James B. Meigs

135.5k total citations · 15 hit papers
400 papers, 44.5k citations indexed

About

James B. Meigs is a scholar working on Endocrinology, Diabetes and Metabolism, Epidemiology and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, James B. Meigs has authored 400 papers receiving a total of 44.5k indexed citations (citations by other indexed papers that have themselves been cited), including 161 papers in Endocrinology, Diabetes and Metabolism, 99 papers in Epidemiology and 91 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in James B. Meigs's work include Diabetes, Cardiovascular Risks, and Lipoproteins (100 papers), Genetic Associations and Epidemiology (71 papers) and Adipokines, Inflammation, and Metabolic Diseases (51 papers). James B. Meigs is often cited by papers focused on Diabetes, Cardiovascular Risks, and Lipoproteins (100 papers), Genetic Associations and Epidemiology (71 papers) and Adipokines, Inflammation, and Metabolic Diseases (51 papers). James B. Meigs collaborates with scholars based in United States, Australia and United Kingdom. James B. Meigs's co-authors include Ralph B. D’Agostino, Peter W.F. Wilson, Ramachandran S. Vasan, Caroline S. Fox, Lisa Sullivan, JoAnn E. Manson, Frank B. Hu, Paul F. Jacques, L. Adrienne Cupples and Richard W. Grant and has published in prestigious journals such as New England Journal of Medicine, The Lancet and JAMA.

In The Last Decade

James B. Meigs

396 papers receiving 43.0k citations

Hit Papers

Abdominal Visceral and Su... 2004 2026 2011 2018 2007 2005 2006 2007 2007 500 1000 1.5k 2.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Abdominal Visceral and Subcutaneous Adipose Tissue Compartments
    2007 2.2k citations Caroline S. Fox, Joseph M. Massaro et al. Circulation profile →
  2. Metabolic Syndrome as a Precursor of Cardiovascular Disease and Type 2 Diabetes Mellitus
    2005 1.6k citations Peter W.F. Wilson, Ralph B. D’Agostino et al. Circulation profile →
  3. Body Mass Index, Metabolic Syndrome, and Risk of Type 2 Diabetes or Cardiovascular Disease
    2006 820 citations James B. Meigs, Peter W.F. Wilson et al. profile →
  4. Visceral and Subcutaneous Adipose Tissue Volumes Are Cross-Sectionally Related to Markers of Inflammation and Oxidative Stress
    2007 739 citations Joseph M. Massaro, Ramachandran S. Vasan et al. Circulation profile →
  5. Soft Drink Consumption and Risk of Developing Cardiometabolic Risk Factors and the Metabolic Syndrome in Middle-Aged Adults in the Community
    2007 719 citations Ravi Dhingra, Lisa Sullivan et al. Circulation profile →
