Research Article An Anthropometric Risk Index Based on Combining Height, Weight, Waist, and Hip Measurements Nir Y. Krakauer 1 and Jesse C. Krakauer 2 1 Department of Civil Engineering, Te City College of New York, New York, NY, USA 2 Metro Detroit Diabetes and Endocrinology, Southfeld, MI, USA Correspondence should be addressed to Nir Y. Krakauer; nirkrakauer@gmail.com Received 4 March 2016; Revised 8 August 2016; Accepted 22 September 2016 Academic Editor: Sharon Herring Copyright © 2016 N. Y. Krakauer and J. C. Krakauer. Tis 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. Body mass index (BMI) can be considered an application of a power law model to express body weight independently of height. Based on the same power law principle, we previously introduced a body shape index (ABSI) to be independent of BMI and height. Here, we develop a new hip index (HI) whose normalized value is independent of height, BMI, and ABSI. Similar to BMI, HI demonstrates a U-shaped relationship to mortality in the Tird National Health and Nutrition Examination Survey (NHANES III) population. We further develop a new anthropometric risk index (ARI) by adding log hazard ratios from separate nonlinear regressions of the four indicators, height, BMI, ABSI, and HI, against NHANES III mortality hazard. ARI far outperforms any of the individual indicators as a linear mortality predictor in NHANES III. Te superior performance of ARI also holds for predicting mortality hazard in the independent Atherosclerosis Risk in Communities (ARIC) cohort. Tus, HI, along with BMI and ABSI, can capture the risk profle associated with body size and shape. Tese can be combined in a risk indicator that utilizes complementary information from height, weight, and waist and hip circumference. Te combined ARI is promising for further research and clinical applications. 1. Introduction Body mass index (BMI) (weight [] relative to height [] as / 2 ) [1] has been robustly established to be independent of height in numerous and diverse population studies and is currently used in the defnition of overweight and obesity. Waist circumference (WC) has been used to indicate the presence of abdominal obesity, with WC above threshold forming one criterion for diagnosis of metabolic syndrome [2, 3]. However, the high correlation (0.8–0.9) found between BMI and WC or WC-derived measures such as WC/H ratio [4–6] and body roundness index [7, 8] limit the utility of these measures beyond BMI. BMI traces back to the pioneering 1800s statistician Quetelet, who postulated a power-law relationship between height and weight [9]. BMI can be considered a special case of the concept of power-law scaling of body dimensions (allometry), developed in biology during the early 1900s [10]. Building on these ideas, we previously applied regression based on power-law scaling to derive an index (a body shape index [ABSI]) that expresses waist circumference (WC) relative to height and weight and was therefore statistically independent of BMI [11]. Odds ratios for mortality in several longitudinal studies showed a U-shaped distribution across BMI and a positive linear association with ABSI [12–14]. Hip circumference (HC) and derived measures such as waist to hip ratio (WC/HC, WHR) have also been studied extensively as risk factors [15–17]. However, HC and WHR are typically highly correlated to BMI or WC, and several studies have failed to show added value of HC-based indicators compared to those only based on , , and WC [18–20]. In general, comparisons of various indices based on , , WC, and HC have shown that diferent individual indices may perform approximately equally well as predictors of mortality and conditions such as heart disease [14, 20–25]. While the joint use of multiple indices could improve risk prediction, high correlations between diferent measures are one reason that there is as yet no clear methodology to Hindawi Publishing Corporation Journal of Obesity Volume 2016, Article ID 8094275, 9 pages http://dx.doi.org/10.1155/2016/8094275