1. Patterns of insulin concentration during the OGTT predict the risk of type 2 diabetes in Japanese Americans.
- Author
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Hayashi T, Boyko EJ, Sato KK, McNeely MJ, Leonetti DL, Kahn SE, and Fujimoto WY
- Subjects
- Asian, Female, Humans, Male, Middle Aged, Odds Ratio, Diabetes Mellitus, Type 2 epidemiology, Diabetes Mellitus, Type 2 metabolism, Glucose Tolerance Test methods, Insulin metabolism
- Abstract
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.
- Published
- 2013
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