Circulating biomarkers and polygenic scores for cardiovascular risk prediction in healthy older adults: differential and complementary contributions.
TL;DR
In older adults, circulating biomarkers and PGSs provide cumulative information for CVD risk prediction, contributing differently to discrimination and risk reclassification.
Abstract
Background
AND
Aims
Atherothrombotic cardiovascular disease (CVD) risk prediction in older adults remains suboptimal. The relative contributions of circulating protein biomarkers and polygenic scores (PGS) are uncertain.
Methods
In 10,433 older individuals aged ≥70 years without prior CVD events, we evaluated traditional risk factors alongside three circulating biomarkers (high-sensitivity C-reactive protein, hsCRP; N-terminal pro-b-type natriuretic peptide, NT proBNP; and high-sensitivity troponin I, hsTnI), and two PGSs (coronary artery disease, ischemic stroke) for prediction of major adverse cardiovascular events (MACE). Associations were assessed using Cox proportional hazards models. Model performance was evaluated using the C-index, calibration, and continuous net reclassification improvement (NRI).
Results
Over a median follow-up of 4.5 years (interquartile range 3.4-5.5), 359 MACE occurred. Each biomarker and both PGSs were independently associated with MACE, with NT-proBNP showing the strongest association (adjusted HR per SD 1.50, 95% CI 1.36-1.65). Compared with the base clinical model, the addition of the three circulating biomarkers (hsCRP, NT-proBNP, and hsTnI) resulted in a greater improvement in discrimination than the addition of the two PGSs (ΔC-index +0.041 vs. +0.016). The fully combined model achieved the highest discrimination (C-index 0.734) with good calibration. Circulating biomarkers improved reclassification primarily through correct identification of non-cases (NRI 0.34), whereas PGSs contributed relatively more to identification of cases (NRI 0.31).
Conclusions
In older adults, circulating biomarkers and PGSs provide cumulative information for CVD risk prediction, contributing differently to discrimination and risk reclassification. Integrating biomarkers and genetic risk may improve CVD risk prediction in older people beyond traditional risk factors.