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This Medical News article discusses the role of the first messenger RNA influenza vaccine to receive US Food and Drug Administration approval.

After a rocky regulatory road, the August 5 approval of the first messenger RNA (mRNA) influenza vaccine in the US provides not only a more effective flu shot for older adults but also the ability to make last-minute changes in its composition should unanticipated strains begin circulating.

“This is a very good tool to have in your toolbox,” said Jesse Goodman, MD, MPH, professor of medicine and infectious diseases and attending physician at Georgetown University.

The US Food and Drug Administration (FDA) granted Moderna’s mRNA-1010 (mFLUSIVA) influenza vaccine traditional approval for use in adults 50 through 64 years of age and accelerated approval for those aged 65 years or older. Moderna says it expects the new flu shot will be available for the 2026-2027 flu season.

“Right now, every indication is the payors will cover it” for everyone 50 years or older, said Michael Osterholm, PhD, MPH, director of the Center for Infectious Disease Research and Policy at the University of Minnesota.

The US Centers for Disease Control and Prevention (CDC) Advisory Committee on Immunization Practices (ACIP), which hasn’t yet met this year “due to ongoing litigation,” hasn’t weighed in on mRNA-1010, leaving official federal guidance on its use uncertain.

US Department of Health and Human Services (HHS) Secretary Robert F. Kennedy Jr completely revamped ACIP’s membership last year, spurring concerns that it is now driven by ideology instead of evidence. As a result, major medical organizations have been advising that members ignore ACIP and turn to independent sources of vaccine recommendations, such as the American Academy of Pediatrics and the Vaccine Integrity Project, which Osterholm founded.

Especially given Kennedy’s vaccine skepticism, Goodman, Osterholm, and other public health leaders applauded mRNA-1010’s approval, granted exactly 1 year after HHS announced that the Biomedical Advanced Research and Development Authority (BARDA) was terminating 22 mRNA vaccine development grants totaling nearly $500 million.

“[T]he data show these vaccines fail to protect effectively against upper respiratory infections like COVID and flu,” Kennedy stated in a press release about the BARDA move.

Just days before HHS issued that press release, though, a study in JAMA Health Forum estimated that COVID-19 vaccination, most of which involved Moderna’s or Pfizer-BioNTech’s mRNA shots, averted more than 2.5 million deaths worldwide, a figure that the authors acknowledged was conservative compared with previous estimates.

And in May of this year, researchers reported that the phase 3 trial of Moderna’s mRNA-1010 vaccine in volunteers 50 years or older found it to be 27% more effective than standard-dose flu shots containing inactivated viruses, which for more than 70 years have typically been grown in fertilized chicken eggs, a lengthier process than achievable with the mRNA platform.

A Matter of Time

Because the conventional methods of producing flu vaccines take so long, the decision about which 3 strains should be targeted must be made months in advance, usually in late February or March for the Northern Hemisphere, based on what’s circulating in the Southern Hemisphere.

However, the influenza virus is constantly evolving, so by the time immunization starts in the fall, new strains could be circulating, rendering the seasonal vaccine a poor match and increasing the risk of hospitalizations and deaths from the disease. “With flu, every day is a new day,” Osterholm explained.

The time it took to manufacture and deliver vaccines slowed the response to the 2009 H1N1 influenza pandemic, said Goodman, the FDA’s lead scientist at the time. “Although people died, we were fortunate that that pandemic was relatively mild,” he pointed out, noting that H1N1, which was not targeted by that season’s flu shots, had begun to recede by the time sufficient supplies of vaccines against it became available.

With an mRNA flu vaccine, “we’re going from a manufacturing process that takes about 6 months to one that takes about 6 weeks,” said Georges Benjamin, MD, MPH, chief executive officer of the American Public Health Association. The mRNA flu vaccine “is a game changer in terms of what you can do.”

The nimbleness the mRNA platform affords is especially valuable because “our surveillance systems, while they’re good, they’re still not real time,” Benjamin noted. “We’re still always looking retroactively at data.”

Ronald Nahass, MD, president of the Infectious Diseases Society of America, issued a statement along the same lines. “Unlike conventional manufacturing, the mRNA platform allows for streamlined development and rapid responses to emerging mutations in viruses,” said Nahass, a clinical professor of medicine at Rutgers Robert Wood Johnson Medical School.

A Change of Heart

The FDA based its approval of mRNA-1010 on results from a phase 3 trial that enrolled approximately 41 000 adults 50 years or older in 11 Northern Hemisphere countries, including the US. Half the participants were randomly assigned to receive the mRNA vaccine, half to a standard-dose licensed vaccine.

In the US but not all 10 other countries in the study, the standard of care for people 65 years or older is a high-dose influenza vaccine, which was not included in the phase 3 trial. For that stated reason, the FDA initially refused even to consider Moderna’s application.

Via a “refusal to file” letter dated February 3 of this year and signed by Vinay Prasad, MD, MPH, then director of the FDA’s Center for Biologics Evaluation and Research (CBER), the agency notified Moderna that it would not review the company’s application for mRNA-1010.

Prasad wrote that his center was refusing to consider the application because the “control arm does not reflect the best-available standard of care in the United States at the time of the study.”

However, according to Moderna, CBER had previously said that although it recommended using the high-dose flu vaccine as a comparator for the older participants, the trial could instead proceed with a standard-dose vaccine comparator for all age groups as long as the company carried out its plan to make note of that in the informed consent form.

A week after Moderna published the refusal-to-file letter on its website, FDA officials reversed course following a quickly scheduled formal meeting with Moderna representatives. A Politico report based on anonymous sources suggested that President Donald Trump had pressured then−FDA Commissioner Martin Makary, MD, MPH, to rescind the agency’s refusal to consider the mRNA flu vaccine because that decision would be unpopular with many voters, a claim that a White House official denied.

In a briefing document for a June meeting of the FDA’s Vaccines and Related Biological Products Advisory Committee (VRBPAC), agency scientists noted that the trial covered only 1 flu season and that it did not establish efficacy in immunocompromised or very frail older adults, who face the greatest risk of severe flu complications and may respond differently to mRNA vaccines. However, the FDA document said it had identified “no major deficiencies,” and VRBPAC voted unanimously to recommend that the agency approve the shot for adults ages 50 through 64 years and grant accelerated approval for those 65 or older.

Still, Goodman noted, the FDA waited until the very last minute to approve the shot, given that Moderna had said that the deadline for the decision was August 5.

“I’m really gratified that FDA made and promulgated the decision that its senior scientists had recommended and that its advisory committee had supported,” Goodman, a former CBER director, said.

“I’ve been really concerned about protecting the scientific integrity of these federal agencies,” he continued. “It’s good that nobody seemed to interfere with this decision.”

A Choice for Those 65 or Older

In the pivotal phase 3 trial, the mRNA vaccine was as effective in participants 65 years or older as it was in trial participants overall, although whether its clinical efficacy is superior to that of the high-dose or other enhanced flu vaccines remains to be seen.

With a median follow-up of 181 days, the phase 3 trial found that the mRNA vaccine across all ages was approximately 27% more effective in preventing polymerase chain reaction–confirmed illness caused by any influenza A or B strain than the standard dose comparator vaccine, researchers reported in May. About 2% of participants who received the mRNA vaccine were diagnosed with the flu, compared with 2.8% who received a comparator vaccine.

Among the more than 19 000 trial participants who were at least 65 years of age, the mRNA vaccine’s relative efficacy was the same as that for all ages, which suggests benefits similar to those of high-dose and other enhanced influenza vaccines in the oldest adults, according to the authors. (Previous research has shown that the relative efficacy of high-dose flu vaccines vs standard-dose shots is about the same as that of the mRNA flu vaccine vs standard-dose shots, Goodman explained.)

Overall, those who received the mRNA vaccine were more likely to report experiencing the adverse events researchers asked about, including injection site pain, fatigue, headache, and muscle aches, which have also been seen after immunization with mRNA COVID-19 vaccines. Most adverse events were mild to moderate and transient, the scientists noted.

In another Moderna-sponsored phase 3 trial, researchers studied the immunogenicity of mRNA-1010 compared with a standard-dose comparator vaccine in adults ages 18 through 64 years or the high-dose flu vaccine in about 3000 people 65 years or older. The mRNA vaccine demonstrated an immune response superior to both the comparator vaccines, the scientists reported in February 2025.

“Based on immunogenicity data, this vaccine will perform in a superior manner” to the high-dose vaccine, Osterholm said. “Obviously, they’re going to be doing postmarketing evaluation.”

Accelerated approval is based on surrogate end points, in this case the immunogenicity data for people 65 years or older. Higher immunogenicity, such as increased neutralizing antibody titers, often correlates with better protection, but Moderna must confirm its vaccine’s clinical benefit in a postmarketing trial involving individuals who are at least 65 years old.

According to the FDA briefing document for the June VRBPAC meeting, the postmarket, or phase 4, confirmatory study will enroll approximately 400 000 adults 65 years or older in each of 2 flu seasons. Vaccine clinics that administer the shots will alternate weekly between mRNA-1010 and an agreed upon CDC-recommended vaccine throughout the flu seasons.

A Growing Family

The new flu shot is not the first approved mRNA vaccine besides those against COVID-19.

In 2024, the FDA greenlighted Moderna’s mRNA vaccine against respiratory syncytial virus (RSV) for adults 60 years or older (in June 2025, the FDA expanded the use of the RSV vaccine [mRESVIA] to include high-risk adults aged 18 through 59 years).

And in April of this year, the European Commission authorized Moderna’s mCOMBRIAX, the world’s first combination influenza and COVID-19 vaccine.

Whether the combination vaccine will eventually become available in the US isn’t clear. Moderna announced in May 2025 that, after consulting with the FDA, it had voluntarily withdrawn its application for the vaccine in the US, saying the company planned to resubmit it after phase 3 trial efficacy data for mRNA-1010 became available.

“Now that mFLUSIVA is approved, we plan to reengage with regulators for further guidance on refiling the submission for our flu plus COVID combination vaccine,” spokesperson Kelly Cunningham said in an email.

Moderna has not yet disclosed whether it plans to pursue approval of mRNA-1010 for younger adults or adolescents and children, Cunningham said.

“Those over 50 years of age face a disproportionate flu burden, so we focused our research on this more vulnerable population,” she wrote in her email. “While they make up less than 20% of the total US population, people 65 years and older account for an estimated 70% to 85% of flu-related deaths and 50% to 70% of flu-related hospitalizations in the 2024-2025 season.”

Despite the billions of doses of COVID-19 vaccines administered worldwide, though, “for some people, there’s still a lot of mistrust” in vaccines in general and mRNA vaccines in particular, acknowledged Goodman, who chairs the Expert Vaccine Analysis Team, a group of scientists, researchers, and former government officials that provides “facts and rapid analysis on emerging vaccine and outbreak issues.”

“We all still have a lot of work to do,” he said, “and no one knows all the answers for how we can do a better job in dealing with all the misinformation and rumors.”

This study from the Global Burden of Disease quantifies the global, regional, and national burden of cardiovascular disease (CVD) attributable to elevated low-density lipoprotein cholesterol (LDL-C) and assesses the contributions of population growth, aging, changes in LDL-C exposure, and...

A, Map shows location-level estimates of mean low-density lipoprotein cholesterol (LDL-C) levels in 2023.

B, Map shows location-level estimates of annualized percentage change in age-standardized mean LDL-C levels, 1990-2023. To convert LDL-C to mmol/L, multiply by 0.0259.

Figure 2.  Stacked Area Chart of Disability-Adjusted Life-Years (DALYs) by LDL-C Level and Region, Both Sexes, Age ≥25 Years, 2023

To convert low-density lipoprotein cholesterol (LDL-C) to mmol/L, multiply by 0.0259.

Figure 3.  Maps of Age-Standardized DALY Rates and Annualized Percentage Change in Age-Standardized DALY Rates Attributable to High LDL-C

A, Map shows age-standardized rates per 100 000 population of disability-adjusted life-years (DALYs) of cardiovascular disease attributable to high low-density lipoprotein cholesterol (LDL-C), both sexes, 2023.

B, Map shows annualized percentage change in age-standardized DALY rates of cardiovascular disease attributable to high LDL-C, both sexes, 1990-2023.

Figure 4.  Box-and-Whisker Plot of Percentage Change in Number of LDL-C–Attributable DALYs Due to Population Growth, Population Aging, Changes in Exposure, and Changes in Risk-Deleted DALY Rates for Cardiovascular Disease by SDI Region, Both Sexes, 1990-2023

Decomposition of change in all-ages, all-sexes combined cardiovascular disease disability-adjusted life-years (DALYs) attributable to low-density lipoprotein cholesterol (LDL-C) from 1990 to 2023 by sociodemographic index (SDI) region due to population growth, population aging, LDL-C exposure, and risk-deleted DALYs. Risk-deleted DALYs are the number of DALYs left after removing the effect of LDL-C exposure, population growth, and population aging on overall DALYs, calculated for overall cardiovascular disease, ischemic heart disease, and ischemic stroke DALY counts multiplied by 1 − the LDL-C population-attributable fraction. Circles represent means and whiskers represent 95% uncertainty intervals in percentage change in number of DALYs attributable to the risk from 1990 to 2023. The SDI is a summary measure of a geographic location’s sociodemographic development based on average income per person, educational attainment, and total fertility rate. The SDI ranges from 0 to 1, where high SDI indicates higher levels of development and low SDI indicates lower levels.

Figure 5.  Scatterplot of Relationship Between Changes in LDL-C Exposure and Risk-Deleted DALY Rates for CVD by SDI Region, 1990-2023

The scatterplot illustrates the relationship between percentage changes in low-density lipoprotein cholesterol (LDL-C) exposure (y-axis) and changes in risk-deleted disability-adjusted life-year (DALY) rates (x-axis). Risk-deleted DALYs represent the change in the disease burden not explained by population growth, population aging, or LDL-C exposure (for example, improvements in health care). Points in the lower left quadrant reflect countries experiencing simultaneous reductions in both LDL-C exposure and cardiovascular disease (CVD) rates, a pattern typical of high sociodemographic index (SDI) regions (dark red). Named countries were identified as outliers based on extreme distributions, with labels shown only for countries falling within the extreme 2.5% of either axis’s global quantile distribution. The SDI is a summary measure of a geographic location’s sociodemographic development based on average income per person, educational attainment, and total fertility rate. The SDI ranges from 0 to 1, where high SDI indicates higher levels of development and low SDI indicates lower levels.

aThe United Arab Emirates (−503.2%) and Qatar (−317.4%) are extreme outliers regarding reductions in risk-deleted DALY rates and have been placed at the left edge of the plot to preserve the symmetry and legibility of the remaining data points. Their positions indicate a profound decline in CVD burden that significantly exceeds global averages, likely reflecting rapid advancements in health care infrastructure and access to cardiovascular interventions.

Key Points

Question  What is the global disease burden attributable to elevated low-density lipoprotein cholesterol (LDL-C)?

Findings  In 2023, high LDL-C caused 3.6 million deaths (6.0% of all deaths) and 90.7 million disability-adjusted life years (DALYs) (3.2% of total DALYs). Despite declines of 45.6% in age-standardized mortality rates and 39.5% in DALY rates, the absolute LDL-C–related burden increased by 38.5% from 1990 to 2023, driven by population growth and aging, with notable regional disparities shifting toward middle-sociodemographic settings.

Meaning  Strategic approaches to prevent, diagnose, and treat elevated LDL-C across populations globally are needed.

Abstract

Importance  Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies.

Objectives  To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends.

Design, Setting, and Population  This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals.

Exposure  Population-level LDL-C concentrations.

Main Outcomes and Measures  Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals.

Results  In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings.

Conclusions and Relevance  Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

Introduction

Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for atherosclerotic cardiovascular disease (CVD), primarily ischemic heart disease (IHD) and ischemic stroke.1 These conditions are the leading causes of premature mortality worldwide, accounting for 22% of global deaths and 10% of disability-adjusted life-years (DALYs) in 2023.1-3

LDL-C is central in CVD risk prediction models, a primary therapeutic target in clinical guidelines,4-7 and a key indicator for monitoring global noncommunicable disease trends.8 Quantifying the LDL-C–attributable burden is therefore essential for guiding prevention and treatment strategies.

The objective of this study was to quantify the global, regional, and national burden of CVD attributable to elevated LDL-C from 1990 to 2023 and to assess the contributions of population growth, aging, changes in LDL-C exposure, and risk-deleted rates to trends in this burden. Building on prior Global Burden of Disease (GBD) estimates,9 this study incorporates updated data and relative risks, revised outcome definitions, and improved counterfactual theoretical minimum risk exposure level (TMREL) distributions.10 This report was produced as part of the GBD Collaborator Network and adheres to the GBD Protocol.11

Methods

As part of GBD 2023,1 ,3 we quantified the burden attributable to high LDL-C (relative to the TMREL of 35-54 mg/dL) among adults aged 25 years or older across 204 countries and territories by age and sex from 1990 to 2023. LDL-C was selected over other lipid biomarkers to align with clinical guidelines, reflect causal evidence, maximize data availability, and avoid double counting. A summary of the methods specific to LDL-C burden estimation is provided herein, with details provided in Sections 4-8 and eFigure 1 in Supplement 1 and in prior publications.3 GBD 2023 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER)12 and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)13 guidelines (eTables 1 and 2 in Supplement 1).

The GBD used deidentified data, and the waiver of informed consent was reviewed and approved by the University of Washington Institutional Review Board (study No. 9060). Analyses were performed using R version 4.4.2 (R Foundation) and Python version 3.10.4 (Python Software Foundation). GBD 2023 estimates are publicly available through the GBD 2023 results tool and associated visualizations. All statistical code is accessible on request.

Estimating LDL-C Distributions

LDL-C population levels (reported in mg/dL; to convert to mmol/L, multiply by 0.0259) were derived from 806 population-based studies from 161 countries, covering 4.7 billion individuals from 1962 to 2023 (Section 4.1, eTable 3, and eFigure 2 in Supplement 1). All data sources are available at the Global Health Data Exchange (GHDx) repository.14

Population-based studies reporting LDL-C, high-density lipoprotein cholesterol (HDL-C), total cholesterol, and/or triglycerides were included in the analysis (Section 4.1 and eFigure 3 in Supplement 1). When LDL-C measurements were not reported, we use the Friedewald equation15 (Section 4.2 in Supplement 1) to calculate the mean LDL-C for a study population when complete data on total cholesterol, HDL-C, and/or triglycerides were available (Section 4.2 in Supplement 1). We also developed a regression model to characterize the relationships between measured lipid components (total cholesterol, HDL-C, triglycerides) and LDL-C, enabling the estimation of LDL-C values when at least 1 lipid parameter was reported (Section 4.2.1 and eTable 4 in Supplement 1). Among all data sources, 30.7% reported LDL-C values, and in 69.3%, LDL-C was estimated from other lipid measures using the regression method. Surveys reporting hypercholesterolemia prevalence without mean lipid data were excluded, except for the US Behavioral Risk Factors Surveillance System (BRFSS), which was included due to the availability of a comparable population representative survey (National Health and Nutrition Examination Survey [NHANES]). BRFSS estimates were adjusted for self-reported bias (Section 4.2 and eTables 5-7 in Supplement 1).

Mean LDL-C values were estimated using spatiotemporal gaussian process regression16 incorporating covariates to inform data-sparse locations (Section 4.3.2 and eFigure 2 in Supplement 1). LDL-C standard deviation was modeled using a log-linear regression (Section 4.3.3 and eTable 8 in Supplement 1). Global sex-specific distributions were derived from a weighted ensemble of 12 parametric families based on fit to person-level data (Section 4.3.4 and eFigure 4 in Supplement 1).

Relative Risks for Outcomes Related to LDL-C

We conducted a systematic review and meta-analysis of 38 randomized clinical trials,17 following PRISMA guidelines,13 to estimate relative risks for IHD and ischemic stroke using the Burden of Proof framework3 ,10 (Section 5, eFigure 5, and eTables 9-10 in Supplement 1). Only IHD and ischemic stroke were included as outcomes (eFigures 6-7 and eTable 11 in Supplement 1), based on evidence that LDL-C–related burden is primarily driven by atherosclerotic pathways.18

CVD was defined as the aggregate of IHD and ischemic stroke. IHD encompassed myocardial infarction,19 coronary artery disease,20,21 and ischemic cardiomyopathy. Ischemic stroke was defined based on brain imaging demonstrating arterial occlusion.22

Theoretical Minimum Risk Exposure Level

The TMREL was defined as the LDL-C concentration associated with the lowest combined risk of IHD and ischemic stroke incidence and mortality. Distinct from clinical treatment targets,5 ,6,23 the TMREL was empirically derived using the Burden of Proof meta-analytic framework and set as a uniform distribution ranging from 35 mg/dL to 54 mg/dL. These bounds were based on the fifth and 15th percentiles of mean LDL-C levels in the reference groups of the randomized clinical trials used to estimate the relative risk for LDL-C (Section 6, eFigure 8, and eTable 12 in Supplement 1).

Attributable Burden

We estimated disease-specific population-attributable fractions and the burden attributable to high LDL-C in terms of deaths and DALYs by age, sex, location, and year, using standard equations for continuous risk factors (Section 7.1.1 in Supplement 1). Population-attributable fractions and DALYs were calculated for each 38.67-mg/dL (1-mmol/L) increment of LDL-C using the method of Rose24 (Section 7.1.2 in Supplement 1). LDL-C–attributable burden was calculated by applying population-attributable fractions to corresponding outcome-specific deaths and DALYs (Section 7.1.3 in Supplement 1).

Uncertainty Intervals

Uncertainty was quantified using a Monte Carlo simulation with 250 draws. We define 95% uncertainty intervals (UIs) using the 2.5th and 97.5th percentiles of the posterior distribution.

Sociodemographic Index Analysis

We assessed LDL-C disease burden by sociodemographic index (SDI), calculated as the geometric mean of fertility, education, and income in each location on a scale of 0 to 1,25 grouping locations into 5 SDI categories based on 2023 quintiles (eTable 13 in Supplement 1).

Decomposition Analysis

Using the Das Gupta method,26 we decomposed changes in CVD DALYs attributable to elevated LDL-C from 1990 to 2023 into 4 components: population growth, aging, LDL-C exposure shifts, and risk-deleted DALY rates. The risk-deleted DALY rates are defined as the changes in the disease burden not explained by population growth and aging or by changes in LDL-C exposure levels. This metric captures the residual change in health outcomes driven by improvements in clinical care, emergency response, and overall health care infrastructure, reflecting gains in prevention, treatment, and survival that are independent of changes in LDL-C exposure, population age structure, and population growth (Section 8 and eFigure 9 in Supplement 1).

Results

Trends in LDL-C Levels and Prevalence

Between 1990 and 2023, the number of adults aged 25 years or older with LDL-C levels of 54 mg/dL or greater increased from approximately 2.5 billion (95% UI, 2.4-2.6 billion) to 4.6 billion (95% UI, 4.3-4.7 billion) (eFigures 10-12 in Supplement 1).

In 2023, age-standardized mean LDL-C was lowest in Burkina Faso, Lesotho, Somalia, and Rwanda (<80 mg/dL) and highest in Serbia, Slovenia, Russia, Norway, and Austria (>135 mg/dL) (Figure 1A).

Figure 1.  Maps of Mean LDL-C Levels and Change in Mean LDL-C Levels, Both Sexes

A, Map shows location-level estimates of mean low-density lipoprotein cholesterol (LDL-C) levels in 2023.

B, Map shows location-level estimates of annualized percentage change in age-standardized mean LDL-C levels, 1990-2023. To convert LDL-C to mmol/L, multiply by 0.0259.

From 1990 to 2023, the sharpest increases in age-standardized mean LDL-C levels occurred in countries with lower LDL-C baseline levels, such as São Tomé and Príncipe, Cameroon, and Bangladesh, with annualized rates of change greater than 0.6%. In contrast, most high-SDI countries, including Belgium, the Netherlands, Norway, Finland, Germany, Switzerland, and Sweden, showed consistent declines (annual reduction −0.4%) (Figure 1B). Regional trends by sex are shown in eFigure 13 in Supplement 1.

Attributable Burden of Disease Due to High LDL-C

Since 1990, the number of deaths and DALYs attributable to high LDL-C increased by approximately 39%, reaching 3.6 million (95% UI, 2.2-5.4 million) deaths and 90.7 million (95% UI, 58.9-123.2 million) DALYs in 2023 (eTable 14 in Supplement 1). High LDL-C was the second leading driver of CVD mortality and a top-10 contributor to the global all-cause burden in 2023. It accounted for 6.0% (95% UI, 3.7%-8.9%) of all-cause deaths, 8.1% (95% UI, 5.0%-11.9%) of noncommunicable disease deaths, and 18.9% (95% UI, 12.4%-27.6%) of CVD deaths. High LDL-C contributed to nearly one-third of IHD deaths (2.7 million [95% UI, 1.8-3.8 million]) and 27% of ischemic stroke deaths (0.87 million [95% UI, 0.29-1.5 million]). It accounted for 3.2% (95% UI, 2.1%-4.5%) of all-cause DALYs; 5.0% (95% UI, 3.2%-6.9%) of noncommunicable disease DALYs, and 20.8% (95% UI, 13.6%-29.0%) of CVD DALYs—specifically, 36.4% (95% UI, 26.7%-47.2%) and 30.7% (95% UI, 11.5%-49.2%) of IHD and ischemic stroke DALYs, respectively.

In 2023, 33% of LDL-C–attributable DALYs occurred in individuals with LDL-C levels between 54 mg/dL and 116 mg/dL, while the remaining LDL-C–attributable DALYs occurred in individuals with LDL-C levels of 116 mg/dL or higher (Figure 2). IHD accounted for 72% of total LDL-C–related DALYs globally and consistently exceeded ischemic stroke across regions, sexes, and locations (eFigures 14 and 15 in Supplement 1).

Figure 2.  Stacked Area Chart of Disability-Adjusted Life-Years (DALYs) by LDL-C Level and Region, Both Sexes, Age ≥25 Years, 2023

To convert low-density lipoprotein cholesterol (LDL-C) to mmol/L, multiply by 0.0259.

Over the study period, global all-ages rates for deaths and DALYs attributable to high LDL-C decreased by approximately 8%, reaching 45.0 (95% UI, 27.6-66.5) deaths and 1124.9 (95% UI, 731.2-1529.0) DALYs per 100 000 in 2023. However, age-standardized CVD global deaths and DALY rates due to high LDL-C declined by 45.6% and 39.5%, respectively, with an annual decrease of 0.45%. Specifically, age-standardized global CVD death rates due to high LDL-C decreased from 74.3 (95% UI, 42.7-109.0) deaths per 100 000 in 1990 to 40.5 (95% UI, 24.9-59.9) deaths per 100 000 in 2023. Age-standardized CVD DALY rates declined from 1656.7 (95% UI, 1018.0-2368.0) DALYs per 100 000 in 1990 to 1002.6 (95% UI, 653.6-1363.9) DALYs per 100 000 in 2023 (eTable 14 in Supplement 1).

Age and Sex Differences

DALY rates increased steadily with age in males and females. In 2023, the number of LDL-C–related DALYs was higher in males than females (55.0 million [95% UI, 37.3-75.3 million] vs 35.7 million [95% UI, 22.4-51.8 million]). From 1990 to 2023, age-specific LDL-C DALY rates declined across all ages and both sexes globally. Age-standardized DALY rates declined annually by 0.92% (95% UI, 0.80%-1.03%) in males and by 1.37% (95% UI, 1.26%-1.50%) in females (eFigure 16 in Supplement 1).

LDL-C–Attributable Burden by Region and SDI

In 2023, age-standardized DALY rates ranged from 2252.6 (95% UI, 1507.4-3038.2) per 100 000 in Eastern Europe to 322.0 (95% UI, 208.7-446.6) in high-income Asia-Pacific (eTables 14 and 15 in Supplement 1). Across countries, rates ranged from 4333.8 (95% UI, 2758.1-6174.8) in Nauru to 265.3 (95% UI, 153.2-379.2) in the Republic of Korea (Figure 3A; eTable 15 in Supplement 1).

Figure 3.  Maps of Age-Standardized DALY Rates and Annualized Percentage Change in Age-Standardized DALY Rates Attributable to High LDL-C

A, Map shows age-standardized rates per 100 000 population of disability-adjusted life-years (DALYs) of cardiovascular disease attributable to high low-density lipoprotein cholesterol (LDL-C), both sexes, 2023.

B, Map shows annualized percentage change in age-standardized DALY rates of cardiovascular disease attributable to high LDL-C, both sexes, 1990-2023.

From 1990 to 2023, age-standardized DALY rates declined in high-income regions (mean reduction, 64%). In contrast, trends in sub-Saharan Africa were heterogeneous, with increases in the Eastern (23%), Western (7.2%), and Central (6.2%) subregions and a decline in Southern sub-Saharan Africa (−17.7%) (Figure 3B; eTable 15 in Supplement 1). Overall, 114 countries experienced declining LDL-C–related CVD burden, including 25 with reductions exceeding 70%, whereas increases of at least 35% were observed in 7 countries, including Bangladesh, Ethiopia, and Tanzania (eTable 15 in Supplement 1).

Globally, the number of DALYs peaked at LDL-C levels of 116 mg/dL to 155 mg/dL and declined sharply above 213 mg/dL. Southeast Asia and high-income regions contributed most to the global burden, with India and China accounting for 34% of LDL-C–attributable DALYs (Figure 2).

The relationship between LDL-C burden and SDI followed an inverted U-shaped pattern (eFigure 17 in Supplement 1). Middle-SDI countries experienced the highest age-standardized DALY rates, with 1439.2 DALYs (95% UI, 947.4-1939.4) per 100 000 in 2023, whereas high-SDI settings had the lowest, with 831.9 DALYs (95% UI, 558.6-1132.9) per 100 000. Over time, high-SDI countries achieved sustained reductions in all-ages and age-standardized DALY rates (−54%), while low-SDI countries experienced paradoxical increases (eTable 14 and eFigure 17 in Supplement 1).

Decomposition Analysis

Globally, the 25.2 million (95% UI, 17.9-30.7 million) increase in DALYs since 1990, a 38.5% (95% UI, 33.2%-43.7%) rise, was driven primarily by demographic changes that outweighed improvements in risk-deleted DALY rates. Population aging and growth contributed 33.9 million DALYs (95% UI, 20.7-49.3 million) and 33.4 million DALYs (95% UI, 21.1-47.0 million), respectively. Declines in risk-deleted DALY rates—reflecting improvements in prevention, treatment, and survival due to causes other than LDL-C exposure—reduced DALYs by 41.3 million (95% UI, 24.9-63.4 million), partially offsetting demographic effects. The impact of LDL-C exposure varied by development status, globally neutral but divergent across SDI quintiles. After accounting for LDL-C exposure and risk-deleted trends, the combined demographic impact yielded a net increase of 25.3 million DALYs (95% UI, −38.5 million to 87.6 million). Most of the rise in IHD and ischemic stroke DALYs (21.1 million and 4.1 million, respectively) was similarly driven by demographic shifts (eFigure 18 in Supplement 1).

A key finding of this analysis was that the contributions of these components varied by SDI (Figure 4). The impact of population growth and aging was largest in low-SDI regions (+150% and +100%) and smallest in high- and high- to middle-SDI areas (+50% and +30%), where larger reductions in risk-deleted DALYs were observed. Declining LDL-C exposure acted as a negative driver of LDL-C burden only in high-SDI regions, whereas rising exposure increased the LDL-C burden across all other SDI quintiles (eFigures 9 and 19-21 in Supplement 1).

Figure 4.  Box-and-Whisker Plot of Percentage Change in Number of LDL-C–Attributable DALYs Due to Population Growth, Population Aging, Changes in Exposure, and Changes in Risk-Deleted DALY Rates for Cardiovascular Disease by SDI Region, Both Sexes, 1990-2023

Decomposition of change in all-ages, all-sexes combined cardiovascular disease disability-adjusted life-years (DALYs) attributable to low-density lipoprotein cholesterol (LDL-C) from 1990 to 2023 by sociodemographic index (SDI) region due to population growth, population aging, LDL-C exposure, and risk-deleted DALYs. Risk-deleted DALYs are the number of DALYs left after removing the effect of LDL-C exposure, population growth, and population aging on overall DALYs, calculated for overall cardiovascular disease, ischemic heart disease, and ischemic stroke DALY counts multiplied by 1 − the LDL-C population-attributable fraction. Circles represent means and whiskers represent 95% uncertainty intervals in percentage change in number of DALYs attributable to the risk from 1990 to 2023. The SDI is a summary measure of a geographic location’s sociodemographic development based on average income per person, educational attainment, and total fertility rate. The SDI ranges from 0 to 1, where high SDI indicates higher levels of development and low SDI indicates lower levels.

Figure 5 further characterizes distinct patterns in the transition of LDL-C–attributable CVD burden between 1990 and 2023 by SDI. High-SDI countries are clustered in the lower left quadrant, reflecting reductions in CVD DALYs driven by both declining LDL-C exposure and improvements in risk-deleted rates. The United Arab Emirates, Qatar, Bahrain, and Jordan are examples, where declines in risk-deleted DALYs rank among the highest globally, alongside exposure-driven DALY reductions. Mostly low-SDI countries are located in the upper right quadrant, indicating increases in LDL-C exposure compounded by limited improvements (or even increases) in risk-deleted CVD DALY rates. Examples from this quadrant include countries such as Bangladesh, Ethiopia, Niger, and Somalia, where the overall LDL-C–related burden is accelerating due to both lifestyle shifts and health care infrastructure gaps. The upper left quadrant includes countries such as South Korea and Cameroon, where the CVD burden attributable to LDL-C is rising due to increased exposure but is partially offset by declines in risk-deleted DALY rates, suggesting that while underlying atherosclerotic risk is rising, improvements in clinical care and other unmeasured factors are potentially preventing a proportional increase in the CVD burden. In the lower right quadrant, in countries like Zambia, Guam, Kiribati, and the Solomon Islands, a decrease in the CVD DALY burden due to LDL-C exposure is observed, yet risk-deleted DALY rates are increasing. This suggests that gains from reduced population LDL-C levels are being offset by insufficient progress in health care delivery or worsening health conditions. There was substantial variability in the contribution of each decomposition component across the 204 countries analyzed (eFigures 21-24 in Supplement 1).

Figure 5.  Scatterplot of Relationship Between Changes in LDL-C Exposure and Risk-Deleted DALY Rates for CVD by SDI Region, 1990-2023

The scatterplot illustrates the relationship between percentage changes in low-density lipoprotein cholesterol (LDL-C) exposure (y-axis) and changes in risk-deleted disability-adjusted life-year (DALY) rates (x-axis). Risk-deleted DALYs represent the change in the disease burden not explained by population growth, population aging, or LDL-C exposure (for example, improvements in health care). Points in the lower left quadrant reflect countries experiencing simultaneous reductions in both LDL-C exposure and cardiovascular disease (CVD) rates, a pattern typical of high sociodemographic index (SDI) regions (dark red). Named countries were identified as outliers based on extreme distributions, with labels shown only for countries falling within the extreme 2.5% of either axis’s global quantile distribution. The SDI is a summary measure of a geographic location’s sociodemographic development based on average income per person, educational attainment, and total fertility rate. The SDI ranges from 0 to 1, where high SDI indicates higher levels of development and low SDI indicates lower levels.

aThe United Arab Emirates (−503.2%) and Qatar (−317.4%) are extreme outliers regarding reductions in risk-deleted DALY rates and have been placed at the left edge of the plot to preserve the symmetry and legibility of the remaining data points. Their positions indicate a profound decline in CVD burden that significantly exceeds global averages, likely reflecting rapid advancements in health care infrastructure and access to cardiovascular interventions.

Discussion

Our findings reveal a persistent and uneven disease burden attributable to elevated LDL-C. While the global age-standardized mortality and DALY rates declined, the absolute burden continues to rise due to population growth and aging, particularly in low- and middle-SDI settings. This divergence between declining age-standardized rates and the rising absolute burden indicates that advances in lipid-lowering therapies and clinical management are being outpaced by demographic forces, with population growth and aging contributing nearly equally to the increase in total burden. At the same time, while high-SDI settings have experienced sustained declines in absolute burden, an increasing share of the burden is concentrated in middle- and low-SDI settings, where gaps in prevention, detection, and treatment remain substantial. These patterns underscore the need for context-specific strategies that explicitly account for the interaction between demographic changes and health system capacity across settings.

Although all-ages and age-standardized CVD DALY rates from high LDL-C declined in most high-SDI countries,27,28 low- and middle-SDI countries show rising or plateauing burdens, often masked by global trends.29 Low- and middle-SDI regions continue to exhibit rising exposure levels30,31 and minimal progress in care27 due to limited testing and access to lipid-lowering therapies, particularly access to statins,32 restricting early detection and long-term risk reduction. This growing burden is not solely driven by aging but also by prolonged exposure to elevated LDL-C beginning earlier in life, compounded by low awareness and undertreatment leading to earlier onset and greater lifetime CVD risk, highlighting the need for earlier screening, prevention, and sustained management across the life course.

Statins remain the cornerstone of LDL-C management; their real-world impact is constrained by underuse, even where available. Barriers include the asymptomatic nature of hypercholesterolemia, low awareness, costs of testing and treatment, inconsistent supply, health system inefficiencies, and concerns about adverse effects. Addressing these gaps requires strengthening primary care systems, improving access to affordable diagnostics and medications, and standardized, guideline-based treatment.33-36

Fixed-dose combination therapies (polypills) with statins may simplify treatment, improve adherence, lower costs, and expand access, particularly in resource-limited settings.37,38 However, their impact depends not only on availability but also on effective implementation, including low cost, broad distribution, and targeted delivery to high-risk groups. Without overcoming these challenges, their population-level benefits will remain limited.

Similarly, novel lipid-lowering therapies such as proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors and small interfering RNA–based approaches have broadened the therapeutic landscape by enabling more intensive LDL-C reduction and introducing alternative delivery mechanisms, including injectable formulations.39,40 However, their widespread adoption is constrained by high costs, health care infrastructure, and the need for appropriate patient selection through individual risk stratification. As such, these therapies are unlikely to substantially reduce the global burden in the near term, particularly in resource-limited settings, where the LDL-C burden is increasing the fastest.

Addressing the global LDL-C–related burden requires a comprehensive framework beyond pharmacological intervention.6 Primordial and primary prevention are fundamental; robust evidence supports regular physical activity and dietary patterns high in fiber and plant-based foods, alongside reduced intake of trans and saturated fats, to improve lipid profiles and cardiovascular health.41 However, individual behavior change must be reinforced by population-level policies, including industrial food reformulation, nutritional labeling, and urban design that promotes active transport.6

Ultimately, addressing the global LDL-C crisis requires integrating these public health strategies with contemporary risk frameworks like Predicting Risk of CVD Events–Atherosclerotic CVD (PREVENT-ASCVD) to identify high-risk individuals earlier in the life course. By embedding cardiovascular prevention within primary care and expanding universal health coverage, clinical gains seen in high-SDI nations can be equitably translated to global population.8

Limitations

This study has several limitations. First, estimates depend on the availability and quality of the input data and may mask local disparities. Second, use of the Friedewald equation likely introduced minimal systematic bias. Third, the LDL-C TMREL (35-54 mg/dL) represents an empirical floor for global health benchmarking and is not intended to serve as a clinical treatment target. In clinical practice, recommended LDL-C thresholds are substantially higher; typically, targets range from less than 130 mg/dL to less than 100 mg/dL for primary prevention, with more intensive targets of less than 70 mg/dL for intermediate-risk individuals and less than 55 mg/dL for those at high risk.6 Fourth, the lack of individual-level treatment data limited our ability to stratify for treatment status globally. Fifth, by focusing on IHD and ischemic stroke, the results likely underestimate the total LDL-C burden across other diseases like peripheral artery disease, vascular dementia, and carotid arterial disease; however, these outcomes are much more challenging to estimate given data sparsity.42,43 Sixth, this framework does not capture cumulative life course exposure or interactions with other risks. Seventh, the risk-deleted DALY rate is a composite metric that cannot disaggregate the specific contributions of unmeasured factors.

Conclusions

Preventing and controlling elevated LDL-C could avert CVD deaths and extend healthy life expectancy globally. Despite advances in LDL-C management, the absolute burden has continued to rise since 1990 due to population growth and aging, shifting increasingly toward middle-SDI countries. With the United Nations Sustainable Development Goals deadline approaching, these findings underscore the urgency of scaling equitable screening and targeted interventions to expand access to care and promote lifelong cardiovascular health.

Corresponding Author: Christian Razo, PhD, Department of Health Metrics Sciences, School of Medicine, Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave NE, Seattle, WA 98195 (razoc@uw.edu).

Accepted for Publication: April 8, 2026.

