Application of an analytical framework for multivariate mediation analysis of environmental data

Opportunities offered by latent-based multiblock strategies to integrate biomarkers of chemical exposure and biomarkers of effect in environmental health studies

Modern environmental epidemiology benefits from a new generation of technologies that enable comprehensive profiling of biomarkers, including environmental chemical exposure and omic datasets. The integration and analysis of large and structured datasets to identify functional associations is constrained by computational challenges that cannot be overcome using conventional regression methods. Some extensions of Partial Least Squares (PLS) regression have been developed to efficently integrate multiple datasets, including Multiblock PLS (MB-PLS) and Sequential and Orthogonalized PLS; however, these approaches remain seldom applied in environmental epidemiology. To address that research gap, this study aimed to assess and compare the applicability of PLS-based multiblock models in an observational case study, where biomarkers of exposure to environmental chemicals and endogenous biomarkers of effect were simultaneously integrated to highlight biological links related to a health outcome. The methods were compared with and without sparsity coupling two metrics to support the variable selection: Variable Importance in Projection (VIP) and Selectivity Ratio (SR). The framework was applied to a case-study dataset mimicking the structure of 36 environmental exposure biomarkers (E-block), 61 inflammation biomarkers (M-block), and their relationships with the gestational age at delivery of 161 mother-infant pairs. The results showed an overall consistency in the selected variables across models, although some specific selection patterns were identified. The block-scaled concatenation-based approaches (e.g. MB-PLS) tended to select more variables from the E-block, while these methods were unable to identify certain variables in the M-block. Overall, the number of variables selected using the SR criterion was higher than using the VIP criterion, with lower predictive performances. The multiblock models coupled to VIP, appeared to be the methods of choice for identifying relevant variables with similar statistical performances. Overall, the use of multiblock PLS-based methods appears to be a good strategy to efficiently support the variable selection process in modern environmental epidemiology.

Analytical challenges and opportunities in the study of endocrine disrupting chemicals within an exposomics framework

Exposomics aims to measure human exposures throughout the lifespan and the changes they produce in the human body. Exposome-scale studies have significant potential to understand the interplay of environmental factors with complex multifactorial diseases widespread in our society and whose origin remain unclear. In this framework, the study of the chemical exposome aims to cover all chemical exposures and their effects in human health but, today, this goal still seems unfeasible or at least very challenging, which makes the exposome for now only a concept. Furthermore, the study of the chemical exposome faces several methodological challenges such as moving from specific targeted methodologies towards high-throughput multitargeted and non-targeted approaches, guaranteeing the availability and quality of biological samples to obtain quality analytical data, standardization of applied analytical methodologies, as well as the statistical assignment of increasingly complex datasets, or the identification of (un)known analytes. This review discusses the various steps involved in applying the exposome concept from an analytical perspective. It provides an overview of the wide variety of existing analytical methods and instruments, highlighting their complementarity to develop combined analytical strategies to advance towards the chemical exposome characterization. In addition, this review focuses on endocrine disrupting chemicals (EDCs) to show how studying even a minor part of the chemical exposome represents a great challenge. Analytical strategies applied in an exposomics context have shown great potential to elucidate the role of EDCs in health outcomes. However, translating innovative methods into etiological research and chemical risk assessment will require a multidisciplinary effort. Unlike other review articles focused on exposomics, this review offers a holistic view from the perspective of analytical chemistry and discuss the entire analytical workflow to finally obtain valuable results.

Environmental Toxicant Exposure and Depressive Symptoms

Importance

Recognizing associations between exposure to common environmental toxicants and mental disorders such as depression is crucial for guiding targeted mechanism research and the initiation of disease prevention efforts.

Objectives

To comprehensively screen and assess the associations between potential environmental toxicants and depressive symptoms and to assess whether systemic inflammation serves as a mediator.

Design, Setting, and Participants

A total of 3427 participants from the 2013-2014 and 2015-2016 waves of the National Health and Nutrition Examination and Survey who had information on blood or urine concentrations of environmental toxicants and depression scores assessed by the 9-item Patient Health Questionnaire (PHQ-9) were included. Statistical analysis was performed from July 1, 2023, to January 31, 2024.

Exposures

Sixty-two toxicants in 10 categories included acrylamide, arsenic, ethylene oxide, formaldehyde, iodine, metals, nicotine metabolites, polycyclic aromatic hydrocarbons, volatile organic compound (VOC) metabolites; and perchlorate, nitrate, and thiocyanate.

Main Outcomes and Measures

An exposome-wide association study and the deletion-substitution-addition algorithm were used to assess associations with depression scores (PHQ-9 ≥5) adjusted for other important covariates. A mediation analysis framework was used to evaluate the mediating role of systemic inflammation assessed by the peripheral white blood cell count.

