A Comprehensive Non-targeted Analysis Study of the Prenatal Exposome

Chemical exposure in females of childbearing age associated with sex hormones: Evidence from an untargeted exposomic approach

Exposure to organic chemicals can cause reproductive hormones disturbance in women. However, there is very limited evidence regarding real-world chemical exposures in reproductive-aged women and their joint effects on sex hormone levels. Here, we applied non-targeted screening workflow based on High-Performance Liquid Chromatography-High-Resolution Mass Spectrometry to investigate the serum chemical exposome of 156 women of childbearing age from Jinan, China. A total of 185 exogenous chemicals from 19 categories were identified in at least 80% of serum samples with confidence levels 1-3, 84 of which have never been reported in humans, and 9 of those showed active effects on multiple biological targets in ToxCast program. A combination of grouped weighted quantile sum regression (GWQS), weighted quantile sum regression (WQS), quantile g calculation (q g-comp), and Bayesian kernel machine regression (BKMR) models indicated significant associations of chemical mixture exposure with progesterone (P4), testosterone (T), and luteinizing hormone (LH)/follicle-stimulating hormone (FSH) ratios, and 7, 4, and 8 priority contributors were identified, respectively, such as fipronil sulfone for P4, dicyclohexyl phthalate for T, and 3-hydroxybenzyl alcohol for LH/FSH. Three chemicals closely related to androgen synthesis and metabolism were proposed. Restricted cubic spline curves showed that 10 of the 28 priority compound-hormone pairs displayed significant non-monotonic exposure-response relationships. This study provides more information on the chemical exposome in Chinese women of childbearing age and has important implications for understanding the effect of chemical co-exposure on sex hormone homeostasis in women.

An Enhanced Protocol to Expand Human Exposome and Machine Learning-Based Prediction for Methodology Application

The human exposome remains limited due to the challenging analytical strategies used to reveal low-level endocrine-disrupting chemicals (EDCs) and their metabolites in serum and urine. This limits the integrity of the EDC exposure assessment and hinders understanding of their cumulative health effects. In this study, we propose an enhanced protocol based on multi-solid-phase extraction (multi-SPE) to expand human exposome with polar EDCs and metabolites and train a machine learning (ML) model for methodology prediction based on molecular descriptors. The protocol enhanced the measurement of 70 (25%) and 34 (12%) out of 295 well-acknowledged EDCs in serum and urine compared to the hydrophilic-lipophilic balance sorbent alone. In a nontarget analysis of serum and urine from 20 women of childbearing age in a cohort of 498, controlling occupational factors and daily behaviors for high chemical exposure potential, the multi-SPE protocol increased the measurement of 10 (40%) and 16 (53%) target EDCs and identification of 17 (77%) and 70 (36%) nontarget chemicals (confidence ≥ level 3) in serum and urine, respectively. Interestingly, the ML model predicted that the multi-SPE protocol could identify an additional 38% of the most bioactive chemicals. In conclusion, the multi-SPE protocol advances human exposome by expanding the measurement and identification of exposure profiles.

MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data

MLinvitroTox is an automated Python pipeline developed for high-throughput hazard-driven prioritization of toxicologically relevant signals detected in complex environmental samples through high-resolution tandem mass spectrometry (HRMS/MS). MLinvitroTox is a machine learning (ML) framework comprising 490 independent XGBoost classifiers trained on molecular fingerprints from chemical structures and target-specific endpoints from the ToxCast/Tox21 invitroDBv4.1 database. For each analyzed HRMS feature, MLinvitroTox generates a 490-bit bioactivity fingerprint used as a basis for prioritization, focusing the time-consuming molecular identification efforts on features most likely to cause adverse effects. The practical advantages of MLinvitroTox are demonstrated for groundwater HRMS data. Among the 874 features for which molecular fingerprints were derived from spectra, including 630 nontargets, 185 spectral matches, and 59 targets, around 4% of the feature/endpoint relationship pairs were predicted to be active. Cross-checking the predictions for targets and spectral matches with invitroDB data confirmed the bioactivity of 120 active and 6791 nonactive pairs while mislabeling 88 active and 56 non-active relationships. By filtering according to bioactivity probability, endpoint scores, and similarity to the training data, the number of potentially toxic features was reduced by at least one order of magnitude. This refinement makes the analytical confirmation of the toxicologically most relevant features feasible, offering significant benefits for cost-efficient chemical risk assessment.Scientific Contribution:In contrast to the classical ML-based approaches for toxicity prediction, MLinvitroTox predicts bioactivity for HRMS features (i.e., distinct m/z signals) based on MS2 fragmentation spectra rather than the chemical structures from the identified features. While the original proof of concept study was accompanied by the release of a MLinvitroTox v1 KNIME workflow, in this study, we release a Python MLinvitroTox v2 package, which, in addition to automation, expands functionality to include predicting toxicity from structures, cleaning up and generating chemical fingerprints, customizing models, and retraining on custom data. Furthermore, as a result of improvements in bioactivity data processing, realized in the concurrently released pytcpl Python package for the custom processing of invitroDBv4.1 input data used for training MLinvitroTox, the current release introduces enhancements in model accuracy, coverage of biological mechanistic targets, and overall interpretability.

Per- and poly-fluoroalkyl substances (PFAS) accelerate biological aging mediated by increased C-reactive protein

Unhealthy biological aging is related to higher incidence of varied age-related diseases, even higher all-cause mortality. Previous small sample size study suggested that Per- and poly-fluoroalkyl substances (PFAS) was associated with biological aging, but the evidence of exposure-response relationships, potential effect modifiers, and potential mediators were not investigated. Therefore, we conducted a cross-sectional analysis of national study including 14, 865 adults in the US from 8 survey cycles of NHANES from 2003 to 2018, to investigate the associations of PFAS compounds in body serum, including perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), perfluorononanoic acid (PFNA), and perfluorohexane sulfonic acid (PFHxS), with biological aging. Generalized linear models showed that higher human exposure to PFAS was associated with accelerated biological aging. Importantly, human exposure to PFOA, PFOS, PFNA, and PFHxS with detected level (above 0.10 ng/mL) was associated with an average of 3.3 year (95 %CI: 2.7, 3.9, P < 0.001), 14.9 year (95 %CI: 7.2, 22.7, P < 0.001), 10.9 years (95 %CI: 3.9, 17.7, P < 0.001), and 8.8 years (95 %CI: 4.8, 12.9, P < 0.001) of biological aging acceleration. Cubic spline models indicated exposure-response relationships where there was no safe threshold of PFAS level regarding harms to human healthy aging. The weighted sum regression model found the significant associations of PFAS compound mixture with biological aging acceleration, and PFOA was the dominant contributor among 4 PFAS compounds. Mediation analysis suggested that C-reactive protein, one of the inflammation biomarkers, might play as mediator in PFAS-induced accelerated biological aging, but not Triglyceride-glucose index. In summary, our study suggests that the effects of PFAS on biological aging acceleration should be of concern and more action plans to address their negative impact on human health should be launched.