Published Online: July 29, 2026. doi:10.1001/jama.2026.8628

The GBD 2023 LDL Cholesterol Collaborators: Christian Razo, PhD; Nicole K. DeCleene, BS; Catherine O. Johnson, PhD; Benjamin A. Stark, MSc; Kate LeGrand, MPH; Hasan Aalruz, PhD; Ukachukwu O. Abaraogu, PhD; Samar Abd ElHafeez, DrPH; Michael Abdelmasseh, MD; Mahmoud Abdelnabi, MBBCh; Sherief Abd-Elsalam, PhD; Rasha Abdelsalam Elshenawy, PhD; Siddig Ibrahim Abdelwahab, PhD; Ali Abdolizadeh, MSc; Arash Abdollahi, MD; Meriem Abdoun, PhD; Mostafa M. Abdrabou, PhD; Deldar Morad Abdulah, MPH; Auwal Abdullahi, PhD; Toufik Abdul-Rahman, MD; Melese Shenkut Abebe, PhD; Habtamu Abebe Getahun, MSc; Olugbenga Olusola Abiodun, FWACP; Olumide Abiodun, MPH; Shady Abohashem, MPH; Ulric Sena Abonie, PhD; Nagah M. Abourashed, PhD; Dmitry Abramov, MD; Mohammed Mehdi Abrar, PhD; Dariush Abtahi, MD; Rana Kamal Abu Farha, PhD; Bilyaminu Abubakar, PhD; Ahmad Y. Abuhelwa, PhD; Hana J Abukhadijah, MPH; Salahdein Aburuz, PhD; Dina Abushanab, MSc; Lisa C. Adams, PhD; Nuhu Lawan Lawan Adamu, MSc; Mesafint Molla Adane, PhD; Isaac Yeboah Addo, PhD; Kamoru Ademola Adedokun, MSc; Oluwatobi E Adegbile, MD; Nurudeen A. Adegoke, PhD; Saheed Ayodeji Adekola, MSc; Olumide Thomas Adeleke, MD; Miracle Ayomikun Adesina, BPT; Molalign Aligaz Adisu, MSc; Mohd Adnan, PhD; Clifford Afoakwah, PhD; Aanuoluwapo Adeyimika Afolabi, MPH; Fatemeh Afrashteh, MD; Ebenezer Afrifa-Yamoah, PhD; Saira Afzal, PhD; Percival Delali Agordoh, MPhil; Williams Agyemang-Duah, PhD; Aqeel Ahmad, PhD; Khabir Ahmad, PhD; Sajjad Ahmad, PhD; Suhaib Ahmad, MD; Asma Ahmed, PhD; Gasha Salih Ahmed, MSc; Luai A. Ahmed, PhD; Mehrunnisha Sharif Ahmed, MSc; Meqdad Saleh Ahmed, PhD; Mohamed Sherif Ali Ahmed, MBBCh; Mushood Ahmed, MBBS; Nesredin Ahmed, MSc; Syed Anees Ahmed, PhD; Zohra Ahmed, MBBS; Aishwarya Aiyer, MD; Marjan Ajami, PhD; Kasuni H.M. Akalanka, PhD; Muhammad Nadeem Akhtar, PhD; Mukadas Oyeniran Akindele, PhD; Mohammed Ahmed Akkaif, PhD; Ashley E. Akrami, BS; Ralph Kwame Akyea, PhD; Syed Mahfuz Al Hasan, PhD; Mohammad Ahmmad Mahmoud Al Zoubi, PhD; Fares Alahdab, MD; Mohammad M. Al-Ahmad, PhD; Stephen Alajajian, MS; Muaaz M. Alajlani, PhD; Tariq A. Alalwan, PhD; Ziyad Al-Aly, MD; Issa Sulaiman Al-Amri, PhD; Turki M. Alanzi, PhD; Abdullah Alarifi, MD; Fahmi Y. Al-Ashwal, PhD; Ahmad Al-Azayzih, PhD; Sayer Al-Azzam, PharmD; Ahmad M. Al-Bashaireh, PhD; Mohammed Albashtawy, PhD; Nader Al-Dewik, PhD; Shereen M. Aleidi, PhD; Fahad D. Algahtani, PhD; Abdelazeem M Algammal, PhD; Khalid F Alhabib, MD; Fadwa Naji Alhalaiqa, PhD; Abid Ali, PhD; Asgar Ali, PhD; Bassam R. Ali, PhD; Mohammad Daud Ali, PhD; Mohammed Usman Ali, PhD; Rafat Ali, PhD; Syed Shujait Ali, PhD; Montaha Al-Iede, MD; Sheikh Mohammad Alif, PhD; Morteza Alipour, BSc; Samah W. Al-Jabi, PhD; Syed Mohamed Aljunid, PhD; Mayson H. Alkhatib, PhD; Sameer Abdulmalik Alkubati, PhD; Raad Hatim Allawi, BS; Khaled S. Allemailem, PhD; Mohammed Z. Allouh, PhD; Wesam Taher Almagharbeh, PhD; Wael Almahmeed, MD; Sabah Al-Marwani, MSc; Joseph Uy Almazan, PhD; Mohmmad Minwer Alnaeem, PhD; Hasan Yaser Alniss, PhD; Mahmoud A. Alomari, PhD; Haifa F. F. Alotaibi, MD; Saleh A Alqahtani, MD; Mohammad AlQudah, MD; Mohammad R Alqudimat, PhD; Ahmad Rajeh Al-Qudimat, MPH; Rajaa M Al-Raddadi, MD; Intima Alrimawi, PhD; Sahel Majed Alrousan, PhD; Mohammed Alsbou, PhD; Najim Z. Alshahrani, MD; Ali Salman Al-Shami, MS; Zaid Altaany, PhD; Awais Altaf, PhD; Malik A. Althobiani, PhD; Diala Altwalbeh, PhD; Hassan Alwafi, PhD; Yaser Mohammed Al-Worafi, PhD; Hany Aly, MD; Abdulmohsen Hamdan Al-Zalabani, PhD; Abdallah Alzoubi, PhD; Karem H. Alzoubi, PhD; Adel S. Al-Zubairi, PhD; Joy Amafah, MPH; Masoud Aman Mohammadi, PhD; Amr Amin, PhD; Saeed Amini, PhD; Ehsan Amini-Salehi, MD; Ganiyu Adeniyi Amusa, MD; Roshan A. Ananda, MD; Robert Ancuceanu, PhD; Song Peng Ang, MD; Ebuka Miracle Anieto, MSc; Abhishek Anil, MD; Prapti Anjana, MPT; Boluwatife Stephen Anuoluwa, PhD; Saeid Anvari, MD; Sumadi Lukman Anwar, PhD; Anayochukwu Edward Anyasodor, PhD; Walter Appati, MD; Jalal Arabloo, PhD; Elshaimaa A. Arafa, PhD; Mosab Arafat, PhD; Aleksandr Y. Aravkin, PhD; Demelash Areda, PhD; Reza Arefnezhad, MSc; Brhane Berhe Aregawi, PhD; Jorge Arias de la Torre, PhD; Timur Aripov, PhD; Jesu Arockiaraj, PhD; Gustavo Aires de Arruda, PhD; Syed Mohammed Basheeruddin Asdaq, PhD; Mohammad Asghari-Jafarabadi, PhD; Saad Ashraf, MD; Syed Amir Ashraf, PhD; Tahira Ashraf, PhD; Bernard Kwadwo Yeboah Asiamah-Asare, PhD; Omer Atac, PhD; Mohammad Athar, PhD; Seyyed Shamsadin Athari, PhD; Prince Atorkey, PhD; Khursheed Aurangzeb, PhD; Sana Javaid Awan, PhD; Usman Ayub Awan, PhD; Kemal Lemnuro Lemnuro Awol, MD; Adedapo Wasiu Awotidebe, PhD; Camila Ospina Ayala, MSc; Berrak Itir Ayli, MD; A.K.M. Azad, PhD; Alireza Azarboo, MD; Sadat Abdulla Aziz, PhD; Ahmed Y. Azzam, MD; Domenico Azzolino, PhD; Rasha Babiker, PhD; Abraham Samuel Babu, PhD; Giridhara Rathnaiah Babu, PhD; Muhammad Badar, PhD; Ashish D. Badiye, PhD; Alaa Aboelnour Badran, MD; Khlood K. Baghlaf, PhD; Atif Amin Baig, PhD; Mohamad Amin Bakhshali, PhD; Marina Balabekova, DSc; Senthilkumar Balakrishnan, PhD; Ovidiu Constantin Baltatu, PhD; Maciej Banach, PhD; Palash Chandra Banik, MPhil; Hiba Jawdat Barqawi, MPhil; Amadou Barrow, MPH; Lingkan Barua, MPH; Muhammad Irfan Bashir, PhD; Shahid Bashir, PhD; Azadeh Bashiri, PhD; Mohammad-Mahdi Bastan, MD; Mahdis Bayat, MD; Mulat Tirfie Bayih, MSc; Narasimha M Beeraka, PhD; Tahmina Begum, PhD; Priyamadhaba Behera, MD; Surama Manjari Behera, PhD; Babak Behnam, MD; Diana Fernanda Bejarano Ramirez, MSc; Melesse Belayneh, PhD; Abdulrahman Babatunde Bello, PhD; Luis Belo, PhD; Abiye Assefa Berihun, MPH; Sonu Bhaskar, MD; Priyadarshini Bhattacharjee, MD; Gurjit Kaur Bhatti, PhD; Jasvinder Singh Bhatti, PhD; Atanu Biswas, DM; Bijit Biswas, MD; Bruno Bizzozero-Peroni, PhD; Mahmut Bodur, PhD; Sitotaw Kerie Bogale, MS; Lucimere Bohn, PhD; Archith Boloor, MD; Paria Bolourinejad, MD; Aime Bonny, MD; Alejandro Botero Carvajal, PhD; Souad Bouaoud, DrPH; Sofiane Boudalia, PhD; Gabrielle Britton, PhD; Raffaele Bugiardini, MD; Felix Busch, MD; Yasser Bustanji, PhD; Zahid A. Butt, PhD; Luciana Aparecida Campos, PhD; Yu Cao, BSc; Alberico L. Catapano, PhD; Luca Cegolon, PhD; Francieli Cembranel, DSc; Edina Cenko, MD; Ester Cerin, PhD; Joshua Chadwick, MD; Chiranjib Chakraborty, PhD; Rama Mohan Chandika, PhD; Eeshwar K Chandrasekar, MD; Baskaran Chandrasekaran, PhD; Vijay Kumar Chattu, PhD; Lam Duc Chau, BA; Anis Ahmad Chaudhary, PhD; Sirshendu Chaudhuri, MD; Asad Ali Ahmed Cheema, MD; An-Tian Chen, PhD; Hana Chen, MSc; Haowei Chen, PhD; Haojin Cheng, BSc; Ka Ching Cheung, MSc; Nicholas W.S. Chew, MD; Gerald Chi, MD; Fatemeh Chichagi, MD; Patrick R. Ching, MD; William C. S. Cho, PhD; Dong-Woo Choi, PhD; Bryan Chong, MBBS; Deepti Chopra, MD; Hitesh Chopra, PhD; Shivani Chopra, MPH; Sonali Gajanan Choudhari, MD; Dinh-Toi Chu, PhD; Chidozie Williams Chukwu, PhD; Sheng-Chia Chung, PhD; Sunghyun Chung, MPH; Arrigo Francesco Giuseppe Cicero, PhD; Claudia Cosma, MD; Michael H. Criqui, MD; Natalia Cruz-Martins, PhD; Omid Dadras, PhD; Lina Dahabiyeh, PhD; Bishal Dahal Khatri, BPH; Xiaochen Dai, PhD; Zhaoli Dai, PhD; Mayank Dalakoti, MPH; Emanuele D’Amico, MD; Endalamaw Tesfa Damtie, MSc; Lalit Dandona, MD; Rakhi Dandona, PhD; Lucio D’Anna, PhD; Pojsakorn Danpanichkul, MD; Samuel Demissie Darcho, MPH; Latefa Ali Dardas, PhD; Sayan Kumar Das, MD; Dimash Davletov, MD; Kairat Davletov, PhD; Tadesse Asmamaw Dejenie, MSc; Cristian Del Bo’, PhD; Ivan Delgado-Enciso, DSc; Edgar Denova-Gutiérrez, DSc; Ismail Dergaa, PhD; Hunegnaw Almaw Derseh, MPH; Emina Dervišević, PhD; Aragaw Tesfaw Desale, MPH; Vinoth Gnana Chellaiyan Devanbu, MD; Devananda Devegowda, PhD; Syed Masudur Rahman Dewan, PhD; Arkadeep Dhali, MBBS; Meghnath Dhimal, PhD; Bibha Dhungel, DrPH; Diana Dias da Silva, PhD; Kimia Didehvar, MD; Xueting Ding, MA; Thanh Chi Do, MD; Thao Huynh Phuong Do, MD; Sushil Dohare, MD; Dachuan Dong, PhD; Mario D’Oria, MD; Fariba Dorostkar, PhD; Ojas Prakashbhai Doshi, MS; Rajkumar Prakashbhai Doshi, MD; Paulo Magno Martins Dourado, PhD; Robert Kokou Dowou, MPhil; Menayit Tamrat Dresse, MD; Mi Du, PhD; Bruce B. Duncan, MD; Andre Rodrigues Duraes, PhD; Osamudiamen Ebohon, MPH; Lamiaa Labieb Mahmoud Ebraheim, PhD; Cynthia Edeh, MD; Behrad Eftekhari, MD; Ashkan Eighaei Sedeh, MD; Michael Ekholuenetale, PhD; Rabie Adel El Arab, PhD; Mohamed Ahmed Eladl, PhD; Ahmed Eldaboush, MD; Faris El-Dahiyat, PhD; Islam Y. Elgendy, MD; Muhammed Elhadi, MD; Mohamed Elhoumed, PhD; Waseem El-Huneidi, PhD; Omar Abdelsadek Abdou Elmeligy, PhD; Mohamed A. Elmonem, PhD; Adel B. Elmoselhi, PhD; Mohamed Hassan Elnaem, PhD; Abdelgawad Salah Eltahawy, PhD; Farshid Etaee, MD; Natalia Fabin, MD; Adewale Oluwaseun Fadaka, PhD; Adeniyi Francis Fagbamigbe, PhD; Ildar Ravisovich Fakhradiyev, PhD; Sodiq Fakorede, BSc; Abdullah Farasani, PhD; Mohammad Fareed, PhD; Aisha Farhana, PhD; MoezAlIslam Ezzat Mahmoud Faris, PhD; Farima Farsi, MD; Zareen Fatima, PhD; Mohammad Fayaz, PhD; Timur Fazylov, MD; Ginenus Fekadu, PhD; Rodrigo Fernandez-Jimenez, PhD; Nuno Ferreira, PhD; Florian Fischer, PhD; Federica Fogacci, MD; Marco Fonzo, MD; Matteo Foschi, MD; Muktar A. Gadanya, MD; Márió Gajdács, PhD; Yaseen Galali, ResM; Balasankar Ganesan, PhD; Balasubramanian Ganesh, PhD; Shivaprakash Gangachannaiah, MD; Dingwei Gao, PhD; David Garcia-Azorin, MD; Paarth Garg, MD; Danijela Gasevic, PhD; Rupesh K. Gautam, PhD; Bamba Gaye, PhD; Mizan Habtemichael Gebresilassie, MSc; Lemma Getacher, PhD; Kalab Yigermal Gete, MD; Fataneh Ghadirian, PhD; Kazem Ghaffari, PhD; Amir Ghaffari Jolfayi, MD; Arin Ghamkhar, BSc; Lobna Gharaibeh, PhD; Mohammadreza Ghasemi, MD; Ramy Mohamed Ghazy, PhD; Sailaja Ghimire, MPH; Nermin Ghith, PhD; Artyom Urievich Gil, PhD; Syed Abdullah Gilani, PhD; Paramjit Singh Gill, DM; Elena V. Gnedovskaya, PhD; Lay Hoon Goh, PhD; Kimiya Gohari, MS; Megha Nishith Gohil, PhD; Mahaveer Golechha, PhD; Pouya Goleij, MSc; Melika Golmohammadi, MD; Alessandra C Goulart, PhD; Aman Goyal, MD; Ayman Grada, MD; Ashna Grover, MD; Long Gu, MD; Mohammed Ibrahim Mohialdeen Gubari, PhD; Zenawi Hagos Gufue, MPH; Avirup Guha, MD; Damitha Asanga Gunawardane, MD; Zheng Guo, PhD; Rajeev Gupta, MD; Sapna Gupta, PhD; Filippo Luca Gurgoglione, MD; Jose Guzman-Esquivel, DSc; Eduardo Guzmán-Muñoz, PhD; Arian Haghtalab, MD; Nguyen Hai Nam, PhD; Kirubel Tesfaye Hailu, MD; Pritam Halder, MD; Nadia M. Hamdy, PhD; Hossein Hamidi, MD; Samer Hamidi, DrPH; Eman A Hammad, PhD; Rifat Hamoudi, PhD; Asif Hanif, PhD; Nasrin Hanifi, PhD; Graeme J. Hankey, MD; Sara Harsini, MD; Phillipp Hartmann, MD; Ikramul Hasan, MPharm; Mohammad Hashem Hashempur, PhD; Md Saquib Hasnain, PhD; Amr Hassan, MD; Ibrahim Nagmeldin Hassan, MD; Ikrama Hassan, PhD; Nageeb Hassan, PhD; Treska S. Hassan, PhD; Omed Hassan Ahmed, PhD; Khezar Hayat, MS; Jeffrey J. Hebert, PhD; Yosra A. Helmy, PhD; Kamal Hezam, PhD; Yuta Hiraike, PhD; Ramesh Holla, MD; Mehdi Hoseinzadeh, PhD; Mihaela Hostiuc, PhD; Sorin Hostiuc, PhD; Priya Hotwani, MD; Mila Nu Nu Htay, PhD; Yifei Hu, PhD; Junjie Huang, MD; Yu-Song Huang, MD; Kiavash Hushmandi, PhD; Dursa Hussein, MPH; Mohammed Asif Hussein, MSc; Bing-Fang Hwang, PhD; Segun Emmanuel Ibitoye, PhD; Ismail A. Ibrahim, BSc; Khalid S. Ibrahim, PhD; Anel Ibrayeva, PhD; Adalia Ikiroma, PhD; Jibran Ikram, MD; Olayinka Stephen Ilesanmi, PhD; Irena M. Ilic, PhD; Milena D. Ilic, PhD; Mohammad Tarique Imam, PhD; Masoud Imani, MSc; Sikiru Opeyemi Imodoye, MSc; Lucius Chidiebere Imoh, MPH; Leeberk Raja Inbaraj, MD; Andreea Iuliana Ionescu, MD; Radu Tudor Ionescu, PhD; Muhammad Iqhrammullah, PhD; Lalu Muhammad Irham, PhD; Azfar Athar Ishaqui, PhD; Duygu Islek, PhD; Nahlah Elkudssiah Ismail, PhD; Yerlan Ismoldayev, PhD; Gaetano Isola, PhD; Masao Iwagami, PhD; Mahalaxmi Iyer, PhD; Juan S. S. Izquierdo-Condoy, MSc; Jaison Jacob, MSc; Ali Jafari-Khounigh, PhD; Ali Jahanshahi, MD; Haitham Jahrami, PhD; Mihajlo Jakovljevic, PhD; Armaan Jamal, BS; Navid Jamali, PhD; Jazlan Jamaluddin, MMed; Jerin James, MD; Masoud Jamshidi, PhD; Robin Jansen, MD; Esmaeil Jarrahi, MSc; Syed Sarmad Javaid, MD; Talha Jawaid, PhD; Yovanthi Anurangi Jayasinghe, BSc; Felix K. Jebasingh, DM; Mohammed Jemal, MSc; Seogsong Jeong, PhD; Bijay Mukesh Jeswani, MBBS; Zixiang Ji, MD; Nathan T. Jibat, MD; Wenyi Jin, MD; Mohammad Jokar, DVM; Tamas Joo, PhD; Akaninyene Paul Joseph, PhD; Mickael Antoine Joseph, PhD; Nitin Joseph, MD; Jesu Vijay Sheldon Joseph Michael Raj, MD; Krupal Joshi, MD; Charity Ehimwenma Joshua, BSc; Sang-Hyuk Jung, PhD; Mikk Jürisson, PhD; Vaishali K, PhD; Dler H. Hussein Kadir, PhD; Farima Kahe, MD; Ashish Kumar Kakkar, MD; Rizwan Kalani, MD; Khalil Kalavani, PhD; Sanjay Kalra, DM; Saltanat Kamenova, DMedSc; Ramat T. Kamorudeen, MPH; Oleksandr Kamyshnyi, DSc; Saddam Fuad Kanaan, PhD; Kehinde Kazeem Kanmodi, MPH; Subham Kansal, MD; Rami S. Kantar, MD; Neeti Kapoor, PhD; Debasish Kar, MD; Mehrdad Karajizadeh, PhD; Paschalis Karakasis, MD; Reema A. Karasneh, PhD; Aliasghar Karimi, MD; Arman Karimi Behnagh, MD; Sadanand Karun, MA; Faizan Zaffar Kashoo, PhD; Manoj Kumar Kashyap, PhD; Mohd Adnan Kausar, PhD; Sina Kazemian, MD; Yabets Tesfaye Kebede, MD; Emmanuelle Kesse-Guyot, PhD; Reza Khademi, MD; Yousef Saleh Khader, PhD; Himanshu Khajuria, PhD; Anas Khaleel, PhD; Nauman Khalid, PhD; Sidra Khalid, PhD; Anees Ahmed Khalil, PhD; Anita Khalili, MD; Pantea Khalili, MD; Ajmal Khan, PhD; Maseer Khan, MD; Md Abdullah Saeed Khan, MPH; Mohammad Idreesh Khan, PhD; Moien A.B. Khan, MSc; Muhammad Hamza Khan, MBBS; Serab Khan, PhD; Sumaiya Khan, MSc; Yusuf Saleem Khan, MD; Sameer Uttamaro Khasbage, MD; Moawiah Mohammad Khatatbeh, PhD; Khalid A. Kheirallah, PhD; Samira Khoshvaght, MSc; Pavan Khosla, BA; Sepehr Khosravi, MD; Hye Jun Kim, MS; Kwanghyun Kim, PhD; Ruth W. Kimokoti, MD; Adnan Kisa, PhD; Ladli Kishore, PhD; Mika Kivimäki, PhD; Michail Kokkorakis, MD; Ali-Asghar Kolahi, MD; Farzad Kompani, MD; Aida Kondybayeva, PhD; Oleksii Korzh, DSc; Karel Kostev, PhD; Sindhura Lakshmi Koulmane Laxminarayana, MD; Kewal Krishan, PhD; Chong-Han Kua, PhD; Mohammed Kuddus, PhD; Mukhtar Kulimbet, MSc; Vishnutheertha Kulkarni, MS; Chandan Kumar, PhD; Dewesh Kumar, MD; G. Anil Kumar, PhD; Jogender Kumar, MD; Laksh Kumar, MBBS; Nitesh Kumar, PhD; Sanjay Kirshan Kumar, FCPS; Amartya Kundu, MD; Satyajit Kundu, MPH; Setor K. Kunutsor, PhD; Maria Dyah Kurniasari, PhD; Krishna Prasad Kurpad, MD; Dian Kusuma, DSc; Ville Kytö, MD; Chandrakant Lahariya, MD; Daphne Teck Ching Lai, PhD; Hanpeng Lai, PhD; Timo Lajunen, PhD; Balzhan Lakanova, MD; Tea Lallukka, PhD; Tamás Lantos, PhD; Yilma Markos Larebo, PhD; Anders O. Larsson, PhD; Savita Lasrado, MS; Duc Tin Le, PhD; Thao Thi Thu Le, MD; Thoa Le, MD; Trang Thi Bich Le, MD; Caterina Ledda, PhD; Hayeon Lee, PhD; Hwamin Lee, PhD; Sang-woong Lee, PhD; Sergey Vadimovich Lee, PhD; Seung Won Lee, MD; Wei-Chen Lee, PhD; Vasileios Leivaditis, PhD; Gebretsadik Kiros Lema, MPH; Dawit Alemu Lemma, MSc; Jimma Lenjisa, PhD; Mitchelle Shilpa Lewis, MSc; Chengfeng Li, MPH; Ming-Chieh Li, PhD; Qiang Li, MD; Shaojie Li, MD; Xingang Li, PhD; Yongze Li, PhD; Lee-Ling Lim, PhD; Daniel Lindholm, MD; Haipeng Liu, PhD; Xianliang Liu, PhD; Xuefeng Liu, PhD; Yubo Liu, PhD; Katherine M. Livingstone, PhD; Erand Llanaj, PhD; Madeeha Shahzad Lodhi, PhD; Niroshan Chandrajith Lokunarangoda, MD; José Francisco López-Gil, PhD; Stefan Lorkowski, PhD; Giancarlo Lucchetti, PhD; Pauline Po Yee Lui, PhD; Peng Luo, MD; Lei Lv, PhD; Ellina Lytvyak, MD; Hawraz Ibrahim M. Amin, PhD; Kevin Sheng-Kai Ma, DDS; Mahmoud Mabrok, PhD; Monika Machoy-Rakoczy, PhD; Seyed Ataollah Madinehzad, MD; Pasquale Maffia, PhD; Javier A. Magaña Gómez, PhD; Mehrdad Mahalleh, MD; Nozad Hussein Mahmood, PhD; Ahmad Azam Malik, PhD; Md. Zubbair Malik, PhD; Tabarak Malik, PhD; Deborah Carvalho Malta, PhD; Doste Rashid Mamand, PhD; Muhammad Talha Maniya, MD; Kamaruddeen Mannethodi, MPH; Farheen Mansoor, MS; Marjan Mansourian, PhD; Shaista Manzoor, PhD; Kaleem Maqsood, PhD; Hamid Reza Marateb, PhD; Basma Hamed Marghani, PhD; Mirko Marino, PhD; Ramon Martinez-Piedra, BSc; Daniela Martini, PhD; Santi Martini, PhD; Miquel Martorell, PhD; Winfried März, MD; Sammer Marzouk, MA; Stefano Masi, PhD; Soroush Masrouri, MD; Neeta Mathur, MD; Fernanda Penido Matozinhos, PhD; Rita Mattiello, PhD; Richard James Maude, PhD; Georgios Mavrovounis, MSc; Steven M. McPhail, PhD; Subhash Mehto, PhD; Hadush Negash Meles, MSc; Walter Mendoza, MD; Godfred Antony Menezes, PhD; Ritesh G. Menezes, MD; Emiru Ayalew Mengistie, MSc; Tara P. Menon, BS; George A. Mensah, MD; Giovanni Merlino, PhD; Tomislav Mestrovic, PhD; Chamila Dinushi Kukulege Mettananda, PhD; Sachith Mettananda, DPhil; Mohamed M. M. Metwally, PhD; Junmei Miao Jonasson, PhD; Muhammad Agus Naufal Mikrajab, MPH; Abdul Mannan Khan Minhas, MBBS; Erkin M. Mirrakhimov, MD; Sayan Mitra, PhD; Tridip Mitra, MSc; Madhukar Mittal, MD; Dhruti Modi, MPT; Mona Gamal Mohamed, PhD; Nouh Saad Mohamed, MSc; Khabab Abbasher Hussien Mohamed Ahmed, MD; Ameen Mosa Mohammad, MD; Taj Mohammad, PhD; Abbas Mohammadi, MD; Mokhtar Mohammadi, PhD; Saeed Mohammadi, PhD; Abdollah Mohammadian-Hafshejani, PhD; Omer Mohammed, MBBS; Syam Mohan, PhD; Yazan Mohsen, MD; Tewodros Shibabaw Molla, MSc; Mariam Molokhia, PhD; Amirabbas Monazzami, PhD; Mohammad Ali Moni, PhD; Sara Montazeri Namin, MD; AmirAli Moodi Ghalibaf, MD; Mahdis Morovvati, MD; Reza Mosaddeghi Heris, MD; Rohith Motappa, MD; Seyed Ahmad Mousavi, MSc; Seyed Mohamad Sadegh Mousavi Kiasary, DVM; Mehrdad Mozafar, MD; Kimia Mozahheb Yousefi, MD; Sumaira Mubarik, PhD; Jibran Sualeh Muhammad, PhD; Yanjinlkham Munkhsaikhan, MD; Anjana Munshi, PhD; Sani Musa, MSc; Sathish Muthu, PhD; Julius C. Mwita, MD; Woojae Myung, PhD; SeyedAli Nabipoorashrafi, MD; Ahamarshan Jayaraman Nagarajan, MTech; Ganesh R. Naik, PhD; Hiten Naik, MS; Firzan Nainu, PhD; Shumaila Nargus, PhD; Yvonne Nartey, PhD; Amir Nasrollahizadeh, MD; Mahmoud Nassar, PhD; Zuhair S. Natto, DrPH; Javaid Nauman, PhD; Zakira Naureen, PhD; Samidi Nirasha Kumari Navaratna, MD; Hala Khaled Nawaiseh, PhD; Biswa Prakash Nayak, PhD; Vinod C. Nayak, MD; Mojdeh Nazari, PhD; Andrew Kamsoko Ndakotsu, MD; Amanuel Tebabal Nega, MSc; Mulata H. Nega, MSc; Abigia Ashenafi Negash, MD; Ionut Negoi, PhD; Reza Nejad Shahrokh Abadi, MD; Mohammad Hadi Nematollahi, PhD; Cao Duy Nguyen, MD; Cuong Tat Nguyen, MPH; Hau Thi Hien Nguyen, MD; Nghia Phu Nguyen, MD; Phat Tuan Nguyen, MD; Van Thanh Nguyen, MD; Robina Khan Niazi, PhD; Luciano Nieddu, PhD; Richmond Nketia, MSc; Shuhei Nomura, PhD; Mamoona Noreen, PhD; Nawsherwan, PhD; Jean Jacques Noubiap, MD; Chisom Adaobi Nri-Ezedi, PhD; Mpiko Ntsekhe, PhD; Fred Nugen, PhD; Aqsha Nur, MPH; Bogdan Oancea, PhD; Ramez M Odat, MD; Fabio Massimo Oddi, PhD; Saheed Adeyinka Odediji, MSc; Oluwaseyi Samson Ogunmodede, PhD; In-Hwan Oh, MD; Olusegun Olatunji Ojedoyin, PhD; Oluwafemi Adeleke Ojo, PhD; Hassan Okati-Aliabad, PhD; Akinkunmi Paul Okekunle, PhD; Osaretin Christabel Okonji, MSc; Gláucia Maria Moraes Oliveira, PhD; Abdulhakeem Abayomi Olorukooba, MD; Qi Chwen Ong, MBBS; Michal Ordak, PhD; Hans Orru, PhD; Atakan Orscelik, MD; Alberto Ortiz, MD; Esteban Ortiz-Prado, PhD; Augustus Osborne, MSc; George Nkrumah Osei, BSc; John W. Ostrominski, MD; Uchechukwu Levi Osuagwu, PhD; Abdu Oumer, PhD; Amel Ouyahia, PhD; Mayowa O. Owolabi, DrM; Kolapo Oyebola, PhD; Ilker Ozsahin, PhD; Mahesh Padukudru Anand, DNB; Alicia Padron-Monedero, PhD; Jagadish Rao Padubidri, MD; Tamás Palicz, PhD; Sujogya Kumar Panda, PhD; Seithikurippu R. Pandi-Perumal, MSc; Georgios D. Panos, MD; Mario Virgilio Papa, MD; Ilias Papadimopoulos, MD; Shahina Pardhan, PhD; Pragyan Paramita Parija, MD; Romil R. Parikh, MD; Eleonora Pascucci, MSc; Maja Pasovic, MEd; Roberto Passera, PhD; Kunal Nitinkumar Patel, MD; Mitesh Patel, PhD; Neel Navinkumar Patel, MD; Niki Rut Patel, MPT; Satyananda Patel, PhD; Ashlesh Patil, MD; Shankargouda Patil, PhD; Dimitrios Patoulias, PhD; Snigdha Pattnaik, MD; Hamidreza Pazoki Toroudi, PhD; Jarmila Pekarcikova, PhD; Prince Peprah, MSc; Gavin Pereira, PhD; Norberto Perico, MD; Simone Perna, PhD; Fanny Petermann-Rocha, PhD; Edoardo Pirera, MD; Habibollah Pirnejad, PhD; Evgenii Plotnikov, PhD; Dimitri Poddighe, PhD; Ramesh Poluru, PhD; Thantrira Porntaveetus, PhD; Pranil Man Singh Pradhan, MD; Manya Prasad, MD; Akila Prashant, PhD; Elton Junio Sady Prates, BS; Nicola Riccardo Pugliese, PhD; Jagadeesh Puvvula, PhD; Xiang Qi, PhD; Jia-Yong Qiu, MD; Ali Qureshi, PhD; Venkatraman Radhakrishnan, MD; Pankaja Raghav, MD; Yashpal Singh Raghav Raghav, PhD; Pracheth Raghuveer, MD; Sheu Kadiri Rahamon, PhD; Fakher Rahim, PhD; Hawbash Mohammed-Amin Rahim, MSc; Sajjad Rahimi, PhD; Fryad Majeed Rahman, PhD; Md. Mosfequr Rahman, PhD; Mohammad Hifz Ur Rahman, PhD; Mosiur Rahman, DrPH; Muhammad Aziz Rahman, PhD; Jeffrey Pradeep Raj, DM; Adarsh Raja, MD; Judah Rajendran, MD; Prashant Rajput, PhD; Mahmoud Mohammed Ramadan, PhD; Chitra Ramasamy, MD; Shakthi Kumaran Ramasamy, MD; Asad Gul Rao, MBBS; Mithun Rao, MD; Sowmya J. Rao, MDS; Vahid Rashedi, PhD; Ashkan Rasouli-Saravani, PhD; Shree Rath, MBBS; Isha Rathi, MPH; Devarajan Rathish, PhD; Santosh Kumar Rauniyar, PhD; David Laith Rawaf, MD; Salman Rawaf, MD; Reza Rawassizadeh, PhD; Asit Ray, PhD; Murali Mohan Rama Krishna Reddy, MD; Elrashdy M. Redwan, PhD; Najeeb Ur Rehman, PhD; Giuseppe Remuzzi, MD; Mina Rezaei, PhD; Nazila Rezaei, MD; Mohsen Rezaeian, PhD; Jefferson Antonio Buendia Rodriguez, PhD; Leonardo Roever, PhD; Debby Syahru Romadlon, PhD; Michele Romoli, MD; Moustaq Karim Khan Rony, MPH; Allen Guy Patrick Ross, MD; Sharmistha Roy, MD; Shubhanjali Roy, MSc; Polani Rubeshkumar, MPH; Michele Russo, PhD; Godfrey Mutashambara Rwegerera, MD; Aly M. A. Saad, MD; Maha Mohamed Saber-Ayad, PhD; Cameron John Sabet, MA; Kabir P. Sadarangani, PhD; Seyed Kiarash Sadat Rafiei, MD; Muhammad Nabeel Saddique, MD; Bashdar Abuzed Sadee, PhD; Ehsan Sadeghi, PhD; Erfan Sadeghi, PhD; Masoumeh Sadeghi, MD; Bassem Sadek, PhD; Mustafa Sadek, PhD; Hossein Sadr, PhD; Mohammad Reza Saeb, PhD; Mohd Saeed, PhD; Umar Saeed, PhD; Maryam Saeedi, PhD; Muhammad Safiullah, MBBS; Amene Saghazadeh, MD; Dominic Sagoe, PhD; Ashok Kumar Sah, PhD; Fatemeh Saheb Sharif-Askari, PhD; Amirhossein Sahebkar, PhD; S Mohammad Sajadi, PhD; Mirza Rizwan Sajid, PhD; Morteza Saki, PhD; Joseph W. Sakshaug, PhD; Afeez Abolarinwa Salami, BDS; Rahman Shah Zaib Saleem, PhD; Mohamed A. Saleh, PhD; Mahdi Salehi, MD; Abubakar Tijjani Salihu, PhD; Sohrab Salimi, MD; Saad Samargandy, PhD; Yoseph Leonardo Samodra, PhD; Abdallah M. Samy, PhD; Aswini Saravanan, MD; Jacob Owusu Sarfo, PhD; Mohammad Sarmadi, MSc; Gargi Sachin Sarode, PhD; Sachin C. Sarode, PhD; Nizal Sarrafzadegan, MD; Michele Sassano, MD; Brijesh Sathian, PhD; Mukesh Kumar Sathya Narayanan, MBBS; Mehrdad Savabi Far, MD; Markus P. Schlaich, MD; Art Schuermans, BSc; Ghil Schwarz, MD; Anita Sejben, PhD; Siddharthan Selvaraj, PhD; Mohammad H. Semreen, PhD; Yigit Can Senol, MD; Dragos Serban, PhD; Francesco Sessa, PhD; Yashendra Sethi, MD; Christianus Heru Setiawan, MSc; Christian Sewor, MPH; Allen Seylani, MD; Samiah Shahid, PhD; Endrit Shahini, MD; Fatemeh Shahrahmani, MD; Moyad Jamal Shahwan, PhD; Sunder Sham, MD; Muhammad Aaqib Shamim, MBBS; Anas Shamsi, PhD; Dan Shan, PhD; Mohd Shanawaz, MD; Mohammed Shannawaz, PhD; Nadim Sharif, MSc; Amin Sharifan, PharmD; Bhoopesh Kumar Sharma, PhD; Bunty Sharma, PhD; Kamal Sharma, MD; Manoj Sharma, PhD; Ravi Kumar Sharma, PhD; Ujjawal Sharma, PhD; Vishal Sharma, PhD; Shamee Shastry, MD; Somia Shehzadi, PhD; Ali Sheidaei, PhD; Rekha Raghuveer Shenoy, PhD; Mahabalesh Shetty, MD; Pavanchand H. Shetty, MD; Fanchao Shi, PhD; Hui-Zhong Shi, MD; Yongfeng Shi, PhD; Tariku Shimels, MSc; Rahman Shiri, PhD; Aminu Shittu, MSc; Ivy Shiue, PhD; Ambreen Shoaib, PhD; Sina Shool, MD; Seyed Afshin Shorofi, PhD; Sunil Shrestha, PhD; Kerem Shuval, PhD; Ching-Hui Sia, MCI; Emmanuel Edwar Siddig, MD; Ahmed Kamal Siddiqi, MD; Ayesha Siddiqua, MD; Diego Augusto Santos Silva, PhD; Luís Manuel Lopes Rodrigues Silva, PhD; Padam Prasad Simkhada, PhD; Amit Singh, PhD; Baljinder Singh, PhD; Harmanjit Singh, DM; Harpreet Singh, PhD; Jasvinder A. Singh, MD; Lucky Singh, PhD; Paramdeep Singh, MD; Poornima Suryanath Singh, PhD; Puneetpal Singh, PhD; Siddharth Singh, MSc; Sunil Kumar Singh, PhD; Surjit Singh, MD; Vanshika Singh, MBBS; Mukesh Kumar Sinha, PhD; Ratnesh Sinha, MD; Valentin Yurievich Skryabin, MD; Balamrit Singh Sokhal, MD; Prashant Sood, PhD; Soroush Soraneh, MD; Reed J. D. Sorensen, PhD; Michele Sorrentino, MD; Michael Spartalis, PhD; Prateek Srivastav, PhD; Panagiotis Stachteas, MSc; Muhammad Haroon Stanikzai, MPH; Antonina V. Starodubova, DSc; Sebastian Straube, DPhil; Peter Stubbs, PhD; Chen-Yang Su, PhD; Vetriselvan Subramaniyan, PhD; Alisha Suhag, PhD; Oksana Sulaieva, PhD; Sahabi K. Sulaiman, MD; Muritala Suleiman Odidi, PhD; Desy Sulistiyorini, MSc; Jing Sun, PhD; Zhong Sun, PhD; Chandan Kumar Swain, MPhil; Lukasz Szarpak, PhD; Sree Sudha Ty, MD; Payam Tabaee Damavandi, MD; Rafael Tabarés-Seisdedos, PhD; Seyyed Mohammad Tabatabaei, PhD; Seyed-Amir Tabatabaeizadeh, PhD; Shima Tabatabai, PhD; Celine Tabche, MSc; Afework Tadele, MPH; Balamurugan Tagiisuran, PhD; Eman Taha Osman Ali, MSc; Moslem Taheri Soodejani, PhD; Jabeen Taiba, PhD; Shima Tajabadi, MSc; Stella Talic, PhD; Ashis Talukder, MSc; Seyed Saeed Tamehri Zadeh, MD; Dessalegn Tamiru Adugna, PhD; Mircea Tampa, PhD; Jianye Tan, MD; Dariga Tanabayeva, BSc; Mohammad Tanashat, MD; Jiahao Tang, BMed; Ekamol Tantisattamo, MD; Mengistie Kassahun Tariku, MPH; Saba Tariq, PhD; Seyed Mohammad Tavangar, MD; Mebrahtu G. Tedla, PhD; Reem Temsah, PharmD; Masayuki Teramoto, MD; Wegen Beyene Tesfamariam, MSc; Azimeraw Arega Tesfu, MSc; Jay Tewari, MBBS; Vidhi Thakar, MPT; Lakshmi Thangavelu, PhD; Ismaeel Tharwat, PhD; Samar Tharwat, MD; Friedrich Thienemann, PhD; Muthu Thiruvengadam, PhD; Nikhil Kenny Thomas, MD; Jansje Henny Vera Ticoalu, MPH; Tenaw Yimer Tiruye, PhD; Krishna Tiwari, MBBS; Madi Tleshev, MSc; Sojit Tomo, MD; Marcello Tonelli, MD; Roman Topor-Madry, PhD; Mathilde Touvier, PhD; Marcos Roberto Tovani-Palone, PhD; An Thien Tran, MD; Thang Huu Tran, MD; Nguyen Tran Minh Duc, MD; Domenico Trico, MD; Christopher Daniel Tristan, BMed; Gary Tse, PhD; Vasilis-Spyridon Tseriotis, MSc; Abdul Rohim Tualeka, PhD; Sok Cin Tye, PhD; Aniefiok John Udoakang, PhD; Atta Ullah, MS; Riaz Ullah, PhD; Saeed Ullah, MSc; Muhammad Umair, PhD; Lawan Umar, PhD; Dinesh Upadhya, PhD; Era Upadhyay, PhD; Jibrin Sammani Usman, PhD; Dilber Uzun Ozsahin, PhD; Hande Uzunçıbuk, PhD; Asokan Govindaraj Vaithinathan, MSc; Omid Vakili, PhD; Jef Van den Eynde, BSc; Joe Varghese, PhD; Tommi Juhani Vasankari, PhD; Balachandar Vellingiri, PhD; Narayanaswamy Venketasubramanian, MSc; Akshaya Kumar Verma, PhD; Poonam Verma, PhD; Victor E. Villalobos-Daniel, PhD; Vasily Vlassov, MD; Jin-Yi Wan, PhD; Cong Wang, PhD; Nelson Wang, MD; Qingzhi Wang, PhD; Shaopan Wang, PhD; Shu Wang, MD; Wanzhou Wang, PhD; Wei Wang, MD; Wei Wang, PhD; Yanzhong Wang, PhD; Youxin Wang, MD; Zhihua Wang, PhD; Tanveer A. A. Wani, PhD; Ahmed Bilal Waqar, PhD; Melissa Y. Wei, MD; Robert G. Weintraub, MB; Yohanes Cakrapradipta Wibowo, MD; Anggi Lukman Wicaksana, PhD; Dakshitha Praneeth Wickramasinghe, MD; Nuwan Darshana Wickramasinghe, MD; Angga Wilandika, PhD; William Kwame Witts, MSc; Tewodros Eshete Wonde, MPH; Utoomporn Wongsin, PhD; Peng Wu, PhD; Guangqin Xiao, MD; Wanqing Xie, DrPH; Site Xu, MPH; Suowen Xu, PhD; Wanqing Xu, MPH; Xiaoyue Xu, PhD; Vikas Yadav, MD; Amirhossein Yadegar, MD; Saba Yahoo, MD; Kazumasa Yamagishi, MD; Hanwen Yang, PhD; Yuichiro Yano, MD; Haiqiang Yao, PhD; Amir Yarahmadi, PhD; Habib Yaribeygi, PhD; Subah Abderehim Yesuf, MSc; Dehui Yin, DrPH; Yazachew Engida Yismaw, MSc; Dong Keon Yon, MD; Naohiro Yonemoto, PhD; Chuanhua Yu, PhD; Elaine A. Yu, PhD; Hairui Yu, PhD; Yong Yu, MS; Quan Yuan, MD; Emad Yuzbashian, PhD; Mubashir Zafar, PhD; Manijeh Zaghampour, MD; Giulia Zamagni, MSc; Nelson Zamora, MD; Aurora Zanghì, MD; Seema Zargar, PhD; Mohammed Zawiah, PhD; Mohammed G M Zeariya, PhD; Eyael M Zeru, MPH; Beijian Zhang, PhD; Casper J. P. Zhang, PhD; Hanyu Zhang, MD; Julio Min Fei Zhang, MD; Yunquan Zhang, PhD; Zhichang Zhang, MEng; Murat Zhanuzakov, DSc; Zhongyi Zhao, PhD; Ming-Hua Zheng, MD; Anthony Zhong, MA; Claire Chenwen Zhong, PhD; Hao Zhou, MD; Jiayan Zhou, PhD; Xiao-Dong Zhou, MD; Zhengyang Zhu, PhD; Abzal Zhumagaliuly, MD; Osama A. Zitoun, MD; Ghazal Zoghi, MD; Mohamed Ali Zoromba, PhD; Alimuddin Zumla, PhD; Ahed H. Zyoud, PhD; Sa’ed H. Zyoud, PhD; Shaher H. Zyoud, PhD; Simon I. Hay, DSc; Ali H. Mokdad, PhD; Christopher J. L. Murray, DPhil; Gregory A. Roth, MD, MPH.

