Environmental inhibitors of the expression of cytochrome P450 17A1 in mammals

Maternal urinary phthalate metabolites are associated with lipidomic signatures among pregnant women in Puerto Rico

BACKGROUND

Phthalates have been reported to alter circulating lipid concentrations in animals, and investigation of these associations in humans will provide greater understanding of potential mechanisms for health outcomes.

OBJECTIVE

To explore associations between phthalate metabolite biomarkers and lipidomic profiles among pregnant women (n = 99) in the Puerto Rico PROTECT cohort.

METHODS

We measured 19 urinary phthalate metabolites during 24-28 weeks of pregnancy. Lipidomic profiles were identified from plasma samples by liquid chromatography-mass spectrometry-based shotgun lipidomics. Relationships between phthalate metabolites and lipid profiles were estimated using compound-by-compound comparisons in multiple linear regression and dimension reduction techniques. We derived sums for each lipid class and sub-class (saturated, mono-unsaturated, polyunsaturated) which were then regressed on phthalate metabolites. Associations were adjusted for false discovery.

RESULTS

After controlling for multiple comparisons, 33 phthalate-lipid associations were identified (False discovery rate adjusted p value < 0.05), and diacylglycerol 40:7 and plasmenyl-phosphatidylcholine 35:1 were the most strongly associated with multiple phthalate metabolites. Metabolites of di-2-ethylhexyl phthalate, bis(2-ethylhexyl) phthalate, dibutyl phthalates, and diisobutyl phthalate were associated with increased ceramides, lysophosphatidylcholines, lysophosphatidylethanolamines, and triacylglycerols, particularly those containing saturated and mono-unsaturated fatty acid chains.

SIGNIFICANCE

Characterization of associations between lipidomic markers and phthalate metabolites during pregnancy will yield mechanistic insight for maternal and child health outcomes.

IMPACT

This study leverages emerging technology to evaluate lipidome-wide signatures of phthalate exposure during pregnancy. The greatest lipid signatures of phthalate exposure were observed for diacylglycerol 40:7 and plasmenyl-phosphatidylcholine 35:1. Polymerized glycerides are important for energy production and regulated through hormone signaling, while plasmenyl-phosphatidylcholines have been implicated in membrane dynamics and important for cell-to-cell signaling. Characterization of these mechanisms are relevant for informing the etiology of maternal and children's health outcomes.

Cross-Sectional Estimation of Endogenous Biomarker Associations with Prenatal Phenols, Phthalates, Metals, and Polycyclic Aromatic Hydrocarbons in Single-Pollutant and Mixtures Analysis Approaches

BACKGROUND

Humans are exposed to mixtures of toxicants that can impact several biological pathways. We investigated the associations between multiple classes of toxicants and an extensive panel of biomarkers indicative of lipid metabolism, inflammation, oxidative stress, and angiogenesis.

METHODS

We conducted a cross-sectional study of 173 participants (median 26 wk gestation) from the LIFECODES birth cohort. We measured exposure analytes of multiple toxicant classes [metals, phthalates, phenols, and polycyclic aromatic hydrocarbons (PAHs)] in urine samples. We also measured endogenous biomarkers (eicosanoids, cytokines, angiogenic markers, and oxidative stress markers) in either plasma or urine. We estimated pair-wise associations between exposure analytes and endogenous biomarkers using multiple linear regression after adjusting for covariates. We used adaptive elastic net regression, hierarchical Bayesian kernel machine regression, and sparse-group LASSO regression to evaluate toxicant mixtures associated with individual endogenous biomarkers.

RESULTS

After false-discovery adjustment (q<0.2), single-pollutant models yielded 19 endogenous biomarker signals associated with phthalates, 13 with phenols, 17 with PAHs, and 18 with trace metals. Notably, adaptive elastic net revealed that phthalate metabolites were selected for several positive signals with the cyclooxygenase (n=7), cytochrome p450 (n=7), and lipoxygenase (n=8) pathways. Conversely, the toxicant classes that exhibited the greatest number of negative signals overall in adaptive elastic net were phenols (n=20) and metals (n=21).

DISCUSSION

This study characterizes cross-sectional endogenous biomarker signatures associated with individual and mixtures of prenatal toxicant exposures. These results can help inform the prioritization of specific pairs or clusters of endogenous biomarkers and exposure analytes for investigating health outcomes. https://doi.org/10.1289/EHP7396.

The Role of Genetic Variation of BMI, Body Composition, and Fat Distribution for Mental Traits and Disorders: A Look-Up and Mendelian Randomization Study

Anthropometric traits and mental disorders or traits are known to be associated clinically and to show genetic overlap. We aimed to identify genetic variants with relevance for mental disorders/traits and either (i) body mass index (or obesity), (ii) body composition, (and/or) (iii) body fat distribution. We performed a look-up analysis of 1,005 genome-wide significant SNPs for BMI, body composition, and body fat distribution in 15 mental disorders/traits. We identified 40 independent loci with one or more SNPs fulfilling our threshold significance criterion (P < 4.98 × 10-5) for the mental phenotypes. The majority of loci was associated with schizophrenia, educational attainment, and/or intelligence. Fewer associations were found for bipolar disorder, neuroticism, attention deficit/hyperactivity disorder, major depressive disorder, depressive symptoms, and well-being. Unique associations with measures of body fat distribution adjusted for BMI were identified at five loci only. To investigate the potential causality between body fat distribution and schizophrenia, we performed two-sample Mendelian randomization analyses. We found no causal effect of body fat distribution on schizophrenia and vice versa. In conclusion, we identified 40 loci which may contribute to genetic overlaps between mental disorders/traits and BMI and/or shape related phenotypes. The majority of loci identified for body composition overlapped with BMI loci, thus suggesting pleiotropic effects.