Prediabetes
Analysis of the predictive effects of anthropometric indicators on prediabetes
Han Jinyu, Ma Xiaoyu, Li Xiaomeng, Zheng Xin, Qu Jiashu, Duan Tingting, Liu Siruo, Wang Qiuyue
Published 2022-03-20
Cite as Chin J Diabetes Mellitus, 2022, 14(3): 218-224. DOI: 10.3760/cma.j.cn115791-20210803-00426
Abstract
ObjectiveTo explore the predictive effects of anthropometric indicators on prediabetes.
MethodsThis study was a cross-sectional survey research. A total of 239 cases from June 2020 to April 2021 were recruited as study subjects from the people aged 18 to 70 years with high risk of diabetes and the healthy people in the Physical Examination Center of the First Affiliated Hospital of China Medical University. All patients′ sex, age, height, weight, systolic blood pressure, diastolic blood pressure, heart rate and so on were collected, oral glucose tolerance test (OGTT) was performed and pancreatic islet function and glycated hemoglobin A1c (HbA1c) were detected. Anthropometric indicators including abdominal volume index (AVI), body roundness index (BRI), body adiposity index (BAI), conicity index (C-index), body shape index (ABSI), clínica universidad de navarra-body adiposity estimator (Cun-BAE) and homeostasis model assessment insulin resistance index (HOMA-IR) were calculated. All the subjects were divided into the normal glucose tolerance (NGT) group and the prediabetes group according to the OGTT test results. Rank sum test and chi-square test were used to analyze the difference in basic data between the two groups. Spearman correlation was used to analyze the correlation between HbA1c, HOMA-IR and anthropometric indicators. The receiver operating characteristic (ROC) curve and logistic regression were used to analyze the predictive effects of anthropometric indicators on prediabetes.
ResultsThere were 119 cases in NGT group (including 46 healthy people in the physical examination center), 120 cases in prediabetes group. AVI, BRI, BAI, C-index, ABSI, CUN-BAE were statistically different between the two groups (all P<0.05), and they were all related to HbA1c and HOMA-IR (all P<0.05). ROC curve analysis showed that 3.698 as the boundary value the BRI had the maximum ROC area (AUC=0.710). The multivariate logistic regression analysis showed that BRI (OR=4.408), C-index (OR=4.540, all P<0.05) had the greatest influence on prediabetes, and stepwise logistic regression showed AVI, BAI and ABSI could be combined to predict prediabetes. The area under the ROC curve of the combined application of AVI, BAI and ABSI was also the largest (AUC=0.735).
ConclusionsBRI and C-index are good predictors of prediabetes, but they cannot be used as independent indicators for predicting prediabetes. The combined application of AVI, BAI, and ABSI is more effective in predicting prediabetes.
Key words:
Prediabetic state; Forecasting; Anthropometric indicators
Contributor Information
Han Jinyu
Department of Geriatrics, First Affiliated Hospital of China Medical University, Shenyang 110001, China
Ma Xiaoyu
Department of Geriatrics, First Affiliated Hospital of China Medical University, Shenyang 110001, China
Li Xiaomeng
Department of Endocrinology, First Affiliated Hospital of China Medical University, Shenyang 110001, China
Zheng Xin
Department of Endocrinology, First Affiliated Hospital of China Medical University, Shenyang 110001, China
Qu Jiashu
Department of Endocrinology, First Affiliated Hospital of China Medical University, Shenyang 110001, China
Duan Tingting
Department of Endocrinology, First Affiliated Hospital of China Medical University, Shenyang 110001, China
Liu Siruo
Department of Endocrinology, First Affiliated Hospital of China Medical University, Shenyang 110001, China
Wang Qiuyue
Department of Endocrinology, First Affiliated Hospital of China Medical University, Shenyang 110001, China
