ArticleFrontiers in endocrinology2024
Surrogate indices of insulin resistance using the Matsuda index as reference in adult men-a computational approach.
Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- The Bidirectional Relationship Between Type 1 Diabetes Mellitus and Obesity in Pediatric Patients: A Systematic Review.Children (Basel, Switzerland) · 2026Review
- Muscle Ultrasound Echo Intensity Detects Insulin Resistance Before Changes in HbAJournal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- Ectopic fat distribution and its correlation with insulin resistance indices in non-obese individuals with type 2 diabetes.Acta diabetologica · 2026Article
- Diagnostic Accuracy of the Triglyceride-Glucose Derived Indices in Detecting Metabolic Syndrome in Pediatric Patients.Journal of obesity & metabolic syndrome · 2026Article
- Article
- Comparison of eight insulin-independent indices for identifying insulin resistance in Chinese young and middle-aged adults without diabetes: favorable performance of METS-IR and TyG-BMI.Frontiers in medicine · 2026Article
- Bayesian Youden index for algorithmic evaluation under class imbalance: mathematical foundations with applications to insulin resistance and diabetes progression.Frontiers in epidemiology · 2026Article
- Role of Matsuda Index in Identifying Patients at Risk for Cystic Fibrosis-Related Diabetes Development.Children (Basel, Switzerland) · 2025Article
- Enhanced oral glucose tolerance test for early detection of insulin resistance and metabolic complications in children with obesity.American journal of preventive cardiology · 2025Article
- Obesity in chronic spinal cord injury is associated with poorer body composition and increased risk of cardiometabolic disease.Scientific reports · 2025Article
- Article
- Association between triglyceride glucose-body mass index and all-cause mortality in critically ill patients with acute myocardial infarction: retrospective analysis of the MIMIC-IV database.Frontiers in nutrition · 2024Article
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Abstract
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 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. Methods: A 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. Results: The 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). Conclusions: The 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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