Predictive Value of Three Insulin Resistance Surrogate Indices for Hypoglycemia Risk in Hospitalized Patients with Type 2 Diabetes: A Retrospective Cohort Study

Background Inpatient hypoglycemia is a serious complication in hospitalized patients with type 2 diabetes mellitus (T2DM), associated with adverse outcomes. While insulin resistance (IR) is central to hyperglycemia, its relationship with hypoglycemia risk in acute care settings remains unclear. Convenient IR surrogate indices, such as the triglyceride-glucose index (TyG), triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C), and metabolic syndrome insulin resistance index (METS-IR), warrant evaluation for hypoglycemia prediction. Aims To evaluate and compare the predictive utility of three IR indices (TyG, TG/HDL-C, METS-IR) for in-hospital hypoglycemia in T2DM patients. Methods This retrospective cohort study included 660 hospitalized T2DM patients (April 2024–April 2025) with ≥ 8-day stay and valid CGM data. Hypoglycemia was defined by CGM < 3.9 mmol/L, capillary glucose confirmation, and clinical symptoms. Survival analysis, multivariable Cox regression, restricted cubic splines, time-dependent ROC, and decision curve analysis were used. Results During follow-up, 193 (29.2%) patients developed hypoglycemia. TyG and METS-IR were independently inversely associated with hypoglycemia risk (per 1-SD increase: HR = 0.680, 95%CI = 0.600–0.773; HR = 0.748, 95%CI = 0.680–0.823), with linear dose-response relationships. TG/HDL-C showed no significant association. TyG exhibited superior model fit (lower AIC), better predictive performance on hospital day 2 (AUC = 0.721), and higher net clinical benefit at 20%–30% risk thresholds. TyG was more predictive in patients with good glycemic control, while METS-IR performed better in those with long diabetes duration. Conclusion TyG and METS-IR are valid predictors of in-hospital hypoglycemia in T2DM patients. TyG demonstrates optimal predictive performance and clinical utility, supporting its integration into early risk stratification for individualized glycemic management.