Modifiable health-factor burden and stroke outcomes across three population databases: cross-sectional and...

Background Modifiable vascular and lifestyle factors often cluster in middle-aged and older adults. We examined whether a simple harmonized health profile provides comparable stroke-related information across public population databases. Methods We used NHANES 2007-2018, CHARLS 2018 and HRS 2018 for cross-sectional analyses, and CHARLS 2011-2018 and HRS 2014-2022 for longitudinal validation. The cross-sectional outcome was self-reported physician-diagnosed stroke history. The main exposure was a six-factor burden score covering physical inactivity, hypertension, diabetes, dyslipidemia or high cholesterol, heart disease and current smoking. Database-specific weighted logistic regression estimates were pooled using random-effects meta-analysis. Longitudinal analyses used person-wave discrete-time survival models. Prediction analyses used CHARLS for model development and HRS for external validation. Results The adjusted cross-sectional models included 14,475 NHANES participants, 16,070 CHARLS participants and 8,175 HRS participants. Stroke history increased monotonically across burden categories in all three databases. Each additional unfavorable factor was associated with higher odds of stroke history in NHANES (odds ratio, 1.69; 95% confidence interval, 1.55-1.86), CHARLS (1.67; 1.58-1.77) and HRS (1.63; 1.52-1.76). The pooled odds ratio was 1.66 (95% confidence interval, 1.60-1.73), with little observed heterogeneity. In longitudinal models, each 1-point higher brain-health score was associated with lower hazards of incident self-reported stroke in CHARLS (hazard ratio, 0.740; 95% confidence interval, 0.712-0.769) and HRS (0.830; 0.789-0.874). The brain-health score showed moderate discrimination in CHARLS (area under the curve, 0.645) and HRS (0.648). Conclusions A higher burden of unfavorable modifiable health factors was consistently associated with stroke history across three public datasets. Longitudinal analyses linked a higher brain-health score to lower incident self-reported stroke hazards. These profiles may support low-cost population risk stratification, but they do not support causal or individual-level clinical interpretation.