Effects of gestational exposures to chemical mixtures on birth weight using Bayesian factor analysis in the Health Outcome and Measures of Environment (HOME) Study

Studying the effects of gestational exposures to chemical mixtures on infant birth weight is inconclusive due to several challenges. One of the challenges is which statistical methods to rely on. Bayesian factor analysis (BFA), which has not been utilized for chemical mixtures, has advantages in variance reduction and model interpretation.

METHODS

We analyzed data from a cohort of 384 pregnant women and their newborns using urinary biomarkers of phthalates, phenols, and organophosphate pesticides (OPs) and serum biomarkers of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), perfluoroalkyl substances (PFAS), and organochlorine pesticides (OCPs). We examined the association between exposure to chemical mixtures and birth weight using BFA and compared with multiple linear regression (MLR) and Bayesian kernel regression models (BKMR).

RESULTS

For BFA, a 10-fold increase in the concentrations of PCB and PFAS mixtures was associated with an 81 g (95% confidence intervals [CI] = -132 to -31 g) and 57 g (95% CI = -105 to -10 g) reduction in birth weight, respectively. BKMR results confirmed the direction of effect. However, the 95% credible intervals all contained the null. For single-pollutant MLR, a 10-fold increases in the concentrations of multiple chemicals were associated with reduced birth weight, yet the 95% CI all contained the null. Variance inflation from MLR was apparent for models that adjusted for copollutants, resulting in less precise confidence intervals.

CONCLUSION

We demonstrated the merits of BFA on mixture analysis in terms of precision and interpretation compared with MLR and BKMR. We also identified the association between exposure to PCBs and PFAS and lower birth weight.

Prenatal exposure to endocrine disrupting chemical mixtures and infant birth weight: A Bayesian analysis using kernel machine regression

BACKGROUND

Pregnant women are regularly exposed to a multitude of endocrine disrupting chemicals (EDCs). EDC exposures, both individually and as mixtures, may affect fetal growth. The relationship of EDC mixtures with infant birth weight, however, remains poorly understood. We examined the relations between prenatal exposure to EDC mixtures and infant birth weight.

METHODS

We used data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pan-Canadian cohort of 1857 pregnant women enrolled between 2008 and 2011. We quantified twenty-one chemical concentrations from five EDC classes, including organochlorine compounds (OCs), metals, perfluoroalkyl substances (PFAS), phenols and phthalate metabolites that were detected in >70% of urine or blood samples collected during the first trimester. In our primary analysis, we used Bayesian kernel machine regression (BKMR) models to assess variable importance, explore EDC mixture effects, and identify any interactions among EDCs. Our secondary analysis used traditional linear regression to compare the results with those of BKMR and to quantify the changes in mean birth weight in relation to prenatal EDC exposures.

RESULTS

We found evidence that mixtures of OCs and metals were associated with monotonic decreases in mean birth weight across the whole range of exposure. trans-Nonachlor from the OC mixture and lead (Pb) from the metal mixture had the greatest impact on birth weight. Our linear regression analysis corroborated the BKMR results and found that a 2-fold increase in trans-nonachlor and Pb concentrations reduced mean birth weight by -38 g (95% confidence interval (CI): -67, -10) and -39 g (95% CI: -69, -9), respectively. A sex-specific association for OC mixture was observed among female infants. PFAS, phenols and phthalates were not associated with birth weight. No interactions were observed among the EDCs.

CONCLUSIONS

Using BKMR, we observed that both OC and metal mixtures were associated with decreased birth weight in the MIREC Study. trans-Nonachlor from the OC mixture and Pb from the metal mixture contributed most to the adverse effects.

Associations of prenatal urinary phthalate exposure with preterm birth: the Maternal-Infant Research on Environmental Chemicals (MIREC) Study

OBJECTIVES

To examine the relation between prenatal urinary phthalate metabolite concentrations and preterm birth (PTB).

METHODS

The data were drawn from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pan-Canadian cohort of 1857 pregnant women enrolled between 2008 and 2011. We quantified urinary concentrations of 7 phthalate metabolites that were detected in > 70% of urine samples collected during the first trimester. Gestational age was obtained from either the last menstrual period or early ultrasound. We used Cox proportional hazard models to examine the associations of urinary phthalate metabolite concentrations, plus the molar sum of di-2-ethylhexyl phthalate metabolites (∑DEHP), with time to delivery before 37 weeks of gestation. We also examined PTB by clinical presentation. PTBs presented with either spontaneous labour or premature rupture of the membrane were considered spontaneous PTB (sPTB). Additionally, we used multiple linear regression to model changes in mean gestational age in relation to phthalate exposure.

RESULTS

We found no evidence of an association between first trimester phthalate metabolite concentrations and PTB among the MIREC study participants. For example, each 2-fold increase in any of the 7 phthalate concentrations or ∑DEHP was associated with hazard ratios (HRs) for PTB ranging from 0.95 to 1.07 with 95% confidence intervals including the null. An assessment of non-linear trends showed some evidence of non-monotonic dose-response relationships between phthalates and PTB. Furthermore, male infants exposed to MCPP showed higher sPTB risk compared with female infants.

CONCLUSION

Phthalate exposure during early pregnancy is not clearly associated with the risk of PTB among this Canadian population.