Studying Structural Details in Complex Samples. I. Combining two Chromatographic Separation Methods with Ultrahigh Resolution Mass Spectrometry

The analysis of complex mixtures poses a challenge due to the high number of compounds present in a mixture, which often exceed the capabilities of analytical methods and instruments. Even more challenging is understanding the structural details of compounds within a complex sample. Most analytical methods provide just bulk information on complex samples, and individual structural details cannot be observed. High-resolution mass spectrometry, the best method to analyze complex samples, suffers from inherent problems for structural studies in complex systems because collision-induced fragmentation (CID) measurements cannot provide data from individual compounds alone. The combination of different steps of chromatographic separation, here the combination of size exclusion chromatography with argentation chromatography, provides sufficient reduction in complexity to implement a method that allows gaining structural details of individual compounds within a complex mixture. The combination of offline size exclusion chromatography followed by online argentation chromatography effectively creates fractions based on the respective properties of the compounds in the mixture (size and number of π-bonds and heteroatoms) and reduces matrix effects to a great extent. Mass spectrometry with ultrahigh resolution provides basic chemical information for each detected compound and also provides the opportunity to gain structural information from MS/MS experiments. The results indicate effectively separated sample fractions yielded by the chromatographic steps with tremendously decreased total numbers of compounds. Especially, argentation chromatography proved to be a valuable separation tool when it comes to heteroatom-containing constituents. In the end, the fragmentation experiments indicated high-quality data due to the clean ion isolation enabled by prior separation. The structural elucidations provided deep insights into the carbon space of crude oil.

Cord plasma metabolomic signatures of prenatal per- and polyfluoroalkyl substance (PFAS) exposures in the Boston Birth Cohort

BACKGROUND

Prenatal per- and polyfluoroalkyl substance (PFAS) exposures are associated with adverse offspring health outcomes, yet the underlying pathological mechanisms are unclear. Cord blood metabolomics can identify potentially important pathways associated with prenatal PFAS exposures, providing mechanistic insights that may help explain PFAS' long-term health effects.

METHODS

The study included 590 mother-infant dyads from the Boston Birth Cohort. We measured PFAS in maternal plasma samples collected 24-72 h after delivery and metabolites in cord plasma samples. We used metabolome-wide association studies and pathway enrichment analyses to identify metabolites and pathways associated with individual PFAS, and quantile-based g-computation models to examine associations of metabolites with the PFAS mixture. We used False Discovery Rate to account for multiple comparisons.

RESULTS

We found that 331 metabolites and 18 pathways were associated with ≥ 1 PFAS, and 38 metabolites were associated with the PFAS mixture, predominantly amino acids and lipids. Amino acids such as alanine and lysine and their pathways, crucial to energy generation, biosynthesis, and bone health, were associated with PFAS and may explain PFAS' effects on fetal growth restriction. Carnitines and carnitine shuttle pathway, associated with 7 PFAS and the PFAS mixture, are involved in mitochondrial fatty acid β-oxidation, which may predispose higher risks of fetal and child growth restriction and cardiovascular diseases. Lipids, such as glycerophospholipids and their related pathway, can contribute to insulin resistance and diabetes by modulating transporters on cell membranes, participating in β-cell signaling pathways, and inducing oxidative damage. Neurotransmission-related metabolites and pathways associated with PFAS, including cofactors, precursors, and neurotransmitters, may explain the PFAS' effects on child neurodevelopment. We observed stronger associations between prenatal PFAS exposures and metabolites in males.

CONCLUSIONS

This prospective birth cohort study contributes to the limited literature on potential metabolomic perturbations for prenatal PFAS exposures. Future studies are needed to replicate our findings and link prenatal PFAS associated metabolomic perturbations to long-term child health outcomes.

Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data

Background: Dry eye disease is a common disorder of the ocular surface, leading patients to seek eye care. Clinical signs and symptoms are currently used to diagnose dry eye disease. Metabolomics, a method for analyzing biological systems, has been found helpful in identifying distinct metabolites in patients and in detecting metabolic profiles that may indicate dry eye disease at early stages. In this study, we explored the use of machine learning and metabolomics data to identify cataract patients who suffer from dry eye disease, a topic that, to our knowledge, has not been previously explored. As there is no one-size-fits-all machine learning model for metabolomics data, choosing the most suitable model can significantly affect the quality of predictions and subsequent metabolomics analyses. Methods: To address this challenge, we conducted a comparative analysis of eight machine learning models on two metabolomics data sets from cataract patients with and without dry eye disease. The models were evaluated and optimized using nested k-fold cross-validation. To assess the performance of these models, we selected a set of suitable evaluation metrics tailored to the data set's challenges. Results: The logistic regression model overall performed the best, achieving the highest area under the curve score of 0.8378, balanced accuracy of 0.735, Matthew's correlation coefficient of 0.5147, an F1-score of 0.8513, and a specificity of 0.5667. Additionally, following the logistic regression, the XGBoost and Random Forest models also demonstrated good performance. Conclusions: The results show that the logistic regression model with L2 regularization can outperform more complex models on an imbalanced data set with a small sample size and a high number of features, while also avoiding overfitting and delivering consistent performance across cross-validation folds. Additionally, the results demonstrate that it is possible to identify dry eye in cataract patients from tear film metabolomics data using machine learning models.

Insights into the Chemical Exposome during Pregnancy: A Non-Targeted Analysis of Preterm and Term Births

Human-made chemicals are ubiquitous, leading to chronic exposure to complex mixtures of potentially harmful substances. We investigated chemical exposures in pregnant women in New York City by applying a non-targeted analysis (NTA) workflow to 95 paired prenatal urine and serum samples (35 pairs of preterm birth) collected as part of the New York University Children's Health and Environment Study. We analyzed all samples using liquid chromatography coupled with Orbitrap high-resolution mass spectrometry in both positive and negative electrospray ionization modes, employing full scan and data-dependent MS/MS fragmentation scans. We detected a total of 1524 chemical features for annotation, with 12 chemicals confirmed by authentic standards. Two confirmed chemicals dodecyltrimethylammonium and N,N-dimethyldecylamine N-oxide appear to not have been previously reported in human blood samples. We observed a statistically significant differential enrichment between urine and serum samples, as well as between preterm and term birth (p < 0.0001) in serum samples. When comparing between preterm and term births, an exogenous contaminant, 1,4-cyclohexanedicarboxylic acid (tentative), showed a statistical significance difference (p = 0.003) with more abundance in preterm birth in serum. An example of chemical associations (12 associations in total) observed was between surfactants (tertiary amines) and endogenous metabolites (fatty acid amides).

Modeling the relative response factor of small molecules in positive electrospray ionization

Technological advancements in liquid chromatography (LC) electrospray ionization (ESI) high-resolution mass spectrometry (HRMS) have made it an increasingly popular analytical technique in non-targeted analysis (NTA) of environmental and biological samples. One critical limitation of current methods in NTA is the lack of available analytical standards for many of the compounds detected in biological and environmental samples. Computational approaches can provide estimates of concentrations by modeling the relative response factor of a compound (RRF) expressed as the peak area of a given peak divided by its concentration. In this paper, we explore the application of molecular dynamics (MD) in the development of a computational workflow for predicting RRF. We obtained measurements of RRF for 48 compounds with LC - quadrupole time-of-flight (QTOF) MS and calculated their RRF. We used the CGenFF force field to generate the topologies and GROMACS to conduct the (MD) simulations. We calculated the Lennard-Jones and Coulomb interactions between the analytes and all other molecules in the ESI droplet, which were then sampled to construct a multilinear regression model for predicting RRF using Monte Carlo simulations. The best performing model showed a coefficient of determination (R 2) of 0.82 and a mean absolute error (MAE) of 0.13 log units. This performance is comparable to other predictive models including machine learning models. While there is a need for further evaluation of diverse chemical structures, our approach showed promise in predictions of RRF.