Affiliations of The GBD 2023 LDL Cholesterol Collaborators: Institute for Health Metrics and Evaluation (IHME), University of Washington, Seattle, Washington (Razo, DeCleene, Johnson, Stark, LeGrand, Hay, Mokdad, Roth); Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, Washington (Razo, Hay, Mokdad, Roth); Al Zaytoonah University of Jordan, Amman, Jordan (Aalruz); University of the West of Scotland, Paisley, United Kingdom (Abaraogu); Alexandria University, Alexandria, Egypt (Abd ElHafeez); Marshall University, Huntington, West Virginia (Abdelmasseh); Mayo Clinic, Phoenix, Arizona (Abdelnabi); Tanta University, Tanta, Egypt (Abd-Elsalam); West Hertfordshire Hospitals NHS Trust, Hertfordshire, United Kingdom (Abdelsalam Elshenawy); Jazan University, Jazan, Saudi Arabia (Abdelwahab); St George’s University, St George’s, Grenada (Abdolizadeh); Iran University of Medical Sciences, Tehran, Iran (Abdollahi); University of Setif Algeria, Setif, Algeria (Abdoun); Cairo University, Cairo, Egypt (Abdrabou); University of Duhok, Duhok, Iraq (Abdulah); Bayero University Kano, Kano, Nigeria (Abdullahi); Toufik’s World Medical Association, Sumy, Ukraine (Abdul-Rahman); Wollo University, Dessie, Ethiopia (Abebe); University of Gondar, Gondar, Ethiopia (Abebe Getahun); Federal Medical Centre, Abuja, Nigeria (O. O. Abiodun); Babcock University, Ilishan-Remo, Nigeria (O. Abiodun); Massachusetts General Hospital, Boston, Massachusetts (Abohashem); Northumbria University, Newcastle, United Kingdom (Abonie); University of Ha’il, Ha’il, Saudi Arabia (Abourashed); Loma Linda University Medical Center, Loma Linda, California (Abramov); Addis Ababa University, Addis Ababa, Ethiopia (Abrar); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Abtahi); Applied Science Private University, Amman, Jordan (Abu Farha); Usmanu Danfodiyo University, Sokoto, Sokoto, Nigeria (Abubakar); University of Sharjah, Sharjah, United Arab Emirates (Abuhelwa); Hamad Bin Khalifa University, Doha, Qatar (Abukhadijah); United Arab Emirates University, Al Ain, United Arab Emirates (Aburuz); Hamad Medical Corporation, Doha, Qatar (Abushanab); Technical University of Munich, Munich, Germany (Adams); Modibbo Adama University, Yola, Yola, Nigeria (Adamu); Bahir Dar University, Bahir Dar, Ethiopia (Adane); University of Sydney, Sydney, New South Wales, Australia (Addo); Roswell Park Comprehensive Cancer Center, Buffalo, New York (Adedokun); East Tennessee State University, Johnson City, Tennessee (Adegbile); The University of Sydney, Sydney, New South Wales, Australia (Adegoke); University of Abuja, Abuja, Nigeria (Adekola); Bowen University, Iwo, Nigeria (Adeleke); Slum and Rural Health Initiative, Ibadan, Nigeria (Adesina); Woldia University, Woldia, Ethiopia (Adisu); University of Hail, Hail, Saudi Arabia (Adnan); Queensland University of Technology, Kelvin Grove, Queensland, Australia (Afoakwah); MSI Nigeria Reproductive Choices, Abuja, Nigeria (Afolabi); Iran University of Medical Sciences, Tehran, Iran (Afrashteh); Edith Cowan University, Perth, Western Australia, Australia (Afrifa-Yamoah); King Edward Memorial Hospital, Lahore, Pakistan (Afzal); University of Health and Allied Sciences, Ho, Ghana (Agordoh); Queen’s University, Kingston, Ontario, Canada (Agyemang-Duah); Shaqra University, Shaqra, Saudi Arabia (A. Ahmad); King Khaled Eye Specialist Hospital & Research Center, Riyadh, Saudi Arabia (K. Ahmad); Abasyn University, Peshawar, Pakistan (Sajjad Ahmad); School of Surgery Wales, Bangor, United Kingdom (Suhaib Ahmad); The University of Lahore, Lahore, Pakistan (A. Ahmed); University of Human Development, Sulaymaniyah, Iraq (G. S. Ahmed); United Arab Emirates University, Al Ain, United Arab Emirates (L. A. Ahmed); Majmaah University, Al Majmaah, Saudi Arabia (Mehrunnisha Sharif Ahmed); University of Duhok, Duhok, Iraq (Meqdad Saleh Ahmed); Mansoura University Hospital, Mansoura, Egypt (M. S. A. Ahmed); Rawalpindi Medical University, Rawalpindi, Pakistan (M. Ahmed); Haramaya University, Harar, Ethiopia (N. Ahmed); East Carolina University, Greenville, North Carolina (S. A. Ahmed); Shaheed Zulfiqar Ali Bhutto University Islamabad Pakistan, Islamabad, Pakistan (Z. Ahmed); Maharashtra University of Health Sciences, Pune, India (Aiyer); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Ajami); Charles Sturt University, Orange, New South Wales, Australia (Akalanka); The University of Lahore, Lahore, Pakistan (Akhtar); Bayero University Kano, Kano, Nigeria (Akindele); Fudan University, Shanghai, China (Akkaif); Midwestern University, Downers Grove, Illinois (Akrami); University of Nottingham, Nottingham, United Kingdom (Akyea); Washington University in St Louis, St Louis, Missouri (Al Hasan); University of Texas, Houston, Texas (Al Zoubi); University of Missouri, Columbia, Missouri (Alahdab); Al Ain University, Abu Dhabi, United Arab Emirates (Al-Ahmad); Wuqu’ Kawoq, Maya Health Alliance, Tecpán, Guatemala (Alajajian); Arab International University, Damascus, Syria (Alajlani); University of Bahrain, Zallaq, Bahrain (Alalwan); Washington University in St Louis, St Louis, Missouri (Al-Aly); University of Nizwa, Nizwa, Oman (Al-Amri); Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia (Alanzi); Ministry of Health, Riyadh, Saudi Arabia (Alarifi); Al-Ayen Iraqi University, Thi-Qar, Iraq (Al-Ashwal); Jordan University of Science and Technology, Irbid, Jordan (Al-Azayzih); Jordan University of Science and Technology, Irbid, Jordan (Al-Azzam); Philadelphia University, Amman, Jordan (Al-Bashaireh); Al al-Bayt University, Mafraq, Jordan (Albashtawy); Hamad Medical Corporation, Doha, Qatar (Al-Dewik); University of Sharjah, Sharjah, United Arab Emirates (Aleidi); University of Hail, Hail, Saudi Arabia (Algahtani); Suez Canal University, Ismailia, Egypt (Algammal); King Saud University, Riyadh, Saudi Arabia (Alhabib); Qatar University, Doha, Qatar (Alhalaiqa); Abdul Wali Khan University Mardan, Mardan, Pakistan (Abid Ali); All India Institute of Medical Sciences, Patna, India (Asgar Ali); United Arab Emirates University, Al Ain, United Arab Emirates (B. R. Ali); Mohammed Al-Mana College for Medical Sciences, Dammam, Saudi Arabia (M. D. Ali); University of Maiduguri, Maiduguri, Nigeria (M. U. Ali); Jamia Millia Islamia, New Delhi, India (R. Ali); University of Swat, Swat, Pakistan (S. S. Ali); The University of Jordan, Amman, Jordan (Al-Iede); Federation University Australia, Melbourne, Victoria, Australia (Alif); University of Hamburg, Hamburg, Germany (Alipour); An-Najah National University, Nablus, Palestine (Al-Jabi); International Medical University, Kuala Lumpur, Malaysia (Aljunid); University of Nizwa, Nizwa, Oman (Alkhatib); University of Hail, Hail, Saudi Arabia (Alkubati); Northwestern University, Chicago, Illinois (Allawi); Qassim University, Saudi Arabia, Buraydah, Saudi Arabia (Allemailem); United Arab Emirates University, Al Ain, United Arab Emirates (Allouh); University of Tabuk, Tabuk, Saudi Arabia (Almagharbeh); Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates (Almahmeed); Independent Consultant, Amman, Jordan (Al-Marwani); Nazarbayev University, Astana, Kazakhstan (Almazan); Al-Zaytoonah University of Jordan, Amman, Jordan (Alnaeem); University of Sharjah, Sharjah, United Arab Emirates (Alniss); Jordan University of Science and Technology, Irbid, Jordan (Alomari); Ministry of Defense Health Services, Riyadh, Saudi Arabia (Alotaibi); King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia (Alqahtani); Jordan University of Science and Technology, Irbid, Jordan (AlQudah); American University of the Middle East, Egaila, Kuwait (Alqudimat); Hamad Medical Corporation, Doha, Qatar (Al-Qudimat); King Abdulaziz University, Jeddah, Saudi Arabia (Al-Raddadi); Georgetown University, Washington, DC (Alrimawi); Ministry of Finance, Dubai, United Arab Emirates (Alrousan); Ajman University, Ajman, United Arab Emirates (Alsbou); University of Jeddah, Jeddah, Saudi Arabia (Alshahrani); Sana’a University, Sana’a, Yemen (Al-Shami); Higher College of Technology, Abu Dhabi, United Arab Emirates (Altaany); The University of Lahore, Lahore, Pakistan (Altaf); King Abdulaziz University, Jeddah, Saudi Arabia (Althobiani); Al-Balqa Applied University, Karak, Jordan (Altwalbeh); Umm Al-Qura University, Makkah, Saudi Arabia (Alwafi); Fakeeh College for Medical Sciences, Jeddah, Saudi Arabia (Al-Worafi); Cleveland Clinic, Cleveland, Ohio (Aly); Taibah University, Madinah, Saudi Arabia (Al-Zalabani); Ajman University, Ajman, United Arab Emirates (A. Alzoubi); Qatar University, Doha, Qatar (K. H. Alzoubi); Sana’a University, Sana’a, Yemen (Al-Zubairi); University of Central Nicaragua, Washington, DC (Amafah); Urmia University of Medical Sciences, Urmia, Iran (M. A. Mohammadi); University of Sharjah, Sharjah, United Arab Emirates (Amin); Khomein University of Medical Sciences, Khomein, Iran (Amini); Guilan University of Medical Sciences, Rasht, Iran (Amini-Salehi); University of Jos, Jos, Nigeria (Amusa); Eastern Health, Box Hill, Victoria, Australia (Ananda); Carol Davila University of Medicine and Pharmacy, Bucharest, Romania (Ancuceanu); Rutgers University, Toms River, New Jersey (Ang); University of Suffolk, Ipswich, United Kingdom (Anieto); All India Institute of Medical Sciences, Bhubaneswar, India (Anil); Charotar University of Science and Technology, Petlad, India (Anjana); University of Medical Sciences, Ondo, Ondo, Nigeria (Anuoluwa); Guilan University of Medical Sciences, Rasht, Iran (Anvari); Gadjah Mada University, Yogyakarta, Indonesia (Anwar); Charles Sturt University, Orange, New South Wales, Australia (Anyasodor); Komfo Anokye Teaching Hospital, Kumasi, Ghana (Appati); Iran University of Medical Sciences, Tehran, Iran (Arabloo); Ajman University, Ajman, United Arab Emirates (Arafa); Al Ain University, Abu Dhabi, United Arab Emirates (Arafat); University of Washington, Seattle, Washington (Aravkin); Ottawa University, Surprise, Arizona (Areda); Shiraz University of Medical Sciences, Shiraz, Iran (Arefnezhad); Adigrat University, Adigrat, Ethiopia (Aregawi); King’s College London, London, United Kingdom (Arias de la Torre); Tashkent Institute of Postgraduate Medical Education, Tashkent, Uzbekistan (Aripov); Sri Ramaswamy Memorial Institute of Science and Technology, Kattankulathur, India (Arockiaraj); University of Pernambuco-UPE, Recife, Brazil (de Arruda); AlMaarefa University, Riyadh, Saudi Arabia (Asdaq); Cabrini Health, Malvern, Victoria, Australia (Asghari-Jafarabadi); Dow University of Health Sciences, Karachi, Pakistan (S. Ashraf); University of Hail, Hail, Saudi Arabia (S. A. Ashraf); Pioneer Journal of Biostatistics and Medical Research (PJBMR), Lahore, Pakistan (T. Ashraf); Deakin University, Melbourne, Victoria, Australia (Asiamah-Asare); Marmara University, Istanbul, Turkiye (Atac); Umm Al-Qura University, Makkah, Saudi Arabia (Athar); Zanjan University of Medical Sciences, Zanjan, Iran (Athari); University of Newcastle, Newcastle, New South Wales, Australia (Atorkey); King Saud University, Riyadh, Saudi Arabia (Aurangzeb); The University of Lahore, Lahore, Pakistan (S. J. Awan); The University of Haripur, Haripur, Pakistan (U. A. Awan); Saint Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia (Awol); Bayero University Kano, Kano, Nigeria (Awotidebe); Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil (Ayala); Hacettepe University, Ankara, Turkiye (Ayli); Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia (Azad); Tehran University of Medical Sciences, Tehran, Iran (Azarboo); University of Sulaimani, Sulaymaniyah, Iraq (Aziz); ASIDE Healthcare, Lewes, Delaware (Azzam); Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy (Azzolino); RAK Medical and Health Sciences University, Ras Alkhaimah, United Arab Emirates (Babiker); Manipal Academy of Higher Education, Manipal, India (A. S. Babu); Qatar University, Doha, Qatar (G. R. Babu); Gomal University, Dera Ismail Khan, Pakistan (Badar); Government Institute of Forensic Science Nagpur, Nagpur, India (Badiye); Mansoura University, Mansoura, Egypt (Badran); King Abdulaziz University, Jeddah, Saudi Arabia (Baghlaf); University of Tabuk, Tabuk, Saudi Arabia (Baig); Mashhad University of Medical Sciences, Mashhad, Iran (Bakhshali); Kazakh National Medical University, Almaty, Kazakhstan (Balabekova); Tamil Nadu State Council for Science and Technology, Chennai, India (Balakrishnan); Alfaisal University, Riyadh, Saudi Arabia (Baltatu); Medical University of Lodz, Lodz, Poland (Banach); Bangladesh University of Health Sciences, Dhaka, Bangladesh (Banik); University of Sharjah, Sharjah, United Arab Emirates (Barqawi); University of The Gambia, Banjul, The Gambia (Barrow); Bangladesh University of Health Sciences, Dhaka, Bangladesh (Barua); Universiti Sultan Zainal Abidin, Malaysia, Terengganu, Malaysia (M. I. Bashir); The University of Lahore, Lahore, Pakistan (S. Bashir); Shiraz University of Medical Sciences, Shiraz, Iran (Bashiri); Tehran University of Medical Sciences, Tehran, Iran (Bastan); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Bayat); Bahir Dar University, Bahir Dar, Ethiopia (Bayih); IM Sechenov First Moscow State Medical University, Moscow, Russia (Beeraka); The University of Queensland, Brisbane, Queensland, Australia (Begum); All India Institute of Medical Sciences, Bhubaneswar, India (P. Behera); All India Institute of Medical Sciences, Bhubaneswar, India (S. M. Behera); American University, Washington, DC (Behnam); Hospital Universitario Fundación Santa Fe de Bogotá (University Hospital Santa Fe Foundation of Bogotá), Bogota, Colombia (Bejarano Ramirez); Bahir Dar University, Bahir Dar, Ethiopia (Belayneh); University of Ibadan, Ibadan, Nigeria (Bello); University of Porto, Porto, Portugal (Belo); Johns Hopkins University, Baltimore, Maryland (Berihun); NSW Brain Clot Bank, Sydney, New South Wales, Australia (Bhaskar); Newcastle University, Newcastle Upon Tyne, United Kingdom (Bhattacharjee); Chandigarh University, Mohali, India (G. K. Bhatti); Central University of Punjab, Bathinda, India (J. S. Bhatti); Institute of Post-Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial Hospital, Kolkata, India (A. Biswas); All India Institute of Medical Sciences, Deoghar, India (B. Biswas); Karolinska Institute, Stockholm, Sweden (Bizzozero-Peroni); Ankara University, Ankara, Turkiye (Bodur); Australian National University, Canberra, New South Wales, Australia (Bogale); University of Porto, Porto, Portugal (Bohn); Manipal Academy of Higher Education, Mangalore, India (Boloor); Isfahan University of Medical Sciences, Isfahan, Iran (Bolourinejad); University of Douala, Douala, Cameroon (Bonny); Universidad Santiago de Cali, Cali, Colombia (Botero Carvajal); University Ferhat Abbas of Setif, Setif, Algeria (Bouaoud); Guelma University, Guelma, Algeria (Boudalia); CEVAXIN, Panama City, Panama (Britton); University of Bologna, Bologna, Italy (Bugiardini); Technical University of Munich, Munich, Germany (Busch); The University of Jordan, Amman, Jordan (Bustanji); University of Waterloo, Waterloo, Ontario, Canada (Butt); Anhembi Morumbi University, Sao Jose dos Campos, Brazil (Campos); National University of Singapore, Singapore, Singapore (Cao); University of Milan, Milan, Italy (Catapano); University of Trieste, Trieste, Italy (Cegolon); Federal University of Santa Catarina, Florianópolis, Brazil (Cembranel); University of Bologna, Bologna, Italy (Cenko); Australian Catholic University, Melbourne, Victoria, Australia (Cerin); National Institute of Epidemiology, Chennai, India (Chadwick); Adamas University, Kolkata, India (Chakraborty); Jazan University, Jazan, Saudi Arabia (Chandika); University of Rochester, Rochester, New York (Chandrasekar); Symbiosis International University, Pune, India (Chandrasekaran); Semey Medical University (SMU), Semey, Kazakhstan (Chattu); The University of Texas Health Science Center at Houston (UTHealth Houston), Houston, Texas (Chau); Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia (Chaudhary); Indian Institute of Public Health, Hyderabad, India (Chaudhuri); International University of Kyrgyzstan, Bishkek, Kyrgyzstan (Cheema); Peking Union Medical College Hospital, Beijing, China (A.-T. Chen); Curtin University, Miri, Malaysia (Hana Chen); Zhujiang Hospital of Southern Medical University, Guangzhou, China (Haowei Chen); Hong Kong Baptist University, Hong Kong, China (Cheng); Hong Kong Polytechnic University, Hong Kong, China (Cheung); National University of Singapore, Singapore, Singapore (Chew); Harvard University, Boston, Massachusetts (Chi); Tehran University of Medical Sciences, Tehran, Iran (Chichagi); Virginia Commonwealth University, Richmond, Virginia (Ching); Queen Elizabeth Hospital, Hong Kong, China (Cho); National Cancer Center, Goyang, South Korea (Choi); National University of Singapore, Singapore, Singapore (Chong); All India Institute of Medical Sciences, Bilaspur, India (D. Chopra); Chitkara University, Rajpura, India (H. Chopra); Saveetha University, Chennai, India (S. Chopra); Jawaharlal Nehru Medical College, Wardha, India (Choudhari); VNU International School (VNUIS), Hanoi, Vietnam (Chu); Georgia Southern University, Statesboro, Georgia (Chukwu); University College London, London, United Kingdom (S.-C. Chung); Texas A&M University, College Station, Texas (S. Chung); University of Bologna, Bologna, Italy (Cicero); University of Florence, Florence, Italy (Cosma); University of California San Diego, La Jolla, California (Criqui); University of Minho, Braga, Portugal (Cruz-Martins); Northern Territory Government, Darwin, South Australia, Australia (Dadras); The University of Jordan, Amman, Jordan (Dahabiyeh); Nepal Health Research Council, Kathmandu, Nepal (Dahal Khatri); University of Washington, Seattle, Washington (X. Dai); University of New South Wales, Sydney, New South Wales, Australia (Z. Dai); University of Cambridge, Cambridge, United Kingdom (Dalakoti); University of Foggia, Foggia, Italy (D’Amico); Bahir Dar University, Bahir Dar, Ethiopia (Damtie); Public Health Foundation of India, Gurugram, India (L. Dandona); Public Health Foundation of India, Gurugram, India (R. Dandona); Imperial College London, London, United Kingdom (D’Anna); Texas Tech University, Lubbock, Texas (Danpanichkul); Haramaya University, Harar, Ethiopia (Darcho); The University of Jordan, Amman, Jordan (Dardas); Apollo Institute of Medical Sciences and Research, Chittoor, India (Das); Kazakh National Medical University, Almaty, Kazakhstan (D. Davletov); Kazakh National Medical University, Almaty, Kazakhstan (K. Davletov); University of Gondar, Gondar, Ethiopia (Dejenie); Università degli Studi di Milano (University of Milan), Milan, Italy (Del Bo’); University of Colima, Colima, Mexico (Delgado-Enciso); Salvador Zubiran National Institute of Medical Sciences and Nutrition, Mexico City, Mexico (Denova-Gutiérrez); University of Manouba, Manouba, Tunisia (Dergaa); Bahir Dar University, Bahir Dar, Ethiopia (Derseh); University of Sarajevo, Sarajevo, Bosnia and Herzegovina (Dervišević); Debre Tabor University, Debre Tabor, Ethiopia (Desale); Chettinad Academy of Research and Education, Chennai, India (Devanbu); Jagadguru Sri Shivarathreeswara Academy of Health Education and Research, Mysuru, India (Devegowda); United International University, Dhaka, Bangladesh (Dewan); Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom (Dhali); Nepal Health Research Council, Kathmandu, Nepal (Dhimal); University of Melbourne, Melbourne, Victoria, Australia (Dhungel); Instituto Politécnico do Porto (Polytechnic Institute of Porto), Porto, Portugal (Dias da Silva); Rutgers University, Newark, New Jersey (Didehvar); University of California Irvine, Irvine, California (Ding); Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam (T. C. Do); Can Tho University of Medicine and Pharmacy, Can Tho, Vietnam (T. H. P. Do); Jazan University, Jazan, Saudi Arabia (Dohare); Stanford University, Palo Alto, California (Dong); University of Trieste, Trieste, Italy (D’Oria); Iran University of Medical Sciences, Tehran, Iran (Dorostkar); Independent Consultant, Bridgewater, New Jersey (O. P. Doshi); Hackettstown Medical Center, Hackettstown, New Jersey (R. P. Doshi); University of São Paulo, São Paulo, Brazil (Dourado); University of Health and Allied Sciences, Ho, Ghana (Dowou); University of Pittsburgh, Pittsburgh, Pennsylvania (Dresse); Shandong University, Jinan, China (Du); Federal University of Rio Grande do Sul, Porto Alegre, Brazil (Duncan); Federal University of Bahia, Salvador, Brazil (Duraes); Northwestern University, Chicago, Illinois (Ebohon); Zagazig University, Zagazig, Egypt (Ebraheim); Johns Hopkins University, Baltimore, Maryland (Edeh); Guilan University of Medical Sciences, Rasht, Iran (Eftekhari); University of Massachusetts Amherst, Amherst, Massachusetts (Sedeh); University of Portsmouth, Hampshire, United Kingdom (Ekholuenetale); Almoosa College of Health Sciences, Al Ahsa, Saudi Arabia (El Arab); University of Sharjah, Sharjah, United Arab Emirates (Eladl); University of Pennsylvania, Philadelphia, Pennsylvania (Eldaboush); Al Ain University, Al Ain, United Arab Emirates (El-Dahiyat); University of Kentucky, Lexington, Kentucky (Elgendy); Korea University, Seoul, South Korea (Elhadi); Ministry of Health, Nouakchott, Mauritania (Elhoumed); University of Sharjah, Sharjah, United Arab Emirates (El-Huneidi); Alexandria University, Alexandria, Egypt (Elmeligy); Cairo University, Cairo, Egypt (Elmonem); University of Sharjah, Sharjah, United Arab Emirates (Elmoselhi); Ulster University, Coleraine, United Kingdom (Elnaem); Damanhour University, Damanhur, Egypt (Eltahawy); Yale University, New Haven, Connecticut (Etaee); Humanitas University, Milan, Italy (Fabin); Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio (Fadaka); University of Ibadan, Ibadan, Nigeria (Fagbamigbe); Kazakh National Medical University, Almaty, Kazakhstan (Fakhradiyev); University of Kansas Medical Center, Kansas City, Kansas (Fakorede); Jazan University, Jazan, Saudi Arabia (Farasani); AlMaarefa University, Riyadh, Saudi Arabia (Fareed); Jouf University, Sakaka, Saudi Arabia (Farhana); Applied Science Private University, Amman, Jordan (Faris); Tehran University of Medical Sciences, Tehran, Iran (Farsi); The University of Lahore, Lahore, Pakistan (Fatima); Shahed University, Tehran, Iran (Fayaz); Kazakh National Medical University, Almaty, Kazakhstan (Fazylov); City University of Hong Kong, Hong Kong, China (Fekadu); Centro Nacional de Investigaciones Cardiovasculares (CNIC) (National Centre for Cardiovascular Disease Research), Madrid, Spain (Fernandez-Jimenez); University of Nicosia, Nicosia, Cyprus (Ferreira); Charité Universitätsmedizin Berlin (Charité Medical University Berlin), Berlin, Germany (Fischer); University of Bologna, Bologna, Italy (Fogacci); University of Padova, Italy, Padova, Italy (Fonzo); Multiple Sclerosis Research Center, Ravenna, Italy (Foschi); Bayero University Kano, Kano, Nigeria (Gadanya); University of Szeged, Szeged, Hungary (Gajdács); Salahaddin University–Erbil, Erbil, Iraq (Galali); Institute of Health & Management, Australia, Melbourne, Victoria, Australia (Ganesan); National Institute of Epidemiology, Chennai, India (Ganesh); Manipal Academy of Higher Education, Manipal, India (Gangachannaiah); Shanghai Tenth People’s Hospital, Shanghai, China (Gao); University of Valladolid, Valladolid, Spain (Garcia-Azorin); Autonomous State Medical College Firozabad, Firozabad, India (Garg); Monash University, Melbourne, Victoria, Australia (Gasevic); Amity Institute of Pharmacy, Noida, India (Gautam); Alliance for Medical Research in Africa (AMedRA), Dakar, Senegal (Gaye); Addis Ababa University, Addis Ababa, Ethiopia (Gebresilassie); Debre Berhan University, Debre Berhan, Ethiopia (Getacher); Bahir Dar University, Bahir Dar, Ethiopia (Gete); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Ghadirian); Khomein University of Medical Sciences, Khomein, Iran (Ghaffari); Iran University of Medical Sciences, Tehran, Iran (Ghaffari Jolfayi); Qom University of Medical Sciences, Qom, Iran (Ghamkhar); Amman Arab University, Amman, Jordan (Gharaibeh); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Ghasemi); Alexandria University, Alexandria, Egypt (Ghazy); Nepal Health Research Council, Kathmandu, Nepal (Ghimire); Danish Cancer Research Institute, Copenhagen, Denmark (Ghith); World Health Organization (WHO), Astana, Kazakhstan (Gil); University of Nizwa, Nizwa, Oman (Gilani); University of Warwick, Coventry, United Kingdom (Gill); Research Center of Neurology, Moscow, Russia (Gnedovskaya); National University of Singapore, Singapore, Singapore (Goh); Tarbiat Modares University, Tehran, Iran (Gohari); Charotar University of Science and Technology, Changa, India (Gohil); Indian Institute of Public Health, Gandhinagar, India (Golechha); Sana Institute of Higher Education, Sari, Iran (Goleij); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Golmohammadi); Universidade de São Paulo (University of São Paulo), São Paulo, Brazil (Goulart); Cleveland Clinic, Cleveland, Ohio (Goyal); Case Western Reserve University, Cleveland, Ohio (Grada); National Institutes of Health, Bethesda, Maryland (Grover); Southwest Medical University, Luzhou, China (Gu); University of Sulaimani, Sulaymaniyah, Iraq (Gubari); Adigrat University, Adigrat, Ethiopia (Gufue); Case Western Reserve University, Cleveland, Ohio (Guha); University of Peradeniya, Kandy, Sri Lanka (Gunawardane); Vanderbilt University Medical Center, Nashville, Tennessee (Guo); Eternal Heart Care Centre & Research Institute, Jaipur, India (R. Gupta); Shriram Institute for Industrial Research, Delhi, India (S. Gupta); Parma University Hospital, Parma, Italy (Gurgoglione); Mexican Institute of Social Segurity, Colima, Mexico (Guzman-Esquivel); Universidad Santo Tomás, Talca, Chile (Guzmán-Muñoz); Urmia University of Medical Sciences, Urmia, Iran (Haghtalab); Thong Nhat Hospital, Ho Chi Minh City, Vietnam (Nam); University College Cork, Cork, Ireland (Hailu); Post Graduate Institute of Medical Education and Research, Chandigarh, India (Halder); Ain Shams University, Cairo, Egypt (Hamdy); Quinnipiac University, Frank H Netter MD School of Medicine, St Vincent’s Medical Center, Bridgeport, Connecticut (H. Hamidi); Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates (S. Hamidi); University of Jordan, Amman, Jordan (Hammad); University of Sharjah, Sharjah, United Arab Emirates (Hamoudi); Sakarya University, Sakarya, Turkiye (Hanif); Zanjan University of Medical Sciences, Zanjan, Iran (Hanifi); The University of Western Australia, Perth, Western Australia, Australia (Hankey); University of Toronto, Toronto, Ontario, Canada (Harsini); University of California San Diego, La Jolla, California (Hartmann); University of Dhaka, Dhaka, Bangladesh (Hasan); Shiraz University of Medical Sciences, Shiraz, Iran (Hashempur); Adamas University, Kolkata, India (Hasnain); Cairo University, Cairo, Egypt (A. Hassan); University of Khartoum, Khartoum, Sudan (I. N. Hassan); Federal University Teaching Hospital, Lafia, Nigeria (I. Hassan); Ajman University, Ajman, United Arab Emirates (N. Hassan); Salahaddin University–Erbil, Erbil, Iraq (T. S. Hassan); Duy Tan University, Da Nang, Vietnam (Hassan Ahmed); University of Veterinary and Animal Sciences, Lahore, Pakistan (Hayat); University of New Brunswick, Fredericton, New Brunswick, Canada (Hebert); University of Kentucky, Lexington, Kentucky (Helmy); Taiz University, Taiz, Yemen (Hezam); University of Tokyo, Tokyo, Japan (Hiraike); Manipal Academy of Higher Education, Manipal, India (Holla); Duy Tan University, Da Nang, Vietnam (Hoseinzadeh); Carol Davila University of Medicine and Pharmacy, Bucharest, Romania (M. Hostiuc); Carol Davila University of Medicine and Pharmacy, Bucharest, Romania (S. Hostiuc); Parkview Health, Fort Wayne, Indiana (Hotwani); University of Malaya, Kuala Lumpur, Malaysia (Htay); Capital Medical University, Beijing, China (Hu); The Chinese University of Hong Kong, Hong Kong, China (J. Huang); Shanghai Jiao Tong University, Shanghai, China (Y.-S. Huang); Baqiyatallah University of Medical Sciences, Tehran, Iran (Hushmandi); Salale University, Gerba Guracha, Ethiopia (D. Hussein); Saveetha University, Chennai, India (M. A. Hussein); China Medical University, Taiwan, Taichung, Taiwan (Hwang); University of Ibadan, Ibadan, Nigeria (Ibitoye); Fenerbahce University, Istanbul, Turkiye (I. A. Ibrahim); University of Zakho, Zakho, Iraq (K. S. Ibrahim); Kazakh National Medical University, Almaty, Kazakhstan (Ibrayeva); Episcope Research Service, Aberdeen, Scotland (Ikiroma); Cleveland Clinic, Cleveland, Ohio (Ikram); Africa Centre for Disease Control and Prevention, Abuja, Nigeria (Ilesanmi); University of Belgrade, Belgrade, Serbia (I. M. Ilic); University of Kragujevac, Kragujevac, Serbia (M. D. Ilic); Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia (Imam); Tehran University of Medical Sciences, Tehran, Iran (Imani); University of Utah, Salt Lake City, Utah (Imodoye); University of Jos, Jos, Nigeria (Imoh); ICMR National Institute for Research in Tuberculosis, Chennai, India (Inbaraj); Carol Davila University of Medicine and Pharmacy, Bucharest, Romania (A. I. Ionescu); University of Bucharest, Bucharest, Romania (R. T. Ionescu); Universitas Muhammadiyah Aceh (Muhammadiyah University of Aceh), Banda Aceh, Indonesia (Iqhrammullah); Universitas Ahmad Dahlan, Yogyakarta, Indonesia (Irham); King Khalid University, Abha, Saudi Arabia (Ishaqui); Emory University, Atlanta, Georgia (Islek); Asian Institute of Medicine, Science and Technology, Bedong, Malaysia (Ismail); Kazakh National Medical University, Almaty, Kazakhstan (Ismoldayev); University of Catania, Catania, Italy (Isola); University of Tsukuba, Tsukuba, Japan (Iwagami); Karpagam Academy of Higher Education, Coimbatore, India (Iyer); Universidad de las Américas, Quito, Ecuador (Izquierdo-Condoy); All India Institute of Medical Sciences, Bhubaneswar, India (Jacob); Tabriz University of Medical Sciences, Tabriz, Iran (Jafari-Khounigh); Guilan University of Medical Sciences, Rasht, Iran (Jahanshahi); Arabian Gulf University, Manama, Bahrain (Jahrami); The World Academy of Sciences UNESCO-TWAS, Trieste, Italy (Jakovljevic); University of Chicago, Chicago, Illinois (Jamal); Sirjan School of Medical Sciences, Sirjan, Iran (Jamali); Universiti Malaya, Kuala Lumpur, Malaysia (Jamaluddin); Sri Ramaswamy Memorial Institute of Science and Technology, Kattankulathur, India (James); Sydney Local Health District, Sydney, New South Wales, Australia (Jamshidi); Universityhospital Düsseldorf, Düsseldorf, Germany (Jansen); Royan Institute, Tehran, Iran (Jarrahi); University of Mississippi Medical Center, Jackson, Mississippi (Javaid); Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia (Jawaid); Cephas Health Research Initiative, Inc, Ibadan, Nigeria (Jayasinghe); Christian Medical College and Hospital (CMC), Vellore, India (Jebasingh); Debre Markos University, Debre Markos, Ethiopia (Jemal); Korea University, Seoul, South Korea (Jeong); Johns Hopkins University, Baltimore, Maryland (Jeswani); Shanghai City Huangpu District Center for Disease Control and Prevention (Shanghai City Huangpu District Health Supervision Bureau), Shanghai, China (Ji); Johns Hopkins University, Baltimore, Maryland (Jibat); City University of Hong Kong, Hong Kong, China (Jin); University of Calgary, Calgary, Alberta, Canada (Jokar); Semmelweis University, Budapest, Hungary (Joo); The University of New South Wales, Canberra, New South Wales, Australia (A. P. Joseph); Sultan Qaboos University, Muscat, Oman (M. A. Joseph); Manipal Academy of Higher Education, Mangalore, India (N. Joseph); Conway Medical Center, Conway, South Carolina (J. V. S. J. M. Raj); All India Institute of Medical Sciences, Rajkot, India (Joshi); National Open University, Benin City, Nigeria (Joshua); Kangwon National University College of Medicine in South Korea, Chuncheon, South Korea (Jung); University of Tartu, Tartu, Estonia (Jürisson); Manipal Academy of Higher Education, Manipal, India (K); Salahaddin University, Erbil, Iraq (Kadir); Wayne State University, Detroit, Michigan (Kahe); Post Graduate Institute of Medical Education and Research, Chandigarh, India (Kakkar); University of Washington, Seattle, Washington (Kalani); Khoy Medical Sciences, Khoy, Iran (Kalavani); Bharti Hospital Karnal, Karnal, India (Kalra); Kazakh National Medical University, Almaty, Kazakhstan (Kamenova); South Wales University, Treforest, United Kingdom (Kamorudeen); I Horbachevsky Ternopil National Medical University, Ternopil, Ukraine (Kamyshnyi); Qatar University, Doha, Qatar (Kanaan); Cephas Health Research Initiative Inc, Ibadan, Nigeria (Kanmodi); All India Institute of Medical Sciences, New Delhi, India (Kansal); New York University, New York, New York (Kantar); Government Institute of Forensic Science Nagpur, Nagpur, India (Kapoor); Plymouth University, Plymouth, United Kingdom (Kar); Shiraz University of Medical Sciences, Shiraz, Iran (Karajizadeh); Aristotle University of Thessaloniki, Thessaloniki, Greece (Karakasis); Yarmouk University, Irbid, Jordan (Karasneh); Shiraz University of Medical Sciences, Shiraz, Iran (Karimi); Iran University of Medical Sciences, Tehran, Iran (Karimi Behnagh); International Institute for Population Sciences, Mumbai, India (Karun); Majmaah University, Al Majmaah, Saudi Arabia (Kashoo); Amity University Haryana, Gurugram, India (Kashyap); University of Hail, Hail, Saudi Arabia (Kausar); Tehran University of Medical Sciences, Tehran, Iran (Kazemian); Yale New Haven Health—Bridgeport Hospital, Bridgeport, Connecticut (Kebede); National Research Institute for Agriculture, Food and Environment, Bobigny, France (Kesse-Guyot); Mashhad University of Medical Sciences, Mashhad, Iran (Khademi); Jordan University of Science and Technology, Irbid, Jordan (Khader); Amity University, Noida, India (Khajuria); University of Petra, Amman, Jordan (Khaleel); Abu Dhabi University, Abu Dhabi, United Arab Emirates (N. Khalid); Lahore Medical Research Center, Lahore, Pakistan (S. Khalid); The University of Lahore, Lahore, Pakistan (Khalil); Guilan University of Medical Sciences, Rasht, Iran (A. Khalili); Iran University of Medical Sciences, Tehran, Iran (P. Khalili); University of Nizwa, Nizwa, Oman (A. Khan); Jazan University, Jazan, Saudi Arabia (M. Khan); National Institute of Preventive and Social Medicine, Dhaka, Bangladesh (M. A. S. Khan); Qassim University, Buraydah, Saudi Arabia (M. I. Khan); United Arab Emirates University, Al Ain, United Arab Emirates (M. A. Khan); Abbasi Shaheed Hospital, Karachi, Pakistan, Karachi, Pakistan (M. H. Khan); International Center for Chemical and Biological Sciences, Karachi, Pakistan (Serab Khan); Jamia Millia Islamia, New Delhi, India (Sumaiya Khan); University of Hail, Hail, Saudi Arabia (Y. S. Khan); All India Institute of Medical Sciences, Raipur, India (Khasbage); Yarmouk University, Irbid, Jordan (Khatatbeh); Jordan University of Science and Technology, Irbid, Jordan (Kheirallah); Duy Tan University, Da Nang, Vietnam (Khoshvaght); Yale University, New Haven, Connecticut (Khosla); Northern Care Alliance NHS Foundation Trust, Salford, United Kingdom (Khosravi); Seoul National University, Seoul, South Korea (H. J. Kim); Creighton University, Omaha, Nebraska (K. Kim); Health and Healing Research, Education, and Service, Inc, Boston, Massachusetts (Kimokoti); Kristiania University College, Oslo, Norway (Kisa); Baddi University of Emerging Sciences & Technology, Himachal Pradesh, India (Kishore); University College London, London, United Kingdom (Kivimäki); Harvard University, Boston, Massachusetts (Kokkorakis); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Kolahi); Tehran University of Medical Sciences, Tehran, Iran (Kompani); Kazakh National Medical University, Almaty, Kazakhstan (Kondybayeva); Kharkiv National Medical University, Kharkiv, Ukraine (Korzh); IQVIA, Frankfurt am Main, Germany (Kostev); Manipal Academy of Higher Education, Udupi, India (Koulmane Laxminarayana); Panjab University, Chandigarh, India (Krishan); Republic Polytechnic, Singapore, Singapore (Kua); University of Hail, Hail, Saudi Arabia (Kuddus); Research Institute of Cardiology and Internal Diseases, Almaty, Kazakhstan (Kulimbet); Queensland Health, Brisbane, Queensland, Australia (Kulkarni); Amity University, Noida, India (C. Kumar); Rajendra Institute of Medical Sciences, Ranchi, India (D. Kumar); Public Health Foundation of India, Gurugram, India (G. A. Kumar); Post Graduate Institute of Medical Education and Research, Chandigarh, India (J. Kumar); Dow University of Health Sciences, Karachi, Pakistan (L. Kumar); National Institute of Pharmaceutical Education and Research, Hajipur, Hajipur, India (N. Kumar); Ahalia Hospital, Abu Dhabi, United Arab Emirates (S. K. Kumar); University of Kentucky, Lexington, Kentucky (A. Kundu); Griffith University, Gold Coast, Queensland, Australia (S. Kundu); University of Manitoba, Winnipeg, Manitoba, Canada (Kunutsor); Universitas Kristen Satya Wacana (Satya Wacana Christian University), Salatiga, Indonesia (Kurniasari); University of Texas, Galveston, Texas (Kurpad); Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates (Kusuma); Turku University Hospital, Turku, Finland (Kytö); Foundation for People-Centric Health Systems, New Delhi, India (Lahariya); Universiti Brunei Darussalam (University of Brunei Darussalam), Bandar Seri Begawan, Brunei (D. T. C. Lai); Huazhong University of Science and Technology, Wuhan, China (H. Lai); University of Helsinki, Helsinki, Finland (Lajunen); Research Institute of Cardiology and Internal Diseases, Almaty, Kazakhstan (Lakanova); University of Helsinki, Helsinki, Finland (Lallukka); University of Szeged, Szeged, Hungary (Lantos); Wachemo University, Hosanna, Ethiopia (Larebo); Uppsala University, Uppsala, Sweden (Larsson); Father Muller Medical College, Mangalore, India (Lasrado); Cho Ray Hospital, Vietnam, Ho Chi Minh City, Vietnam (D. T. Le); University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam (T. T. T. Le); University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam (T. Le); Methodist Hospital, Merrillville, Indiana (T. T. B. Le); University of Catania, Catania, Italy (Ledda); Kyung Hee University, Seoul, South Korea (Hayeon Lee); Korea University, Seoul, South Korea (Hwamin Lee); Gachon University, Seongnam, South Korea (S.- Lee); Kazakh National Medical University, Almaty, Kazakhstan (S. V. Lee); Sungkyunkwan University, Suwon-si, South Korea (S. W. Lee); University of Texas Medical Branch, Galveston, Texas (W.-C. Lee); Westpfalz Klinikum, Kaiserslautern, Germany (Leivaditis); Mekelle University, Mekelle, Ethiopia (Lema); Wachemo University, Hosanna, Ethiopia (Lemma); University of South Australia, Adelaide, South Australia, Australia (Lenjisa); Manipal Academy of Higher Education, Udupi, India (Lewis); Edith Cowan University, Perth, Western Australia, Australia (C. Li); National Taiwan Normal University, Taipei, Taiwan (M.-C. Li); Capital Medical University, Beijing, China (Q. Li); Peking University, Beijing, China (S. Li); Edith Cowan University, Joondalup, Western Australia, Australia (X. Li); The First Hospital of China Medical University, Shenyang, China (Y. Li); University of Malaya, Kuala Lumpur, Malaysia (Lim); Uppsala University, Uppsala, Sweden (Lindholm); National Medical Research Association, Leicester, United Kingdom (H. Liu); Hong Kong Metropolitan University, Hong Kong, China (Xianliang Liu); Cleveland Clinic, Cleveland, Ohio (Xuefeng Liu); Central South University, Changsha, China (Y. Liu); Deakin University, Burwood, Victoria, Australia (Livingstone); German Institute of Human Nutrition Potsdam-Rehbrücke, Potsdam, Germany (Llanaj); The University of Lahore, Lahore, Pakistan (Lodhi); University of Moratuwa, Sri Lanka, Moratuwa, Sri Lanka (Lokunarangoda); Universidad Espíritu Santo, Samborondón, Ecuador (López-Gil); Friedrich Schiller University Jena, Jena, Germany (Lorkowski); Federal University of Juiz de Fora, Juiz de Fora, Brazil (Lucchetti); The Chinese University of Hong Kong, Hong Kong, China (Lui); Southern Medical University, Guangzhou, China (Luo); Novo Nordisk, Plainsboro, New Jersey (Lv); University of Alberta, Edmonton, Alberta, Canada (Lytvyak); Salahaddin University–Erbil, Erbil, Iraq (M. Amin); University of Pennsylvania, Philadelphia, Pennsylvania (Ma); Suez Canal University, Ismailia, Egypt (Mabrok); Pomeranian Medical University, Szczecin, Poland (Machoy-Rakoczy); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Madinehzad); University of Glasgow, Glasgow, United Kingdom (Maffia); Autonomous University of Sinaloa, Culiacán, Mexico (Magaña Gómez); Tehran University of Medical Sciences, Tehran, Iran (Mahalleh); Cihan University–Sulaimaniya, Sulaymaniyah, Iraq (Mahmood); King Abdulaziz University, Jeddah, Saudi Arabia (A. A. Malik); Dasman Diabetes Institute, Kuwait City, Kuwait (M. Z. Malik); Jimma University, Jimma, Ethiopia (T. Malik); Federal University of Minas Gerais, Belo Horizonte, Brazil (Malta); Karolinska Institute, Stockholm, Sweden (Mamand); Ziauddin University, Karachi, Pakistan (Maniya); Hamad Medical Corporation, Doha, Qatar (Mannethodi); University of Karachi, Karachi, Pakistan (Mansoor); Isfahan University of Medical Sciences, Isfahan, Iran (Mansourian); University of Sharjah, Sharjah, United Arab Emirates (Manzoor); Lahore Garrison University, Lahore, Pakistan (Maqsood); University of Isfahan, Isfahan, Iran (Marateb); Mansoura University, Mansoura, Egypt (Marghani); University of Milan, Milan, Italy (Marino); Pan American Health Organization, Washington, DC (Martinez-Piedra); University of Milan, Milan, Italy (D. Martini); Universitas Airlangga (Airlangga University), Surabaya, Indonesia (S. Martini); University of Concepción, Concepción, Chile (Martorell); Medical University of Graz, Graz, Austria (März); Northwestern University, Chicago, Illinois (Marzouk); University of Pisa, Pisa, Italy (Masi); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Masrouri); Apollo Institute of Medical Sciences and Research, Hyderabad, India (Mathur); Federal University of Minas Gerais, Belo Horizonte, Brazil (Matozinhos); Federal University of Rio Grande do Sul, Porto Alegre, Brazil (Mattiello); Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand (Maude); University of Thessaly, Larissa, Greece (Mavrovounis); Queensland University of Technology, Kelvin Grove, Queensland, Australia (McPhail); Indian Institute of Technology Dharwad, Dharwad, India (Mehto); Adigrat University, Adigrat, Ethiopia (Meles); Universidad Científica del Sur (University of the South), Lima, Peru (Mendoza); Trinity Medical Sciences University, St Vincent, Saint Vincent and the Grenadines (G. A. Menezes); Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia (R. G. Menezes); Bahir Dar University, Bahir Dar, Ethiopia (Mengistie); Virginia Tech, Roanoke, Virginia (Menon); National Institutes of Health, Bethesda, Maryland (Mensah); University of Udine, Udine, Italy (Merlino); University North, Varazdin, Croatia (Mestrovic); University of Kelaniya, Ragama, Sri Lanka (C. D. K. Mettananda); University of Kelaniya, Ragama, Sri Lanka (S. Mettananda); Zagazig University, Zagazig, Egypt (Metwally); University of Gothenburg, Gothenburg, Sweden (Miao Jonasson); National Research and Innovation Agency, Bogor District, Indonesia (Mikrajab); Baylor College of Medicine, Houston, Texas (Minhas); Kyrgyz State Medical Academy, Bishkek, Kyrgyzstan (Mirrakhimov); University of Sydney, Sydney, New South Wales, Australia (S. Mitra); Sri Ramaswamy Memorial Institute of Science and Technology, Chengalpattu, India (T. Mitra); All India Institute of Medical Sciences, Raebareli, India (Mittal); Charotar University of Science and Technology, Anand, India (Modi); RAK Medical and Health Sciences University, Ras Alkhaimah, United Arab Emirates (M. G. Mohamed); Sirius Training and Research Centre, Khartoum, Sudan (N. S. Mohamed); University of Khartoum, Khartoum, Sudan (Mohamed Ahmed); University of Duhok, Duhok, Iraq (A. M. Mohammad); All India Institute of Medical Sciences, New Delhi, India (T. Mohammad); Valley Health System, Las Vegas, Nevada (A. Mohammadi); Lebanese French University, Erbil, Iraq (M. Mohammadi); University of Nizwa, Nizwa, Oman (S. Mohammadi); Shahrekord University of Medical Sciences, Shahrekord, Iran (Mohammadian-Hafshejani); Government Medical College Kozhikode, Kozhikode, India (Mohammed); Saveetha University, Chennai, India (Mohan); Johns Hopkins University, Baltimore, Maryland (Mohsen); University of Gondar, Gondar, Ethiopia (Molla); King’s College London, London, United Kingdom (Molokhia); Department of Sport Physiology, Razi University, Kermanshah, Iran (Monazzami); Charles Sturt University, Bathurst, New South Wales, Australia (Moni); Islamic Azad University, Tehran, Iran (Montazeri Namin); Mashhad University of Medical Sciences, Mashhad, Iran (Moodi Ghalibaf); Tehran University of Medical Sciences, Tehran, Iran (Morovvati); Tabriz University of Medical Sciences, Tabriz, Iran (Mosaddeghi Heris); Manipal Academy of Higher Education, Mangalore, India (Motappa); Royan Institute, Tehran, Iran (Mousavi); Texas A&M University, Corpus Christi, Texas (Mousavi Kiasary); Tehran University of Medical Sciences, Tehran, Iran (Mozafar); Iran University of Medical Sciences, Tehran, Iran (Mozahheb Yousefi); University of Groningen (Rijksuniversiteit Groningen), Groningen, Netherlands (Mubarik); University of Birmingham, Birmingham, United Kingdom (Muhammad); The University of Tokyo, Tokyo, Japan (Munkhsaikhan); Central University of Punjab, Bathinda, India (Munshi); Ahmadu Bello University, Zaria, Nigeria (Musa); Orthopaedic Research Group, Coimbatore, India (Muthu); University of Botswana, Gaborone, Botswana (Mwita); Seoul National University, Seoul, South Korea (Myung); University of Washington, Seattle, Washington (Nabipoorashrafi); Initiative for Financing Health and Human Development, Chennai, India (Nagarajan); Flinders University, Adelaide, South Australia, Australia (G. R. Naik); University of British Columbia, Vancouver, British Columbia, Canada (H. Naik); Hasanuddin University, Makassar, Indonesia (Nainu); The University of Lahore, Lahore, Pakistan (Nargus); University of Bristol, Bristol, United Kingdom (Nartey); Tehran University of Medical Sciences, Tehran, Iran (Nasrollahizadeh); University of Vermont, South Burlington, Vermont (Nassar); King Abdulaziz University, Jeddah, Saudi Arabia (Natto); United Arab Emirates University, Al Ain, United Arab Emirates (Nauman); University of Nizwa, Nizwa, Oman (Naureen); University of Peradeniya, Kandy, Sri Lanka (Navaratna); University of Jordan, Amman, Jordan (Nawaiseh); Amity University, Noida, India (B. P. Nayak); Manipal Academy of Higher Education, Manipal, India (V. C. Nayak); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Nazari); MedStar Health, Baltimore, Maryland (Ndakotsu); Bahir Dar University, Bahir Dar, Ethiopia (A. T. Nega); Mekelle University, Mekelle, Ethiopia (M. H. Nega); Johns Hopkins University, Baltimore, Maryland (Negash); Carol Davila University of Medicine and Pharmacy, Bucharest, Romania (Negoi); Mashhad University of Medical Sciences, Mashhad, Iran (Nejad Shahrokh Abadi); Kerman University of Medical Sciences, Kerman, Iran (Nematollahi); Duy Tan University, Da Nang, Vietnam (C. D. Nguyen); Duy Tan University, Hanoi, Vietnam (C. T. Nguyen); Duy Tan University, Da Nang, Vietnam (H. T. H. Nguyen); Nam Can Tho University, Can Tho City, Vietnam (N. P. Nguyen); Danang Family Hospital, Da Nang, Vietnam (P. T. Nguyen); Oxford University Clinical Research Unit, Vietnam, Ho Chi Minh City, Vietnam (V. T. Nguyen); International Islamic University Islamabad, Islamabad, Pakistan (Niazi); University for International Studies in Rome, Rome, Italy (Nieddu); University of Glasgow, Glasgow, United Kingdom (Nketia); Tohoku University, Miyagi, Japan (Nomura); The Women University Multan, Multan, Pakistan (Noreen); Fujian Branch of National Clinical Research Center for Cardiovascular Diseases, Xiamen, China (Nawsherwan); University of California San Francisco (Noubiap); Nnamdi Azikiwe University, Awka, Nigeria (Nri-Ezedi); University of Cape Town, Cape Town, South Africa (Ntsekhe); Mayo Clinic, Rochester, Minnesota (Nugen); London School of Hygiene & Tropical Medicine, London, United Kingdom (Nur); University of Bucharest, Bucharest, Romania (Oancea); Jordan University of Science and Technology, Irbid, Jordan (Odat); University of Rome “Tor Vergata,” Rome, Italy (Oddi); Ladoke Akintola University, Ogbomoso, Nigeria (Odediji); University of New South Wales, Sydney, New South Wales, Australia (Ogunmodede); University of Ulsan, Seoul, South Korea (Oh); University of KwaZulu-Natal, Durban, South Africa (Ojedoyin); University of Turku, Turku, Finland (Ojo); Zahedan University of Medical Sciences, Zahedan, Iran (Okati-Aliabad); Seoul National University, Seoul, South Korea (Okekunle); University of the Western Cape, Cape Town, South Africa (Okonji); Federal University of Rio de Janeiro, Rio de Janeiro, Brazil (Oliveira); Ahmadu Bello University, Zaria, Nigeria (Olorukooba); University of Oxford, Oxford, United Kingdom (Ong); Centre of Regenerative Medicine, Medical University of Bialystok, Bialystok, Poland (Ordak); University of Tartu, Tartu, Estonia (Orru); University of California San Francisco, San Francisco, California (Orscelik); IIS-Fundacion Jimenez Diaz, Madrid, Spain (Ortiz); Universidad de las Americas (University of the Americas), Quito, Ecuador (Ortiz-Prado); Njala University, Freetown, Sierra Leone (Osborne); University of Cape Coast, Cape Coast, Ghana (Osei); Harvard University, Boston, Massachusetts (Ostrominski); Western Sydney University, Bathurst, New South Wales, Australia (Osuagwu); Haramaya University, Harar, Ethiopia (Oumer); University Ferhat Abbas of Setif, Setif, Algeria (Ouyahia); University of Ibadan, Ibadan, Nigeria (Owolabi); Nigerian Institute of Medical Research, Lagos, Nigeria (Oyebola); Operational Research Center in Healthcare, Nicosia, Turkiye (Ozsahin); Jagadguru Sri Shivarathreeswara University, Mysore, India (Padukudru Anand); Institute of Health Carlos III, Madrid, Spain (Padron-Monedero); Manipal Academy of Higher Education, Mangalore, India (Padubidri); Semmelweis University, Budapest, Hungary (Palicz); Siksha ‘O’ Anndhan Deemed to be University, Bhubaneswar, India (Panda); Chandigarh University, Punjab, India (Pandi-Perumal); University of Nottingham, Nottingham, United Kingdom (Panos); University of Padua, Padua, Italy (Papa); University of Bologna, Bologna, Italy (Papadimopoulos); Anglia Ruskin University, Cambridge, United Kingdom (Pardhan); All India Institute of Medical Sciences, Jammu, India (Parija); University of Minnesota, Minneapolis, Minnesota (Parikh); Catholic University of Sacred Heart, Rome, Italy (Pascucci); University of Washington, Seattle, Washington (Pasovic); University of Torino, Torino, Italy (Passera); University of Kansas Medical Center, Kansas City, Kansas (K. N. Patel); Marwadi University, Rajkot, India (M. Patel); University of Tennessee, Nashville, Tennessee (N. N. Patel); Charotar University of Science and Technology, Changa, India (N. R. Patel); Regional Institute of Education (RIE), Ajmer, India (S. Patel); All India Institute of Medical Sciences, Nagpur, India (A. Patil); Roseman University of Health Sciences, South Jordan, Utah (S. Patil); Aristotle University of Thessaloniki, Thessaloniki, Greece (Patoulias); Mallareddy Medical College for Womens’,Mallareddy Vishwavidya Peeth, Hyderabad, India (Pattnaik); Iran University of Medical Sciences, Tehran, Iran (Pazoki Toroudi); Trnava University, Trnava, Slovakia (Pekarcikova); Macquarie University, Sydney, New South Wales, Australia (Peprah); Curtin University, Bentley, Western Australia, Australia (Pereira); Mario Negri Institute for Pharmacological Research, Bergamo, Italy (Perico); University of Milan, Milan, Italy (Perna); Universidad Diego Portales (Diego Portales University), Santiago, Chile (Petermann-Rocha); University of Palermo, Palermo, Italy (Pirera); University of Amsterdam, Amsterdam, Netherlands (Pirnejad); Tomsk National Research Medical Center, Tomsk, Russia (Plotnikov); VinUniversity, Hanoi, Vietnam (Poddighe); The INCLEN Trust International, New Delhi, India (Poluru); Chulalongkorn University, Bangkok, Thailand (Porntaveetus); Tribhuvan University, Kathmandu, Nepal (Pradhan); Institute of Liver and Biliary Sciences, New Delhi, India (Prasad); JSS Academy of Higher Education and Research, Mysuru, India (Prashant); Federal University of Minas Gerais, Belo Horizonte, Brazil (Prates); University of Pisa, Pisa, Italy (Pugliese); University of Pennsylvania, Philadelphia, Pennsylvania (Puvvula); New York University, New York, New York (Qi); Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China (Qiu); Universiti Sains Malaysia in Malaysia, Penang, Malaysia (Qureshi); Cancer Institute (WIA), Chennai, India (Radhakrishnan); All India Institute of Medical Sciences, Jodhpur, India (P. Raghav); Jazan University, Jazan, Saudi Arabia (Y. S. R. Raghav); National Institute of Mental Health and Neurosciences, Bengaluru, India (Raghuveer); University of Ibadan, Ibadan, Nigeria (Rahamon); Kocaeli University, Izmit, Turkiye (F. Rahim); University of Human Development, Sulaymaniyah, Iraq (H. M.-A. Rahim); Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran (Rahimi); Cihan University–Sulaimaniya, Sulaymaniyah, Iraq (F. M. Rahman); University of Rajshahi, Rajshahi, Bangladesh (M. M. Rahman); National University of Science and Technology, Sohar, Oman (M. H. U. Rahman); University of Rajshahi, Rajshahi, Bangladesh (M. Rahman); Federation University Australia, Berwick, Victoria, Australia (M. A. Rahman); Manipal Academy of Higher Education, Manipal, India (J. P. Raj); Dow University of Health Sciences, Karachi, Pakistan (Raja); Cleveland Clinic, Cleveland, Ohio (Rajendran); Centre for Chronic Disease Control, New Delhi, India (Rajput); University of Sharjah, Sharjah, United Arab Emirates (Ramadan); Govt Siddhartha Medical College, Vijayawada, India (C. Ramasamy); Stanford University, Stanford, California (S. K. Ramasamy); Dow University of Health Sciences, Karachi, Pakistan (A. G. Rao); Manipal Academy of Higher Education, Manipal, India (M. Rao); Sharavathi Dental College and Hospital, Shimogga, India (S. J. Rao); University of Social Welfare and Rehabilitation Sciences, Tehran, Iran (Rashedi); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Rasouli-Saravani); All India Institute of Medical Sciences, Bhubaneswar, India (Rath); Amity University in Noida, Noida, India (Rathi); Rajarata University of Sri Lanka, Anuradhapura, Sri Lanka (Rathish); University of Tokyo, Tokyo, Japan (Rauniyar); Imperial College London, London, United Kingdom (D. L. Rawaf); Imperial College London, London, United Kingdom (S. Rawaf); Boston University, Boston, Massachusetts (Rawassizadeh); Siksha ‘O’ Anndhan (Deemed to be University), Bhubaneswar, India (Ray); Northwest Health, Porter, Valparaiso, Indiana (Reddy); King Abdulaziz University, Jeddah, Egypt (Redwan); Kohat University of Science and Technology, Kohat, Pakistan (Rehman); Mario Negri Institute for Pharmacological Research, Bergamo, Italy (Remuzzi); Tehran University, Tehran, Iran (M. Rezaei); Tehran University of Medical Sciences, Tehran, Iran (N. Rezaei); Rafsanjan University of Medical Sciences, Rafsanjan, Iran (Rezaeian); University of Antioquia, Medellin, Colombia (Rodriguez); University of Sao Paulo, Ribeirão Preto, Brazil (Roever); Chulalongkorn University, Bangkok, Thailand (Romadlon); Maurizio Bufalini Hospital, Cesena, Italy (Romoli); International University of Business Agriculture and Technology, Dhaka, Bangladesh (Rony); Ajman University, Ajman, United Arab Emirates (Ross); New Mexico State University, Las Cruces, New Mexico (Sharmistha Roy); Indian Institute of Public Health, Delhi, India (Shubhanjali Roy); Public Health Wales, Cardiff, United Kingdom (Rubeshkumar); SS Annunziata Hospital-ASL Abruzzo, Chieti, Italy (Russo); Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania (Rwegerera); Zagazig University, Zagazig, Egypt (Saad); University of Sharjah, Sharjah, United Arab Emirates (Saber-Ayad); Georgetown University, Washington, DC (Sabet); Universidad Diego Portales (Diego Portales University), Santiago, Chile (Sadarangani); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Sadat Rafiei); King Edward Medical University, Lahore, Pakistan, Lahore, Pakistan (Saddique); Salahaddin University, Erbil, Iraq (Sadee); Kermanshah University of Medical Sciences, Kermanshah, Iran (Ehsan Sadeghi); Shiraz University of Medical Sciences, Shiraz, Iran (Erfan Sadeghi); Isfahan University of Medical Sciences, Isfahan, Iran (M. Sadeghi); United Arab Emirates University, Al Ain, United Arab Emirates (B. Sadek); Qena University, Qena City, Egypt (M. Sadek); Iran University of Medical Sciences, Tehran, Iran (Sadr); International Medical University, Gdańsk, Poland (Saeb); University of Hail, Hail, Saudi Arabia (M. Saeed); Széchenyi István University, Győr, Hungary (U. Saeed); Saveh University of Medical Sciences, Saveh, Iran (Saeedi); King Edward Medical University, Lahore, Pakistan (Safiullah); Tehran University of Medical Sciences, Tehran, Iran (Saghazadeh); University of Bergen, Bergen, Norway (Sagoe); A’ Sharqiyah University, Ibra, Oman (Sah); University of Sharjah, Sharjah, United Arab Emirates (Sharif-Askari); Saveetha University, Chennai, India (Sahebkar); Al-Hadba University, Mosul, Iraq (Sajadi); University of Gujrat, Gujrat, Pakistan (Sajid); Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran (Saki); LMU-Munich, Munich, Germany (Sakshaug); University College Hospital, Ibadan, Ibadan, Nigeria (Salami); Lahore University of Management Sciences, Lahore, Pakistan (Saleem); University of Sharjah, Sharjah, United Arab Emirates (Saleh); Mayo Clinic, Rochester, Minnesota (Salehi); Monash University, Melbourne, Victoria, Australia (Salihu); Shahid Beheshti University of Medical Sciences, Tehran, Iran (Salimi); King Abdulaziz University, Jeddah, Saudi Arabia (Samargandy); National Taiwan University, Taipei, Taiwan (Samodra); Ain Shams University, Cairo, Egypt (Samy); All India Institute of Medical Sciences, Jodhpur, India (Saravanan); University of Cape Coast, Cape Coast, Ghana (Sarfo); Queensland University of Technology, Brisbane, Queensland, Australia (Sarmadi); Dr D Y Patil Vidyapeeth, Pune (Deemed to be University), Pune, India (G. S. Sarode); Dr D Y Patil Vidyapeeth, Pune (Deemed to be University), Pune, India (S. C. Sarode); Isfahan University of Medical Sciences, Isfahan, Iran (Sarrafzadegan); University of Bologna, Bologna, Italy (Sassano); Hamad Medical Corporation, Doha, Qatar (Sathian); National Institute for Research in Tuberculosis, Chennai, India (Sathya Narayanan); Università degli studi della Campania Luigi Vanvitelli (University of Campania Luigi Vanvitelli), Naples, Italy (Savabi Far); The University of Western Australia, Perth, Western Australia, Australia (Schlaich); Katholieke Universiteit Leuven, Leuven, Belgium (Schuermans); ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy (Schwarz); University of Szeged, Szeged, Hungary (Sejben); University of Puthisastra, Phnom Penh, Cambodia (Selvaraj); University of Sharjah, Sharjah, United Arab Emirates (Semreen); University of California San Francisco, San Francisco, California (Senol); Carol Davila University of Medicine and Pharmacy, Bucharest, Romania (Serban); University of Catania, Catania, Italy (Sessa); Swami Vivekanand Subharti University, Meerut, India (Sethi); Sanata Dharma University, Yogyakarta, Indonesia (Setiawan); Colorado State University, Fort Collins, Colorado (Sewor); National Institutes of Health, Rockville, Maryland (Seylani); The University of Lahore, Lahore, Pakistan (Shahid); National Institute of Gastroenterology–IRCCS “Saverio de Bellis,” Castellana Grotte, Italy (Shahini); Mashhad University of Medical Sciences, Mashhad, Iran (Shahrahmani); Ajman University, Ajman, United Arab Emirates (Shahwan); Northwell Health, New York, New York (Sham); All India Institute of Medical Sciences, Jodhpur, India (Shamim); Ajman University, Ajman, United Arab Emirates (Shamsi); Lancaster University, Lancaster, United Kingdom (Shan); Jazan University, Jazan, Saudi Arabia (Shanawaz); Amity University, Noida, India (Shannawaz); University of Vermont, Burlington, Vermont (Sharif); University for Continuing Education Krems, Krems, Austria (Sharifan); Shree Guru Gobind Singh Tricentenary University, Gurugram, India (B. K. Sharma); Graphic Era (Deemed to be University), Dehradun, India (B. Sharma); BJ Medical College, Ahmedabad, India (K. Sharma); University of Nevada Las Vegas, Las Vegas, Nevada (M. Sharma); Chandigarh University, Punjab, India (R. K. Sharma); Central University of Punjab, Bathinda, India (U. Sharma); Panjab University, Chandigarh, India (V. Sharma); Manipal Academy of Higher Education, Manipal, India (Shastry); The University of Lahore, Lahore, Pakistan (Shehzadi); Tehran University of Medical Sciences, Tehran, Iran (Sheidaei); Manipal Academy of Higher Education, Manipal, India (Shenoy); Rajiv Gandhi University of Health Sciences, Moodubidire, India (M. Shetty); Nitte University, Mangalore, India (P. H. Shetty); Shanghai Jiao Tong University, Shanghai, China (F. Shi); Katholieke Universiteit Leuven, Leuven, Belgium (H.-Z. Shi); Jilin University, Changchun, China (Y. Shi); Saint Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia (Shimels); Finnish Institute of Occupational Health, Helsinki, Finland (Shiri); Usmanu Danfodiyo University, Sokoto, Sokoto, Nigeria (Shittu); University of Oulu, Oulu, Finland (Shiue); Jazan University, Jazan, Saudi Arabia (Shoaib); Iran University of Medical Sciences, Tehran, Iran (Shool); Mazandaran University of Medical Sciences, Sari, Iran (Shorofi); Kathmandu Cancer Center, Bhaktapur, Nepal (Shrestha); Texas Tech University Health Sciences Center, Dallas, Texas (Shuval); National University of Singapore, Singapore, Singapore (Sia); University of Khartoum, Khartoum, Sudan (Siddig); Emory University, Atlanta, Georgia (Siddiqi); University of Health Sciences, Bahawalpur, Pakistan (Siddiqua); Federal University of Santa Catarina, Florianópolis, Brazil (D. A. S. Silva); Polytechnic Institute of Guarda, Guarda, Portugal (L. M. L. R. Silva); University of Huddersfield, Huddersfield, United Kingdom (Simkhada); Central University of Punjab, Bathinda, India (A. Singh); Central University of Punjab, Bathinda, India (B. Singh); Government Medical College and Hospital, Chandigarh, India (Harmanjit Singh); IFTM University, Moradabad, India (Harpreet Singh); Baylor College of Medicine, Houston, Texas (J. A. Singh); Indian Council of Medical Research, New Delhi, India (L. Singh); All India Institute of Medical Sciences, Bathinda, India (Paramdeep Singh); Amity University, Noida, India (P. S. Singh); Punjabi University Patiala, Patiala, India (Puneetpal Singh); Indian Institute of Technology Indore, Indore, India (Siddharth Singh); Central University of Punjab, Bathinda, India (S. K. Singh); All India Institute of Medical Sciences, Jodhpur, India (Surjit Singh); King George’s Medical University, Lucknow, India (V. Singh); Manipal Academy of Higher Education, Manipal, India (M. K. Sinha); ESIC Medical College and Hospital, Ranchi, India (R. Sinha); Royal College of Psychiatrists, London, United Kingdom (Skryabin); Keele University, Stoke-on-Trent, United Kingdom (Sokhal); Independent Consultant, New Delhi, India (Sood); Urmia University of Medical Sciences, Urmia, Iran (Soraneh); University of Washington, Seattle, Washington (Sorensen); University of Naples “Federico II,” Naples, Italy (Sorrentino); University of Athens, Athens, Greece (Spartalis); Manipal Academy of Higher Education, Manipal, India (Srivastav); Aristotle University of Thessaloniki, Thessaloniki, Greece (Stachteas); Kandahar University, Kandahar, Afghanistan (Stanikzai); Federal Research Institute of Nutrition, Biotechnology and Food Safety, Moscow, Russia (Starodubova); University of Alberta, Edmonton, Alberta, Canada (Straube); University of Technology Sydney, Sydney, New South Wales, Australia (Stubbs); McGill University, Montreal, Quebec, Canada (Su); Sunway University, Subang Jaya, Malaysia (Subramaniyan); University of Bristol, Bristol, United Kingdom (Suhag); Kyiv Medical University, Kyiv, Ukraine (Sulaieva); Yobe State University Teaching Hospital, Yobe, Nigeria (Sulaiman); Federal University, Dutse, Dutse, Nigeria (Suleiman Odidi); Universitas Indonesia Maju, Jakarta, Indonesia (Sulistiyorini); Charles Sturt University, Orange, New South Wales, Australia (J. Sun); Universiti Putra Malaysia, Selangor, Malaysia (Z. Sun); Utkal University, Bhubaneswar, India (Swain); The John Paul II Catholic University of Lublin, Lublin, Poland (Szarpak); All India Institute of Medical Sciences, Deoghar, India (Ty); Neurocenter of Southern Switzerland (NSI), Lugano, Switzerland (Tabaee Damavandi); University of Valencia, Valencia, Spain (Tabarés-Seisdedos); Mashhad University of Medical Sciences, Mashhad, Iran (Tabatabaei); Islamic Azad University, Mashhad, Iran (Tabatabaeizadeh); Department of Medical Education, Shahid Beheshti University of Medical Sciences, Tehran, Iran (Tabatabai); Imperial College London, London, United Kingdom (Tabche); Jimma University, Jimma, Ethiopia (Tadele); Harvard University, Boston, Massachusetts (Tagiisuran); Ewha Womans University, Seoul, South Korea (Taha Osman Ali); Shahid Sadoughi University of Medical Sciences, Yazd, Iran (Taheri Soodejani); University of Nebraska Medical Center, Omaha, Nebraska (Taiba); Università degli studi della Campania Luigi Vanvitelli (University of Campania Luigi Vanvitelli), Naples, Italy (Tajabadi); Monash University, Melbourne, Victoria, Australia (Talic); Australian National University, Acton, Australian Capital Territory, Australia (Talukder); The University of Western Australia, Perth, Western Australia, Australia (Tamehri Zadeh); Jimma University, Jimma, Ethiopia (Tamiru Adugna); Carol Davila University of Medicine and Pharmacy, Bucharest, Romania (Tampa); Southern Medical University, Guangzhou, China (Tan); University of British Columbia, Vancouver, British Columbia, Canada (Tanabayeva); Yarmouk University, Irbid, Jordan (Tanashat); Zhejiang Chinese Medical University, Hangzhou, China (Tang); University of California Irvine, Orange, California (Tantisattamo); Debre Markos University, Debre Markos, Ethiopia (Tariku); The University of Faisalabad, Faisalabad, Pakistan (Tariq); Tehran University of Medical Sciences, Tehran, Iran (Tavangar); University of Missouri, Indianapolis, Indiana (Tedla); Alfaisal University, Riyadh, Saudi Arabia (Temsah); Northwestern University, Chicago, Illinois (Teramoto); Mekelle University, Mekelle, Ethiopia (Tesfamariam); Bahir Dar University, Bahir Dar, Ethiopia (Tesfu); King George’s Medical University, Lucknow, India (Tewari); Charotar University of Science and Technology, Changa, India (Thakar); Saveetha University, Chennai, India (Thangavelu); The American University in Cairo, Cairo, Egypt (I. Tharwat); Mansoura University, Mansoura, Egypt (S. Tharwat); University of Cape Town, Cape Town, South Africa (Thienemann); Konkuk University, Seoul, South Korea (Thiruvengadam); St Luke’s Hospital, Patanamthitta, India (Thomas); Universitas Sam Ratulangi (Sam Ratulangi University), Manado, Indonesia (Ticoalu); University of South Australia, Adelaide, South Australia, Australia (Tiruye); All India Institute of Medical Sciences, Jodhpur, India (Tiwari); Kazakh National Medical University, Almaty, Kazakhstan (Tleshev); All India Institute of Medical Sciences, Jodhpur, India (Tomo); University of Calgary, Calgary, Alberta, Canada (Tonelli); Jagiellonian University Medical College, Kraków, Poland (Topor-Madry); National Institute for Health and Medical Research (INSERM), Paris, France (Touvier); Saveetha University, Chennai, India (Tovani-Palone); Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam (A. T. Tran); University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam (T. H. Tran); ALS Vietnam, Quang Ngai, Vietnam (Tran Minh Duc); University of Pisa, Pisa, Italy (Trico); Sebelas Maret University, Surakarta, Indonesia (Tristan); Hong Kong Metropolitan University, Hong Kong, China (Tse); Aristotle University of Thessaloniki, Thessaloniki, Greece (Tseriotis); University of Development, Surabaya, Indonesia (Tualeka); Harvard University, Boston, Massachusetts (Tye); University of Medical Sciences, Ondo, Ondo, Nigeria (Udoakang); University of Nizwa, Nizwa, Oman (A. Ullah); King Saud University, Riyadh, Saudi Arabia (R. Ullah); University of Karachi, Karachi, Pakistan (S. Ullah); King Abdullah International Medical Research Center, Riyadh, Saudi Arabia (Umair); Federal Ministry of Health, Azare, Nigeria (Umar); Manipal Academy of Higher Education, Manipal, India (Upadhya); Amity University Rajasthan, Jaipur, India (Upadhyay); Bayero University, Kano, Nigeria (Usman); University of Sharjah, Sharjah, United Arab Emirates (Uzun Ozsahin); University of Trakya, Edirne, Turkiye (Uzunçıbuk); University of Bahrain, Zallaq, Bahrain (Vaithinathan); Isfahan University of Medical Sciences, Isfahan, Iran (Vakili); Katholieke Universiteit Leuven, Leuven, Belgium (Van den Eynde); Jazan University, Jazan, Saudi Arabia (Varghese); UKK Institute, Tampere, Finland (Vasankari); Central University of Punjab, Bathinda, India (Vellingiri); Raffles Hospital, Singapore, Singapore (Venketasubramanian); Siksha ‘O’ Anndhan (Deemed to be University), Bhubaneswar, India (A. K. Verma); Siksha ‘O’ Anndhan Deemed to be University, Bhubaneswar, India (P. Verma); Ministry of Health, Mexico City, Mexico (Villalobos-Daniel); National Research University Higher School of Economics, Moscow, Russia (Vlassov); Beijing University of Chinese Medicine, Beijing, China (Wan); Brigham and Women’s Hospital, Boston, Massachusetts (C. Wang); The George Institute for Global Health, Sydney, New South Wales, Australia (N. Wang); Xuzhou Medical University, Xuzhou, China (Q. Wang); Xiamen University, Xiamen, China (Shaopan Wang); Beijing Tiantan Hospital, Beijing, China (Shu Wang); Peking University, Beijing, China (Wanzhou Wang); Shandong University, Jinan, China (Wei Wang); Xuzhou Medical University, Xuzhou, China (Wei Wang); King’s College London, London, United Kingdom (Yanzhong Wang); Peking University, Beijing, China (Youxin Wang); University of Science and Technology of China, Heifei, China (Z. Wang); King Saud University, Riyadh, Saudi Arabia (Wani); The University of Lahore, Lahore, Pakistan (Waqar); University of California Los Angeles, Los Angeles, California (Wei); Royal Children’s Hospital, Melbourne, Victoria, Australia (Weintraub); Heidelberg University, Mannheim, Germany (Wibowo); Gadjah Mada University, Yogyakarta, Indonesia (Wicaksana); University of Colombo, Colombo, Sri Lanka (D. P. Wickramasinghe); Rajarata University of Sri Lanka, Anuradhapura, Sri Lanka (N. D. Wickramasinghe); Universitas Aisyiyah Bandung, Bandung, Indonesia (Wilandika); University of Ghana, Accra, Ghana (Witts); University of Technology Sydney, Sydney, New South Wales, Australia (Wonde); Mahidol University, Bangkok, Thailand (Wongsin); Chinese Academy of Sciences, Shenzhen, China (Wu); Huazhong University of Science and Technology, Wuhan, China (Xiao); Anhui Medical University, Anhui, China (Xie); Shanghai Jiao Tong University, Shanghai, China (Site Xu); University of Science and Technology of China, Hefei, China (Suowen Xu); Tufts University, Boston, Massachusetts (W. Xu); University of New South Wales, Sydney, New South Wales, Australia (X. Xu); National Institute for Research in Environmental Health, Bhopal, India (Yadav); Tehran University of Medical Sciences, Tehran, Iran (Yadegar); Apollo Institute of Medical Sciences and Research, Hyderabad, India (Yahoo); Juntendo University, Tokyo, Japan (Yamagishi); Capital Medical University, Beijing, China (Yang); Juntendo University, Tokyo, Japan (Yano); Beijing University of Chinese Medicine, Beijing, China (Yao); Thomas Jefferson University, Philadelphia, Pennsylvania (Yarahmadi); Semnan University of Medical Sciences, Semnan, Iran (Yaribeygi); St Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia (Yesuf); Xuzhou Medical University, Xuzhou, China (Yin); Bahir Dar University, Bahir Dar, Ethiopia (Yismaw); Kyung Hee University, Seoul, South Korea (Yon); University of Toyama, Toyama, Japan (Yonemoto); Wuhan University, Wuhan, China (C. Yu); Vitalant Research Institute, San Francisco, California (E. A. Yu); University of Alabama, Birmingham, Alabama (H. Yu); Hubei University of Medicine, Shiyan, China (Y. Yu); Harbin Medical University Cancer Hospital, Harbin, China (Yuan); University of British Columbia–Okanagan, Kelowna, British Columbia, Canada (Yuzbashian); University of Hail, Hail, Saudi Arabia (Zafar); Islamic Azad University, Tehran, Iran (Zaghampour); Burlo Garofolo Institute for Maternal and Child Health, Trieste, Italy (Zamagni); Universidad Católica Boliviana San Pablo, Tarija, Bolivia (Zamora); University of Foggia, Foggia, Italy (Zanghì); King Saud University, Riyadh, Saudi Arabia (Zargar); Northern Border University, Rafha, Saudi Arabia (Zawiah); University of Hail, Hail, Saudi Arabia (Zeariya); Johns Hopkins University, Baltimore, Maryland (Zeru); Zhongshan Hospital, Shanghai, China (B. Zhang); University of Hong Kong, Hong Kong, China (C. J. P. Zhang); ACS Medical College and Hospital, ShangHai, China (H. Zhang); Columbia University, New York, New York (J. M. F. Zhang); Wuhan University of Science and Technology, Wuhan, China (Y. Zhang); China Medical University, Shenyang, China (Z. Zhang); Al Farabi Kazakh National University, Almaty, Kazakhstan (Zhanuzakov); Shengjing Hospital of China Medical University, Shenyang, China (Zhao); Wenzhou Medical University, Wenzhou, China (Zheng); Harvard University, Boston, Massachusetts (A. Zhong); The Chinese University of Hong Kong, Hong Kong, China (C. C. Zhong); Zhejiang University, Yiwu, China (H. Zhou); Stanford University, Stanford, California (J. Zhou); Wenzhou Medical University, Wenzhou, China (X.-D. Zhou); Anhui University of Chinese Medicine, Hefei, China (Zhu); Kazakh National Medical University, Almaty, Kazakhstan (Zhumagaliuly); University of Waterloo, Waterloo, Ontario, Canada (Zitoun); Hormozgan University of Medical Sciences, Bandar Abbas, Iran (Zoghi); Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia (Zoromba); University College London, London, United Kingdom (Zumla); An-Najah National University, Nablus, Palestine (A. H. Zyoud); An-Najah National University, Nablus, Palestine (Sa’ed H. Zyoud); Palestine Technical University (Kadoorie), Tulkarem, Palestine (Shaher H. Zyoud); University of Washington, Seattle, Washington (Murray); Division of Cardiology, School of Medicine, University of Washington, Seattle, Washington (Roth).

Author Contributions: Dr Razo had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Razo, Johnson, LeGrand, Mokdad, Roth, Aalruz, Abdelwahab, Abdrabou, Abohashem, Aburuz, Adegbile, Afrashteh, Afzal, Agyemang-Duah, Suhaib Ahmad, Mehrunnisha Ahmed, Meqdad Ahmed, Mohamed Sherif Ali Ahmed, Akhtar, Al-Kaif, Al Zoubi, Alanzi, Al-Bashaireh, Albashtawy, Al-Dewik, Algammal, Alif, Alkhatib, Allouh, Almazan, Alnaeem, Alshahrani, Al-Shami, Altwalbeh, Alwafi, Al-Worafi, Amini, Amini-Salehi, Amusa, Arafa, Arafat, Aravkin, Areda, Arefnezhad, Aripov, Aurangzeb, Ayli, Azarboo, Azzam, Baig, Banik, Barrow, Bastan, Bhaskar, B. Biswas, Bouaoud, Busch, Chau, A. Chen, Cheng, Cho, Chong, H. Chopra, S. Chopra, Cruz-Martins, D’Amico, Danpanichkul, Darcho, Devegowda, Dhimal, Didehvar, T. C. Do, Dohare, O. Doshi, R. Doshi, Ekholuenetale, El Arab, Eldaboush, El-Dahiyat, Etaee, Fadaka, Fagbamigbe, Farasani, Gadanya, Ghadirian, Ghaffari, Ghaffari Jolfayi, Ghamkhar, Ghimire, Gohari, Golechha, Golmohammadi, Hamdy, Hanifi, Hassan Ahmed, Helmy, Hoseinzadeh, Hotwani, Irham, Islek, Isola, Iwagami, Jahrami, Jamal, Jayasinghe, Jeswani, Jin, Joshua, Jürisson, K, Kalavani, Kantar, Kashoo, Khalil, P. Khalili, Khoshvaght, Khosla, Kisa, Kolahi, L. Kumar, Kytö, Lahariya, Larebo, Lasrado, D. Le, Thi Thu Thao Le, Thoa Le, Ledda, Hayeon Lee, Sang-woong Lee, Sergey Lee, W. Lee, Lemma, Q. Li, S. Li, Llanaj, Ma, Mahmood, A. Malik, Mamand, Maqsood, Marzouk, R. Menezes, Mohamed Ahmed, A. Mohammadi, M. Mohammadi, Mohammadian-Hafshejani, Montazeri Namin, Mosaddeghi Heris, Motappa, Musa, Nabipoorashrafi, Nargus, B. Nayak, Nejad Shahrokh Abadi, C. Nguyen, C. T. Nguyen, N. Nguyen, P. Nguyen, Nketia, Oancea, Odediji, Ojedoyin, Orscelik, Osborne, Padubidri, Papa, Pasovic, K. Patel, N. Patel, N. R. Patel, Peprah, Perna, Porntaveetus, Qureshi, P. Raghav, Y. Raghav, Rajendran, A. Rao, M. Rao, S. Rao, Rath, Rawassizadeh, Redwan, Remuzzi, Rezaeian, Rodriguez, Roever, Romadlon, Sharmistha Roy, Sabet, Sadat Rafiei, Saddique, Sadee, U. Saeed, Saheb Sharif-Askari, Saleh, Salehi, Samy, Savabi Far, Senol, Sethi, Seylani, Shahid, Shamsi, Shan, Sharif, Bunty Sharma, R. Sharma, L. Singh, R. Sinha, Skryabin, Spartalis, Stanikzai, Starodubova, Taheri Soodejani, Taiba, Tajabadi, Tan, Tanashat, Thakar, Thomas, Tiwari, Topor-Madry, Tualeka, Tye, Uzun Ozsahin, Venketasubramanian, N. Wang, Shu Wang, Wanzhou Wang, Wei Wang, Youxin Wang, D. Wickramasinghe, Site Xu, Suowen Xu, Yarahmadi, Yonemoto, H. Yu, Yuan, Zaghampour, Zhao, Zhu.

