China 
Africa 
Explore chapters and articles related to this topic
The Scale of the Problem—Overweight and Obesity
Published in Ruth Chambers, Paula Stather, Tackling Obesity and Overweight Matters in Health and Social Care, 2022
Indices of central obesity, including waist circumference, waist-to-hip ratio, waist-to-height ratio, waist-to-thigh ratio, body adiposity index and a body shape index independent of overall adiposity, were all found to be positively associated with a higher risk of all-cause mortality in the general population in a systematic review of 72 published research studies.1
Anthropometry in Physical Performance and Health
Published in Henry C. Lukaski, Body Composition, 2017
Other combined measurements have been used to derive composite indices. The conicity index (Valdez 1991) includes height, weight, and waist girth in a theoretical model of opposed truncated cones. The body adiposity index (Bergman et al. 2011) uses body height raised to the power of 1.5 and hip circumference, but subsequent work showed it was no better than BMI, waist or hip girth at predicting fatness (Freedman et al. 2012). The body roundness index considers the eccentricity of the body’s height in relation to waist, and was successfully shown to relate to % fat and % VAT (Thomas et al. 2013). While this study appears very robust, its assumed circularity of the waist is a limitation, and the assumption that waist circumference should increase with stature is at odds with the aforementioned observations of Wells et al. (2007). Taken together, there are many anthropometric health indices to select from, all of which have their benefits and limitations. Although their applicability may be limited in some groups, especially athletes (Santos et al. 2015), the emergence of newer indices especially those that predict VAT is likely to supplant the overreliance on some of the more traditional indices that have been used ubiquitously in the past.
Hedgehog interacting protein as a circulating biomarker in women with obesity: a cross-sectional study and intervention studies
Published in Annals of Medicine, 2023
Hao Wang, Yanping Wang, Hongmin Zhang, Zerong Liang, Wenjing Hu, Sheng Qiu, Ke Li, Lili Zhang, Han Dai, Mengliu Yang, Gangyi Yang, Ling Li
SPSS 19.0 was used to conduct statistical analyses. The data were presented as mean ± SD or median with interquartile range. Data of non-normal distribution, as determined using the Kolmogorov–Smirnov test, were logarithmically transformed before analysis. Comparisons between the two groups were analysed by independent Student’s t-test. The Association of HHIP with other variables was analysed by simple and multiple correlation coefficients. Binary logistic regression analyses were used to examine the association between serum HHIP and obesity. The receiver operating characteristic (ROC) curve was made by SPSS 19.0 for investigating the sensitivity and specificity of HHIP to predict obesity. The free androgen index (FAI) was calculated as FAI = (TEST/SHBG) × 100 [22]. Body adiposity index (BAI) was calculated as (hip circumference/height 1.5–18). Visceral adiposity index (VAI) = WC/[36.58 + (1.89 × BMI)] × TG/0.81 × 1.52/HDL-C] [23]. The sample size was calculated using the following equation: n = (Z1-α/2σ/εμ)2 (σ, standard error; μ, mean; Z1-α/2 = 1.96, α = 0.05; and ε = 10%). p < .05 was considered significant in comparison to the control.
Validity of cardiometabolic index, lipid accumulation product, and body adiposity index in predicting the risk of hypertension in Chinese population
Published in Postgraduate Medicine, 2018
Haoyu Wang, Yintao Chen, Guozhe Sun, Pengyu Jia, Hao Qian, Yingxian Sun
Recently, the body adiposity index (BAI), calculated from measurements of the hip circumference (HC) and height, has been recently proposed by Bergman et al. as a new method intended to substitute BMI to estimate the percentage of body fat and SAT [21,22]. In prior studies, BAI has been associated with insulin sensitivity in obese women [23], diabetes in men [24], and urinary albumin excretion [25], reflecting its superior clinical utility and prognostic value in the evaluation of cardiovascular risk [26,27]. The cardiometabolic index (CMI) was posited as a new VAT distribution and dysfunction indicator to assess the presence of diabetes and atherosclerotic progression using triglyceride (TG)/high-density lipoprotein cholesterol (HDL-C) and WHtR [28–30]. As such, a novel sex-specific index based on WC and TG, termed lipid accumulation product (LAP), has been suggested to detect insulin resistance, metabolic syndrome, and incident cardiovascular events [31–33]. Taken together, incorporating the concomitant of anthropometric and lipid parameters into a clinically simple and reproducible marker such as CMI and LAP seems to be potentially able to discriminate the visceral compartment and cardiometabolic risk. Notably, knowledge of the properties of VAT and specific role of anthropometric indices have the potential to further our understanding of the hypertension risk conferred by obesity and provide more accurate means for prevention.
Is Body Adiposity Index a Better and Easily Applicable Measure for Determination of Body Fat?
Published in Journal of the American College of Nutrition, 2020
Esen Yeşil, Beril Köse, Merve Özdemir
In a study conducted on European and American participants, BAI was reported to provide a better indicator of adiposity in adults than BMI, but it was found to not provide valid estimates of BF%, particularly on lower levels of BF (19). In another study, BAI was not determined to be more strongly associated with the incidence of diabetes than BMI. Moreover, waist circumference was the strongest predictor in both sexes in two large prospective studies (14). Segheto et al. (23) compared several methods (skinfold thicknesses, bioelectrical impedance analysis and DXA) for assessing body compositions using the body adiposity index (BAI). It was found that BAI was not a predictor of BF in Brazilian adults (21).