Rapid identification of reproductive toxicants among environmental chemicals using an in vivo evaluation of gametogenesis in budding yeast Saccharomyces cerevisiae

Infertility affects ∼12 % of couples, with environmental chemical exposure as a potential contributor. Of the chemicals that are actively manufactured, very few are assessed for reproductive health effects. Rodents are commonly used to evaluate reproductive effects, which is both costly and time consuming. Thus, there is a pressing need for rapid methods to test a broader range of chemicals. Here, we developed a strategy to evaluate large numbers of chemicals for reproductive toxicity via a yeast, S. cerevisiae high-throughput assay to assess gametogenesis as a potential new approach method (NAM). By simultaneously assessing chemicals for growth effects, we can distinguish if a chemical affects gametogenesis only, proliferative growth only or both. We identified a well-known mammalian reproductive toxicant, bisphenol A (BPA) and ranked 19 BPA analogs for reproductive harm. By testing mixtures of BPA and its analogs, we found that BPE and 17 β-estradiol each together with BPA showed synergistic effects that worsened reproductive outcome. We examined an additional 179 environmental chemicals including phthalates, pesticides, quaternary ammonium compounds and per- and polyfluoroalkyl substances and found 57 with reproductive effects. Many of the chemicals were found to be strong reproductive toxicants that have yet to be tested in mammals. Chemicals having affect before meiosis I division vs. meiosis II division were identified for 16 gametogenesis-specific chemicals. Finally, we demonstrate that in general yeast reproductive toxicity correlates well with published reproductive toxicity in mammals illustrating the promise of this NAM to quickly assess chemicals to prioritize the evaluation for human reproductive harm.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Prenatal exposure to persistent and non-persistent chemical mixtures and associations with adverse birth outcomes in the Atlanta African American Maternal-Child Cohort

BACKGROUND

African Americans (AAs) experience higher rates of preterm birth and fetal growth restriction relative to other pregnant populations. Differential in utero exposure to environmental chemicals may partially explain these health disparities, as AAs are disproportionately exposed to environmental hazards.

OBJECTIVE

We examined the individual and mixture effects of non-persistent chemicals and persistent organic pollutants (POPs) on gestational age at birth and birthweight for gestational age z-scores within a prospective cohort of pregnant AAs.

METHODS

First-trimester serum and urine samples obtained from participants within the Atlanta African American Maternal-Child cohort were analyzed for 43 environmental chemicals, including per-and polyfluoroalkyl substances (PFAS), polybrominated diphenyl ethers (PBDEs), organochlorine pesticides, pyrethroid insecticides, phthalates, bisphenol A, nicotine, and the primary metabolite of delta-9-tetrahydrocannabinol. Linear regression was used to estimate individual associations between chemicals and gestational age and birthweight z-scores (N ranging from 107 to 523). Mixture associations were estimated using quantile g-computation, principal component (PC) analyses, and hierarchical Bayesian kernel machine regression among complete cases (N = 86).

RESULTS

Using quantile g-computation, increasing all chemical exposures by one quantile was modestly associated with a reduction in gestational age (mean change per quartile increase = -0.47, 95% CI = -1.56, 0.61) and birthweight z-scores (mean change per quartile increase = -0.49, 95% CI = -1.14, 0.15). All PCs were associated with a reduction in birthweight z-scores; associations were greatest in magnitude for the two PCs reflecting exposure to combined tobacco, insecticides, PBDEs, and phthalates. In single pollutant models, we observed inconsistent and largely non-significant associations.

SIGNIFANCE

We conducted multiple targeted exposure assessment methods to quantify levels of environmental chemicals and leveraged mixture methods to quantify their joint effects on gestational age and birthweight z-scores. Our findings suggest that prenatal exposure to multiple classes of persistent and non-persistent chemicals is associated with reduced gestational age and birthweight z-scores in AAs.

IMPACT

African Americans (AAs) experience higher rates of preterm birth and fetal growth restriction relative to other pregnant populations. Differential in utero exposure to environmental chemicals may partially explain these health disparities, as AAs are disproportionately exposed to environmental hazards. In the present study, we analyzed serum and urine samples for levels of 43 environmental chemicals. We used quantile g-computation, principal component analysis, and BKMR to assess associations between chemical exposure mixtures and adverse birth outcomes. Our findings suggest that prenatal exposure to multiple classes of chemicals is associated with reduced birthweight z-scores, a proxy for fetal growth, in AAs.

The importance of developmental programming in the beef industry

The beef industry relies on multiple focused segments (e.g., cow-calf, stocker/feeder, and meat packing) to supply the world with beef. Thus, the potential impact of developmental programming on the beef industry needs to be evaluated with regards to the different production traits that drive profitability within each segment. For example, when nutrient restriction of dams occurred early in gestation embryo survival was decreased and the ovarian reserve of heifer progeny was negatively affected. Restriction during mid- to late gestation negatively impacted first service conception rates and pregnancy success of daughters. Even non-nutrient stress has been reported to impact transgenerational embryo development through the male progeny. Primary and secondary muscle fibers form during months two to eight (Days 60-240) of gestation. Therefore, external stimuli (nutrition or environmental) during this window have the potential to decrease the postnatal number of muscle fibers; which has an irreversible impact on animal growth and performance. Nutrient restriction during the last third of gestation resulted in decreased weaning weights, and in some instances decreased dry mater intake, hot carcass weight, and marbling scores. Protein supplementation during late gestation; however, increased weaning weight and ADG to weaning, but progeny of dams restricted in protein in late gestation had greater ribeye area. The importance of developmental programming is recognized; however, its precise application depends on comprehension of its integrated effects across the multiple-focused segments of the beef industry.

Data-driven insights can transform women's reproductive health

This perspective explores the transformative potential of data-driven insights to understand and address women's reproductive health conditions. Historically, clinical studies often excluded women, hindering comprehensive research into conditions such as adverse pregnancy outcomes and endometriosis. Recent advances in technology (e.g., next-generation sequencing techniques, electronic medical records (EMRs), computational power) provide unprecedented opportunities for research in women's reproductive health. Studies of molecular data, including large-scale meta-analyses, provide valuable insights into conditions like preterm birth and preeclampsia. Moreover, EMRs and other clinical data sources enable researchers to study populations of individuals, uncovering trends and associations in women's reproductive health conditions. Despite these advancements, challenges such as data completeness, accuracy, and representation persist. We emphasize the importance of holistic approaches, greater inclusion, and refining and expanding on how we leverage data and computational integrative approaches for discoveries so that we can benefit not only women's reproductive health but overall human health.

Exploring applications of non-targeted analysis in the characterization of the prenatal exposome

Capturing the breadth of chemical exposures in utero is critical in understanding their long-term health effects for mother and child. We explored methodological adaptations in a Non-Targeted Analysis (NTA) pipeline and evaluated the effects on chemical annotation and discovery for maternal and infant exposure. We focus on lesser-known/underreported chemicals in maternal and umbilical cord serum analyzed with liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS). The samples were collected from a demographically diverse cohort of 296 maternal-cord pairs (n = 592) recruited in San Francisco Bay area. We developed and evaluated two data processing pipelines, primarily differing by detection frequency cut-off, to extract chemical features from non-targeted analysis (NTA). We annotated the detected chemical features by matching with EPA CompTox Chemicals Dashboard (n = 860,000 chemicals) and Human Metabolome Database (n = 3140 chemicals) and applied a Kendrick Mass Defect filter to detect homologous series. We collected fragmentation spectra (MS/MS) on a subset of serum samples and matched to an experimental MS/MS database within the MS-Dial website and other experimental MS/MS spectra collected from standards in our lab. We annotated ~72 % of the features (total features = 32,197, levels 1-4). We confirmed 22 compounds with analytical standards, tentatively identified 88 compounds with MS/MS spectra, and annotated 4862 exogenous chemicals with an in-house developed annotation algorithm. We detected 36 chemicals that appear to not have been previously reported in human blood and 9 chemicals that were reported in less than five studies. Our findings underline the importance of NTA in the discovery of lesser-known/unreported chemicals important to characterize human exposures.