Results

Among the 3427 adults included, 1735 (50.6%) were women, 2683 (78.3%) were younger than 65 years, and 744 (21.7%) were 65 years or older, with 839 (24.5%) having depressive symptoms. In terms of race and ethnicity, 570 participants (16.6%) were Mexican American, 679 (19.8%) were non-Hispanic Black, and 1314 (38.3%) were non-Hispanic White. We identified associations between 27 chemical compounds or metals in 6 of 10 categories of environmental toxicants and the prevalence of depressive symptoms, including the VOC metabolites N-acetyl-S-(2-hydroxy-3-butenyl)-l-cysteine (odds ratio [OR], 1.74 [95% CI, 1.38, 2.18]) and total nicotine equivalent-2 (OR, 1.42 [95% CI, 1.26-1.59]). Men and younger individuals appear more vulnerable to environmental toxicants than women and older individuals. Peripheral white blood cell count mediated 5% to 19% of the associations.

Conclusions and Relevance

In this representative cross-sectional study of adults with environmental toxicant exposures, 6 categories of environmental toxicants were associated with depressive symptoms with mediation by systemic inflammation. This research provides insight into selecting environmental targets for mechanistic research into the causes of depression and facilitating efforts to reduce environmental exposures.

Metabolic Perturbations Associated with both PFAS Exposure and Perinatal/Antenatal Depression in Pregnant Individuals: A Meet-in-the-Middle Scoping Review

PURPOSE OF REVIEW

Depression during the perinatal or antenatal period affects at least 1 in 10 women worldwide, with long term health implications for the mother and child. Concurrently, there is increasing evidence associating maternal exposure to per- and poly-fluoroalkyl substances (PFAS) to adverse pregnancy outcomes. We reviewed the body of evidence examining both the associations between PFAS exposure and perturbations in the maternal metabolome, and the associations between the maternal metabolome and perinatal/antenatal depression. Through this, we sought to explore existing evidence of the perinatal metabolome as a potential mediation pathway linking PFAS exposure and perinatal/antenatal depression.

RECENT FINDINGS

There are few studies examining the metabolomics of PFAS exposure-specifically in pregnant women-and the metabolomics of perinatal/antenatal depression, let alone studies examining both simultaneously. Of the studies reviewed (N = 11), the majority were cross sectional, based outside of the US, and conducted on largely homogenous populations. Our review identified 23 metabolic pathways in the perinatal metabolome common to both PFAS exposure and perinatal/antenatal depression. Future studies may consider findings from our review to conduct literature-derived hypothesis testing focusing on fatty acid metabolism, alanine metabolism, glutamate metabolism, and tyrosine metabolism when exploring the biochemical mechanisms conferring the risk of perinatal/antenatal depression due to PFAS exposure. We recommend that researchers also utilize heterogenous populations, longitudinal study designs, and mediation approaches to elucidate key pathways linking PFAS exposures to perinatal/antenatal depression.

Statistical methods for chemical mixtures: a roadmap for practitioners

Quantitative characterization of the health impacts associated with exposure to chemical mixtures has received considerable attention in current environmental and epidemiological studies. With many existing statistical methods and emerging approaches, it is important for practitioners to understand when each method is best suited for their inferential goals. In this study, we conduct a review and comparison of 11 analytical methods available for use in mixtures research, through extensive simulation studies for continuous and binary outcomes. These methods fall in three different classes: identifying important components of a mixture, identifying interactions and creating a summary score for risk stratification and prediction. We carry out an illustrative data analysis in the PROTECT birth cohort from Puerto Rico. Most importantly we develop an integrated package "CompMix" that provides a platform for mixtures analysis where the practitioner can implement a pipeline for several types of mixtures analysis. Our simulation results suggest that the choice of methods depends on the goal of analysis and there is no clear winner across the board. For selection of important toxicants in the mixture and for identifying interactions, Elastic net by Zou et al. (Enet), Lasso for Hierarchical Interactions by Bien et al (HierNet), Selection of nonlinear interactions by a forward stepwise algorithm by Narisetty et al. (SNIF) have the most stable performance across simulation settings. Additionally, the predictive performance of the Super Learner ensembling method by Van de Laan et al. and HierNet are found to be superior to the rest of the methods. For overall summary or a cumulative measure, we find that using the Super Learner to combine multiple Environmental Risk Scores can lead to improved risk stratification properties. We have developed an R package "CompMix: A comprehensive toolkit for environmental mixtures analysis", allowing users to implement a variety of tasks under different settings and compare the findings. In summary, our study offers guidelines for selecting appropriate statistical methods for addressing specific scientific questions related to mixtures research. We identify critical gaps where new and better methods are needed.

