Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Zhuang LH

PROVIDER: S-EPMC8196215 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundPer- and polyfluoroalkyl substances (PFAS) are environmentally persistent chemicals commonly used in the production of household and consumer goods. While exposure to PFAS has been associated with greater adiposity in children and adults, less is known about associations with gestational weight gain (GWG).MethodsWe quantified using mass spectrometry perfluorooctanoate (PFOA), perfluorooctanesulfonate (PFOS), perfluorohexanesulfanoate (PFHxS) and perfluorononanoate (PFNA) in maternal serum from 18 ± 5 weeks' gestation (mean ± standard deviation (std)) in a prospective pregnancy and birth cohort (2003-2006, Cincinnati, Ohio) (n = 277). After abstracting weight data from medical records, we calculated GWG from 16 ± 2 weeks' gestation (mean ± std) to the measured weight at the last visit or at delivery, rate of weight gain in the 2nd and 3rd trimesters (GWR), and total weight gain z-scores standardized for gestational age at delivery and pre-pregnancy BMI. We investigated covariate-adjusted associations between individual PFAS using multivariable linear regression; we assessed potential effect measure modification (EMM) by overweight/obese status (pre-pregnancy BMI<25 kg/m2 v. ≥25 kg/m2). Using weighted quantile sum regression, we assessed the combined influence of these four PFAS on GWG and GWR.ResultsEach doubling in serum concentrations of PFOA, PFOS, and PFNA was associated with a small increase in GWG (range 0.5-0.8 lbs) and GWR (range 0.03-0.05 lbs/week) among all women. The association of PFNA with GWG was stronger among women with BMI≥25 kg/m2 (β = 2.6 lbs; 95% CI:-0.8, 6.0) than those with BMI<25 kg/m2 (β = -1.0 lbs; 95% CI:-3.8, 1.8; p-EMM = 0.10). We observed associations close to the null between PFAS and z-scores and between the PFAS exposure index (a combined summary measure) and the outcomes.ConclusionAlthough there were consistent small increases in gestational weight gain with increasing PFOA, PFOS, and PFNA serum concentrations in this cohort, the associations were imprecise. Additional investigation of the association of PFAS with GWG in other cohorts would be informative and could consider pre-pregnancy BMI as a potential modifier.

Project description:BackgroundReduced fetal growth increases the risk for adverse health outcomes. Growing evidence suggests that metal exposures contribute to reduced fetal growth, but little is known about the effects of complex metal mixtures.ObjectivesWe investigated the impact of a complex mixture of metals on birth weight for gestational age (BW for GA) in the Maternal and Developmental Risks from Environmental and Social Stressors study, a predominately lower-income Hispanic pregnancy cohort in Los Angeles, California.MethodsCadmium (Cd), cobalt (Co), mercury (Hg), nickel (Ni), molybdenum (Mo), lead (Pb), antimony (Sb), tin (Sn), and thallium (Tl) were measured by inductively coupled plasma mass spectrometry (ICP-MS) in maternal urine samples collected in early pregnancy (median GA: 13.1 wk). Speciated urinary arsenic (As) (inorganic+monomethyl+dimethyl As) was measured by high-performance liquid chromatography coupled to ICP-MS. Primary analyses focused on a mixture of seven metals that have previously been associated individually with fetal growth (i.e., As, Cd, Co, Hg, Ni, Pb, Tl) (n=262). In exploratory analyses, we additionally examined three metals that have been less studied in relation to fetal growth (i.e., Mo, Sb, Sn). Covariate-adjusted Bayesian kernel machine regression was used to investigate metal mixture associations with BW for GA z-scores.ResultsIn primary analyses, Hg and Ni ranked highest as predictors of BW for GA. An inverse linear association was estimated for Hg, whereas a positive association was estimated for Ni at low-to-moderate concentrations. A potential interaction between Hg and Ni was also identified. In our exploratory analysis, Sb ranked highest as a predictor of BW for GA, followed by Hg and Ni.ConclusionsOur findings suggest that in this understudied population, Hg may reduce fetal growth, whereas Ni may promote fetal growth. We also identified Sb as a potential metal of concern for this population, which merits additional investigation. https://doi.org/10.1289/EHP7201.