Identification of transformation products during Doxylamine chloramination for NDMA mitigation

N-nitrosodimethylamine (NDMA) is a disinfection byproduct that forms at the presence of an organic nitrogen precursor. Doxylamine, an antihistaminic pharmaceutical, is a precursor of NDMA and has been shown to form NDMA in the presence of chloramine. In this study, the effect of Doxylamine as an NDMA precursor has been further studied during chloramination. The end product and byproducts during chloramination were investigated using a high-resolution mass spectrometer by taking samples at different time intervals. Results suggest that NDMA is not the only end product forming during chloramination of Doxylamine and several transformation products that do not end up as NDMA may form. A group of these transformation products have been selected based on their relative amounts during chloramination with time and notated as Focus Tentative Transformation Products (FTTPn). The identification of these byproducts will make it easier to study the conditions during chloramination that may favour these 'known' transformation products with the use of less sophisticated analytical instruments. Then, it might lead to the establishment of chloramination protocols that will minimise the formation of NDMA from its precursors.

Blurred lines: Crossing the boundaries between the chemical exposome and the metabolome

The aetiology of every human disease lies in a combination of genetic and environmental factors, each contributing in varying proportions. While genomics investigates the former, a comparable holistic paradigm was proposed for environmental exposures in 2005, marking the onset of exposome research. Since then, the exposome definition has broadened to include a wide array of physical, chemical, and psychosocial factors that interact with the human body and potentially alter the epigenome, the transcriptome, the proteome, and the metabolome. The chemical exposome, deeply intertwined with the metabolome, includes all small molecules originating from diet as well as pharmaceuticals, personal care and consumer products, or pollutants in air and water. The set of techniques to interrogate these exposures, primarily mass spectrometry and nuclear magnetic resonance spectroscopy, are also extensively used in metabolomics. Recent advances in untargeted metabolomics using high resolution mass spectrometry have paved the way for the development of methods able to provide in depth characterisation of both the internal chemical exposome and the endogenous metabolome simultaneously. Herein we review the available tools, databases, and workflows currently available for such work, and discuss how these can bridge the gap between the study of the metabolome and the exposome.

Scannotation: A Suspect Screening Tool for the Rapid Pre-Annotation of the Human LC-HRMS-Based Chemical Exposome

In an increasingly chemically polluted environment, rapidly characterizing the human chemical exposome (i.e., chemical mixtures accumulating in humans) at the population scale is critical to understand its impact on health. High-resolution mass spectrometry (HRMS) profiling of complex biological matrices can theoretically provide a comprehensive picture of chemical exposures. However, annotating the detected chemical features, particularly low-abundant ones, remains a significant obstacle to implementing such approaches at a large scale. We present Scannotation (https://github.com/scannotation/Scannotation_software), an automated and user-friendly suspect screening tool for the rapid pre-annotation of HRMS preprocessed data sets. This software tool combines several MS1 chemical predictors, i.e., m/z, experimental and predicted retention times, isotopic patterns, and neutral loss patterns, to score the proximity between features and suspects, thus efficiently prioritizing tentative annotations to verify. Scannotation and MS-DIAL4 were used to annotate blood serum samples of 75 Breton adolescents. Scannotation's combination of MS1-based chemical predictors allowed us to annotate 89 chemically diverse environmental compounds with high confidence (confirmed by MS2 when available). These compounds included 62% of emerging molecules, for which no toxicological or human biomonitoring data are reported in the literature. The complementarity observed with MS-DIAL4 results demonstrates the relevance of Scannotation for the efficient pre-annotation of large-scale exposomics data sets.

Nontargeted Analysis in Tobacco Research: Challenges and Opportunities

Tobacco products are evolving at a pace that has outstripped tobacco control, leading to a high prevalence of tobacco use in the population. Researchers have been tirelessly developing suitable techniques to assess these products' emissions, toxicity, and public health impact. The nonclinical testing of tobacco products to assess the chemical profile of emissions is needed for evidence-based regulations. This testing has largely relied on targeted analytical methods that focus on constituent lists that may fall short in determining the toxicity of newly designed tobacco products. Nontargeted analysis (NTA), or the process of identifying and quantifying compounds within a complex matrix without prior knowledge of its chemical composition, is a promising technique for tobacco regulation, but it is not without challenges. The lack of standardized methods for sample generation, sample preparation, chromatographic separation, compound identification, and data analysis and reporting must be addressed so that the quality and reproducibility of the data generated by NTA can be benchmarked. This review discusses the challenges and highlights the opportunities of NTA in studying tobacco product constituents and emissions.

The plastic health map: A systematic evidence map of human health studies on plastic-associated chemicals

BACKGROUND

The global production and use of plastic materials has increased dramatically since the 1960s and there is increasing evidence of human health impacts related to exposure to plastic-associated chemicals. There is, however, no comprehensive, regulatory, post-market monitoring for human health effects of plastic-associated chemicals or particles and it is unclear how many of these have been investigated for effects in humans, and therefore what the knowledge gaps are.

OBJECTIVE

To create a systematic evidence map of peer-reviewed human studies investigating the potential effects of exposure to plastic-associated particles/chemicals on health to identify research gaps and provide recommendations for future research and regulation policy.

METHODS

Medline and Embase databases were used to identify peer-reviewed primary human studies published in English from Jan 1960 - Jan 2022 that investigated relationships between exposures to included plastic-associated particles/chemicals measured and detected in bio-samples and human health outcomes. Plastic-associated particles/chemicals included are: micro and nanoplastics, due to their widespread occurrence and potential for human exposure; polymers, the main building blocks of plastic; plasticizers and flame retardants, the two most common types of plastic additives with the highest concentration ranges in plastic materials; and bisphenols and per- or polyfluoroalkyl substances, two chemical classes of known health concern that are common in plastics. We extracted metadata on the population and study characteristics (country, intergenerational, sex, age, general/special exposure risk status, study design), exposure (plastic-associated particle/chemical, multiple exposures), and health outcome measures (biochemical, physiological, and/or clinical), from which we produced the interactive database 'Plastic Health Map' and a narrative summary.

RESULTS

We identified 100,949 unique articles, of which 3,587 met our inclusion criteria and were used to create a systematic evidence map. The Plastic Health Map with extracted metadata from included studies are freely available at https://osf.io/fhw7d/ and summary tables, plots and overall observations are included in this report.

CONCLUSIONS

We present the first evidence map compiling human health research on a wide range of plastic-associated chemicals from several different chemical classes, in order to provide stakeholders, including researchers, regulators, and concerned individuals, with an efficient way to access published literature on the matter and determine knowledge gaps. We also provide examples of data clusters to facilitate systematic reviews and research gaps to help direct future research efforts. Extensive gaps are identified in the breadth of populations, exposures and outcomes addressed in studies of potential human health effects of plastic-associated chemicals. No studies of the human health effects of micro and/or nanoplastics were found, and no studies were found for 26/1,202 additives included in our search that are of known hazard concern and confirmed to be in active production. Few studies have addressed recent "substitution" chemicals for restricted additives such as organophosphate flame retardants, phthalate substitutes, and bisphenol analogues. We call for a paradigm shift in chemical regulation whereby new plastic chemicals are rigorously tested for safety before being introduced in consumer products, with ongoing post-introduction biomonitoring of their levels in humans and health effects throughout individuals' life span, including in old age and across generations.

