Surrogate indices of insulin resistance using the Matsuda index as reference in adult men—a computational approach - Malagón-Soriano, Víctor Antonio; Ledezma-Forero, Andres Julian; Espinel-Pachon, Cristian Felipe; Burgos-Cárdenas, Álvaro Javier; Garces, Maria Fernanda; Ortega-Ramírez, Gustavo Eduardo; Franco-Vega, Roberto; Peralta-Franco, Jhon Jairo; Maldonado-Acosta, Luis Miguel; Rubio-Romero, Jorge Andres; Mercado-Pedroza, Manuel Esteban; Caminos-Cepeda, Sofia Alexandra; et al. (Frontiers in Endocrinology, 2024)
Background: Overweight and obesity, high blood pressure, hyperglycemia, hyperlipidemia and insulin resistance (IR) are strongly associated with non-communicable diseases (NCDs), including type 2 diabetes, cardiovascular disease, stroke and cancer.Different surrogate indexes of IR are derived and validated with the euglycemic hyper insulinemic clamp (EHC) test. Thus, using a computational approach to predict IR with Matsuda index as reference, this study aimed to determine the optimal cut-off value and diagnosis accuracy for surrogate indices in non-diabetic young adult men. Methods: A cross-sectional descriptive study was carried out with ninety three young men (ages 18-31). Serum levels of glucose and insulin were analyzed in the fasting state and during an oral glucose tolerance test (OGTT). Additionally, clinical, biochemical, hormonal, anthropometric characteristics and body composition (DEXA) were determined. The computational approach to evaluate the IR diagnostic accuracy and cut-off value using difference parameters was examined and other statistical tools to make the output robust.Results: Highest sensitivity, specificity at the optimal cutoff value respectively, were established for HOMA-IR (0.91; 0.98; 3.40), QUICKI (0.98; 0.96; 0.33), TyG-WC (1.00; 1.00; 427.77), TyG-BMI (1.00; 1.00; 132.44), TyG-WHtR (0.98; 1.00; 2.48), WHtR (1.00; 1.00; 0.53), WC (1.00; 1.00; 92.63), BMI (1.00; 1.00; 28.69), TFM (%)(1.00; 1.00; 31.07), AF (%)(1.00; 0.98; 40.33), LAP (0.84; 1.00; 45.49), Leptin (0.91; 1.00; 16.08), LAR (0.84; 1.00; 1.17) and fasting insulin (0.91; 0.98; 16.01). Conclusions: The computational approach was used to determine the diagnosis accuracy and the optimal cut-off value to determine IR to use in preventive health care.