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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Obesityarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Obesity
Article . 2023 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
Obesity
Article . 2023
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Overfat cutoffs and the optimal combination of body fat indices for detecting cardiometabolic risk among school‐aged children

Authors: Hongbo Dong; Hong Cheng; Junting Liu; Yinkun Yan; Xiaoyuan Zhao; Xinying Shan; Guimin Huang; +1 Authors

Overfat cutoffs and the optimal combination of body fat indices for detecting cardiometabolic risk among school‐aged children

Abstract

AbstractObjectiveThis study aimed to develop cutoffs and the optimal combination for body fat indices for screening cardiometabolic risk (CMR) among the pediatric population.MethodsThis cross‐sectional study consisted of 8710 (50.3% boys) Chinese children aged 6 to 18 years. Body fat indices, including fat mass index (FMI), body fat percentage, trunk to leg fat ratio (TLR), and android to gynoid fat ratio, were derived from dual‐energy x‐ray absorptiometry scans. The area under the receiver operating characteristic curve was used to determine the best combination and optimal cutoffs of body fat indices to identify CMR.ResultsCompared with anthropometry‐based obesity measures, i.e., BMI and waist circumference, the FMI + TLR combination presented statistically higher area under the receiver operating characteristic curve values for discriminating CMR and its clustering. The optimal overfat cutoffs of FMI and TLR were respectively determined at the 75th percentile in boys and at the 80th percentile of FMI and the 75th percentile of TLR in girls. Moreover, simplified thresholds derived from age‐group‐merged cutoffs showed similar performance as optimal cutoffs in detecting CMR.ConclusionsBoth the optimal and simplified overfat cutoffs were provided for the Chinese pediatric population. The use of FMI and TLR together allows for adequate screening of CMR and its clustering.

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Keywords

Male, Body Mass Index, Cross-Sectional Studies, Absorptiometry, Photon, Adipose Tissue, Cardiovascular Diseases, Body Composition, Humans, Female, Obesity, Child

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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