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How to Estimate Fat Mass in Overweight and Obese Subjects
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 co...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3639623/ https://www.ncbi.nlm.nih.gov/pubmed/23662101 http://dx.doi.org/10.1155/2013/285680 |
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author | Donini, Lorenzo Maria Poggiogalle, Eleonora del Balzo, Valeria Lubrano, Carla Faliva, Milena Opizzi, Annalisa Perna, Simone Pinto, Alessandro Rondanelli, Mariangela |
author_facet | Donini, Lorenzo Maria Poggiogalle, Eleonora del Balzo, Valeria Lubrano, Carla Faliva, Milena Opizzi, Annalisa Perna, Simone Pinto, Alessandro Rondanelli, Mariangela |
author_sort | Donini, Lorenzo Maria |
collection | PubMed |
description | 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.” |
format | Online Article Text |
id | pubmed-3639623 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-36396232013-05-09 How to Estimate Fat Mass in Overweight and Obese Subjects Donini, Lorenzo Maria Poggiogalle, Eleonora del Balzo, Valeria Lubrano, Carla Faliva, Milena Opizzi, Annalisa Perna, Simone Pinto, Alessandro Rondanelli, Mariangela Int J Endocrinol Clinical Study 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.” Hindawi Publishing Corporation 2013 2013-04-10 /pmc/articles/PMC3639623/ /pubmed/23662101 http://dx.doi.org/10.1155/2013/285680 Text en Copyright © 2013 Lorenzo Maria Donini et al. https://creativecommons.org/licenses/by/3.0/ 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. |
spellingShingle | Clinical Study Donini, Lorenzo Maria Poggiogalle, Eleonora del Balzo, Valeria Lubrano, Carla Faliva, Milena Opizzi, Annalisa Perna, Simone Pinto, Alessandro Rondanelli, Mariangela How to Estimate Fat Mass in Overweight and Obese Subjects |
title | How to Estimate Fat Mass in Overweight and Obese Subjects |
title_full | How to Estimate Fat Mass in Overweight and Obese Subjects |
title_fullStr | How to Estimate Fat Mass in Overweight and Obese Subjects |
title_full_unstemmed | How to Estimate Fat Mass in Overweight and Obese Subjects |
title_short | How to Estimate Fat Mass in Overweight and Obese Subjects |
title_sort | how to estimate fat mass in overweight and obese subjects |
topic | Clinical Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3639623/ https://www.ncbi.nlm.nih.gov/pubmed/23662101 http://dx.doi.org/10.1155/2013/285680 |
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