Development and Evaluation of a Comprehensive Prediction Model for Incident Coronary Heart Disease Using Genetic, Social, and Lifestyle-Psychological Factors: A Prospective Analysis of the UK Biobank

Mohammadreza Naderian; Kristjan Norland; Daniel J Schaid; Iftikhar J Kullo

doi:10.7326/ANNALS-24-00716

Development and Evaluation of a Comprehensive Prediction Model for Incident Coronary Heart Disease Using Genetic, Social, and Lifestyle-Psychological Factors: A Prospective Analysis of the UK Biobank

Ann Intern Med. 2024 Dec 10. doi: 10.7326/ANNALS-24-00716. Online ahead of print.

Authors

Mohammadreza Naderian¹, Kristjan Norland¹, Daniel J Schaid², Iftikhar J Kullo³

Affiliations

¹ Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota (M.N., K.N.).
² Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota (D.J.S.).
³ Department of Cardiovascular Medicine and Gonda Vascular Center, Mayo Clinic, Rochester, Minnesota (I.J.K.).

PMID: 39652873
DOI: 10.7326/ANNALS-24-00716

Abstract

Background: Clinical risk calculators for coronary heart disease (CHD) do not include genetic, social, and lifestyle-psychological risk factors.

Objective: To improve CHD risk prediction by developing and evaluating a prediction model that incorporated a polygenic risk score (PRS) and a polysocial score (PSS), the latter including social determinants of health and lifestyle-psychological factors.

Design: Cohort study.

Setting: United Kingdom.

Participants: UK Biobank participants recruited between 2006 and 2010.

Measurements: Incident CHD (myocardial infarction and/or coronary revascularization); 10-year clinical risk based on pooled cohort equations (PCE), Predicting Risk of cardiovascular disease EVENTs (PREVENT), and QRISK3; PRS (Polygenic Score Catalog identification: PGS000018) for CHD (PRS_CHD); and PSS_CHD from 100 related covariates. Machine-learning and time-to-event analyses and model performance indices.

Results: In 388 224 participants (age, 55.5 [SD, 8.1] years; 42.5% men; 94.9% White), the hazard ratio for 1 SD increase in PSS_CHD for incident CHD was 1.43 (95% CI, 1.38 to 1.49; P < 0.001) and for 1 SD increase in PRS_CHD was 1.59 (CI, 1.53 to 1.66, P < 0.001). Non-White persons had higher PSS_CHD than White persons. The effects of PSS_CHD and PRS_CHD on CHD were independent and additive. At a 10-year CHD risk threshold of 7.5%, adding PSS_CHD and PRS_CHD to PCE reclassified 12% of participants, with 1.86 times higher CHD risk in the up- versus down-reclassified persons and showed superior performance compared with PCE as reflected by improved net benefit while maintaining good calibration relative to the clinical risk calculators. Similar results were seen when incorporating PSS_CHD and PRS_CHD into PREVENT and QRISK3.

Limitation: A predominantly White cohort; possible healthy participant effect and ecological fallacy.

Conclusion: A PSS_CHD was associated with incident CHD and its joint modeling with PRS_CHD improved the performance of clinical risk calculators.

Primary funding source: National Human Genome Research Institute.