Associations of cumulative exposure and dynamic trajectories of the combined atherogenic and frailty index with incident cardiometabolic multimorbidity: a longitudinal analysis based on the China Health and Retirement Longitudinal Study (CHARLS)

BACKGROUND AND OBJECTIVE: The atherogenic index of plasma (AIP) reflects atherogenic dyslipidemia and insulin resistance, whereas the frailty index (FI) quantifies cumulative physiological deficits across multiple organ systems. Although both the AIP and FI are independently associated with cardiometabolic multimorbidity (CMM), the joint impact of the atherogenic index of plasma-frailty index (AIPFI) on the risk of CMM remains unclear. In this study, the associations between baseline levels, cumulative AIPFI and longitudinal changes of AIPFI and the incidence of CMM were evaluated. METHODS: Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS). A total of 7995 participants were included in the baseline analysis, and 4,483 participants with repeated biomarker measurements in 2012 and 2015 were included in the longitudinal analysis. AIPFI was calculated via the following formula: AIPFI = AIP × FI. Multivariate Cox proportional hazards models and restricted cubic splines were applied to evaluate the associations of AIPFI with the incidence of CMM. K-means clustering characterized longitudinal AIPFI patterns. The clinical prediction model was performed in both the training and validation cohorts, as validated by receiver operating characteristic curve analysis, calibration curve analysis, and decision curve analysis. The shapley additive explanations (SHAP) method was employed to provide further explanation. RESULTS: A total of 7995 participants were included and followed for a median of 9.0 years, during which 747 (9.3%) incident CMMs occurred. Across quartiles of the AIPFI, the risk of CMM increased progressively, with adjusted HR of 2.46 (95% CI 1.77-3.43) for Q4 compared with those for Q1. In longitudinal analyses (n = 4,483), participants in cluster 2, with persistently high and increasing AIPFI values, presented increased risks of CMM (HR 1.54, 95% CI 1.19-2.01). An elevated cumulative AIPFI was associated with an increased incidence of CMM (HR 1.01, 95% CI 1.01-1.01). The RCS revealed a significant positive nonlinear relationship between the baseline AIPFI and cumulative AIPFI with the risk of CMM (all P < 0.05, and all P for nonlinear values < 0.05). SHAP model analysis revealed hypertension, heart disease, and AIPFI as the most influential predictors. CONCLUSIONS: Both baseline and longitudinal changes in the AIPFI were independently associated with the risk of CMM. The incorporation of longitudinal monitoring of the AIPFI into routine health evaluations may enhance population-level CMM risk prediction and support more effective prevention strategies.