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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...

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Autores principales: Donini, Lorenzo Maria, Poggiogalle, Eleonora, del Balzo, Valeria, Lubrano, Carla, Faliva, Milena, Opizzi, Annalisa, Perna, Simone, Pinto, Alessandro, Rondanelli, Mariangela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
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.”
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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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