Use of Polygenic Risk Score for Prediction of Heart Failure in Cancer Survivors.

BACKGROUND

The risk for heart failure (HF) is increased among cancer survivors, but predicting individual HF risk is difficult. Polygenic risk scores (PRS) for HF prediction summarize the combined effects of multiple genetic variants specific to the individual.

OBJECTIVES

The aim of this study was to compare clinical HF prediction models with PRS in both cancer and noncancer populations.

METHODS

Cancer and HF diagnoses were identified using International Classification of Diseases-10th Revision codes. HF risk was calculated using the ARIC (Atherosclerosis Risk in Communities) HF score (ARIC-HF). The PRS for HF (PRS-HF) was calculated according to the Global Biobank Meta-analysis Initiative. The predictive performance of the ARIC-HF and PRS-HF was compared using the area under the curve (AUC) in both cancer and noncancer populations.

RESULTS

After excluding 2,644 participants with HF prior to consent, 440,813 participants without cancer (mean age 57 years, 53% women) and 43,720 cancer survivors (mean age 60 years, 65% women) were identified at baseline. Both the ARIC-HF and PRS-HF were significant predictors of incident HF after adjustment for chronic kidney disease, overall health rating, and total cholesterol. The PRS-HF performed poorly in predicting HF among cancer (AUC: 0.552; 95% CI: 0.539-0.564) and noncancer (AUC: 0.561; 95% CI: 0.556-0.566) populations. However, the ARIC-HF predicted incident HF in the noncancer population (AUC: 0.804; 95% CI: 0.800-0.808) and provided acceptable performance among cancer survivors (AUC: 0.748; 95% CI: 0.737-0.758).

CONCLUSIONS

The prediction of HF on the basis of conventional risk factors using the ARIC-HF score is superior compared to the PRS, in cancer survivors, and especially among the noncancer population.