Identification of BMI-related high-risk feature combinations for diabetes among young adults with normal baseline fasting plasma glucose using interpretable machine learning: a health check-up cohort study.

Body mass index (BMI) is an easily obtainable indicator for diabetes risk screening, but its residual risk value among young adults with normal fasting plasma glucose (FPG) remains insufficiently understood. This cohort study investigated the association between BMI and incident diabetes, its nonlinear risk pattern, and BMI-related risk structures among young adults with normal baseline FPG. Data were obtained from the Rich Healthcare Group health check-up database in China. Participants aged <40 years without diabetes at baseline, with complete BMI data and at least one follow-up visit, were included; those with baseline FPG <5.6 mmol/L were defined as the primary analytic population. Cox regression and restricted cubic spline analysis were used to examine the association between BMI and incident diabetes. Four machine learning models were compared, with logistic regression selected as the primary interpretable model and XGBoost used as an exploratory nonlinear model. SHapley Additive exPlanations were applied to interpret model-derived variable contributions. A total of 103,693 participants were included, and 266 incident diabetes events occurred during a median follow-up of 2.99 years. BMI was independently associated with incident diabetes in the multivariable Cox model (HR = 1.284, 95% CI: 1.250-1.319; P <0.001). Restricted cubic spline analysis showed a significant nonlinear association, with risk increasing more steeply beyond approximately 28 kg/m². In the validation set, logistic regression and XGBoost achieved ROC-AUC values of 0.812 and 0.817, respectively; however, their low PR-AUC values indicated limited ability to identify true positive cases under the very low event rate. SHAP analysis identified BMI as the most influential predictor in the exploratory XGBoost model and suggested possible model-derived joint contribution patterns involving triglycerides and systolic blood pressure, but formal Cox-based interaction testing did not confirm statistically significant multiplicative interactions. These findings suggest that BMI-related diabetes risk among normoglycemic young adults is nonlinear and embedded within a broader metabolic risk structure. Combining conventional regression with interpretable machine learning may support earlier identification and refined risk stratification of young adults at increased diabetes risk before fasting glucose becomes abnormal.