Triglyceride glucose-related indexes and lipid accumulation products-reliable markers of insulin resistance in the Chinese population

Insulin resistance (IR) is a pivotal pathogenic component of metabolic diseases. It is crucial to identify convenient and reliable indicators of insulin resistance for its early detection. This study aimed at assessing the predictive ability of seven novel obesity and lipid-related indices. A total of 5,847 female and 3,532 male healthy subjects were included in the study. The triglyceride glucose (TyG) index, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), lipid accumulation products (LAP), body roundness index (BRI), body adiposity index (BAI), and visceral adiposity index (VAI) were measured and calculated using the established formulae. IR was diagnosed using the homeostatic model assessment of insulin resistance (HOMA-IR) index over the third quantile. The levels of all seven lipid-related indices were significantly higher in subjects with higher HOMA-IR values than in those with lower HOMA-IR values. These indices displayed moderate to high effectiveness [receiver operating characteristic (ROC) curve-area under the curve (AUC) > 0.6] in predicting IR. Among them, TyG-BMI (AUC: 0.729), LAP (AUC: 0.708), and TyG-WC (AUC: 0.698) showed the strongest association with HOMA-IR. In the female population, the AUC for TyG-BMI, LAP, and TyG-WC in predicting IR was 0.732, 0.705, and 0.718, respectively. Logistic regression analysis showed the optimal cut-off values of those indicators in predicting IR as follows: TyG-BMI: male subjects - 115.16 [odds ratio (OR) = 6.05, 95% CI: 5.09-7.19], female subjects - 101.58 (OR = 4.55, 95% CI: 4.00-5.16); LAP: male subjects - 25.99 (OR = 4.53, 95% CI: 3.82-5.38), female subjects - 16.11 (OR = 3.65, 95% CI: 3.22-4.14); and TyG-WC: male subjects - 409.43 (OR = 5.23, 95% CI: 4.48-6.24), female subjects - 342.48 (OR = 4.07, 95% CI: 3.59-4.61). TyG-index-related parameters and LAP appear to be effective predictors of IR in the Chinese population. Specifically, TyG-BMI may be the most appropriate predictor of IR.