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

Figures

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  Figure 1. Flow chart of the study population. A total of 4281 participants were recruited for this survey. AUC indicates area under the curve; BMI, body mass index; DCA, decision curve analysis; MetS, metabolic syndrome; RCS, restricted cubic spline; and SHAP, Shapley additive explanations.

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  Figure 2. Linear associations between baseline and cumulative MetS scores and the incidence of new‐onset stroke. A, Crude RCS model analysis of the association between baseline MetS scores and the risk of new‐onset stroke. Crude model was adjusted with no covariates. B, Adjusted Model 1 adjusted for age, sex, educational attainment, residential status (urban/rural), and marital status. C, Adjusted Model 2 further adjusted for BMI, alcohol consumption, smoking status, dyslipidemia, CVD, diabetes, and hypertension. D, Crude RCS model analysis of the association between cumulative MetS scores and the risk of new‐onset stroke. Crude model was adjusted with no covariates. E, Adjusted Model 1 adjusted for age, sex, educational attainment, residential status (urban/rural), and marital status. F, Adjusted Model 2 further adjusted for BMI, alcohol consumption, smoking status, dyslipidemia, CVD, diabetes, and hypertension. BMI indicates body mass index; CVD, cardiovascular disease; MetS, metabolic syndrome; and RCS, restricted cubic spline.

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  Figure 3. Clustering of changes in the MetS score between Wave 1 and Wave 3. A, Determination of the optimal number of clusters (k=4) for changes in MetS scores using the K‐means clustering algorithm. B, Visualization of the 4 identified clusters based on K‐means clustering with Euclidean distance; the x and y axes represent the principal components of the changes in MetS scores. C, Data visualization for the classes of the change in the MetS score from 2011 to 2012 (Wave 1) and 2015 to 2016 (Wave 3). MetS indicates metabolic syndrome.

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  Figure 4. An assessment of the discriminatory capabilities of 8 machine learning models, accompanied by a feature importance analysis using SHAP. A, Feature importance scores derived from the Boruta algorithm across 100 classifier runs. Each line represents different variable. B, Analysis of feature selection using the Boruta algorithm; the horizontal axis represents the names of each variable, and the vertical axis displays the Z‐values. The box plot illustrates the Z‐values for each variable in the model, with green boxes indicating the selected 8 important variables, yellow boxes representing tentative variables, and red boxes marking unimportant variables. Blue boxes are calculated as reference levels during the run of Boruta algorithm. Notably, MetS score emerged as the top predictor. C, Receiver operating characteristic curves for the machine learning models evaluated on the testing data set. D, Calibration curves for machine learning models applied to the testing data set. E, Decision curve analysis illustrating the net benefits of the various models. F, SHAP summary plot detailing the impact of each feature on stroke risk within the GBM model, as indicated by SHAP values. The…