Acquisition, analysis, or interpretation of data: DeCleene, Johnson, Stark, Hay, Mokdad, Murray, Abaraogu, Abd ElHafeez, Abdelmasseh, Abdelnabi, Abd-Elsalam, Elshenawy, Abdelwahab, Abdolizadeh, Abdollahi, Abdoun, Abdulah, Abdullahi, Abdul-Rahman, Abebe, Getahun, Olugbenga Abiodun, Olumide Abiodun, Abohashem, Abonie, Abourashed, Abramov, Abrar, Abtahi, Abu-Farha, Abubakar, Abuhelwa, Abukhadijah, Aburuz, Abushanab, Adams, Adamu, Adane, Addo, Adedokun, Adegbile, Adegoke, Adekola, Adeleke, Adesina, Adisu, Adnan, Afoakwah, Afolabi, Afrifa-Yamoah, Afzal, Agordoh, A. Ahmad, K. Ahmad, Sajjad Ahmad, Suhaib Ahmad, A. Ahmed, G. Ahmed, L. Ahmed, Mohamed Sherif Ali Ahmed, Mushood Ahmed, N. Ahmed, S. Ahmed, Z. Ahmed, Aiyer, Ajami, Akalanka, Akhtar, Akindele, Akrami, Akyea, Al Hasan, Alahdab, Al-Ahmad, Alajajian, Alajlani, Alalwan, Al-Aly, Al-Amri, Alarifi, Al-Ashwal, Al-Azayzih, Al-Azzam, Al-Bashaireh, Albashtawy, Al-Dewik, Aleidi, Algahtani, Algammal, Alhabib, Alhalaiqa, Abid Ali, Asgar Ali, B. Ali, Mohammad Daud Ali, Mohammed Ali, R. Ali, S. Ali, Al-Iede, Alif, Alipour, Al-Jabi, Aljunid, Alkhatib, Alkubati, Allawi, Allemailem, Almagharbeh, Almahmeed, Al-Marwani, Alnaeem, Alniss, Alomari, Alotaibi, Alqahtani, AlQudah, Alqudimat, Al-Qudimat, Al-Raddadi, Alrimawi, Alrousan, Alsbou, Alshahrani, Al-Shami, Altaany, Altaf, Althobiani, Alwafi, Al-Worafi, Aly, Al-Zalabani, A. Alzoubi, K. Alzoubi, Al-Zubairi, Amafah, Aman Mohammadi, Amin, Amini, Amusa, Ananda, Ancuceanu, Ang, Anieto, Anil, Anjana, Anuoluwa, Anvari, Anwar, Anyasodor, Appati, Arabloo, Arafa, Arafat, Aregawi, Arias de la Torre, Arockiaraj, Arruda, Asdaq, Asghari-Jafarabadi, Saad Ashraf, Syed Amir Ashraf, T. Ashraf, Asiamah-Asare, Atac, Athar, Athari, Atorkey, S. Awan, U. Awan, Awol, Awotidebe, Ayala, Azad, Aziz, Azzam, Azzolino, Babiker, Babu, Rathnaiah Babu, Badar, Badiye, Badran, Baghlaf, Baig, Bakhshali, Balabekova, Balakrishnan, Baltatu, Banach, Banik, Barqawi, Barrow, Barua, M. Bashir, S. Bashir, Bashiri, Bastan, Bayat, Bayih, Beeraka, Begum, P. Behera, S. Behera, Behnam, Bejarano Ramirez, Belayneh, Bello, Belo, Berihun, Bhaskar, Bhattacharjee, G. Bhatti, J. Bhatti, A. Biswas, B. Biswas, Bizzozero-Peroni, Bodur, Bogale, Bohn, Boloor, Bolourinejad, Bonny, Botero Carvajal, Bouaoud, Boudalia, Britton, Bugiardini, Busch, Bustanji, Butt, Campos, Cao, Catapano, Cegolon, Cembranel, Cenko, Cerin, Chadwick, Chakraborty, Chandika, Chandrasekar, Chandrasekaran, Chattu, Chau, Chaudhary, Chaudhuri, Cheema, A. Chen, Hana Chen, Haowei Chen, Cheung, Chew, Chi, Chichagi, Ching, Choi, Chong, D. Chopra, Choudhari, Chu, Chukwu, Sheng-Chia Chung, Sunghyun Chung, Cicero, Cosma, Criqui, Cruz-Martins, Dadras, Dahabiyeh, Dahal Khatri, X. Dai, Z. Dai, Dalakoti, D’Amico, Damtie, L. Dandona, R. Dandona, D’Anna, Darcho, Dardas, Das, D. Davletov, K. Davletov, Dejenie, Del Bo’, Delgado-Enciso, Denova-Gutiérrez, Dergaa, Derseh, Dervišević, Tesfaw, Devanbu, Devegowda, Dewan, Dhali, Dhimal, Dhungel, Dias da Silva, Didehvar, Ding, T. C. Do, T. Do, Dong, D’Oria, Dorostkar, O. Doshi, Dourado, Dowou, Dresse, Du, Duncan, Duraes, Ebohon, Ebraheim, Edeh, Eftekhari, Eighaei Sedeh, Ekholuenetale, El Arab, Eladl, El-Dahiyat, Elgendy, Elhadi, Elhoumed, El-Huneidi, Elmeligy, Elmonem, Elmoselhi, Elnaem, Eltahawy, Fabin, Fagbamigbe, Fakhradiyev, Fakorede, Fareed, Farhana, Faris, Farsi, Fatima, Fayaz, Fazylov, Fekadu, Fernandez-Jimenez, Ferreira, Fischer, Fogacci, Fonzo, Foschi, Gadanya, Gajdács, Galali, Ganesan, Ganesh, Gangachannaiah, Gao, Garcia-Azorin, Garg, Gasevic, Gautam, Gaye, Gebresilassie, Getacher, Gete, Ghaffari, Ghamkhar, Gharaibeh, Ghasemi, Ghazy, Ghimire, Ghith, Gil, Gilani, Gill, Gnedovskaya, Goh, Gohari, Gohil, Goleij, Golmohammadi, Goulart, Goyal, Grada, Grover, Gu, Gubari, Gufue, Guha, Gunawardane, Guo, R. Gupta, S. Gupta, Gurgoglione, Guzman-Esquivel, Guzmán-Muñoz, Haghtalab, Hai Nam, Hailu, Halder, Hamdy, H. Hamidi, S. Hamidi, Hammad, Hamoudi, Hanif, Hanifi, Hankey, Harsini, Hartmann, Hasan, Hashempur, Hasnain, A. Hassan, Ibrahim Nagmeldin Hassan, Ikrama Hassan, N. Hassan, T. Hassan, Hayat, Hebert, Helmy, Hezam, Hiraike, Holla, M. Hostiuc, S. Hostiuc, Hotwani, Htay, Hu, J. Huang, Y. Huang, Hushmandi, D. Hussein, M. Hussein, Hwang, Ibitoye, Ibrahim, Ibrayeva, Ikiroma, Ikram, Ilesanmi, I. Ilic, M. Ilic, Imam, Imani, Imodoye, Imoh, Inbaraj, A. Ionescu, R. Ionescu, Iqhrammullah, Irham, Ishaqui, Islek, Ismail, Ismoldayev, Isola, Iyer, Izquierdo-Condoy, Jacob, Jafari-Khounigh, Jahanshahi, Jahrami, Jakovljevic, Jamal, Jamali, Jamaluddin, James, Jamshidi, Jansen, Jarrahi, Javaid, Jawaid, Jayasinghe, Jebasingh, Jemal, Jeong, Jeswani, Ji, Jibat, Jin, Jokar, Joo, A. Joseph, M. Joseph, N. Joseph, Joseph Michael Raj, Joshi, Joshua, Jung, Jürisson, Kadir, Kahe, Kakkar, Kalani, Kalavani, Kalra, Kamenova, Kamorudeen, Kanaan, Kanmodi, Kansal, Kantar, Kapoor, Kar, Karajizadeh, Karakasis, Karasneh, Karimi, Karimi Behnagh, Karun, Kashoo, Kashyap, Kausar, Kazemian, Kebede, Kesse-Guyot, Khademi, Khader, Khajuria, khaleel, N. Khalid, S. Khalid, Khalil, A. Khalili, A. Khan, Maseer Khan, Md Abdullah Saeed Khan, M. I. Khan, Moien Khan, Muhammad Hamza Khan, Serab Khan, Sumaiya Khan, Y. Khan, Khasbage, Khatatbeh, Kheirallah, Khosla, Khosravi, H. Kim, K. Kim, Kimokoti, Kisa, Kishore, Kivimaki, Kokkorakis, Kompani, Kondybayeva, Korzh, Kostev, Koulmane Laxminarayana, Krishan, Kua, Kuddus, Kulimbet, Kulkarni, C. Kumar, D. Kumar, G. Kumar, J. Kumar, L. Kumar, N. Kumar, S. Kumar, A. Kundu, S. Kundu, Kunutsor, Kurniasari, Kurpad, Kusuma, Kytö, Lahariya, D. Lai, H. Lai, Lajunen, Lakanova, Lallukka, Lantos, Larsson, D. Le, Thi Thu Thao Le, Thoa Le, Trang Le, Hwamin Lee, Seung Won Lee, W. Lee, Leivaditis, Lema, Lenjisa, Lewis, C. Li, M. Li, Q. Li, X. Li, Y. Li, Lim, Lindholm, H. Liu, X. Liu, X. C. Liu, Y. Liu, Livingstone, Llanaj, Lodhi, Lokunarangoda, López-Gil, Lorkowski, Lucchetti, Lui, Luo, Lv, Lytvyak, M. Amin, Ma, Mabrok, Machoy-Rakoczy, Madinehzad, Maffia, Magaña Gómez, Mahalleh, Mahmood, A. Malik, M. Malik, T. Malik, Malta, Maniya, Mannethodi, Mansoor, Mansourian, Manzoor, Marateb, Marghani, Marino, Martinez, D. Martini, S. Martini, Martorell, März, Marzouk, Masi, Masrouri, Mathur, Matozinhos, Mattiello, Maude, Mavrovounis, McPhail, Mehto, Meles, Mendoza, G. Menezes, Mengistie, Menon, Mensah, Merlino, Mestrovic, C. Mettananda, S. Mettananda, Metwally, Miao Jonasson, Mikrajab, Minhas, Mirrakhimov, S. Mitra, T. Mitra, Mittal, Modi, M. Mohamed, N. Mohamed, Mohamed Ahmed, A. Mohammad, T. Mohammad, S. Mohammadi, Mohammadian-Hafshejani, Mohammed, Mohan, Mohsen, Molla, Molokhia, Monazzami, Moni, Montazeri Namin, Moodi Ghalibaf, Morovvati, Mosaddeghi Heris, Motappa, Mousavi, Mousavi Kiasary, Mozafar, Mozahheb Yousefi, Mubarik, Muhammad, Munkhsaikhan, Munshi, Muthu, Mwita, Myung, Nagarajan, G. Naik, H. Naik, Nainu, Nartey, Nasrollahizadeh, Nassar, Natto, Nauman, Naureen, Navaratna, Nawaiseh, Nawsherwan, B. Nayak, V. Nayak, Nazari, Ndakotsu, A. Nega, M. Nega, Negash, Negoi, Nematollahi, C. Nguyen, C. T. Nguyen, H. Nguyen, N. Nguyen, P. Nguyen, V. Nguyen, Niazi, Nieddu, Nketia, Nomura, Noreen, Noubiap, Nri-Ezedi, Ntsekhe, Nugen, Nur, Oancea, Odat, Oddi, Odediji, Ogunmodede, Oh, Ojedoyin, Ojo, Okati-Aliabad, Okekunle, Okonji, Moraes de Oliveira, Olorukooba, Ong, Ordak, Orru, Orscelik, Ortiz, Ortiz-Prado, Osei, Ostrominski, Osuagwu, Oumer, Ouyahia, Owolabi, Oyebola, Ozsahin, Mahesh, Padron-Monedero, Padubidri, Palicz, Panda, None, Pandi-Perumal, Panos, Papa, Papadimopoulos, Pardhan, Parija, Parikh, Pascucci, Pasovic, Passera, K. Patel, M. Patel, S. Patel, A. Patil, S. Patil, Patoulias, Pattnaik, Pazoki Toroudi, Pekarcikova, Pereira, Perico, Petermann-Rocha, Pirera, Pirnejad, Plotnikov, Poddighe, Poluru, Pradhan, Prasad, Prashant, Prates, Pugliese, Puvvula, Qi, Qiu, Radhakrishnan, P. Raghav, Raghuveer, Rahamon, F. Rahim, H. Rahim, Rahimi, F. Rahman, Mosfequr Rahman, Hifz Ur Rahman, Mosiur Rahman, Muhammad Aziz Rahman, Raj, Raja, Rajput, Ramadan, C. Ramasamy, S. Ramasamy, M. Rao, S. Rao, Rath, Rathi, Rathish, Rauniyar, D. Rawaf, S. Rawaf, Ray, Reddy, Redwan, Rehman, M. Rezaei, N. Rezaei, Roever, Romoli, Rony, Ross, Sharmistha Roy, Shubhanjali Roy, Rubeshkumar, Russo, Rwegerera, Saad, Saber-Ayad, Sabet, Sadarangani, Sadat Rafiei, Ehsan Sadeghi, Erfan Sadeghi, M. Sadeghi, B. Sadek, M. Sadek, Sadr, Saeb, M. Saeed, U. Saeed, Saeedi, Safiullah, Saghazadeh, Sagoe, Sah, Sahebkar, Sajadi, Sajid, Saki, Sakshaug, Salami, Saleem, Salehi, Salihu, Salimi, Samargandy, Samodra, Samy, Saravanan, Sarfo, Sarmadi, G. Sarode, S. Sarode, Sarrafzadegan, Sassano, Sathian, Sathya Narayanan, Schlaich, Schuermans, Schwarz, Sejben, Selvaraj, Semreen, Senol, Serban, Sessa, Sethi, Setiawan, Sewor, Shahid, Shahini, Shahrahmani, Shahwan, Sham, Shamim, Shamsi, Shan, Shanawaz, Shannawaz, Sharif, Sharifan, Bhoopesh Sharma, K. Sharma, M. Sharma, R. Sharma, U. Sharma, V. Sharma, Shastry, Shehzadi, Sheidaei, Shenoy, M. Shetty, P. Shetty, F. Shi, H. Shi, Y. Shi, Shimels, Shiri, Shittu, Shiue, Shoaib, Shorofi, Shrestha, Shuval, Sia, Siddig, Siddiqi, Siddiqua, D. Silva, L. Silva, Simkhada, A. Singh, B. Singh, Harmanjit Singh, Harpreet Singh, J. Singh, L. Singh, Paramdeep Singh, P. S. Singh, Puneetpal Singh, Siddharth Singh, S. K. Singh, Surjit Singh, V. Singh, M. Sinha, Skryabin, Sokhal, Sood, Soraneh, Sorensen, Sorrentino, Spartalis, Srivastav, Stachteas, Stanikzai, Starodubova, Straube, Stubbs, Su, Subramaniyan, Suhag, Sulaieva, Sulaiman, Suleiman Odidi, Sulistiyorini, J. Sun, Z. Sun, Swain, Szarpak, T Y, Tabaee Damavandi, Tabares-Seisdedos, Tabatabaei, Tabatabaeizadeh, Tabatabai, Tabche, Tadele, Tangiisuran, Taha Osman Ali, Taiba, Talic, Talukder, Tamehri Zadeh, Tamiru Adugna, Tampa, Tan, Tanabayeva, Tang, Tantisattamo, Tariku, Tariq, Tavangar, Tedla, Temsah, Teramoto, Tesfamariam, Tesfu, Tewari, Thangavelu, I. Tharwat, S. Tharwat, Thienemann, Thiruvengadam, Ticoalu, Tiruye, Tleshev, Tomo, Tonelli, Topor-Madry, Touvier, Tovani-Palone, A. Tran, T. Tran, Tran Minh Duc, Trico, Tristan, Tse, Tseriotis, Tye, Udoakang, A. Ullah, R. Ullah, S. Ullah, Umair, Umar, Upadhya, Upadhyay, Usman, Uzun Ozsahin, Uzunçıbuk, Vaithinathan, Vakili, Van den Eynde, Varghese, Vasankari, Vellingiri, Venketasubramanian, A. Verma, P. Verma, Villalobos-Daniel, Vlassov, Wan, C. Wang, N. Wang, Q. Wang, Shaopan Wang, Shu Wang, Wanzhou Wang, Wei Wang, Yanzhong Wang, Z. Wang, Wani, Waqar, Wei, Weintraub, Wibowo, Wicaksana, D. Wickramasinghe, N. Wickramasinghe, Wilandika, Witts, Wonde, Wongsin, Wu, Xiao, Xie, Site Xu, Suowen Xu, W. Xu, X. Xu, Yadav, Yahoo, Yamagishi, Yang, Yano, Yao, Yarahmadi, Yaribeygi, Yesuf, Yin, Yismaw, Yon, Yonemoto, C. Yu, E. Yu, H. Yu, Y. Yu, Yuzbashian, Zafar, Zamagni, Zamora, Zanghì, Zargar, Zawiah, Zeariya, Zeru, B. Zhang, C. Zhang, H. Zhang, J. Zhang, Y. Zhang, Z. Zhang, Zhanuzakov, Zheng, A. Zhong, C. Zhong, H. Zhou, J. Zhou, X. Zhou, Zhu, Zhumagaliuly, Zitoun, Zoghi, Zoromba, Zumla, A. Zyoud, S. H. Zyoud, S. Zyoud, Kamyshnyi.

Drafting of the manuscript: Razo, Aalruz, Abd-Elsalam, Abdolizadeh, Olumide Abiodun, Abourashed, Aburuz, Afzal, Suhaib Ahmad, Mohamed Sherif Ali Ahmed, Mushood Ahmed, N. Ahmed, Akhtar, Al-Kaif, Al-Dewik, Algahtani, Algammal, Abid Ali, Alif, Aljunid, Alkhatib, Alkubati, Almagharbeh, Alnaeem, Alqahtani, AlQudah, Al-Qudimat, Alsbou, Alshahrani, Al-Shami, Altaany, Althobiani, Alwafi, Al-Worafi, Amini, Arafa, Arafat, Aurangzeb, Azzam, Baig, Barrow, M. Bashir, S. Bashir, Bastan, Bashiri, Bhaskar, G. Bhatti, J. Bhatti, B. Biswas, Bodur, Bohn, Bouaoud, Busch, Cegolon, Cembranel, Chadwick, Chew, Choi, Chong, S. Chopra, Darcho, Delgado-Enciso, Dergaa, Dewan, Didehvar, T. C. Do, R. Doshi, Ekholuenetale, El Arab, Eldaboush, El-Dahiyat, Elhadi, Etaee, Fadaka, Fagbamigbe, Faris, Gadanya, Gajdács, Ghaffari, Ghamkhar, Golmohammadi, Goyal, Grada, Grover, Gunawardane, S. Gupta, Hamdy, S. Hamidi, Hammad, Hanifi, A. Hassan, Hassan Ahmed, Helmy, Hotwani, Y. Huang, D. Hussein, Ikram, Imani, Ishaqui, Islek, Jamal, Javaid, Jeswani, Jin, Joshi, Joshua, Kahe, Kalavani, Kantar, Kar, Kashoo, N. Khalid, Khalil, Moien Khan, Khoshvaght, Kisa, L. Kumar, Lahariya, Larebo, Thi Thu Thao Le, Hayeon Lee, Sang-woong Lee, Leivaditis, Lokunarangoda, Ma, Mabrok, Mahmood, A. Malik, Marghani, Marzouk, Mattiello, Meles, Merlino, M. Mohamed, N. Mohamed, Mohamed Ahmed, A. Mohammadi, M. Mohammadi, Mohammadian-Hafshejani, Mohammed, Molokhia, Motappa, Mousavi, Mozahheb Yousefi, Nagarajan, Nejad Shahrokh Abadi, C. Nguyen, C. T. Nguyen, P. Nguyen, Nketia, Noreen, Odat, Okonji, Orscelik, Padubidri, Papa, Papadimopoulos, Pardhan, Pascucci, K. Patel, N. Patel, S. Patil, Pattnaik, Perna, Porntaveetus, Qi, Radhakrishnan, Raghuveer, Rahamon, F. Rahim, Raja, A. Rao, M. Rao, S. Rao, Rath, Rawassizadeh, Rodriguez, Roever, Ross, Sharmistha Roy, Sabet, U. Saeed, Samy, Selvaraj, Sethi, Seylani, Shahid, Shahini, Shamsi, Shanawaz, Sharif, K. Sharma, Shenoy, H. Shi, Shorofi, P. S. Singh, R. Sinha, Spartalis, Srivastav, Stanikzai, Starodubova, T Y, Tabaee Damavandi, Taha Osman Ali, Tanashat, Tang, Tewari, Tiwari, Tovani-Palone, Tseriotis, Tualeka, Uzun Ozsahin, Uzunçıbuk, N. Wang, Shu Wang, Youxin Wang, D. Wickramasinghe, Wonde, Site Xu, Suowen Xu, Yarahmadi, Yadegar, H. Yu, Yuan, Zamora, Zeariya, H. Zhang, Zhao, Zhu.

Critical review of the manuscript for important intellectual content: DeCleene, Johnson, Stark, LeGrand, Hay, Mokdad, Murray, Roth, Aalruz, Abaraogu, Abd ElHafeez, Abdelmasseh, Abdelnabi, Abd-Elsalam, Elshenawy, Abdelwahab, Abdolizadeh, Abdollahi, Abdoun, Abdrabou, Abdulah, Abdullahi, Abdul-Rahman, Abebe, Getahun, Olugbenga Abiodun, Olumide Abiodun, Abohashem, Abonie, Abourashed, Abramov, Abrar, Abtahi, Abu-Farha, Abubakar, Abuhelwa, Abukhadijah, Aburuz, Abushanab, Adams, Adamu, Adane, Addo, Adedokun, Adegbile, Adegoke, Adekola, Adeleke, Adesina, Adisu, Adnan, Afoakwah, Afolabi, Afrashteh, Afrifa-Yamoah, Afzal, Agordoh, Agyemang-Duah, A. Ahmad, K. Ahmad, Sajjad Ahmad, Suhaib Ahmad, A. Ahmed, G. Ahmed, L. Ahmed, Mehrunnisha Ahmed, Meqdad Ahmed, Mohamed Sherif Ali Ahmed, Mushood Ahmed, N. Ahmed, S. Ahmed, Z. Ahmed, Aiyer, Ajami, Akalanka, Akhtar, Akindele, Al-Kaif, Akrami, Akyea, Al Hasan, Al Zoubi, Alahdab, Al-Ahmad, Alajajian, Alajlani, Alalwan, Al-Aly, Al-Amri, Alanzi, Alarifi, Al-Ashwal, Al-Azayzih, Al-Azzam, Al-Bashaireh, Albashtawy, Al-Dewik, Aleidi, Algammal, Alhabib, Alhalaiqa, Abid Ali, Asgar Ali, B. Ali, Mohammad Daud Ali, Mohammed Ali, R. Ali, S. Ali, Al-Iede, Alif, Alipour, Al-Jabi, Aljunid, Alkhatib, Alkubati, Allawi, Allemailem, Allouh, Almahmeed, Al-Marwani, Almazan, Alnaeem, Alniss, Alomari, Alotaibi, Alqahtani, AlQudah, Alqudimat, Al-Qudimat, Al-Raddadi, Alrimawi, Alrousan, Alshahrani, Al-Shami, Altaany, Altaf, Althobiani, Altwalbeh, Alwafi, Al-Worafi, Aly, Al-Zalabani, A. Alzoubi, K. Alzoubi, Al-Zubairi, Amafah, Aman Mohammadi, Amin, Amini, Amini-Salehi, Amusa, Ananda, Ancuceanu, Ang, Anieto, Anil, Anjana, Anuoluwa, Anvari, Anwar, Anyasodor, Appati, Arabloo, Arafa, Arafat, Aravkin, Areda, Arefnezhad, Aregawi, Arias de la Torre, Aripov, Arockiaraj, Arruda, Asdaq, Asghari-Jafarabadi, Saad Ashraf, Syed Amir Ashraf, T. Ashraf, Asiamah-Asare, Atac, Athar, Athari, Atorkey, S. Awan, U. Awan, Awol, Awotidebe, Ayala, Ayli, Azad, Azarboo, Aziz, Azzam, Azzolino, Babiker, Babu, Rathnaiah Babu, Badar, Badiye, Badran, Baghlaf, Baig, Bakhshali, Balabekova, Balakrishnan, Baltatu, Banach, Banik, Barqawi, Barrow, Barua, M. Bashir, S. Bashir, Bastan, Bayat, Bayih, Beeraka, Begum, P. Behera, S. Behera, Behnam, Bejarano Ramirez, Belayneh, Bello, Belo, Berihun, Bhaskar, Bhattacharjee, G. Bhatti, J. Bhatti, A. Biswas, B. Biswas, Bizzozero-Peroni, Bodur, Bogale, Bohn, Boloor, Bonny, Botero Carvajal, Bolourinejad, Bouaoud, Boudalia, Britton, Bugiardini, Busch, Bustanji, Butt, Campos, Cao, Catapano, Cegolon, Cembranel, Cenko, Cerin, Chakraborty, Chandika, Chandrasekar, Chandrasekaran, Chattu, Chau, Chaudhary, Chaudhuri, Cheema, A. Chen, Hana Chen, Haowei Chen, Cheng, Cheung, Chew, Chi, Chichagi, Ching, Cho, Choi, Chong, D. Chopra, H. Chopra, Choudhari, Chu, Chukwu, Sheng-Chia Chung, Sunghyun Chung, Cicero, Cosma, Criqui, Cruz-Martins, Dadras, Dahabiyeh, Dahal Khatri, X. Dai, Z. Dai, Dalakoti, D’Amico, Damtie, L. Dandona, R. Dandona, D’Anna, Danpanichkul, Darcho, Dardas, Das, D. Davletov, K. Davletov, Dejenie, Del Bo’, Delgado-Enciso, Denova-Gutiérrez, Dergaa, Derseh, Dervišević, Tesfaw, Devanbu, Devegowda, Dewan, Dhali, Dhimal, Dhungel, Dias da Silva, Didehvar, Ding, T. C. Do, T. Do, Dohare, Dong, D’Oria, Dorostkar, O. Doshi, R. Doshi, Dourado, Dowou, Dresse, Du, Duncan, Duraes, Ebohon, Ebraheim, Edeh, Eftekhari, Eighaei Sedeh, Ekholuenetale, El Arab, Eladl, Eldaboush, El-Dahiyat, Elgendy, Elhadi, Elhoumed, El-Huneidi, Elmeligy, Elmonem, Elmoselhi, Elnaem, Eltahawy, Fabin, Fadaka, Fagbamigbe, Fakhradiyev, Fakorede, Farasani, Fareed, Farhana, Faris, Farsi, Fatima, Fayaz, Fazylov, Fekadu, Fernandez-Jimenez, Ferreira, Fischer, Fogacci, Fonzo, Foschi, Gadanya, Gajdács, Galali, Ganesan, Ganesh, Gangachannaiah, Gao, Garcia-Azorin, Garg, Gasevic, Gautam, Gaye, Gebresilassie, Getacher, Gete, Ghadirian, Ghaffari, Ghaffari Jolfayi, Ghamkhar, Gharaibeh, Ghasemi, Ghazy, Ghimire, Ghith, Gil, Gilani, Gill, Gnedovskaya, Goh, Gohari, Gohil, Golechha, Goleij, Golmohammadi, Goulart, Goyal, Grada, Grover, Gu, Gubari, Gufue, Guha, Gunawardane, Guo, R. Gupta, S. Gupta, Gurgoglione, Guzman-Esquivel, Guzmán-Muñoz, Haghtalab, Hai Nam, Hailu, Halder, Hamdy, H. Hamidi, Hammad, Hamoudi, Hanif, Hanifi, Hankey, Harsini, Hartmann, Hasan, Hashempur, Hasnain, Ibrahim Nagmeldin Hassan, Ikrama Hassan, N. Hassan, T. Hassan, Hayat, Hebert, Helmy, Hezam, Hiraike, Holla, Hoseinzadeh, M. Hostiuc, S. Hostiuc, Hotwani, Htay, Hu, J. Huang, Y. Huang, Hushmandi, D. Hussein, M. Hussein, Hwang, Ibitoye, Ibrahim, Ibrayeva, Ikiroma, Ikram, Ilesanmi, I. Ilic, M. Ilic, Imam, Imodoye, Imoh, Inbaraj, A. Ionescu, R. Ionescu, Iqhrammullah, Irham, Ishaqui, Islek, Ismail, Ismoldayev, Isola, Iwagami, Iyer, Izquierdo-Condoy, Jacob, Jafari-Khounigh, Jahanshahi, Jahrami, Jakovljevic, Jamal, Jamali, Jamaluddin, James, Jamshidi, Jansen, Jarrahi, Javaid, Jawaid, Jayasinghe, Jebasingh, Jemal, Jeong, Jeswani, Ji, Jibat, Jin, Jokar, Joo, A. Joseph, M. Joseph, N. Joseph, Joseph Michael Raj, Joshi, Joshua, Jung, Jürisson, K, Kadir, Kahe, Kakkar, Kalani, Kalra, Kamenova, Kamorudeen, Kanaan, Kanmodi, Kansal, Kantar, Kapoor, Kar, Karajizadeh, Karakasis, Karasneh, Karimi, Karimi Behnagh, Karun, Kashoo, Kashyap, Kausar, Kazemian, Kebede, Kesse-Guyot, Khademi, Khader, Khajuria, khaleel, N. Khalid, S. Khalid, Khalil, A. Khalili, P. Khalili, A. Khan, Maseer Khan, Md Abdullah Saeed Khan, M. I. Khan, Moien Khan, Muhammad Hamza Khan, Serab Khan, Sumaiya Khan, Y. Khan, Khasbage, Khatatbeh, Kheirallah, Khosla, Khosravi, H. Kim, K. Kim, Kimokoti, Kisa, Kishore, Kivimaki, Kokkorakis, Kolahi, Kompani, Kondybayeva, Korzh, Kostev, Koulmane Laxminarayana, Krishan, Kua, Kuddus, Kulimbet, Kulkarni, C. Kumar, D. Kumar, G. Kumar, J. Kumar, L. Kumar, N. Kumar, S. Kumar, A. Kundu, S. Kundu, Kunutsor, Kurniasari, Kurpad, Kusuma, Kytö, Lahariya, D. Lai, H. Lai, Lajunen, Lakanova, Lallukka, Lantos, Larebo, Larsson, Lasrado, D. Le, Thoa Le, Trang Le, Ledda, Hayeon Lee, Hwamin Lee, Sergey Lee, Seung Won Lee, W. Lee, Leivaditis, Lema, Lemma, Lenjisa, Lewis, C. Li, M. Li, Q. Li, S. Li, X. Li, Y. Li, Lim, Lindholm, H. Liu, X. Liu, X. C. Liu, Y. Liu, Livingstone, Llanaj, Lodhi, Lokunarangoda, López-Gil, Lorkowski, Lucchetti, Lui, Luo, Lv, Lytvyak, M. Amin, Ma, Mabrok, Machoy-Rakoczy, Madinehzad, Maffia, Magaña Gómez, Mahalleh, Mahmood, A. Malik, M. Malik, T. Malik, Malta, Mamand, Maniya, Mannethodi, Mansoor, Mansourian, Manzoor, Maqsood, Marateb, Marghani, Marino, Martinez, D. Martini, S. Martini, Martorell, März, Marzouk, Masi, Masrouri, Mathur, Matozinhos, Mattiello, Maude, Mavrovounis, McPhail, Mehto, Meles, Mendoza, G. Menezes, R. Menezes, Mengistie, Menon, Mensah, Mestrovic, C. Mettananda, S. Mettananda, Metwally, Miao Jonasson, Mikrajab, Minhas, Mirrakhimov, S. Mitra, T. Mitra, Mittal, Modi, M. Mohamed, N. Mohamed, Mohamed Ahmed, A. Mohammad, T. Mohammad, A. Mohammadi, S. Mohammadi, Mohammadian-Hafshejani, Mohammed, Mohan, Mohsen, Molla, Molokhia, Monazzami, Moni, Montazeri Namin, Moodi Ghalibaf, Morovvati, Mosaddeghi Heris, Motappa, Mousavi, Mousavi Kiasary, Mozafar, Mozahheb Yousefi, Mubarik, Muhammad, Munkhsaikhan, Munshi, Musa, Muthu, Mwita, Myung, Nabipoorashrafi, Nagarajan, G. Naik, H. Naik, Nainu, Nargus, Nartey, Nasrollahizadeh, Nassar, Natto, Nauman, Naureen, Navaratna, Nawaiseh, Nawsherwan, B. Nayak, V. Nayak, Nazari, Ndakotsu, A. Nega, M. Nega, Negash, Negoi, Nematollahi, C. Nguyen, C. T. Nguyen, H. Nguyen, N. Nguyen, P. Nguyen, V. Nguyen, Niazi, Nieddu, Nketia, Nomura, Noreen, Noubiap, Nri-Ezedi, Ntsekhe, Nugen, Nur, Oancea, Odat, Oddi, Odediji, Ogunmodede, Oh, Ojedoyin, Ojo, Okati-Aliabad, Okekunle, Okonji, Moraes de Oliveira, Olorukooba, Ong, Ordak, Orru, Orscelik, Ortiz, Ortiz-Prado, Osborne, Osei, Ostrominski, Osuagwu, Oumer, Ouyahia, Owolabi, Oyebola, Ozsahin, Mahesh, Padron-Monedero, Padubidri, Palicz, Panda, None, Pandi-Perumal, Panos, Papa, Papadimopoulos, Parija, Parikh, Pascucci, Pasovic, Passera, K. Patel, M. Patel, N. Patel, N. R. Patel, S. Patel, A. Patil, S. Patil, Patoulias, Pattnaik, Pazoki Toroudi, Pekarcikova, Peprah, Pereira, Perico, Perna, Petermann-Rocha, Pirera, Pirnejad, Plotnikov, Poddighe, Poluru, Pradhan, Prasad, Prashant, Prates, Pugliese, Puvvula, Qi, Qiu, Qureshi, Radhakrishnan, P. Raghav, Y. Raghav, Raghuveer, Rahamon, F. Rahim, H. Rahim, Rahimi, F. Rahman, Mosfequr Rahman, Hifz Ur Rahman, Mosiur Rahman, Muhammad Aziz Rahman, Raj, Raja, Rajendran, Rajput, Ramadan, C. Ramasamy, S. Ramasamy, M. Rao, S. Rao, Rath, Rathi, Rathish, Rauniyar, D. Rawaf, S. Rawaf, Ray, Reddy, Redwan, Rehman, Remuzzi, M. Rezaei, N. Rezaei, Rezaeian, Rodriguez, Roever, Romadlon, Romoli, Rony, Ross, Sharmistha Roy, Shubhanjali Roy, Rubeshkumar, Russo, Rwegerera, Saad, Saber-Ayad, Sabet, Sadarangani, Sadat Rafiei, Saddique, Sadee, Ehsan Sadeghi, Erfan Sadeghi, M. Sadeghi, B. Sadek, M. Sadek, Sadr, Saeb, M. Saeed, U. Saeed, Saeedi, Safiullah, Saghazadeh, Sagoe, Sah, Saheb Sharif-Askari, Sahebkar, Sajadi, Sajid, Saki, Sakshaug, Salami, Saleem, Saleh, Salehi, Salihu, Salimi, Samargandy, Samodra, Samy, Saravanan, Sarfo, Sarmadi, G. Sarode, S. Sarode, Sarrafzadegan, Sassano, Sathian, Sathya Narayanan, Savabi Far, Schlaich, Schuermans, Schwarz, Sejben, Selvaraj, Semreen, Senol, Serban, Sessa, Sethi, Setiawan, Sewor, Seylani, Shahid, Shahini, Shahrahmani, Shahwan, Sham, Shamim, Shan, Shanawaz, Shannawaz, Sharif, Sharifan, Bhoopesh Sharma, Bunty Sharma, K. Sharma, M. Sharma, R. Sharma, U. Sharma, V. Sharma, Shastry, Shehzadi, Sheidaei, Shenoy, M. Shetty, P. Shetty, F. Shi, H. Shi, Y. Shi, Shimels, Shiri, Shittu, Shiue, Shoaib, Shorofi, Shrestha, Shuval, Sia, Siddig, Siddiqi, Siddiqua, D. Silva, L. Silva, Simkhada, A. Singh, B. Singh, Harmanjit Singh, Harpreet Singh, J. Singh, L. Singh, Paramdeep Singh, P. S. Singh, Puneetpal Singh, Siddharth Singh, S. K. Singh, Surjit Singh, V. Singh, M. Sinha, R. Sinha, Skryabin, Sokhal, Sood, Soraneh, Sorensen, Sorrentino, Spartalis, Stachteas, Stanikzai, Starodubova, Straube, Stubbs, Su, Subramaniyan, Suhag, Sulaieva, Sulaiman, Suleiman Odidi, Sulistiyorini, J. Sun, Z. Sun, Swain, Szarpak, T Y, Tabaee Damavandi, Tabares-Seisdedos, Tabatabaei, Tabatabaeizadeh, Tabatabai, Tabche, Tadele, Tangiisuran, Taha Osman Ali, Taheri Soodejani, Taiba, Tajabadi, Talic, Talukder, Tamehri Zadeh, Tamiru Adugna, Tampa, Tan, Tanabayeva, Tanashat, Tang, Tantisattamo, Tariku, Tariq, Tavangar, Tedla, Temsah, Teramoto, Tesfamariam, Tesfu, Tewari, Thakar, Thangavelu, I. Tharwat, S. Tharwat, Thienemann, Thiruvengadam, Thomas, Ticoalu, Tiruye, Tleshev, Tomo, Tonelli, Topor-Madry, Touvier, Tovani-Palone, A. Tran, T. Tran, Tran Minh Duc, Trico, Tristan, Tse, Tseriotis, Tye, Udoakang, A. Ullah, R. Ullah, S. Ullah, Umair, Umar, Upadhya, Upadhyay, Usman, Uzun Ozsahin, Uzunçıbuk, Vaithinathan, Vakili, Van den Eynde, Varghese, Vasankari, Vellingiri, Venketasubramanian, A. Verma, P. Verma, Villalobos-Daniel, Vlassov, Wan, C. Wang, N. Wang, Q. Wang, Shaopan Wang, Shu Wang, Wanzhou Wang, Wei Wang, Wei Wang, Yanzhong Wang, Z. Wang, Wani, Waqar, Wei, Weintraub, Wibowo, Wicaksana, D. Wickramasinghe, N. Wickramasinghe, Wilandika, Witts, Wonde, Wongsin, Wu, Xiao, Xie, Site Xu, Suowen Xu, W. Xu, X. Xu, Yadav, Yadegar, Yahoo, Yamagishi, Yang, Yano, Yao, Yarahmadi, Yaribeygi, Yesuf, Yin, Yismaw, Yon, Yonemoto, C. Yu, E. Yu, H. Yu, Y. Yu, Yuzbashian, Zafar, Zaghampour, Zamagni, Zamora, Zanghì, Zargar, Zawiah, Zeariya, Zeru, B. Zhang, C. Zhang, J. Zhang, Y. Zhang, Z. Zhang, Zhanuzakov, Zheng, A. Zhong, C. Zhong, H. Zhou, J. Zhou, X. Zhou, Zhu, Zhumagaliuly, Zitoun, Zoghi, Zoromba, Zumla, A. Zyoud, S. H. Zyoud, S. Zyoud, Kamyshnyi.

Statistical analysis: Razo, DeCleene, Johnson, Stark, Abd ElHafeez, Abdelmasseh, Elshenawy, Abdullahi, Abdul-Rahman, Abebe, Abohashem, Abourashed, Abuhelwa, Aburuz, Adane, Adegbile, Adeleke, Adnan, Afolabi, Afrifa-Yamoah, Afzal, Suhaib Ahmad, Mohamed Sherif Ali Ahmed, Aiyer, Ajami, Alajlani, Al-Amri, Alanzi, Albashtawy, Al-Dewik, Algahtani, Algammal, S. Ali, Alif, Alkhatib, Al-Marwani, Alqudimat, Al-Qudimat, Alrousan, Alsbou, Al-Shami, Althobiani, Aly, K. Alzoubi, Aman Mohammadi, Amini, Amusa, Ananda, Ancuceanu, Ang, Anuoluwa, Anwar, Arafat, Aravkin, Areda, Arias de la Torre, Asdaq, Asghari-Jafarabadi, T. Ashraf, Aurangzeb, U. Awan, Awotidebe, Azzam, Bakhshali, Baltatu, Banik, Barrow, M. Bashir, Bastan, Beeraka, P. Behera, S. Behera, Berihun, Bhaskar, Bhattacharjee, G. Bhatti, J. Bhatti, B. Biswas, Bogale, Boloor, Bouaoud, Bugiardini, Chau, Chaudhuri, Chichagi, Chong, H. Chopra, S. Chopra, Choudhari, Dahabiyeh, Dahal Khatri, X. Dai, Darcho, Dardas, Delgado-Enciso, Denova-Gutiérrez, Dergaa, Dhali, Didehvar, Ding, Du, Eighaei Sedeh, Ekholuenetale, El Arab, El-Dahiyat, Elhoumed, Eltahawy, Fagbamigbe, Fayaz, Gadanya, Ganesh, Garg, Gete, Ghamkhar, Gohari, Grada, Gu, Gubari, Gunawardane, Guo, S. Gupta, Haghtalab, Hai Nam, Hamdy, Hamoudi, Hanifi, Harsini, Hasnain, Hassan Ahmed, Helmy, Hoseinzadeh, S. Hostiuc, Hotwani, Hu, Y. Huang, D. Hussein, Ikram, Imani, Imodoye, Irham, Ishaqui, Islek, Ismail, Isola, Jacob, James, Jeswani, Ji, Jibat, A. Joseph, Joseph Michael Raj, Joshi, Joshua, Jürisson, Kadir, Kakkar, Kalavani, Karakasis, Karimi Behnagh, Kashoo, Kashyap, khaleel, N. Khalid, Maseer Khan, M. I. Khan, Sumaiya Khan, Khasbage, Khatatbeh, Khoshvaght, Khosla, Kisa, Kishore, Korzh, Kulkarni, C. Kumar, S. Kundu, Lahariya, Lasrado, D. Le, Thoa Le, Hwamin Lee, Sang-woong Lee, Sergey Lee, Seung Won Lee, C. Li, X. Li, Y. Li, X. C. Liu, Lokunarangoda, Ma, Machoy-Rakoczy, Mahmood, M. Malik, Manzoor, Martinez, Marzouk, McPhail, Meles, R. Menezes, Mikrajab, M. Mohamed, N. Mohamed, Mohamed Ahmed, M. Mohammadi, Mohsen, Molla, Morovvati, Mosaddeghi Heris, Mubarik, Muhammad, Munkhsaikhan, Myung, Nagarajan, Nartey, Nasrollahizadeh, Nassar, Natto, B. Nayak, M. Nega, C. Nguyen, C. T. Nguyen, P. Nguyen, Niazi, Nieddu, Nketia, Nomura, Oancea, Odediji, Ogunmodede, Ojedoyin, Ordak, Ouyahia, Oyebola, Padubidri, Panda, Papa, Pasovic, K. Patel, N. Patel, Perna, Poluru, Porntaveetus, Prasad, Prates, Qi, Qiu, Raghuveer, F. Rahman, Rajput, A. Rao, S. Rao, Rath, Rauniyar, Rawassizadeh, Ray, Redwan, Roever, Romadlon, Ross, Sharmistha Roy, Rubeshkumar, Saad, Sabet, Erfan Sadeghi, U. Saeed, Safiullah, Sah, Saki, Salimi, Samy, Sarmadi, Schwarz, Sethi, Shanawaz, Shannawaz, Sharif, M. Sharma, R. Sharma, V. Sharma, Shehzadi, M. Shetty, H. Shi, Shimels, B. Singh, L. Singh, Siddharth Singh, V. Singh, M. Sinha, R. Sinha, Sood, Sorensen, Spartalis, Srivastav, Stanikzai, Starodubova, Su, J. Sun, Swain, T Y, Tabatabaei, Tabatabai, Taiba, Tamiru Adugna, Tampa, Tan, Tesfu, Tewari, I. Tharwat, Thomas, Tse, Tye, Umair, Umar, Upadhyay, Uzun Ozsahin, Varghese, Wan, N. Wang, Shaopan Wang, Shu Wang, Wanzhou Wang, Wei Wang, Yanzhong Wang, Youxin Wang, D. Wickramasinghe, Wonde, Wongsin, Xie, Site Xu, Suowen Xu, Yang, Yarahmadi, C. Yu, H. Yu, Y. Yu, Yuan, Zafar, Zaghampour, Zargar, Zeariya, B. Zhang, Z. Zhang, Zhao, Zheng, C. Zhong, Zhu, Zoromba, Kamyshnyi.

Obtained funding: Mokdad, Mohamed Sherif Ali Ahmed, Alshahrani, Bouaoud, Ghamkhar, A. Ionescu, Kalavani, Ma, S. Martini, Mohamed Ahmed, Nomura, Sabet, Stanikzai, Site Xu, Zhu.

Administrative, technical, or material support: Razo, LeGrand, Hay, Mokdad, Murray, Abaraogu, Abdelnabi, Abdrabou, Abdullahi, Olugbenga Abiodun, Olumide Abiodun, Abohashem, Abu-Farha, Abubakar, Abushanab, Adams, Adegbile, Adekola, Adesina, Afzal, Agordoh, A. Ahmed, Meqdad Ahmed, Mohamed Sherif Ali Ahmed, Mushood Ahmed, Akalanka, Akhtar, Al-Ahmad, Alanzi, Albashtawy, Al-Dewik, Algammal, Alhabib, Abid Ali, Asgar Ali, Mohammed Ali, Al-Iede, Alipour, Aljunid, Allawi, Almazan, Alnaeem, Alqahtani, Al-Shami, Alwafi, Al-Worafi, Aly, Al-Zalabani, Amin, Amini, Amini-Salehi, Amusa, Ancuceanu, Anieto, Anwar, Aripov, Athari, Aziz, Azzam, Azzolino, Rathnaiah Babu, Baig, Barqawi, M. Bashir, S. Bashir, Bayih, Bhattacharjee, B. Biswas, Bouaoud, Boudalia, Britton, Chadwick, Chattu, Cheema, A. Chen, Chong, H. Chopra, S. Chopra, Chu, Criqui, X. Dai, Dalakoti, Damtie, L. Dandona, Danpanichkul, Dardas, Devanbu, Dhali, T. C. Do, Dohare, O. Doshi, Dowou, Ebraheim, Ekholuenetale, Eldaboush, El-Dahiyat, Elhadi, Elnaem, Fatima, Fekadu, Gadanya, Gajdács, Ganesan, Gao, Ghaffari Jolfayi, Ghamkhar, Gohari, Golechha, Gufue, Hailu, Halder, Hanif, Hanifi, Harsini, Ibrahim Nagmeldin Hassan, T. Hassan, Hayat, Hebert, Ibitoye, Ilesanmi, Imam, Irham, Izquierdo-Condoy, Jahrami, Jamaluddin, Jansen, Jawaid, Jeong, Ji, Jin, Joshi, Joshua, Jung, Kahe, Kalavani, Kamenova, Kanmodi, Kansal, Kantar, Kar, Karimi Behnagh, Kashoo, Kausar, Khajuria, Khalil, Moien Khan, Muhammad Hamza Khan, Sumaiya Khan, Kheirallah, Khosla, Khosravi, Kokkorakis, Kondybayeva, Krishan, Kulimbet, C. Kumar, G. Kumar, L. Kumar, S. Kumar, Kurpad, D. Lai, Lakanova, Larsson, Trang Le, Hayeon Lee, Leivaditis, Lema, Lim, Livingstone, Lodhi, Lorkowski, Lui, Ma, Magaña Gómez, A. Malik, Mansourian, Maqsood, Marateb, März, Mathur, Mattiello, Mensah, C. Mettananda, S. Mettananda, M. Mohamed, Mohamed Ahmed, Mohammadian-Hafshejani, Mohan, Molla, Motappa, Natto, Nawaiseh, Nazari, Negoi, C. Nguyen, C. T. Nguyen, N. Nguyen, Nomura, Noreen, Nur, Odediji, Ogunmodede, Ojedoyin, Okonji, Olorukooba, Orscelik, Osei, Oyebola, Mahesh, Parikh, K. Patel, N. Patel, A. Patil, Patoulias, Pradhan, Qureshi, Radhakrishnan, Raghuveer, Rahamon, F. Rahim, Rahimi, Rajendran, Ramadan, Rath, Rauniyar, M. Rezaei, Romadlon, Saad, Sabet, Sadr, M. Saeed, U. Saeed, Sagoe, Sah, Sakshaug, Salehi, Salimi, Samy, Sarfo, Sathian, Schlaich, Senol, Sethi, Shahid, Shahini, Shamsi, Shan, K. Sharma, Sheidaei, Shrestha, Shuval, L. Silva, Paramdeep Singh, Siddharth Singh, Skryabin, Spartalis, Stanikzai, Subramaniyan, Tabaee Damavandi, Tamiru Adugna, Tan, Tanashat, Tantisattamo, S. Tharwat, Tiruye, A. Tran, Tran Minh Duc, Trico, Tseriotis, Udoakang, Umair, Upadhya, Uzun Ozsahin, Uzunçıbuk, Vasankari, Wan, Shu Wang, Wanzhou Wang, Waqar, Witts, Wongsin, Xiao, Site Xu, W. Xu, Yahoo, Yao, E. Yu, Zafar, Zeariya, J. Zhang, Y. Zhang, Zhanuzakov, Zhao, Zheng, H. Zhou, Zhu, Zitoun, Zoromba.

Supervision: LeGrand, Hay, Mokdad, Murray, Roth, Abd-Elsalam, Abdullahi, Afzal, Agordoh, L. Ahmed, Mohamed Sherif Ali Ahmed, N. Ahmed, Aiyer, Al-Azayzih, Albashtawy, Algammal, Abid Ali, Alotaibi, Alshahrani, Alwafi, Aly, Amini, Anil, Arefnezhad, Arias de la Torre, Saad Ashraf, Azarboo, Azzam, Azzolino, Babiker, Banach, S. Bashir, Belayneh, B. Biswas, Bohn, Bolourinejad, Bouaoud, Catapano, Cegolon, Chadwick, Cheng, Chew, H. Chopra, S. Chopra, Cruz-Martins, Dalakoti, D’Amico, D’Anna, Delgado-Enciso, Dergaa, Dervišević, T. C. Do, Eldaboush, Elgendy, Etaee, Fakhradiyev, Farsi, Fazylov, Fogacci, Gajdács, Gangachannaiah, Garcia-Azorin, Ghamkhar, Ghazy, Goyal, Grada, Guha, S. Gupta, Gurgoglione, Haghtalab, Hai Nam, Halder, Hamdy, H. Hamidi, Hanifi, Ibrahim Nagmeldin Hassan, Hwang, Iqhrammullah, Isola, Jakovljevic, Jibat, M. Joseph, Joshi, Kamenova, Kantar, Karakasis, Karimi, Kashoo, P. Khalili, Kostev, C. Kumar, A. Kundu, Kusuma, Lahariya, Thi Thu Thao Le, Ledda, Q. Li, Y. Li, López-Gil, Luo, Ma, Machoy-Rakoczy, A. Malik, Malta, Maniya, Marino, D. Martini, Marzouk, Masi, Mirrakhimov, Mohamed Ahmed, A. Mohammadi, Monazzami, Morovvati, Motappa, Nasrollahizadeh, N. Nguyen, V. Nguyen, Noreen, Odat, Oddi, Moraes de Oliveira, Orru, Osuagwu, Palicz, Passera, K. Patel, M. Patel, S. Patil, Petermann-Rocha, Pugliese, Puvvula, Raghuveer, Hifz Ur Rahman, Raj, Raja, Remuzzi, Rodriguez, Romadlon, Sharmistha Roy, Russo, U. Saeed, Saeedi, Sah, Saheb Sharif-Askari, Salami, Salimi, Samy, Selvaraj, Sessa, Sethi, Sewor, Shamim, K. Sharma, P. Shetty, Y. Shi, Shrestha, D. Silva, A. Singh, P. S. Singh, Skryabin, Spartalis, Stanikzai, Starodubova, Subramaniyan, Szarpak, Tabares-Seisdedos, Tadele, Tampa, Tan, Tanabayeva, Tanashat, Tang, Tariq, Tavangar, Tiwari, Umair, Venketasubramanian, Wei Wang, Z. Wang, Site Xu, Suowen Xu, W. Xu, Yang, Yano, Yarahmadi, Yon, Yuan, Zamora, Zanghì, H. Zhang, Zhanuzakov, Zhu.

Conflict of Interest Disclosures: Disclosed grants and personal fees reflect collaborators’ general institutional and personal funding as researchers and did not directly support the present article or the collaborator’s role in it, unless otherwise noted in the Funding/Support section. Dr Abramov reported receipt of speaker fees from AstraZeneca outside the submitted work. Dr Ancuceanu reported receipt of personal fees from AbbVie, Merck Romania, Laropharm, Reckit, Magnapharm, Biessen Pharma, and ALK Slovakia outside the submitted work. Dr Baltatu reported support from the National Council for Scientific and Technological Development (CNPq) under fellowship grant 304224/2022-7; institutional research support from the Anima Institute and Alfaisal University; and leadership or fiduciary roles for VividiWise Analytics (as managing partner) and São José dos Campos Tech Park–CITE (as biotech advisory board member). Dr Bhaskar reported receipt of grants from the Japan Society for Promotion of Science and personal fees from the Japan Stroke Society (travel grant) outside the submitted work; leadership or a fiduciary role in other board, society, committee, or advocacy group, paid or unpaid, with the National Cerebral and Cardiovascular Center (Osaka, Japan) as visiting director (2023-2025), Rotary District 9675 (Sydney, Australia) as district chair for diversity, equity, and inclusion, and the Global Health and Migration Hub Community, Global Health Hub Germany as chair, founding member, and manager; and editorial board memberships at PLOS One, BMC Neurology, TouchNeurology, Frontiers in Neurology, Frontiers in Stroke, Frontiers in Public Health, Journal of Aging Research, Neurology International, Diagnostics, and BMC Medical Research Methodology. Additionally, Dr Bhaskar serves as a member of the College of Reviewers for the Canadian Institutes of Health Research, director of research for the World Headache Society, member of the Scientific Review Committee at Cardiff University Biobank, chair of the Rotary Reconciliation Action Plan, Rotary District 9675 (Sydney, Australia), health care and medical adviser for Japan Connect, and expert adviser/reviewer for the Cariplo Foundation. Dr A. Biswas reported receipt of grants from the Indian Council of Medical Research, Department of Biotechnology, Government of India, and Department of Science and Technology, Government of India, and personal fees from Lupin Pharmaceuticals, Intas Pharmaceuticals, Eisai Pharmaceuticals, and Eli Lilly outside the submitted work. Dr Catapano reported receipt of personal fees from Amarin, Amgen, AstraZeneca, Chiesi, Daiichi Sankyo, Eli Lilly, Esperion, Ionis Pharmaceutical, Menarini, New Amsterdam Pharma, Novartis, Novo Nordisk, Regeneron, Sanofi, Ultragenyx, and Viatris outside the submitted work. Dr Cicero reported receipt of personal fees from Zentiva SA, Dompé SpA, Italfarmaco SpA, and Baryer SpA outside the submitted work. Dr R. Dandona reported receipt of grants from Mariwala Health Initiative and the Gates Foundation, being UK cochair for RIGHT Call 8 Funding Committee, National Institute for Health and Care Research (NIHR), being vice chair for the International Stillbirth Alliance, being chair for the Independent Programme Oversight Committee for COPE-BP (NIHR 206976), being a member of the Funding Committee, Global NIHR Global Health Research Groups Call 5, NIHR, and being a member of the Health Metrics Sciences MS Admissions Committee, University of Washington, outside the submitted work. Dr Dhungel reported contract work payment (to institution) from Eli Lilly outside the submitted work. Dr Garcia-Azorin reported receipt of personal fees from the World Health Organization, nonfinancial support from the Spanish Society of Neurology, and grants from the Carlos III Health Research Institute outside the submitted work. Dr Ghith reported receipt of grants from Danish Academy of Data Science outside the submitted work. Dr Hankey reported receipt of honoraria from the American Heart Association (Associate Editor, Circulation) and Janssen Research and Development (cochair, Executive Committee, Librexia Stroke Trial) outside the submitted work. Dr Hartmann reported receipt of grants from Nterica Bio outside the submitted work. Dr A. Hassan reported payment or honoraria for lectures, presentations, and speakers bureaus from Novartis, Allergan, AbbVie, Merck, Biologix, Viatris, Pfizer, Eli Lilly, Janssen, Roche, Sanofi Genzyme, Bayer, AstraZeneca, Hikma Pharma, Al Andalus, Chemipharm, Lundbeck, Elixir, EvaPharma, Inspire Pharma, Future Pharma and Habib Scientific Office, and Everpharma; support for attending meetings and/or travel from Novartis, Allergan, Merz, Pfizer, Merck, Biologix, Roche, Sanofi Genzyme, Bayer, Hikma Pharma, Chemipharm, Al Andalus, and Clavita Pharm; being president of MENA Headache Society, board member of the Multiple Sclerosis chapter of the Egyptian Society of Neurology, board member of Headache chapter of the Egyptian Society of Neurology, member of the Committee of Education of the International Headache Society (IHS), member of the Membership Committee of IHS, and member of the Regional Committee of IHS. Dr Hebert reported receipt of grants from Research NB in the form of salary and project support during the conduct of the study. Dr Kalani reported receipt of grants from the National Institutes of Health/National Institute of Neurological Disorders and Stroke (R01NS138297) outside the submitted work. Dr Kashyap reported receipt of an extramural grant (2022-2025; 5/13/55/2020/NCD-III) from the Indian Council of Medical Research outside the submitted work. Dr Kishore reported being reviewer & data analyst for the School of Pharmacy and Emerging Sciences outside the submitted work. Dr Kivimaki reported receipt of grants from the Wellcome Trust (221854/Z/20/Z), the Medical Research Council (MR/Y014154/1), and the Research Council of Finland (350426) (to university) outside the submitted work. Dr Kokkorakis reported receipt of grants from ZonMW outside the submitted work. Dr Kostev reported employment with IQVIA outside the submitted work. Dr Krishan reported nonfinancial support from the UGC Centre of Advanced Study, CAS II awarded to the Department of Anthropology and a RUSA 2.0 grant awarded by the Ministry of Education to Panjab University outside the submitted work. Dr Lallukka reported receipt of grants (to university) from the Research Council of Finland (grant 330527) during the conduct of the study. Dr M. Li reported receipt of grants from the National Science and Technology Council of Taiwan (NSTC 113-2314-B-003-002) and National Taiwan Normal University (Higher Education Sprout Project) during the conduct of the study. Dr Lim reported receipt of grants from Novartis, AstraZeneca, and Abbott Diabetes Care and personal fees from Novartis, Viatris, AstraZeneca, Boehringer Ingelheim, Novo Nordisk, Zuellig Pharma, Roche Diabetes Care, and Abbott Diabetes Care outside the submitted work. Dr Lorkowski reported receipt of grants from DSM-Firmenich and personal fees from Amarin Germany, Amedes Holding, Amgen, Berlin-Chemie, Boehriger Ingelheim Pharma, Daiichi Sankyo Deutschland, Danone, Hubert Burda Media, Janssen-Cilag, Lilly Deutschland, Novartis Pharma, Novo Nordisk Pharma, Roche Pharma, Sanofi-Aventis, Swedish Orphan Biovitrum, and Synlab Holding Deutschland outside the submitted work. Dr Maffia reported receipt of personal fees from Elsevier and being vice president and chair of the Engagement Committee for the British Pharmacological Society, vice chair of the Basic and Translational Section for the International Union of Basic and Clinical Pharmacology, chair of the Translational Research Medical Review Panel for Heart Research UK, nucleus member of the European Society of Cardiology Working Group on Atherosclerosis and Vascular Biology and Cell Biology of the Heart, member of the Executive Committee of the British Atherosclerosis Society, chair of the Publication Committee of the International Union of Immunological Societies, member of the Translational Clinical Studies Grant Panel for the Chief Scientist Office, and deputy editor of cardiovascular research, associate editor of pharmacological research, and editor-in-chief of the Human Health Section of Frontiers for Young Minds outside the submitted work. Dr Marateb reported receipt of grants from Universitat Politècnica de Catalunya Barcelona Tech-UPC (salary) outside the submitted work. Dr März reported receipt of personal fees from Amgen, Sanofi, Amryt, Abbott Diagnostics, Akzea, Novartis, and Sobi and grants from Novartis, Amgen, Abbott Diagnostics, and Sobi outside the submitted work. Dr Masi reported receipt of personal fees for lectures, advisory board participation, educational activities, and editorial activities from Servier; receipt of indirect payments from Servier for attending conferences (travel, hotels, and conference registrations); participation in a trial for Novartis; and receipt of grants (to institution) from CertMedica and Novo Nordisk to conduct trials serving as the local principal investigator. Dr Maude reported receipt of grants from the Wellcome Trust (grant 220211), which provides core funding for the Mahidol Oxford Tropical Medicine Research Unit and contributes to his salary, outside the submitted work. Dr Molokhia reported receipt of grants from UK NHS Race & Health Observatory. Dr Nomura reported receipt of grants from the Ministry of Education, Culture, Sports, Science and Technology of Japan. Dr Oancea reported receipt of grants from the Romanian National Research, Development, and Innovation Plan 2022-2027 (project PNRR/2022/C9/MCID/I8 No. 1672173292, contract 760231). Dr Ostrominski reported receipt of grants from the National Institutes of Health and personal fees from Bayer AG, Corcept Therapeutics, and Viking Therapeutics outside the submitted work. Dr Panda reported receipt of grants or contracts from the Central Council for Research in Homoeopathy (India) (17-59/2023-24/CCRH/Tech./Coll./ ICMR-Diabetes/960) and Siksha O. Anusandhan (deemed to be a university) outside the submitted work. Dr Panos reported receipt of grants and honoraria from Bayer Greece, Thea Greece, and Roche Hellas outside the submitted work. Dr Raj reported receipt of grants from the Indian Council of Medical Research outside the submitted work. Dr Samodra reported being cofounder of Benang Merah Research Center, Indonesia. Dr Schlaich reported receipt of personal fees from Medtronic, Abbott, and AstraZeneca and grants from Boehringer Ingelheim outside the submitted work. Dr J. Singh reported receipt of personal fees from ROMTech, Atheneum, Clearview Healthcare Partners, Yale, Hulio, Horizon Pharmaceuticals/DINORA, ANI/Exeltis USA Inc, Frictionless Solutions, Schipher, Crealta/Horizon, Medisys, Fidia, PK Med, Two Labs Inc, Adept Field Solutions, Clinical Care Options, Putnam Associates, Focus Forward, Navigant Consulting, Spherix, MedIQ, Jupiter Life Science, UBM LLC, Trio Health, Medscape, WebMD, Practice Point Communications, the National Institutes of Health, and the American College of Rheumatology; personal fees from Simply Speaking; nonfinancial support as past steering committee member from Omeract; and stock or stock options from Atyr Pharmaceuticals, Atai Life Sciences, Kintara Therapeutics, Intelligent Biosolutions, Acumen Pharmaceutical, TPT Global Tech, Vaxart Pharmaceuticals, Atyu Biopharma, Adaptimmune Therapeutics, GeoVax Labs, Pieris Pharmaceuticals, Enzolytics Inc, Seres Therapeutics, Tonix Pharmaceuticals Holding Cor, Aebona Pharmaceuticals, and Charlotte’s Web Holdings Inc outside the submitted work. Dr Straube reported receipt of personal fees from MSI Foundation and editorial board membership for SN Comprehensive Clinical Medicine outside the submitted work. Dr Tabares-Seisdedos reported receipt of grants from Valencian Regional Government’s Ministry of Education (PROMETEO/CIPROM/2022/58) and the Spanish Ministry of Science, Innovation and Universities (PID2021-129099OB-I00). Dr Ticoalu reported being a cofounder of Benang Merah Research Center, Indonesia. Dr Tseriotis reported receipt of grants from the European Academy of Neurology and the European Committee for Treatment and Research in Multiple Sclerosis and nonfinancial support from Inovis, Genesis Pharma, and Novartis outside the submitted work. Dr N. Wang reported receipt of grants from The Heart Foundation and drug development investment from George Institute Ventures outside the submitted work. Dr E. Yu reported being an employee of Vitalant Research Institute. No other disclosures were reported.

