# Integrating Polygenic Scores with Clinical, Lifestyle, and Social Risk Factors to Improve Heart Failure Risk Prediction

## TL;DR

This study shows that combining genetic and non-genetic factors improves the prediction of heart failure risk, offering opportunities for early intervention.

## Contribution

The study introduces a novel approach integrating polygenic scores with clinical, lifestyle, and social risk factors for heart failure prediction.

## Key findings

- The integrated model (PGS + CRS + PXS) outperformed individual risk scores in predicting heart failure.
- Including PGS with clinical and exposure risk factors as independent features improved model performance based on AUPRC and F1 score.
- Combining multiple domains of risk factors enhances heart failure risk prediction accuracy.

## Abstract

Heart failure (HF) is highly prevalent, high-burden disorder with its prevalence expected to increase. Early detection of HF can reduce morbidity and mortality; therefore, novel early detection methods are needed. Polygenic scores (PGS) can combine common variants across the genome and provide phenotype-specific risk scores. However, there are also many well-known, non-genomic risk factors of HF, in the clinical, lifestyle, and social determinant of health (SDOH) domains, and it is not clear how genetic and non-genetic risk factors collectively contribute to HF risk. To address this question, we assessed whether combining HF PGS with clinical, lifestyle, and SDOH risk factors improves risk prediction. Leveraging data from the All of Us Research Program (n = 22,275), clinical risk factors were aggregated into a clinical risk score (CRS) while lifestyle and SDOH risk factors were aggregated into a polyexposure score (PXS). Feature selection was conducted with LASSO regression and statistical significance thresholding from logistic regression models (p < 0.05). Features were included in the model if they were statistically significant and important in ≥ 95% of 1000 iterations. To assess model performance, logistic regressions with HF case/control status were conducted with each risk score individually, as well as integrated models. The integrated model (PGS + CRS + PXS) performed better than individual risk scores (AUROC = 0.763, AUPRC = 0.047, F1 score = 0.062, balanced accuracy = 0.683). To assess the validity of the CRS and PXS, an integrated model with the PGS along with clinical and exposure risk factors as independent features was also evaluated. Based on AUPRC and F1 score, this integrated risk model (PGS + CRS risk factors + PXS risk factors) performed better than the combining the PGS with the CRS and PXS (AUROC = 0.738, AUPRC = 0.047, F1 score = 0.066, balanced accuracy = 0.657). 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.