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Updated: May 15, 2026

Study of In Vivo Glucose Metabolism in High-fat Diet-fed Mice Using Oral Glucose Tolerance Test (OGTT) and Insulin Tolerance Test (ITT)
Published on: January 7, 2018
Patterns of insulin concentration during the OGTT predict the risk of type 2 diabetes in Japanese Americans
Tomoshige Hayashi1, Edward J Boyko, Kyoko Kogawa Sato
1Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA.
Objective:
To examine whether the patterns of insulin concentration during the oral glucose tolerance test (OGTT) predict type 2 diabetes.
Research Design And Methods:
We followed 400 nondiabetic Japanese Americans for 10-11 years. Insulin concentrations at 30, 60, and 120 min during a 2-h 75-g OGTT at baseline were used to derive the following possible patterns of insulin: pattern 1 (30-min peak, higher insulin level at 60 than at 120 min), pattern 2 (30-min peak, lower or equal level at 60 vs. 120 min), pattern 3 (60-min peak); pattern 4 (120-min peak, lower level at 30 than at 60 min), and pattern 5 (120-min peak, equal or higher level at 30 vs. 60 min). Insulin sensitivity was estimated by homeostasis model assessment of insulin resistance (HOMA-IR) and Matsuda index. Insulin secretion was estimated by the insulinogenic index (IGI) [Δinsulin/Δglucose (30-0 min)] and disposition index (IGI/HOMA-IR).
Results:
There were 86 incident cases of type 2 diabetes. The cumulative incidence was 3.2, 9.8, 15.4, 47.8, and 37.5% for patterns 1, 2, 3, 4, and 5, respectively. Compared with pattern 1, patterns 4 and 5, characterized by a lasting late insulin response, were associated with significantly less insulin sensitivity as measured by the Matsuda index and lower early insulin response by the disposition index. The multiple-adjusted odds ratios of type 2 diabetes were 12.55 (95% CI 4.79-32.89) for pattern 4 and 8.34 (2.38-29.27) for pattern 5 compared with patterns 1 and 2. This association was independent of insulin secretion and sensitivity.
Conclusions:
The patterns of insulin concentration during an OGTT strongly predict the development of type 2 diabetes.
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