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International Journal of Endocrinology
Volume 2013 (2013), Article ID 285680, 9 pages
Clinical Study

How to Estimate Fat Mass in Overweight and Obese Subjects

1Medical Physiopathology, Food Science and Endocrinology Section, Food Science and Human Nutrition Research Unit, Experimental Medicine Department, Sapienza University of Rome, 00185 Rome, Italy
2Section of Human Nutrition and Dietetics, Endocrinology and Nutrition Unit, Department of Applied Health Sciences, Faculty of Medicine, University of Pavia, ASP, 27100 Pavia, Italy

Received 20 February 2013; Accepted 18 March 2013

Academic Editor: Felice Strollo

Copyright © 2013 Lorenzo Maria Donini 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.


Background. The prevalence of overweight and obesity is increasing and represents a primary health concern. Body composition evaluation is rarely performed in overweight/obese subjects, and the diagnosis is almost always achieved just considering body mass index (BMI). In fact, whereas BMI can be considered an important tool in epidemiological surveys, different papers stated the limitations of the use of BMI in single individuals. Aim. To assess the determinants of body composition in overweight and obese subjects. Methods. In 103 overweight or obese subjects (74 women, aged 41.5 ± 10 years, and 29 men, aged 43.8 ± 8 years), a multidimensional evaluation was performed including the assessment of body composition using Dual Energy X-Ray Absorptiometry (DXA), anthropometry, bioimpedance analysis (BIA), and biochemical parameters (total cholesterol, triacylglycerol, HDL- and LDL-cholesterol, free fatty acids and glycerol, glucose, insulin, C-reactive protein, plasma acylated and unacylated ghrelin, adiponectin, and leptin serum levels). Results. BMI does not represent the main predictor of FM estimated by DXA; FM from BIA and hip circumference showed a better association with FM from DXA. Moreover, models omitting BMI explained a greater part of variance. These data are confirmed by the predictive value analysis where BMI showed a performance similar to a “coin flip.”