Dataset Information

A simple equation to estimate body fat percentage in children with overweightness or obesity: a retrospective study.

ABSTRACT:

Background

Dual-energy X-ray absorptiometry (DXA) provides separate measurements of fat mass, fat-free mass and bone mass, and is a quick, accurate, and safe technique, yet one that is not readily available in routine clinical practice. Consequently, we aimed to develop statistical formulas to predict fat mass (%) and fat mass index (FMI) with simple parameters (age, sex, weight and height).

Methods

We conducted a retrospective observational cross-sectional study in 416 overweight or obese patients aged 4-18 years that involved assessing adiposity by DXA (fat mass percentage and FMI), body mass index (BMI), sex and age. We randomly divided the sample into two parts (construction and validation). In the construction sample, we developed formulas to predict fat mass and FMI using linear multiple regression models. The formulas were validated in the other sample, calculating the intraclass correlation coefficient via bootstrapping.

Results

The fat mass percentage formula had a coefficient of determination of 0.65. This value was 0.86 for FMI. In the validation, the constructed formulas had an intraclass correlation coefficient of 0.77 for fat mass percentage and 0.92 for FMI.

Conclusions

Our predictive formulas accurately predicted fat mass and FMI with simple parameters (BMI, sex and age) in children with overweight and obesity. The proposed methodology could be applied in other fields. Further studies are needed to externally validate these formulas.

SUBMITTER: Cortes-Castell E

PROVIDER: S-EPMC5410155 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

A simple equation to estimate body fat percentage in children with overweightness or obesity: a retrospective study.

Cortés-Castell Ernesto E Juste Mercedes M Palazón-Bru Antonio A Monge Laura L Sánchez-Ferrer Francisco F Rizo-Baeza María Mercedes MM

PeerJ 20170427

<h4>Background</h4>Dual-energy X-ray absorptiometry (DXA) provides separate measurements of fat mass, fat-free mass and bone mass, and is a quick, accurate, and safe technique, yet one that is not readily available in routine clinical practice. Consequently, we aimed to develop statistical formulas to predict fat mass (%) and fat mass index (FMI) with simple parameters (age, sex, weight and height).<h4>Methods</h4>We conducted a retrospective observational cross-sectional study in 416 overweight ...[more]

PMID: 28462041

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Project description:BackgroundAccurate obesity classification is important so that appropriate intervention can be instituted to modify metabolic risk factors. Commonly utilized body mass index (BMI) and percentage body fat (PBF) are influenced by lean mass whereas fat mass index (FMI) measures only body fat. This study compares the prevalence of obesity and metabolic risk factors with FMI, BMI and PBF using DXA (dual-energy x-ray absorptiometry).Methods489 women randomly recruited from the electoral roll were stratified into 4 age groups; 40-49, 50-59, 60-69 and 70-79 years from 2000 to 2001. Clinical data and DXA body composition were obtained. Statistical analyses were performed using Medcalc v15 (Ostend, Belgium) with significance level at p = 0.05 (two-tailed).ResultsThere was higher prevalence of obesity using PBF compared to BMI and FMI (p<0.001). This difference was greater from age 50-59 (p<0.05) which may be explained by age-related lean mass loss. PBF over-classified obesity in over 35% of normal and 95% of overweight categories compared to FMI and BMI. BMI has a sensitivity of 78.9% and specificity of 98.3% for obesity using FMI as the standard. BMI under-classified obesity in the overweight category by 14.9% compared to FMI. There was no difference in diabetes, dyslipidemia, hypertension and metabolic syndrome prevalence within the BMI-obesity and FMI-obesity categories (p>0.05).ConclusionPBF classified more obesity than BMI and FMI because of its low pre-determined threshold. The greater difference with PBF compared to BMI and FMI from the 50-59 decade onwards can be attributed to age-related lean mass loss. BMI had the lowest sensitivity for obesity diagnosis. BMI under-classified obesity in the overweight category compared to FMI due to its inability to differentiate lean mass. However, there was no significant difference in the prevalence of metabolic risk factors between BMI and FMI-obesity categories indicating that fat location may influence metabolic dysregulation.

Project description:ObjectiveUsing meta-regression this paper sets out the minimum change in body mass index-SD score (BMI-SDS) required to improve adiposity as percentage body fat for children and adolescents with obesity.DesignMeta-regression.SettingStudies were identified as part of a large-scale systematic review of the following electronic databases: AMED, Embase, MEDLINE via OVID, Web of Science and CENTRAL via Cochrane library.ParticipantsIndividuals aged 4-19 years with a diagnosis of obesity according to defined BMI thresholds.InterventionsStudies of lifestyle treatment interventions that included dietary, physical activity and/or behavioural components with the objective of reducing obesity were included. Interventions of <2 weeks duration and those that involved surgical and/or pharmacological components (eg, bariatric surgery, drug therapy) were excluded.Primary and secondary outcome measuresTo be included in the review, studies had to report baseline and post-intervention BMI-SDS or change measurements (primary outcome measures) plus one or more of the following markers of metabolic health (secondary outcome measures): adiposity measures other than BMI; blood pressure; glucose; inflammation; insulin sensitivity/resistance; lipid profile; liver function. This paper focuses on adiposity measures only. Further papers in this series will report on other outcome measures.ResultsThis paper explores the potential impact of BMI-SDS reduction in terms of change in percentage body fat. Thirty-nine studies reporting change in mean percentage body fat were analysed. Meta-regression demonstrated that reduction of at least 0.6 in mean BMI-SDS ensured a mean reduction of percentage body fat mass, in the sense that the associated 95% prediction interval for change in mean percentage body fat was wholly negative.ConclusionsInterventions demonstrating reductions of 0.6 BMI-SDS might be termed successful in reducing adiposity, a key purpose of weight management interventions.Trial registration numberCRD42016025317.

Dataset Information

A simple equation to estimate body fat percentage in children with overweightness or obesity: a retrospective study.

Background

Methods

Results

Conclusions

Publications

A simple equation to estimate body fat percentage in children with overweightness or obesity: a retrospective study.

Similar Datasets

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