Head-to-head comparison of six cumulative metabolic indices and joint association with renal function for incident cardiometabolic multimorbidity in non-diabetic adults with CKM syndrome stages 0–3: a prospective cohort study

Abstract

Cumulative exposure to metabolic abnormalities may provide a time-integrated measure of long-term metabolic burden as a complement to single baseline measurements. However, the comparative prognostic value of different cumulative metabolic indices for predicting incident cardiometabolic multimorbidity (CMM) in individuals with cardiovascular-kidney-metabolic (CKM) syndrome remains unclear. We conducted a prospective cohort study using data from the China Health and Retirement Longitudinal Study (CHARLS) including 3,788 non-diabetic adults with CKM stages 0–3 who were free of diabetes at both the 2011 baseline and the 2015 landmark. Six cumulative metabolic indices were calculated using repeated measurements obtained in 2012 and 2015. Associations with incident CMM were evaluated using Cox proportional hazards models and restricted cubic splines. Predictive performance was assessed using 5-year area under the receiver operating characteristic curve (AUC) derived from Cox model-predicted 5-year risks, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Joint analyses with renal function, subgroup analyses, and multiple sensitivity analyses were conducted to evaluate robustness. During a median follow-up of 5.0 years, 154 participants (4.1%) developed CMM. After full adjustment, higher cumulative CHG, NHHR, and TyG were independently associated with an increased risk of CMM, with hazard ratios (highest vs. lowest quartile) of 3.21 (95% CI, 1.92–5.37), 3.52 (95% CI, 2.05–6.05), and 3.18 (95% CI, 1.83–5.53), respectively (all P  < 0.001). Higher cumulative eGDR was associated with a lower CMM risk (HR, 0.30; 95% CI, 0.15–0.58; P for trend < 0.001). Restricted cubic spline analyses showed no evidence of nonlinearity for most cumulative metabolic indices, except for cum_TyG (P for nonlinearity = 0.048). Among all cumulative indices, cum_CHG showed the largest numerical incremental discrimination beyond the reference model (ΔAUC = 0.040; DeLong P  = 0.037; category-free NRI = 0.213; IDI = 0.017); however, no index demonstrated statistically significant superiority after multiple-comparison correction, and cum_eGDR was inversely oriented. Participants with both high cum_CHG and lower estimated glomerular filtration rate (eGFR) had the highest risk of incident CMM, but this joint analysis represents a descriptive joint association rather than a validated risk-stratification tool. No statistically significant multiplicative interaction was observed between cum_CHG and eGFR ( P  = 0.341). These associations remained consistent across subgroup and sensitivity analyses. Among non-diabetic adults with CKM stages 0–3, higher cumulative metabolic burden, particularly as captured by cum_CHG, was independently associated with an increased risk of incident CMM. Among the six cumulative indices, cum_CHG showed the largest numerical incremental discrimination; however, its superiority over other indices was not statistically established, and cum_eGDR exhibited an inverse association. The joint analysis of cum_CHG and eGFR identified a subgroup with the highest observed CMM risk, but this finding should be interpreted as a descriptive joint association rather than a validated risk-stratification tool. These exploratory findings support the potential value of longitudinal metabolic assessment in the early stages of CKM syndrome but require external validation before clinical implementation.