Evidence map›Paper›PMID 41848465›Full record

ArticleJournal of the American College of Cardiology2026

Combining Genomics With Lipid and Inflammatory Biomarkers to Predict Coronary Artery Disease Risk: UK Biobank Study.

Raysha Farah, Min Seo Kim, Buu Truong, Yang Sui, So Mi Jemma Cho, Sarah Margaret Urbut, Aniruddh Patel, Paul M Ridker, Pradeep Natarajan, Akl C Fahed

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In one paragraph

Article in Journal of the American College of Cardiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

10 authors.

Raysha FarahDepartment of Medicine, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Min Seo KimCardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Buu TruongCardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Yang SuiCardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
So Mi Jemma ChoCardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, Seoul, Republic of Korea.
Sarah Margaret UrbutCardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Aniruddh PatelCardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Paul M RidkerCenter for Cardiovascular Disease Prevention, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Pradeep NatarajanCardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Akl C FahedCardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. Electronic address: afahed@mgh.harvard.edu.

Funding

Advancing the clinical actionability of polygenic scores for coronary artery diseaseK08HL168238 · MASSACHUSETTS GENERAL HOSPITAL · 2025 to 2025
$170k
Integration of novel contextual and genomic blood pressure measures to enhance cardiovascular disease prediction and management in young adultsK99HL177340 · BROAD INSTITUTE, INC. · 2025 to 2025
$165k
Integrating genomic and nongenomic risk for coronary artery diseaseK08HL161448 · MASSACHUSETTS GENERAL HOSPITAL · 2025 to 2025
$122k
NHLBI NIH HHS K08 HL161448NHLBI NIH HHS K08 HL168238NHLBI NIH HHS K99 HL177340
6 · The paper itself

Abstract

backgroundCoronary artery disease (CAD) polygenic risk score (PRS), low-density-lipoprotein cholesterol (LDL-C), lipoprotein(a) (Lp(a)), and high-sensitivity C-reactive protein (hsCRP) are biomarkers that predict CAD. It is unclear whether integrating genomics with lipid and inflammatory biomarkers could complement traditional risk scores in identifying people at risk of CAD.

objectivesThis study assesses the predictive value of CAD PRS, LDL-C, Lp(a), and hsCRP for incident CAD across different age and sex groups.

methodsParticipants (n = 215,695) from the UK Biobank aged 40 to 69 years with baseline CAD PRS, LDL-C, Lp(a), and hsCRP values were followed for 12 years to assess the incidence of CAD. We evaluated a multivariable-adjusted Cox model that included all 4 biomarkers, net reclassification index, C-statistics, and population attributable risk across different age and sex groups.

resultsOver a 12-year follow-up, 4,721 men and 2,425 women developed CAD. The HRs for incident CAD associated with each biomarker elevation were 1.79 (95% CI: 1.70-1.89) for CAD PRS, 1.60 (95% CI: 1.48-1.66) for LDL-C, 1.20 (95% CI: 1.12-1.29) for Lp(a), and 1.64 (95% CI: 1.57-1.72) for hsCRP. CAD PRS demonstrated a stronger association in men (HR per SD: 1.49; 95% CI: 1.45-1.54) than women (HR per SD: 1.37; 95% CI: 1.31-1.44; P-interaction ≤ 0.001). All biomarkers conferred greater HRs at younger ages (P < 0.0001). Individuals with all biomarkers elevated had a 4.65-fold increased risk of CAD compared with those with no elevated biomarkers. A combined 4-biomarker model had a higher C-statistic of 0.753 compared with the pooled cohort equations (C-statistic of 0.740). The C-statistic of the combined 4-biomarker model was also higher in younger individuals in both sexes and yielded a 32.0% continuous net reclassification index when compared with the pooled cohort equations.

conclusionsCAD PRS, LDL-C, hsCRP, and Lp(a) show independent age- and sex-specific associations with CAD. Measuring all 4 biomarkers may improve midlife CAD risk prediction for both male and female patients.

Indexed as

Cholesterol, LDLCoronary Artery DiseaseC-Reactive ProteinGenomicsLipidsLipoprotein(a)AdultAgedBiological Specimen BanksBiomarkersFemaleFollow-Up StudiesGenetic Risk ScoreHumansIncidenceMaleBiomarkersCholesterol, LDLC-Reactive ProteinLipidsLipoprotein(a)atherosclerotic diseasehigh-sensitivity C-reactive proteinlipoprotein(a)low-density-lipoprotein cholesterolpolygenic risk score

Identifiers

PMID41848465
PMCPMC13332811

What Socratic holds

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Registered trials

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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 Socratic graph.