Funding/Support: This study was funded by the Gates Foundation.

Role of the Funder/Sponsor: The Gates Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2.

Additional Contributions: This research was conducted as part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2023 (GBD 2023), led by the Institute for Health Metrics and Evaluation at the University of Washington. We thank all the researchers, staff, and collaborators of GBD 2023 who worked to make the project possible.

Additional Information: Generative artificial intelligence (AI) tools, specifically Google Gemini (Google LLC) and ChatGPT (GPT-4, OpenAI), were used between January 2025 and March 2026 to assist with language editing, unit conversion verification (mmol/L to mg/dL), and integration of feedback from the GBD 2023 collaborators. No AI tools were used for data analysis, data interpretation, or generation of figures. All AI-assisted content was reviewed, verified, and revised by the lead author Christian Razo, who takes full responsibility for the integrity and accuracy of the content generated.

This Perspective explores clinical decision support tools and which functions warrant the use of artificial intelligence (AI)–enabled large language models, including data and knowledge, delivery format, and governance.

Consider a primary care physician seeing a patient aged 58 years for routine follow-up. The electronic health record (EHR) alerts that the patient is eligible for statin therapy. The physician overrides it, as clinicians do for the vast majority of alerts. The system verifies what is computationally easy (eg, age, lipid values, and risk score) but ignores what the clinician needs: has this patient been offered statins before? Did they decline, and if so, why (cost concerns, fear of adverse effects, preference for lifestyle modification)? Have they tried statins previously and experienced muscle pain? If they were prescribed a statin, did they ever pick it up from the pharmacy? What did they write in that patient portal message 2 months ago when they mentioned reading online that statins cause memory problems? The answers are scattered across notes, dispensing records, and portal messages. The alert identifies eligibility but not the patient’s decision state or the barriers to action.

Consistent with established definitions, clinical decision support (CDS) includes tools that provide knowledge and patient-specific information to support health decisions and is not limited to guideline adherence.1,2 This Perspective focuses on clinician-facing CDS organized around a defined decision; generic note drafting, inbox management, and open-ended chart summarization are excluded unless they directly support that decision. A prior reason for declining statin therapy is relevant because it changes the next action, not eligibility. Deterministic methods remain preferable when criteria and outputs are explicit; large language models (LLMs) may extend them through flexible synthesis and adaptive presentation.

Early medical LLM applications have focused on drafting replies and summarizing charts.3,4 The larger opportunity is to revisit a long-standing trade-off between clinical fidelity and computational tractability. Health information technology has historically represented complex narratives and knowledge through structured fields and rules because they were computable.5 LLMs do not provide the first access to narrative text; their incremental value is the flexibility to extract, synthesize, and communicate across heterogeneous sources.

Generative models remain nondeterministic: outputs may vary across runs, and the basis for a particular output is not directly inspectable.6 ,7 In a 2024 LLM triage study, concordance with physician judgment varied widely and adding examples reduced 1 model’s concordance.8 Domain- or task-specific training may improve performance but does not ensure that knowledge encoded in model weights is current, comprehensive, or traceable. Knowledge-dependent CDS therefore requires grounding in maintained clinical sources and constraints on their use. The relevant question is which functions warrant LLMs, under what constraints and safeguards. We consider 3 interdependent elements: data and knowledge, delivery format, and governance (Figure).

Figure.  Flowchart of 3 Elements for Reimagining Clinical Decision Support (CDS) in the Age of Generative Artificial Intelligence

In the proposed hybrid model, deterministic logic and conventional clinical NLP remain preferred for explicitly specified tasks, whereas LLMs support decision-bounded synthesis of retrieved patient context and adaptive, decision-specific presentation.

ICD indicates International Classification of Diseases; LLM, large language model; NLP, natural language processing.

Data and Knowledge: From Structured Fields to Decision-Bounded Clinical Context

Many CDS systems operate primarily on structured data to trigger if-then logic. This foundation enables useful capabilities, such as drug allergy alerts, but it also inherits the information losses accumulated across decades of EHR development. Although the problem-oriented medical record was intended to support reasoning, documentation, and billing, reporting requirements have increasingly prioritized structured data capture.9,10 Clinical natural language processing (NLP) has long extracted predefined concepts, assertions, and relations from notes and may remain preferable for narrowly specified tasks. LLMs may add value when decision-relevant information is variably expressed across notes, portal messages, care team communications, and dispensing records and must be synthesized around a defined decision. They do not address data access, interoperability, patient matching, or record linkage; surrounding infrastructure must first retrieve, link, and normalize the records.

LLM-enabled CDS should use decision-bounded retrieval: developers should prospectively specify which contextual information could alter a recommendation or next action. For statins, this may include prior discussions, preferences, adverse effects, dispensing, adherence, access barriers, and contraindications. Outputs should identify the source and date of each material claim and distinguish documented facts from model-generated inferences. Encounter context, including visit type and user role, should determine whether, when, and how support is presented.

LLMs also change how clinical knowledge can be represented and applied. General-purpose pretraining provides broad language, extraction, and synthesis capabilities, but parametric medical knowledge is heterogeneous, incompletely auditable, and not reliably current. Domain adaptation or task-specific fine-tuning may improve performance without establishing knowledge provenance or currency. At design time, LLMs may help informaticists extract candidate criteria, exceptions, and actions from narrative guidelines.11 These knowledge artifacts should remain human-reviewed, versioned, and testable. At runtime, knowledge-dependent outputs should be grounded in curated, versioned sources through retrieval-augmented generation or validated tools, with source attribution. When retrieved evidence conflicts with parametric knowledge, systems should follow the authoritative source, disclose uncertainty, or abstain. Deterministic logic should remain responsible for explicitly represented criteria and hard safety constraints.

Delivery Format: From Interruptive Alerts to Decision-Specific Action Support

Traditional CDS relies on alerts, reminders, documentation templates, and order sets that can fragment attention and add clicks. LLM-enabled CDS need not be chat-based. For recurring decisions, it may deliver a source-linked summary, previsit brief, or context-enriched alert within existing workflows. Conversation should be reserved for unanticipated questions or optional evidence review. The aim is to provide the minimum information needed with minimal interaction burden.

Rather than interrupting a physician with a generic alert, the system could display a concise, noninterruptive summary within the relevant EHR workflow: “Statin therapy was discussed in March 2025; the patient subsequently expressed concern about cognitive adverse effects in a portal message; no prior statin dispensing is documented.” Each statement could link to its source. No conversation would be required; the clinician could expand the supporting evidence or ask a follow-up question only when needed. The system would thus improve the content of decision support without imposing a new interaction burden.

Early deployments support a mixed rather than chat-first model. ChatEHR, an LLM system embedded in the EHR, pairs fixed automations with an optional conversational interface; in that deployment, summarization was the most frequent use, sampled outputs contained inaccuracies and hallucinations, and benchmark-based evaluation was insufficient for monitoring.12 A separate hospital-wide study found use concentrated on retrieval, summarization, and note drafting, while voluntary user feedback declined after scale-up.13 These reports show feasibility and adoption, not improved decisions or outcomes.

Replacing a click with a conversation would not address alert fatigue and could create a new source of burden. The value of LLM-enabled delivery depends on suppressing low-value outputs, minimizing required interaction, and presenting only information likely to alter the decision or next action. Conversational follow-up should be available when useful rather than required for routine use.

Even when clinicians retain final decision authority, requiring them to verify generated summaries carries its own risks. Research on automation bias suggests that humans are poor monitors of automated systems, especially when those systems are usually correct.14 If LLM-based CDS handles routine cases well, clinicians may become less vigilant and miss the cases where the system fails. Designing for appropriate human engagement, not just human oversight, is an unsolved challenge.

Governance: From Predeployment Evaluation to Life Cycle Governance

Rule-based CDS is deterministic, supporting case-based testing, version control, and predeployment review. Generative models complicate rather than invalidate conventional validation: outputs may vary across runs and cannot be fully represented as a fixed rule set. However, variability can be bounded through constrained tasks and outputs, model and prompt version control, deterministic verification, and regression testing. Predeployment evaluation therefore remains necessary but must be supplemented by continuous monitoring.

Knowledge Maintenance

Rule-based CDS represents clinical knowledge explicitly, allowing individual rules and knowledge artifacts to be versioned and revised, although maintenance can be slow. In LLM-enabled CDS, knowledge may reside both in model parameters and external sources supplied at runtime. Updating a curated external source is comparatively tractable; reliably removing superseded information from model parameters is not. Adding a new guideline or fine-tuning updated content does not establish that an older, conflicting recommendation has been erased. Organizations should therefore treat parametric medical knowledge as unverified; maintain authoritative content in versioned external repositories whenever feasible; and revalidate retrieval, source adherence, and conflict handling whenever either the model or the knowledge source changes.

Hybrid Architectures

Not every CDS function requires generative artificial intelligence (AI). Systems should use the least complex method that can reliably perform the intended task: structured queries and deterministic rules for explicitly computable criteria and hard constraints; conventional clinical NLP for stable, narrowly specified extraction tasks; and LLMs for flexible synthesis across heterogeneous sources or adaptive, decision-specific presentation. The architecture should also specify which external knowledge sources the model may use, how those sources are curated and versioned, and what should occur when retrieved evidence conflicts with knowledge encoded in the model. These decisions should be explicitly justified, documented, and tested. When LLMs are used, open-ended generation should be bounded through retrieval from specified patient records and maintained knowledge sources, source-linked attribution, schema-constrained outputs, and deterministic verification of computable claims. Systems should abstain or escalate when relevant evidence is missing, conflicting, or insufficient. Each model, prompt, and knowledge-source version should undergo regression and subgroup testing before deployment, with revalidation after any update.

Operationalization requires an explicit division of responsibility. Clinical domain experts and knowledge stewards, including guideline authors when relevant, should define the target decision, computable criteria, and classes of contextual information that could change the next appropriate action. Clinical informaticists and EHR teams should translate deterministic components into testable logic and determine where support is placed in the workflow. AI developers should implement decision-bounded retrieval and source-linked synthesis of contextual information. Clinical service leaders and end users should determine how outputs are reviewed, corrected, and escalated, while institutional governance bodies should oversee validation, monitoring, incident response, and model or knowledge updates. Each deployed application should have a named clinical owner and a defined mechanism for correction and rollback.

Runtime Monitoring

Because predeployment evaluation cannot characterize all real-world uses, organizations need infrastructure to detect problems in production. Monitoring should be aligned with each component of the hybrid system: errors in deterministic components; retrieval failures, omissions, misattributions, temporal errors, unsupported statements, and failures to follow current authoritative sources in contextual synthesis; and inappropriate or misleading action support. Monitoring should include systematic output sampling, audit logs, subgroup performance review, and predefined thresholds for escalation, pausing, or rollback.

Discussion

The fear that LLMs will pollute the medical record and undermine clinical reasoning is justified if generative AI is treated as a faster way to automate existing workflows. The EHR’s current issues (eg, fragmented, documentation-heavy, alert-fatigued) were not inevitable; they emerged from decades of decisions that prioritized administrative efficiency over clinical reasoning. Using LLMs to generate more of the same content, faster, would compound these mistakes.

However, this is not the only path. LLMs offer an opportunity to synthesize heterogeneous clinical information with greater flexibility and deliver decision support more contextually, but only when they add value beyond deterministic rules, conventional clinical NLP, and retrieval methods. Realizing this opportunity requires 3 commitments from the medical informatics community:

Realistic expectations about LLM capabilities: we should neither dismiss these tools nor oversell them. Rigorous evaluation (eg, red teaming, adversarial testing at the boundaries of training distributions) must precede clinical deployment.

Realistic expectations about LLM capabilities: we should neither dismiss these tools nor oversell them. Rigorous evaluation (eg, red teaming, adversarial testing at the boundaries of training distributions) must precede clinical deployment.

Investment in hybrid architectures: not every function needs generative AI and not every function should have it. Thoughtful decomposition among deterministic rules, conventional clinical NLP, retrieval systems, and generative components is essential.

Investment in hybrid architectures: not every function needs generative AI and not every function should have it. Thoughtful decomposition among deterministic rules, conventional clinical NLP, retrieval systems, and generative components is essential.

Development of new governance infrastructure: runtime monitoring, accountability frameworks, and continuous validation are not optional additions but prerequisites for responsible deployment.

Development of new governance infrastructure: runtime monitoring, accountability frameworks, and continuous validation are not optional additions but prerequisites for responsible deployment.

These commitments define a research agenda for when LLMs improve decisions beyond existing methods without increasing clinician burden (Table).

Table.  Research Agenda for Large Language Model (LLM)–Based Clinical Decision Support (CDS)

Conclusions

Decades have been spent simplifying clinical complexity to fit computational constraints. Generative AI should complement deterministic rules, conventional clinical NLP, robust data integration, and explicitly maintained clinical knowledge, with its use limited to tasks where flexible synthesis and adaptive presentation add value and with outputs grounded in current sources, delivered through the least burdensome interface, and governed throughout the life cycle.

Rakovina slinivky břišní patří dlouhodobě k nejhůře léčitelným nádorovým onemocněním. Pacienti často přicházejí k lékaři až v pokročilém stadiu a možnosti léčby bývají omezené. Naději nyní přináší nový experimentální lék daraxonrasib, který v klinických studiích vykázal výrazné prodloužení přežití pacientů s agresivní formou nádoru. Americký Úřad pro kontrolu potravin a léčiv (FDA) nyní umožnil jeho podávání prvním pacientům ještě před definitivním schválením.

Lék známý také pod označením RMC-6236 vyvíjí biotechnologická společnost Revolution Medicines. Zaměřuje se na mutaci genu KRAS, která stojí za růstem nádoru přibližně u devíti z deseti případů rakoviny slinivky břišní.

Právě mutace genu KRAS byla desítky let považována za prakticky neléčitelnou. Gen KRAS totiž řídí buněčný růst a jeho poškození způsobuje nekontrolované dělení nádorových buněk. Vědcům se však podařilo vyvinout látky, které dokážou zmutovaný protein zablokovat a zastavit signály podporující růst nádoru.

Daraxonrasib patří mezi takzvané inhibitory KRAS. Užívá se jednou denně ve formě tablet a podle dosavadních výsledků by mohl představovat zásadní posun v léčbě pacientů s metastatickým duktálním adenokarcinomem slinivky (PDAC), tedy nejčastější a zároveň velmi agresivní formou tohoto onemocnění.

Mimořádně nadějné

Výsledky klinických testů označují onkologové za mimořádně nadějné. U pacientů, u nichž selhala standardní chemoterapie, se podařilo snížit riziko úmrtí přibližně o 60 procent. Medián přežití se podle zveřejněných dat zvýšil z 6,7 na 13,2 měsíce.

Na první pohled nemusí rozdíl několika měsíců působit dramaticky. U rakoviny slinivky však jde podle odborníků o významný posun. Toto onemocnění totiž patří mezi nádory s nejnižší mírou přežití a nové účinné léky a léčebné metody se donedávna objevovaly jen velmi vzácně.

„Tato možnost je nepodobná ničemu, co jsme v léčbě rakoviny slinivky skutečně viděli po mnoho let,“ uvedl pro magazín National Geographic onkolog Chris Chen ze Stanford University School of Medicine. Podle něj jde o „skutečně převratný okamžik“ v léčbě tohoto typu rakoviny.

Na mimořádně slibné výsledky reagoval také americký Úřad pro kontrolu potravin a léčiv (FDA). Ten na počátku května schválil zahájení takzvaného programu rozšířeného přístupu (Expanded Access Protocol, EAP). Protokol umožňuje lékařům podávat experimentální lék vybraným pacientům ještě před jeho definitivní registrací a uvedením na trh.

Úřad uvedl, že žádost obdržel 28. dubna a schválil ji už o dva dny později. Podle komisaře FDA Martyho Makaryho tak rychlý postup odráží snahu zajistit pacientům s vážnými a život ohrožujícími nemocemi co nejrychlejší přístup k nadějným terapiím.

Daraxonrasib už dříve získal od FDA status průlomové terapie. Ten je vyhrazen léčivům, která mohou znamenat výrazné zlepšení oproti dosavadní léčbě a umožňuje urychlené posuzování.

Společnost Revolution Medicines nyní připravuje žádost o plné schválení léku. Do té doby bude daraxonrasib dostupný pouze omezenému počtu pacientů ve Spojených státech prostřednictvím jejich ošetřujících lékařů v rámci schváleného programu.

Pacient nula, tedy první člověk, který se nakazil nebezpečným hantavirem a nákazu roznesl na lodi MV Hondius, jsou s největší pravděpodobností nizozemský manželský pár, domnívají se experti Světové zdravotnické organizace (WHO). Zřejmě se nakazili v argentinském městě Ushuaia.

Kde se nizozemský pár nakazil hantavirem, není ještě stoprocentní, ale nejpravděpodobněji se tak stalo během pozorování ptáků nedaleko města Ushuaia, které je srdcem Ohňové země, píše web týdeníku Focus. Ushuaia, považovaná za nejjižnější město světa, slouží jako brána do Antarktidy.

Ti, kteří se účastnili výletu pozorování ptáků, mezi nimiž byli nizozemští manželé, údajně navštívili skládku, kde mohli přijít do kontaktu s hlodavci, kteří virus přenášejí. Vyšetřování přesných okolností nákazy ale pokračuje.

Luxusní výletní loď vyplula z jižní Argentiny. Na palubě se rozšířil hantavirus, který způsobuje vážné dýchací onemocnění. Onemocnělo osm lidí, z nichž tři zemřeli – nizozemský pár a jedna žena z Německa. Čtvrtý je na jednotce intenzivní péče v Johannesburgu.

Na palubě bylo celkem 149 lidí: 88 cestujících a 61 členů posádky. Největší národnostní skupinu tvoří Britové (19) a Američané (17), následují Španělé (14).

Nákazu provázejí bolesti hlavy, závratě, horečka, nevolnost, průjem a bolesti žaludku, po nichž následuje náhlý nástup vážných dýchacích potíží. Na nákazu zatím neexistuje žádná léčba, lék ani vakcína. Úmrtnost dosahuje až 50 procent. Z laboratorních výsledků vyplývá, že na lodi řádí vir typu Andes, který se může přenášet i mezi lidmi.

Hantavirus

Přirozeně se vyskytuje u hlodavců a u lidí může způsobit závažné onemocnění. Infekce může vést ke dvěma hlavním typům onemocnění: hantavirovému plicnímu syndromu (HPS) a hemorrhagické horečce s renálním syndromem (HFRS).

Člověk se nakazí prostřednictvím infikovaných hlodavců, především myší a krys. Buď vdechne aerosoly obsahující částice moči, trusu nebo slin hlodavců, nebo přímým kontaktem s infikovaným materiálem, kousnutím či konzumací kontaminovaných potravin nebo vody.

Přenos z člověka na člověka je vzácný, vyskytuje se u typu hantaviru označovaného jako virus Andes.

Smrtnost se liší podle konkrétního typu viru. Dosahuje ale až 50 procent. Tyto smrtící typy jsou nicméně extrémně vzácné.

Nákaza může vést k plicnímu syndromu. Vyskytuje se hlavně v Severní a Jižní Americe a je velmi závažný. Příznaky se nejprve podobají chřipce, člověk trpí horečkou, únavou, bolestmi svalů, nevolností, zvracením a bolestmi břicha. Později se přidává kašel a dušnost, což může vést k selhání plic.

Další možností je hemorrhagická horečka s renálním syndromem. Je běžnější v Evropě a Asii. Příznaky zahrnují náhlou horečku, zimnici a bolesti hlavy, nízký krevní tlak, selhání ledvin, krvácení do kůže a sliznic.

Smrtnost HFRS se liší podle typu viru - u typu Hantaan jde o 5 až 10 procent, u typu Puumala a Seoul je to 1 procento.

Na hantavirus neexistuje specifická antivirová léčba.

For years, we've thought of autism as lying on a spectrum, but emerging evidence suggests that it comes in several distinct types. The implications for how we support autistic people could be profound

“On the spectrum.” These three words have become synonymous with autism, yet behind them lies a common misunderstanding. The idea of “the spectrum” suggests that all autistic people share similar experiences and behave in similar ways – only to a greater or lesser extent. The reality couldn’t be further from the truth.

Some autistic people may not speak at all; others are hyperverbal and extremely fluent. Some are highly sensitive to bright lights and noise, or the opposite. And some have rigid routines and make repetitive movements like hand-flapping, while others are more flexible but spend a lot of time on “special interests” – anything from Tudor history to Rubik’s cubes.

Autism’s incredible diversity is something to celebrate. However, it has long presented an immense challenge to researchers trying to understand this seeming jumble of traits. Strides are now being made, as several recent studies have identified apparent groups within the catch-all term of autism that are also underpinned by patterns of genes and brain activity.

Researchers are exploring if and how these subtypes can be leveraged to help autistic people get better, more personalised support, and gain a great understanding of themselves. “There is now a more concrete basis for understanding where their experiences are coming from,” says neuroscientist Conor Liston at Weill Cornell Medicine in New York.

Yet this isn’t the first time that researchers have tried to separate autism into different guises and some advocates are wary of how these subtypes will play out in society. “You might feel like [subtyping] is value-neutral, but for someone else, it really isn’t,” says Amy Pearson, a psychologist at Durham University, UK.

Neurodiversity

Autism is a developmental condition that affects how people interact with others and the world around them. Broadly speaking, this means that autistic people often have difficulties with socialising, communicating and sensory sensitivity, and they may have restricted behaviours and interests. In numerous countries, autism is legally classed as a disability, which can help autistic people access support. However, many autistic people argue that it isn’t a disability, but instead a form of neurodivergence – and others are happy with both designations.

A growing awareness of neurodiversity has led to increased rates of autism diagnosis, with estimates now putting the number of diagnoses in the US at 1 in every 32 people. This includes many women and girls, who are often diagnosed later in life, as autism tends to present differently in them, especially when it comes to social motivations and behaviour. Traditionally, this hasn’t been accounted for.

In some ways, the idea of an autism spectrum, first coined by psychologists in 1979, fits this encompassing approach to diagnosis – and many autistic people still find the concept helpful. At the same time, the growing need to describe large variations in behaviour and experience has revealed the spectrum’s limitations.

Paul, a project manager from Maryland in his early 50s, is currently going through this diagnostic process. He struggles with interpersonal skills, such as “understanding what other people are feeling” if they don’t express themselves literally, he says. “It affects me at work, but it’s also helped me at work, because I ask a lot of questions so that I can understand something, and until I understand it, I don’t let it go.” Because of well-worn stereotypes about the spectrum and what autism is like, it never occurred to Paul that he might be autistic until his therapist suggested it. “I don’t think anybody fits all of this stuff,” he says.

Liston says autism is a large catch-all category that lumps together “people with probably many different kinds of molecular, cellular and brain circuit mechanisms”. In order to get a better handle on the underlying biology, we need to think about more precise ways of identifying it and embrace the condition’s heterogeneity, he says. This, in turn, could lead to earlier diagnosis and personalised support for autistic people. “Ultimately, that’s the goal,” says Adriana Di Martino at the Child Mind Institute in New York.

Searching for autism subtypes

So, in recent years, researchers have tried to demarcate autism subtypes by identifying clusters of people with similar sets of traits and symptoms, which may also have shared biological mechanisms. One early attempt was published in 2020 by developmental psychologist Mirko Uljarević at Stanford University in California and his colleagues. They asked the parents of 164 autistic children to rate their children’s social abilities and found five clusters that had distinct patterns of strengths and weaknesses across different social traits that didn’t map onto a simple line from more to less severe.

However, it became clear that this and similar studies could improve on their methods. Some studies relied heavily on parents’ reports about their autistic children, limiting their reliability. Moreover, it wasn’t clear in some research if these were true clusters or if the basic idea of a spectrum fit the data better. A 2020 review led by Di Martino concluded that there are probably “at least 2 to 4” distinct autism neurosubtypes, but that the studies were too small and relied on qualitative measures of autistic traits.

Since then, researchers like Di Martino have refined their methods, using larger sample sizes and identifying more granular behaviours and traits. They have also turned to brain imaging and genetic analysis to help match up behaviour with biological mechanisms. “We believe that is a more effective way to understand and characterise the features that are relevant for autism,” says Di Martino.

Now, what look to be genuine subtypes are appearing out of the fog. In a 2023 study, Liston and his colleagues analysed several existing datasets that comprised 432 autistic people whose brain activity had been measured and whose specific autistic traits had been identified. They reliably identified three distinct dimensions along which brain activity and behaviour were correlated in this group compared with a control group of neurotypical people – meaning people whose brains develop and work like most other people’s do.

One dimension related to intellectual functioning, especially verbal intelligence. The second was about social behaviour and relationships with other people, called “social affect”, and the third was linked to restricted interests and repetitive behaviours. Then, the team looked at how the autistic group scored on these three dimensions and found that their traits cluster into four subgroups.

Those in subgroup one had high verbal intelligence and strong connectivity in their language-processing centres, while it was the opposite for those in subgroup two. Likewise, while those in subgroup three had poor social affect but fewer restricted and repetitive behaviours, these traits were flipped in those in subgroup four. “Having identified those four subtypes, we can begin to ask questions about [why] they’re different,” says Liston.

Surprisingly, the team found that atypical connections in a given brain system didn’t lead to traits related to that system. “A lot of work to date has tended to assume that what is causing your symptoms, or what explains the severity of your symptoms, is also somehow abnormal,” he says. “And that is, in fact, not the case.” Some of the neurological changes may reflect one part of the brain compensating for problems elsewhere, says Liston. This detail would never show up in a study that lumped all autistic people together, he says, whereas the subgroup analysis revealed the underlying biology.

Genetic roots

Throwing genes into the mix offers further insights. Gene variants that are associated with autism often play a role in the connections that form between neurons, known as synapses. Liston’s team found that brain regions with altered circuitry in autistic people, compared with neurotypical people, also showed characteristic changes in gene expression. This implies that it should one day be possible to join the dots from genes to brain circuitry to behaviour, says Liston.

Another landmark subtyping study was published in July last year by geneticist Natalie Sauerwald at the Flatiron Institute in New York and her colleagues. They used a dataset from Simons Powering Autism Research, a research study that is led by the autistic community, which included 5392 autistic people – an order of magnitude more than previous studies. For each individual, the researchers examined 239 traits spanning seven categories: communication, restricted and repetitive behaviour, attention, disruptive behaviour, mood, developmental delay and self-injury. (It is worth noting that some autistic people feel that any greater incidence of self-harming, or other challenging behaviours among the autistic community is indicative of how they are treated by a world not built to support their needs.)

The researchers looked for patterns in the combinations of these traits and also found that they naturally fell into four subgroups, but these differed in several ways from the subgroups that Liston’s team found. Those in the first subgroup had a lot of difficulty with communication and restricted or repetitive behaviours, as well as disruptive behaviour, attention and anxiety, but no sign of developmental delay. Meanwhile, those in the second subgroup displayed developmental delay and a nuanced mix of other traits; those in the third subgroup had mild difficulties in all seven categories; and those in the final group had severe difficulties across the board.

Team member and geneticist Olga Troyanskaya at Princeton University and the Flatiron Institute says the researchers were surprised by how strongly the four groups came out of the data. “Every individual is unique, but there do seem to be these replicable groups.”

The idea that some subtypes might feature developmental delay was backed up by a study published in October last year, which looked at children diagnosed with autism between 5 and 17 years old. Researchers found evidence of two subgroups: those in subgroup one began experiencing social, emotional and behavioural difficulties early in life, while those in the other subgroup experienced upticks in difficulties in late childhood and early adolescence. These two subgroups were also linked to different sets of genetic variants, though there was some overlap between the two groups.

So far, it isn’t entirely clear how these and other subtyping studies fit together – or if there are two, three, four or more different subtypes. “I’ve sat down and tried to write down what each group is [in other studies] and how that fits with our groups,” says Sauerwald. Some of the categories her team identified are clearly distinct, but some seem to line up with those found by Liston and others. “We’re hopefully getting closer to the reality,” she says.

In one sense, the mismatch isn’t surprising, as these research teams have taken different approaches. Sauerwald and her colleagues concentrated on outward traits, whereas Liston and his colleagues focused more on connectivity within the brain. What’s more, the teams looked at different kinds of genetic variation: Sauerwald’s team examined changes in the genome itself, whereas Liston’s team looked at gene expression.

Switching subtypes

Confounding matters further, an autistic person might not stay in the same subtype all their life. “There’s a lot of clinical information pointing to changes over time and through development,” says Sauerwald. “As kids get older, sometimes they might switch.” In fact, one subtyping study published in 2024, which reassessed autistic people several years after an initial clustering assessment, found that nearly half of them changed subgroups within five years.

“I don’t think subtypes capture the multidimensionality of development,” says Pearson, who is herself autistic. Just like everyone else, autistic people change in a huge variety of ways over the course of their lives, so she says the subgroups can only ever be a crude approximation of behaviour and experience.

Indeed, it remains an open question whether truly distinct subtypes exist. These studies strongly suggest that some combinations of traits are more common than others, but it is still possible that every combination exists in somebody, somewhere. Because of these uncertainties, none of the subtyping researchers interviewed wants to see the subgroups they have identified used in healthcare clinics, at least not yet.

Still, with further advances, they hope their research can offer a framework that is helpful for the autistic community. Autism diagnoses already help many people make sense of themselves. Breaking down the broad autism diagnosis into subtypes could help autistic people understand each other’s varying experiences – and further validate their own, says Liston.

There has been greater celebration of autism and neurodivergence in recent years, enabled in part by the internet, and finer subgroups would be a natural extension of that, he says. Many autistic people are still told that they can’t really be autistic because they don’t have some trait or other, says Paul. With subtyping, “you would be able to say, ‘Well, I’m in this category’,” he says. Some might benefit from that, says Paul, although he doesn’t personally believe he needs it.

Troyanskaya, meanwhile, envisions doctors using subtypes to forewarn autistic people or their families of the specific challenges that they may encounter, perhaps years in advance. This would lead to “having the awareness to try to get support in place before the crisis, as opposed to after the crisis”, she says.

Another possibility that could one day emerge is targeted pharmacological treatments for specific adverse effects. This is a delicate topic because it may be conflated with the concept of a “cure” for autism, and that idea implies that being autistic is inherently bad. Many autistic people would say the condition is their strength, says Di Martino. Nevertheless, she argues that such treatments may be useful for some specific behaviours such as self-harm.

There is tentative evidence that autistic people in different subgroups respond differently to these treatments. For instance, one proposed drug suggested to improve social responsivity is the hormone oxytocin. So far, the results have been inconsistent. But a study published in 2024 that divided participants into two autism subtypes found that one group responded more strongly to oxytocin than the other. This may help explain oxytocin’s variable results, but won’t resolve the argument over whether pharmaceutical treatments are ever necessary, or if society needs to support autistic people more.

In a discussion in an online forum with New Scientist, a user called Neonatal RRT, an autistic hospital worker, wrote that more personalised approaches to healthcare are better, but that this also risks individuals slipping through the gaps if they don’t fit into finer categories. “People can be denied the care they need,” they wrote.

Asperger’s syndrome

Previous attempts at further categorisation haven’t worked out. Autism is currently an umbrella diagnosis used by doctors, but from 1994 to 2013, psychologists recognised a second, “milder” form of autism called Asperger’s syndrome, which was applied to people who lacked social skills and had restricted interests, but did acquire fairly typical language skills. Some autistic people still embrace the term “Asperger’s”, but many avoid it, either because of its namesake Hans Asperger’s links with the Nazi child euthanasia programme or because they disagree with the idea that autistic people can be divided into one group that needs less support and is perceived to be “high-functioning”, and another that is “low-functioning”. What’s more, some autistic people judged to be high-functioning sometimes found it harder to access treatment and support.

Anoushka Pattenden at the National Autistic Society in the UK is concerned that this new wave of subtyping research, while well meaning, could similarly backfire. “We fear that further categorising of autism is unhelpful and may lead to more stigma or discrimination,” she says. Pattenden, who is autistic, is glad that researchers have avoided labels such as high-functioning or low-functioning in these new subcategories, but says “you don’t have control over how that gets used, and what ends up happening with it”.

Sauerwald recognises these potential risks and says her team consulted with the autistic community when naming their subgroups. “We are constantly learning and doing our best to ensure that our work is beneficial to the communities involved rather than harmful, to the extent we can,” she says.

Ultimately, subtypes can only be beneficial if societies also become more empathetic towards autistic people, says Paul, which wasn’t his experience growing up. Pearson points out that many schools, universities and employers still don’t offer generic accommodations for autistic people, let alone personalised support.

“The first hoop to jump through with all of this is education,” writes Neonatal RRT, which may go some way to dismantling harmful stereotypes about autism. Instead of a uniform label of being “on the spectrum”, the autistic community, responding to the variety of their experiences, has alighted on another metaphor in recent years: the colour wheel. Every spoke of the wheel has a unique colour and represents an autistic trait, such as restricted interests and sensitivity to sensory stimuli, extending along the spoke to a different degree. In this way, the colour wheel, which contains many possible “plots”, underlines autistic individuality.

Sauerwald and other researchers hope that a respectful approach to subtyping can reveal autism’s underlying biology in a way that also brings this colour wheel, and the lived experiences it contains, into focus. What we choose to do with those subtypes – and how societies choose to treat neurodivergent people – is then up to all of us.

Urban populations in southern Britain experienced a decline in health that lasted for generations after the Romans arrived

The health of populations in Britain declined under Roman occupation, particularly in more urban areas.

There is a widely held belief that the Romans brought civilisation and its many benefits to those they conquered, perhaps best exemplified in

Monty Python’s Life of Brian

, in which John Cleese’s character Reg asks “Apart from the sanitation, the medicine, education, wine, public order, irrigation, roads, the freshwater system and public health, what have the Romans ever done for us?”

Yet researchers have been aware for at least a decade that there was a decline in the health of the population in Iron Age Britain after the Romans conquered the territory in AD 43 – and that populations thrived after they left.

Now, Rebecca Pitt at the University of Reading, UK, has studied 646 ancient skeletons, 372 belonging to children who were less than 3.5 years old when they died, as well as 274 from adult females aged between 18 and 45 years old. These came from 24 Iron Age and Romano-British sites across south and central England, dating from four centuries before the Romans turned up until the fourth century AD, when they withdrew.

Pitt estimated the ages of the individuals from features of the pelvis in adults and from the teeth of the children. Looking at the experiences of potential mothers and infants together, she says, should give a better impression of the stressors affecting different generations under Roman occupation.

“Environmental exposures during critical periods of early development can have lasting effects on an individual’s health,” says Pitt, just as a mother’s health can influence that of a child.

Pitt examined the bones and teeth and looked for abnormalities such as lesions or fractures that could indicate tuberculosis, osteomyelitis or dental disease. She also used X-rays to look at the internal structures of bones, which can reveal changes to how the bones develop caused by malnutrition or deficiencies in vitamin C and D.

This revealed that the negative health impacts of the Roman occupation were concentrated in the two larger urban centres in the study – the Roman administrative towns of Venta Belgarum, now Winchester, and Corinium Dobunnorum or Cirencester.

Overall, 81 per cent of the urban Roman adults had bone abnormalities compared with 62 per cent of people dating from the Iron Age, but the Iron Age and rural Roman cohorts didn’t differ significantly. And just 26 per cent of Iron Age children featured such effects compared with 41 per cent or those in rural Roman settlements and 61 per cent in urban Roman sites.

“One of the things that was really apparent in the urban non-adults was rickets, which means that people weren’t getting enough access to vitamin D from sunlight,” says Pitt.

She suggests these health effects, which lasted for many generations, were down to new diseases the Romans brought with them as well as the class divides and infrastructure they introduced, resulting in limited access to resources for those lower down the social ladder and overcrowded, polluted living situations.

“My dad always jokes about

The Life of Brian

, but the Romans had quite a negative impact on our health, which affected quite a few generations,” says Pitt.

Martin Millett at the University of Cambridge says the finding is interesting, and that the effect might even be underestimated if the people who were being buried were those of higher status who might have been healthier, but he doesn’t think it’s necessarily an urban effect.

“These urban centres are not huge medieval towns with deep poverty and huge densities,” he says. “What we may be seeing is an increasing differentiation between the rich and the poor. The Roman Empire has an economic and a social system that means the difference between the rich and poor is getting greater through time.”

Richard Madgwick at Cardiff University, UK, also says that the legacy of the Romans didn’t benefit everyone equally. “Greater hygiene, sanitation and medical know-how was there, but the access to it? That’s a totally different matter,” he says. “The reality is that not everyone benefited and it took a little while to trickle down to the different elements of society.”

Walking Hadrian's Wall and Roman innovation: England

Follow in the footsteps of the Romans on this immersive walking tour along Hadrian’s Wall, one of Britain’s most iconic, ancient landmarks and a UNESCO World Heritage Site.

Lung cancer has remained the leading cause of cancer death among men in the US since the early 1950s, among women in the US since 1987, and worldwide for several decades. Lung cancer screening with low-dose computed tomography (LDCT) significantly reduced lung cancer mortality in 2 large-scale...

Lung cancer has remained the leading cause of cancer death among men in the US since the early 1950s, among women in the US since 1987, and worldwide for several decades. Lung cancer screening with low-dose computed tomography (LDCT) significantly reduced lung cancer mortality in 2 large-scale randomized clinical trials

1

,2

and is currently recommended in the US for an estimated 13 million high-risk adults,

3

offering a tremendous opportunity to reduce mortality from the deadliest cancer. Yet, implementation of lung cancer screening has been challenging, and more than 80% of adults eligible for screening do not get screened.

3

Moreover, recent evidence suggests that current eligibility criteria for screening inadequately identify high-risk populations and exclude approximately 50% of adults diagnosed with lung cancer.

4

,5

These challenges raise 2 critical questions. Under current conditions, how many lives does screening for lung cancer save? And how many more lives could be saved with better uptake and eligibility criteria for screening?

In this issue of JAMA, Bandi and colleagues sought to address these questions

6

using data from the 2024 National Health Interview Survey, as well as 2 validated models that predict lung cancer deaths prevented and life-years gained from screening. Their analysis quantified the impact of lung cancer screening under current conditions and projected potential gains from (1) improving screening uptake and (2) broadening screening eligibility criteria.

The analysis yielded several key findings, all of which argue for action. First, the authors found that only 18.7% of adults eligible for lung cancer screening reported undergoing screening in 2024, which is unacceptably low. Even at this low level of uptake, the authors estimate that screening prevents nearly 15 000 lung cancer deaths and saves more than 190 000 life-years over a 5-year period. If screening uptake were improved to include 100% of those meeting current screening eligibility criteria, an estimated 62 000 lung cancer deaths could be prevented and more than 870 000 life-years could be gained over 5 years, representing an increase greater than 300% in lung cancer deaths prevented and life-years gained compared with that estimated under the current level of uptake for lung cancer screening. It is worth highlighting that these gains in life-years attributed to screening with 100% uptake are substantially greater than the estimated gains in life-years from therapeutic advances for late-stage disease.

7

The data communicate clearly and unambiguously that increasing the uptake of lung cancer screening must be elevated to a leading public health priority.

Moreover, the authors found that a surprising number of lung cancer deaths that could be prevented by screening (nearly 30 000 over 5 years) occur among adults with a history of smoking who did not qualify for screening under the 2021 US Preventive Services Task Force (USPSTF) criteria. Of these nearly 30 000 potentially preventable deaths, 36% occurred among adults who have at least 20 pack-years of smoking but stopped smoking more than 15 years before. Another 36% of these deaths occurred among adults who either currently smoke or quit within the past 15 years and have at least 20 years of smoking, but have fewer than 20 pack-years of smoking. Notably, recent research has supported both the removal of the 15 years since quitting requirement from lung cancer screening recommendations

8

and expanding screening criteria to individuals who have smoked for 20 or more years but, due to a low daily intensity, have fewer than 20 pack-years of smoking.

9

,10

Indeed, adopting a 20-year smoking duration criterion over the complicated-to-calculate pack-year criterion would expand eligibility for lung cancer screening among those at high risk

9

,10

with minimal losses in screening efficiency.

10

In light of these data, both the National Comprehensive Cancer Network and the American Cancer Society have removed the 15 years since quitting smoking requirement in their guidelines. In 2024, the National Comprehensive Cancer Network also updated their guideline to include a 20-year smoking duration criterion, recommending lung cancer screening for individuals who have smoked at least 20 years (even if they do not have ≥20 pack-years of smoking).

While increasing evidence suggests that the current USPSTF lung cancer screening guidelines miss many high-risk individuals who ultimately develop lung cancer, there are concerns that expanding criteria may open screening to low-risk populations in whom the potential harms of screening may outweigh benefits.

11

,12

This is an important concern, and we agree that the impact of any proposed revisions to the USPSTF criteria on low-risk populations would need to be evaluated. However, it is helpful to consider the meaning of the metrics commonly used to understand the impact of proposed lung cancer screening eligibility criteria.

When eligibility criteria for lung cancer screening are expanded, the sensitivity of the criteria will almost always increase (ie, more individuals who develop lung cancer will qualify for screening). That in itself is the fundamental benefit of expanding lung cancer screening eligibility criteria. On the other hand, specificity of the expanded lung cancer screening eligibility criteria will almost always decline because more individuals without lung cancer will also become eligible for screening under the expanded criteria; people without lung cancer who become newly eligible under the expanded criteria are reclassified from what could be considered true negatives under the original criteria to what could be considered false positives under the expanded criteria, thereby decreasing specificity. The only exception to this would be if every person who became newly eligible for lung cancer screening under the expanded criteria had lung cancer, which is unrealistic. However, specificity itself provides no information about the degree of lung cancer risk among a population that becomes newly eligible for lung cancer screening under a given guideline, and a decline in specificity should not be used as an argument against expanding screening eligibility criteria. Importantly, screening efficiency (ie, the number of individuals needed to screen to detect one lung cancer) complements sensitivity and specificity by offering insight into the cancer risk among individuals eligible under expanded lung cancer screening eligibility criteria. When evaluating the impact of expanded lung cancer screening eligibility criteria, sensitivity, specificity, and screening efficiency should all be considered.

Lastly, it is worth noting that Bandi and colleagues focused on adults with any smoking history aged 50 to 80 years and did not include individuals who have never smoked or individuals outside of the age range recommended for screening by the USPSTF. Every year, an estimated 20 000 individuals who have never smoked die of lung cancer in the US.

13

Moreover, approximately 4700 cases of lung cancer are diagnosed among adults younger than 50 years in the US each year.

14

These data highlight the need to study the potential benefits and harms of extending opportunities for early lung cancer detection to individuals without a history of tobacco use or who fall outside of the age criteria for lung cancer screening set by the USPSTF.

Prospective nonrandomized studies in Asia

15

as well as the Female Asian Nonsmoker Screening Study in the US have demonstrated the feasibility of LDCT screening in Asian individuals who have never smoked. However, it is unlikely that screening recommendations in the US will be expanded to include populations who have never used tobacco without high-quality randomized trial data. Randomized trials evaluating LDCT screening in populations who have never used tobacco have long been regarded as being prohibitively expensive. However, the advent of improved lung cancer risk stratification tools offer hope. Risk stratification tools, such as Sybil (a deep learning image–based machine learning algorithm),

16

can enable the identification of individuals at high risk of lung cancer regardless of tobacco history, which may provide an opportunity to study LDCT screening among a subset of the general population who have never smoked but who are nevertheless at high risk of lung cancer.

Lung cancer screening has realized only a fraction of its lifesaving potential. Each year, many individuals die of lung cancer who probably would still be alive had they been screened—reflecting both low uptake among currently eligible adults and overly restrictive eligibility criteria that exclude many at high risk. Achieving its full potential in the US will require a concerted national effort to increase uptake, expand eligibility thoughtfully, and harness advances in risk prediction to reach all high-risk individuals, not only those with a tobacco use history. Lung cancer screening has already saved tens of thousands of lives in the decade since its implementation. We cannot afford to leave its potential untapped.

Lung cancer has remained the leading cause of cancer death among men in the US since the early 1950s, among women in the US since 1987, and worldwide for several decades. Lung cancer screening with low-dose computed tomography (LDCT) significantly reduced lung cancer mortality in 2 large-scale randomized clinical trials and is currently recommended in the US for an estimated 13 million high-risk adults,  offering a tremendous opportunity to reduce mortality from the deadliest cancer. Yet, implementation of lung cancer screening has been challenging, and more than 80% of adults eligible for screening do not get screened.  Moreover, recent evidence suggests that current eligibility criteria for screening inadequately identify high-risk populations and exclude approximately 50% of adults diagnosed with lung cancer.

These challenges raise 2 critical questions. Under current conditions, how many lives does screening for lung cancer save? And how many more lives could be saved with better uptake and eligibility criteria for screening?

In this issue of JAMA, Bandi and colleagues sought to address these questions  using data from the 2024 National Health Interview Survey, as well as 2 validated models that predict lung cancer deaths prevented and life-years gained from screening. Their analysis quantified the impact of lung cancer screening under current conditions and projected potential gains from (1) improving screening uptake and (2) broadening screening eligibility criteria.

The analysis yielded several key findings, all of which argue for action. First, the authors found that only 18.7% of adults eligible for lung cancer screening reported undergoing screening in 2024, which is unacceptably low. Even at this low level of uptake, the authors estimate that screening prevents nearly 15 000 lung cancer deaths and saves more than 190 000 life-years over a 5-year period. If screening uptake were improved to include 100% of those meeting current screening eligibility criteria, an estimated 62 000 lung cancer deaths could be prevented and more than 870 000 life-years could be gained over 5 years, representing an increase greater than 300% in lung cancer deaths prevented and life-years gained compared with that estimated under the current level of uptake for lung cancer screening. It is worth highlighting that these gains in life-years attributed to screening with 100% uptake are substantially greater than the estimated gains in life-years from therapeutic advances for late-stage disease.  The data communicate clearly and unambiguously that increasing the uptake of lung cancer screening must be elevated to a leading public health priority.

Moreover, the authors found that a surprising number of lung cancer deaths that could be prevented by screening (nearly 30 000 over 5 years) occur among adults with a history of smoking who did not qualify for screening under the 2021 US Preventive Services Task Force (USPSTF) criteria. Of these nearly 30 000 potentially preventable deaths, 36% occurred among adults who have at least 20 pack-years of smoking but stopped smoking more than 15 years before. Another 36% of these deaths occurred among adults who either currently smoke or quit within the past 15 years and have at least 20 years of smoking, but have fewer than 20 pack-years of smoking. Notably, recent research has supported both the removal of the 15 years since quitting requirement from lung cancer screening recommendations  and expanding screening criteria to individuals who have smoked for 20 or more years but, due to a low daily intensity, have fewer than 20 pack-years of smoking.  Indeed, adopting a 20-year smoking duration criterion over the complicated-to-calculate pack-year criterion would expand eligibility for lung cancer screening among those at high risk with minimal losses in screening efficiency.  In light of these data, both the National Comprehensive Cancer Network and the American Cancer Society have removed the 15 years since quitting smoking requirement in their guidelines. In 2024, the National Comprehensive Cancer Network also updated their guideline to include a 20-year smoking duration criterion, recommending lung cancer screening for individuals who have smoked at least 20 years (even if they do not have ≥20 pack-years of smoking).

While increasing evidence suggests that the current USPSTF lung cancer screening guidelines miss many high-risk individuals who ultimately develop lung cancer, there are concerns that expanding criteria may open screening to low-risk populations in whom the potential harms of screening may outweigh benefits.  This is an important concern, and we agree that the impact of any proposed revisions to the USPSTF criteria on low-risk populations would need to be evaluated. However, it is helpful to consider the meaning of the metrics commonly used to understand the impact of proposed lung cancer screening eligibility criteria.

When eligibility criteria for lung cancer screening are expanded, the sensitivity of the criteria will almost always increase (ie, more individuals who develop lung cancer will qualify for screening). That in itself is the fundamental benefit of expanding lung cancer screening eligibility criteria. On the other hand, specificity of the expanded lung cancer screening eligibility criteria will almost always decline because more individuals without lung cancer will also become eligible for screening under the expanded criteria; people without lung cancer who become newly eligible under the expanded criteria are reclassified from what could be considered true negatives under the original criteria to what could be considered false positives under the expanded criteria, thereby decreasing specificity. The only exception to this would be if every person who became newly eligible for lung cancer screening under the expanded criteria had lung cancer, which is unrealistic. However, specificity itself provides no information about the degree of lung cancer risk among a population that becomes newly eligible for lung cancer screening under a given guideline, and a decline in specificity should not be used as an argument against expanding screening eligibility criteria. Importantly, screening efficiency (ie, the number of individuals needed to screen to detect one lung cancer) complements sensitivity and specificity by offering insight into the cancer risk among individuals eligible under expanded lung cancer screening eligibility criteria. When evaluating the impact of expanded lung cancer screening eligibility criteria, sensitivity, specificity, and screening efficiency should all be considered.

Lastly, it is worth noting that Bandi and colleagues focused on adults with any smoking history aged 50 to 80 years and did not include individuals who have never smoked or individuals outside of the age range recommended for screening by the USPSTF. Every year, an estimated 20 000 individuals who have never smoked die of lung cancer in the US. Moreover, approximately 4700 cases of lung cancer are diagnosed among adults younger than 50 years in the US each year. These data highlight the need to study the potential benefits and harms of extending opportunities for early lung cancer detection to individuals without a history of tobacco use or who fall outside of the age criteria for lung cancer screening set by the USPSTF.

Prospective nonrandomized studies in Asia as well as the Female Asian Nonsmoker Screening Study in the US have demonstrated the feasibility of LDCT screening in Asian individuals who have never smoked. However, it is unlikely that screening recommendations in the US will be expanded to include populations who have never used tobacco without high-quality randomized trial data. Randomized trials evaluating LDCT screening in populations who have never used tobacco have long been regarded as being prohibitively expensive. However, the advent of improved lung cancer risk stratification tools offer hope. Risk stratification tools, such as Sybil (a deep learning image–based machine learning algorithm),  can enable the identification of individuals at high risk of lung cancer regardless of tobacco history, which may provide an opportunity to study LDCT screening among a subset of the general population who have never smoked but who are nevertheless at high risk of lung cancer.

Lung cancer screening has realized only a fraction of its lifesaving potential. Each year, many individuals die of lung cancer who probably would still be alive had they been screened—reflecting both low uptake among currently eligible adults and overly restrictive eligibility criteria that exclude many at high risk. Achieving its full potential in the US will require a concerted national effort to increase uptake, expand eligibility thoughtfully, and harness advances in risk prediction to reach all high-risk individuals, not only those with a tobacco use history. Lung cancer screening has already saved tens of thousands of lives in the decade since its implementation. We cannot afford to leave its potential untapped.

This study examines the current national prevalence of up-to-date lung cancer screening and estimates lung cancer deaths prevented and life-years gained from screening.

Figure.  Projected Lung Cancer Deaths Prevented Over 5 Years and Life-Years Gained via Lung Cancer Screening of Individuals With 100% Uptake Among Screening-Ineligible Individuals, US 2024

Screening-ineligible sample included ever-smoking individuals aged 50 to 80 years ineligible for screening based on US Preventive Services Task Force (USPSTF) 2021 lung cancer screening recommendation criteria with no report of ever being diagnosed with lung cancer. Deaths prevented and life-years gained estimated with multiple imputation for missing data on variables were included in the models (eMethods in Supplement 1). Estimates were population-weighted to be nationally representative. Deaths averted and life-years-gained estimates were rounded to tenths place if the value was 0-99, rounded to ones place if the value was 100-999, and rounded to tens place if the value was >1000. Screening-ineligible individuals were categorized into 7 groups based on USPSTF, American Cancer Society (ACS), and National Comprehensive Cancer Network (NCCN) screening eligibility criteria (eMethods in Supplement 1). The data values for the population number (in millions), lung cancer deaths averted, and life-years gained, respectively, for the 7 groups in order of appearance are 12.8, 4690, and 91260 among ≥15 years since quitting (YSQ), <20 pack-years, and <20 smoking-years; 3.45, 3360, and 53 380 among ≥15 YSQ, <20 pack-years, and ≥20 smoking-years (NCCN); 5.35, 10 700, and 145 970 among ≥15 YSQ, ≥20 pack-years (ACS and NCCN); 0.42, 111, 3130 among <15 YSQ, <20 pack-years, <20 smoking-years; 2.53, 2450, 44 950 among <15 YSQ, <20 pack-years, ≥20 smoking-years (NCCN); 0.21, 170, 3500 among currently smoking, <20 pack-years, <20 smoking-years; and 3.94, 8220, and 140 230 among currently smoking, <20 pack-years, and ≥20 smoking-years (NCCN).

Table.  Lung Cancer Screening (LCS) Prevalence and Projected Lung Cancer Deaths Prevented and Life-Years Gained Due to Screening (N = 2124)

a

Annual lung cancer screening (LCS) reduces LC mortality and is recommended by the US Preventive Services Task Force (USPSTF).

1

Recent state-level data showed LCS uptake is low (9%-31%),

2

but true nationally representative estimates are lacking. This study estimated the current national prevalence of up-to-date LCS and deaths prevented and life-years gained from LCS at current and 100% screening uptake.

Methods

This study used the 2024 National Health Interview Survey, a cross-sectional population-based household survey of noninstitutionalized civilians (response rate: 47.9%). The study used deidentified public-use data, was exempt from institutional review board review, and followed STROBE guidelines.

Among individuals aged 50 to 80 years without LC who ever smoked, weighted prevalence of up-to-date LCS (self-report of past-year receipt of chest computed tomography [CT] scan to “check” or “screen” for LC) was estimated among screening-eligible individuals (based on USPSTF 2021 recommendation criteria of currently smoking or <15 years since quitting with history of ≥20 pack-years) (eMethods and eFigure in Supplement 1).

1

The validated Lung Cancer Death Risk Assessment Tool model

3

estimated LC deaths preventable by screening over 5 years and the life-years-gained from CT screening model

4

estimated life-years gained from screening. Models assumed LCS prevented 20.4% of LC deaths with adherence to 3 annual screens based on National Lung Cancer Screening Trial results (Supplement 1). Estimates were generated for 3 scenarios: (1) at current uptake among screening-eligible individuals; (2) at 100% uptake among unscreened screening-eligible individuals, and (3) at 100% uptake in screening-ineligible individuals with ever-history of smoking. Race and ethnicity were self-reported based on fixed categories. Analyses were weighted to be nationally representative and conducted in SUDAAN version 9.4 (SAS Institute Inc) and RStudio version 4.4.1 (R Foundation).

Results

Of the analytic sample, 31.2% (n = 2124) were screening-eligible, translating to 12.76 million individuals (55% male; 66.4% ≥ 60 years) (Table). An additional 4390 individuals (weighted n = 28.08 million) were screening-ineligible, of whom 18.1% had at least 15 years since quitting and at least 20-pack-years of smoking (eFigure in Supplement 1).

Table.  Lung Cancer Screening (LCS) Prevalence and Projected Lung Cancer Deaths Prevented and Life-Years Gained Due to Screening (N = 2124)

a

Of screening-eligible individuals, 18.7% (95% CI, 16.7%-20.8%) reported up-to-date LCS, with lower prevalence in those younger than 60 years vs 60 years or older (eg, 7.9% for those aged 50-54 y vs 22.8% for those aged 70-80 y) and higher prevalence with increasing comorbidities (eg, 28.1% for those with ≥3 comorbidities vs 9.6% for none) (Table).

Among screening-eligible individuals, 100% screening uptake was projected to prevent 62 110 lung cancer deaths over 5 years and gain 872 270 life-years. Current up-to-date LCS uptake was estimated to prevent 24% (weighted n = 14 970) of these deaths and gain 22% (190 030) life-years (Table). For each additional 1000 screening-eligible individuals screened, similar life-years would be gained for those with 1, 2, or 3 or more comorbidities (65.3, 61.6, and 64.1 years) even as prevented deaths increased (4.1, 4.3, and 5.6 deaths).

If screening-ineligible ever-smoking individuals had 100% screening uptake, an estimated 29 690 additional deaths would be prevented and 482 410 additional life-years would be gained (Table). An estimated 64% of deaths averted occurred in 2 groups: those who formerly smoked with at least 15 years since quitting and at least 20 pack-years and those currently smoking with at least 20 smoking years (Figure).

