Assessment of six surrogate insulin resistance indexes for predicting cardiometabolic multimorbidity incidence in Chinese middle-aged and older populations: Insights from the China health and retirement longitudinal study

Abstract

Aims

Insulin resistance (IR) is associated with cardiometabolic multimorbidity (CMM). We aimed to explore the predictive value of six surrogate IR indexes—Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), triglyceride-glucose (TyG), atherogenic index of plasma (AIP), TyG-body mass index (TyGBMI), and TyG-waist circumference (TyGwaist)—to establish the CMM incidence in Chinese middle-aged and older populations.

Material and Methods

To estimate the odds ratio (OR) with a 95% confidence interval (95% CI) for incident CMM using six surrogates, we analysed data from the China Health and Retirement Longitudinal Study using multivariate logistic regression models. The nonlinear dose-response correlation was evaluated using restricted cubic spline analysis; predictive performance was assessed using receiver operator characteristic curves.

Results

Among 6451 eligible participants, 268 (4.2%) developed CMM during the 4-year follow-up period. The ORs (95% CI) for incident CMM increased with increasing CVAI quartiles (Q) [Q2: 1.71, 1.03–2.90; Q3: 2.72, 1.70–4.52; Q4: 5.16, 3.29–8.45; all p < 0.05] after full adjustment, with Q1 as the reference. Other indexes yielded similar results. These associations remained significant in individuals with a normal body mass index. Notably, CVAI, AIP, and TyG exhibited a linear dose-response relationship with CMM (P_nonlinear ≥0.05), whereas LAP, TyGBMI, and TyGwaist displayed significant nonlinear correlations (P_nonlinear <0.05). The area under the curve for the CVAI (0.691) was significantly superior to that of other indexes (all p < 0.05).

Conclusions

The six IR surrogates were independently associated with CMM incidence. CVAI may be the most appropriate indicator for predicting CMM in middle-aged and older Chinese populations.