Comparative analysis of insulin resistance indicators in predicting reversion from prediabetes to normoglycemia
摘要
Indicators for insulin resistance (IR) have been proposed for predicting dysglycemia; however, their comparative effectiveness in predicting reversion from prediabetes to normoglycemia remains unclear. This study aimed to compare the predictive performance of four IR indicators for normoglycemia reversion in Chinese adults with prediabetes.
MethodsThis retrospective cohort study included 15,415 participants with prediabetes—fasting plasma glucose (FPG) levels of 5.6–6.9 mmol/L. Normoglycemia reversion was defined as FPG levels < 5.6 mmol/L without a diabetes diagnosis at follow-up. Baseline metabolic scores for IR (MetS-IR), triglyceride glucose (TyG) index, TyG-body mass index (TyG-BMI), and triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio were calculated. Cox proportional hazards models assessed associations between IR indicators and normoglycemia reversion. C-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to assess the predictive ability of IR indicators in addition to a base model.
ResultsOver 3.2 years, 6,624 (43.0%) participants reverted to normoglycemia. After adjusting for potential confounders, a one-standard-deviation increase in MetS-IR, TyG, TyG-BMI, and TG/HDL-C was significantly associated with a lower likelihood of normoglycemia reversion (hazard ratios [95% confidence interval {CI}]: 0.79 [0.77, 0.82], 0.89 [0.87, 0.91], 0.84 [0.82, 0.86], and 0.88 [0.85, 0.90], respectively). The base model showed modest discrimination (C-statistic: 0.608). Adding MetS-IR improved C-statistics most (+ 0.018), followed by TyG-BMI (+ 0.009), TG/HDL-C (+ 0.008), and TyG (+ 0.006). MetS-IR significantly improved NRI (0.109, 95% CI 0.071–0.172) and IDI (0.016, 95% CI 0.009–0.025).
ConclusionsMetS-IR appears to be a more effective predictor of normoglycemia reversion compared to other IR indicators, potentially aiding in the early identification of individuals with prediabetes likely to achieve spontaneous reversion, thereby supporting personalized interventions.