A Critical Review on the Opportunity to Use Placenta and Innovative Biomonitoring Methods to Characterize the Prenatal Chemical Exposome

Adverse effects associated with chemical exposures during pregnancy include several developmental and reproductive disorders. However, considering the tens of thousands of chemicals present on the market, the effects of chemical mixtures on the developing fetus is still likely underestimated. In this critical review, we discuss the potential to apply innovative biomonitoring methods using high-resolution mass spectrometry (HRMS) on placenta to improve the monitoring of chemical exposure during pregnancy. The physiology of the placenta and its relevance as a matrix for monitoring chemical exposures and their effects on fetal health is first outlined. We then identify several key parameters that require further investigations before placenta can be used for large-scale monitoring in a robust manner. Most critical is the need for standardization of placental sampling. Placenta is a highly heterogeneous organ, and knowledge of the intraplacenta variability of chemical composition is required to ensure unbiased and robust interindividual comparisons. Other important variables include the time of collection, the sex of the fetus, and mode of delivery. Finally, we discuss the first applications of HRMS methods on the placenta to decipher the chemical exposome and describe how the use of placenta can complement biofluids collected on the mother or the fetus.

Extracting Structural Information from Physicochemical Property Measurements Using Machine Learning─A New Approach for Structure Elucidation in Non-targeted Analysis

Non-targeted analysis (NTA) has made critical contributions in the fields of environmental chemistry and environmental health. One critical bottleneck is the lack of available analytical standards for most chemicals in the environment. Our study aims to explore a novel approach that integrates measurements of equilibrium partition ratios between organic solvents and water (KSW) to predictions of molecular structures. These properties can be used as a fingerprint, which with the help of a machine learning algorithm can be converted into a series of functional groups (RDKit fragments), which can be used to search chemical databases. We conducted partitioning experiments using a chemical mixture containing 185 chemicals in 10 different organic solvents and water. Both a liquid chromatography quadrupole time-of-flight mass spectrometer (LC-QTOF MS) and a LC-Orbitrap MS were used to assess the feasibility of the experimental method and the accuracy of the algorithm at predicting the correct functional groups. The two methods showed differences in log KSW with the QTOF method showing a mean absolute error (MAE) of 0.22 and the Orbitrap method 0.33. The differences also culminated into errors in the predictions of RDKit fragments with the MAE for the QTOF method being 0.23 and for the Orbitrap method being 0.31. Our approach presents a new angle in structure elucidation for NTA and showed promise in assisting with compound identification.

Association between a Mixture of Per- and Polyfluoroalkyl Substances (PFAS) and Inflammatory Biomarkers in the Atlanta African American Maternal-Child Cohort

Per- and polyfluoroalkyl substances (PFAS) have been identified as environmental contributors to adverse birth outcomes. One potential mechanistic pathway could be through PFAS-related inflammation and cytokine production. Here, we examined associations between a PFAS mixture and inflammatory biomarkers during early and late pregnancy from participants enrolled in the Atlanta African American Maternal-Child Cohort (N = 425). Serum concentrations of multiple PFAS were detected in >90% samples at 8-14 weeks gestation. Serum concentrations of interferon-γ (IFN-γ), interleukin 6 (IL-6), interleukin 10 (IL-10), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) were measured at up to two time points (8-14 weeks and 24-30 weeks gestation). The effect of the PFAS mixture on each inflammatory biomarker was examined using quantile g-computation, Bayesian kernel machine regression (BKMR), Bayesian Weighted Sums (BWS), and weighted quantile sum (WQS) regression. Across all models, the PFAS mixture was associated with increased IFN-γ, IL-10, and TNF-α at both time points, with the strongest effects being observed at 24-30 weeks. Using quantile g-computation, increasing concentrations of a PFAS mixture were associated with a 29% (95% confidence interval = 18.0%, 40.7%) increase in TNF-α at 24-30 weeks. Similarly, using BWS, the PFAS mixture was associated with increased TNF-α at 24-30 weeks (summed effect = 0.29, 95% highest posterior density = 0.17, 0.41). The PFAS mixture was also positively associated with TNF-α at 24-30 weeks using BKMR [75th vs 50th percentile: 17.1% (95% credible interval = 7.7%, 27.4%)]. Meanwhile, PFOS was consistently the main drivers of overall mixture effect across four methods. Our findings indicated an increase in prenatal PFAS exposure is associated with an increase in multiple pro-inflammatory cytokines, potentially contributing to adverse pregnancy outcomes.

PFAS Exposures and the Human Metabolome: A Systematic Review of Epidemiological Studies

Purpose of Review

There is a growing interest in understanding the health effects of exposure to per- and polyfluoroalkyl substances (PFAS) through the study of the human metabolome. In this systematic review, we aimed to identify consistent findings between PFAS and metabolomic signatures. We conducted a search matching specific keywords that was independently reviewed by two authors on two databases (EMBASE and PubMed) from their inception through July 19, 2022 following PRISMA guidelines.

Recent Findings

We identified a total of 28 eligible observational studies that evaluated the associations between 31 different PFAS exposures and metabolomics in humans. The most common exposure evaluated was legacy long-chain PFAS. Population sample sizes ranged from 40 to 1,105 participants at different stages across the lifespan. A total of 19 studies used a non-targeted metabolomics approach, 7 used targeted approaches, and 2 included both. The majority of studies were cross-sectional (n = 25), including four with prospective analyses of PFAS measured prior to metabolomics.

Summary

Most frequently reported associations across studies were observed between PFAS and amino acids, fatty acids, glycerophospholipids, glycerolipids, phosphosphingolipids, bile acids, ceramides, purines, and acylcarnitines. Corresponding metabolic pathways were also altered, including lipid, amino acid, carbohydrate, nucleotide, energy metabolism, glycan biosynthesis and metabolism, and metabolism of cofactors and vitamins. We found consistent evidence across studies indicating PFAS-induced alterations in lipid and amino acid metabolites, which may be involved in energy and cell membrane disruption.

Prenatal and childhood exposure to per-/polyfluoroalkyl substances (PFASs) and its associations with childhood overweight and/or obesity: a systematic review with meta-analyses

BACKGROUND

Per-/polyfluoroalkyl substances (PFASs) are persistent organic pollutants and suspected endocrine disruptors.

OBJECTIVE

The aim of this work was to conduct a systematic review with meta-analysis to summarise the associations between prenatal or childhood exposure to PFASs and childhood overweight/obesity.

METHODS

The search was performed on the bibliographic databases PubMed and Embase with text strings containing terms related to prenatal, breastfeeding, childhood, overweight, obesity, and PFASs. Only papers describing a biomonitoring study in pregnant women or in children up to 18 years that assessed body mass index (BMI), waist circumference (WC), or fat mass in children were included. When the estimates of the association between a PFAS and an outcome were reported from at least 3 studies, a meta-analysis was conducted; moreover, to correctly compare the studies, we developed a method to convert the different effect estimates and made them comparable each other. Meta-analyses were performed also stratifying by sex and age, and sensitivity analyses were also performed.