Project description:BackgroundGestational diabetes mellitus (GDM) is associated with increased availability of glucose and macronutrients in fetal circulation and macrosomia. Therefore, the role of GDM in the association between metabolism-disrupting chemicals and birth size deserves attention.ObjectiveWe examined whether GDM may mediate or modify the associations between maternal environmental pollutant exposures and offspring birth size measures.MethodsWe analyzed 604 Faroese pregnant women and their offsprings born in 1997-2000. Maternal pregnancy serum concentrations of organochlorine compounds (OCs: polychlorinated biphenyl (PCB) congeners and dichlorodiphenyldichloroethylene (DDE)), and five perfluoroalkyl substances (PFASs), and hair and cord blood mercury concentrations were measured. We used regression (single-pollutants) and structural equation models (SEMs) (multiple-pollutant analyses using latent constructs of OCs, PFASs and mercury) to estimate the associations with GDM and birth size measures, accounting for mediation and/or effect modification by GDM.ResultsSerum-DDE and hair-mercury concentrations were associated with GDM (adjusted OR per concentration doubling: 1.29; 95% CI: 0.94, 1.77 for DDE, and 0.79; 95% CI: 0.62, 0.99 for mercury), but in multiple pollutant-adjusted SEMs only a positive association between OC exposure and GDM remained significant (change in GDM odds per OC doubling: 0.45; 95% CI: 0.05, 0.86). PCB and overall OC exposure were positively associated with head circumference (SEM; mean change per OC doubling: 0.13cm; 95% CI, 0.01. 0.25). Overall PFAS exposure was inversely associated with birth weight (SEM; mean change per PFAS doubling: -169g; 95% CI: -359, 21), and for many single-PFASs we found a pattern of inverse associations with birth weight and head circumference in boys, and positive or null associations in girls. None of the environmental pollutants was associated with offspring length. GDM neither modified nor mediated the associations with birth size measures.ConclusionsWe found associations with GDM and offspring birth size to be specific to the environmental pollutant or pollutant group. Associations with birth size measures appear to be independent of GDM occurrence.

Project description:There has been a growing interest in the literature on multiple environmental risk factors for diseases and an increasing emphasis on assessing multiple environmental exposures simultaneously in epidemiologic studies of cancer. One method used to analyze exposure to multiple chemical exposures is weighted quantile sum (WQS) regression. While WQS regression has been demonstrated to have good sensitivity and specificity when identifying important exposures, it has limitations including a two-step model fitting process that decreases power and model stability and a requirement that all exposures in the weighted index have associations in the same direction with the outcome, which is not realistic when chemicals in different classes have different directions and magnitude of association with a health outcome. Grouped WQS (GWQS) was proposed to allow for multiple groups of chemicals in the model where different magnitude and direction of associations are possible for each group. However, GWQS shares the limitation of WQS of a two-step estimation process and splitting of data into training and validation sets. In this paper, we propose a Bayesian group index model to avoid the estimation limitation of GWQS while having multiple exposure indices in the model. To evaluate the performance of the Bayesian group index model, we conducted a simulation study with several different exposure scenarios. We also applied the Bayesian group index method to analyze childhood leukemia risk in the California Childhood Leukemia Study (CCLS). The results showed that the Bayesian group index model had slightly better power for exposure effects and specificity and sensitivity in identifying important chemical exposure components compared with the existing frequentist method, particularly for small sample sizes. In the application to the CCLS, we found a significant negative association for insecticides, with the most important chemical being carbaryl. In addition, for children who were born and raised in the home where dust samples were taken, there was a significant positive association for herbicides with dacthal being the most important exposure. In conclusion, our approach of the Bayesian group index model appears able to make a substantial contribution to the field of environmental epidemiology.