• Chieh-Hua Lu, Sen-Wen Teng, Chung-Ze Wu, Chang-Hsun Hsieh, Jin-Biou Chang, Yen-Lin Chen, Yao-Jen Liang, Po-Shiuan Hsieh, Dee Pei, and Jiunn-Diann Lin.
• Department of Internal Medicine, Division of Endocrinology and Metabolism, Tri-Service General Hospital, National Defense Medical Center.
• Medicine (Baltimore). 2020 Mar 1; 99 (12): e19562e19562.

AbstractIt has been established that prediabetes can causes significant comorbidities, particularly in the elderly. The deterioration of glucose metabolism are generally considered to be results of the impairment of the 4 factors: first, second insulin secretion (FPIS, SPIS, respectively), glucose effectiveness (GE), and insulin resistance. In this study, we enrolled older women to investigate their relationships with prediabetes.Five thousand four hundred eighty-two nonobese, nondiabetic women were included. They were divided into normal glucose tolerance and prediabetes groups. Receiver operating characteristic curve was performed to investigate the effects on whether to have prediabetes for each factors. Two models were built: Model 1: FPIS + SPIS, and Model 2: model 1 + GE. The area under the receiver operating characteristic (aROC) curve was used to determine the predictive power of these models.The aROC curve of GE was significantly higher than the diagonal line followed by SPIS and FPIS accordingly. The aROC curve of Model 1 (0.611) was not different from GE. However, Model 2 improved significantly up to 0.663. Based on this model, an equation was built (-0.003 × GE - 212.6 × SPIS - 17.9 × insulin resistance + 4.8). If the calculated value is equal or higher than 0 (≥0), then the subject has higher chance to have prediabetes (sensitivity = 0.607, specificity = 0.635).Among the 4 factors, GE is the most important contributor for prediabetes in older women. By building a model composed of FPIS, SPIS, and GE, the aROC curve increased significantly. The equation built from this model could predict prediabetes precisely.

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