RESULTS

In total, 484 and 779 articles were retrieved from PubMed and Embase, respectively, resulting in a total of 826 articles after merging duplicates. The papers included in this systematic review were 49: 26 evaluating prenatal exposure to PFASs, 17 childhood exposure, and 6 both. Considering a qualitative evaluation, results were conflicting, with positive, negative, and null associations. 30 papers were included in meta-analyses (19 prenatal, 7 children, and 4 both). Positive associations were evidenced between prenatal PFNA and BMI, between PFOA and BMI in children who were more than 3 years, and between prenatal PFNA and WC. Negative associations were found between prenatal PFOS and BMI in children who were 3 or less years, and between PFHxS and risk of overweight. Relatively more consistent negative associations were evidenced between childhood exposure to three PFASs (PFOA, PFOS, and PFNA) and BMI, in particular PFOS in boys. However, heterogeneity among studies was high.

CONCLUSION

Even though heterogeneous across studies, the pooled evidence suggests possible associations, mostly positive, between prenatal exposure to some PFASs and childhood BMI/WC; and relatively stronger evidence for negative associations between childhood exposure to PFASs and childhood BMI.

Non-targeted analysis (NTA) and suspect screening analysis (SSA): a review of examining the chemical exposome

Non-targeted analysis (NTA) and suspect screening analysis (SSA) are powerful techniques that rely on high-resolution mass spectrometry (HRMS) and computational tools to detect and identify unknown or suspected chemicals in the exposome. Fully understanding the chemical exposome requires characterization of both environmental media and human specimens. As such, we conducted a review to examine the use of different NTA and SSA methods in various exposure media and human samples, including the results and chemicals detected. The literature review was conducted by searching literature databases, such as PubMed and Web of Science, for keywords, such as "non-targeted analysis", "suspect screening analysis" and the exposure media. Sources of human exposure to environmental chemicals discussed in this review include water, air, soil/sediment, dust, and food and consumer products. The use of NTA for exposure discovery in human biospecimen is also reviewed. The chemical space that has been captured using NTA varies by media analyzed and analytical platform. In each media the chemicals that were frequently detected using NTA were: per- and polyfluoroalkyl substances (PFAS) and pharmaceuticals in water, pesticides and polyaromatic hydrocarbons (PAHs) in soil and sediment, volatile and semi-volatile organic compounds in air, flame retardants in dust, plasticizers in consumer products, and plasticizers, pesticides, and halogenated compounds in human samples. Some studies reviewed herein used both liquid chromatography (LC) and gas chromatography (GC) HRMS to increase the detected chemical space (16%); however, the majority (51%) only used LC-HRMS and fewer used GC-HRMS (32%). Finally, we identify knowledge and technology gaps that must be overcome to fully assess potential chemical exposures using NTA. Understanding the chemical space is essential to identifying and prioritizing gaps in our understanding of exposure sources and prior exposures. IMPACT STATEMENT: This review examines the results and chemicals detected by analyzing exposure media and human samples using high-resolution mass spectrometry based non-targeted analysis (NTA) and suspect screening analysis (SSA).

Extending Nontargeted Discovery of Environmental Chemical Exposures during Pregnancy and Their Association with Pregnancy Complications-A Cross-Sectional Study

BACKGROUND

Nontargeted analysis (NTA) methods identify novel exposures; however, few chemicals have been quantified and interrogated with pregnancy complications.

OBJECTIVES

We characterized levels of nine exogenous and endogenous chemicals in maternal and cord blood identified, selected, and confirmed in prior NTA steps, including linear and branched isomers perfluorooctane sulfonate (PFOS), perfluorohexane sulfonate (PFHxS), monoethylhexyl phthalate, 4-nitrophenol, tetraethylene glycol, tridecanedioic acid, octadecanedioic acid, and deoxycholic acid. We evaluated relationships between maternal and cord levels and between gestational diabetes mellitus (GDM) and hypertensive disorders of pregnancy in a diverse pregnancy cohort in San Francisco.

METHODS

We collected matched maternal and cord serum samples at delivery from 302 pregnant study participants from the Chemicals in Our Bodies cohort in San Francisco. Chemicals were identified via NTA and quantified using targeted approaches. We calculated distributions and Spearman correlation coefficients testing the relationship of chemicals within and between the maternal and cord blood matrices. We used adjusted logistic regression to calculate the odds of GDM and hypertensive disorders of pregnancy associated with an interquartile range increase in maternal chemical exposures.

RESULTS

We detected linear PFOS, PFHxS, octadecanedioic acid, and deoxycholic acid in at least 97% of maternal samples. Correlations ranged between - 0.1 and 0.9. We observed strong correlations between cord and maternal levels of PFHxS, linear PFOS, and branched PFOS (coefficient = 0.9 , 0.8, and 0.8, respectively). An interquartile range increase in linear and branched PFOS, tridecanedioic acid, octadecanedioic acid, and deoxycholic acid was associated with increased odds ratio (OR) of GDM [OR = 1.33 (95% CI: 0.89, 2.01), 1.24 (95% CI: 0.86, 1.80), 1.26 (95% CI: 0.93, 1.73), 1.24 (95% CI: 0.86, 1.80), and 1.23 (95% CI: 0.87, 1.75), respectively]. Tridecanedioic acid was positively associated with hypertensive disorders of pregnancy [OR = 1.28 (95% CI: 0.90, 1.86)].

DISCUSSION

We identified both exogenous and endogenous chemicals seldom quantified in pregnant study participants that were also related to pregnancy complications and demonstrated the utility of NTA to identify chemical exposures of concern. https://doi.org/10.1289/EHP11546.

Investigating geographic differences in environmental chemical exposures in maternal and cord sera using non-targeted screening and silicone wristbands in California

BACKGROUND

Differential risks for adverse pregnancy outcomes may be influenced by prenatal chemical exposures, but current exposure methods may not fully capture data to identify harms and differences.

METHODS

We collected maternal and cord sera from pregnant people in Fresno and San Francisco, and screened for over 2420 chemicals using LC-QTOF/MS. We matched San Francisco participants to Fresno participants (N = 150) and compared detection frequencies. Twenty-six Fresno participants wore silicone wristbands evaluated for over 1500 chemicals using quantitative chemical analysis. We assessed whether living in tracts with higher levels of pollution according to CalEnviroScreen correlated with higher numbers of chemicals detected in sera.

RESULTS

We detected 2167 suspect chemical features across maternal and cord sera. The number of suspect chemical features was not different by city, but a higher number of suspect chemicals in cosmetics or fragrances was detected in the Fresno versus San Francisco participants' sera. We also found high levels of chemicals used in fragrances measured in the silicone wristbands. Fresno participants living in tracts with higher pesticide scores had higher numbers of suspect pesticides in their sera.

CONCLUSIONS

Multiple exposure-assessment approaches can identify exposure to many chemicals during pregnancy that have not been well-studied for health effects.

Associations of prenatal exposure to per- and polyfluoroalkyl substances (PFAS) with measures of cognition in 7.5-month-old infants: An exploratory study

BACKGROUND

Prenatal exposure to per- and polyfluoroalkyl substances (PFAS) has been linked to a wide array of adverse maternal and child health outcomes. However, studies examining PFAS in relation to offspring cognition have been inconclusive.

OBJECTIVE

We examined whether prenatal exposure to a mixture of PFAS was related to cognition in 7.5-month-old infants.