  6. Major dietary patterns are related to plasma concentrations of markers of inflammation and endothelial dysfunction
    2004 672 citations James B. Meigs et al. profile →
  7. Diet-quality scores and plasma concentrations of markers of inflammation and endothelial dysfunction
    2005 669 citations James B. Meigs et al. profile →
  8. Diet-quality scores and plasma concentrations of markers of inflammation and endothelial dysfunction
    2005 665 citations James B. Meigs et al. profile →
  9. Inflammatory Markers and Risk of Developing Type 2 Diabetes in Women
    2004 641 citations James B. Meigs et al. profile →
  10. Carbohydrate Nutrition, Insulin Resistance, and the Prevalence of the Metabolic Syndrome in the Framingham Offspring Cohort
    2004 589 citations Nicola M. McKeown, James B. Meigs et al. Diabetes Care profile →
  11. Low-Grade Albuminuria and Incidence of Cardiovascular Disease Events in Nonhypertensive and Nondiabetic Individuals
    2005 558 citations James B. Meigs, Thomas J. Wang et al. Circulation profile →
  12. Genotype Score in Addition to Common Risk Factors for Prediction of Type 2 Diabetes
    2008 550 citations James B. Meigs, Lisa Sullivan et al. profile →
  13. C-Reactive Protein, the Metabolic Syndrome, and Prediction of Cardiovascular Events in the Framingham Offspring Study
    2004 546 citations James B. Meigs, Lisa Sullivan et al. Circulation profile →
  14. Biomarkers of Endothelial Dysfunction and Risk of Type 2 Diabetes Mellitus
    2004 516 citations James B. Meigs profile →
  15. Increasing Cardiovascular Disease Burden Due to Diabetes Mellitus
    2007 500 citations Caroline S. Fox, Ralph B. D’Agostino et al. Circulation profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
James B. Meigs 15.0k 10.5k 10.3k 9.4k 8.5k 400 44.5k
Barbara V. Howard 15.6k 1.0× 10.0k 1.0× 5.3k 0.5× 11.9k 1.3× 8.6k 1.0× 559 45.1k
Jaakko Tuomilehto 23.2k 1.6× 14.1k 1.3× 9.1k 0.9× 12.4k 1.3× 12.7k 1.5× 776 61.5k
Elizabeth Barrett‐Connor 17.3k 1.2× 8.3k 0.8× 5.3k 0.5× 7.7k 0.8× 6.7k 0.8× 669 49.5k
Jean‐Pierre Després 11.5k 0.8× 14.3k 1.4× 8.8k 0.9× 11.0k 1.2× 8.7k 1.0× 395 39.9k
Julie E. Buring 12.5k 0.8× 9.9k 0.9× 10.8k 1.1× 15.8k 1.7× 12.6k 1.5× 602 66.5k
Earl S. Ford 15.7k 1.0× 12.2k 1.2× 10.0k 1.0× 9.8k 1.0× 13.3k 1.6× 349 54.1k
Coen D.A. Stehouwer 15.7k 1.0× 12.4k 1.2× 9.9k 1.0× 17.6k 1.9× 5.2k 0.6× 1.1k 57.4k
Robert H. Eckel 22.0k 1.5× 16.8k 1.6× 14.4k 1.4× 14.7k 1.6× 11.5k 1.4× 242 65.3k
Matti Uusitupa 13.4k 0.9× 12.0k 1.1× 5.2k 0.5× 4.6k 0.5× 6.5k 0.8× 483 37.7k
Kay‐Tee Khaw 9.4k 0.6× 7.8k 0.7× 4.6k 0.4× 6.6k 0.7× 8.9k 1.0× 617 41.4k

