Using three statistical methods to analyze the association between exposure to 9 compounds and obesity in children and adolescents: NHANES 2005-2010
Abstract Background Various risk factors influence obesity differently, and environmental endocrine disruption may increase the occurrence of obesity. However, most of the previous studies have considered only a unitary exposure or a set of similar exposures instead of mixed exposures, which entail...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2020-08-01
|
Series: | Environmental Health |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s12940-020-00642-6 |
id |
doaj-c3c9a588b5b14e87b48f58fa984ac9e9 |
---|---|
record_format |
Article |
spelling |
doaj-c3c9a588b5b14e87b48f58fa984ac9e92020-11-25T02:25:03ZengBMCEnvironmental Health1476-069X2020-08-0119111310.1186/s12940-020-00642-6Using three statistical methods to analyze the association between exposure to 9 compounds and obesity in children and adolescents: NHANES 2005-2010Bangsheng Wu0Yi Jiang1Xiaoqing Jin2Li He3Emergency Department, Zhongnan Hospital of Wuhan UniversityEmergency Department, Zhongnan Hospital of Wuhan UniversityEmergency Department, Zhongnan Hospital of Wuhan UniversityInternal hematology, Zhongnan Hospital of Wuhan UniversityAbstract Background Various risk factors influence obesity differently, and environmental endocrine disruption may increase the occurrence of obesity. However, most of the previous studies have considered only a unitary exposure or a set of similar exposures instead of mixed exposures, which entail complicated interactions. We utilized three statistical models to evaluate the correlations between mixed chemicals to analyze the association between 9 different chemical exposures and obesity in children and adolescents. Methods We fitted the generalized linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) to analyze the association between the mixed exposures and obesity in the participants aged 6–19 in the National Health and Nutrition Examination Survey (NHANES) 2005–2010. Results In the multivariable logistic regression model, 2,5-dichlorophenol (2,5-DCP) (OR (95% CI): 1.25 (1.11, 1.40)), monoethyl phthalate (MEP) (OR (95% CI): 1.28 (1.04, 1.58)), and mono-isobutyl phthalate (MiBP) (OR (95% CI): 1.42 (1.07, 1.89)) were found to be positively associated with obesity, while methylparaben (MeP) (OR (95% CI): 0.80 (0.68, 0.94)) was negatively associated with obesity. In the multivariable linear regression, MEP was found to be positively associated with the body mass index (BMI) z-score (β (95% CI): 0.12 (0.02, 0.21)). In the WQS regression model, the WQS index had a significant association (OR (95% CI): 1.48 (1.16, 1.89)) with the outcome in the obesity model, in which 2,5-DCP (weighted 0.41), bisphenol A (BPA) (weighted 0.17) and MEP (weighted 0.14) all had relatively high weights. In the BKMR model, despite no statistically significant difference in the overall association between the chemical mixtures and the outcome (obesity or BMI z-score), there was nonetheless an increasing trend. 2,5-DCP and MEP were found to be positively associated with the outcome (obesity or BMI z-score), while fixing other chemicals at their median concentrations. Conclusion Comparing the three statistical models, we found that 2,5-DCP and MEP may play an important role in obesity. Considering the advantages and disadvantages of the three statistical models, our study confirms the necessity to combine different statistical models on obesity when dealing with mixed exposures.http://link.springer.com/article/10.1186/s12940-020-00642-6ObesityAdolescentChildWeighted quantile sum (WQS) regressionBayesian kernel machine regression (BKMR) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bangsheng Wu Yi Jiang Xiaoqing Jin Li He |
spellingShingle |
Bangsheng Wu Yi Jiang Xiaoqing Jin Li He Using three statistical methods to analyze the association between exposure to 9 compounds and obesity in children and adolescents: NHANES 2005-2010 Environmental Health Obesity Adolescent Child Weighted quantile sum (WQS) regression Bayesian kernel machine regression (BKMR) |
author_facet |
Bangsheng Wu Yi Jiang Xiaoqing Jin Li He |
