N.L.S. Corral, B.R. Ely, and C.T. Minson, FACSM

University of Oregon, Eugene, OR

The oral glucose tolerance test (OGTT) is a common clinical tool to assess metabolic health, and is often used in exercise training or diet intervention studies. Various calculations can be done with the data obtained from an OGTT, however, the relationship between static (baseline) and dynamic (change over time) markers of insulin sensitivity and metabolic health have not been examined in women.

PURPOSE: To compare static and dynamic markers of insulin sensitivity obtained through an OGTT in female subjects through a range of BMI (lean to obese) and metabolic health status.

METHODS: Ten female subjects (Age: 18-41; BMI: 20.4-40.9) underwent an OGTT, which consisted of ingesting a 75g glucose drink following a 12-hr overnight fast. Blood samples were taken at 0, 10, 20, 30 45, 60, 90 and 120 min and analyzed for glucose and insulin. Common clinical calculations for insulin sensitivity were performed, including static (homeostatic model assessment of insulin resistance [HOMA-IR] and quantitative insulin sensitivity check index [QUICKI]; both based on fasting glucose and insulin)) and dynamic indexes (Matsuda index and glucose area under the curve [AUC]; based on changes in glucose and insulin during an OGTT). Indexes were compared using linear regression analysis and relationships were considered significant at p<0.05. RESULTS: Based on HOMA-IR, QUICKI, Matsuda Index, and AUC, 4 of the 10 women were classified as insulin resistant, and 3 of these 4 had a BMI >30 (obese). BMI was significantly (p<0.05) correlated with QUICKI (r=0.64), HOMA-IR(r=0.66), and AUC (r=0.66), but not Matsuda. Significant correlations were found between HOMA-IR & QUICKI (r=0.77, p=0.009), HOMA-IR & AUC(r=0.87, p=0.001), and QUICKI & Matsuda (r= 0.97, p<0.001). No other significant relationships were observed between indexes. CONCLUSIONS: Strong correlations were observed between static and dynamic measures, however these relationships varied, suggesting that each index may provide unique insight. If possible, static and dynamic indexes should be considered when analyzing OGTT data to gain a more complete picture of metabolic health in women. These results can be applied to future intervention studies aimed at improving metabolic health in women.

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