Triglyceride-Based Indices Versus Homeostasis Model Assessment of Insulin Resistance for Quantitative Insulin Sensitivity Check Index-Defined Insulin Resistance in Metabolic Syndrome: Implications for Resource-Constrained Healthcare

Background Insulin resistance (IR) is an important pathogenic factor in metabolic syndrome (MetS) and its associated cardiometabolic complications. Insulin-based indices such as the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and the Quantitative Insulin Sensitivity Check Index (QUICKI) are well-validated surrogates of IR; however, their dependence on fasting insulin assays restricts their implementation in limited-resource healthcare facilities. Triglyceride-based indices, especially the triglyceride-glucose (TyG) index, have been considered as affordable, insulin-free alternatives. However, their diagnostic accuracy compared with HOMA-IR has been inconsistent across different categories of individuals. Hence, this study aimed to evaluate the incremental efficacy of composite indices comprising anthropometric and glycemic parameters among patients with MetS, and to compare the diagnostic performance of triglyceride-based indices with HOMA-IR for detecting QUICKI-defined IR. Methodology This cross-sectional study included 101 participants, comprising 50 healthy controls and 51 participants diagnosed with MetS. Assessments included anthropometric, clinical, and fasting biochemical parameters. The QUICKI index was used to categorize IR. Triglycerides, the TyG index, fasting insulin, HOMA-IR, and composite indicators (body mass index (BMI) + glycated hemoglobin (HbA1c)), TyG + BMI, TyG + BMI + HbA1c) were all subjected to receiver operating characteristic curve analysis. Diagnostic odds ratios (DORs), sensitivity, specificity, and area under the curve (AUC) were calculated at cut-offs derived using the Youden Index. To determine the independent variables associated with IR as determined by QUICKI, multivariate logistic regression was used. Results Participants with MetS exhibited substantially lower QUICKI scores (p < 0.001) and significantly higher BMI, waist circumference, blood pressure, fasting insulin, HOMA-IR, HbA1c, and TyG-based parameters. The differentiation efficacy of HOMA-IR (AUC = 0.978) and fasting insulin (AUC = 0.972) was relatively high. The diagnostic effectiveness of triglycerides alone was inadequate (AUC = 0.572). Strong diagnostic accuracy was achieved using composite indices, specifically BMI + HbA1c (AUC = 0.868) and TyG + BMI + HbA1c (AUC = 0.874), with high specificity (91.7%) and high DORs (132) among the MetS subgroup. Triglycerides were not independently significant (p = 0.121), whereas BMI (odds ratio (OR) = 1.257; p = 0.002) and HbA1c (OR = 20.552; p < 0.001) were independent predictors of IR on multivariate analysis. Conclusions Compared with HOMA-IR, independent triglyceride-based indices show inadequate diagnostic significance for identifying QUICKI-defined IR. However, composite indices that include BMI, HbA1c, and lipid characteristics provide reliable, effective substitutes that are not dependent on insulin assays, which makes them suitable for medical facilities with limited resources. These results strengthen a multi-parameter approach to scalable IR screening and risk assessment in MetS.