Mediation effect of serum zinc on insulin secretion inhibited by methyl tert-butyl ether in gas station workers

Methyl tert-butyl ether (MTBE), a type of gasoline additive, has been found to affect insulin function and glucose homeostasis in animal experiments, but there is still no epidemiological evidence. Zinc (Zn) is a key regulatory element of insulin secretion and function, and Zn homeostasis can be disrupted by MTBE exposure through inducing oxidative stress. Therefore, we suspected that Zn might be involved and play an important role in the process of insulin secretion inhibited by MTBE exposure. In this study, we recruited 201 male subjects including occupational and non-occupational MTBE exposure from Anhui Province, China in 2019. Serum insulin and functional analog fibroblast growth factor 1 (FGF1) and blood MTBE were detected by Elisa and headspace solid-phase microextraction and gas chromatography-high-resolution mass spectrometry. According to MTBE internal exposure level, the workers were divided into low- and high-exposed groups and found that the serum insulin level in the high-exposed group was significantly lower than that in the low-exposed group (p = 0.003) while fasting plasma glucose (FPG) level increased obviously in the high-exposed group compared to the low-exposed group (p = 0.001). Further analysis showed that MTBE exposure level was positively correlated with FPG level, but negatively correlated with serum insulin level, which suggested that the FPG level increase might be related to the decrease of serum insulin level induced by MTBE exposure. The results of further mediation effect analysis showed that changes in serum zinc levels played a major intermediary role in the process of insulin secretion inhibition and blood glucose elevation caused by MTBE exposure. In addition, a significant negative correlation was found between MTBE exposure and serum Zn level, which might play a strong mediating effect on the inhibition of insulin secretion induced by MTBE exposure. In conclusion, our study provided evidence that MTBE could inhibit insulin secretion and interfere with Zn metabolism in gas station workers for the first time, and found that Zn might play an important mediation effect during the process of inhibiting insulin secretion and interfering with glucose metabolism induced by MTBE exposure.

The complex role of air pollution on the association between greenness and respiratory mortality: Insight from a large cohort, 2009-2020

BACKGROUND

Although emerging studies have illuminated the protective association between greenness and respiratory mortality, efforts to quantify the potentially complex role of air pollution in the causal pathway are still limited. We aimed to examine the potential roles of air pollution in the causal pathway between greenness and respiratory mortality in China.

METHODS

We used data from a community-based prospective cohort of 654,115 participants in southern China (Jan 2009-Dec 2020). We evaluated the greenness exposure as a three-year moving average Normalized Difference Vegetation Index (NDVI) within the 500 m buffer around the residence. Cox proportional hazards model was applied to estimate the association between greenness and respiratory mortality. Causal mediation analysis combined with a four-way dimensional decomposition method was utilized to simultaneously quantify the interaction and mediation role of air pollution including PM2.5, PM10, or NO2 on the greenness-respiratory mortality relationship.

FINDINGS

We observed 6954 respiratory deaths during 12 years of follow-up. Increasing NDVI level from the lowest to the highest quartile is associated with a 19 % (95%CI: 13-25 %) reduction in the respiratory mortality risk. For the total protective effect, the proportion attributable to the overall negative interaction between greenness and air pollution (PM2.5, PM10, or NO2) was 2.2 % (1.7-3.2 %), 3.5 % (0.4-3.7 %), or 25.0 % (22.8-27.1 %), respectively. Simultaneously, we estimated 25.5 % (20.1-32.0 %), 49.5 % (32.5-71.9 %), or 1.0 % (0.8-1.2 %) of the total protective association was mediated through a reduction in PM2.5, PM10, or NO2, respectively.

INTERPRETATION

Increased greenness exposure mitigated respiratory mortality through both the antagonistic interaction and mediation pathway of air pollution (PM2.5, PM10, or NO2).

Methods for mediation analysis with high-dimensional DNA methylation data: Possible choices and comparisons

Epigenetic researchers often evaluate DNA methylation as a potential mediator of the effect of social/environmental exposures on a health outcome. Modern statistical methods for jointly evaluating many mediators have not been widely adopted. We compare seven methods for high-dimensional mediation analysis with continuous outcomes through both diverse simulations and analysis of DNAm data from a large multi-ethnic cohort in the United States, while providing an R package for their seamless implementation and adoption. Among the considered choices, the best-performing methods for detecting active mediators in simulations are the Bayesian sparse linear mixed model (BSLMM) and high-dimensional mediation analysis (HDMA); while the preferred methods for estimating the global mediation effect are high-dimensional linear mediation analysis (HILMA) and principal component mediation analysis (PCMA). We provide guidelines for epigenetic researchers on choosing the best method in practice and offer suggestions for future methodological development.

Higher HDL-C levels attenuated the association of plasma polybrominated diphenyl ethers with prediabetes and type 2 diabetes mellitus in rural Chinese adults

BACKGROUND

Plasma polybrominated diphenyl ethers (PBDE) were used as flame retardants widely, however, epidemiological evidence for the association between PBDEs and type 2 diabetes mellitus (T2DM) is inconsistent. Moreover, the combined effects of PBDEs and blood lipid indicators on impaired fasting glucose (IFG) and T2DM remains largely unknown in rural areas lacking good waste recycling infrastructure.

