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Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations

BACKGROUND: Recent evidence suggests that a substantial subgroup of the population who have a high-risk waist circumference (WC) do not have an obese body mass index (BMI). This study aimed to explore whether including those with a non-obese BMI but high risk WC as ‘obese’ improves prediction of adi...

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Autores principales: Tanamas, Stephanie K., Permatahati, Viandini, Ng, Winda L., Backholer, Kathryn, Wolfe, Rory, Shaw, Jonathan E., Peeters, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734864/
https://www.ncbi.nlm.nih.gov/pubmed/26855785
http://dx.doi.org/10.1186/s40608-016-0085-5
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author Tanamas, Stephanie K.
Permatahati, Viandini
Ng, Winda L.
Backholer, Kathryn
Wolfe, Rory
Shaw, Jonathan E.
Peeters, Anna
author_facet Tanamas, Stephanie K.
Permatahati, Viandini
Ng, Winda L.
Backholer, Kathryn
Wolfe, Rory
Shaw, Jonathan E.
Peeters, Anna
author_sort Tanamas, Stephanie K.
collection PubMed
description BACKGROUND: Recent evidence suggests that a substantial subgroup of the population who have a high-risk waist circumference (WC) do not have an obese body mass index (BMI). This study aimed to explore whether including those with a non-obese BMI but high risk WC as ‘obese’ improves prediction of adiposity-related metabolic outcomes. METHODS: Eleven thousand, two hundred forty-seven participants were recruited. Height, weight and WC were measured. Ten thousand, six hundred fifty-nine participants with complete data were included. Adiposity categories were defined as: BMI(N)/WC(N), BMI(N)/WC(O), BMI(O)/WC(N), and BMI(O)/WC(O) (N = non-obese and O = obese). Population attributable fraction, area under the receiver operating characteristic curve (AUC), and odds ratios (OR) were calculated. RESULTS: Participants were on average 48 years old and 50 % were men. The proportions of BMI(N)/WC(N), BMI(N)/WC(O), BMI(O)/WC(N) and BMI(O)/WC(O) were 68, 12, 2 and 18 %, respectively. A lower proportion of diabetes was attributable to obesity defined using BMI alone compared to BMI and WC combined (32 % vs 47 %). AUC for diabetes was also lower when obesity was defined using BMI alone (0.62 vs 0.66). Similar results were observed for all outcomes. The odds for hypertension, dyslipidaemia, diabetes and CVD were increased for those with BMI(N)/WC(O) (OR range 1.8–2.7) and BMI(O)/WC(O) (OR 1.9–4.9) compared to those with BMI(N)/WC(N). CONCLUSIONS: Current population monitoring, assessing obesity by BMI only, misses a proportion of the population who are at increased health risk through excess adiposity. Improved identification of those at increased health risk needs to be considered for better prioritisation of policy and resources. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40608-016-0085-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-47348642016-02-05 Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations Tanamas, Stephanie K. Permatahati, Viandini Ng, Winda L. Backholer, Kathryn Wolfe, Rory Shaw, Jonathan E. Peeters, Anna BMC Obes Research Article BACKGROUND: Recent evidence suggests that a substantial subgroup of the population who have a high-risk waist circumference (WC) do not have an obese body mass index (BMI). This study aimed to explore whether including those with a non-obese BMI but high risk WC as ‘obese’ improves prediction of adiposity-related metabolic outcomes. METHODS: Eleven thousand, two hundred forty-seven participants were recruited. Height, weight and WC were measured. Ten thousand, six hundred fifty-nine participants with complete data were included. Adiposity categories were defined as: BMI(N)/WC(N), BMI(N)/WC(O), BMI(O)/WC(N), and BMI(O)/WC(O) (N = non-obese and O = obese). Population attributable fraction, area under the receiver operating characteristic curve (AUC), and odds ratios (OR) were calculated. RESULTS: Participants were on average 48 years old and 50 % were men. The proportions of BMI(N)/WC(N), BMI(N)/WC(O), BMI(O)/WC(N) and BMI(O)/WC(O) were 68, 12, 2 and 18 %, respectively. A lower proportion of diabetes was attributable to obesity defined using BMI alone compared to BMI and WC combined (32 % vs 47 %). AUC for diabetes was also lower when obesity was defined using BMI alone (0.62 vs 0.66). Similar results were observed for all outcomes. The odds for hypertension, dyslipidaemia, diabetes and CVD were increased for those with BMI(N)/WC(O) (OR range 1.8–2.7) and BMI(O)/WC(O) (OR 1.9–4.9) compared to those with BMI(N)/WC(N). CONCLUSIONS: Current population monitoring, assessing obesity by BMI only, misses a proportion of the population who are at increased health risk through excess adiposity. Improved identification of those at increased health risk needs to be considered for better prioritisation of policy and resources. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40608-016-0085-5) contains supplementary material, which is available to authorized users. BioMed Central 2016-01-29 /pmc/articles/PMC4734864/ /pubmed/26855785 http://dx.doi.org/10.1186/s40608-016-0085-5 Text en © Tanamas et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Tanamas, Stephanie K.
Permatahati, Viandini
Ng, Winda L.
Backholer, Kathryn
Wolfe, Rory
Shaw, Jonathan E.
Peeters, Anna
Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations
title Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations
title_full Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations
title_fullStr Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations
title_full_unstemmed Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations
title_short Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations
title_sort estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734864/
https://www.ncbi.nlm.nih.gov/pubmed/26855785
http://dx.doi.org/10.1186/s40608-016-0085-5
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