Cross-Sectional Associations between Prenatal Per- and Poly-Fluoroalkyl Substances and Bioactive Lipids in Three Environmental Influences on Child Health Outcomes (ECHO) Cohorts

Abstract

Prenatal per- and poly-fluoroalkyl substances (PFAS) exposure may influence gestational outcomes through bioactive lipids─metabolic and inflammation pathway indicators. We estimated associations between prenatal PFAS exposure and bioactive lipids, measuring 12 serum PFAS and 50 plasma bioactive lipids in 414 pregnant women (median 17.4 weeks' gestation) from three Environmental influences on Child Health Outcomes Program cohorts. Pairwise association estimates across cohorts were obtained through linear mixed models and meta-analysis, adjusting the former for false discovery rates. Associations between the PFAS mixture and bioactive lipids were estimated using quantile g-computation. Pairwise analyses revealed bioactive lipid levels associated with PFDeA, PFNA, PFOA, and PFUdA (p < 0.05) across three enzymatic pathways (cyclooxygenase, cytochrome p450, lipoxygenase) in at least one combined cohort analysis, and PFOA and PFUdA (q < 0.2) in one linear mixed model. The strongest signature revealed doubling in PFOA corresponding with PGD2 (cyclooxygenase pathway; +24.3%, 95% CI: 7.3-43.9%) in the combined cohort. Mixture analysis revealed nine positive associations across all pathways with the PFAS mixture, the strongest signature indicating a quartile increase in the PFAS mixture associated with PGD2 (+34%, 95% CI: 8-66%), primarily driven by PFOS. Bioactive lipids emerged as prenatal PFAS exposure biomarkers, deepening insights into PFAS' influence on pregnancy outcomes.

Keywords: PFAS; bioactive lipids; eicosanoids; inflammatory pathways; metabolic pathways; mixtures; pregnancy outcomes.

Figure 1

Heatmap of β estimates corresponding to percentage change in bioactive lipids as a result of doubling log-transformed PFAS for adjusted joint analyses and meta-analyses run on adjusted within-cohort models. The magnitude of effect estimates in each cell in the heatmap corresponds to the intensity of the color band in the legend. Nonsignificant values (p > .05) are marked with black “X”. Sample sizes range from 343 to 383 (see Supplemental Tables 7, 8, 9, and 10 for exact pairwise sample sizes).

Figure 2

Forest plot of quantile g-computation effect estimates in the combined cohort analysis with IKIDS and CIOB cohorts (N = 343), showing of β estimates corresponding to percentage change in bioactive lipids associated with a simultaneous 1-quartile increase in all log-transformed PFAS. Model adjusted for maternal age, maternal education, prepregnancy BMI, parity, gestational age at visit, and cohort.