METHODS

In this study, a total of 2607 subjects aged 18-79 years were included from the Henan Rural Cohort. Generalized linear and logistic regression models were applied to evaluate the associations of various PBDE pollutants on IFG and T2DM. Quantile g-computation regression and PBDE pollution score created by the adaptive elastic net were applied to evaluate the impact of PBDEs mixtures on IFG and T2DM. Interaction effects of individual PBDE pollutants and blood lipid indicators on IFG and T2DM were assessed by using Interaction plots.

RESULTS

The geometric mean concentrations (detection rates) were 0.09 ng/mL (100.0%), 0.12 ng/mL (97.8%), 0.22 ng/mL (94.7%), 0.16 ng/mL (99.2%) and 0.28 ng/mL (100.0%) for PBDE-28, PBDE-47, PBDE-99, and PBDE-153 respectively. However, PBDE-28, PBDE-99, PBDE-100, and ΣPBDEs were positively associated with IFG (odds ratios (ORs) (95% confidence intervals (CIs)): 1.14 (1.06, 1.23), 1.16 (1.04, 1.29), 1.25 (1.14, 1.37), and 1.27 (1.08, 1.50)). Similarly, PBDE-28, PBDE-47, PBDE-99, PBDE-100, and ΣPBDEs were positively associated with T2DM (ORs (95% CIs): 1.30 (1.10, 1.54), 1.13 (1.06, 1.22), 1.27 (1.13, 1.43), 1.27 (1.15, 1.40), and 1.30 (1.10, 1.54)). Moreover, five PBDE mixtures or jointly as PBDE pollution score, were significantly associated with an increased risk of T2DM (P < 0.05 for all). In addition, the harmful effect of PBDE exposure on T2DM was decreased with accompanying high-density lipoprotein cholesterol (HDL-C) levels increased.

CONCLUSIONS

Our findings highlight the importance of managing PBDEs contamination and suggest that HDL-C may be a novel way to prevent T2DM.

Mediating effect of endocrine hormones on association between per- and polyfluoroalkyl substances exposure and birth size: Findings from sheyang mini birth cohort study

BACKGROUND

Prenatal exposure to per- and polyfluoroalkyl substances (PFAS) has been reported to affect fetus growth, but current results were inconsistent and their mechanism remained unclear.

OBJECTIVES

We aimed to evaluate the associations of prenatal exposure to single and/or multiple PFAS with birth size and to elucidate whether thyroid hormones and reproductive hormones mediate these associations.

METHODS

A total of 1087 mother-newborn pairs from Sheyang Mini Birth Cohort Study were included in the present cross-sectional analysis. 12 PFAS, 5 thyroid hormones and 2 reproductive hormones were measured in cord serum. Multiple linear regression models and Bayesian kernel machine regression (BKMR) models were used to examine the associations of PFAS with either birth size or endocrine hormones. One-at-a-time pairwise mediating effect analysis was applied to estimate the mediating effect of single hormone in the association between individual chemical and birth size. High-dimensional mediation approach including elastic net regularization and Bayesian shrinkage estimation were further performed to reduce exposure dimension and figure out the global mediation effects of joint endocrine hormones.

RESULTS

Perfluorononanoic acid (PFNA) exposure was positively associated to weight for length z score [WLZ, per log10-unit: regression coefficient (β) = 0.26, 95% confidence intervals (CI): 0.04, 0.47] and ponderal index (PI, β = 0.56, 95% CI: 0.09, 1.02), and PFAS mixture results fit by BKMR model showed consistent consequences. High-dimensional mediating analyses revealed that thyroid stimulating hormone (TSH) explained 6.7% of the positive association between PFAS mixtures exposure and PI [Total effect (TE) = 1.499 (0.565, 2.405); Indirect effect (IE) = 0.105 (0.015, 0.231)]. Besides, 7.3% of the PI variance was indirectly explained by 7 endocrine hormones jointly [TE = 0.810 (0.802, 0.819); IE = 0.040 (0.038, 0.041)].

CONCLUSIONS

Prenatal PFAS mixtures exposure, especially PFNA, was positively associated to birth size. Such associations were partly mediated by cord serum TSH.

