The cut-off values of surrogate measures for insulin resistance in the Korean population according to the Korean Genome and Epidemiology Study (KOGES)

ObjectiveThe current methods available for determining insulin resistance are complicated; hence, they are only applicable to small-scale studies. Therefore, this study aimed to classify the characteristics of surrogate measures for insulin resistance and establish valid cut-off values for predicting the development of type 2 diabetes mellitus (DM) in Korean populations.MethodsThis prospective study included 7,643 participants aged 40-69 years from the Ansung-Ansan cohort database (2001-2012). Four surrogate measures, namely homeostasis model assessment-insulin resistance (HOMA-IR), visceral adiposity index (VAI), lipid accumulation product (LAP), and triglycerides and glucose (TyG) index, were analyzed. We analyzed each measure using receiver operating characteristic (ROC) curve for the development of type 2 DM. The cut-off value was determined as the value with the highest Youden index score in the specificity dominant area.ResultsThe area under the curve (AUC) was 0.566 (95% confidence interval [CI], 0.548-0.583) for HOMA-IR, 0.622 (95% CI, 0.605-0.639) for VAI, 0.642 (95% CI, 0.625-0.658) for LAP, and 0.672 (95% CI, 0.656-0.687) for TyG index. The AUC of TyG index was significantly higher than that of HOMA-IR, VAI, and LAP (p < 0.001). The cut-off value was 2.54 (sensitivity 36.8%; specificity 73.1%; hazard ratio [HR], 1.41, 95% CI, 1.25-1.59) for HOMA-IR, 2.54 (sensitivity 50.4%; specificity 68.8%; HR, 1.75, 95% CI, 1.55-1.96) for VAI, 36.6 (sensitivity 59.2%; specificity 63.9%; HR, 1.87, 95% CI, 1.64-2.14) for LAP, and 4.69 (sensitivity 62.1%; specificity 63.1%; HR, 2.17, 95% CI, 1.92-2.45) for TyG index.ConclusionsThe TyG index was a better predictor for DM than HOMA-IR. VAI and LAP showed the modest predictability for DM. The TyG index could be a useful supplementary method for identifying individuals at risk for insulin resistance and DM development.This research was supported by Hallym University Research Fund 2017 (HURF-2017-48)