Integrating Polygenic Scores with Clinical, Lifestyle, and Social Risk Factors to Improve Heart Failure Risk Prediction
This repository contains the code for this publication: https://doi.org/10.1142/9789819824755_0046
- Author: Katie M. Cardone
- Published at Pacific Symposium on Biocomputing 2026
Paper Summary
- Previous research as developed independent risk scores for genomic (polygenic scores, PGS), clinical (clinical risk scores, CRS), and lifestyle and social risk factors (polyexposure score, PXS), but their combined predictive power remains largely under-explored in the context of HF.
- Leveraging data from the All of Us Research Program, we assessed whether combining HF PGS with clinical, lifestyle, and SDOH risk factors improves risk prediction
- Clinical, lifestyle, and social risk factors were aggregated into CRS and PXS using average and weighted average methods
- The integrated model (PGS + CRS + PXS) performed better than individual risk scores.
- These findings demonstrate that integration of risk factors across multiple domains can improve HF prediction. Knowing that PGS combined with clinical, lifestyle, and SDOH risk factors is predictive of HF risk provides greater opportunity for the identification of individuals at risk of HF prior to disease onset with the goal of prevention or early intervention