Lifestyle factors as predictors of general cardiovascular disease: use for early self-screening.

This study aims to examine the effectiveness of lifestyle factors in predicting general cardiovascular events and to investigate the feasibility of using the lifestyle model as a self-screening tool. The authors conducted a longitudinal study over a 10-year follow-up in Japan. Logistic regression analysis was used to create prediction models for general cardiovascular disease (CVD) death. The authors estimated the predictive power of the models by calculating the area under the receiver operating characteristic (AUROC) curve. The total of 6 traditional and 5 lifestyle risk factors were significantly associated with the incidence of CVD events. Hazard ratios (HRs) were 0.26 (95% confidence interval [CI] = 0.17, 0.41) for regular physical activity, 0.57 (95% CI = 0.50, 0.67) for moderate- or high-intensity work, and 1.72 (95% CI = 1.31-2.26) for short sleep duration; the HRs for traditional and Western dietary patterns were 1.53 (95% CI = 1.12, 2.09) and 2.62 (95% CI = 1.46, 4.68), respectively. The AUROC curve was significantly different between the classic and lifestyle prediction models. These results suggest that lifestyle factors are significant predictors of CVD events.