Long-term exposure to di(2-ethylhexyl) phthalate, diisononyl phthalate, and a mixture of phthalates alters estrous cyclicity and/or impairs gestational index and birth rate in mice

Phthalates are found in plastic food containers, medical plastics, and personal care products. However, the effects of long-term phthalate exposure on female reproduction are unknown. Thus, this study investigated the effects of long-term, dietary phthalate exposure on estrous cyclicity and fertility in female mice. Adult female CD-1 mice were fed chow containing vehicle control (corn oil) or 0.15-1500 ppm of di(2-ethylhexyl) phthalate (DEHP), diisononyl phthalate (DiNP), or a mixture of phthalates (Mix) containing DEHP, DiNP, benzyl butyl phthalate, di-n-butyl phthalate, diisobutyl phthalate, and diethyl phthalate. Measurements of urinary phthalate metabolites confirmed effective delivery of phthalates. Phthalate consumption for 11 months did not affect body weight compared to control. DEHP exposure at 0.15 ppm for 3 and 5 months increased the time that the mice spent in estrus and decreased the time the mice spent in metestrus/diestrus compared to control. DiNP exposure (0.15-1500 ppm) did not significantly affect time in estrus or metestrus/diestrus compared to control. Mix exposure at 0.15 and 1500 ppm for 3 months decreased the time the mice spent in metestrus/diestrus and increased the time the mice spent in estrus compared to control. DEHP (0.15-1500 ppm) or Mix (0.15-1500 ppm) exposure did not affect fertility-related indices compared to control. However, long-term DiNP exposure at 1500 ppm significantly reduced gestational index and birth rate compared to control. These data indicate that chronic dietary exposure to phthalates alters estrous cyclicity, and long-term exposure to DiNP reduces gestational index and birth rate in mice.

A review of statistical strategies to integrate biomarkers of chemical exposure with biomarkers of effect applied in omic-scale environmental epidemiology

Humans are exposed to a growing list of synthetic chemicals, some of them becoming a major public health concern due to their capacity to impact multiple biological endpoints and contribute to a range of chronic diseases. The integration of endogenous (omic) biomarkers of effect in environmental health studies has been growing during the last decade, aiming to gain insight on the potential mechanisms linking the exposures and the clinical conditions. The emergence of high-throughput omic platforms has raised a list of statistical challenges posed by the large dimension and complexity of data generated. Thus, the aim of the present study was to critically review the current state-of-the-science about statistical approaches used to integrate endogenous biomarkers in environmental-health studies linking chemical exposures with health outcomes. The present review specifically focused on internal exposure to environmental chemical pollutants, involving both persistent organic pollutants (POPs), non-persistent pollutants like phthalates or bisphenols, and metals. We identified 42 eligible articles published since 2016, reporting 48 different statistical workflows, mostly focused on POPs and using metabolomic profiling in the intermediate layer. The outcomes were mainly binary and focused on metabolic disorders. A large diversity of statistical strategies were reported to integrate chemical mixtures and endogenous biomarkers to characterize their associations with health conditions. Multivariate regression models were the most predominant statistical method reported in the published workflows, however some studies applied latent based methods or multipollutant models to overcome the specific constraints of omic or exposure of data. A minority of studies used formal mediation analysis to characterize the indirect effects mediated by the endogenous biomarkers. The principles of each specific statistical method and overall workflow set-up are summarized in the light of highlighting their applicability, strengths and weaknesses or interpretability to gain insight into the causal structures underlying the triad: exposure, effect-biomarker and outcome.

Inhalable mixture of polycyclic aromatic hydrocarbons and metals, DNA oxidative stress and nasal ribosomal DNA copy number amplification: Direct and indirect effect analyses among population

The ribosomal DNA (rDNA) plays a crucial role in maintaining genome stability. So far, alterations of rDNA from airborne pollutants exposure remain unclear. Nasal epithelial cells are the earliest respiratory barrier, which has an accessible surrogate for evaluating respiratory impairment. We developed a mixture-centered biomarkers study integrated epidemiological and biological evidence among 768 subjects, a mixture of polycyclic aromatic hydrocarbons (PAHs) and metals. We identified the mixed exposure of PAHs and metals by environmental and biological monitoring, selected urinary 8-hydroxy-2'-deoxyguanosine as DNA oxidative stress marker, and measured their rDNA copy number (rDNA CN) in nasal epithelial cells. We performed linear regression, adaptive elastic net regression, BKMR, and mediation analyses to assess the direct and indirect effects. We found a 10% elevation in urinary 1-hydroxypyrene was correlated with a separate 0.31% and 0.82% amplification of nasal 5S and 45S rDNA CN, respectively (all P < 0.05). A 10% increment of urine nickel was associated with a separate 0.37% and 1.18% elevation of nasal 5S and 45S rDNA CN, respectively (all P < 0.05). BKMR results also confirmed our findings of PAHs and nickel. Our findings suggested that DNA oxidative stress might trigger rDNA instability induced by inhaled PAHs and metals.

