Dynamic Changes in Metabolic Syndrome Scores and New‐Onset Stroke Risk in Middle‐Aged and Older Adults: A Nationwide Prospective Cohort Study in China Aligned With Predictive, Preventive, and Personalized Medicine

BACKGROUND: Despite the established link between metabolic syndrome (MetS) and stroke incidence, the effects of dynamic and cumulative MetS scores on stroke risk among middle-aged and older populations in China remain inadequately explored. Furthermore, it is unclear whether MetS scores could serve as a more robust predictor of new-onset stroke. METHODS: Using data from 4281 participants aged 45 years and older from Waves 1 and 3 of the CHARLS (China Health and Retirement Longitudinal Study), time-varying MetS scores were classified via K-means clustering into 4 distinct subgroups spanning 2012 to 2015. Associations between MetS scores and incident stroke were evaluated employing logistic regression, and machine learning predicted new-onset stroke risk based on MetS score and other covariates. RESULTS: Elevated baseline and cumulative MetS scores were independently associated with an increased risk of stroke. Participants categorized within Class 3 (persistent moderate-to-high MetS levels) and Class 4 (highly fluctuating elevated MetS levels) exhibited significantly higher stroke risk relative to Class 1 (stable low MetS levels). The gradient boosting machine model achieved superior predictive accuracy, reflected by an area under the curve of 0.76 (95% CI, 0.72-0.79). Shapley additive explanations identified age, MetS score, and body mass index as the most influential predictors. CONCLUSIONS: Fluctuations in MetS scores, along with baseline and cumulative MetS measurements, are independently associated with an elevated risk of stroke. Moreover, the MetS score is anticipated to be a reliable and clinically relevant indicator for the prediction of new-onset stroke risk.