Figure.  Projected Lung Cancer Deaths Prevented Over 5 Years and Life-Years Gained via Lung Cancer Screening of Individuals With 100% Uptake Among Screening-Ineligible Individuals, US 2024

Screening-ineligible sample included ever-smoking individuals aged 50 to 80 years ineligible for screening based on US Preventive Services Task Force (USPSTF) 2021 lung cancer screening recommendation criteria with no report of ever being diagnosed with lung cancer. Deaths prevented and life-years gained estimated with multiple imputation for missing data on variables were included in the models (eMethods in Supplement 1). Estimates were population-weighted to be nationally representative. Deaths averted and life-years-gained estimates were rounded to tenths place if the value was 0-99, rounded to ones place if the value was 100-999, and rounded to tens place if the value was >1000. Screening-ineligible individuals were categorized into 7 groups based on USPSTF, American Cancer Society (ACS), and National Comprehensive Cancer Network (NCCN) screening eligibility criteria (eMethods in Supplement 1). The data values for the population number (in millions), lung cancer deaths averted, and life-years gained, respectively, for the 7 groups in order of appearance are 12.8, 4690, and 91260 among ≥15 years since quitting (YSQ), <20 pack-years, and <20 smoking-years; 3.45, 3360, and 53 380 among ≥15 YSQ, <20 pack-years, and ≥20 smoking-years (NCCN); 5.35, 10 700, and 145 970 among ≥15 YSQ, ≥20 pack-years (ACS and NCCN); 0.42, 111, 3130 among <15 YSQ, <20 pack-years, <20 smoking-years; 2.53, 2450, 44 950 among <15 YSQ, <20 pack-years, ≥20 smoking-years (NCCN); 0.21, 170, 3500 among currently smoking, <20 pack-years, <20 smoking-years; and 3.94, 8220, and 140 230 among currently smoking, <20 pack-years, and ≥20 smoking-years (NCCN).

Discussion

Only approximately 1 in 5 eligible individuals in the US underwent LCS in 2024. Increasing current uptake to 100% could increase deaths prevented and life-years gained 3-fold. Efforts to increase uptake include improving awareness of LCS recommendations and access to LCS facilities,

5

and targeting subgroups in whom LCS maximizes life-years gained.

4

Unscreened eligible individuals in this study with fewer comorbidities had similar life-years gained because they were less likely to die of comorbid causes. Revisiting current eligibility recommendations is warranted. In 2023, the American Cancer Society eliminated the years-since-quit requirement

6

and the National Comprehensive Cancer Network followed suit in 2025 (Supplement 1).

Limitations of this study include survey nonresponse and potential recall bias, inability to distinguish screening vs diagnostic CT, and inability to verify modeling assumptions including adherence to 3 annual scans.

Enamel does not naturally regenerate, which can lead to painful cavities, but a gel that harnesses some of the properties of saliva could restore the hard, shiny layer to teeth

A gel uses chemicals found in saliva to repair and regenerate tooth enamel, which could prevent people from developing cavities that require fillings.

Enamel – the hard, shiny layer on the surface of teeth – shields the sensitive inner layers from wear and tear, acids and bacteria. “Enamel is the first line of defence. Once that line of defence starts to break down, tooth decay becomes accelerated,” says Alvaro Mata at the University of Nottingham, UK. Enamel doesn’t naturally regenerate, and treatments such as fluoride varnishes and remineralisation solutions only prevent the situation from worsening.

Looking for a solution, Mata and his colleagues have developed a gel containing a modified version of a protein that they manipulated to act like amelogenin, a protein that helps guide the growth of our enamel when we are infants.

Experiments that involved pasting the gel onto human teeth under a microscope in solutions containing calcium and phosphate – the primary building blocks of enamel – show that it creates a thin and robust layer that stays on teeth for a few weeks, even during brushing.

The gel fills holes and cracks, creating a scaffold that uses the calcium and phosphate to promote the organised growth of new crystals in the enamel below the gel layer, even when so much was gone that the underlying dentine below was exposed.

“The gel was able to grow crystals epitaxially, which means it’s in the same crystallographic orientation as existing enamel,” says Mata.

That orientation means that the new growth – which reached up to 10 micrometres thick – is integrated into the underlying natural tissue, rebuilding the structure and properties of enamel. “The growth actually happens within a week,” says Mata. The process also worked when using donated saliva, which also naturally contains calcium and phosphate, rather than just in the solution the team used that comprised these chemicals.

A similar approach was reported in 2019, but that produced thinner coatings, and the recovery of the architecture of inner layers of enamel was only partial.

Clinical trials in people are set for early next year. Mata has also launched a company called Mintech-Bio and hopes to have a first product out towards the end of 2026, which he sees dentists using.

A subcutaneously injectable version of Merck’s intravenous pembrolizumab, marketed as Keytruda, will be available for the almost 20 cancers that the immune checkpoint inhibitor can treat, the FDA announced. The new option, marketed as Keytruda Qlex, seeks to make administration more convenient than current intravenous methods. Merck said the shot will be available in late September.

In Merck’s open-label phase 3 clinical trial of 377 patients with metastatic non–small cell lung cancer, researchers compared pembrolizumab exposure in those receiving it in either injected or intravenous form.

The results found no difference in exposure or survival rates across the intravenous and subcutaneous options, concluding that both methods may be used for the same indications. The injection only takes 2 minutes to administer, whereas intravenous infusions last about 30 minutes.

The injectable form also allows for greater accessibility at smaller health care settings and potentially avoids the need for a port, a venous access device implanted in the chest. However, patients with known contraindications to berahyaluronidase, the ingredient enabling subcutaneous permeability, should avoid the new version.

Can you escape your genetic inheritance, and do lifestyle changes actually make a difference? Daniel Cossins set out to understand what the evidence on Alzheimer’s really means for him

A few years ago, my dad was diagnosed with Alzheimer’s disease, just like his older brother and his mum before him. Slowly, his personality began to ebb away. Now, at the age of 75, his cognitive decline is accelerating: he no longer recognises his granddaughters, for instance, and he lives in a near-constant state of confusion, which means he is losing his independence, too.

As I process this loss and try to support my parents, I have become increasingly curious about what my family history means for me. I am 43, an age at which the misfolded proteins thought to underlie Alzheimer’s can begin to build up in the brain. I found myself wanting to better understand my own risk – and what, if anything, I can do about it. Would taking a DNA test to reveal my hereditary risk of Alzheimer’s be a good idea? And how could I make sense of the increasingly prominent idea that we can all “prevent” Alzheimer’s by addressing key lifestyle factors like diet and exercise? Given the prevalence of the disease in my family, I was sceptical about this.

What I learned was by turns confusing and frustrating – in the sense that Alzheimer’s is such a complex disease that almost everything we learn requires careful unpicking – but also surprisingly empowering. As Rudolph Tanzi, a neurologist at Massachusetts General Hospital, puts it: “Genetics is the hand that you’re dealt and you may get a lousy hand, but how you play it really does matter.”

As difficult as it is, my family’s situation is far from unusual. Some 55 million people worldwide are living with dementia, of which Alzheimer’s is the most common form; this figure is expected to rise to 78 million by 2030. Meanwhile, despite significant progress, we haven’t nailed down the causes of Alzheimer’s beyond the frustratingly broad statement that it has to do with age-related changes in the brain as well as genetic, health and lifestyle factors.

Neuroinflammation

The leading idea is the amyloid hypothesis, which suggests that the clumping of a misfolded protein called amyloid-beta between brain cells triggers the abnormal formation of another protein called tau inside them in what are called tangles. This, in turn, leads to neuroinflammation, disruption of neural connections – or synapses – and cell death. “Amyloids are the match and tangles are the brush fires,” says Tanzi. “You won’t get Alzheimer’s from that alone, because that has to trigger neuroinflammation – and that’s the wildfire that kills enough neurons and synapses to get the disease.”

There are reasons, however, to think the amyloid hypothesis isn’t the whole picture. For a long time, the new treatments it inspired – antibodies engineered to remove misfolded proteins from the brain – failed to reduce symptoms. But in the past few years, some of these drugs, such as Lecanemab, have been approved by the US Food and Drug Administration, having shown modest effectiveness at slowing cognitive decline in people with early-stage Alzheimer’s.

Many experts still consider the benefits too small to compensate for the risk of swelling and bleeding in the brain that this treatment carries. But as the first “disease-modifying” therapy, these drugs are still a positive step – and they suggest that more effective options are coming. Indeed, earlier this year pharmaceutical company Roche announced positive early results from a small trial of a drug called Trontinemab, which appears to remove amyloid deposits with a lower risk of brain bleeds.

All of this comes too late for my dad, of course. For me, though, it is motivation to investigate my own risk: if the ideal scenario is “early prediction, early detection, early intervention”, as Tanzi says, then I want to know if and when I might need to act.

Genetic risk

When it comes to early prediction, the first port of call is my genetic inheritance. We know that genes play a significant role in Alzheimer’s risk, primarily through the gene that codes for the apolipoprotein E (APOE) protein, which helps transport fats and cholesterol in the body and brain.

Everyone has two copies of the

APOE

gene, one inherited from each parent, and there are three variants.

APOE3,

the most common, isn’t thought to affect Alzheimer’s risk.

APOE2

, which is relatively rare, may provide some protection against the disease.

APOE4

, meanwhile, is clearly associated with a heightened risk. Studies suggest that having one copy of this increases the likelihood of developing Alzheimer’s by three to four times compared with someone with none, while two copies can increase risk as much as 15-fold.

Given my family history, I suspect I will be among the 25 per cent of people worldwide with at least one copy of the

APOE4

variant – and possibly even among the 2 per cent with two.

These days, it is easy enough to find out, thanks to direct-to-consumer genetic tests. The best-known option is the “health and ancestry kit” sold by the company 23andMe, which can include reports on the

APOE

gene. But a quick search for what is available in the UK reveals several other products advertised solely as

APOE

tests. Initially, I was gung-ho. When the kit arrived, however, I found myself hesitating.

“

Initially, I was gung-ho. When the test kit arrived, however, I found myself hesitating

“

All of the Alzheimer’s organisations in the UK and the US recommend against such a test. Their primary reasoning is that

APOE

isn’t deterministic because there are many other risk factors involved. “The situation where you have two copies of

APOE4

does increase your risk quite substantially, but it doesn’t mean that you will inevitably get Alzheimer’s disease,” says Charles Marshall who studies dementia at Queen Mary University of London.

Besides, dementia is so common that most people have a family history, says Marshall. “So, unless someone in the family had very young onset dementia, it doesn’t make a huge difference in terms of an individual’s risk to have had a parent who had it later in life.”

The widespread caution over

APOE

tests also has to do with the potential psychological distress they can cause, says Ashvini Keshavan at University College London’s Dementia Research Centre. “The utility [of genetic testing] is so minimal and the downsides so high, in terms of anxiety generation, that people shouldn’t be doing it,” she says.

As someone with a tendency to ruminate, this gave me pause. Ultimately, however, I decided to go for it. Rightly or wrongly, I had it in my head that I was likely to have at least one copy of

APOE4

and possibly two, so that anything else would feel like a bonus.

As I awaited my results, my thoughts turned to detection – and the misfolded proteins thought to be the cause of Alzheimer’s, which can begin to build up in the brain 20 years before even mild cognitive impairment. “If you do carry

APOE4

, you might want to know whether you have amyloid beginning in your brain and [tau] tangles being induced, because that’s how it works,” says Tanzi. The idea is that you would then have a window of opportunity to intervene at an early stage of the disease’s pathology.

What’s the deal with biomarker tests?

In the past few years, researchers have demonstrated that blood biomarker tests can detect amyloid-beta and tau in the brain more easily than the methods currently used to diagnose early-stage Alzheimer’s. One of the most promising blood tests detects the presence of a particular protein called p-tau217, a tell-tale sign of disease pathology, well in advance of symptoms. A 2024 study evaluating a p-tau217 test showed that it was just as accurate as analysis of cerebrospinal fluid, and more so than PET scans.

For now, the focus is on how best to roll out these blood tests in clinical settings. But they could eventually be used to screen everyone over 50, say, in much the same way people are currently screened for high cholesterol. “That’s the goal,” says Tanzi. “We’re not going to end Alzheimer’s by waiting until the brain has deteriorated enough that you have symptoms.”

But there is still nowhere near enough evidence that blood tests can reliably predict your risk of getting Alzheimer’s, or when, says Keshavan. “These blood tests do show changes in people who are asymptomatic, but their presence doesn’t necessarily mean you’re going to develop symptoms within your lifetime.” Some people live for decades with amyloid and tau in their brain without developing Alzheimer’s – something the researchers refer to as “resilience”.

Unsurprisingly, these blood biomarker tests are already available on the open market. Again, though, Tanzi urges caution: “If you think finding out you carry

APOE4

causes stress, imagine discovering that amyloid is accumulating in your brain.”

Keshavan is even more steadfast. She worries that we will end up in a situation similar to what doctors face with at-home

APOE

tests today, where “people come with their results and we’re having to pick up the pieces, in terms of dealing with the anxiety and stress”, she says. “That is why we are singing it from the rooftops: people should not do this!”

I heed the advice. It is probably too early for me anyway. That said, I wouldn’t rule out getting a blood biomarker test in a few years’ time, especially if new amyloid-busting treatments are available by then.

The prospect of Alzheimer’s vaccines

On that front, perhaps the most exciting prospect are vaccines against amyloid-beta and tau. The idea is simple: with the help of vaccine additives called adjuvants, you turbocharge the body’s natural immune response to clear out the misfolded proteins. Several are already in clinical trials, with a view to using them to not only slow or halt disease progression, but even to help prevent it. The reality, however, is that there is no guarantee that any of these Alzheimer’s vaccines will ever be approved for medical use – never mind in time to help me or others in a similar position.

Which brings us to the big question of what, if anything, we can do in the meantime. If you have been following the news, you could be forgiven for thinking we already have the answer. There is a steady stream of headlines confidently declaring that we can all “prevent” dementia by making healthier lifestyle choices. I was instinctively sceptical, though, possibly because the coverage tends to gloss over the details of these studies and how robust their findings are and what they mean for people like me with a family history of the disease.

The most recent flurry of stories was inspired by a

Lancet

Commission report from 2024, in which 27 experts assessed the best available evidence and concluded that 45 per cent of dementia cases could be avoided by addressing 14 key lifestyle factors. They included lower levels of education, hearing and vision impairment, high blood pressure, high cholesterol, obesity, diabetes, smoking, excessive alcohol consumption, air pollution, social isolation, depression, traumatic brain injury and physical inactivity.

The take-home message is that “there’s a lot we can do to prevent dementia or delay its onset”, says Gill Livingston, a neurologist at University College London and lead author of the report. “Some people will still develop dementia, but [if they address these lifestyle factors], in general, people will develop it later and have it for less long. And that’s really important, because if you delay it for 10 or 15 years, you may never get it in your lifetime.”

However, critics of the

Lancet

report pointed out that the analysis relied largely on observational studies, which can establish correlations, but not causality. “We don’t really have evidence that dementia cases are prevented by addressing any of these risk factors,” wrote Marshall at the time.

Livingston, for her part, says that some things just aren’t amenable to randomised controlled trials (RCTs), which are considered the gold standard of research, for both practical and ethical reasons. She also points out that the observational studies the

Lancet

considered tended to be large, of high quality and show effects of a similar magnitude in the same direction. “People always say that correlation does not equal causation, but, equally, it often does,” says Livingston. “There are no RCTs of smoking and lung cancer, for example, because that would be unethical. But none of us have a problem believing they’re related based on the weight of the evidence.”

It is also fair to say that the

Lancet

report did include a few RCTs – on the role of blood pressure and diabetes, for example. And they aren’t the only ones. Perhaps the most compelling is the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER), the first large-scale RCT to demonstrate that lifestyle interventions can prevent cognitive decline among older adults at risk of dementia.

The first FINGER study, published in 2015, involved more than 1200 participants, aged 60 to 77, with an increased risk of dementia but no obvious memory problems. Half of them were put through a programme of lifestyle interventions involving diet, physical activity, cognitive training and blood pressure monitoring, whereas the control group received only regular health advice. After two years, cognitive performance improved in both groups, but the total average improvement of the intervention group was 25 per cent greater.

“On top of that, we saw that the control group had 30 per cent higher risk for cognitive decline,” says Miia Kivipelto at the Karolinska Institute in Stockholm, Sweden, who leads the FINGER programme. The team was also able to demonstrate a reduction in the estimated risk of dementia.

The success of the FINGER trials provided a model for multiple follow-up studies in which the interventions have been adapted and refined for different populations in more than 60 countries, with further positive results.

All of this seems encouraging, and many of the researchers I spoke to praised the rigorous nature of the FINGER studies. But we have to keep in mind that these trials haven’t yet demonstrated a reduction in cases of dementia, says Marshall. “What we see is that your scores on cognitive tests get better over time, which is not what happens normally,” he says. “And if you are in the intervention arm, they get better by a little bit more than the people in the control arm. So, it’s not clear how that translates into dementia prevention in the real world.”

Becoming more resilient

What we can say, however, is that these lifestyle interventions do seem to affect how resilient your brain is to dementia, and possibly Alzheimer’s pathology more specifically, if and when either do arise. “The likelihood is that they delay when you get symptoms in the face of Alzheimer’s pathology, which in practice means that some people will then die of something else before they get symptoms,” says Marshall.

Which does sound a lot like prevention, to be fair. “I think there’s really quite clear evidence that the brain resilience makes a difference,” says Livingston. And according to Tanzi, it is never too early to adopt the habits that help to build this resilience, regardless of your

APOE

status. “The message should go out that in 98 per cent of cases of family history or genetic predisposition, lifestyle does make a difference,” he says.

As for which of the various lifestyle factors are the most important, every researcher I spoke to had a slightly different take. The FINGER studies suggest that two key pillars should be priorities: eating a Mediterranean-style diet – high in vegetables, fruits, nuts and whole grains and low in red meat – and being active, physically, mentally and socially. But Livingston emphasises blood pressure and cholesterol levels, on the basis that many lines of evidence suggest that good vascular health seems to reduce your risk of dementia.

It can also depend on your age and life situation. Like me, many people in their 40s are juggling work, childcare and elderly parents, so stress is another important factor, says Kivipelto. “Rather than cognitive stimulation, someone in your position might want to focus on sleep and stress reduction, perhaps by increasing your exercise, as well as checking for hypertension.”

When my genetic test results come in, I discover that I have just one copy of the

APOE4

gene variant. To recap, that means I am three to four times more likely to develop Alzheimer’s than someone with no copies of that variant. The news doesn’t induce much anxiety, though, because at this stage, I have learned enough to know that my

APOE

status doesn’t seal my fate – and to have some confidence, tentative as much of the evidence may be, that I might be able to delay the onset of cognitive decline.

If anything, the

APOE

results make me feel more motivated to make good on long-held intentions to undertake a more health-focused lifestyle – especially given that Kivipelto and her colleagues were recently able to demonstrate that

APOE4

carriers get greater benefits from the FINGER interventions than non-carriers.

I am aware that essentially amounts to generic health advice – eat better, exercise and so on. But it does feel empowering. As daft as it might sound, every time I pick the mackerel salad for lunch, set off for a run in the woods or even arrange to meet up with friends, I remind myself that I am building my brain’s long-term resilience to neurodegeneration.

Besides, until better drugs come along, that is all anyone can do. “At the individual level, no one can say they’re preventing Alzheimer’s because there are no guarantees,” says Tanzi. “But keeping your brain healthy and boosting its resilience is obviously worth doing, and it is likely to mean that you live for longer without dementia.”

Pokud bude tato jednoduchá technika osvojena, mohla by otevřít nový způsob pozorování orgánů v těle.

Běžné potravinářské barvivo v kombinaci s roztokem zprůhlednilo myší kůži.

Výzkumníci z texaské a stanfordské univerzity provedli tento průlomový výzkum, který by mohl posunout medicínské zobrazování.

Ke zprůhlednění kůže vědci použili jednoduchý roztok vody a tartrazinu, oblíbeného potravinářského barviva.

Při experimentu byla směs nanesena na lebky a břicha myší. Barvivo se rychle rozptýlilo do kůže, čímž se tato místa stala průhlednými.

„Trvalo několik minut, než se průhlednost objevila. Funguje to podobně jako pleťový krém nebo maska: Potřebná doba závisí na tom, jak rychle molekuly difundují do kůže,“ řekl Dr. Zihao Ou, odborný asistent fyziky na Texaské univerzitě v Dallasu a hlavní autor.

Tento dočasný účinek je vratný. Barvivo je navíc „biokompatibilní“ a nepředstavuje pro zvířata žádnou újmu.

Pokud bude tato jednoduchá technika přijata, mohla by otevřít nový způsob pozorování orgánů v těle.

Odborníci předpokládají, že tato technologie bude využita ke zviditelnění žil při odběrech krve, zjednodušení laserového odstraňování tetování a dokonce i k včasnému odhalení a léčbě rakoviny.

Snížení rozptylu světla

Živá kůže rozptyluje světlo podobně jako mlha, takže je obtížné ji prohlédnout.

Klíčem bylo najít kombinaci, která by snížila rozptyl světla v kožní tkáni.

„Zkombinovali jsme žluté barvivo, což je molekula, která absorbuje většinu světla, zejména modré a ultrafialové světlo, s kůží, která je rozptylujícím prostředím. Samostatně tyto dvě věci blokují průchod většiny světla. Když jsme je však spojili dohromady, podařilo se nám dosáhnout průhlednosti myší kůže,“ vysvětlil Ou.

Ke „kouzlu“ dojde, když se molekuly pohlcující světlo rozpustí ve vodě a změní index lomu roztoku. To odpovídá indexu lomu složek kožní tkáně, jako jsou lipidy. Zajímavé je, že se tím snižuje rozptyl světla v kůži, takže vypadá průhledná.

„Molekuly barviva v podstatě snižují míru rozptylu světla v kožní tkáni, podobně jako se rozptyluje mlžný opar,“ uvádí tisková zpráva.

Představila vnitřní struktury myší.

Průhledná kůže poskytla vědcům jedinečnou možnost pozorovat různé vnitřní struktury myší.

Výzkumníci si skrze průhlednou lebku přímo prohlíželi krevní cévy na mozku. Na druhé straně průhledné břicho odhalilo vnitřní orgány a svalové kontrakce, které posouvají potravu trávicím systémem.

Zatímco u myší byl tento postup úspěšný, jeho účinnost u lidí bude teprve testována. Lidská kůže je zhruba desetkrát silnější než myší, což vyžaduje jinou dávku barviva nebo jinou techniku jeho podání, aby proniklo dovnitř.

Budoucí výzkum se zaměří na stanovení optimální dávky barviva pro lidskou tkáň. Kromě toho se tým snaží prozkoumat alternativní molekuly, včetně upravených materiálů, které by mohly být účinnější než tartrazin.

Ultrazvuk je nejčastěji používanou metodou pro zobrazení vnitřních struktur živých bytostí. Technologie založená na tomto řešení by mohla být levnější variantou takových vyšetření. Navíc ji lze použít se stávající optickou zobrazovací technikou.

„Optická zařízení, jako je mikroskop, se ke studiu živých lidí nebo zvířat přímo nepoužívají, protože světlo nemůže procházet živou tkání. Ale nyní, když dokážeme tkáň zprůhlednit, nám to umožní podívat se na detailnější dynamiku. To zcela změní dosavadní optický výzkum v biologii,“ dodal Ou.

Scientists “reverse engineer” placebo effect in mice by stimulating brain areas involved in pain-relief response

If adopted, this simple technique could open a new way of observing organs within a body.

Lidé s chronickými diabetickými bércovými vředy by mohli brzy získat nový způsob léčby ran, který by urychlil jejich hojení a snížil počet hospitalizací. Výzkumníci z Michiganské státní univerzity a nemocnice South Shore Hospital zjistili, že kombinace dvou běžných léků na cukrovku - injekčního inzulínu a perorálně podávaného metforminu - zvyšuje množství metforminu v místě rány. Vzhledem k tomu, že metformin může urychlit hojení ran, mohla by to být vítaná zpráva pro 18,6 milionu lidí na celém světě (1,6 milionu v USA), u nichž se během života objeví vřed na diabetické noze neboli DFU.

„Shromáždili jsme lidské exsudáty z vředů diabetické nohy a analyzovali jejich složení,“ řekl Morteza Mahmoudi, docent na katedře radiologie a programu Precision Health na MSU College of Human Medicine. „Jednou z věcí, které jsme si ve složení exudátů všimli - a která nebyla nikde jinde pozorována - byla přítomnost metforminu.

„Až dosud farmakologické studie nezjistily interakci mezi inzulinem a metforminem,“ dodal. „Naše studie ukazuje, že by mohla existovat přinejmenším nepřímá role konzumace inzulínu i metforminu tak, že metformin může skončit v oblasti rány, kde zvyšuje schopnost organismu hojit se.“

Mahmoudi a jeho spoluřešitelka Lisa Gouldová, plastická chirurgička a lékařka pro péči o rány v nemocnici South Shore Hospital a klinická docentka medicíny na Brownově univerzitě, nedávno publikovali v časopise ACS Pharmacology and Translational Science článek, který podrobně popisuje dosud neslýchané spojení mezi inzulinem a metforminem v exsudátech DFU.

Práce týmu byla financována z grantu Národního institutu pro diabetes a nemoci trávicího ústrojí a ledvin.

„Naše zjištění mohou ovlivnit přístup lékařů k hojení chronických ran,“ řekl Mahmoudi. „Například pokud pacient dostane ránu, mohla by mu pomoci synergická role inzulínu a metforminu.

„Kromě toho musí vývojáři obvazů na rány zvážit interakce všeho, co dávají na rány s exsudáty,“ pokračoval. „Exsudáty mohou s obvazy na rány interagovat a ovlivňovat jejich bezpečnost a terapeutickou účinnost. To bude vyhodnocovat další výzkum.“

Scientists in Japan say they have reversed the signs of Alzheimer’s disease in lab mice by restoring the healthy function of synapses, critical parts of neurons that shoot chemical messages to other neurons.

The secret was developing a synthetic peptide, a small package of amino acids — a mini-protein, if you will — and injecting it up the nostrils of the mice, in an experiment they detailed in a study published in the journal Brain Research.

Needless to say, mice are very different from humans. But if the treatment successfully survives the gauntlet of clinical studies with human participants, it could potentially lead to a new treatment for Alzheimer’s disease, a tragic degenerative condition that burdens tens of millions of people around the world.

"We strongly hope that our peptide could go through the tests and reach AD (Alzheimer’s disease) patients without much delay and rescue their cognitive symptoms, which is the primary concern of patients and their families," Okinawa Institute of Science and Technology neuroscience professor and the study's principal investigator Tomoyuki Takahashi said in a statement.

For the study, researchers focused on how the protein tau disrupts the chemical communication between neurons.

In Alzheimer’s disease, tau accumulates in the brain and interferes with the normal processes within synapses by using up a type of enzyme called dynamin, a key component in healthy neuron synaptic function.

Injection of the peptide seems to prevent this interaction with dynamin, which then leads to the reversal of Alzheimer’s disease symptoms in mice and restores their cognitive function, as long as they're treated early.

Members of the research team seem very optimistic that the study could be translated into a viable medication that could treat this devastating disease, but acknowledge that it's going to take a long time.

Going from experiments with mice to clinical trials and then finally into a drug that's commercially available can take decades.

"The coronavirus vaccine showed us that treatments can be rapidly developed, without sacrificing scientific rigor or safety," said Chia-Jung Chang, Okinawa Institute research scientist and the study's first author, in a statement. "We don’t expect this to go as quickly, but we know that governments — especially in Japan — want to address Alzheimer’s disease, which is affecting so many people. And now, we have learned that it is possible to effectively reverse cognitive decline if treated at an early stage."

If it's too late for our grandparents and parents, that's terrible. But perhaps this treatment will be ready in time for us.

Nerovnováha kožního mikrobiomu může způsobit řadu kožních problémů, například přispět ke vzniku akné. Vztahem mikrobiomu a akné se zabývá MUDr. Zuzana Nevoralová, Ph.D., z Kožního oddělení Nemocnice Jihlava, která s přednáškou Nové možnosti ovlivnění mikrobiomu vystoupila i na únorovém Dermatologickém update 2024 v Praze.

• O mikrobiomu se v poslední době mluví stále častěji…

Ano, mikrobiom je v současné době zkoumán v celé řadě odvětví medicíny. Ukazuje se, že může být jednou z příčin některých nemocí. Jeho úprava napomáhá léčbě řady chorob. A nejde jen o střevní nemoci, ale například i onemocnění kožní.

• Vy se aktuálně zaměřujete na to, jak konkrétně mikrobiom ovlivňuje vznik akné…

Ano. Víme, že akné vzniká ze čtyř příčin – dochází k ucpávání pórů, hromadění mazu, ke změně v poměru bakterií a přítomnosti zánětu. Co se týče bakteriálních příčin, dříve se myslelo, že dochází ke zmnožení pouze jedné bakterie, a to Propionibacterium acnes. Nejnovější poznatky ale ukazují, že vůbec nejde o zmnožení této bakterie, ale o změnu v poměru jejích fylotypů. Roli hraje také Staphylococcus epidermidis, resp. poměr obou bakterií, který je u akné změněn ve prospěch Propionibacterium acnes. Základem léčby akné je stále „otvírat“ ucpané póry, snižovat množství mazu, zánět, ale novou metodou léčby je právě změnit tyto špatné poměry bakterií a jejich fylotypů v poměry pozitivní. K tomu není účelné podávat antibiotika. Již dávno se potvrdilo, že u akné se antibiotika nesmějí podávat samotná, ale pouze v kombinaci s jinými léky. Naším novým cílem je antibiotickou léčbu minimalizovat a podávat léky zcela jiného typu, které jinou cestou změní špatný poměr bakterií v ten správný.

• Čemu konkrétně se ve své přednášce věnujete?

Zaměřila jsem se na současné možnosti úpravy poměru bakterií u akné, kdy cílem je, aby bylo dosaženo správného poměru bakterií, a tudíž došlo i ke zlepšení akné. Produktů, které to umožňují, je již několik, další jsou ve vývoji. Testovala jsem produkty z USA, které zatím v České republice dostupné nejsou. Speciálně se jednalo o tamponky k aplikaci na celý obličej. Tamponky obsahují právě u akné „oslabenou“ bakterii, resp. pozitivní produkty Staphylococcus epidermidis spolu s jeho „potravou“ – tedy prebiotikum a postbiotikum, plus 1% kyselinu salicylovou, která napomáhá lepšímu vstřebání produktu do kůže. Přípravek se aplikuje dvakrát denně na celou postiženou kůži a díky jeho působení se zlepšuje poměr bakterií a dochází ke správnému zastoupení jak Propionibacterium acnes, tak i Staphylococcus epidermidis. Efekt této léčby je především na pupínky. Možností působení na mikrobiom je již ale více, a firmy dále vyvíjejí řadu nových produktů, zatím z řady dermokosmetiky.

• Je pouhá změna mikrobiomu pro odstranění akné dostačující?

Jak jsem již řekla, základem léčby akné je léčba kombinovaná. Je potřeba pokud možno odstranit všechny patogenetické příčiny akné. Obecně lze tedy říci, že jakýkoli dobrý produkt zlepšující poměr bakterií aplikovaný na postiženou kůži má pozitivní efekt. Podle mých zkušeností je potřeba aplikovat ještě další lék, který zároveň uvolní „ucpané“ komedony. Následně dochází ke snížení počtu jak komedonů, tak i pupínků, eventuálně hnisavých pustulek. Výsledkem této kombinované léčby, kterou jsem zatím testovala na dvaceti pacientech, je výrazné zlepšení onemocnění.

• Mohou tyto poznatky nějak využít ve svých ordinacích praktičtí lékaři?

Určitě mohou. Již nyní několik společností nabízí dermokosmetiku, která pozitivně mění poměr obou hlavních bakterií nebo poměr fylotypů Propionibacterium acnes.Myslím si, že tyto přípravky budou nyní hitem na trhu. Nesmí se ale zapomínat aplikovat i přípravky snižující hyperkeratózu (především retinoidy nebo kyselinu azelaovou).

• Existují nějaká doporučení ohledně stravování, jak v tomto ohledu mikrobiom pozitivně ovlivnit?

Ukázalo se, že při vzniku a rozvoji akné hraje roli i střevní mikrobiom. Ten totiž interaguje s kožním mikrobiomem. Interakce mezi bakteriemi zapojenými do vzniku akné se tak rozšiřují za kůži samotnou. Pacienti s akné mají střevní mikrobiom, který se odlišuje od zdravých kontrol, a tato porucha byla stanovena jako enterotyp západní diety. Ukázalo se, že konzumace mléčných produktů, rafinovaných cukrů, čokolády a nasycených tuků podporuje rozvoj akné cestou aktivace metabolických signálů. Souvislost mezi střevním mikrobiomem a vznikem akné může být spojena se skutečností, že bakteriální dysbióza ve střevě způsobuje zvýšenou střevní permeabilitu vedoucí k uvolnění zánětlivých mediátorů do cirkulace. Americká firma, jejíž produkty jsem používala, má vyvinut celý komplex produktů k léčbě akné, včetně správných střevních probiotik. Vhodná je zřejmě i dieta s omezeným množstvím cukrů, čokolády a nasycených tuků. Komplex všech výše uvedených opatření pak vede ke zlepšení nálezu na kůži a ke spokojenosti pacienta i lékaře. Závěrem je nutno zdůraznit, že léčba musí být samozřejmě dlouhodobá.

Od začátku pandemie koronaviru došlo v Anglii a Walesu k vyššímu počtu úmrtí na onemocnění jater a cukrovku, než se očekávalo. Od začátku pandemie koronaviru zemřelo v Anglii a Walesu na onemocnění jater a cukrovku mnohem více lidí, než se očekávalo. Přesný důvod není jasný, ale některá úmrtí mohou být způsobena obtížemi v přístupu ke zdravotní péči v době výluky a rostoucí spotřebou alkoholu.

Podle britského Úřadu pro národní statistiku (ONS) došlo v období od března 2020 do června 2022 v Anglii a Walesu k 3834 nadměrným úmrtím způsobeným cirhózou a dalšími jaterními chorobami ve srovnání s klouzavými pětiletými průměry těchto onemocnění. To představuje nárůst o 19,7 % oproti očekávaným hodnotám. Ve stejném období došlo k 3466 nadměrným úmrtím způsobeným cukrovkou, což představuje nárůst o 24,4 procenta.

Pětileté průměry nezahrnují rok 2020, aby se snížily vysoké hodnoty úmrtnosti zaznamenané na vrcholu pandemie, a ONS se zabýval pouze primární příčinou úmrtí, takže žádná úmrtí nebyla započítána dvakrát.

"Víme, že naprostá většina úmrtí v důsledku onemocnění jater je způsobena alkoholickými chorobami jater," říká Ian Rowe z univerzity v Leedsu ve Velké Británii. "Během pandemie došlo k nárůstu konzumace alkoholu u osob, které již dříve hodně pily. Je velmi pravděpodobné, že tato zvýšená spotřeba vedla k nárůstu počtu úmrtí na jaterní onemocnění, který byl v tomto období zaznamenán."

Počet úmrtí na onemocnění jater mezi březnem 2020 a červnem 2022 vzrostl nad pětileté průměry u žen více než u mužů - o 22,4 % oproti 18 % - i když příčina není zcela jasná. "Může to souviset s dopady pandemie, která neúměrně postihla ženy, ale ve skutečnosti nevíme," říká Rowe. Studie ONS zjistila, že ženy ve Spojeném království během pandemie obvykle zažívaly nižší životní spokojenost a štěstí než muži. Jagpreet Chhatwal z Harvardovy univerzity říká, že podobné trendy byly zaznamenány i v USA, kde v letech 2019 až 2020 vzrostl počet úmrtí na jaterní choroby způsobené alkoholem o 22,4 %. "Nejsou to jen výluky, které způsobily nárůst spotřeby alkoholu, k trvalému nárůstu alkoholu přispívá i přetrvávající zvýšený stres ve společnosti," říká. "I krátkodobé zvýšení konzumace alkoholu může vést k chronickému onemocnění jater a s ním spojené úmrtnosti."

Podle Joanne Morlingové z Nottinghamské univerzity ve Velké Británii mohla pandemie vést také k pozdějšímu diagnostikování jaterních onemocnění.

"Víme, že cirhóza je často diagnostikována pozdě - téměř 50 % případů je poprvé zjištěno po urgentním příjmu s cirhózou v konečném stadiu, kdy je přežití špatné," říká. "Pokud v období pandemie skutečně dojde k nárůstu počtu úmrtí, mohlo by to souviset se zpožděním v diagnostice a pozdními prezentacemi souvisejícími s omezenou dostupností služeb."

Vysvětlení nadměrného počtu úmrtí na cukrovku je méně jasné, říká Jonathan Shaw z Monash University v australském Melbourne. Jedním z problémů je, že tato úmrtí mohou být v závislosti na lékaři zaznamenávána různě. "Například je známo, že lidé s cukrovkou jsou vystaveni zvýšenému riziku úmrtí na kardiovaskulární onemocnění, ale to, zda lékař, který vyplňuje úmrtní list osoby s cukrovkou, která zemřela na infarkt, skutečně přisuzuje toto úmrtí cukrovce, je značně rozdílné," říká Shaw.

Poznamenává však, že svou roli pravděpodobně sehrálo i používání uzávěrů. "Dá se očekávat, že to negativně ovlivní léčbu cukrovky, takže by nemuselo být překvapením, že počet úmrtí u lidí s cukrovkou vzrostl," říká.

Kromě toho Morling říká, že nevíme, kolik úmrtí v souvislosti s cukrovkou během pandemie se týkalo také kovidu-19. "Ve skupině nad 50 let to pravděpodobně souvisí, protože víme, že diabetes byl rizikovým faktorem pro covid-19," říká.

Některé studie také naznačují, že covid-19 může někdy způsobit cukrovku. "Důkazy o tom jsou však stále nejasné, zejména v případě diabetu 1. typu," říká Richard Oram z Exeterské univerzity ve Velké Británii.

V konečném důsledku je vyšší úmrtnost na cukrovku pravděpodobně způsobena kombinací několika těchto faktorů, říká Morling.

"Od pandemie se zvyšuje počet diabetiků, kteří absolvují všech osm doporučených zdravotních prohlídek, přičemž NHS investuje 36 milionů liber na pomoc při řešení nerovností v oblasti zdraví," říká mluvčí britského ministerstva zdravotnictví a sociální péče. Otevřelo také více než 80 komunitních diagnostických center s cílem zvýšit počet testů na jaterní onemocnění, říká mluvčí.

Přihlaste se k odběru bezplatného zpravodaje Health Check, který vám každou sobotu přináší novinky z oblasti zdraví, stravy a fitness, kterým můžete věřit.

Fyzikální ústav Akademie věd přišel s nadějným biosenzorem, který by měl během několika minut odhalit i množství viru v těle. „Teď jsme ve fázi jednání s partnery z průmyslu a na základě dohod, které z toho vyplynou, bychom rádi pokročili směrem ke komercializaci a k uvedení biosenzoru co nejdříve na trh,“ říká vedoucí výzkumného týmu Hana Lísalová.

Lísalová zdůrazňuje, že průběžné výsledky výzkumu ukazují, že citlivost biosenzoru je srovnatelná s běžnými PCR testy, které se nyní k odhalení nákazy koronavirem používají:

„Dostali jsme se do fáze, kdy se podařilo potvrdit funkčnost biočipu – celého toho systému. A došli jsme ještě o krůček dál, když jsme provedli zaslepenou studii a srovnávací studii s metodou PCR.“

A vysvětluje: „Všechny PCR vzorky, které byly negativní, byly negativní i na našem biosenzoru, stejné to bylo s pozitivními případy. Můžeme určit i koncentraci virových antigenů. To by byla opravdu veliká výhoda oproti stávajícím antigenovým testům, které ukazují jen rozdíl mezi ano a ne.“

Hlavně rychle a spolehlivě

Z testů, které jsou dnes českým lékařům oficiálně k dispozici, je přesný a spolehlivý pouze PCR test, který využívá stěr ze sliznice nosohltanu. Vědci po celém světě proto usilovně pracují na vývoji stejně spolehlivé, ale rychlejší varianty.

PCR test totiž odhaluje genetickou informaci viru prostřednictvím rozštěpení a namnožení vzorku RNA. Problém ale je, že se tak děje v několika krocích, což zabere i několik hodin. Ale aby bylo testování efektivní, trvá to klidně i půl dne nebo i celý.

A naopak rychlotesty, které dají výsledek do čtvrthodiny, bývají nepřesné, a to především u lidí bezpříznakových nebo s mírnými příznaky. 

Velké naděje pro rychlé odhalení nemoci covid-19 se vkládají do antigenních testů, které jsou založené na detekci specifického koronavirového antigenu. Testování zabere zhruba půl hodiny.

Jak ale ukázala srovnávací studie Fakultní nemocnice v pražské Motole, jejich citlivost je hluboko pod 70 procenty. To podle přednosty tamního Ústavu lékařské mikrobiologie Pavla Dřevínka znamená, že tyto antigenní testy nerozpoznají nemoc u tří z deseti pacientů, přestože měli i příznaky.

Proto výzkumníci pracují na rychlém testu, který by byl dostatečně přesný i rychlý. Své varianty už z českých vědeckých řad představily pražská a olomoucká lékařská fakulta nebo Zdravotní ústav Ostrava. Na své variantě testu pracuje také Přírodovědecká fakulta Univerzity Karlovy.

Studie ve velmi rané fázi.

Metoda IPS zvyšuje riziko rakoviny

Deaths from liver disease and diabetes have been higher than expected in England and Wales since the coronavirus pandemic began. Far more people have died from liver disease and diabetes in England and Wales since the coronavirus pandemic began than expected. Exactly why is unclear, but some deaths may be due to difficulties in accessing healthcare during periods of lockdown, along with rising alcohol consumption.

Between March 2020 and June 2022, there were 3834 excess deaths caused by cirrhosis and other liver diseases in England and Wales compared with the rolling five-year averages for these conditions, according to the UK Office for National Statistics (ONS). This is a 19.7 per cent rise on expected levels. Meanwhile, in the same period, there were 3466 excess deaths caused by diabetes – a 24.4 per cent rise.

The five-year averages exclude 2020 to discount the high mortality figures seen at the height of the pandemic, and the ONS only looked at the primary cause of death, so no deaths were counted twice.

“We know the vast majority of deaths due to liver disease result from alcohol-related liver disease,” says Ian Rowe at the University of Leeds in the UK. “There was an increase in alcohol consumption in previously already heavy drinkers during the pandemic. It is very likely that this increased consumption has led to the increase in liver disease deaths seen over that period.”

Deaths from liver disease between March 2020 and June 2022 rose higher above the five‑year averages in women than in men – by 22.4 per cent compared with 18 per cent – although the cause isn’t entirely clear. “It may be related to impacts of the pandemic that disproportionately affected women, but really we don’t know,” says Rowe. An ONS study found that women in the UK typically experienced lower life satisfaction and happiness than men during the pandemic. Jagpreet Chhatwal at Harvard University says similar trends were seen in the US, with deaths from alcohol-related liver disease rising 22.4 per cent between 2019 and 2020. “It’s not just the lockdowns that caused an increase in alcohol consumption, the lingering elevated stress in society is contributing to sustained increase in alcohol,” he says. “Even a short-term increase in alcohol can result in chronic liver disease and associated mortality.”

The pandemic may have also led to later diagnoses of liver diseases, says Joanne Morling at the University of Nottingham, UK.

“We know that cirrhosis is often diagnosed late – almost 50 per cent of cases are first detected following an emergency presentation with end-stage cirrhosis, when survival is poor,” she says. “If there is a true increase in the number of deaths during the pandemic period, it could relate to delays in diagnosis and late presentations related to reduced access to services.”

Explanations for the excess diabetes deaths are less clear, says Jonathan Shaw at Monash University in Melbourne, Australia. One issue is that these deaths may be recorded differently depending on the doctor. “For example, people with diabetes are known to be at increased risk of death from cardiovascular disease, but whether or not the doctor completing the death certificate for a person with diabetes who died of a heart attack actually attributes the death to diabetes is quite variable,” says Shaw.

But he notes that the use of lockdowns is likely to have played a part. “This might be expected to adversely affect treatment of diabetes, so it might not be a surprise to see a rise in deaths in people with diabetes,” he says.

In addition, Morling says that we don’t know how many of the deaths involving diabetes during the pandemic also involved covid‑19. “In the 50-plus group, it is likely to be related, as we know diabetes was a risk factor for covid‑19,” she says.

Some studies also suggest that covid-19 can sometimes cause diabetes. “But the evidence for this is still unclear, especially for type 1 diabetes,” says Richard Oram at the University of Exeter in the UK.

Ultimately, the higher diabetes deaths are likely to be due to a combination of several of these factors, says Morling.

“Since the pandemic, the number of diabetics receiving all eight recommended health checks is increasing, with the NHS investing £36 million to help tackle health inequalities,” says a spokesperson for the UK government’s Department of Health and Social Care. It has also opened more than 80 community diagnostic centres in order to increase tests for liver disease, says the spokesperson.

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Společnost Google plánuje ukončit provoz kontroverzní aplikace Streams, která analyzovala informace ze zdravotnické dokumentace a jejímž cílem bylo zlepšit sledování životních funkcí a dalších testů za účelem zlepšení péče.

Dceřiná společnost technologické společnosti DeepMind, která se zabývá umělou inteligencí, v únoru 2016 poprvé oznámila, že spolupracuje se správami Národní zdravotní služby (NHS) na analýze údajů o pacientech. Společnost měla v úmyslu kombinovat strojové učení s hromadnými lékařskými údaji a vyvinout modely, které by mohly předpovídat nebo diagnostikovat akutní poškození ledvin.

Šetření časopisu New Scientist z téhož roku však odhalilo, že jedna z těchto dohod o sdílení dat, uzavřená s Royal Free London NHS Foundation Trust, by společnosti DeepMind umožnila přístup k rozsáhlým údajům o 1,6 milionu lidí, včetně citlivých informací, jako například zda jim byl diagnostikován virus HIV nebo deprese nebo zda někdy podstoupili potrat. Později se ukázalo, že tato dohoda nebyla v souladu se zákony na ochranu údajů.

V listopadu 2018 bylo oznámeno, že Streams přejde do oddělení Google Health, které v rámci Googlu dohlíží na všechny zdravotnické projekty, a následující rok podepsaly trusty NHS se společností Google nové smlouvy. Společnost Google nyní uzavírá specializované oddělení Google Health a přesouvá zaměstnance pracující na jeho projektech do jiných oblastí podnikání. Součástí této změny je i úplné zrušení služby Streams.

Podle mluvčího společnosti Google přestal fond Imperial College Healthcare NHS Trust používat Streams 31. července, ale lékaři fondu Royal Free London NHS Trust jej budou v blízké budoucnosti používat i nadále, dokud se nenajde řešení. Smlouvy podepsané společností Google a trusty NHS vyžadují, aby data byla vymazána do šesti měsíců od ukončení partnerství.

Somerset NHS Foundation Trust potvrdil časopisu New Scientist, že také provedl krátký pilotní projekt se službou Streams, ale že na konci zkušebního provozu nebyla přijata natrvalo a všechna data byla smazána.

Sam Smith, který ve Velké Británii vede skupinu MedConfidential zabývající se ochranou soukromí zdravotnických údajů, říká: "NHS se spoléhá na důvěryhodné dodavatele, ale společnosti, které se po rozbití věcí přesunou jinam, vytvářejí pro NHS problémy s dědictvím. Google by měl přiznat své rozhodnutí, smazat data a naučit se, že experimentování na pacientech je z nějakého důvodu regulováno."

Webové stránky společnosti Google stále inzerují Streamy, včetně funkce pro sledování výsledků testů covid-19. Mluvčí Googlu však časopisu New Scientist sdělil, že společnost "učinila strategické rozhodnutí zaměřit se v budoucnu na jeden klinický nástroj, přičemž naše týmy ve Velké Británii a USA nyní pracují na softwaru Care Studio, který poskytuje ucelenější řešení, jež pomáhá lékařům a sestrám vyhledávat a prohlížet informace o pacientech".

Worrying about jet lag could actually make it worse, so try to relax on your next long-haul flight.

Eva Winnebeck at Ludwig Maximilian University of Munich, Germany, and her colleagues have looked at potential psychological causes of jet leg – the temporary impact of a long flight in which someone feels out of sync with the new time zone. They may struggle to sleep, feel tired and have trouble concentrating.

“In biology when we think about a topic, we often think about what we can measure – like a molecule or something,” Winnebeck says. “But a psychologist would think about the world very differently… what you think about a disease can have a huge impact on it.”

The team asked 90 people to keep a sleep diary for a week before and after taking a long-haul flight in 2018. Before the flight, they were also asked whether they expected to get jet lag and how bad they thought it would be. After the flight, they were asked to detail what their jet lag was like every day for a week using a questionnaire which quantified symptoms on a 60-point scale.

The researchers also took into account whether the participants were travelling east or west and how many time-zones they were planning to cross – six, on average.

The team found that, on average, jet lag lasted for around four days, but that it was less common than people thought it would be. More than 75 per cent of the participants said they expected to get jet lag, but only 54 per cent did.

The direction of travel and the number of time zones crossed had no notable effect on the extent of jet lag. “People are so variable,” Winnebeck says. “Length of travel could affect someone really badly, but have no effect on someone else.”

The team did find that people travelling from west to east took longer to go to sleep in the week post-flight, though this group didn’t say that their jet lag felt worse compared with those who travelled in the other direction. Previous research has shown that this direction of travel makes your sleep worse because it is harder to fall asleep earlier than you’re used to, as opposed to staying awake for longer.

Instead, the researchers found that a person’s expectation for how bad their jet lag would be was the strongest predictor of how they said they felt. For every extra day someone expected their jet lag to last, the peak intensity of how bad they actually felt post-flight increased by a small amount. For example, someone who expected five days of jet lag reported a peak intensity nearly twice as high as someone expecting just one day.

This could be a form of “nocebo” effect, a version of the placebo effect in which the expectation of harm can lead to a greater negative outcome, says Winnebeck.

“It could be a nocebo effect or it could be that the people who didn’t worry about jet lag just went about their normal lives after their flight – which helped the body get in sync quickly,” Winnebeck says.

Stuart Peirson at the University of Oxford says it “makes complete sense” that expectation would play a role in how bad jet lag is. But he notes that human studies are extremely complicated and lots of factors may be at play beyond anxiety. “Few people get good sleep on a long flight – how might that affect jet lag?” he says.

Robust new research, published in the journal Translational Psychiatry, is reporting on several years of animal studies showing how a psychedelic drug called dimethyltryptamine (DMT) can promote brain plasticity and induce the formation of new neurons. The research presents evidence to suggest the hallucinogenic effects of the drug may be able to be separated from this neuron-generating mechanism.

Ayahuasca is a hallucinogenic preparation known to be consumed in shamanic and religious contexts by indigenous populations in South America. DMT is the main psychoactive compound in the psychedelic brew, and it has become the focus of a great deal of research due to its profoundly powerful, but short-acting, hallucinogenic qualities.

The recent renaissance in psychedelic science has found hallucinogenic drugs such as psilocybin can induce potent antidepressant effects. Preliminary studies investigating ayahuasca have seen similar antidepressant outcomes. It has been hypothesized that the positive mental health outcomes from these psychedelic compounds stems from their ability to stimulate new neuron production, a process referred to as neurogenesis.

This new research, led by a team of Spanish scientists, set out to understand by what mechanism DMT could induce neurogenesis. Across several mouse experiments the study first established DMT does indeed promote acute neurogenesis, and furthermore, these new neurons can be linked to detectable improvements in the animals’ memory and cognition.

“… these [new hippocampal neurons] have a functional impact since DMT treatment during 21 days clearly improved mouse performance in learning and memory tasks, in which the hippocampus is considered to play an essential role,” the researchers write in the new study. “These observations are in agreement with previous works showing that adult hippocampal neurogenesis plays an important role in these cognitive functions.”

Perhaps the most compelling finding in the new research is the confirmation that this psychedelic-induced neurogenesis seems to be produced by a mechanism that is separate to that which generates the drug’s hallucinogenic effect.

The hallucinogenic qualities of most psychedelics are commonly thought to be generated through the stimulation of 5-HT2A serotonin receptors in the brain but it is still up for debate whether neurogenesis induced by psychedelics is mediated through the same serotonin receptor activity.

This new research suggests neurogenesis may be mediated through sigma-1 receptors (S1R), which prior research has established are also influenced by DMT. The study reveals the neurogenic effect of DMT could be effectively blocked when mice were administered a S1R antagonist.

“The results here obtained indicate that the observed effects of DMT are mediated by the activation of the S1R,” the researchers write in the study. “In this regard, it has been shown that the stimulation of the S1R by different agonists enhances neurogenesis in the hippocampus.”

What all this ultimately means is that is seems possible the new-neuron-stimulating effect of DMT could be divorced from its hallucinogenic and psychoactive properties. José Ángel Morales, an author on the new research, suggests this promisingly points to new research pathways investigating ways to harness the therapeutic potential of neurogenesis.

“This capacity to modulate brain plasticity suggests that it has great therapeutic potential for a wide range of psychiatric and neurological disorders, including neurodegenerative diseases," says Morales.

This research is not the first to raise the possibility of divorcing the therapeutic potential of psychedelics from their hallucinatory effects. Both the US government and commercial pharmaceutical companies are investigating ways to either moderate, or eliminate altogether, the psychedelic effect of psychedelics. However, there is considerable debate within the psychedelic research community as to how fundamentally important the overwhelming psychoactive experience actually is to the drug's subsequent therapeutic benefits.

"The challenge is to activate our dormant capacity to form neurons and thus replace the neurons that die as a result of the disease,” notes Morales. “This study shows that DMT is capable of activating neural stem cells and forming new neurons.”

The new study was published in the journal Translational Psychiatry.

Díky tomu, že se nemocnice a zdravotnické instituce po celém světě snaží najít bezpečnou péči o své pacienty během probíhající pandemie koronavirových chorob 2019 (COVID-19), se telehealth dostává do popředí medicíny. Ačkoli pandemie COVID-19 zdůraznila výhody virtuálního vidění pacientů, telehealth se po celá desetiletí používá ke zlepšení přístupu k péči.1
Ačkoli se článek zaměřil na klinické návštěvy, existují další důležitá použití pro telehealth, která pandemie COVID-19 zdůraznila. Existuje rozsáhlá literatura podporující účinnost intervencí založených na telehealth pro podporu změn stravování10
a redukce indexu tělesné hmotnosti; málo se však ví o tom, jak nejlépe využít telehealth k zajištění nutriční podpory během pandemie COVID-19. V tomto článku nastíníme naléhavost telehealthu, jeho současné použití registrovanými odborníky na výživu (RDN) během pandemie COVID-19, překážky implementace a budoucí důsledky.16. března 2020 Bílý dům ve spolupráci s Centry pro kontrolu a prevenci nemocí oznámil nové pokyny pro sociální distancování.Sociální distancování, nebo akt fyzického distancování se od ostatních lidí a omezení skupinových shromáždění, je účinným způsobem prevence šíření infekčních agens, včetně koronavirů
U RDN byla zpochybněna nezbytná setkání, jako je výuka diety pro eliminaci čtyř potravin u dítěte s nově diagnostikovanou eozinofilní ezofagitidou, návštěvy kontroly hmotnosti u obezity nebo podvýživy a návštěvy u osob s enterální nebo parenterální výživou. Telehealth návštěvy nabízejí nejen schopnost udržet pacienty a poskytovatele zdravotní péče v bezpečí, ale také umožňují nepřetržitou péči o pacienty. V některých případech může telehealth dokonce poskytnout schopnost zlepšit poskytování zdravotní péče nad úroveň standardní péče.
Návštěvy jeden na jednoho

U pacientů s parenterální výživou má přímé sledování domácích potřeb a vybavení možnost zabránit readmisi a infekcím krevního řečiště spojeným s centrální linií.

Why Use Telehealth?

On March 16, 2020, the White House, in conjunction with the Centers for Disease Control and Prevention, announced new guidelines for social distancing.

Social distancing, or the act of physically distancing from other people and limiting group gatherings, is an effective way to prevent the spread of infectious agents, including coronavirus. In health care settings, social distancing has had huge implications on the ability to provide care. Although social distancing might halt the spread of the virus, some health care providers are worried that it might lead to poorer health outcomes for patients without COVID-19.

Nonemergency surgery and procedures were halted, many in-person visits were cancelled and delayed, and providers were left in limbo regarding balancing societal needs and the needs of individual patients.

For RDNs, necessary encounters, such as education on a four-food elimination diet for a child with newly diagnosed eosinophilic esophagitis, weight management visits for obesity or malnutrition, and visits for those with enteral or parenteral nutrition, were questioned. Telehealth visits offer not only the ability to keep patients and health care providers safe, but also allow for the continued care of patients. In some cases, telehealth can even provide the ability to improve health care delivery beyond the standard of care.

Current Uses of Telehealth

 One-on-One Visits

Perhaps one of the most intuitive first steps in using telehealth is to transform existing outpatient one-on-one visits to telehealth visits with an RDN. Telehealth visits have been used in multiple diseases to provide nutrition-related interventions.

Through this type of visit, RDNs can both obtain a thorough history and visualize a patient’s home environment. For example, an RDN providing gluten-free diet education might note that gluten-free flours are directly adjacent to wheat flours. In children specifically, observing mealtime and parent–child interactions in the home environment has provided insight about how to best counsel families. For patients with parenteral nutrition, direct observation of home supplies and equipment setup has the potential to prevent readmissions and central line–associated bloodstream infections.

Although anthropometry cannot be independently measured using direct contact, several interventions have been tested and can be used as limited surrogates. For example, there is some evidence, but no strong agreement, that self-reported weight and height are accurate. For those without a scale, waist circumferences can be used, and are even more accurate when paired with video instructions.

 Group Visits

Telehealth allows for easier coordination of group visits, while still allowing for social distancing. A group visit can consist of multiple guardians of the same patient, a multidisciplinary visit with several health care providers caring for the same patient, or several patients and one health care provider. This is especially useful for pediatric patients who might spend time in two different households. By using telehealth, guardians who split custody, grandparents, or even home health nurses can join the visit without physically being in the same location. This helps ensure that all people engaged in the care of the patient hear the same message and stimulates conversations and questions among the group.

Similarly, telehealth has also been used to facilitate the ongoing use of multidisciplinary clinics. Multidisciplinary clinics consisting of providers in multiple specialties have been shown to improve outcomes.

