Development and validation of a risk prediction algorithm for high-risk populations combining genetic and conventional risk factors of cardiovascular disease

Résumé fourni par la source

AIM: To develop a model for cardiovascular disease (CVD) risk, combining polygenic risk score (PRS) with traditional risk factors while assessing the added value of PRS in two cohorts of biobank participants. METHODS: Data of 128 209 participants from the Estonian Biobank recruited between 2002-2017 and 2018-2022 without prevalent cardiovascular disease, was included. Hazard ratios (HR) for polygenic risk versus conventional risk factors were estimated with Cox proportional hazards models, cumulative incidence was assessed with Aalen-Johansen curves. Predictive performance was tested using a split-sample approach and competing risk modelling. Age at CVD event served as the outcome, and the impact of the PRS was evaluated by age group (25-59 vs. 60+), sex, and recruitment period, using HRs, Harrell's C-index, and net reclassification indices (NRI). RESULTS: The estimated HR per one standard deviation (SD) of PRS ranged from 1.1, 95% CI 1.06-1.15 (age 60 + , earlier cohort) to 1.36, 95% CI 1.24-1.49 (men 25-59, later cohort). Adding PRS to the conventional risk factors in the age group 25-59 increased the C-statistic by 0.028 (p < 0.0001) for men. In the age group 60 + , the increase was 0.016 (p = 0.0002) across all. In the independent validation set, the continuous NRI was 19.1% (95% CI 13.3%-24.9%) in the 25-59 group and 13.9% (95% CI 8.1%-19.6%) in the 60 + group. CONCLUSIONS: In a high-risk population, PRS is a strong independent risk factor for CVD and should be considered in routine risk assessment, starting at a relatively young age.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.