Frontiers in Endocrinology (Apr 2024)

Surrogate indices of insulin resistance using the Matsuda index as reference in adult men—a computational approach

  • Víctor Antonio Malagón-Soriano,
  • Andres Julian Ledezma-Forero,
  • Cristian Felipe Espinel-Pachon,
  • Álvaro Javier Burgos-Cárdenas,
  • Maria Fernanda Garces,
  • Gustavo Eduardo Ortega-Ramírez,
  • Roberto Franco-Vega,
  • Jhon Jairo Peralta-Franco,
  • Luis Miguel Maldonado-Acosta,
  • Jorge Andres Rubio-Romero,
  • Manuel Esteban Mercado-Pedroza,
  • Sofia Alexandra Caminos-Cepeda,
  • Ezequiel Lacunza,
  • Carlos Armando Rivera-Moreno,
  • Aquiles Enrique Darghan-Contreras,
  • Ariel Iván Ruiz-Parra,
  • Jorge E. Caminos

DOI
https://doi.org/10.3389/fendo.2024.1343641
Journal volume & issue
Vol. 15

Abstract

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BackgroundOverweight 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 indices of IR are derived and validated with the euglycemic–hyperinsulinemic clamp (EHC) test. Thus, using a computational approach to predict IR with Matsuda index as reference, this study aimed to determine the optimal cutoff value and diagnosis accuracy for surrogate indices in non-diabetic young adult men.MethodsA cross-sectional descriptive study was carried out with 93 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, and anthropometric characteristics and body composition (DEXA) were determined. The computational approach to evaluate the IR diagnostic accuracy and cutoff value using difference parameters was examined, as well as other statistical tools to make the output robust.ResultsThe highest sensitivity and specificity at the optimal cutoff value, respectively, were established for the Homeostasis model assessment of insulin resistance index (HOMA-IR) (0.91; 0.98; 3.40), the Quantitative insulin sensitivity check index (QUICKI) (0.98; 0.96; 0.33), the triglyceride-glucose (TyG)-waist circumference index (TyG-WC) (1.00; 1.00; 427.77), the TyG-body mass index (TyG-BMI) (1.00; 1.00; 132.44), TyG-waist-to-height ratio (TyG-WHtR) (0.98; 1.00; 2.48), waist-to-height ratio (WHtR) (1.00; 1.00; 0.53), waist circumference (WC) (1.00; 1.00; 92.63), body mass index (BMI) (1.00; 1.00; 28.69), total body fat percentage (TFM) (%) (1.00; 1.00; 31.07), android fat (AF) (%) (1.00; 0.98; 40.33), lipid accumulation product (LAP) (0.84; 1.00; 45.49), leptin (0.91; 1.00; 16.08), leptin/adiponectin ratio (LAR) (0.84; 1.00; 1.17), and fasting insulin (0.91; 0.98; 16.01).ConclusionsThe computational approach was used to determine the diagnosis accuracy and the optimal cutoff value for IR to be used in preventive healthcare.

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