Abstract
BACKGROUND: Atherogenic dyslipidemia significantly influences diabetes development, yet the longitudinal prognostic value of the atherogenic index of plasma (AIP) in prediabetic populations remains understudied. This study investigated whether dynamic AIP measures predict diabetes conversion in middle-aged and older Chinese adults with prediabetes while exploring interactions with other metabolic risk factors through interpretable machine learning. METHODS: We analyzed data from 1965 prediabetic adults (≥ 45 years) from the China Health and Retirement Longitudinal Study (2012-2015), assessing baseline AIP, cumulative AIP exposure (AIPcum), and change patterns via K-means clustering. Associations with incident diabetes were evaluated using logistic regression with progressive confounder adjustment. Five machine learning models were developed using predictors identified through the Boruta algorithm and recursive feature elimination, with the best model interpreted via SHAP (SHapley Additive exPlanations) analysis. RESULTS: During follow-up, 15.0% of participants progressed to diabetes. Elevated AIPcum showed the strongest association with diabetes risk (OR for highest vs. lowest quartile = 2.37; 95% CI: 1.60-3.57; P < 0.001), while "Persistently High" and "Increasing" AIP change patterns exhibited ∼2.5-fold higher risk versus "Persistently Low" patterns. Random forest achieved the best predictive performance (AUROC = 0.760), with glucose, hand grip strength, AIPcum, and waist circumference as key predictors. Interaction analyses revealed significant synergistic effects between AIPcum and other metabolic factors. CONCLUSION: Cumulative AIP exposure and unfavorable AIP change patterns independently predict prediabetes-to-diabetes progression. Incorporating these measures into risk assessment may enhance early identification of high-risk individuals and inform targeted interventions.