Table of Contents Author Guidelines Submit a Manuscript
International Journal of Endocrinology
Volume 2013 (2013), Article ID 239376, 10 pages
Research Article

Over Time, Do Anthropometric Measures Still Predict Diabetes Incidence in Chinese Han Nationality Population from Chengdu Community?

Department of Cardiovascular Medicine, West China Hospital, Sichuan University, Chengdu 610041, China

Received 20 June 2013; Revised 3 September 2013; Accepted 11 September 2013

Academic Editor: Andre Pascal Kengne

Copyright © 2013 Kai Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objective. To examine whether anthropometric measures could predict diabetes incidence in a Chinese population during a 15-year follow-up. Design and Methods. The data were collected in 1992 and then again in 2007 from the same group of 687 individuals. Waist circumference, body mass index, waist to hip ratio, and waist to height ratio were collected based on a standard protocol. To assess the effects of baseline anthropometric measures on the new onset of diabetes, Cox's proportional hazards regression models were used to estimate the hazard ratios of them, and the discriminatory power of anthropometric measures for diabetes was assessed by the area under the receiver operating curve (AROC). Results. Seventy-four individuals were diagnosed with diabetes during a 15-year follow-up period (incidence: 10.8%). These anthropometric measures also predicted future diabetes during a long follow-up ( ). At 7-8 years, the AROC of central obesity measures (WC, WHpR, WHtR) were higher than that of general obesity measures (BMI) ( ). But, there were no significant differences among the four anthropometric measurements at 15 years. Conclusions. These anthropometric measures could still predict diabetes with a long time follow-up. However, the validity of anthropometric measures to predict incident diabetes may change with time.