About

James B Meigs MD, MPH is a Professor of Medicine at Harvard Medical School and a primary care internist at Massachusetts General Hospital. He serves as the Director of the MGH Division of Clinical Research’s Clinical Effectiveness Research Unit and is an Associate Member of the Broad Institute. His research focuses on the cause and prevention of type 2 diabetes and cardiovascular disease, utilizing biochemical and genetic epidemiology as well as health services translational research approaches. He has been recognized with the ADA’s Kelly West Award for Outstanding Achievement in Diabetes Epidemiology in 2009. Dr. Meigs is a senior leader in numerous major international T2D genomics consortia, including MAGIC, DIAGRAM, AAGILE, CHARGE, and TOPMed-diabetes, and is involved in several NIH grants related to omics and cardiometabolic research. He has mentored over 50 early career investigators, most of whom have continued in academic medicine.

Research topics

• Genetics
• Biology
• Medicine
• Endocrinology
• Internal medicine
• Evolutionary biology
• Computational biology
• Political Science
• Demography
• Computer Science
• Sociology
• Machine Learning
• Bioinformatics
• Pathology
• Intensive care medicine
• Astronomy
• Mathematics
• Physical therapy
• Environmental health
• Pediatrics
• Physics
• Family medicine
• Virology
• Immunology

Selected publications

• Loci for insulin processing and secretion provide insight into type 2 diabetes risk

  UNC Libraries · 2025-11-08

  articleOpen access
  PublisherDOI

• A large-scale genome-wide study of gene-sleep duration interactions for blood pressure in 811,405 individuals from diverse populations

  Molecular Psychiatry · 2025-04-04 · 1 citations

  articleOpen access
  PublisherOA PDFDOI

• Genome-wide gene-sleep interaction study identifies novel lipid loci in 732,564 participants

  Atherosclerosis · 2025-11-26 · 1 citations

  articleOpen access

  BACKGROUND AND AIMS: Deviations from the population mean in sleep duration have been associated with increased risk for developing dyslipidemia and atherosclerotic cardiovascular disease, but the mechanism of effect is poorly characterized. We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets. METHODS: We collected data from 55 cohorts with a combined sample size of 732,564 participants (87 % European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20 % of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for one-degree of freedom tests of interaction and two-degree of freedom joint tests of the SNP-main and -interaction effect on lipid levels. RESULTS: <6.6e-6). Multiple loci, including those mapped to APSH (target for aspartic and succinic acid) and SLC8A1 showed biological plausibility and druggability potential based on literature. CONCLUSIONS: Collectively, the 17 (9 with short and 8 with long sleep) loci provided evidence into the biomolecular mechanisms underlying sleep-associated lipid changes, including potential involvement of the vitamin D receptor pathway. Collectively, these findings may contribute developing novel interventions for treating dyslipidemia in people with sleep disturbances.

  PublisherOA PDFDOI

• Cross-Ancestry Comparison of Aptamer and Antibody Proteomics Measures

  Research Square · 2025-02-12 · 1 citations

  preprintOpen access
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• Compartment-specific Metabolic Alterations to Insulin Reflect Adiposity-driven Variation and Predict Type 2 Diabetes

  The Journal of Clinical Endocrinology & Metabolism · 2025-08-11 · 2 citations

  article

  CONTEXT: The metabolic mechanisms underlying insulin resistance are not fully understood. Metabolomic profiling can reveal compartment-specific variations and identify individuals at risk for type 2 diabetes (T2D). OBJECTIVE: To characterize insulin-induced metabolomic changes during a hyperinsulinemic-euglycemic clamp and evaluate a derived risk score's predictive value for T2D. DESIGN, SETTING, AND PARTICIPANTS: Clamp studies were conducted in 80 adults (38 with T2D, 42 without) to measure plasma and muscle metabolites in fasting and hyperinsulinemic states. An insulin resistance metabolomic score was developed and tested in a prospective case-control study (367 cases, 910 controls) from the Women's Health Initiative (28.5-year follow-up). MAIN OUTCOME MEASURES: Metabolite changes during hyperinsulinemia and incident T2D. RESULTS: Hyperinsulinemia altered 79.5% of plasma metabolites (notably fatty acids, lactate, and pyruvate) and 15.8% of muscle metabolites (eg, branched-chain and aromatic amino acids). T2D was associated with higher triglycerides and lower tricarboxylic acid intermediates during clamp. Adiposity amplified insulin-induced increases in plasma lipids. The risk score predicted incident T2D (hazard ratio, 1.20 per SD; 95% CI, 1.09-1.32; P = 7.4 × 10-4). CONCLUSION: Compartment-specific metabolic responses to insulin are shaped by adiposity and predict future T2D risk, supporting use of metabolomic signatures for early identification and prevention.

