Clinical use of polygenic risk scores for detection of peripheral artery disease and cardiovascular events

• DOI: 10.1371/journal.pone.0303610
• Publication Date: 2024-05-17T18:19:51Z
• Authors: Jesutofunmi A. Omiye; Ilies Ghanzouri; Ivan Lopez; Fudi Wang; John Cabot; Saeed Amal; Jianqin Ye; Nicolas Gabriel L...; Faatihat Adebayo-...; Elsie Gyang Ross
• Keywords: Brier score

We have previously shown that polygenic risk scores (PRS) can improve stratification of peripheral artery disease (PAD) in a large, retrospective cohort. Here, we evaluate the potential PRS improving detection PAD and prediction major adverse cardiovascular cerebrovascular events (MACCE) (AE) an institutional patient created cohort 278 patients (52 cases 226 controls) fit PAD-specific based on weighted sum alleles. built traditional clinical models machine learning (ML) using genetic variables to detect PAD, MACCE, AE. The models’ performances were measured area under curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), Brier score. also evaluated utility our model decision analysis (DCA). found modest, but not statistically significant model’s performance with inclusion from 0.902 (95% CI: 0.846–0.957) (clinical only) 0.909 0.856–0.961) PRS). significantly improved re-classification NRI 0.07 0.002–0.137), p = 0.04. For ML predicting addition did AUC, however, demonstrated ( 2e-05). Decision showed higher benefit combined PRS-clinical across all thresholds detection. Including PAD-risk was associated utility, although see change AUC. This result underscores incorporating data into for prevalent need use evaluation metrics discern impact new biomarkers smaller populations.