METHODS

Our analytic sample included participants enrolled in the Chemicals in Our Bodies (CIOB) and Illinois Kids Development Study (IKIDS) cohorts (N = 163). Seven PFAS were measured in 2nd trimester maternal serum samples and were detected in >65% of participants. Infant cognition was measured with a visual recognition memory task using an infrared eye tracker when infants were 7.5 months old. This task included familiarization trials where each infant was shown two identical faces and test trials where each infant was shown the familiar face paired with a novel face. In familiarization, we assessed average run duration (time looking at familiarization stimuli before looking away) as a measure of information processing speed, in addition to time to familiarization (time to reach 20 s of looking at stimuli) and shift rate (the number of times infants looked between stimuli), both as measures of attention. In test trials, we assessed novelty preference (proportion of time looking to the novel face) to measure recognition memory. Linear regression was used to estimate associations of individual PFAS with cognitive outcomes, while Bayesian kernel machine regression (BKMR) was used to estimate mixture effects.

RESULTS

In adjusted single-PFAS linear regression models, an interquartile range increase in PFNA, PFOA, PFOS, PFHxS, PFDeA, and PFUdA was associated with an increase in shift rate, reflecting better visual attention. Using BKMR, increasing quartiles of the PFAS mixture was similarly associated with a modest increase in shift rate. There were no significant associations between PFAS exposure and time to reach familiarization (another measure of attention), average run duration (information processing speed), or novelty preference (visual recognition memory).

CONCLUSION

In our study population, prenatal PFAS exposure was modestly associated with an increase in shift rate and was not strongly associated with any adverse cognitive outcomes in 7.5-month-old infants.

Inflammation and the Potential Implication of Macrophage-Microglia Polarization in Human ASD: An Overview

Autism spectrum disorder (ASD) is a heterogeneous collection of neurodevelopmental disorders, difficult to diagnose and currently lacking treatment options. The possibility of finding reliable biomarkers useful for early identification would offer the opportunity to intervene with treatment strategies to improve the life quality of ASD patients. To date, there are many recognized risk factors for the development of ASD, both genetic and non-genetic. Although genetic and epigenetic factors may play a critical role, the extent of their contribution to ASD risk is still under study. On the other hand, non-genetic risk factors include pollution, nutrition, infection, psychological states, and lifestyle, all together known as the exposome, which impacts the mother's and fetus's life, especially during pregnancy. Pathogenic and non-pathogenic maternal immune activation (MIA) and autoimmune diseases can cause various alterations in the fetal environment, also contributing to the etiology of ASD in offspring. Activation of monocytes, macrophages, mast cells and microglia and high production of pro-inflammatory cytokines are indeed the cause of neuroinflammation, and the latter is involved in ASD's onset and development. In this review, we focused on non-genetic risk factors, especially on the connection between inflammation, macrophage polarization and ASD syndrome, MIA, and the involvement of microglia.

Per- and polyfluoroalkyl substances and psychosocial stressors have a joint effect on adverse pregnancy outcomes in the Atlanta African American Maternal-Child cohort

BACKGROUND

African Americans (AAs) experience high rates of adverse pregnancy outcomes relative to Whites. Differential in utero exposure to environmental chemicals and psychosocial stressors may explain some of the observed health disparities, as exposures to per- and polyfluoroalkyl substances (PFAS) and experiences of discrimination have been linked to adverse birth outcomes. Few studies have examined chemicals and non-chemical stressors together as an exposure mixture, which may better reflect real-life exposure patterns. Here, we adapted methods designed for the analysis of exposure mixtures to examine joint effects of PFAS and psychosocial stress on birth outcomes among AAs.

METHODS

348 participants from the Atlanta African American Maternal-Child cohort were included in this study. Four PFAS were measured in first trimester serum samples. Self-report questionnaires were administered during the first trimester and were used to assess psychosocial stress (perceived stress, depression, anxiety, gendered racial stress). Quantile g-computation and Bayesian kernel machine regression (BKMR) were used to estimate the joint effects between PFAS and psychosocial stressors on gestational age at delivery and birthweight for gestational age z-scores. All models were adjusted for maternal education, maternal age, parity, and any alcohol, tobacco and marijuana use.

RESULTS

Our analytic sample included a socioeconomically diverse group of pregnant women, with 79 % receiving public health insurance. In quantile g-computation models, a simultaneous one-quartile increase in all PFAS, perceived stress, depression, anxiety, and gendered racial stress was associated with a reduction in birthweight z-scores (mean %change per quartile increase = -0.24, 95 % confidence interval = -0.43, -0.06). BKMR similarly showed that increasing all exposures in the mixture was associated with a modest decrease in birthweight z-scores, but not a reduced length of gestation.

DISCUSSION

Using methods designed for analyzing exposure mixtures, we found that a simultaneous increase in in utero PFAS and psychosocial stressors was associated with reduced birthweight for gestational age z-scores.

Plasma PFOA and PFOS Levels, DNA Methylation, and Blood Lipid Levels: A Pilot Study

Exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) is associated with blood lipids in adults, but the underlying mechanisms remain unclear. This pilot study aimed to investigate the associations between PFOA or PFOS and epigenome-wide DNA methylation and assess the mediating effect of DNA methylation on the PFOA/PFOS-blood lipid association. We measured plasma PFOA/PFOS and leukocyte DNA methylation in 98 patients enrolled from the hospital between October 2018 and August 2019. The median plasma PFOA/PFOS levels were 0.85 and 2.29 ng/mL. Plasma PFOA and PFOS levels were significantly associated with elevated total cholesterol (TC) and low-density lipoprotein cholesterol (LDL) levels. There were 63/87 CpG positions and 8/11 differentially methylated regions (DMRs) associated with plasma PFOA/PFOS levels, respectively. In addition, 5 CpG positions (annotated to AFF3, CREB5, NRG2, USF2, and intergenic region) and one DMR annotated to IRF6 may mediate the association between plasma PFOA/PFOS and LDL levels (mediated proportion from 7.29 to 46.77%); two CpG positions may mediate the association between plasma PFOA/PFOS and TC levels (annotated to CREB5 and USF2, mediated proportion is around 30%). The data suggest that PFOA/PFOS exposure alters DNA methylation. More importantly, the association of PFOA/PFOS with lipid indicators was partly mediated by DNA methylation changes in lipid metabolism-related genes.

Disparities in chemical exposures among pregnant women and neonates by socioeconomic and demographic characteristics: A nontargeted approach

BACKGROUND

Exposure to environmental chemicals during pregnancy adversely affects maternal and infant health, and identifying socio-demographic differences in exposures can inform contributions to health inequities.

METHODS

We recruited 294 demographically diverse pregnant participants in San Francisco from the Mission Bay/Moffit Long (MB/ML) hospitals, which serve a primarily higher income population, and Zuckerberg San Francisco General Hospital (ZSFGH), which serves a lower income population. We collected maternal and cord sera, which we screened for 2420 unique formulas and their isomers using high-resolution mass spectrometry using LC-QTOF/MS. We assessed differences in chemical abundances across socioeconomic and demographic groups using linear regression adjusting for false discovery rate.

RESULTS

Our participants were racially diverse (31% Latinx, 16% Asian/Pacific Islander, 5% Black, 5% other or multi-race, and 43% white). A substantial portion experienced financial strain (28%) and food insecurity (20%) during pregnancy. We observed significant abundance differences in maternal (9 chemicals) and cord sera (39 chemicals) between participants who delivered at the MB/ML hospitals versus ZSFGH. Of the 39 chemical features differentially detected in cord blood, 18 were present in pesticides, one per- or poly-fluoroalkyl substance (PFAS), 21 in plasticizers, 24 in cosmetics, and 17 in pharmaceuticals; 4 chemical features had unknown sources. A chemical feature annotated as 2,4-dichlorophenol had higher abundances among Latinx compared to white participants, those delivering at ZSFGH compared to MB/ML, those with food insecurity, and those with financial strain. Post-hoc QTOF analyses indicated the chemical feature was either 2,4-dichlorophenol or 2,5-dichlorophenol, both of which have potential endocrine-disrupting effects.

