Longitudinal Trajectories of Body Roundness Index and Risk of Incident Hypertension in Non-Diabetic Elderly Adults: A Prospective Nationwide Cohort Study Involving Two Cohorts

Figures

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  Figure 1. Study design, participant selection, and analytical workflow.

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  Figure 2. Longitudinal trajectories of body roundness index and incidence of hypertension in the CHARLS cohort. (A) Three-group BRI trajectory model. (B) Incidence of hypertension according to trajectory group.

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  Figure 3. The comprehensive rank of five independent base ML models and various stacking ensemble combinations. (A) Features panel selection strategy implemented in the training set. Bars represent feature importance ranked by contribution to the prediction of incident hypertension. The line plot shows the cumulative area under the receiver operating characteristic curve (AUC; right y-axis) as features were sequentially added. Shaded areas indicate 95% confidence intervals estimated using 10-fold cross-validation. Features selected for the final model are highlighted in red and were identified based on the absence of significant incremental improvement in model performance over two consecutive iterations, as assessed by DeLong’s test (two-sided P > 0.05). (B) Comprehensive ranking of five individual machine learning models, random forest (RF), decision tree (DT), gradient boosting decision tree (GBDT), support vector machine (SVM), and logistic regression, and multiple stacking ensemble models in the testing set. The SVM–BDT–DT–Logistic stacking ensemble achieved the highest overall rank based on combined performance metrics. (C) Comparison of AUC values between the SVM–BDT–DT–Logistic…

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  Figure 4. Global model interpretability analysis. (A) Combined SHAP summary and bar plots for the six features included in the hypertension risk prediction model. SHAP values represent the contribution of each feature to the model output. Each dot corresponds to an individual participant in the testing dataset and is colored according to the feature value, with vertical stacking indicating feature value density. The bar plot shows the mean absolute SHAP value for each feature. (B) Pie chart showing the relative percentage contribution of each of the six features to the prediction model, calculated based on the mean absolute SHAP values. (C) Bar plots and pie charts illustrating heterogeneity in feature contributions to the hypertension prediction model, quantified using SHAP values, across BRI trajectory groups (trajectory 1, trajectory 2, and trajectory 3) and age strata (45–50 years, 50–60 years, and >60 years). Feature groups include lifestyle factors (smoking and alcohol consumption) and biochemical markers (C-reactive protein and remnant cholesterol), with bars representing absolute contributions and pie charts indicating relative proportions. (D) SHAP dependence plots with locally…