Association between maternal exposure to per- and polyfluoroalkyl substances and serum markers of liver function during pregnancy in China: A mixture-based approach

Previous studies have shown that per- and polyfluoroalkyl substances (PFAS) may have hepatotoxic effects in animals. However, epidemiological evidence in humans, especially pregnant women, is limited. This study aimed to assess the association of single and multiple PFAS exposure with serum markers of liver function in pregnant women. A total of 420 pregnant women from the Guangxi Zhuang Birth Cohort were enrolled from June 2015 to April 2019. Nine PFAS were measured in the maternal serum in early pregnancy. Data for liver function biomarkers, namely, alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyltransferase (GGT), total bilirubin (TBIL), direct bilirubin (DBIL), and indirect bilirubin (IBIL), were obtained from medical records. In generalized linear model (GLM), there was a positive association of perfluorooctane sulfonate (PFOS) with ALT, perfluorodecanoic acid (PFDA) and perfluorobutanesulfonic acid (PFBS) with GGT, and perfluorohexane sulfonate (PFHxS) with TBIL and IBIL. In contrast, there was a negative association of perfluoroheptanoic acid (PFHpA) with TBIL. There were inverse U-shaped relationships of PFUnA with ALT and AST and PFDA with ALT by restricted cubic spline. The weighted quantile sum (WQS) regression model revealed the positive effects of the PFAS mixture on GGT, TBIL, DBIL, and IBIL. Bayesian kernel machine regression (BKMR) analysis confirmed that the PFAS mixture was positively associated with GGT, and PFBS was the main contributor. In addition, the BKMR model showed a positive association of individual PFBS with GGT, individual PFHxS with TBIL and IBIL, and a negative association of individual PFHpA with TBIL. Our findings provide evidence of an association between individual PFAS, PFAS mixture and maternal serum markers of liver function during pregnancy. Additionally, these findings also enhance concerns over PFAS exposure on maternal liver function and PFAS monitoring in pregnancy, reducing the effect of maternal liver dysfunction on maternal and infant health.

PM2.5-bound polycyclic aromatic hydrocarbons of a megacity in eastern China: Source apportionment and cancer risk assessment

Ninety-six fine particulate matter (PM_2.5) samples covering four seasons from October 2020 to August 2021 were collected at a 'super' site in Hangzhou, a megacity in eastern China. These samples were analyzed to determine the sources and potential cancer risks to humans of 16 United States Environmental Protection Agency priority polycyclic aromatic hydrocarbons (PAHs). The average concentrations of the PAHs in PM_2.5 in autumn, winter, spring, and summer were 8.35 ± 4.90, 27.9 ± 13.6, 8.3 ± 5.97, and 1.05 ± 0.50 ng/m³, respectively, and with an annual average of 11.9 ± 13.2 ng/m3. The source apportionment by positive matrix factorization analysis indicated that, based on the yearly average, the major sources of PAHs were traffic emissions (38.2 %), coal combustion (28.9 %), coke (21.7 %), and volatilization (11.1 %). Strong correlations between high concentrations of carbonaceous aerosols and high-molecular-weight PAHs in winter could be attributed to incomplete combustion. Long-range transport of air from the sea to the southeast resulted in low concentrations of carbonaceous aerosols and low-molecular-weight PAHs in summer. Trajectory clustering and the potential source contribution function both indicated that the Yangtze River Delta was the main source region of PAHs for PM_2.5 in Hangzhou in spring and summer. In autumn and winter, it was dominated by long-range transport from northern China. Lifetime lung cancer risk assessment revealed that the PAHs in PM_2.5 impose moderate human health risks in Hangzhou due to traffic emissions. The results of this study provide important information for policymakers to establish abatement strategies to reduce PAH emissions in Hangzhou, and perhaps other urban centers across China.

Global and Partial Effect Assessment in Metabolic Syndrome Explored by Metabolomics

In nutrition and health research, untargeted metabolomics is actually analyzed simultaneously with clinical data to improve prediction and better understand pathological status. This can be modeled using a multiblock supervised model with several input data blocks (metabolomics, clinical data) being potential predictors of the outcome to be explained. Alternatively, this configuration can be represented with a path diagram where the input blocks are each connected by links directed to the outcome—as in multiblock supervised modeling—and are also related to each other, thus allowing one to account for block effects. On the basis of a path model, we show herein how to estimate the effect of an input block, either on its own or conditionally to other(s), on the output response, respectively called “global” and “partial” effects, by percentages of explained variance in dedicated PLS regression models. These effects have been computed in two different path diagrams in a case study relative to metabolic syndrome, involving metabolomics and clinical data from an older men′s cohort (NuAge). From the two effects associated with each path, the results highlighted the complementary information provided by metabolomics to clinical data and, reciprocally, in the metabolic syndrome exploration.

Methods for Mediation Analysis with High-Dimensional DNA Methylation Data: Possible Choices and Comparison

Epigenetic researchers often evaluate DNA methylation as a mediator between social/environmental exposures and disease, but modern statistical methods for jointly evaluating many mediators have not been widely adopted. We compare seven methods for high-dimensional mediation analysis with continuous outcomes through both diverse simulations and analysis of DNAm data from a large national cohort in the United States, while providing an R package for their implementation. Among the considered choices, the best-performing methods for detecting active mediators in simulations are the Bayesian sparse linear mixed model by Song et al. (2020) and high-dimensional mediation analysis by Gao et al. (2019); while the superior methods for estimating the global mediation effect are high-dimensional linear mediation analysis by Zhou et al. (2021) and principal component mediation analysis by Huang and Pan (2016). We provide guidelines for epigenetic researchers on choosing the best method in practice and offer suggestions for future methodological development.

