Dynamic Changes in Metabolic Syndrome Scores and New-Onset Stroke Risk in Middle-Aged and Older Adults: A Nationwide...

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.

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.

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.

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.