Metabolomic Alterations Associated with Phthalate Exposures among Pregnant Women in Puerto Rico.

ABSTRACT

Although phthalate exposure has been linked with multiple adverse pregnancy outcomes, their underlying biological mechanisms are not fully understood. We examined associations between biomarkers of phthalate exposures and metabolic alterations using untargeted metabolomics in 99 pregnant women and 86 newborns [mean (SD) gestational age = 39.5 (1.5) weeks] in the PROTECT cohort. Maternal urinary phthalate metabolites were quantified using isotope dilution high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS), while metabolic profiles in maternal and cord blood plasma were characterized via reversed-phase LC-MS. Multivariable linear regression was used in metabolome-wide association studies (MWAS) to identify individual metabolic features associated with elevated phthalate levels, while clustering and correlation network analyses were used to discern the interconnectedness of biologically relevant features. In the MWAS adjusted for maternal age and prepregnancy BMI, we observed significant associations between specific phthalates, namely, di(2-ethylhexyl) phthalate (DEHP) and mono(3-carboxypropyl) phthalate (MCPP), and 34 maternal plasma metabolic features. These associations predominantly included upregulation of fatty acids, amino acids, purines, or their derivatives and downregulation of ceramides and sphingomyelins. In contrast, fewer significant associations were observed with metabolic features in cord blood. Correlation network analysis highlighted the overlap of features associated with phthalates and those identified as differentiating markers for preterm birth in a previous study. Overall, our findings underscore the complex impact of phthalate exposures on maternal and fetal metabolism, highlighting metabolomics as a tool for understanding associated biological processes. Future research should focus on expanding the sample size, exploring the effects of phthalate mixtures, and validating identified metabolic features in larger, more diverse populations.