Wen Wei1, Xie Xin2, Bing Shao3, Fang-Fang Zeng4, Edgar J Love4, Bin-You Wang3. 1. 1Longquanyi Center for Disease Control and Prevention,Chengdu,People's Republic of China. 2. 3Department of Social Medicine,School of Public Health,Harbin Medical University,Harbin,People's Republic of China. 3. 2Department of Epidemiology,School of Public Health,Harbin Medical University,No. 157 Baojian Street,Harbin 150081,People's Republic of China. 4. 4Guangdong Provincial Key Laboratory of Food,Nutrition, and Health,School of Public Health,Sun Yat-sen University,Guangzhou,People's Republic of China.
Abstract
OBJECTIVE: The present study was performed to test the predictive value of different cut-off points of anthropometric parameters for the presence of type 2 diabetes (T2DM) or glucose tolerance abnormalities in north-east Chinese adults. DESIGN: Multistage random cluster sampling method in a cross-sectional study. SETTING:Height, body weight, maximal body weight in the past, waist and hip circumferences, blood pressure, 2 h post-load glucose and other lifestyle factors were measured. SUBJECTS: We used data from 1058 adults aged 20 years or over, selected in the city of Mudanjiang, in 2005. RESULTS:BMI, maximal BMI in the past (MAXBMI), waist:hip ratio (WHR), waist:height ratio (WHtR) and waist circumference (WC) were significantly correlated with each other. Partial correlation coefficients between WHtR and WC, and between MAXBMI and BMI, were higher than those between the other indices. The association of anthropometric indices with T2DM or glucose tolerance abnormalities was significantly highest for the collaboration cut-off points of MAXBMI (≥ 23.0 kg/m(2) for T2DM, ≥ 22.0 kg/m(2) for glucose tolerance abnormalities) with WHtR (≥ 0.52). Areas under the receiver-operating characteristic curves also showed that WHtR was a better anthropometric index that discriminated between the presence and absence of T2DM and an excellent indicator with high Youden's index. CONCLUSIONS:MAXBMI combined with WHtR was a better anthropometric index associated with T2DM or glucose tolerance abnormalities. The combined use of these two measures is a good choice for T2DM prevention and screening.
RCT Entities:
OBJECTIVE: The present study was performed to test the predictive value of different cut-off points of anthropometric parameters for the presence of type 2 diabetes (T2DM) or glucose tolerance abnormalities in north-east Chinese adults. DESIGN: Multistage random cluster sampling method in a cross-sectional study. SETTING: Height, body weight, maximal body weight in the past, waist and hip circumferences, blood pressure, 2 h post-load glucose and other lifestyle factors were measured. SUBJECTS: We used data from 1058 adults aged 20 years or over, selected in the city of Mudanjiang, in 2005. RESULTS: BMI, maximal BMI in the past (MAXBMI), waist:hip ratio (WHR), waist:height ratio (WHtR) and waist circumference (WC) were significantly correlated with each other. Partial correlation coefficients between WHtR and WC, and between MAXBMI and BMI, were higher than those between the other indices. The association of anthropometric indices with T2DM or glucose tolerance abnormalities was significantly highest for the collaboration cut-off points of MAXBMI (≥ 23.0 kg/m(2) for T2DM, ≥ 22.0 kg/m(2) for glucose tolerance abnormalities) with WHtR (≥ 0.52). Areas under the receiver-operating characteristic curves also showed that WHtR was a better anthropometric index that discriminated between the presence and absence of T2DM and an excellent indicator with high Youden's index. CONCLUSIONS: MAXBMI combined with WHtR was a better anthropometric index associated with T2DM or glucose tolerance abnormalities. The combined use of these two measures is a good choice for T2DM prevention and screening.
Entities:
Keywords:
Anthropometry; Impaired glucose tolerance; Maximal BMI in the past; Type 2 diabetes; Waist:height ratio
Authors: Ping Rao; Yong Zhou; Si-Qi Ge; An-Xin Wang; Xin-Wei Yu; Mohamed Ali Alzain; Andrea Katherine Veronica; Jing Qiu; Man-Shu Song; Jie Zhang; Hao Wang; Hong-Hong Fang; Qing Gao; You-Xin Wang; Wei Wang Journal: Int J Environ Res Public Health Date: 2016-08-30 Impact factor: 3.390