Gut microbiota regulates chronic ethanol exposure-induced depressive-like behavior through hippocampal NLRP3-mediated neuroinflammation

Chronic ethanol exposure (CEE), which can lead to neuroinflammation, is an increasing risk factor for depression disorder, but the underlying mechanism is not clear. Recent observations have revealed the associations among psychiatric disorders, ethanol exposure and alterations of the gut microbiota. Here, we found that CEE induced depressive-like behavior, which could be alleviated by probiotics and transferred from donor to recipient mice by fecal microbiota transplantation (FMT). Neuroinflammation and the activation of the NLRP3 inflammasome were also observed in recipient mice. The downregulation of NLRP3 in the hippocampus mitigated CEE-induced depressive-like behavior and neuroinflammation but had no significant effect on FMT recipient mice. Moreover, elevated serum inflammatory factors in recipient mice showed a significant mediation effect between the gut microbiota and depressive-like behavior. Together, our study findings indicate that the gut microbiota contributes to both hippocampal NLRP3-mediated neuroinflammation and depressive-like behavior induced by CEE, which may open avenues for potential interventions against CEE-associated psychiatric disorders.

Inflammation and oxidative stress as mediators of the impacts of environmental exposures on human pregnancy: Evidence from oxylipins

Inflammation and oxidative stress play major roles in healthy and pathological pregnancy. Environmental exposure to chemical pollutants may adversely affect maternal and fetal health in pregnancy by dysregulating these critical underlying processes of inflammation and oxidative stress. Oxylipins are bioactive lipids that play a major role in regulating inflammation and increasing lines of evidence point towards an importance in pregnancy. The biosynthetic production of oxylipins requires oxygenation of polyunsaturated fatty acids, which can occur through several well-characterized enzymatic and nonenzymatic pathways. This review describes the state of the science of epidemiologic evidence on oxylipin production in pregnancy and its association with 1) key pregnancy outcomes and 2) environmental exposures. We searched PubMed for studies of pregnancy that measured one or more oxylipin analytes during pregnancy or delivery. We evaluated oxylipin associations with three categories of adverse pregnancy outcomes, including preeclampsia, preterm birth, and fetal growth restriction, along with several categories of environmental pollutants. The majority of studies evaluated one to two oxylipins, most of which focused on oxylipins produced from nonenzymatic processes of oxidative stress. However, an increasing number of recent studies have leveraged technological advancements to profile a large number of oxylipins produced from distinct biosynthetic pathways. Although the literature indicated robust evidence that oxylipins produced via nonenzymatic pathways are associated with pregnancy outcomes and environmental exposures, evidence for enzymatically produced oxylipins showed that associations may differ between biosynthetic pathways. Along with summarizing this evidence, we review promising therapeutic options to regulate oxylipin production and provide a set of recommendations for future epidemiologic studies in these research areas. Further evidence is needed to improve our understanding of how oxylipins may act as key biological mediators for the adverse effects of environmental pollutants on pregnancy outcomes.

Associations Between Prenatal Urinary Biomarkers of Phthalate Exposure and Preterm Birth: A Pooled Study of 16 US Cohorts

Importance

Phthalate exposure is widespread among pregnant women and may be a risk factor for preterm birth.

Objective

To investigate the prospective association between urinary biomarkers of phthalates in pregnancy and preterm birth among individuals living in the US.

Design, Setting, and Participants

Individual-level data were pooled from 16 preconception and pregnancy studies conducted in the US. Pregnant individuals who delivered between 1983 and 2018 and provided 1 or more urine samples during pregnancy were included.

Exposures

Urinary phthalate metabolites were quantified as biomarkers of phthalate exposure. Concentrations of 11 phthalate metabolites were standardized for urine dilution and mean repeated measurements across pregnancy were calculated.

Main Outcomes and Measures

Logistic regression models were used to examine the association between each phthalate metabolite with the odds of preterm birth, defined as less than 37 weeks of gestation at delivery (n = 539). Models pooled data using fixed effects and adjusted for maternal age, race and ethnicity, education, and prepregnancy body mass index. The association between the overall mixture of phthalate metabolites and preterm birth was also examined with logistic regression. G-computation, which requires certain assumptions to be considered causal, was used to estimate the association with hypothetical interventions to reduce the mixture concentrations on preterm birth.