  PublisherDOI

• Genome-Wide Association Study of Hypoglycemia in Adults With Diabetes in the Million Veteran Program

  Diabetes · 2025-09-19

  article

  Hypoglycemia is a preventable adverse treatment effect in diabetes patients, but genetic markers to identify those with increased susceptibility are lacking. We performed a case/control genome-wide association study (GWAS) of hypoglycemia in U.S. Million Veteran Program (MVP) participants with medication-treated diabetes. Case participants had an outpatient random serum/plasma glucose <70 mg/dL or an emergency department visit for hypoglycemia. GWAS was stratified by race/ethnicity, adjusted for age at MVP enrollment, sex, and top 10 population-specific principal components, followed by multipopulation meta-analysis. Secondary analyses examined genetic associations with hypoglycemia stratified by diabetes medication exposure as well as replication in UK Biobank and the Action to Control Cardiovascular Risk in Diabetes clinical trial. The study included 72,244 (22,045 case participants) non-Hispanic White participants, 24,162 (10,441 case participants) non-Hispanic Black participants, and 9,196 (2,800 case participants) Hispanic participants. Four loci had genome-wide significant associations with hypoglycemia in multipopulation meta-analysis: rs12712928 (chromosome 2, SIX2/SIX3 locus), rs1064173 (chromosome 6, HLA-DQB1/DQA2 locus), rs35198068 (chromosome 10, TCF7L2 locus), and rs113748381 (chromosome 17, SCL16A11 locus). All four loci replicated in at least one independent cohort, and the magnitude of associations with hypoglycemia varied by diabetes type. Genome-wide analyses may complement candidate pharmacogenetic studies to identify risk markers of adverse drug effects. ARTICLE HIGHLIGHTS: Genetic variants associated with hypoglycemia risk in individuals with medication-treated diabetes have not been evaluated genome-wide. The specific question we asked was whether common genetic variants are associated with hypoglycemia among individuals with diabetes treated with glucose-lowering medications. We found four genomic loci were associated with hypoglycemia in a genome-wide association study. One locus-on chromosome 6-was associated with hypoglycemia only in individuals with likely type 1 diabetes, and two loci-on chromosome 2 and chromosome 6-were associated with hypoglycemia only in the context of treatment with sulfonylureas (chromosome 2) or with insulin (chromosome 6). Genetic variants may help identify individuals with diabetes at increased hypoglycemia risk, but additional study is needed to address the clinical utility of genetic data to inform hypoglycemia risk.

  PublisherDOI

• Decomposed interaction testing improves detection of genetic modifiers of the relationship of dietary omega-3 fatty acid intake and its plasma biomarkers with hsCRP in the UK Biobank

