Defining Insulin Resistance From Hyperinsulinemic-Euglycemic Clamps

• Autores: Charmaine S Tam, Wenting Xie, William D. Johnson, William T. Cefalu, Leanne Maree Redman, Eric Ravussin
• Localización: Diabetes care, ISSN-e 0149-5992, Vol. 35, Nº. 7, 2012, págs. 1605-1610
• Idioma: inglés
• Enlaces
  • Texto completo
• Dialnet Métricas: 2 Citas
• Resumen

  • This study was designed to determine a cutoff point for identifying insulin resistance from hyperinsulinemic-euglycemic clamp studies performed at 120 mU/m^sup 2^ * min in a white population and to generate equations from routinely measured clinic and blood variables for predicting clamp-derived glucose disposal rate (GDR), i.e., insulin sensitivity. We assembled data from hyperinsulinemic-euglycemic clamps (120 mU/m^sup 2^ * min insulin dose) performed at the Pennington Biomedical Research Center between 2001 and 2011. Subjects were divided into subjects with diabetes (n = 51) and subjects without diabetes (n = 116) by self-report and/or fasting glucose ? 126 mg/dL. We found that 75% of individuals with a GDR <5.6 mg/kg fat-free mass (FFM) + 17.7 * min were truly insulin resistant. Cutoff values for GDRs normalized for body weight, body surface area, or FFM were 4.9 mg/kg * min, 212.2 mg/m^sup 2^ * min, and 7.3 mg/kgFFM * min, respectively. Next, we used classification tree models to predict GDR from routinely measured clinical and biochemical variables. We found that individual insulin resistance could be estimated with good sensitivity (89%) and specificity (67%) from the homeostasis model assessment of insulin resistance (HOMA-IR) >5.9 or 2.8< HOMA-IR <5.9 with HDL <51 mg/dL. We developed a cutoff for defining insulin resistance from hyperinsulinemic-euglycemic clamps. Moreover, we now provide classification trees for predicting insulin resistance from routinely measured clinical and biochemical markers. These findings extend the clamp from a research tool to providing a clinically meaningful message for participants in research studies, potentially providing greater opportunity for earlier recognition of insulin resistance.