PMID- 29453090
OWN - NLM
STAT- MEDLINE
DCOM- 20181214
LR  - 20240314
IS  - 1873-6750 (Electronic)
IS  - 0160-4120 (Print)
IS  - 0160-4120 (Linking)
VI  - 113
DP  - 2018 Apr
TI  - Evaluating effects of prenatal exposure to phthalate mixtures on birth weight: A 
      comparison of three statistical approaches.
PG  - 231-239
LID - S0160-4120(17)31988-8 [pii]
LID - 10.1016/j.envint.2018.02.005 [doi]
AB  - OBJECTIVES: We applied three statistical approaches for evaluating associations 
      between prenatal urinary concentrations of a mixture of phthalate metabolites and 
      birth weight. METHODS: We included 300 women who provided 732 urine samples 
      during pregnancy and delivered a singleton infant. We measured urinary 
      concentrations of metabolites of di(2-ethylhexyl)-phthalate, di-isobutyl-, 
      di-n-butyl-, butylbenzyl-, and diethyl phthalates. We applied 1) linear 
      regressions; 2) classification methods [principal component analysis (PCA) and 
      structural equation models (SEM)]; and 3) Bayesian kernel machine regression 
      (BKMR), to evaluate associations between phthalate metabolite mixtures and birth 
      weight adjusting for potential confounders. Data were presented as mean 
      differences (95% CI) in birth weight (grams) as each phthalate increased from the 
      10th to the 90th percentile. RESULTS: When analyzing individual phthalate 
      metabolites using linear regressions, each metabolite demonstrated a modest 
      inverse association with birth weight [from -93 (-206, 21) to -49 (-164, 65)]. 
      When simultaneously including all metabolites in a multivariable model, inflation 
      of the estimates and standard errors were noted. PCA identified two principal 
      components, both inversely associated with birth weight [-23 (-68, 22), -27 (-71, 
      17), respectively]. These inverse associations were confirmed when applying SEM. 
      BKMR further identified that monoethyl and mono(2-ethylhexyl) phthalate and 
      phthalate concentrations were linearly related to lower birth weight [-51(-164, 
      63) and -122 (-311, 67), respectively], and suggested no evidence of interaction 
      between metabolites. CONCLUSIONS: While none of the methods produced significant 
      results, we demonstrated the potential issues arising using linear regression 
      models in the context of correlated exposures. Among the other selected 
      approaches, classification techniques identified common sources of exposures with 
      implications for interventions, while BKMR further identified specific 
      contributions of individual metabolites.
CI  - Copyright (c) 2018 Elsevier Ltd. All rights reserved.
FAU - Chiu, Yu-Han
AU  - Chiu YH
AD  - Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 
      02215, USA. Electronic address: yuc187@mail.harvard.edu.
FAU - Bellavia, Andrea
AU  - Bellavia A
AD  - Department of Environmental Health, Harvard T.H. Chan School of Public Health, 
      Boston, MA 02215, USA.
FAU - James-Todd, Tamarra
AU  - James-Todd T
AD  - Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 
      02215, USA; Department of Environmental Health, Harvard T.H. Chan School of 
      Public Health, Boston, MA 02215, USA.
FAU - Correia, Katharine F
AU  - Correia KF
AD  - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 
      MA 02215, USA.
FAU - Valeri, Linda
AU  - Valeri L
AD  - Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA 02478, 
      USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA.
FAU - Messerlian, Carmen
AU  - Messerlian C
AD  - Department of Environmental Health, Harvard T.H. Chan School of Public Health, 
      Boston, MA 02215, USA.
FAU - Ford, Jennifer B
AU  - Ford JB
AD  - Department of Environmental Health, Harvard T.H. Chan School of Public Health, 
      Boston, MA 02215, USA.
FAU - Minguez-Alarcon, Lidia
AU  - Minguez-Alarcon L
AD  - Department of Environmental Health, Harvard T.H. Chan School of Public Health, 
      Boston, MA 02215, USA.
FAU - Calafat, Antonia M
AU  - Calafat AM
AD  - National Center for Environmental Health, Centers for Disease Control and 
      Prevention, Atlanta, GA 30341, USA.
FAU - Hauser, Russ
AU  - Hauser R
AD  - Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 
      02215, USA; Department of Environmental Health, Harvard T.H. Chan School of 
      Public Health, Boston, MA 02215, USA; Vincent Department of Obstetrics and 
      Gynecology, Massachusetts General Hospital, Boston, MA 02114, USA.
FAU - Williams, Paige L
AU  - Williams PL
AD  - Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 
      02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public 
      Health, Boston, MA 02215, USA. Electronic address: paige@hsph.harvard.edu.
CN  - EARTH Study Team
LA  - eng
GR  - P30 ES000002/ES/NIEHS NIH HHS/United States
GR  - R01 ES009718/ES/NIEHS NIH HHS/United States
GR  - R01 ES022955/ES/NIEHS NIH HHS/United States
GR  - R01 ES026166/ES/NIEHS NIH HHS/United States
PT  - Comparative Study
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20180220
PL  - Netherlands
TA  - Environ Int
JT  - Environment international
JID - 7807270
RN  - 0 (Environmental Pollutants)
RN  - 0 (Phthalic Acids)
RN  - 6O7F7IX66E (phthalic acid)
RN  - UF064M00AF (diethyl phthalate)
SB  - IM
MH  - Adult
MH  - Bayes Theorem
MH  - Birth Weight/*drug effects
MH  - Environmental Exposure/*statistics & numerical data
MH  - Environmental Pollutants/*toxicity
MH  - Female
MH  - Humans
MH  - Linear Models
MH  - Male
MH  - Phthalic Acids/*toxicity/urine
MH  - Pregnancy
MH  - *Prenatal Exposure Delayed Effects
MH  - Principal Component Analysis
MH  - Prospective Studies
PMC - PMC5866233
MID - NIHMS943665
OTO - NOTNLM
OT  - Bayesian Kernel Machine Regression
OT  - Chemical mixtures
OT  - Principal component analysis
OT  - Structural equation models
COIS- Declarations of interests: None. The findings and conclusions in this report are 
      those of the authors and do not necessarily represent the official position of 
      the Centers for Disease Control and Prevention or the National Institutes of 
      Health.
EDAT- 2018/02/18 06:00
MHDA- 2018/12/15 06:00
PMCR- 2019/04/01
CRDT- 2018/02/18 06:00
PHST- 2017/11/11 00:00 [received]
PHST- 2018/02/05 00:00 [revised]
PHST- 2018/02/05 00:00 [accepted]
PHST- 2018/02/18 06:00 [pubmed]
PHST- 2018/12/15 06:00 [medline]
PHST- 2018/02/18 06:00 [entrez]
PHST- 2019/04/01 00:00 [pmc-release]
AID - S0160-4120(17)31988-8 [pii]
AID - 10.1016/j.envint.2018.02.005 [doi]
PST - ppublish
SO  - Environ Int. 2018 Apr;113:231-239. doi: 10.1016/j.envint.2018.02.005. Epub 2018 
      Feb 20.