Risk prediction for cardiovascular related diseases using PRS and EHR in the Framingham Heart Study
Taegun Kim, Jaeseung Song, Jong Wha J. Joo
Abstract
Cardiovascular disease is a leading cause of mortality and rising healthcare costs worldwide. Fortunately, the disease is preventable, and addressing risk factors can significantly reduce its effects. Over the past decade, risk prediction models have advanced significantly, with polygenic risk scoring analysis, which is often used in combination with clinical health information for prediction.
Introduction
Cardiovascular disease (CVD) is one of the most prevalent diseases in modern society, posing a significant health risk and a substantial financial burden on global healthcare systems [1–13]. CVD is a complex disorder resulting from interactions between genetic and environmental factors; however, the specific contributions of genes and environmental influences remain poorly understood.
Materials and Methods
The study aims to systematically evaluate the predictive utility of PRS and EHR for CVD-related disease risk prediction by comparing different models. PRS was calculated for six disease outcomes AF, stroke, CHD, CHF, dementia, and diabetes using three widely adopted PRS methods.
Results
To select the method for estimating the PRS score, three different PRS computation methods were compared: PRSice2 [60], LDpred2 [61], and lassosum [62]. For each disease outcome, a logistic regression model was fitted using a single PRS variable as the sole predictor. Model performance was assessed using the AUC.
Discussion
CVD remains a leading cause of mortality worldwide and contributes substantially to escalating healthcare expenditures. Effective management of modifiable risk factors can markedly reduce their burden in many cases. Over the past decade, risk prediction methodologies have evolved considerably, particularly with the introduction of PRS, which are often combined with clinical information to enhance prediction.
Conclusion
This study systematically evaluated the predictive performance of PRS, EHR–based clinical variables, and their integration across six CVD–related outcomes using data from the FHS. By comparing multiple prediction models under PRS-only, EHR-only, and PRS + EHR scenarios, we demonstrated that the relative contribution of genetic and clinical information to disease risk prediction varies substantially across diseases.
Citation: Kim T, Song J, Joo JWJ (2026) Risk prediction for cardiovascular related diseases using PRS and EHR in the Framingham Heart Study. PLoS One 21(4):
e0345914. https://doi.org/10.1371/journal.pone.0345914
Editor: Giuseppe Novelli, Universita degli Studi di Roma Tor Vergata, ITALY
Received: July 19, 2025; Accepted: March 12, 2026; Published: April 17, 2026
Copyright: © 2026 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data used in this study were obtained from the FHS through dbGaP (phs000007.v32.p13: Framingham Cohort). The FHS genotype and phenotype data are not publicly available due to participant privacy restrictions. The data are available to other researchers upon approval of data requests through dbGaP. Data can be requested at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000007.v32.p13. Summary statistics used for PRS estimation were obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas/) for the following datasets: GCST006414 (AF), GCST90473543 (Myocardial Ischemia), GCST90480183 (Diastolic heart failure), GCST007320 (Alzheimer’s disease), GCST90267278 (Diabetes), and GCST90044350 (Stroke). All scripts used for data preprocessing, feature engineering, model training, and figure generation are available on GitHub: https://github.com/DGU-CBLAB/FHS-Riskprediction.
Funding: This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2026-RS-2020-II201789), and the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2026-RS-2023-00254592) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: AF, atrial fibrillation; AUC, area under the receiver operating characteristic curve; AUPRC, area under the precision–recall curve; BG, blood glucose; CALC_LDL, calculated low-density lipoprotein cholesterol; CHD, coronary heart disease; CHF, congestive heart failure; CI, confidence interval; CPD, cigarettes per day; CREAT, creatinine; CVD, cardiovascular disease; DBP, diastolic blood pressure; DLVH, definite left ventricular hypertrophy; DOR, diagnostic odds ratio; EHR, electronic health records; FHS, Framingham Heart Study; GWAS, genome-wide association studies; HDL, high-density lipoprotein cholesterol; HGT, height; HIP, hip girth; LR+, positive likelihood ratio; LR−, negative likelihood ratio; MAF, minor allele frequency; ML, machine learning; PCA, principal component analysis; PRS, polygenic risk score; QC, quality control; SBP, systolic blood pressure; SHAP, Shapley Additive Explanations; TRIG, triglycerides; VENT_RT, ventricular rate by electrocardiography