CONCLUSIONS

Chemical exposures differed between delivery hospitals, likely due to underlying social conditions faced by populations served. Differential exposures to 2,4-dichlorophenol or 2,5-dichlorophenol may contribute to disparities in adverse outcomes.

Associations of per- and polyfluoroalkyl substances (PFAS) and their mixture with oxidative stress biomarkers during pregnancy

BACKGROUND

Oxidative stress from excess reactive oxygen species (ROS) is a hypothesized contributor to preterm birth. Per- and polyfluoroalkyl substances (PFAS) exposure is reported to generate ROS in laboratory settings, and is linked to adverse birth outcomes globally. However, to our knowledge, the relationship between PFAS and oxidative stress has not been examined in the context of human pregnancy.

OBJECTIVE

To investigate the associations between prenatal PFAS exposure and oxidative stress biomarkers among pregnant people.

METHODS

Our analytic sample included 428 participants enrolled in the Illinois Kids Development Study and Chemicals In Our Bodies prospective birth cohorts between 2014 and 2019. Twelve PFAS were measured in second trimester serum. We focused on seven PFAS that were detected in >65 % of participants. Urinary levels of 8-isoprostane-prostaglandin-F2α, prostaglandin-F2α, 2,3-dinor-8-iso-PGF2α, and 2,3-dinor-5,6-dihydro-8-iso-PGF2α were measured in the second and third trimesters as biomarkers of oxidative stress. We fit linear mixed-effects models to estimate individual associations between PFAS and oxidative stress biomarkers. We used quantile g-computation and Bayesian kernel machine regression (BKMR) to assess associations between the PFAS mixture and averaged oxidative stress biomarkers.

RESULTS

Linear mixed-effects models showed that an interquartile range increase in perfluorooctane sulfonic acid (PFOS) was associated with an increase in 8-isoprostane-prostaglandin-F2α (β = 0.10, 95 % confidence interval = 0, 0.20). In both quantile g-computation and BKMR, and across all oxidative stress biomarkers, PFOS contributed the most to the overall mixture effect. The six remaining PFAS were not significantly associated with changes in oxidative stress biomarkers.

CONCLUSIONS

Our study is the first to investigate the relationship between PFAS exposure and biomarkers of oxidative stress during human pregnancy. We found that PFOS was associated with elevated levels of oxidative stress, which is consistent with prior work in animal models and cell lines. Future research is needed to understand how prenatal PFAS exposure and maternal oxidative stress may affect fetal development.

Advances in computational methods along the exposure to toxicological response paradigm

Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.

Uncertainty estimation strategies for quantitative non-targeted analysis

Non-targeted analysis (NTA) methods are widely used for chemical discovery but seldom employed for quantitation due to a lack of robust methods to estimate chemical concentrations with confidence limits. Herein, we present and evaluate new statistical methods for quantitative NTA (qNTA) using high-resolution mass spectrometry (HRMS) data from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT). Experimental intensities of ENTACT analytes were observed at multiple concentrations using a semi-automated NTA workflow. Chemical concentrations and corresponding confidence limits were first estimated using traditional calibration curves. Two qNTA estimation methods were then implemented using experimental response factor (RF) data (where RF = intensity/concentration). The bounded response factor method used a non-parametric bootstrap procedure to estimate select quantiles of training set RF distributions. Quantile estimates then were applied to test set HRMS intensities to inversely estimate concentrations with confidence limits. The ionization efficiency estimation method restricted the distribution of likely RFs for each analyte using ionization efficiency predictions. Given the intended future use for chemical risk characterization, predicted upper confidence limits (protective values) were compared to known chemical concentrations. Using traditional calibration curves, 95% of upper confidence limits were within ~tenfold of the true concentrations. The error increased to ~60-fold (ESI+) and ~120-fold (ESI-) for the ionization efficiency estimation method and to ~150-fold (ESI+) and ~130-fold (ESI-) for the bounded response factor method. This work demonstrates successful implementation of confidence limit estimation strategies to support qNTA studies and marks a crucial step towards translating NTA data in a risk-based context.

Prenatal PFAS and psychosocial stress exposures in relation to fetal growth in two pregnancy cohorts: Applying environmental mixture methods to chemical and non-chemical stressors

BACKGROUND

Prenatal exposure to individual per‑ and poly‑fluoroalkyl substances (PFAS) and psychosocial stressors have been associated with reductions in fetal growth. Studies suggest cumulative or joint effects of chemical and non-chemical stressors on fetal growth. However, few studies have examined PFAS and non-chemical stressors together as a mixture, which better reflects real life exposure patterns. We examined joint associations between PFAS, perceived stress, and depression, and fetal growth using two approaches developed for exposure mixtures.

METHODS

Pregnant participants were enrolled in the Chemicals in Our Bodies cohort and Illinois Kids Development Study, which together make up the ECHO.CA.IL cohort. Seven PFAS were previously measured in 2nd trimester maternal serum samples and were natural log transformed for analyses. Perceived stress and depression were assessed using self-reported validated questionnaires, which were converted to t-scores using validated methods. Quantile g-computation and Bayesian kernel machine regression (BKMR) were used to assess joint associations between PFAS, perceived stress and depression t-scores and birthweight z-scores (N = 876).

RESULTS

Individual PFAS, depression and perceived stress t-scores were negatively correlated with birthweight z-scores. Using quantile g-computation, a simultaneous one quartile increase in all PFAS, perceived stress and depression t-scores was associated with a slight reduction in birthweight z-scores (mean change per quartile increase = -0.09, 95% confidence interval = -0.21,0.03). BKMR similarly indicated that cumulative PFAS and stress t-scores were modestly associated with lower birthweight z-scores. Across both methods, the joint association appeared to be distributed across multiple exposures rather than due to a single exposure.

CONCLUSIONS

Our study is one of the first to examine the joint effects of chemical and non-chemical stressors on fetal growth using mixture methods. We found that PFAS, perceived stress, and depression in combination were modestly associated were lower birthweight z-scores, which supports prior studies indicating that chemical and non-chemical stressors are jointly associated with adverse health outcomes.

In vivo and in vitro postovulatory aging: when time works against oocyte quality?

In mammalian species an optimal fertilization window during which successful fertilization occurs. In the majority of mammals estrus marks ovulation time and coincident with mating, thereby allowing the synchronized meeting in the fallopian tubes, between freshly ejaculated sperm and freshly ovulated oocytes. Conversely, women do not show natural visual signs of ovulation such that fertilization can occur hours later involving an aged oocyte and freshly ejaculated spermatozoa. During this time, the oocyte undergoes a rapid degradation known as “postovulatory aging” (POA). POA may become particularly important in the human-assisted reproductive technologies, as the fertilization of retrieved mature oocytes can be delayed due to increased laboratory workload or because of unforeseeable circumstances, like the delayed availability of semen samples. This paper is an updated review of the consequences of POA, either in vivo or in vitro, on oocyte quality with particular attention to modifications caused by POA on oocyte nuclear, cytoplasmic, genomic, and epigenetic maturation, and embryo development.