author_sort |
Bangsheng Wu |
title |
Using three statistical methods to analyze the association between exposure to 9 compounds and obesity in children and adolescents: NHANES 2005-2010 |
title_short |
Using three statistical methods to analyze the association between exposure to 9 compounds and obesity in children and adolescents: NHANES 2005-2010 |
title_full |
Using three statistical methods to analyze the association between exposure to 9 compounds and obesity in children and adolescents: NHANES 2005-2010 |
title_fullStr |
Using three statistical methods to analyze the association between exposure to 9 compounds and obesity in children and adolescents: NHANES 2005-2010 |
title_full_unstemmed |
Using three statistical methods to analyze the association between exposure to 9 compounds and obesity in children and adolescents: NHANES 2005-2010 |
title_sort |
using three statistical methods to analyze the association between exposure to 9 compounds and obesity in children and adolescents: nhanes 2005-2010 |
publisher |
BMC |
series |
Environmental Health |
issn |
1476-069X |
publishDate |
2020-08-01 |
description |
Abstract Background Various risk factors influence obesity differently, and environmental endocrine disruption may increase the occurrence of obesity. However, most of the previous studies have considered only a unitary exposure or a set of similar exposures instead of mixed exposures, which entail complicated interactions. We utilized three statistical models to evaluate the correlations between mixed chemicals to analyze the association between 9 different chemical exposures and obesity in children and adolescents. Methods We fitted the generalized linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) to analyze the association between the mixed exposures and obesity in the participants aged 6–19 in the National Health and Nutrition Examination Survey (NHANES) 2005–2010. Results In the multivariable logistic regression model, 2,5-dichlorophenol (2,5-DCP) (OR (95% CI): 1.25 (1.11, 1.40)), monoethyl phthalate (MEP) (OR (95% CI): 1.28 (1.04, 1.58)), and mono-isobutyl phthalate (MiBP) (OR (95% CI): 1.42 (1.07, 1.89)) were found to be positively associated with obesity, while methylparaben (MeP) (OR (95% CI): 0.80 (0.68, 0.94)) was negatively associated with obesity. In the multivariable linear regression, MEP was found to be positively associated with the body mass index (BMI) z-score (β (95% CI): 0.12 (0.02, 0.21)). In the WQS regression model, the WQS index had a significant association (OR (95% CI): 1.48 (1.16, 1.89)) with the outcome in the obesity model, in which 2,5-DCP (weighted 0.41), bisphenol A (BPA) (weighted 0.17) and MEP (weighted 0.14) all had relatively high weights. In the BKMR model, despite no statistically significant difference in the overall association between the chemical mixtures and the outcome (obesity or BMI z-score), there was nonetheless an increasing trend. 2,5-DCP and MEP were found to be positively associated with the outcome (obesity or BMI z-score), while fixing other chemicals at their median concentrations. Conclusion Comparing the three statistical models, we found that 2,5-DCP and MEP may play an important role in obesity. Considering the advantages and disadvantages of the three statistical models, our study confirms the necessity to combine different statistical models on obesity when dealing with mixed exposures. |
topic |
Obesity Adolescent Child Weighted quantile sum (WQS) regression Bayesian kernel machine regression (BKMR) |
url |
http://link.springer.com/article/10.1186/s12940-020-00642-6 |
work_keys_str_mv |
AT bangshengwu usingthreestatisticalmethodstoanalyzetheassociationbetweenexposureto9compoundsandobesityinchildrenandadolescentsnhanes20052010 AT yijiang usingthreestatisticalmethodstoanalyzetheassociationbetweenexposureto9compoundsandobesityinchildrenandadolescentsnhanes20052010 AT xiaoqingjin usingthreestatisticalmethodstoanalyzetheassociationbetweenexposureto9compoundsandobesityinchildrenandadolescentsnhanes20052010 AT lihe usingthreestatisticalmethodstoanalyzetheassociationbetweenexposureto9compoundsandobesityinchildrenandadolescentsnhanes20052010 |
_version_ |
1724853017688145920 |