Although these clinics are often crowded with people and might not allow for following social distancing guidelines, telehealth allows for continued multidisciplinary care. For example, in our institution, patients from across the country are referred to the Gastrointestinal Eosinophilic Diseases Program. A visit to a multidisciplinary clinic within this program frequently involves consultation with a team of providers, including a gastroenterologist, allergist, RDN, feeding therapist, and psychologist. A typical telehealth visit consists of a nurse screening the patient’s medical concerns and a medical assistant recording weight and height, as well as ensuring the technology is working correctly. Then, each provider sees the patient sequentially and ends by sending a secure message via the electronic health record to all of the other providers, including concerns and suggested plans. The next scheduled provider then sees the patient, formulates a plan, and reports it in the same manner. Frequently, the RDN and feeding therapist see the patient together as a joint visit within this process. This process continues until the patient and family have seen the entire team. The patient then leaves the “virtual room” by disconnecting from the telehealth video app, and all the providers reconvene in the same virtual room to discuss their visits and determine global recommendations for the patient.

Finally, telehealth allows for continued group education. In our own institution, we began offering group gluten-free diet educational sessions for newly diagnosed patients with celiac disease via telehealth in January 2019. This was initiated in response to the fact that many of our patients live in rural areas that require significant travel for specialized pediatric care, and do not have access to RDNs with training on the management of a strict gluten-free diet. After doing so, we measured the effects of the type of education (in-person vs telehealth) on patient gluten-free diet knowledge, health-related quality of life, and diet adherence at their first follow-up visit post diagnosis of celiac disease. During the course of approximately 9 months, 57 families took the in-person classes and 13 took the telehealth-based classes. Not only did we find no differences in self-reported improvements in gluten-free diet management, but for some families the telehealth classes had a marked reduction in hours taken off work and need for childcare. Of the 39 children who returned for follow-up, there were no differences in RDN adherence assessments, scores on a gluten-free diet quiz, or health-related quality of life. Furthermore, no child in either group had a positive urine gluten immunogenic peptide result. Armed with this knowledge and with the spread of COVID-19, we continued to offer individual in-person and individual telehealth educational sessions, but transitioned all group educational sessions to group telehealth classes as of March 25, 2020.

 Inpatient Visits

In order to preserve personal protective equipment and in an effort to avoid unnecessary patient contact, many inpatient consultations have been converted to telehealth encounters during the COVID-19 pandemic. These can occur through a hospital-provided telehealth cart equipped with a telehealth application, or through the patient’s personal device, such as a smartphone or tablet. Some telehealth platforms are designed specifically for inpatient visits, but with the loosening of Health Insurance Portability and Accountability Act guidelines during the COVID-19 pandemic, even other modalities such as FaceTime, Skype, or Zoom can be utilized.

Barriers to Telehealth Implementation

Access to technology, such as available high-speed broadband service or wireless networks, is critical to successful telehealth implementation. It is not surprising that slow internet connection and poor connection to wireless networks negatively impacts communication between health care providers and patients during telehealth visits. Although three-quarters of US adults have broadband internet service at home, this service is not evenly distributed among racial minorities, older adults, rural residents, and those with lower levels of education and income less likely to have broadband service at home. The COVID-19 pandemic has highlighted this digital divide and although the Federal Communications Commission has made some recent efforts at closing this gap, such as creating an interactive broadband map (https://broadbandmap.fcc.gov/#/) and increased financial support for the Rural Digital Opportunity Fund, lack of access to the technology needed for telehealth remains an ongoing barrier.

Other frequently cited barriers to telehealth use include health provider and patient acceptance, reimbursement, and regulatory barriers.

A recent article in the Journal of the Academy of Nutrition and Dietetics provides guidance on how to address many of these barriers and practice telehealth specifically as an RDN However, during the COVID-19 pandemic, it has become easier to practice telehealth now more than ever. Prior training and use of familiar technology lead to better acceptance by both patients and providers, and many institutions perform a “tech-check” before scheduled appointment times. With social distancing in place, many providers have quickly adapted to telehealth and are finding that it is easier than they initially perceived.

Under our current extraordinary circumstances, the federal government is also making telehealth even easier by allowing covered health care providers subject to Health Insurance Portability and Accountability Act rules to communicate with patients in ways that might not fully comply with the requirements of Health Insurance Portability and Accountability Act, such as by using Apple FaceTime, Google Hangouts, and Zoom applications, among others.

In addition, the government has lifted many restrictions limiting telehealth use, now allowing for some doctors to practice across state lines.

To promote the logistical ability to see patients via telehealth both during the COVID-19 pandemic and afterwards, many organizations have recognized the importance of interstate compacts.

The purpose of these compacts vary but all have the goal of safely reducing regulatory barriers to allow practitioners to practice in multiple states. With current licensing for RDNs varying by state, perhaps it is time for RDNs to join the campaign.

Finally, there have been several reimbursement-related changes in the past few months. In response to the COVID-19 pandemic, Medicare beneficiaries were expanded, ensuring that essentially all electronic communications could be paid at the same rate-as an in-person visit.

Whether this will persist after the pandemic subsides and what is covered by private insurers is less clear and is rapidly evolving. To help hospitals and administrators navigate this, several resources exist. The Center for Connected Health Policy has information regarding insurance coverage and easy-to-read fact-sheets summarizing new health policies during the COVID-19 pandemic.

Although no RDN-specific guidelines about initiating telehealth during the current regulations of COVID-19 have been released, several other implementation guidelines are available and applicable.

Future Implications

As the COVID-19 pandemic continues to progress and change, it is unclear what the future holds. Even with the lifting of some restrictions in selected states, scientists believe that some degree of social distancing may be needed until 2022.

COVID-19 is just one of many new viruses and with increasing numbers of outbreaks happening in the past few decades telehealth may be here to stay. Perhaps the most immediate action needed by RDNs and the medical community at large is to appeal to the government regarding the necessity of nutritional support in caring for patients. So far, government legislation in response to COVID-19 does not include RDNs. Because of the need to consider all aspects of patient care, some societies, such as the American Physical Therapy Association, have created a template letter to send to state legislatures advocating for policy changes in regard to licensure and reimbursement.

In times of natural disasters, successful responses and preparedness have been associated with ensuring that structures such as people, equipment, systems, administrators, and legal organizations are already in place to respond effectively. The ability of hospitals to convert in-person visits to telehealth visits is variable, with 89.5% of hospitals reporting telehealth capabilities in Minnesota, but only 36.9% in Louisiana. Moreover, the number of hospitals providing telehealth has not kept up with demands.

In our institution, we were able to quickly adapt to telehealth gluten-free diet classes because the framework for this class was already in place. Because of this, we suggest that institutions continue to offer some form of telehealth even after the effects of this pandemic have subsided. In addition, future research is needed on how to best combine telehealth and in-person visits in order to best meet the needs of patients and improve health-related outcomes.

Celosvětový růst počtu kuřáků se poprvé zastavil, což může znamenat zvrat v globální epidemii, která už připravila o život desítky milionů lidí. Ve zprávě zveřejněné v Londýně to uvedla Světová zdravotnická organizace (WHO). Ukázalo se, že snaha vlád posílit boj s kouřením přináší výsledky, uvedla.

„Po mnoho let jsme byli svědky trvalého růstu počtu mužů, kteří kouří či jinak konzumují tabákovou produkci. Nyní poprvé vidíme, že jejich počet se snižuje, protože vlády jsou vůči tabákovému průmyslu přísnější,“ sdělil generální ředitel WHO Tedros Adhanom Ghebreyesus.

Globální spotřeba klesá

Jestliže v roce 2000 pravidelně konzumovala tabákové výrobky zhruba třetina světové populace, v roce 2015 už to byla jen čtvrtina. V absolutních číslech podle WHO poklesl počet uživatelů tabáku ze zhruba 1,4 miliardy v roce 2000 na 1,3 miliardy v loňském roce. Osmdesát procent uživatelů tabáku přitom loni tvořili kuřáci.

Podle WHO zemře každoročně v souvislosti se škodlivými následky konzumace tabáku přibližně osm milionů lidí, z toho 1,2 milionu tvoří pasivní kuřáci.

Dosud vykazovala statistika konzumentů tabáku snižující se počet kuřaček, loni jich bylo přibližně o 100 milionů méně než v roce 2000. Počet kuřáků se naproti tomu každoročně zvyšoval o 40 milionů. Teď se ale ukazuje, že tento růst se zastavil, a experti WHO očekávají pokles. Do roku 2025 by se počet kuřáků – mužů i žen – měl snížit o 37 milionů lidí.

Co dalšího studie zjistila?

Budou to už dva roky, přesně 13. prosince, co Adam Vojtěch usedl do křesla ministra zdravotnictví. Za tu dobu se mu podle jeho vlastních slov povedlo prosadit v praxi používání elektronického receptu nebo začít skutečně využívat institut dohodovacího řízení. Další věci se mění pomaleji. „Není to úplně jednoduché a přiznávám, že po dvou letech se člověk občas cítí trochu unaven různými půtkami a debatami s krizovým štábem, ale jsem přesvědčen, že jdeme správnou cestou,“ říká ministr Vojtěch, s nímž si Zdravotnický deník povídal mimo jiné o implementaci DRG, vztahu s lékařskou komorou či o personálních změnách, které v posledních letech proběhly ve vedeních nemocnic.

Budete dva roky ve funkci. Které tři, čtyři změny se vám povedly?

Nastavení důvěry s jednotlivými segmenty poskytovatelů péče. Revitalizovali jsme dohodovací řízení o úhradách a umožnili jsme, aby zástupci segmentů měli možnost dohodnout se u kulatého stolu, byť to letos nebylo stoprocentní. Ale nikoho jsme nehodili přes palubu, což je důležité. Obecně jakékoliv porušení důvěry je špatně a v předchozích letech důvěra k ministerstvu taková nebyla. To se změnilo, stejně jako celá atmosféra partnerského vztahu mezi zdravotními pojišťovnami a jednotlivými segmenty. Jde o krok kupředu i z hlediska výsledků, nastavování úhrad a kvalitativních kritérií, které jsou v dohodách obsaženy. Beneficienti nejsou jen poskytovatelé, ale především pacienti.

Druhá věc je nastartování elektronického receptu, kde je penetrace v rámci českého zdravotnictví takřka stoprocentní. Zpočátku to bylo velmi složité na vysvětlování, ale odpracovali jsme si to, jezdili jsme do krajů a podobně, což také pomohlo. Dnes se s tím všichni sžili a počet receptů stále stoupá. Je tedy úspěch, že jsme do toho zapojili prakticky všechny lékaře a elektronická preskripce u nás jede tak, jako se to nepovedlo ani v některých státech na západ od nás. S tím je spojeno i prosazení lékového záznamu, což je také úspěch.

Třetí oblastí jsou kontroly ze strany ministerstva v přímo řízených nemocnicích, i když tam stále máme určité rezervy. Nastavili jsme ale pravidla pro veřejné zakázky, zpětné bonusy a další ukazatele, které se snažíme sledovat. To je posun kupředu, i když ne každý z toho byl nadšený.

Podařilo se nám také prosadit nový model financování medicínského školství. O tom se dlouho mluvilo a nikdy nedotáhlo do konce. Pracovali jsme na tom s ÚZIS a vláda schválila plán podpory lékařských fakult, sedm miliard na 11 let. Už letos došlo k navýšení počtu přijatých studentů, dokonce jsme převýšili oněch 15 procent, myslím, že nyní je to 20 procent studentů navíc. Nebylo to úplně jednoduché, bylo třeba přesvědčit ministerstvo školství, aby cíleně podpořilo lékařské fakulty. Je to vklad do budoucna. Spousta dalších věcí je rozpracovaných, i když výsledky budou až za nějaký čas. Jde třeba o reformu primární péče, což je běh na delší trať.

Můžete uvést alespoň jednu věc, kterou se vám nepodařilo dotlačit tam, kam jste si předsevzal?

Některé věci jdou pomaleji, než jsem si představoval. Týká se to třeba elektronizace a zákona o elektronizaci zdravotnictví. Důvodů je řada, za prvé je to asi ne úplně dostatečná zkušenost lidí, kteří na tom pracují, s tvorbou legislativy a prací v tomto módu. Jsou to lidé, kteří rozumějí technickým otázkám, ale nedokážou je pak dobře přenést na papír, aby šla příprava zákona rychle a všichni jej pochopili. Je to ale komplikovaná věc.

Zákon o elektronickém zdravotnictví je velmi robustní. Vždycky se říká, že bychom měli mít zákony stručné. Je elektronizace tak složitá, nebo je to kvůli ochraně dat? Když laik bude chtít studovat něco o elektronizaci zdravotnictví a začne zákonem, brzy ho to odradí…

Zákon ještě projde připomínkovým řízením a bude se cizelovat, ale je to hodně technická věc o nastavení celé architektury. Nemyslím si však, že by byl zásadně dlouhý, máme tu delší zákony. Text je komplikovaný, je to technická norma. Dívám se na něj ze systémového makropohledu, co by elektronické zdravotnictví mělo umět a jak by mělo být nastaveno. Důležité je, aby byl výsledek funkční a aby se v tom všichni, kterých se to týká, tedy poskytovatelé, ale i pacienti, uměli pohybovat. Věřím, že tomu tak nakonec bude.

Jakým způsobem je upravený itinerář elektronizace českého zdravotnictví?

Trochu jsme museli termín posouvat, z čehož nejsem šťastný. Čas běží a je jasné, že tento zákon bude ve sněmovně velmi detailně diskutován. Čím dříve půjde do legislativního procesu, tím lépe, už jsme ho ale poslali do vnitřního připomínkového řízení. Můj cíl je, aby do března šel na vládu, do léta proběhlo první čtení a schvalování se dokončilo po letních prázdninách tak, aby byl zákon účinný od roku 2021. Neříkám, jestli od začátku nebo od poloviny. Paralelně s tím jdou totiž technické otázky soutěžení integrovaného datového rozhraní resortu, což je platforma, skrze kterou by měla být data sdílena. Musí to jít ruku v ruce: jedna věc je regulatorika a nastavení pravidel, druhá věc je technické řešení. Moje představa je tedy taková, že by systém měl být nastaven v průběhu roku 2021 s tím, že nepředpokládáme, že se systém hned spustí na sto procent – počítáme s přechodným obdobím. Aktuálně diskutujeme o tom, že by mohlo být kratší pro nemocnice a delší pro terén, praktické lékaře a podobně. To je hlavně otázka vedení elektronické zdravotnické dokumentace, kdy je finálním cílem, abychom měli bezpapírové zdravotnictví. Teď jsem byl v Dánsku, kde už nemají v nemocnicích ani ordinacích praktických lékařů žádné šanony. To nám nějaký čas bude trvat, ale nejzazší termín, kdy by všichni měli mít elektronickou dokumentaci, je za osm let. Zvažujeme přitom, že pro nemocnice by to bylo kolem tří let od účinnosti zákona.

Když jsme u věcí, které jsou trochu ve skluzu, jak to v současnosti vypadá s novelou ošetřující vstup inovací? Pan náměstek Vrubel její teze představoval už před létem, po půl roce se ale nic moc nepohnulo.

Je tam určitý skluz, který je dán hlavně tím, že novela je velmi komplexní – neřeší jen část inovací, ale zahrnuje asi čtyři další části. Je tam změna v úhradách zdravotnických prostředků na poukaz na základě toho, jak se shodla k tomu ustanovená komise. Další velká oblast je rozhodování revizních lékařů o nároku pacienta. Je tam takzvaná správní část, tedy nastavení pravidel pro rozhodování revizních lékařů a možnost odvolání. Pak jsou tam drobné úpravy v rámci dohodovacího řízení. Díky tomu, že je zákon otevřený, toho zkrátka chceme využít a přidat další oblasti, takže se to trochu zdrželo. Teď v prosinci by ale novela měla jít do meziresortního připomínkového řízení tak, abychom ho dokončili do konce ledna. Pak bude vypořádání, takže bych chtěl, aby podobně jako u zákona o elektronizaci šla úprava do března na vládu a první čtení jsme zvládli před létem.

Takže teze se za tu dobu nezměnily?

Pokud jde o inovace, ne. Zákon jsme připravovali na základě kulatého stolu s odborníky, pacienty a pojišťovnami a zásadní změny tam nejsou.

S premiérem jsme na podobné vlně

Ve zdravotnictví velká část zásadních změn vyžaduje delší čas než jedno časové období. Životnost politika bývá kratší. Když teď sedíte ve vládě s ostatními kolegy a vidíte, co dělají, bavilo by vás vést i jiný resort než zdravotnictví?

Asi ano, ale nejsem univerzální politik, který se vyjádří ke všemu a všemu rozumí. Na to si nehraju, vyjadřuji se ke věcem, kterým rozumím a mám k nim blízko. Asi si tedy nedokážu představit, že bych dělal například ministra zemědělství, to je mi hodně vzdálené, ale bavil by mě třeba ministr kultury nebo spravedlnosti. To jsou resorty, ke kterým mám blízko profesně i citově, takže si dokážu představit, jak se v tomto prostředí pohybuji.

Kdyby byla možnost, chtěl byste zdravotnictví vést i další roky? Teď, po dvou letech, kdy vidíte, co řízení rezortu obnáší?

Je pravda, že některé věci jsou komplikovanější, než si člověk dřív myslel, i když jsem se zdravotnictví věnoval na ministerstvu financí. Zájmových skupin je velké množství a názorů na to, jak má zdravotnictví vypadat v různých oblastech, jako je třeba postgraduální vzdělávání, je také mnoho a často jsou zcela protichůdné. Není to tedy úplně jednoduché a přiznávám, že po dvou letech se člověk občas cítí trochu unaven různými půtkami a debatami s krizovým štábem. Někdy mě to frustruje a vysává, protože lidé, kteří mají takové postoje, neřeší systémové otázky. Já jsem člověk, který chce řešit i systém, takže mi to vybíjí baterky. Na druhou stranu se některé věci povedou, což mě zase dobije. Delší působení si tedy dokážu představit. Ve zdravotnictví je to tak, jak říkáte, tedy, že se věci nastartují a plody práce bude sklízet někdo další. Nejsou tu rychlá vítězství, systémové kroky jako reforma primární péče, reforma péče o duševní zdraví, elektronizace, vzdělávání či podpora lékařských fakult nabíhají v řádu let. Všechno jsme to udělali, ale efekt se projeví až později. Jestli tu budu já nebo někdo jiný, to nedokážu říct, ale byl bych rád, abych alespoň některé věci mohl dokončit. Jsem přesvědčen, že jdeme správnou cestou. V tomto směru by tedy pro mě byla možnost pokračovat zajímavá.

Jak moc cítíte podporu premiéra? Nakolik ho zdravotnictví zajímá? 

Myslím si, že ho velmi zajímá. Samozřejmě jako premiér řeší všechny resorty, takže je jasné, že ne všude má tak detailní znalosti. Na druhou stranu ve zdravotnictví jsme na podobné vlně, respektive akceptoval některé postuláty, což je pozitivní. V některých věcech má odlišný pohled, ale celkově se o zdravotnictví zajímá nadprůměrně oproti jiným resortům. Kdyby se udělala analýza mediálních výstupů, myslím, že by zdravotnictví bylo v popředí.

Mediálně, navenek, vám určitě podporu vyjadřuje…

Obecně každý ministr zdravotnictví je závislý na podpoře premiéra, potažmo dobrém vztahu s ministrem financí.

Pan premiér vystupuje impulsivně. Neříkáte si někdy, když něco takto vystřelí do mediálního prostoru –  ajaj, to zase budu muset dovysvětlit?

To je moje role. Nemůže znát všechno, ale mnoho věcí pochopil z hlediska principu. To, že dnes podporuje dohodovací řízení a začal rozumět tomu, jak zdravotnictví funguje, nebylo dříve samozřejmé.

Jste ve vládě, kde jsou sociální demokraté a v zádech komunisté, kteří zásadně nepřipouštějí spoluúčast. Co vy osobně, myslíte si, že spoluúčast v českém zdravotnictví chybí? Cítíte se omezen koalicí? Máte pocit, že kdybyste měl volné ruce, mohl byste zdravotnictví posunout dál?

Této debatě se dříve či později stejně nevyhneme. Nějaké vícezdrojové financování bude muset nastat, ale respektuji aktuální stav. Nemyslím si, že řešením je nezbytně návrat regulačních poplatků a podobně, ale třeba rozšíření možnosti připojištění je něco, o čem je možné debatu vést. Jsme teď součástí platformy na Hospodářské komoře, kde se o tom diskutuje. Věřím, že debata nastane a že to vnímají i další strany, možná i někteří kolegové ze sociální demokracie. Na druhou stranu to není o jednom ministrovi, který může prosadit tak zásadní věc. Musí na ní být bazální shoda s většinou stran. V tomto období to není na pořadu dne.

Jak přistupujte k poslancům zdravotního výboru, kteří jsou skoro všichni lékaři? Každý doktor si myslí, že zná zdravotnictví nejlépe, vy ale nejste vzděláním zdravotník. Cítíte to jako handicap?

Výhoda byla, že jsem se zdravotnictví věnoval už předtím a některé kolegy jsem znal. Nebyl jsem tedy člověk, který se objevil zčistajasna a věci se učil. Přímo to od kolegů necítím, možná si to někdy myslí a já to nevím. Někdy ale dostávám zprávy, co o tom můžu vědět, když jsem nikdy nepracoval v nemocnici a nevím, jak to tam funguje. Stále ale opakuji, že rolí ministra zdravotnictví není léčit pacienty a říkat, jaké mají být medicínské postupy, ale nastavovat legislativu a systémové otázky financování. V tomto směru si nemyslím, že je znalost medicíny základní premisa. Na druhou stranu se tady na ministerstvu podařilo vytvořit zajímavé podpůrné orgány, jako je obnovená rada poskytovatelů, což přispívá k důvěře. Sedíme u jednoho stolu a věci diskutujeme, z čehož pak rezultuje dohodovací řízení. Můj poradní orgán je také vědecká rada, kde jsou špičky v oborech, kteří mi pomáhají a mohu se opírat o jejich stanoviska. Mám tedy na koho se obrátit. Co se týče poslanců, mohou být názory odlišné, poukazují na svou praxi a někdy to nekoresponduje s pohledem ministerstva, ale to k tomu patří.

Zákon o komorách chceme novelizovat

Podíváme-li se na zájmové skupiny v českém zdravotnictví, rozčíslo se to podobně, jako už několikrát v minulosti. Je tu část poskytovatelů, kteří kvitují to, že funguje dohodovací řízení a rozjezd různých reforem. Ti vás podporují bez ohledu na to, jaké je jejich politické přesvědčení. Pak jsou tu odboráři a lékařská komora. Spory s odboráři jsou přirozené, ale co se pokazilo s lékařskou komorou? Na začátku jste s nimi komunikoval, byl jste na jejich sjezdech, dokonce jste zpíval na jejich plese… Zdálo se, že komunikace možná je. Teď už jste ale na sjezdu nebyl a komora proti vám brojí. Když si otevřu Tempus Medicorum, jste největší zločinec – i když to zase není nic, co bychom si nepřečetli v minulosti o jiných ministrech. Kde se to zlomilo?

Odbory dělají svou práci, že se na řadě věcí neshodneme, je asi přirozené. Česká lékařská komora, byť to nechce slyšet, tenduje k odborářskému pohledu více než k pohledu profesní komory, která by měla hájit především etiku ve zdravotnictví a medicíně, dohlížet na kvalitu lékařského stavu. Také proto je součástí krizového štábu, což u lékařské komory asi není zcela běžné. Možná je u mě ten rozdíl, a jeden představitel odborů mi to i řekl, že se mnou nehnou a nemůžou mnou manévrovat, kam chtějí. Svoje názory principiálně držím, ne že mě skřípnou u zdi a povolím. Došli jsme sice ke kompromisu, ale nebylo to tak, jak asi byli zvyklí. Zřejmě jim předchozí ministři šli více na ruku. U České lékařské komory mě to mrzí, měla by to být profesní organizace na podobné úrovni jako Česká lékařská společnost, která bude hájit zájmy lékařů i na jiné úrovni, než že největší problém je nedostatek peněz. Měli by být více konstruktivní a navrhovat systémová řešení, o nichž bychom se mohli bavit. To tam nevidím, je tam jen stále stejná rétorika, že zdravotnictví kolabuje a je katastrofálně podfinancované. Rétorika postavená na vyvolávání konfliktu a negaci všeho, co ministerstvo udělá. Na sjezdu komory jsem byl minulý rok a bylo to dost ostré – nebylo to ani věcné, ale čistě emocionální. Komora by mohla uznat, že se něco podařilo. Je spousta problémů, které je potřeba řešit, ale některé věci se udělaly. Když pak slyším, že třeba navýšení kapacit lékařských fakult nic neznamená a je to jen marketing, štve mě to. Vedení komory je trochu zacyklené, asi i proto, že je tam tak dlouho, což u žádné funkce není nejlepší z hlediska nového pohledu na věc.

Uvažujete o změně komorového zákona, který by například omezil funkční období?

Uvažujeme o novelizaci, která částečně vychází z věcí, které musíme udělat například u lékárníků. Byla tam nějaká soudní rozhodnutí, která musíme aplikovat v rámci zákona, a stomatologové také mají nějaké požadavky. Pravdou je, že mezi komorami, byť mají jeden zákon, jsou rozdíly z hlediska organizace fungování. Je to možná i tím, že lékárníci a stomatologové jsou homogenní skupina, byť jsou z různých oblastí. U České lékařské komory to tak úplně není, což vidíme i dnes – praktičtí lékaři, ambulantní specialisté či nemocniční lékaři mají různé zájmy a je to komplikovanější. Přál bych si, aby komory byly více legitimní, a když už mají povinné členství, aby se zapojovalo více členů do rozhodování. Zvažujeme, jakým způsobem by to bylo možné, aby se nezapojovalo pouze zhruba deset procent osob. To se podařilo České lékárnické komoře a hned to vedlo ke změně představenstva, je pestřejší a je tam více názorů.

Na povinné členství nechcete sáhnout?

Nemyslím si, že je to teď to hlavní téma. Ne že bychom to apriori nechtěli řešit, ale spíše by tam měly být nějaké nástroje, aby mohlo více lidí volit a třeba i aby nebylo možné prodlužovat si volební období na doživotí.

Sestry: model 4+1 se zatím nemění

Jak se díváte na vznik komory sester, případně nelékařských pracovníků?

Dokonce už je i legislativní návrh, nakonec asi bude poslanecký. Diskutujeme o něm s profesorkou Adámkovou, která by se toho chtěla ujmout. Nejsme proti, může to být zajímavé, ale nechceme, aby bylo povinné členství. Dohodli jsme se, a souhlasí s tím i Česká asociace sester, že by se mělo vyjít z registračního principu. Měla by to být profesní komora, která se bude věnovat vzdělávání a kultivaci. Nejsem ale úplně příznivcem vzniku komory nelékařských zdravotnických pracovníků, protože jsou velmi heterogenní skupinou – profesí je zhruba padesát nebo šedesát, takže si nedovedu představit, jak by se dohodovali, kdo bude ve vedení. Spíše jsem tedy příznivcem vzniku komory sester, protože bude jednodušší.

Před rokem se uvažovalo o výsluhách či příspěvcích pro sestry na bydlení. Vypadá něco z toho, že by se mohlo realizovat?

Máme k tomu pracovní skupinu a udělali jsme některé pozitivní věci, byť nejsou všechny finanční. Vytvořili jsme metodiku pro psychosociální intervenci, což je docela důležité. Některé nemocnice už něco podobného mají, takový tým funguje třeba na Vinohradech. Teď chystáme změnu fondů kulturně sociálních potřeb, abychom z něj mohli hradit více věcí pro zdravotníky, například dopravu do zaměstnání a další benefity. Prosadili jsme tedy, aby ministerstvo financí, které má vyhlášku v gesci, ji otevřelo. Dokončujeme také kampaň Studuj zdrávku, kterou chceme akcelerovat před podáváním přihlášek. Ve spolupráci s našimi přímo řízenými nemocnicemi chystáme rovněž stipendijní programy pro studenty zdravotnických oborů. Pokud jde o větší otázky typu výsluh, moc jsme se neposunuli. Jednali jsme s ministerstvem financí a ještě jsme nenašli společnou řeč.

Jste spokojen s fungováním modelu 4+1?

V praxi 4+2. Moc sester v modelu není, poslední číslo, které jsem měl, bylo kolem 250. Nechceme to ale teď měnit, část sester model využívá a trendem je, že se nemocnice snaží motivovat praktické sestry, aby v kombinované formě dostudovaly na všeobecnou sestru. Vidíme přitom, že počet praktických sester se v nemocnicích poměrně výrazně zvyšuje a důležité je, aby se kvalifikovaly. Nemocnice jim studium i hradí, v kombinované formě u toho mohou pracovat. Nemyslím si tedy, že je prostor na zásadní změnu.

Nemocnice si změnami ředitelů polepšily

Pojďme se podívat na management a ekonomiku nemocnic. Jak vnímáte dosavadní postup CZ-DRG? Na jednu stranu je udělaný obrovský kus práce, ale na druhou stranu se stále se očekávání míjí s tím, co DRG skutečně může poskytnout. Všichni váhají, co bude dál při překlopení na úhrady. Neznepokojuje vás, že řadu let nemocnice jedou na paušály a obtížně se do hodnocení dostávají nové postupy, někteří to označují dokonce za zahnívání?

Paušály jsou tu od 90. let, takže to skutečně je zahnívání. Pokusů o kultivaci byla celá řada, z různých důvodů, ať už politických, nebo finančních, se ale nepodařily. I kritici současného projektu však musí uznat, že jsme nikdy nedošli tak daleko jako nyní. Byl na tom odveden velký kus práce a dnes už se i reálně posouváme. Už jen to, že máme nový klasifikační systém vycházející z reality, povede ke zlepšení. CZ-DRG už je ve verzi 2.0 vyhlášeno Českým statistickým úřadem. V tomto roce některé vybrané nemocnice vykazovaly nové markery a nyní bude CZ-DRG vykazováno plošně. Plán je takový, že staré DRG bude vypnuto na konci příštího roku a od roku 2021 by se jelo jen v CZ-DRG. Teď bude vycházet třetí verze.

Ta bude použita pro dohodovací řízení pro rok 2021?

Určitě. Velký krok je také to, že jsme DRG posunuli z akademické sféry na ÚZIS do skutečné reálné diskuze se stakeholdery, kterých se to týká. Trochu jsem tomu zazlíval, že se někde DRG tvoří, ale nejsou u toho zdravotní pojišťovny, které péči platí, a také všichni poskytovatelé lůžkové péče. Proto jsme vytvořili řídící radu projektu, kde jsou u stolu všechny asociace nemocnic a pojišťovny. To projekt posunulo hodně dopředu, odkrylo karty a zlepšilo atmosféru. Řídící rada rozhodla, že příští rok spustí v rámci úhrad pilot dvou oblastí, které jsou sice minoritní, ale zajímavé. Jde o onkogynekologii a pneumoonkochirurgii, kde jsou náklady poměrně homogenní v rámci všech poskytovatelů. Všichni se shodli, že zde je možné DRG vyzkoušet. V roce 2021 na to může navázat další oblast, ale nemůžeme si myslet, že ke změně dojde z roku na rok. Pozitivní je, že už nejsme v akademické debatě, ale CZ-DRG se začíná implementovat do vykazování a v pilotech i do úhrad, bude se nabalovat jako sněhová koule.

Ale přechod od homogenních skupin do všudypřítomné heterogenity bude dost obtížný?

Určitě. Nemocnice si to z hlediska nákladů zanalyzovaly a my dnes víme, že se liší i v rámci stejné péče. Náklady na iktus v iktovém a komplexním cerebrovaskulárním centru nejvyššího typu jsou jiné, proto se nyní zvažuje možnost rozdělení nemocnic. Věřím, že se postupně bude klasifikace doplňovat, a byť to bude trvat třeba dalších pět let, než stoprocentně naběhne do úhrad.

Jak byste si zhodnotil své personální změny ve vedení nemocnic? O Vás se ví, že ostentativně nezasahujete do výběrových řízení a spoléháte na výběrové komise. Jste spokojen s tím, jací manažeři přišli do vedení nemocnic za vašeho působení?  

Všichni byli překvapeni, že jsem zvolil jiný přístup. V minulosti se někdy pořádala výběrová řízení jen na oko.

Nicméně podpis je tam pak váš – je to vaše rozhodnutí. Není tedy lepší, aby si ministr výběr ředitelů více ohlídal?

Také ne ve všech případech jsem se doporučením komise řídil. Nedávno byl případ, kde jsem řekl, že jen přes mou mrtvolu, a zrušil jsem výběrové řízení (jednalo se o křeslo ředitele Nemocnice u sv. Anny v Brně, kde výběrová komise navrhla Michala Pohanku – pozn. redakce). Není to tedy tak, že bych slepě podepsal, co komise doporučí. Pokud doporučí někoho, kdo z mého pohledu splňuje určité parametry a schopnosti, nemám s tím problém. Samozřejmě neříkám, že jsou výběrová řízení úplně ideální – pro člověka je tam omezený prostor, je předložena písemná koncepce a v důsledku se člověk projeví až v praxi. Až na pár výjimek si ale myslím, že výměny dopadly dobře a řada nemocnic si tím polepšila. Když se bavím s primáři a přednosty klinik, musím říci, že skoro ve všech případech je feedback pozitivní – že člověk, který tam přišel, změnil atmosféru v nemocnici. Je pravda, že některé nemocnice mají problémy, některé kumulované i historicky, a nedá se to za rok změnit. Jsem ale se změnami docela spokojen.

I s celkovým hospodářským vývojem přímo řízených nemocnic?

Část si historicky nese dluh a situace je tam komplikovanější, jako je Bulovka, takže to řešíme intenzivněji a hledáme cesty, jak je zbavit dluhů. To je zhruba třetina, dvě třetiny jsou ale v zisku, některé z nich ve větším než loni. Zmínil bych třeba Homolku, která pod novým vedením vzkvétá a výsledky jsou vynikající.

Měl byste na pochvalu i někoho mimopražského?

Tam k tolika změnám nedošlo právě z důvodu, že fungují dobře, jako je Olomouc, Hradec Králové či Plzeň. Není důvod dělat radikální změny tam, kde nemocnice, v nichž jsme provedli kontrolu, nevykazovaly problémy z hlediska dodržování zákonů a soutěžení. Ani z hlediska finálního hospodaření nemocnice problém nemají. Je to hodně i o parametrických nastaveních z minulosti, které se v pozitivním i negativním smyslu táhnou mnoho let. Ředitelé také někdy spoléhají na CZ-DRG, což některým může pomoci. Celkově ale situace není špatná. Problém jsou investice, tam musíme zabrat. Jde o společný cíl s panem premiérem, kdy teď bude představovat národní investiční plán, v němž zdravotnictví má svou kapitolu. Podařilo se nám dotáhnout IKEM, ale máme tam řadu dalších, jako je právě Plzeň, Olomouc či Hradec Králové. Často se nemocnice snaží koncentrovat péči, což je trend, který je správný, vytvářet urgentní příjmy a podobně. To jsou velké investice, o kterých musíme jednat s ministerstvem financí. Věřím ale tomu, že ze strategických investic by se ještě dvě, tři do konce mého mandátu začnou realizovat.

Šest stovek pacientů se přesunulo z psychiatrických nemocnic

Byla zřízena Národní rada pro duševní zdraví. Jaká jsou pro vás prioritní témata, která by měla řešit?

První zasedání bude 16. prosince. Rada má naplňovat národní akční plán pro duševní zdraví. Posunuli jsme se v reformě psychiatrie, která běží – vznikají centra duševního zdraví, nyní jsme otevřeli další v Jižních Čechách, a podle posledních čísel už bylo přesunuto z nemocnic do komunitní péče 600 pacientů.

Léčba duševně nemocných, kterou se samozřejmě rada bude zabývat, je ale jen jedna část. Národní akční plán, který je nyní v připomínkách, řeší péči o duševní zdraví obecně i z hlediska prevence. Proto je v radě i ministerstvo školství – chceme se zaměřit na prevenci, dostupnost služeb pro studenty a detekci chorob ve včasném stadiu na školách, i těch vysokých. Víme totiž, že máme problém s počtem sebevražd u mladistvých. Bude tam i oblast justice, ochranného léčení, řešíme také otázku práce se zaměstnanci. Dnes už za námi aktivně chodí velcí zaměstnavatelé a chtějí s námi spolupracovat na prevenci syndromu vyhoření. Téma duševního zdraví, které ještě před pěti, deseti lety bylo spíše tabu a psychiatrie byla Popelkou, se tedy posunulo velmi dopředu. Rada vlády to má potvrdit.

Jaké jsou první zkušenosti s fungováním center duševního zdraví? Už jich je zřízeno 17 a další vybíráte.

Zkušenost je dobrá. Je tam spousta problémů, jako u všeho, co je nové. Ve zdravotní části bylo odpracováno hodně a je připraveno i financování ze strany zdravotních pojišťoven, což je důležité – někteří lidé zpochybňovali udržitelnost projektu z hlediska financování, až doběhnou evropské finance. To je teď největší výzva, aby centra fungovala i po pilotní fázi. Teď bude dobíhat prvních pět center a jsme s pojišťovnami dohodnuti, že je budou hradit, jsou pro to připraveny výkony. Větší problém je oblast sociální, ale v poslední době se situace zlepšila. MPSV je ochotno centra podpořit a pro příští rok na ně vyčlenilo prostředky. Musíme ale jednat i o dalších letech. Je to otázka zdravotně sociálního pomezí, které u nás funguje ne zcela ve všech oblastech. Tady si to můžeme vyzkoušet. Beru to jako velkou výzvu.

Jak postupuje transformace léčeben?

Pracujeme s nimi, každá nemocnice má vypracovaný svůj transformační plán a dochází ke snižování počtu lůžek. Vyjednali jsme s ministerstvem financí pro nemocnice i nějaké peníze navíc, protože se potřebují humanizovat a zlepšovat prostředí. Cílem je, abychom psychiatrické nemocnice měly úspornější, ale na druhou stranu kvalitnější. Nebude tedy osm pacientů na pokoji, ale třeba jenom dva, a bude to vypadat jinak i z hlediska zázemí. Z rozpočtu jsme na to dostali sto milionů navíc. To tedy také běží, i když ne všude zcela ideálně. Ještě před rokem, dvěma byla situace složitější, ne všichni ředitelé byli kooperativní, ale dnes se se nemocnice pomalu, ale jistě transformují.

Dostali jsme se ke zdravotně sociálnímu pomezí, které se týká nejen psychiatrie. Podařilo se vám nějak pokročit spolu s ministryní práce a sociálních věcí například na téma seniorů a péče o ně?

Je to celá řada oblastí, vedle psychiatrie jsou tam dvě zásadní. Jedna se týká domovů pro děti do tří let, bývalých kojeneckých ústavů, kde jsme se shodli, že by měla proběhnout transformace. Byly provedeny analýzy umístěných dětí a my chceme, aby tam nebyly děti se sociálními indikacemi. Druhá oblast je dlouhodobá péče, což je zásadní i z hlediska demografického vývoje – pacientů bude přibývat. Tam také proběhla základní shoda v tom smyslu, že oba naše resorty provedou novely příslušných zákonů, my zákon o zdravotních službách. Tam chceme měnit celou řadu oblastí, jednou by ale měla být dlouhodobá zdravotně sociální péče. MPSV bude upravovat zákon o sociálních službách.

Cílem je vydefinovat dlouhodobou zdravotně sociální péči, jak má vypadat a na jaká lůžka se bude vztahovat. Poskytovatelé by měli mít registraci jak podle zákona o sociálních službách, tak o zdravotních službách, aby byly nastaveny i personální standardy. Pokud poskytují zdravotní péči, jsou za to placeni z veřejného zdravotního pojištění a dnes nejsou registrováni dle zákona o zdravotních službách, není to systémově správně. To by se mělo změnit, zároveň ale chceme upravit platby, opustit vykazování výkonů v odbornosti 913 a přesunout se do úhrady paušální částkou podobně jako u LDN za lůžkoden či ošetřovací den. I pro pojišťovny je to dobré řešení, protože budou moci plánovat. V odbornosti 913 jsou dnes různé komplikace vykazování a sledování, zda někdo nevykázal něco neoprávněně, takže jsou s tím spojeny problémy z hlediska kontroly. To není ideální ani pro poskytovatele. Zároveň by tu měl být příspěvek na péči ze sociálního budgetu a platba za hotelové služby ze strany klienta. Na tom jsme se s paní ministryní bazálně shodli a oba resorty teď pracují na příslušných změnách.

Od zákona o léčitelství se čekalo příliš moc

Na závěr bychom se ještě zeptali na zákon o léčitelích. Vy jste změnil taktiku, rozhodl jste se, že nakonec nevznikne samostatný zákon a léčitelé budou registrovaní jako volná živnost, na což reagovalo ministerstvo průmyslu a obchodu, kterému se to moc nelíbí. Kdysi už léčitelé byli živnostníky a nebylo to vůbec k ničemu.

Nejde ale o jediné opatření, chystáme i další. Problém byl, že jsme chtěli zákon o léčitelských službách, ale sešlo se k němu tolik připomínek, které byly tak diametrálně odlišné, že jsme nakonec od záměru upustili a rozhodli se jít jinou cestou v tom duchu, že je lepší něco udělat a pak na to navázat. Šli jsme tedy cestou živnosti, ale stále chceme mít základní podmínky vedení dokumentace, abychom měli stopu, co léčitel dělal.

Tím pádem se ale dostanou do zákona o zdravotních službách, to je trochu divné.

Ale bude tam jasně napsáno, že léčitelství není zdravotní služba. Vím, že to vzbuzuje debaty, ale alespoň nějaký krok se v tomto směru udělá. Na druhou stranu vnímám diskuzi, že se od zákona o léčitelství čekalo více, než mohl reálně přinést. My jsme nikdy neřekli, že můžeme služby regulovat a že na ministerstvu bude někdo, kdo bude říkat: toto je správná služba a toto je špatná služba. To není možné, protože variant je obrovské množství. Řekli jsme, že chceme nastavit procesní pravidla a podmínky, abychom věděli, kdo službu poskytuje. Dnes nevíme, kde je služba poskytována a za jakých podmínek. Uvidíme, co se s tím v legislativním procesu stane.

Tomáš Cikrt, Michaela Koubová

Cystickou fibrózou v Česku trpí zhruba šest set lidí, pro něž nemoc znamená značný zásah do života. Kvůli hrozbě infekcí pro pacienty platí přísná hygienická opatření, musí pravidelně inhalovat a cvičit a nemoc často provází například cukrovka či cirhóza jater. Zpravidla nejvíce nemocné omezují slábnoucí plíce. Jejich fungování má zlepšit nový lék, který by mohl pomoci devadesáti procentům pacientů.

7 min

Cystickou fibrózou v Česku trpí zhruba šest set lidí, pro něž nemoc znamená značný zásah do života. Kvůli hrozbě infekcí pro pacienty platí přísná hygienická opatření, musí pravidelně inhalovat a cvičit a nemoc často provází například cukrovka či cirhóza jater. Zpravidla nejvíce nemocné omezují slábnoucí plíce. Jejich fungování má zlepšit nový lék, který by mohl pomoci devadesáti procentům pacientů.

Za zásadní komplikaci označuje slabé plíce například Evelína Rudalská, která nemožnost se nadechnout popisuje jako vůbec nejhorší pocit. Pravidelné inhalace a dechová cvičení zaberou pacientům až tři hodiny denně.

Nemocní se musí také vyhýbat třeba stojaté vodě, hlíně a nedezinfikovaným místům. „Když jsem byla malá, tak jsem nemohla jezdit na exkurze, do zoologické zahrady, nemohla jsem jezdit na normální tábory,“ popisovala loni v říjnu Josefina Arellanesová a doplnila, že si nemůže například ani jen tak skočit do rybníka.

Arellanesová kromě toho trpí cirhózou jater. „Vypadá to, že jsem čtyřicetiletý alkoholik,“ komentuje. Její matka ji ale kvůli nemoci nechtěla držet v izolaci. „Preventivně jsem každý den chodila do školky s desinfekčními prostředky a čistila jsem záchodky, abych měla klid,“ popisuje Anna Arellanesová, která předsedá České asociaci pro vzácná onemocnění.

Pro Jakuba Kořínka byla prý nejtěžším obdobím puberta. Naplno si tehdy podle svých slov uvědomil, že svým vrstevníkům nestačí. Taky pro něj bylo složité dělat si životní plány. „To kašlání vám vždycky připomene, že to nebudete mít tak jednoduché a možná to ani nemá smysl. Vždyť tu budu, já nevím, do třiceti,“ konstatoval Kořínek v březnu 2017.

Záhy ale dostal naději. V rámci klinické studie dostal lék na svou vzácnou mutaci cystické fibrózy, takzvanou keltskou, a nyní už mu je lépe. „Najednou plánujete rodinu, plánujete kariéru,“ dodává Kořínek.

Šťastně skončil i příběh Evelíny Rudalské a Martiny Adamcové. Obě se navzdory zdravotním problémům snažily naplno žít a sportovat, jenže poškozené plíce oběma vypověděly službu a připojené na kyslík čekaly na dárce. „Nemohla jsem se ani osprchovat, takže se o mě musela starat mamka,“ popsala Rudalská v červnu 2017.

Obě se transplantace dočkaly. Adamcová po operaci mluvila o neskutečné euforii, že konečně může dýchat. Ona i Rudalská ale nadále musí brát velké množství léků a dávat pozor na případné infekce. Pro řadu pacientů s cystickou fibrózou ovšem transplantace není vhodná, navíc samotnou chorobu nevyléčí.

V takových případech by měl pomoci nový lék Trikafta, který představili američtí badatelé. Podle nich by měl pomoci až devadesáti procentům nemocných. Kromě toho, že jim pomůže dýchat díky lepšímu fungování plic, také útočí na genetické kořeny nemoci.

Při současných léčebných možnostech by bylo možné téměř vymýtit v Česku žloutenku typu C do deseti let. Na vyléčení všech pacientů s touto nemocí ale chybí peníze a Česko nemá ani národní strategii eliminace virové hepatitidy typu C. Na tiskové konferenci k blížícímu se Světovému dni hepatitidy to dnes uvedli odborníci. Odhadli, že léčbu žloutenky v ČR ročně prodělá zhruba 1200 lidí, nakažených je ale kolem 45.000. S léčbou by přitom náklady z dlouhodobého hlediska klesly.

Žloutenku typu C lze vyléčit antivirotiky podávanými ve formě tablet. Podle lékařů je ale problémem nedostatečné rozšíření léčby. "Bez dobrého léčebného pokrytí nedosáhneme účinného snížení výskytu hepatitidy C a jejích nových případů," řekl vedoucí Národního monitorovacího střediska pro drogy a závislost Viktor Mravčík.

"Pokud zvýšíme léčebné pokrytí dvakrát, poměrně blízko a rychle se přiblížíme k eliminaci hepatitidy C. To je aktuální téma i v mezinárodních institucích, agendách udržitelného rozvoje, zdravotnictví," uvedl Mravčík. Podle něj středisko nyní jedná s ministerstvem zdravotnictví a zdravotními pojišťovnami o schválení akčního plánu eliminace žloutenky u uživatelů drog na období 2019 až 2021. "Nenahrazuje národní strategii eliminace žloutenky C pro všechny cílové skupiny podle dokumentů WHO (Světová zdravotnická organizace), ale může být její významnou součástí," doplnil Mravčík.

Pro úspěšnou léčbu nemoci a ochranu společnosti je podle lékařů důležité včasné odhalení virové hepatitidy. To je podle nich současně ale největší problém, protože zhruba 60 procent nakažených jsou injekční uživatelé drog. Jednou z prevencí onemocnění mezi nimi je tak distribuce čistých jehel a stříkaček, kterou zajišťují mimo jiné nízkoprahová a kontaktní centra pracující s drogově závislými. Tyto organizace jsou ale obvykle neziskové a závislé na sponzorech a bez pravidelné finanční podpory. Pro letošní rok navíc stát snížil příspěvek těmto organizacím o asi 25 procent.

Podle předsedy pacientského sdružení Recovery Františka Trantina jsou ale právě nízkoprahová centra klíčová pro prevenci i léčbu hepatitidy. Vedle výměny stříkaček chystají pacienty i na otestování krve. "Jsou to jediné instituce, kteří s nakaženými mluví jako s lidmi," uvedl Trantina. Podle něj jsou lidé se žloutenkou typu C obvykle obtížní pacienti a léčby se bojí. "Panuje mezi nimi obrovské množství urban legend. Že to bolí, že půjdou na biopsii, že jim budeme rvát tkáň. A je těžké, jim to vysvětlit," řekl Trantino. Pracovníci nízkoprahového centra pacienta vyšetří, doprovodí na léčbu a případně mu přímo na místě podávají léky.

Žloutenka typu C se přenáší krví. Zhruba polovina nakažených je uživateli pervitinu, čtvrtina pak užívá heroin. Ne všichni ale onemocněli vlivem drog. Mezi nakaženými jsou i lidé, kteří před rokem 1992 prodělali transfuzi krve. Do té doby se krev dárců proti žloutence netestovala.

Some researchers suggest MDMA, the party drug commonly known as "ecstasy" or "molly," could become a recognized treatment for post-traumatic stress disorder (PTSD) in the coming years.

Case in point: A small clinical trial published last week in the Journal of Psychopharmacology found that therapeutic doses of MDMA, in concert with psychotherapy, reduced the severity of most participants' PTSD symptoms.

And a year after the trial ended, 76 percent of participants no longer met the clinical criteria for a PTSD diagnosis.

Expect Delays

The results, as exciting as they seem on paper, are only from a phase II trial — the second of three stages of safety and efficacy testing required before the U.S. Food and Drug Administration will consider approving a new pharmaceutical.

Many phase II trials, this one included, gather very impressive-sounding results, but the road to FDA approval is littered with the corpses of early-stage research that never made it to the end. That said, phase III trials for treating PTSD with MDMA are underway.

Role a kompetence praktických lékařů jsou sice jedním z věčných témat českého zdravotnictví, aktuálně se ale zdá, že změna postupuje rychleji. Mění se preskripční omezení u desítek léčivých přípravků, ve spolupráci s onkology se popsala možnost sledování onkologických pacientů v remisi u praktiků. Možností, které se diskutují, je mnohem více.

„Potenciál všeobecných praktických lékařů v ČR, ve srovnání s vyspělými zeměmi EU, není využit. To je způsobeno posunem kompetencí, přetrvávajícím z dob socialistického zdravotnictví, který je fixován ekonomickými zájmy ostatních poskytovatelů zdravotní péče a dlouhodobou nekoncepčností řízení českého zdravotnictví,“ uvádí ve Fóru Medical Tribune Doc. MUDr. Svatopluk Býma, CSc., předseda Společnosti všeobecného lékařství ČLS JEP. „Podle OECD mají praktičtí lékaři největší potenciál ke zlepšení a udržení zdraví populace. Z ekonomického pohledu jedna koruna investovaná do primární péče ušetří až sedm korun v následné péči,“ připomíná Doc. Býma.

Argumenty proti posilování kompetencí praktiků, postavené na nedostatečné odborné zdatnosti, podle praktického lékaře z Dobřichovic MUDr. Martina Dudka jen zastírají zájmy těch, které je říkají. „Pohled na kompetence by měl být zcela opačný, tj. stanovit minimální požadavky na spektrum výkonů, lékaře, kteří to nesplňují, postupně vytlačit ze sítě a novým bez daného vybavení smlouvy nedávat,“ myslí si MUDr. Dudek. „Omezení kompetencí, tj. rozsahu poskytované péče, by nemělo vůbec existovat, a pokud daný lékař má erudici, tak nechť to dělá,“ uvádí.

Podle ředitele Nemocnice Jihlava MUDr. Lukáše Veleva, MHA, by správně nastavená role praktického lékaře pomohla zdravotnictví zefektivnit. „Poskytování nepřetržité péče o registrované klienty považuji za samozřejmost. Tomu je potřeba přizpůsobit motivace: praxe, které budou fungovat dobře, by měly být výrazně zvýhodněny. Je to jednoduchý princip, kdy se dobrá práce vyplácí, méně dobrá je placena hůře a nezájem je postihován,“ soudí MUDr. Velev. „Systému to přinese úspory ostatní péče, zlepší výsledky léčby a umožní rozumné nastavení, rozumějme redukci ostatní sítě,“ uvádí.

Myšlenka rozšíření kompetencí praktických lékařů se líbí senátorce MUDr. Aleně Dernerové. Vidí ale i určité riziko. „V současnosti je velký nedostatek těchto lékařů jak pro dospělé, tak pro děti a dorost. A to jsme ještě nedospěli ke kritickému bodu. Nedovedu si představit, že se zatížení praktických lékařů, často v důchodovém věku, ještě zvětší,“ píše MUDr. Dernerová.

Práce praktických lékařů se bez ohledu na diskutované potřebné změny v posledních dobách už výrazně změnila. „Zcela běžně léčíme ambulantně diagnózy, pro které byly děti dříve hospitalizovány,“ připomíná MUDr. Ilona Hülleová, předsedkyně Sdružení praktických lékařů pro děti a dorost ČR. Klíčové podle ní bude motivace prostřednictvím mimokapitačních výkonů. „Pro další rozvoj primární péče o dětskou populaci je potřeba rozšířit spektrum zejména mimokapitačních výkonů a možnosti předepisování léků, kapitační platba by měla zůstat jako předvídatelný základ ekonomického ohodnocení ordinace PLDD,“ uvádí MUDr. Hülleová.

Ve Spojených státech bude lék obchodovat firma Genentech ze skupiny Roche.

„Jedná se o první novou antivirovou léčbu chřipky s novým mechanismem účinku schválenou FDA za téměř dvacet let. Tento nový léčivý přípravek poskytuje důležitou další možnost léčby,“ uvedl komisař FDA Scott Gottlieb, M.D. Poznamenal ale, že léky proti chřipce nejsou náhradou za každoroční očkování. „Každoroční očkování je primárním prostředkem prevence a kontroly výskytu chřipkových onemocnění,“ uvedl.

Lék Xofluza se užívá jen v jedné tabletě, zatímco dosavadní antivirotika jako Tamiflu a Relenza berou pacienti několik dní. Tamiflu se doporučuje užívat dvakrát denně po dobu pěti dnů. Baloxavir marboxil byl ve studiích úspěšnější také ve snížení virové nálože v těle a zkrácení doby, po kterou se virus dále uvolňoval. Tím by se mělo omezit další šíření choroby.

Baloxavir marboxil je FDA schválený jen pro pacienty starší 12 let. Podává se do 48 od vzniku symptomů. „Když je léčba zahájena do 48 hodin po onemocnění příznaky chřipky, mohou antivirové léky utlumit příznaky a zkrátit dobu, kdy se pacienti cítí špatně,“ říká Debra Birnkrant, MD z FDA. „Je důležité mít více možností léčby, které fungují proti viru různými způsoby, protože chřipkové viry se mohou stát odolné proti antivirovým lékům,“ dodává. Nové léčivo má být účinné i proti těm kmenům chřipky, které vyvíjejí rezistenci vůči inhibitorům neuraminidázy.

Firma Roche uvedla, že uvede Xofluzu do USA za cenu 150 dolarů za dávku. Xofluza je od února schválená na trhu v Japonsku. Do konce roku má být předložena ke schválení v Evropské unii.

This genetic association study investigates the independent and combined associations of genetically determined differences in lipoprotein lipase–mediated lipolysis and low-density lipoprotein cholesterol metabolism with risk of coronary disease and diabetes.

Association of Genetically Enhanced Lipoprotein Lipase–Mediated Lipolysis and Low-Density Lipoprotein Cholesterol–Lowering Alleles With Risk of Coronary Disease and Type 2 Diabetes

Figure 1.  Associations of Genotype Category With Cardiometabolic Disease Outcomes in 2 × 2 Factorial Genetic Analyses

Associations of each genetic score group with risk of coronary artery disease and type 2 diabetes compared with the reference group. The reference group includes those with a low-density lipoprotein cholesterol (LDL-C)–lowering score and a triglyceride-lowering LPL score less than or equal to the median score; the genetically lower triglyceride levels only group, those with a triglyceride-lowering LPL score greater than the median but an LDL-C–lowering score less than or equal to the median; the genetically lower LDL-C levels only group, those with an LDL-C–lowering score greater than the median but a triglyceride-lowering LPL score less than or equal to the median; and the group with both exposures, those with both scores greater than the median. Analyses include individual-level genetic data from 390 470 participants of the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies. Median values and interquartile ranges for lipid levels in a given genotype category are from the EPIC-Norfolk study. To convert LDL-C level to micromoles per liter, multiply by 0.0259. To convert triglyceride level to micromoles per liter, multiply by 0.0113. IQR indicates interquartile range; LPL, lipoprotein lipase; NA, not applicable; OR, odds ratio.

Figure 2.  Associations of Triglyceride-Lowering LPL Alleles With Cardiometabolic Disease Outcomes in Individuals Above or Below the Median of the Population Distribution of Low-Density Lipoprotein Cholesterol (LDL-C)–Lowering Genetic Variants

Analyses include individual-level genetic data from 390 470 participants of the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies.

Figure 3.  Associations of Triglyceride-Lowering LPL Alleles With Cardiometabolic Disease Outcomes Within Quintiles of the Population Distribution of Genetic Variants at 58 Low-Density Lipoprotein Cholesterol (LDL-C)–Associated Genetic Loci

Data are from the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies. Median values and interquartile ranges for lipid levels within each stratum are from the EPIC-Norfolk study. To convert LDL-C level to micromoles per liter, multiply by 0.0259. To convert triglyceride level to micromoles per liter, multiply by 0.0113. IQR, interquartile range; OR, odds ratio.

Figure 4.  Associations of Loss-of-Function Alleles With Cardiometabolic Disease Outcomes in ANGPTL4 and ANGPTL3

A, Associations of the ANGPTL4 p.Glu40Lys loss-of-function allele with cardiometabolic disease outcomes. Groups with genetically higher or lower low-density lipoprotein cholesterol (LDL-C) levels were defined on the basis of the median value of the 58-variant LDL-C–lowering genetic score. Associations are scaled to represent the odds ratio (OR) per SD of genetically lower triglyceride levels. Data are from the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies. B, Associations of different genetic exposures associated with lower LDL-C levels with protection against coronary disease. A clear log-linear relationship between genetic difference in LDL-C level and lower risk is observed for several mechanisms, while ANGPTL3 loss-of-function variants are outliers in this relationship. For individual variants, the estimates represent per-allele differences; for quintiles of the LDL-C score, the difference is compared with the bottom quintile; for the overall genetic score, the difference is per SD of genetically lower LDL-C level; and for ANGPTL3 variants, the difference is in carriers compared with noncarriers.

