Article in Circulation. Genomic and precision medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
15 authors.
Romit Bhattacharya *Department of Medicine, Harvard Medical School (R.B., A.P., A.J., S.H., K.L., M.C.H., P.N.), Massachusetts General Hospital, Boston.ORCID 0000-0002-0782-4753
Christopher S Marnell *The Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY (C.S.M.).ORCID 0000-0002-5045-8496
So Mi Jemma ChoDepartment of Medicine, Harvard Medical School (R.B., A.P., A.J., S.H., K.L., M.C.H., P.N.), Massachusetts General Hospital, Boston.ORCID 0000-0003-2460-3335
Aniruddh P PatelDepartment of Medicine, Harvard Medical School (R.B., A.P., A.J., S.H., K.L., M.C.H., P.N.), Massachusetts General Hospital, Boston.ORCID 0000-0001-7871-9638
Yunfeng RuanDepartment of Medicine, Harvard Medical School (R.B., A.P., A.J., S.H., K.L., M.C.H., P.N.), Massachusetts General Hospital, Boston.ORCID 0000-0002-3213-8309
Satoshi KoyamaDepartment of Medicine, Harvard Medical School (R.B., A.P., A.J., S.H., K.L., M.C.H., P.N.), Massachusetts General Hospital, Boston.ORCID 0000-0002-9286-0360
Amanda R JowellDepartment of Medicine, Harvard Medical School (R.B., A.P., A.J., S.H., K.L., M.C.H., P.N.), Massachusetts General Hospital, Boston.ORCID 0000-0001-9030-0979
Mark TrinderProgram in Medical and Population Genetics & Cardiovascular Disease Initiative, Broad Institute of Harvard & MIT, Cambridge (A.P., Y.R., S.K., M.T., S.H., K.L., M.C.H., S.M.Z., I.S., P.N.).ORCID 0000-0001-8447-2897
Sara HaidermotaDepartment of Medicine, Harvard Medical School (R.B., A.P., A.J., S.H., K.L., M.C.H., P.N.), Massachusetts General Hospital, Boston.ORCID 0000-0002-8588-3388
Kim LanneryDepartment of Medicine, Harvard Medical School (R.B., A.P., A.J., S.H., K.L., M.C.H., P.N.), Massachusetts General Hospital, Boston.
Michael C HonigbergDepartment of Medicine, Harvard Medical School (R.B., A.P., A.J., S.H., K.L., M.C.H., P.N.), Massachusetts General Hospital, Boston.ORCID 0000-0001-8630-5021
Seyedeh M ZekevatDepartment of Medicine, Cardiovascular Research Center & Center for Genomic Medicine (R.B., S.M.J.C., A.P., Y.R., S.K., M.K., S.H., K.L., M.C.H., S.M.Z., P.N.), Massachusetts General Hospital, Boston.
Ida SurakkaProgram in Medical and Population Genetics & Cardiovascular Disease Initiative, Broad Institute of Harvard & MIT, Cambridge (A.P., Y.R., S.K., M.T., S.H., K.L., M.C.H., S.M.Z., I.S., P.N.).ORCID 0000-0002-6669-5541
Gina M PelosoDepartment of Biostatistics, Boston University School of Public Health, MA (G.M.P.).ORCID 0000-0002-5355-8636
Pradeep NatarajanDepartment of Medicine, Harvard Medical School (R.B., A.P., A.J., S.H., K.L., M.C.H., P.N.), Massachusetts General Hospital, Boston.ORCID 0000-0001-8402-7435
Funding
Polygenic Risk Scores for Diverse Populations - Bridging Research and Clinical CareR01HL151152 · NHLBI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Christy Leigh Avery, Jennifer Below · 2020 to 2026
$12.3M
Using genetic variation to study biology of blood lipids & coronary heart diseaseR01HL127564 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Pradeep Natarajan, Gina Marie Peloso · 2015 to 2026
$7.3M
CTSA K12 Program at Harvard Medical SchoolK12TR004381 · NCATS · HARVARD MEDICAL SCHOOL · PI Karen K Miller · 2023 to 2026
$6.5M
Enabling improved applicability and transferability of polygenic scores across populationsU01HG011719 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI Alicia Martin · 2021 to 2026
$5.5M
Whole genome sequences in individuals to comprehensively characterize the genetic mechanisms of dyslipidemiasR01HL142711 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Satoshi Koyama, Gina Marie Peloso · 2019 to 2026
$4.7M
Advancing the clinical actionability of polygenic scores for coronary artery diseaseK08HL168238 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Aniruddh Patel · 2024 to 2026
backgroundPrior work suggests modifiable cardiovascular risk factors (CRFs) account for 80% to 90% of the risk for incident myocardial infarction. The contributions of genetic and other novel CRFs have not been simultaneously assessed in contemporary data sets.
methodsIn the United Kingdom Biobank, CRFs were identified and Cox proportional hazards models with traditional CRFs (hypertension, diabetes, dyslipidemia, waist-to-hip ratio, diet, exercise, alcohol, and socioeconomic deprivation) and contemporary/genetic CRFs (Lp(a) [lipoprotein(a)], hsCRP [high-sensitivity C-reactive protein], familial hypercholesterolemia variants, and polygenic risk score for coronary artery disease) were constructed for coronary artery disease. Coronary artery disease was defined as a first-time myocardial infarction diagnosis or coronary revascularization. R
resultsAmong 299 707 individuals, the mean (SD) age was 56.2 (8.1) years, and 166 533 (55.6%) were women. Over a median (interquartile range) follow-up of 11.0 (9.6-12.5) years, 17 409 (5.8%) of participants developed myocardial infarction. R
conclusionsNovel CRFs like polygenic risk score for coronary artery disease, hsCRP, and Lp(a) have similar importance, comparable to traditional CRFs such as hypertension, dyslipidemia, and diabetes, for incident myocardial infarction, highlighting important identifiable residual risk factors.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Importance of Clinical, Laboratory, and Genetic Risk Factors for Incident CAD. · full record | OpenQuestion