PMID- 32867766
OWN - NLM
STAT- MEDLINE
DCOM- 20201013
LR  - 20201013
IS  - 1476-069X (Electronic)
IS  - 1476-069X (Linking)
VI  - 19
IP  - 1
DP  - 2020 Aug 31
TI  - Using three statistical methods to analyze the association between exposure to 9 
      compounds and obesity in children and adolescents: NHANES 2005-2010.
PG  - 94
LID - 10.1186/s12940-020-00642-6 [doi]
LID - 94
AB  - 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 (beta (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.
FAU - Wu, Bangsheng
AU  - Wu B
AD  - Emergency Department, Zhongnan Hospital of Wuhan University, 169 Donghu Road, 
      Wuhan, 430071, Hubei, China.
AD  - Second Clinical School, Wuhan University, Wuhan, 430071, Hubei, China.
FAU - Jiang, Yi
AU  - Jiang Y
AD  - Emergency Department, Zhongnan Hospital of Wuhan University, 169 Donghu Road, 
      Wuhan, 430071, Hubei, China.
AD  - Second Clinical School, Wuhan University, Wuhan, 430071, Hubei, China.
FAU - Jin, Xiaoqing
AU  - Jin X
AUID- ORCID: 0000-0002-2571-3512
AD  - Emergency Department, Zhongnan Hospital of Wuhan University, 169 Donghu Road, 
      Wuhan, 430071, Hubei, China. redjin@whu.edu.cn.
FAU - He, Li
AU  - He L
AD  - Internal hematology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, 
      Wuhan, 430071, Hubei, China. lihe126@hotmail.com.
LA  - eng
PT  - Journal Article
DEP - 20200831
PL  - England
TA  - Environ Health
JT  - Environmental health : a global access science source
JID - 101147645
RN  - 0 (Environmental Pollutants)
SB  - IM
MH  - Adolescent
MH  - Bayes Theorem
MH  - Child
MH  - Environmental Exposure/*adverse effects
MH  - Environmental Monitoring/*methods
MH  - Environmental Pollutants/*adverse effects
MH  - Female
MH  - Humans
MH  - Linear Models
MH  - Male
MH  - Models, Statistical
MH  - Nutrition Surveys
MH  - Pediatric Obesity/chemically induced/*epidemiology
MH  - Prevalence
MH  - United States/epidemiology
PMC - PMC7457345
OTO - NOTNLM
OT  - Adolescent
OT  - Bayesian kernel machine regression (BKMR)
OT  - Child
OT  - Obesity
OT  - Weighted quantile sum (WQS) regression
COIS- The authors declare that they have no competing interests.
EDAT- 2020/09/02 06:00
MHDA- 2020/10/21 06:00
PMCR- 2020/08/31
CRDT- 2020/09/02 06:00
PHST- 2020/02/21 00:00 [received]
PHST- 2020/08/12 00:00 [accepted]
PHST- 2020/09/02 06:00 [entrez]
PHST- 2020/09/02 06:00 [pubmed]
PHST- 2020/10/21 06:00 [medline]
PHST- 2020/08/31 00:00 [pmc-release]
AID - 10.1186/s12940-020-00642-6 [pii]
AID - 642 [pii]
AID - 10.1186/s12940-020-00642-6 [doi]
PST - epublish
SO  - Environ Health. 2020 Aug 31;19(1):94. doi: 10.1186/s12940-020-00642-6.