Table.  Characteristics of Participants From the UK Biobank, EPIC-InterAct, and EPIC-Norfolk Included in This Study

Key Points

Question  Are genetically determined differences in lipoprotein lipase (LPL)–mediated lipolysis and low-density lipoprotein cholesterol (LDL-C)–lowering pathways independently associated with risk of coronary disease and diabetes?

Findings  In this genetic association study including 392 220 people, triglyceride-lowering alleles in LPL or its inhibitor ANGPTL4 were associated with lower risk of coronary artery disease and type 2 diabetes in a consistent fashion across quantiles of the population distribution of LDL-C–lowering alleles. For a given genetic difference in LDL-C, the association with lower risk of coronary disease conveyed by rare loss-of-function variants in ANGPTL3, which are associated with lower LDL-C levels and enhanced LPL lipolysis, was greater than that conveyed by other LDL-C–lowering genetic mechanisms.

Meaning  LPL-mediated lipolysis and LDL-C–lowering mechanisms independently contribute to the risk of coronary disease and diabetes, which supports the development of LPL-enhancing agents for use in the context of LDL-C–lowering therapy.

Abstract

Importance  Pharmacological enhancers of lipoprotein lipase (LPL) are in preclinical or early clinical development for cardiovascular prevention. Studying whether these agents will reduce cardiovascular events or diabetes risk when added to existing lipid-lowering drugs would require large outcome trials. Human genetics studies can help prioritize or deprioritize these resource-demanding endeavors.

Objective  To investigate the independent and combined associations of genetically determined differences in LPL-mediated lipolysis and low-density lipoprotein cholesterol (LDL-C) metabolism with risk of coronary disease and diabetes.

Design, Setting, and Participants  In this genetic association study, individual-level genetic data from 392 220 participants from 2 population-based cohort studies and 1 case-cohort study conducted in Europe were included. Data were collected from January 1991 to July 2018, and data were analyzed from July 2014 to July 2018.

Exposures  Six conditionally independent triglyceride-lowering alleles in LPL, the p.Glu40Lys variant in ANGPTL4, rare loss-of-function variants in ANGPTL3, and LDL-C–lowering polymorphisms at 58 independent genomic regions, including HMGCR, NPC1L1, and PCSK9.

Main Outcomes and Measures  Odds ratio for coronary artery disease and type 2 diabetes.

Results  Of the 392 220 participants included, 211 915 (54.0%) were female, and the mean (SD) age was 57 (8) years. Triglyceride-lowering alleles in LPL were associated with protection from coronary disease (approximately 40% lower odds per SD of genetically lower triglycerides) and type 2 diabetes (approximately 30% lower odds) in people above or below the median of the population distribution of LDL-C–lowering alleles at 58 independent genomic regions, HMGCR, NPC1L1, or PCSK9. Associations with lower risk were consistent in quintiles of the distribution of LDL-C–lowering alleles and 2 × 2 factorial genetic analyses. The 40Lys variant in ANGPTL4 was associated with protection from coronary disease and type 2 diabetes in groups with genetically higher or lower LDL-C. For a genetic difference of 0.23 SDs in LDL-C, ANGPTL3 loss-of-function variants, which also have beneficial associations with LPL lipolysis, were associated with greater protection against coronary disease than other LDL-C–lowering genetic mechanisms (ANGPTL3 loss-of-function variants: odds ratio, 0.66; 95% CI, 0.52-0.83; 58 LDL-C–lowering variants: odds ratio, 0.90; 95% CI, 0.89-0.91; P for heterogeneity = .009).

Conclusions and Relevance  Triglyceride-lowering alleles in the LPL pathway are associated with lower risk of coronary disease and type 2 diabetes independently of LDL-C–lowering genetic mechanisms. These findings provide human genetics evidence to support the development of agents that enhance LPL-mediated lipolysis for further clinical benefit in addition to LDL-C–lowering therapy.

Introduction

Lipoprotein lipase (LPL) is an endothelium-bound enzyme that catalyzes the rate-limiting step in the clearance of atherogenic triglyceride-rich particles.1 There is genetic evidence of a causal link between impaired LPL-mediated lipolysis and coronary artery disease. Gain-of-function genetic variants in LPL2,3 and loss-of-function variants in its intravascular inhibitors ANGPTL3,4-6ANGPTL4,2,7 and APOC38,9 are associated with lower triglyceride levels and lower coronary disease risk, while loss-of-function variants in LPL2,3,10 and its natural activator APOA511 are associated with higher triglyceride levels and higher coronary risk. Impaired LPL-mediated lipolysis has also been linked to insulin resistance12 and a higher risk of type 2 diabetes,12-15 but the associations of this pathway with glucose metabolism are incompletely understood.

There is growing interest around LPL-mediated lipolysis as a target for pharmacological intervention. Several new medicines that enhance LPL-mediated clearance of triglyceride-rich lipoprotein particles by directly activating LPL16,17 or by inhibiting its intravascular inhibitors6,7,18-20 are in preclinical7,16,17 or early clinical6,18-21 development for cardiovascular prevention. However, it is not known whether these agents will provide further benefits in addition to low-density lipoprotein cholesterol (LDL-C)–lowering therapy, which is the mainstay of lipid-lowering therapy in cardiovascular prevention. Drugs that accelerate LPL-mediated clearance of triglyceride-rich lipoprotein particles are being developed for use in addition to statins and, possibly, other LDL-C–lowering agents. However, statins,22 ezetimibe,23 and PCSK9 inhibitors24-27 also reduce triglyceride-rich particles, and this could limit the clinical benefits and utility of LPL-enhancing agents when used in combination with these drugs.

Large-scale clinical trials and the investment of massive resources would be required to study the effect of each of these LPL-enhancing agents on cardiovascular outcomes in the context of LDL-C–lowering therapy. In advance of outcome trials, human genetic approaches can provide evidence of whether or not genetically determined differences in LPL-mediated lipolysis and LDL-C metabolism have independent associations with cardiometabolic disease risk, which can help prioritize or deprioritize these resource-intensive efforts.28,29

Methods

Study Design

The aims of this study were to (1) investigate associations of genetically enhanced LPL-mediated lipolysis with cardiometabolic risk factors, coronary artery disease, and type 2 diabetes (eFigure 1A in the Supplement), and (2) estimate the independent and combined associations with cardiometabolic outcomes of genetically enhanced LPL-mediated lipolysis and LDL-C–lowering genetic variants (eFigure 1B and C in the Supplement). For the first aim, we estimated associations from summary-level genetic data including up to 672 505 individuals in nonstratified analyses (eFigure 1A in the Supplement). For the second aim, we used individual-level genetic data from up to 390 470 individuals from a pool of 392 220 individuals to perform 2 × 2 factorial (eFigure 1B in the Supplement) or stratified (eFigure 1C in the Supplement) genetic analyses. We also investigated the associations of naturally occurring variation in the genes encoding LPL inhibitors with cardiometabolic outcomes.

Participants and Studies

In nonstratified analyses (eFigure 1A in the Supplement), we used genetic association data on up to 672 505 people from the European Prospective Investigation Into Cancer and Nutrition (EPIC)–InterAct,30 EPIC-Norfolk,31 UK Biobank,32 and large-scale genetic consortia, including the Coronary Artery Disease Genome-Wide Replication and Meta-analysis Plus the Coronary Artery Disease Genetics Consortium (CARDIoGRAMplusC4D),33 Diabetes Genetics Replication and Meta-analysis (DIAGRAM) consortium,34 Genetic Investigation of Anthropometric Traits (GIANT) consortium,35 ,36 Meta-analyses of Glucose and Insulin-Related Traits Consortium (MAGIC),37 ,38 and Global Lipids Genetics Consortium (GLGC).39 In factorial and stratified analyses (eFigure 1B and C in the Supplement), we used individual-level data from up to 390 470 individuals from a pool of 392 220 individuals included in EPIC-InterAct, EPIC-Norfolk, and UK Biobank (Table). EPIC-InterAct30 is a case-cohort study of type 2 diabetes nested within the EPIC study.40 EPIC-Norfolk is a prospective cohort study of more than 20 000 individuals aged 40 to 79 years living in Norfolk county in the United Kingdom at recruitment.31 UK Biobank is a population-based cohort of 500 000 people aged 40 to 69 years who were recruited from 2006 to 2010 from several centers across the United Kingdom.32 Detailed characteristics of the participants with individual-level genotype data included in this study are presented in the Table, and details about the cohorts participating in each analysis, phenotype definitions, and data sources are in eAppendix 1 and eTable 1 in the Supplement. All studies were approved by local institutional review boards and ethics committees, and participants gave written informed consent for collection of samples and genetic analysis.

Table.  Characteristics of Participants From the UK Biobank, EPIC-InterAct, and EPIC-Norfolk Included in This Study

Factorial and Stratified Genetic Analyses

The similarities between the random allocation of genetic variants at conception and that of participants in a randomized trial41 have been used as rationale to study associations of alleles in different genes to gain insights into the likely consequences of the pharmacological modulation of the gene products in a way that simulates a factorial randomized clinical trial.42 ,43 In this study, for each participant, we calculated a weighted LPL genetic score and a weighted LDL-C genetic score by adding the number of triglyceride-lowering LPL alleles or LDL-C–lowering alleles at 58 LDL-C–associated genetic loci, weighted by their effect on the corresponding lipid levels. These genetic scores were dichotomized at the median value to naturally randomize participants into 4 groups: (1) a reference group, (2) a group with genetically lower triglyceride levels via LPL alleles, (3) a group with genetically lower LDL-C levels via alleles at 58 independent genetic loci, and (4) a group with both genetically lower triglyceride levels via LPL alleles and genetically lower LDL-C levels via the 58 genetic loci. We studied associations with lipid traits and cardiometabolic outcomes between groups using a 2 × 2 factorial design (eFigure 1B in the Supplement). Further details about this approach are in eMethods 1 in the Supplement.

In stratified analyses (eFigure 1C in the Supplement), we studied the associations of LPL alleles with cardiometabolic outcomes in quantiles of the population distribution of 58 LDL-C–lowering alleles or alleles at 3 genes encoding the targets of current lipid-lowering therapy, including HMGCR (encoding the target of statins), NPC1L1 (ezetimibe), and PCSK9 (PCSK9 inhibitors). We considered groups above or below the median of overall and gene-specific LDL-C–lowering genetic scores as well as quintiles of the general LDL-C–lowering genetic score.

Selection of Genetic Variants

As a proxy for genetically enhanced LPL lipolysis, we used 6 genetic variants in the LPL gene previously reported to be strongly and independently associated with triglyceride levels (P < 5.0 × 10−8 for each variant in conditional analyses from the GLGC10 ) (eTable 2 in the Supplement). In factorial or stratified analyses, as instruments for genetically lower LDL-C, we used 58 genetic variants from independent genomic regions associated with LDL-C levels in up to 188 577 participants of GLGC39 (P < 5.0 × 10−8 for LDL-C in each region; all variants were more than 500 kb away from each other and had low linkage disequilibrium, with pairwise R2 < 0.01) (eTable 2 in the Supplement). In sensitivity analyses, we used a subset of 22 of the 58 variants that were not associated with triglyceride level in GLGC.39 We also considered 6 HMGCR,43 5 NPC1L1,42 and 7 PCSK943 genetic variants previously used by Ference et al42 ,43 as genetic proxies for statin, ezetimibe, or PCSK9 inhibitor therapy (eTable 2 in the Supplement). Quality checks of genetic data and of analyses presented in this article are described in eMethods 2 in the Supplement.

Loss-of-Function Variants in the Inhibitors of LPL

We estimated associations with cardiometabolic outcomes of a low-frequency variant in ANGPTL4 (p.Glu40Lys; 40Lys allele frequency, 1.9%). The 40Lys allele disrupts the inhibitory effect of ANGPTL4 on LPL in vitro44 and is strongly associated with lower triglyceride levels (approximately 0.27 SDs lower triglycerides per 40Lys allele; P = 4.2 × 10−175) but not with LDL-C (approximately 0.004 SDs lower LDL-C per 40Lys allele; P = .70) in GLGC.14 The variant is also associated with protection from cardiometabolic disease.2,7,14,45

Rare loss-of-function alleles in the LPL inhibitor ANGPTL3 are associated with lower LDL-C and triglyceride levels,4 -6 offering a unique genetic model for the combined reduction of LDL-C levels and enhancement of LPL-mediated lipolysis. Genetic studies and clinical trials show that different LDL-C–lowering mechanisms protect against coronary disease with a log-linear relationship that is observed independently of the mechanism by which this reduction is attained.42 ,46,47 If the association with lower risk of ANGPTL3 variants is only via lower LDL-C levels, one would expect their association to be the same as that of LDL-C–lowering variants in other genes for a given genetic difference in LDL-C levels. We investigated this hypothesis by meta-analyzing and modeling data from previously published genetic studies5 ,6 about the association of rare loss-of-function variants of ANGPTL3 with LDL-C and coronary disease risk (eAppendix 2 in the Supplement). We also attempted to estimate the associations with cardiometabolic outcomes of a rare loss-of-function variant in the APOC3 gene captured by direct genotyping in UK Biobank, but the analysis was uninformative likely because of low statistical power (eAppendix 3 in the Supplement).

Statistical Analysis

In nonstratified and stratified genetic analyses, associations of the 6 triglyceride-lowering genetic variants in LPL with outcomes were estimated using weighted generalized linear regression models that accounted for correlation between genetic variants.48 Estimates of the association of LPL alleles with triglyceride levels and of LPL alleles with a given outcome were used to calculate estimates of the association of genetically lower triglyceride levels via LPL alleles with that outcome. Correlation values were obtained from the LDlink software (eTable 3 in the Supplement).49 Results were scaled to represent the β coefficient or the odds ratio (OR) per SD genetically lower triglyceride levels via LPL alleles. Triglyceride associations are expressed in natural log–transformed and standardized units. In factorial genetic analyses (eFigure 1B in the Supplement), the associations of each group relative to the reference group were estimated using linear regression for plasma LDL-C and triglyceride levels and either logistic or Prentice-weighted Cox regression (as appropriate for the study design) for coronary artery disease and type 2 diabetes.

All analyses were adjusted for age, sex, and genetic principal components. Analyses were conducted within each study and pooled using fixed-effect inverse variance–weighted meta-analysis. Statistical analyses were performed using Stata version 14.2 (StataCorp) and R version 3.2.2 (The R Foundation for Statistical Computing). A 2-tailed P value less than .05 was considered statistically significant.

Results

Associations of LPL Alleles With Cardiometabolic Risk Factors and Outcomes

Triglyceride-lowering alleles in LPL were associated with lower risk of type 2 diabetes both in combined analyses (OR per SD of genetically lower triglycerides, 0.69; 95% CI, 0.62-0.76; P = 2.6 × 10−13) (eFigure 2 and eTable 4 in the Supplement) and individual-variant analyses (eFigure 3 and eTable 5 in the Supplement). Comparisons with estimates from multiple triglyceride-lowering genetic mechanisms50 showed that this association is specific to LPL and does not reflect a general association in a protective direction of lower triglyceride levels (eAppendix 4 and eTable 6 in the Supplement). Associations with lower coronary risk (OR per SD of genetically lower triglycerides, 0.59; 95% CI, 0.53-0.66; P = 1.3 × 10−22) (eFigures 2 and 3 and eTables 4 and 5 in the Supplement) were consistent with previous studies.10 Triglyceride-lowering LPL alleles were associated with lower fasting insulin levels, fasting plasma glucose levels, and body mass index–adjusted waist-to-hip ratio (ie, a more favorable fat distribution; β in SD of body mass index–adjusted waist-to-hip ratio per SD of genetically lower triglycerides, −0.09; 95% CI, −0.12 to −0.06; P = 7.9 × 10−5) (eFigure 2 in the Supplement), a novel association consistent with evidence of the preferential LPL-mediated lipid distribution to peripheral, rather than central, adipocytes.51

Independent and Combined Associations of LPL Alleles and LDL-C–Lowering Alleles With Cardiometabolic Outcomes

In factorial genetic analyses, people naturally randomized to genetically lower triglycerides via LPL alleles had lower triglyceride levels but similar LDL-C levels compared with the reference group (eFigure 4 in the Supplement). The association with lipid levels was additive to that of LDL-C–lowering alleles (eFigure 4 in the Supplement), which were also associated with lower triglyceride levels, consistent with the observed reduction in triglyceride-rich particles in people taking statins,22 ezetimibe,23 or PCSK9 inhibitors.24-27

People naturally randomized to lower LDL-C levels, lower triglyceride levels via LPL alleles, or both had a lower risk of coronary artery disease compared with the reference group, with the lowest odds in people naturally randomized to both genetic exposures (OR, 0.73; 95% CI, 0.70-0.76; P = 2.8 × 10−52) (Figure 1). In this group, the OR for coronary disease compared with the reference group was a further 7% (95% CI, 1%-12%) lower than expected on the basis of the association of the 2 exposures alone (P for interaction = .02). However, stratified analyses in groups above or below the median or in quintiles of the distribution of LDL-C–lowering alleles were not consistent with an interaction (Figure 2A and Figure 3).

Figure 1.  Associations of Genotype Category With Cardiometabolic Disease Outcomes in 2 × 2 Factorial Genetic Analyses

Associations of each genetic score group with risk of coronary artery disease and type 2 diabetes compared with the reference group. The reference group includes those with a low-density lipoprotein cholesterol (LDL-C)–lowering score and a triglyceride-lowering LPL score less than or equal to the median score; the genetically lower triglyceride levels only group, those with a triglyceride-lowering LPL score greater than the median but an LDL-C–lowering score less than or equal to the median; the genetically lower LDL-C levels only group, those with an LDL-C–lowering score greater than the median but a triglyceride-lowering LPL score less than or equal to the median; and the group with both exposures, those with both scores greater than the median. Analyses include individual-level genetic data from 390 470 participants of the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies. Median values and interquartile ranges for lipid levels in a given genotype category are from the EPIC-Norfolk study. To convert LDL-C level to micromoles per liter, multiply by 0.0259. To convert triglyceride level to micromoles per liter, multiply by 0.0113. IQR indicates interquartile range; LPL, lipoprotein lipase; NA, not applicable; OR, odds ratio.

Figure 2.  Associations of Triglyceride-Lowering LPL Alleles With Cardiometabolic Disease Outcomes in Individuals Above or Below the Median of the Population Distribution of Low-Density Lipoprotein Cholesterol (LDL-C)–Lowering Genetic Variants

Analyses include individual-level genetic data from 390 470 participants of the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies.

Figure 3.  Associations of Triglyceride-Lowering LPL Alleles With Cardiometabolic Disease Outcomes Within Quintiles of the Population Distribution of Genetic Variants at 58 Low-Density Lipoprotein Cholesterol (LDL-C)–Associated Genetic Loci

Data are from the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies. Median values and interquartile ranges for lipid levels within each stratum are from the EPIC-Norfolk study. To convert LDL-C level to micromoles per liter, multiply by 0.0259. To convert triglyceride level to micromoles per liter, multiply by 0.0113. IQR, interquartile range; OR, odds ratio.

People naturally randomized to lower LDL-C had a higher risk of type 2 diabetes compared with the reference group (Figure 1), consistent with previous studies.43 ,50,52-55 However, people naturally randomized to both genetic exposures had a similar risk of type 2 diabetes compared with the reference group (Figure 1), as the association of LPL alleles with lower risk cancelled out the risk-increasing association of LDL-C–lowering alleles. Consistently, triglyceride-lowering LPL alleles were strongly associated with lower diabetes risk also in people with genetically lower LDL-C levels (Figure 2A).

In stratified analyses, triglyceride-lowering LPL alleles were strongly and consistently associated with protection from coronary disease and diabetes in subgroups of people above or below the median of the population distribution of the 58 LDL-C–lowering alleles (Figure 2A) and of the 22 of 58 LDL-C–lowering alleles that were not associated with triglyceride levels in GLGC (eTable 7 in the Supplement), HMGCR, NPC1L1, or PCSK9 alleles (Figure 2) (eFigure 5 in the Supplement). Associations of LPL alleles with lower risk were consistent in quintiles of the population distribution of the 58 LDL-C–lowering alleles (Figure 3) (eFigure 6 in the Supplement).

Evidence From ANGPTL4 and ANGPTL3 Genetic Variants

The ANGPTL4 p.Glu40Lys variant was associated with protection from coronary disease and diabetes, with effect estimates nearly identical to the ones of triglyceride-lowering alleles in LPL for a given genetic difference in triglyceride levels (Figure 4A) (eFigure 2 in the Supplement). Associations were consistent in people above or below the median of the 58-variant LDL-C–lowering genetic score (Figure 4A). Also, the 40Lys allele was associated with a more favorable fat distribution in the UK Biobank (n = 350 450; SD of body mass index–adjusted waist-to-hip ratio per allele, −0.024; SE, 0.0086; P = .005).

Figure 4.  Associations of Loss-of-Function Alleles With Cardiometabolic Disease Outcomes in ANGPTL4 and ANGPTL3

A, Associations of the ANGPTL4 p.Glu40Lys loss-of-function allele with cardiometabolic disease outcomes. Groups with genetically higher or lower low-density lipoprotein cholesterol (LDL-C) levels were defined on the basis of the median value of the 58-variant LDL-C–lowering genetic score. Associations are scaled to represent the odds ratio (OR) per SD of genetically lower triglyceride levels. Data are from the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies. B, Associations of different genetic exposures associated with lower LDL-C levels with protection against coronary disease. A clear log-linear relationship between genetic difference in LDL-C level and lower risk is observed for several mechanisms, while ANGPTL3 loss-of-function variants are outliers in this relationship. For individual variants, the estimates represent per-allele differences; for quintiles of the LDL-C score, the difference is compared with the bottom quintile; for the overall genetic score, the difference is per SD of genetically lower LDL-C level; and for ANGPTL3 variants, the difference is in carriers compared with noncarriers.

In previous sequencing studies, carrying a rare loss-of-function variant in ANGPTL3 has been associated with 36-mg/dL (to convert to millimoles per liter, multiply by 0.0113) lower triglyceride levels and 0.23-SD lower LDL-C levels (approximately 9 mg/dL).6 In this study, for variants at HMGCR, NPC1L1, and PCSK9 and for the 58-variant LDL-C–lowering genetic score, a genetic difference of 0.23 SD in LDL-C was consistently associated with approximately 10% lower odds of coronary disease (OR, 0.90; 95% CI, 0.89-0.91; I2 = 0%; P for heterogeneity in effect estimates = .86) (eFigure 7 in the Supplement). In a meta-analysis of published genetic studies5 ,6 on rare loss-of-function variants in ANGPTL3, we found an association with approximately 34% lower odds of coronary disease for carriers compared with noncarriers (OR, 0.66; 95% CI, 0.52-0.83; P < .001; I2 = 0%; P for heterogeneity = .99) (eFigure 8 in the Supplement). For a given genetic difference in LDL-C level, the association of ANGPTL3 variants with lower coronary disease risk was stronger than that of the LDL-C–lowering genetic score (P for heterogeneity = .009) (Figure 4B) (eFigure 7 and eTable 8 in the Supplement).

Discussion

By analyzing individual-level genetic data in close to 400 000 people, we provide strong evidence that triglyceride-lowering alleles in the LPL pathway and LDL-C–lowering genetic mechanisms are independently associated with a lower risk of coronary artery disease. This is of relevance to the future clinical development and positioning of LPL-enhancing drugs, given that these agents are being developed for use in addition to statins and other existing LDL-C–lowering drugs. Because the LDL-C–lowering alleles studied here included those at genes encoding the targets of current LDL-C–lowering therapy, this study supports the hypothesis that pharmacologically enhancing LPL-mediated lipolysis is likely to provide further cardiovascular benefits in addition to existing LDL-C–lowering agents.

By studying the interplay of these pathways with a study design that is directly relevant to the future clinical development of LPL-enhancing agents, this study adds to previous analyses that have investigated the associations of LPL pathway alleles2,3,10,12,14 or LDL-C–lowering alleles50,53,56-58 with cardiometabolic disease separately. The independent associations with cardiometabolic outcomes of genetically enhanced LPL-mediated lipolysis and of mechanisms that lower LDL-C via PCSK9, NPC1L1, and HMGCR provide direct support for the development of direct enhancers of LPL16,17 for use in the context of existing LDL-C–lowering therapy. They also provide general support for other agents that enhance LPL activity via inhibition of its natural inhibitors in this therapeutic context.6,7,18-21

We also investigated variation at 2 intravascular inhibitors of LPL, ANGPTL4 and ANGPTL3, making 2 important observations. First, the level of protection from coronary disease and diabetes associated with the ANGPTL4 p.Glu40Lys variant is the same as that of LPL alleles for a given genetic difference in triglyceride levels and is consistent across the population distribution of LDL-C–lowering alleles. These findings are relevant for drugs that inhibit ANGPTL47 or directly enhance LPL by disrupting the inhibitory activity of ANGPTL4.17 Second, rare loss-of-function variants in ANGPTL3 are associated with a greater level of protection from coronary disease than other genetic mechanisms for a given genetic difference in LDL-C levels. This result suggests that ANGPTL3 inhibition may be an exception to the LDL paradigm, the mechanism-independent log-linear relationship between LDL-C lowering and coronary disease protection that has been consistently found in genetic studies and clinical trials.42,46 In phase 1 trials, ANGPTL3 inhibitors reduced LDL-C levels by amounts similar to or greater than currently approved LDL-C–lowering drugs.6,20,21 Our findings suggest that ANGPTL3 inhibitors may be more effective than current agents for a given magnitude of LDL-C reduction.

Triglyceride-lowering LPL alleles were also associated with protection against type 2 diabetes. The strong and consistent association of multiple independent LPL alleles with lower risk of type 2 diabetes found in our study extends and reinforces previous reports by us and others limited to the rs180117712 and rs32812,14,15 alleles. We also provide evidence consistent with the association with lower odds of diabetes being specific to the LPL pathway and not being a general association of lower triglyceride levels. In factorial analyses, this association was in a protective direction with a magnitude equivalent to the association of LDL-C–lowering alleles with increased risk of type 2 diabetes. Therefore, our data suggest that enhancing LPL activity may also ameliorate glucose metabolism while further reducing the risk of cardiovascular disease in people taking LDL-C–lowering therapy.

Triglyceride-lowering alleles in LPL were also associated with greater insulin sensitivity, lower glucose levels, and a more favorable body fat distribution pattern, strengthening the link of this pathway with insulin and glucose metabolism.12,45 The novel finding from this study of robust associations of triglyceride-lowering LPL alleles and the ANGPTL4 p.Glu40Lys variant with a lower waist-to-hip ratio is consistent with the known role of LPL as a lipid-buffering molecule51 and corroborates the notion that the association of this pathway with insulin sensitivity and lower diabetes risk may be at least partially because of improved capacity to preferentially store excess calories in peripheral adipose compartments.12

Limitations

A number of assumptions and possible limitations of the genetic approach used in this study are worth considering when interpreting its results. Mendelian randomization generally assumes that genetic variants are associated with the end point exclusively via the risk factor of interest.41 In this case, the risk factor of interest is genetic differences in LPL-mediated lipolysis, of which triglyceride levels are a proxy, and therefore, the association of LPL alleles with different metabolic risk factors and diseases does not invalidate the approach. The consequences of modest genetically determined differences in LPL-mediated lipolysis over several decades as assessed in this study may differ from the short-term pharmacological modulation of LPL-mediated lipolysis in randomized clinical trials or clinical practice. While our analyses show a strong association of LPL alleles with coronary disease and diabetes, this does not necessarily mean that pharmacologically enhancing lipolysis over a short time will yield clinically relevant changes in future risk of coronary disease or new-onset diabetes in high-risk adults for whom these agents are being developed. Therefore, the effect estimates from our genetic analysis reflect a life-long exposure to genetic differences in LPL-mediated lipolysis and should not be interpreted as an exact prediction of the magnitude of the clinical effect for studies of the short-term pharmacological modulation of this pathway.

Conclusions

Triglyceride-lowering alleles in the LPL pathway are associated with lower risk of coronary disease and type 2 diabetes independently of LDL-C–lowering genetic mechanisms. These findings provide human genetics evidence to support the development of agents that enhance LPL-mediated lipolysis for further clinical benefit in addition to LDL-C–lowering therapy.

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Article Information

Accepted for Publication: July 26, 2018.

Published Online: September 19, 2018. doi:10.1001/jamacardio.2018.2866

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 Lotta LA et al. JAMA Cardiology.

Corresponding Author: Luca A. Lotta, MD, PhD (luca.lotta@mrc-epid.cam.ac.uk), and Nicholas J. Wareham, MBBS, PhD (nick.wareham@mrc-epid.cam.ac.uk), MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Box 285, Cambridge CB2 0QQ, United Kingdom.

Author Contributions: Dr Lotta had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Langenberg and Wareham contributed equally.

Study concept and design: Lotta, Langenberg, Wareham.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Lotta, Langenberg, Wareham.

Critical revision of the manuscript for important intellectual content: Lotta, Stewart, Sharp, Day, Burgess, Luan, Bowker, Cai, Li, Wittemans, Kerrison, Khaw, McCarthy, O’Rahilly, Scott, Savage, Perry, Langenberg.

Statistical analysis: Lotta, Stewart, Sharp, Day, Burgess, Luan, Bowker, Cai, Li, Wittemans, Scott, Perry.

Obtained funding: Khaw, Savage, Langenberg, Wareham.

Administrative, technical, or material support: Kerrison, Khaw, McCarthy, Langenberg.

Study supervision: Lotta, Langenberg, Wareham.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr McCarthy has received grants from Eli Lilly and Company, Roche, AstraZeneca, Merck & Company, AbbVie, Janssen, Servier, Novo Nordisk, Sanofi Aventis, Boehringer Ingelheim, Pfizer, and Takeda; honoraria from Eli Lilly and Company, Novo Nordisk, and Pfizer; and serves on the advisory panels of Novo Nordisk and Pfizer. Dr O’Rahilly has received personal fees from Pfizer, AstraZeneca, MedImmune (iMed), and ERX Pharmaceuticals for serving on advisory boards and scientific panels. Dr Scott is an employee and shareholder of GlaxoSmithKline. No other disclosures were reported.

Funding/Support: This research has been conducted using the UK Biobank resource. This study has been conducted using data from the EPIC-InterAct and EPIC-Norfolk studies. This study was funded by the United Kingdom’s Medical Research Council through grants MC_UU_12015/1, MC_PC_13046, MC_PC_13048, and MR/L00002/1. This work was supported by grant MC_UU_12012/5 from the MRC Metabolic Diseases Unit and grant 115372 from the Cambridge NIHR Biomedical Research Centre and European Union/European Federation of Pharmaceutical Industries and Associations Innovative Medicines Initiative Joint Undertaking. The EPIC-InterAct Study was funded by grant LSHM_CT_2006_037197 from the EU FP6 programme. Dr Burgess is supported by Sir Henry Dale Fellowship grant 204623/Z/16/Z jointly funded by the Wellcome Trust and the Royal Society. Dr McCarthy is a Wellcome Trust Senior Investigator and is supported by the grants 090532 and 098381from the Wellcome Trust. Dr McCarthy was supported by the National Institute for Health Research Oxford Biomedical Research Centre. Dr O’Rahilly is funded by Wellcome Trust Senior Investigator award 095515/Z/11/Z and Wellcome Trust Strategic award 100574/Z/12/Z from the Wellcome Trust. Dr Savage is supported by grant 107064 from the Wellcome Trust.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute of Health Research, or the UK Department of Health.

Additional Contributions: We acknowledge the help of the MRC Epidemiology Unit support teams, including the field, laboratory, and data management teams.

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This cluster randomized trial compares the effects of greening vacant urban land (removing trash, grading land, planting grass and trees, installing fences, and regular maintenance) vs no intervention on the self-reported mental health of adults living in neighborhoods with vacant urban lots in...

Original Investigation

Public Health

Figure 1.  Distribution of Study Vacant Lots Across Philadelphia, Pennsylvania

This map shows the distribution of randomly selected study vacant lots across 3 groups of the trial: the greening intervention, the trash cleanup intervention, and no intervention. The distribution of vacant lots shown is representative of those in the study, although for the purposes of confidentiality are not the locations of actual study lots.

Figure 2.  Vacant Lot Main Greening Intervention

Images show blighted preperiod conditions and remediated postperiod restorations. A, The image shows the grass seeding method used to rapidly complete the treatment process. B, The after image shows the low wooden perimeter fence. Vacant lots shown here are representative of those in the study, although for purposes of confidentiality are not actual study lots.

Figure 3.  Flowchart of Vacant Lots and Participants Through Vacant Lot Greening Trial

aVacant lots were classified as blighted if they (1) had existing violations signaling blight, including illegal dumping, abandoned cars, and/or unmanaged vegetation growth; and (2) had been abandoned, as confirmed through contact with the owner of record who, within a 10-day period, either authorized the intervention or did not reply. Those excluded as having insufficient blight or not confirmed as abandoned did not meet these conditions.

Table 1.  Baseline Characteristics Demonstrating Balance Across Study Groupsa

Table 2.  Intention-to-Treat Analyses of Vacant Lot Interventions and Self-reported Mental Health Outcomes

Key Points

Question  Does the greening of vacant urban land reduce self-reported poor mental health in community-dwelling adults?

Findings  In this cluster randomized trial of urban greening and mental health, 110 randomly sampled vacant lot clusters were randomly assigned to 3 study groups. Among 342 participants included in the analysis, feeling depressed significantly decreased by 41.5% and self-reported poor mental health showed a reduction of 62.8% for those living near greened vacant lots compared with control participants.

Meaning  The remediation of vacant and dilapidated physical environments, particularly in resource-limited urban settings, can be an important tool for communities to address mental health problems, alongside other patient-level treatments.

Abstract

Importance  Neighborhood physical conditions have been associated with mental illness and may partially explain persistent socioeconomic disparities in the prevalence of poor mental health.

Objective  To evaluate whether interventions to green vacant urban land can improve self-reported mental health.

Design, Setting, and Participants  This citywide cluster randomized trial examined 442 community-dwelling sampled adults living in Philadelphia, Pennsylvania, within 110 vacant lot clusters randomly assigned to 3 study groups. Participants were followed up for 18 months preintervention and postintervention. This trial was conducted from October 1, 2011, to November 30, 2014. Data were analyzed from July 1, 2015, to April 16, 2017.

Interventions  The greening intervention involved removing trash, grading the land, planting new grass and a small number of trees, installing a low wooden perimeter fence, and performing regular monthly maintenance. The trash cleanup intervention involved removal of trash, limited grass mowing where possible, and regular monthly maintenance. The control group received no intervention.

Main Outcomes and Measures  Self-reported mental health measured by the Kessler-6 Psychological Distress Scale and the components of this scale.

Results  A total of 110 clusters containing 541 vacant lots were enrolled in the trial and randomly allocated to the following 1 of 3 study groups: the greening intervention (37 clusters [33.6%]), the trash cleanup intervention (36 clusters [32.7%]), or no intervention (37 clusters [33.6%]). Of the 442 participants, the mean (SD) age was 44.6 (15.1) years, 264 (59.7%) were female, and 194 (43.9%) had a family income less than $25 000. A total of 342 participants (77.4%) had follow-up data and were included in the analysis. Of these, 117 (34.2%) received the greening intervention, 107 (31.3%) the trash cleanup intervention, and 118 (34.5%) no intervention. Intention-to-treat analysis of the greening intervention compared with no intervention demonstrated a significant decrease in participants who were feeling depressed (−41.5%; 95% CI, −63.6% to −5.9%; P = .03) and worthless (−50.9%; 95% CI, −74.7% to −4.7%; P = .04), as well as a nonsignificant reduction in overall self-reported poor mental health (−62.8%; 95% CI, −86.2% to 0.4%; P = .051). For participants living in neighborhoods below the poverty line, the greening intervention demonstrated a significant decrease in feeling depressed (−68.7%; 95% CI, −86.5% to −27.5%; P = .007). Intention-to-treat analysis of those living near the trash cleanup intervention compared with no intervention showed no significant changes in self-reported poor mental health.

Conclusions and Relevance  Among community-dwelling adults, self-reported feelings of depression and worthlessness were significantly decreased, and self-reported poor mental health was nonsignificantly reduced for those living near greened vacant land. The treatment of blighted physical environments, particularly in resource-limited urban settings, can be an important treatment for mental health problems alongside other patient-level treatments.

Trial Registration  isrctn.org Identifier: ISRCTN92582209

Introduction

Almost 1 in 5 US adults report some form of mental illness. Depression is the second largest contributor to years lived with disability in the United States,1 with more than 16 million adults experiencing an episode annually.2,3 Yet patient mental health services only account for an estimated 5% of total medical care spending in the United States.4 A broadening of treatment options to improve mental health is necessary, including interventions that fundamentally change harmful environmental surroundings that may be key contributors to mental illness.

Neighborhood physical conditions, including vacant or dilapidated spaces, trash, and lack of quality infrastructure such as sidewalks and parks, are associated with depression5-9 and are factors that may explain the persistent prevalence of mental illness in resource-limited communities.10 Vacant and dilapidated spaces are unavoidable neighborhood conditions that residents in low-resource communities encounter every day, making the very existence of these spaces a constant source of stress11,12 and possibly mental illness.

However, neighborhood physical conditions can also positively influence mental health.13,14 Spending time and living near green spaces have been associated with various improved mental health outcomes, including less depression, anxiety, and stress.15-19 Several studies have demonstrated a dose-response relationship between more time spent in green spaces and lower depression rates.20,21 Therefore, green space may be a potential buffer between inequitable neighborhood conditions and poor mental health outcomes.22-24

While patient-level therapies for mental illness will always be a vital aspect of treatment, changing the places where people live, work, and play may have broad population-level effects on mental health outcomes.25 There have been calls for the development of urban environmental interventions to improve mental health outcomes and well-being.1,26 In support of this, a number of observational studies have demonstrated the positive effect of vacant land greening interventions on urban health, crime, and stress.12,27-29 However, these prior studies have not been experimental and have not tested mental health outcomes. Given this, we evaluated data from, to our knowledge, the first citywide cluster randomized trial with the objective to test the effects of inexpensive, standardized, and reproducible vacant land remediation interventions—greening and trash cleanup—on health and safety. We report here on the mental health outcomes. Analysis of crime outcomes is reported elsewhere.30

Methods

Study Design

This citywide cluster randomized trial of a standardized, reproducible vacant lot greening intervention and vacant lot trash cleanup intervention was conducted in Philadelphia, Pennsylvania. The University of Pennsylvania institutional review board approved this trial. All participants provided written informed consent. All sections of this article were written using the Consolidated Standards of Reporting Trials (CONSORT) reporting guideline.31 The trial protocol can be found in the Supplement.

Vacant Lot Random Sampling and Random Assignment

A master list was compiled of all vacant lots citywide available from the city administrative records throughout January 2011. Vacant lots that were authorized by municipal ordinance as blighted and eligible for the intervention were randomly sampled for the trial. Eligible lots were included if they specifically (1) had existing violations signaling blight, including illegal dumping, abandoned cars, and/or unmanaged vegetation growth; and (2) had been abandoned, as confirmed through contact with the owner of record who, within a 10-day period, either authorized the intervention or did not reply. Owners included the city itself for publicly owned lots. We excluded lots that had insufficient blight or lack of abandonment, lots that were greater than 5500 sq ft, and lots that were fully paved parking lots.

Vacant lot clusters served as the intervention unit for the study. To form these clusters, the master list of eligible vacant lots was ordered based on the assignment of random numbers within 4 sections of the city.32 In each section of the city, the first vacant lot in the randomly ordered list was chosen as an index lot and a 0.25-mile radius buffer was created around that lot. All other eligible vacant lots on the master list that fell within this radius were used to form a cluster grouping of geographically proximal vacant lots that summed between 4500 to 5500 total sq ft; these lots were then removed from consideration as future index lots. This process then cycled to the next randomly ordered index lot on the list that was at least 0.25 miles away from the edge of prior clusters until all clusters were formed. This process guaranteed that no clusters overlapped, reducing potential spillover and contamination effects across trial arms.

Within each city section, clusters were randomly assigned to 1 of 3 study groups—the greening intervention, trash cleanup intervention, or no intervention (Figure 1). A repeated randomization procedure33 was used under a predetermined protocol that permitted repeated random allocation of the 3 study groups until a statistically significant balance was achieved with a set of potential confounding variables, including the total area and mean separating distance of the study vacant lots, the total vacant lots, resident population, and number of serious crimes (part I violent and property crimes), in each cluster.

Figure 1.  Distribution of Study Vacant Lots Across Philadelphia, Pennsylvania

This map shows the distribution of randomly selected study vacant lots across 3 groups of the trial: the greening intervention, the trash cleanup intervention, and no intervention. The distribution of vacant lots shown is representative of those in the study, although for the purposes of confidentiality are not the locations of actual study lots.

Vacant Lot Interventions and Control Group

The vacant lot greening intervention involved the cleaning and greening of vacant lots via a standard, reproducible process of removing trash and debris, grading the land, planting new grass and a small number of trees, installing a low wooden perimeter fence with openings, and performing regular maintenance (Figure 2). The vacant lot trash cleanup intervention group involved removal of trash and debris, limited grass mowing on the lot where possible, and regular maintenance. The Pennsylvania Horticultural Society designed and carried out the interventions over a 2-month period, from April 1, 2013, to May 31, 2013, followed by monthly maintenance. At the end of the postintervention period, vacant lots assigned to the control condition were scheduled for cleaning and greening.

Figure 2.  Vacant Lot Main Greening Intervention

Images show blighted preperiod conditions and remediated postperiod restorations. A, The image shows the grass seeding method used to rapidly complete the treatment process. B, The after image shows the low wooden perimeter fence. Vacant lots shown here are representative of those in the study, although for purposes of confidentiality are not actual study lots.

Random Sampling of Participants

Two preintervention interview survey waves were conducted from October 1, 2011, to March 31, 2013, and 2 postintervention survey waves were conducted from June 1, 2013, to November 30, 2014, with a sample of residents from each cluster. All participants completed at least 1 preintervention survey and 1 postintervention survey. The outer-bounding polygon and its centroid were calculated for each grouping of vacant lots per cluster. This centroid represented the point location that was mathematically closest to all the study vacant lots in each cluster. The address of the closest building to this point location was then determined as the starting point for house-to-house random sampling and enrollment of survey participants. At each starting address, a 2-person survey team walked in a predetermined random direction on the corresponding city block followed by randomly chosen adjacent city blocks within the cluster until a total of 5 participants had been identified, consented, and were interviewed. Both the survey team and participants were blinded to cluster intervention. Participants were told the study was about improving our understanding of urban health. One participant per household was chosen; in households with multiple eligible participants, the individual with the most recent birthday was chosen. All baseline interviews and most follow-up interviews were conducted in person; a handful of follow-up interviews were conducted by telephone. Both English-speaking and Spanish-speaking individuals 18 years and older were administered the survey in the language of their choice; only 2 Spanish-language surveys were administered. Each participant was compensated $25 per interview, which took an average of 39.6 minutes to complete. Based on the American Association for Public Opinion Research response rate calculator, our survey response rate was 47.4%.34 Our response rate matched or exceeded that of other surveys and was high enough to produce a reasonably representative sample of our target population.35-37

Outcome Measures

At each interview, participants responded to questions about their perceptions of mental health, focusing on their experiences within the past 30 days to anchor responses in time relative to the intervention period and to avoid telescoping and overestimation. We used the validated short-form Kessler-6 Psychological Distress Scale (K6), a widely used community screening tool. The K6 was designed to evaluate the prevalence of serious mental illness in the community and does not make a clinical diagnosis of mental illness. Participants were asked to indicate how often they felt nervous, hopeless, restless, depressed, that everything was an effort, and worthless using the following scale: all of the time, most of the time, more than half of the time, less than half of the time, some of the time, or at no time. In keeping with the K6 order and scoring, the 2 middle categories were combined to create a score of 0 to 4 for each marker, which was then summed for a total score of 0 to 24. Using standard scoring guidelines, a score of 13 or greater indicated higher prevalence of serious mental illness or what we call self-reported poor mental health.38,39 Participants self-reported their race and/or ethnicity.

Statistical Analysis

Prior to the study, sample size was determined by taking into account anticipated intracluster correlation, participant response prevalence, number of crimes reported to the police in each area, effect size, and power. The minimally detectable effect size, given 80% power and 4 time points based on the group before vs after interaction test for any pairwise comparison among the randomly allocated groups of lots, was calculated.40 From this, and predicting a 25% loss-to-follow-up rate, we estimated that we would maintain more than 80% power if we randomly surveyed 3 people per cluster twice before and twice after the intervention.

Intention-to-treat analyses of participants were conducted according to the randomly assigned vacant lot cluster intervention group in which they lived. Pairwise comparisons were completed for all study outcomes between the greening intervention group and the no intervention group as well as the trash cleanup intervention group and the no intervention group. These pairwise comparisons were tested for statistical significance (all tests were 2-sided and statistical significance was defined as P ≤ .05) using unadjusted random-effects, cross-sectional time series regressions that accounted for the cluster design of the trial. Random-effects regressions were chosen because we assumed that unobserved lot-specific effects were correlated over time at the cluster level. All statistical analyses were conducted using Stata, version 14.1 (StataCorp LLC).

Difference-in-differences analyses were calculated as interaction terms of 1-0 intervention-control differences multiplied by 0-1 pre-post differences. These difference-in-differences interaction terms were the primary independent variables of interest interpreted as the true effect of the interventions on the outcomes studied. The estimates from the difference-in-differences analysis were then divided by the overall magnitude of occurrence for each outcome in the intervention group to obtain percentage reductions.27,29,41 Additional subset analyses were also completed by neighborhood poverty levels using the census tracts within which study participants lived. The poverty threshold for 2013 was determined to be $19 530 per the average size of persons per household in Philadelphia and the 2013 poverty guidelines from the US Census Bureau and the Department of Health and Human Services Office of the Assistant Secretary for Planning and Evaluation.42

Results

Vacant Lots and Clusters

The master list included 44 768 vacant lots, 34 149 (76.3%) of which were deemed eligible for inclusion in the study. Ineligible lots were excluded owing to insufficient blight or not being abandoned (4284), being greater than 5500 sq ft (3755), and being existing private or commercial parking lots (2580). A total of 110 clusters containing 541 vacant lots were enrolled in the trial and randomly allocated to the following 1 of 3 study arms: the greening intervention (37 clusters [33.6%]), the trash cleanup intervention (36 clusters [32.7%]), or no intervention (37 clusters [33.6%]) (Figure 3). Of the clusters, 47 (42.7%) were included in neighborhood poverty subset analysis.

Figure 3.  Flowchart of Vacant Lots and Participants Through Vacant Lot Greening Trial

aVacant lots were classified as blighted if they (1) had existing violations signaling blight, including illegal dumping, abandoned cars, and/or unmanaged vegetation growth; and (2) had been abandoned, as confirmed through contact with the owner of record who, within a 10-day period, either authorized the intervention or did not reply. Those excluded as having insufficient blight or not confirmed as abandoned did not meet these conditions.

Balance was evident at the cluster level between the 3 intervention conditions in terms of total number of study lots per study arm (range, 161-206 lots), the mean number of study lots per cluster (range, 4.5-5.4 lots), the total square footage of study lots per cluster (range, 4844-4935 sq ft), the mean number of residents per cluster (range, 285-297 people), and the mean number of serious crimes, as reported by the Philadelphia Police Department, occurring within each cluster during the 18-month baseline period (range, 16.5-18.3 crimes) (Table 1).

Table 1.  Baseline Characteristics Demonstrating Balance Across Study Groupsa

Participant Baseline Characteristics

Of the 442 participants, the mean (SD) age was 44.6 (15.1) years, 264 (59.7%) were female, and 194 (43.9%) had a family income less than $25 000. A total of 442 participants were interviewed during the preintervention period, and 342 (77.4%) of these original participants were interviewed during the postintervention period and are included in this analysis. This amounted to a 22.6% loss to follow-up; of the 100 lost participants, 78% could not be found in their original cluster, and 22% refused to participate in subsequent waves. Of the 442 participants, 149 (33.7%) were assigned to the greening intervention, 145 (32.8%) to the trash cleanup intervention, and 148 (33.5%) to no intervention. Of the 342 participants included in the analysis, 117 (34.2%) received the greening intervention, 107 (31.3%) the trash cleanup intervention, and 118 (34.5%) no intervention. A total of 139 people (40.6%) were included in the neighborhood poverty subset analyses, including 45 (32.4%) receiving the greening intervention, 51 (36.7%) the trash cleanup intervention, and 43 (30.9%) no intervention. Participant demographic characteristics were balanced between the 3 study arms, including mean tenure in the home (range, 12.0-13.7 years), mean age (range, 43.3-45.3 years), and percentage with family income less than $25 000 (range, 41.0%-46.9%) (Table 1).

Participant-Reported Mental Health Outcomes

Intention-to-treat analyses demonstrated significant changes in participant-reported mental health outcomes. Intention-to-treat analyses of the greening intervention compared with no intervention demonstrated a significant decrease in feeling depressed (−41.5%; 95% CI, −63.6% to −5.9%; P = .03) and feeling worthless (−50.9%; 95% CI, −74.7% to −4.7%; P = .04). Analysis also demonstrated a nonsignificant reduction in overall self-reported poor mental health (−62.8%; 95% CI, −86.2% to 0.4%; P = .051), as calculated by the K6 (Table 2). When looking only at neighborhoods below the poverty line, feeling depressed significantly decreased (−68.7%; 95% CI, −86.5% to −27.5%; P = .007). There was no significant difference in self-reported poor mental health in neighborhoods below the poverty line.

Table 2.  Intention-to-Treat Analyses of Vacant Lot Interventions and Self-reported Mental Health Outcomes

Intention-to-treat analyses of the trash cleanup intervention compared with no intervention did not show any statistically significant differences between self-reported poor mental health measured by the K6 (Table 2). There was also no difference between groups for the individual components of the K6. The analysis of neighborhoods below the poverty line also did not indicate any difference in self-reported mental health between the groups.

Discussion

In this citywide cluster randomized trial of 2 vacant land remediation interventions, greening was associated with a significant reduction in feeling depressed and worthless as well as a nonsignificant reduction in overall self-reported poor mental health for randomly sampled residents living nearby. The trash cleanup intervention was not associated with a reduction in feeling depressed or self-reported poor mental health.

To our knowledge, this is the first citywide cluster randomized trial of actual place-based changes to urban spaces. These results add much needed experimental evidence to a growing body of literature calling for structural changes to neighborhoods as a method for improving health and safety.43,44 This study extends previous work showing a clear association between green space and mental illness,13-21 by demonstrating that adding green space to people’s neighborhood environment can improve the trajectory of their mental health. Additionally, vacant lot greening is a relatively low-cost intervention (approximately $1597 per vacant lot and $180 per year to maintain) that we have previously shown to be a cost-beneficial solution to firearm violence.29 For these reasons, vacant lot greening may be an extremely attractive intervention for policy makers seeking to address urban blight.

Our findings indicate that the effect of vacant lot greening on feeling depressed was slightly stronger for those living in neighborhoods below the poverty line. Urban blight is an environmental condition that disproportionately affects low-resource neighborhoods, as evidenced by the fact that almost half of our participants had yearly family incomes less than $25 000. Making structural changes to the lowest-resource neighborhoods can make them healthier and may be an important mechanism to address persistent and entrenched socioeconomic health disparities.45

There are several possible mechanisms through which the vacant lot greening intervention but not the trash cleanup intervention improved feeling depressed and self-reported poor mental health. One significant difference between the 2 interventions was the creation of new green space. Green space, particularly in urban environments more likely to have a dearth of vegetation, has been linked to recovery from mental fatigue,46 a state of inattentiveness and irritability resulting from the information-processing demands of daily life. Spending time in or near nature can combat mental fatigue because it allows engagement without paying explicit attention.46-48 A related concept is the association between spending time in or near green space and stress reduction,18,49 which may in turn reduce mental illness. For example, walking past green space has been associated with reduction in heart rate,12 one marker of acute physiological stress.

Additionally, the presence of green space is associated with improved neighborhood social milieu, including the concepts of social cohesion, social capital, and collective efficacy.50-53 The presence of grass and trees is related to use of outdoor space and increased social activity that takes place in those outdoor spaces.54 Improved social conditions are, in turn, associated with better mental health.55,56 For example, living in a low-income neighborhood is associated with worse mental health indicators for people with low but not high social cohesion.57 Studies have found that social cohesion mediated a positive green space–mental health relationship.58-60 Additionally, previous studies have demonstrated an association of vacant lot greening with increased feelings of safety and decreased violent crime, both of which may work to improve mental illness.27,28 Fear of crime, for example, is associated with almost 2-fold higher likelihood of having depression.61

The other significant difference between the greening and trash cleanup interventions was the presence of a simple wooden post and rail fence. The fence delineates the newly greened space as one that is cared for but does have openings to indicate that people can enter the space. The fence is also meant to deter illegal dumping. Previous qualitative work conducted by our team indicated that vacant land causes people to feel stigmatized and abandoned by their community and government.11 Countering this with clear signs of neighborhood investment, such as a clearly marked newly greened vacant lot, may contribute to the improvements seen in feeling depressed and self-reported poor mental health.

Limitations

There were several limitations to this study. We used the K6 to measure our outcome of interest and mental health. While this is a validated and widely used scale, it is still a single scale, and other mental illness screening and diagnosis tools and scales may produce different results. Furthermore, we did not conduct a Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition–level diagnosis of mental illness but rather used a community screening tool. Another limitation is the duration of our study and loss to follow-up. We followed up people for 18 months following the blight remediation interventions and are unable to know if the effect of the interventions on mental health outcomes persisted past the study period. We also made every effort to minimize loss to follow-up of our study participants after they were first enrolled, although differential, nonrandom dropout in our 3 study arms and across all study waves could have affected our results. Finally, we did not specifically track if and how study participants used (or did not use) study vacant lots, although prior work has demonstrated signs of use, such as barbeques or chairs on similar vacant lots.62

Conclusions

Among community-dwelling adults, self-reported feelings of depression and worthlessness were significantly decreased and self-reported poor mental health was nonsignificantly reduced for those living near greened vacant lots compared with control lots. The treatment of dilapidated physical environments can be an important tool for communities to address persistent mental health problems. These findings provide support to health care clinicians concerned with positively transforming the often chaotic and harmful environments that affect their patients. Our findings also offer evidence to policy makers interested in increasing municipal investments in the remediation of blighted urban spaces as an inexpensive29 and scalable way to improve mental health, particularly in low-resource neighborhoods.

Article Information

Accepted for Publication: April 26, 2018.

Published: July 20, 2018. doi:10.1001/jamanetworkopen.2018.0298

Correction: This article was corrected on August 17, 2018, to fix an error in Figure 2B.

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 South EC et al. JAMA Network Open.

Corresponding Author: Eugenia C. South, MD, MS, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Blockley Hall, Room 408, Philadelphia, PA 19104 (eugenia.south@uphs.upenn.edu).

Author Contributions: Drs MacDonald and Branas had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: South, Hohl, MacDonald, Branas.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: South, Hohl, Branas.

Critical revision of the manuscript for important intellectual content: Hohl, Kondo, MacDonald, Branas.

Statistical analysis: MacDonald, Branas.

Obtained funding: Branas.

Administrative, technical, or material support: Hohl, Kondo, Branas.

Supervision: MacDonald, Branas.

Conflict of Interest Disclosures: Dr Hohl reported receiving grants from the National Institutes of Health during the conduct of the study. Dr MacDonald reported receiving grants from the National Institutes of Health and Centers for Disease Control and Prevention during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was funded in part by grants R01AA020331 and R01DA010164 from the National Institutes of Health and grant R49CE002474 from the Centers for Disease Control.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Additional Contributions: Philippe Bourgois, PhD (University of California, Los Angeles), contributed to the study design and execution; Keith Green (Pennsylvania Horticulture Society, Philadelphia), study intervention design and implementation; Jamillah Millner, BA (University of Pennsylvania, Philadelphia), participant recruitment, enrollment, and retention; Vicky Tam, MA (University of Pennsylvania, Philadelphia), geospatial planning and implementation; Douglas Wiebe, PhD (University of Pennsylvania, Philadelphia), study design and execution; and Jeremy Levenson, BA (University of California, Los Angeles), study execution. These individuals were affiliated with this project and key to its success. Drs Bourgois and Wiebe and Mss Millner and Tam received salary support for their contribution. Mr Green’s organization received funds to perform the intervention. Mr Levenson received compensation for his field work.

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Scientifically sound methods for investigating dietary consumption patterns from self-serve salad bars are needed to inform school policies and programs.

Abstract

Background

Scientifically sound methods for investigating dietary consumption patterns from self-serve salad bars are needed to inform school policies and programs.

Objective

To examine the reliability and validity of digital imagery for determining starting portions and plate waste of self-serve salad bar vegetables (which have variable starting portions) compared with manual weights.

Design/methods

In a laboratory setting, 30 mock salads with 73 vegetables were made, and consumption was simulated. Each component (initial and removed portion) was weighed; photographs of weighed reference portions and pre- and post-consumption mock salads were taken. Seven trained independent raters visually assessed images to estimate starting portions to the nearest ¼ cup and percentage consumed in 20% increments. These values were converted to grams for comparison with weighed values.

Statistical analyses

Intraclass correlations between weighed and digital imagery–assessed portions and plate waste were used to assess interrater reliability and validity. Pearson’s correlations between weights and digital imagery assessments were also examined. Paired samples t tests were used to evaluate mean differences (in grams) between digital imagery–assessed portions and measured weights.

Results

Interrater reliabilities were excellent for starting portions and plate waste with digital imagery. For accuracy, intraclass correlations were moderate, with lower accuracy for determining starting portions of leafy greens compared with other vegetables. However, accuracy of digital imagery–assessed plate waste was excellent. Digital imagery assessments were not significantly different from measured weights for estimating overall vegetable starting portions or waste; however, digital imagery assessments slightly underestimated starting portions (by 3.5 g) and waste (by 2.1 g) of leafy greens.

Conclusions

This investigation provides preliminary support for use of digital imagery in estimating starting portions and plate waste from school salad bars. Results might inform methods used in empirical investigations of dietary intake in schools with self-serve salad bars.

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Biography

M. K. Bean is an associate professor, Department of Pediatrics, Children’s Hospital of Richmond at Virginia Commonwealth University, Richmond.

A. Sova is a research assistant, Department of Pediatrics, Children’s Hospital of Richmond at Virginia Commonwealth University, Richmond.

H. A. Raynor is a professor, Department of Nutrition, University of Tennessee, Knoxville.

L. M. Thornton is an associate research professor, Department of Psychiatry, University of North Carolina, Chapel Hill.

M. Dunne Stewart is CEO, Greater Richmond Fit4Kids, Richmond, Virginia.

S. E. Mazzeo is a professor, Department of Psychology, Virginia Commonwealth University, Richmond.

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