Countries citing papers authored by James B. Meigs

Since Specialization
Citations

This map shows the geographic impact of James B. Meigs's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by James B. Meigs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James B. Meigs more than expected).

Fields of papers citing papers by James B. Meigs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James B. Meigs. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by James B. Meigs. The network helps show where James B. Meigs may publish in the future.

Co-authorship network of co-authors of James B. Meigs

This figure shows the co-authorship network connecting the top 25 collaborators of James B. Meigs. A scholar is included among the top collaborators of James B. Meigs based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with James B. Meigs. James B. Meigs is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Arruda, Ana Luiza, Ozvan Bocher, Henry J. Taylor, et al.. (2025). The effect of type 2 diabetes genetic predisposition on non-cardiovascular comorbidities. Nature Communications. 16(1). 9042–9042.
2.
Mandla, Ravi, Philip Schroeder, Bianca Porneala, et al.. (2024). Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study. Genome Medicine. 16(1). 63–63. 4 indexed citations
3.
Triant, Virginia A., Asya Lyass, Leo B. Hurley, et al.. (2024). Cardiovascular Risk Estimation Is Suboptimal in People With HIV. Journal of the American Heart Association. 13(10). e029228–e029228. 11 indexed citations
4.
Rooney, Mary R., Natalie Daya, Aaron Leong, et al.. (2023). Prognostic value of insulin resistance and hyperglycemia biomarkers for long-term risks of cardiometabolic outcomes. Journal of Diabetes and its Complications. 37(9). 108583–108583. 1 indexed citations
5.
Lewis, Anna, Emma Perez, Anya E. R. Prince, et al.. (2022). Patient and provider perspectives on polygenic risk scores: implications for clinical reporting and utilization. Genome Medicine. 14(1). 114–114. 32 indexed citations
6.
Dornbos, Peter, Ryan Koesterer, Trang Thi Huyen Nguyen, et al.. (2022). A combined polygenic score of 21,293 rare and 22 common variants improves diabetes diagnosis based on hemoglobin A1C levels. Nature Genetics. 54(11). 1609–1614. 22 indexed citations
7.
Thaweethai, Tanayott, Carolin C. M. Schulte, Sarah Hsu, et al.. (2022). Gestational Glucose Intolerance and Risk of Future Diabetes. Diabetes Care. 46(1). 83–91. 20 indexed citations
8.
James, Kaitlyn E., Marie‐France Hivert, James B. Meigs, et al.. (2021). Physiological subtypes of gestational glucose intolerance and risk of adverse pregnancy outcomes. American Journal of Obstetrics and Gynecology. 226(2). 241.e1–241.e14. 18 indexed citations
9.
Sosa‐Rubí, Sandra G., Jacqueline A. Seiglie, Jennifer Manne‐Goehler, et al.. (2020). Incremental Risk of Developing Severe COVID-19 Among Mexican Patients With Diabetes Attributed to Social and Health Care Access Disadvantages. Diabetes Care. 44(2). 373–380. 20 indexed citations
10.
Leong, Aaron, Chen Ji, Eleanor Wheeler, et al.. (2019). Mendelian Randomization Analysis of Hemoglobin A1c as a Risk Factor for Coronary Artery Disease. Diabetes Care. 42(7). 1202–1208. 22 indexed citations
11.
O’Seaghdha, Conall M., Shih‐Jen Hwang, Martin G. Larson, et al.. (2013). Analysis of a Urinary Biomarker Panel for Incident Kidney Disease and Clinical Outcomes. Journal of the American Society of Nephrology. 24(11). 1880–1888. 64 indexed citations
12.
Upadhyay, Ashish, Martin G. Larson, Chao‐Yu Guo, et al.. (2010). Inflammation, kidney function and albuminuria in the Framingham Offspring cohort. Nephrology Dialysis Transplantation. 26(3). 920–926. 131 indexed citations
13.
Hwang, Shih-Jen, Evadnie Rampersaud, Josef Coresh, et al.. (2008). TCF7L2 Variants Associate with CKD Progression and Renal Function in Population-Based Cohorts. Journal of the American Society of Nephrology. 19(10). 1989–1999. 36 indexed citations
14.
Dhingra, Ravi, Lisa Sullivan, Paul F. Jacques, et al.. (2007). Soft Drink Consumption and Risk of Developing Cardiometabolic Risk Factors and the Metabolic Syndrome in Middle-Aged Adults in the Community. Circulation. 116(5). 480–488. 719 indexed citations breakdown →
15.
Bhattacharyya, Timothy, Denis Chang, James B. Meigs, Daniel M. Estok, & Henrik Malchau. (2007). Mortality After Periprosthetic Fracture of the Femur. Journal of Bone and Joint Surgery. 89(12). 2658–2662. 319 indexed citations
16.
Gonzalez, Jeffrey S., Steven A. Safren, Enrico Cagliero, et al.. (2007). Depression, Self-Care, and Medication Adherence in Type 2 Diabetes. Diabetes Care. 30(9). 2222–2227. 430 indexed citations
17.
McKeown, Nicola M., et al.. (2004). Dietary glycemic index is related to metabolic risk factors in the Framingham Offspring Cohort. The FASEB Journal. 18. 5821. 1 indexed citations
18.
Elosúa, Roberto, Serkalem Demissie, L. Adrienne Cupples, et al.. (2003). Obesity Modulates the Association among APOE Genotype, Insulin, and Glucose in Men. Obesity Research. 11(12). 1502–1508. 84 indexed citations
19.
Meigs, James B.. (2002). Epidemiology of the metabolic syndrome, 2002.. The American Journal of Managed Care. 8(11 Suppl). S283–92; quiz S293. 136 indexed citations
20.
Dubey, A. K., James B. Meigs, Enrico Cagliero, & Henry C. Chueh. (2002). Implementation, Usage, and Impact of Automated Guidelines in the DMA: A Web-based Diabetes Disease Management System.. PubMed Central. 1011–1011. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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