Evidence map›Paper›PMID 41282843›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Clinical validation of a statin-benefit polygenic score using real-world cohorts of primary prevention participants.

Tanushree Haldar, Kyung Min Lee, Craig C Teerlink, Kruthika R Iyer, Amy L Voorhees, James A Najera, Nanase Toda, Meng Lu, Michael P Douglas, Alice B Popejoy and 9 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

19 authors.

Tanushree HaldarDepartment of Clinical Pharmacy, University of California San Francisco, San Francisco, CA, USA.
Kyung Min LeeVA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA.ORCID 0000-0001-8995-0448
Craig C TeerlinkVA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
Kruthika R IyerDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Amy L VoorheesVA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA.
James A NajeraDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
Nanase TodaDepartment of Clinical Pharmacy, University of California San Francisco, San Francisco, CA, USA.
Meng LuKaiser Permanente Northern California Division of Research, Pleasanton, CA, USA.
Michael P DouglasDepartment of Clinical Pharmacy, University of California San Francisco, San Francisco, CA, USA.
Alice B PopejoyDepartment of Public Health Sciences (Epidemiology Division), School of Medicine, University of California, Davis, Davis, CA, USA.
Adam BressDepartment of Population Health Sciences, Division of Health System Innovation and Research, University of Utah School of Medicine, Salt Lake City, UT, USA.ORCID 0000-0002-2259-5039
Catherine TcheandjieuPalo Alto VA Healthcare System, Palo Alto, CA, USA.ORCID 0000-0001-9559-4339
Philip S TsaoDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0001-7274-9318
Kyong-Mi ChangDepartment of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.ORCID 0000-0001-6811-9364
Ronald M KraussDepartment of Pediatrics, University of California San Francisco, San Francisco, CA, USA.
Neil RischDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0002-4126-2061
Carlos IribarrenKaiser Permanente Northern California Division of Research, Pleasanton, CA, USA.ORCID 0000-0001-5622-1839
Julie A LynchVA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA.ORCID 0000-0003-0108-2127
Akinyemi Oni-OrisanDepartment of Clinical Pharmacy, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0002-5897-5543

Funding

Optimization of statin regimens for atherosclerotic cardiovascular disease prevention using polygenic risk scores and real-world evidenceR56HL161518 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ONI-ORISAN, AKINYEMI · 2022 to 2022
$595k
BLRD VA I01 BX003362NHLBI NIH HHS R56 HL161518
6 · The paper itself

Abstract

Background: Polygenic risk scores derived from coronary artery disease genome-wide association studies are associated with statin relative risk reduction. Objective: Examine the relationship between coronary artery disease polygenic risk scores that include variants below thresholds of genome-wide significance and statin primary prevention of major adverse cardiovascular events. Methods: We generated coronary artery disease polygenic risk scores for participants with no past evidence of myocardial infarction from three electronic health record-linked genetic biobanks: All of Us Research Program, Genetic Epidemiology Research on Adult Health and Aging, and the Million Veteran Program. We scored each participant using three different polygenic risk scores: two with both genome-wide and sub-genome-wide significant variants (metaGRS and PRS2022) and one with only variants meeting genome-wide significance (164SNP). We used covariate-adjusted Cox regression models to compare risk of major adverse cardiovascular events between statin users and nonusers matched on age, sex, smoking status, type 2 diabetes mellitus, and hypertension within strata defined by polygenic risk. For the primary analysis, we performed a meta-analysis across the three cohorts for the delta hazard ratio of statin effectiveness between high and low polygenic risk. Results: Across all cohorts, statin use was more strongly associated with reduced risk of major adverse cardiovascular events among participants with no myocardial infarction at index in the highest versus lowest polygenic risk score group for the PRS2022 (interaction beta 0.19, standard error 0.07, interaction Conclusions: We demonstrated that the association between coronary artery disease polygenic risk scores and statin relative risk reduction can by enhanced with the inclusion of sub-genome-wide variants, paving the way for more research to establish clinical utility.

Identifiers

PMID41282843
PMCPMC12633136

What OpenQuestion holds

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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.