  Genes & Nutrition · 2025-03-04 · 2 citations

  articleOpen access

  Discovery and translation of gene-environment interactions (GxEs) influencing clinical outcomes is limited by low statistical power and poor mechanistic understanding. Molecular omics data may help address these limitations, but their incorporation into GxE testing requires principled analytic approaches. We focused on genetic modification of the established mechanistic link between dietary long-chain omega-3 fatty acid (dN3FA) intake, plasma N3FA (pN3FA), and chronic inflammation as measured by high sensitivity CRP (hsCRP). We considered an approach that decomposes the overall genetic effect modification into components upstream and downstream of a molecular mediator to increase the potential to discover gene-N3FA interactions. Simulations demonstrated improved power of the upstream and downstream tests compared to the standard approach when the molecular mediator for many biologically plausible scenarios. The approach was applied in the UK Biobank (N = 188,700) with regression models that used measures of dN3FA (based on fish and fish oil intake), pN3FA (% of total fatty acids measured by nuclear magnetic resonance), and hsCRP. Mediation analysis showed that pN3FA fully mediated the dN3FA-hsCRP main effect relationship. Next, we separately tested modification of the dN3FA-hsCRP (“standard”), dN3FA-pN3FA (“upstream”), and pN3FA-hsCRP (“downstream”) associations. The known FADS1-3 locus variant rs174535 reached p = 1.6 × 10–12 in the upstream discovery analysis, with no signal in the downstream analysis (p = 0.94). It would not have been prioritized based on a naïve analysis with dN3FA exposure and hsCRP outcome (p = 0.097), indicating the value of the decomposition approach. Gene-level enrichment testing of the genome-wide results further prioritized two genes from the downstream analysis, CBLL1 and MICA, with links to immune cell counts and function. In summary, a molecular mediator-focused interaction testing approach enhanced statistical power to identify GxEs while homing in on relevant sub-components of the dN3FA-hsCRP pathway.

  PublisherOA PDFDOI

• &lt;b&gt;Genome-wide association study of hypoglycemia in adults with diabetes in the Million Veteran Program&lt;/b&gt;

  2025-09-19

  article

  &lt;p dir="ltr"&gt;Hypoglycemia is a preventable adverse treatment effect in diabetes patients, but genetic markers to identify those with increased susceptibility are lacking. We performed a case/control genome-wide association study (GWAS) of hypoglycemia in US Million Veteran Program (MVP) participants with medication-treated diabetes mellitus. Cases had an outpatient random serum/plasma glucose &lt;70 mg/dL or an emergency department visit for hypoglycemia. GWAS was stratified by race/ethnicity, adjusted for age at MVP enrollment, sex, and top 10 population-specific principal components, followed by multi-population meta-analysis. Secondary analyses examined genetic associations with hypoglycemia stratified by diabetes medication exposure as well as replication in UK Biobank and the Action to Control Cardiovascular Risk in Diabetes clinical trial. The study included 72,244 (22,045 cases) non-Hispanic White participants, 24,162 (10,441 cases) non-Hispanic Black participants, and 9,196 (2,800 cases) Hispanic participants. Four loci had genome-wide significant associations with hypoglycemia in multi-population meta-analysis: rs12712928 (chromosome 2, SIX2/SIX3 locus), rs1064173 (chromosome 6, HLA-DQB1/DQA2 locus), rs35198068 (chromosome 10, TCF7L2 locus), and rs113748381 (chromosome 17, SCL16A11 locus). All four loci replicated in at least one independent cohort, and the magnitude of associations with hypoglycemia varied by diabetes type. Genome-wide analyses may complement candidate pharmacogenetic studies to identify risk markers of adverse drug effects.&lt;/p&gt;

  PublisherDOI

• The effect of type 2 diabetes genetic predisposition on non-cardiovascular comorbidities

  Nature Communications · 2025-10-10 · 4 citations

  articleOpen access

  Type 2 diabetes is associated with a range of non-cardiovascular non-oncologic comorbidities. To move beyond associations and evaluate causal effects between type 2 diabetes genetic predisposition and 21 comorbidities, we apply Mendelian randomization analysis using genome-wide association studies across multiple genetic ancestries. Additionally, leveraging eight mechanistic clusters of type 2 diabetes genetic profiles, each representing distinct biological pathways, we investigate causal links between cluster-stratified type 2 diabetes genetic predisposition and comorbidity risk. We identify causal effects of type 2 diabetes genetic predisposition driven by distinct genetic clusters. For example, the risk-increasing effects of type 2 diabetes genetic predisposition on cataracts and erectile dysfunction are primarily attributed to adiposity and glucose regulation mechanisms, respectively. We observe opposing effect directions across different genetic ancestries for depression, asthma and chronic obstructive pulmonary disease. Our findings leverage the heterogeneity underpinning type 2 diabetes genetic predisposition to prioritize biological mechanisms underlying causal relationships with comorbidities.

