Significant interactions between traditional risk factors affect cardiovascular risk prediction in healthy general population

Abstract

Aims. The aim was to carry out a systematic screening of interactions between the traditional risk factors and to evaluate which interactions are truly relevant for estimation of cardiovascular disease (CVD) risk. Methods. Cox regression was used in a meta-analysis of five independent, population-based health examination surveys (the National FINRISK Study). End-points were 10-year incidence of coronary heart disease (CHD), ischemic stroke (IS), and CVD in a population free of cardiovascular disease (n = 35,460). Results. In addition to expected age interactions, systolic blood pressure was found to be a markedly stronger risk factor for CVD (and for CHD) among subjects with normal BMI (BMI < 25: HR 1.42 [1.30-1.55] for one SD increase in systolic blood pressure) when compared to obese subjects (BMI > 30: HR 1.10 [1.01-1.19]) (P < 0.001 for interaction) and among subjects with highest high-density lipoprotein (HDL) (33% tertile: HR 1.43 [1.29-1.58]) when compared to subjects with low HDL (lowest 33% tertile: HR 1.20 [1.13-1.28]) (P < 0.001 for interaction). Interactions improved risk prediction of CVD (cross-validated continuous net reclassification improvement [NRI] 49.4% with 95% CI 44.7%-54.1%, P < 0.0001 and clinical NRI 4.7%, with 95% CI 2.8%-6.5%, P < 0.0001). The C-statistic improved from 0.8438 to 0.8455 (P = 0.010). No significant interaction was associated with the risk of IS. Conclusions. There are significant effect modifications between major risk factors, and accounting for them leads to significantly more accurate estimation of cardiovascular risk.