  PublisherOA PDFDOI

• MON-551 Insulin Resistance (IR) and BMI in a Multiethnic Population of Normoglycemic Adults

  Journal of the Endocrine Society · 2025-10-01

  articleOpen access

  Abstract Disclosure: J.Z. Louie: Quest Diagnostics. D. Shiffman: Quest Diagnostics. J.B. Meigs: Quest Diagnostics. M.J. McPhaul: Quest Diagnostics. The number of individuals with obesity and type 2 diabetes (T2D) has progressively increased in the United States and in developed countries worldwide. Efforts to prevent T2D have centered on individuals with prediabetes through lifestyle changes or prescription medications. Preventing development of T2D earlier, before dysglycemia is manifested, could be an alternative strategy to interrupt disease progression. Therefore, standardized tools to identify those with normoglycemia and insulin resistance, who are at risk for prediabetes and T2D may be useful. We investigated whether BMI elevation would be a useful marker to identify individual with normoglycemia who would benefit from insulin resistance assessment. The study included 16,333 working-age adults with normoglycemia (fasting plasma glucose &amp;lt;100 mg/dL and HbA1c &amp;lt;5.7%) who participated in an annual wellness program. Insulin resistance was assessed with an insulin resistance risk score (IRRS) based on fasting intact insulin and C-peptide. IR was defined as IRRS in the top tertile (IRRS &amp;gt; 20%). The relationships between log-transformed IRRS levels and ethnicity or BMI were assessed in linear models.The 16,333 study participants had mean (SD) age of 44 (12) years. Of these, 66% were women. The population were ethnically diverse: 2015 (12.3%) self-identified as African American, 2321 (14.2%) as Asian, 2269 (13.9%) as Hispanic, 6898 (42.2%) as White, and 2830 (17.3%) as Other. Median (IQR) IRRS levels differed between the populations: 14% (7 - 33) in African American; 10% (5 - 22) in Asian; 16% (8 - 39) in Hispanic; 11% (6 - 27) in White; 12% (6 - 28) in Other (P &amp;lt;0.001 for difference between each ethnic group and Whites). The association between IRRS and BMI was significant (P &amp;lt;0.001). The IRRS levels rose as BMI levels increased in each ethnicity. The rate of IRRS levels per BMI level was higher in Asians (P interaction [P intxn] &amp;lt;0.001), lower in African Americans (P intxn &amp;lt;0.001), and similar in Hispanics (P intxn =0.11) compared with Whites. The participants with obesity or severe obesity (BMI ≥30) were 233 (10.0%) in Asians, 968 (48.0%) in African Americans, 756 (33.3) in Hispanics, 2104 (30.5%) in Whites, and 858 (30.3%) in others. In those with BMI ≥30, greater than 50% had IRRS &amp;gt;20%: 67.4% in Asians, 51.8% in African Americans, 67.1% in Hispanics, 61.3 % in Whites, and 57.1% in others. Our study found that higher IRRS levels in normoglycemic adults was significantly associated with higher BMI levels, and the effect of BMI levels on IRRS levels differed between ethnicities. BMI may be used to identify normoglycemic adults for insulin resistance screening to prevent or delay type 2 diabetes or CVD development and to assess insulin resistance differently in each ethnicity. Presentation: Monday, July 14, 2025

  PublisherOA PDFDOI

Recent grants

• NIH Grant U01DK078616

  NIH · $3.6M · 2020

• TOPMed Omics of Type 2 Diabetes and Quantitative Traits

  NIH · $2.9M · 2008–2025

• NIH Grant R21DK065732

  NIH · $268k · 2007

• NIH Grant R13DK085836

  NIH · $15k · 2010

• NIH Grant R01DK078616

  NIH · $5.0M · 2015

Frequent coauthors

• Ramachandran S. Vasan

  National Heart Lung and Blood Institute

  1269 shared

• Emelia J. Benjamin

  Boston Medical Center

  775 shared

• Daniel Levy

  National Heart Lung and Blood Institute

  753 shared

• Ralph B. D’Agostino

  Wake Forest University

  739 shared

• José C. Florez

  Harvard University

  738 shared

• Caroline S. Fox

  Birmingham Women’s and Children’s NHS Foundation Trust

  701 shared

• Paul F. Jacques

  655 shared

• Josée Dupuis

  Boston University

  653 shared

Awards & honors

• ADA’s prestigious Kelly West Award for Outstanding Achieveme…