Results

The final analytic sample included 6045 participants (mean [SD] age, 29.1 [6.1] years). Overall, 802 individuals (13.3%) were Black, 2323 (38.4%) were Hispanic/Latina, 2576 (42.6%) were White, and 328 (5.4%) had other race and ethnicity (including American Indian/Alaskan Native, Native Hawaiian, >1 racial identity, or reported as other). Most phthalate metabolites were detected in more than 96% of participants. Higher odds of preterm birth, ranging from 12% to 16%, were observed in association with an interquartile range increase in urinary concentrations of mono-n-butyl phthalate (odds ratio [OR], 1.12 [95% CI, 0.98-1.27]), mono-isobutyl phthalate (OR, 1.16 [95% CI, 1.00-1.34]), mono(2-ethyl-5-carboxypentyl) phthalate (OR, 1.16 [95% CI, 1.00-1.34]), and mono(3-carboxypropyl) phthalate (OR, 1.14 [95% CI, 1.01-1.29]). Among approximately 90 preterm births per 1000 live births in this study population, hypothetical interventions to reduce the mixture of phthalate metabolite levels by 10%, 30%, and 50% were estimated to prevent 1.8 (95% CI, 0.5-3.1), 5.9 (95% CI, 1.7-9.9), and 11.1 (95% CI, 3.6-18.3) preterm births, respectively.

Conclusions and Relevance

Results from this large US study population suggest that phthalate exposure during pregnancy may be a preventable risk factor for preterm delivery.

Causal Inference in Radiomics: Framework, Mechanisms, and Algorithms

The widespread use of machine learning algorithms in radiomics has led to a proliferation of flexible prognostic models for clinical outcomes. However, a limitation of these techniques is their black-box nature, which prevents the ability for increased mechanistic phenomenological understanding. In this article, we develop an inferential framework for estimating causal effects with radiomics data. A new challenge is that the exposure of interest is latent so that new estimation procedures are needed. We leverage a multivariate version of partial least squares for causal effect estimation. The methodology is illustrated with applications to two radiomics datasets, one in osteosarcoma and one in glioblastoma.

Human impacts as the main driver of tropical forest carbon

Understanding the mechanisms controlling forest carbon storage is crucial to support "nature-based" solutions for climate change mitigation. We used a dataset of 892 Atlantic Forest inventories to assess the direct and indirect effects of environmental conditions, human impacts, tree community proprieties, and sampling methods on tree above-ground carbon stocks. We showed that the widely accepted drivers of carbon stocks, such as climate, soil, topography, and forest fragmentation, have a much smaller role than the forest disturbance history and functional proprieties of the Atlantic Forest. Specifically, within-forest disturbance level was the most important driver, with effect at least 30% higher than any of the environmental conditions individually. Thus, our findings suggest that the conservation of tropical carbon stocks may be dependable on, principally, avoiding forest degradation and that conservation policies focusing only on carbon may fail to protect tropical biodiversity.

Mediation by hormone concentrations on the associations between repeated measures of phthalate mixture exposure and timing of delivery

BACKGROUND

Phthalates are used in the manufacturing of consumer products, resulting in ubiquitous human exposure to phthalate mixtures. Previous work has suggested that phthalates display endocrine-disrupting capabilities, and exposure is associated with early delivery.

OBJECTIVE

To assess mediating effects of hormone concentrations on associations between phthalate mixtures and preterm birth (PTB).

METHODS

Repeated urinary phthalates and serum hormones were measured among 1011 women in the PROTECT Puerto Rico birth cohort from 2011-2019. We utilized ridge regression to create phthalate environmental risk scores (ERS), which represent weighted summaries of total phthalate exposure. Mediation analyses were conducted on a subset of 705 women. We additionally conducted fetal sex-specific analyses.

RESULTS

Free thyroxine (FT4) mediated 9.6% of the association between high molecular weight (HMW) ERS at 18 weeks and reduced gestational age at delivery (95%CI:1.07-29.9). Progesterone at 26 weeks mediated 21.1% and 16.2% of the association between HMW ERS at 18 and 22 weeks, and spontaneous PTB, respectively. Among male fetuses, corticotropin releasing hormone (CRH) at 18 weeks mediated 28.2% of the association between low molecular weight ERS and spontaneous PTB.

SIGNIFICANCE

We provide introductory evidence of hormone disruption on the causal pathway between phthalate exposure and early delivery. We also show differences by fetal sex, but larger sample size is necessary to validate our findings.

IMPACT STATEMENT

This study provides introductory evidence that an alteration of hormone concentrations occurs on the causal pathway between gestational phthalate mixture exposure and subsequent PTB. In addition to the novel application of repeated biomarker measurements and mixtures methods in causal mediation analyses, we also explored differences between classes of phthalate compounds and between fetal sexes. We show that differential endocrine pathways may be disrupted with exposures to low versus HMW phthalate compounds, and that pregnancies with a male fetus may be more susceptible to endocrine disruption than those with a female fetus.

Multimodal data integration via mediation analysis with high-dimensional exposures and mediators

Motivated by an imaging proteomics study for Alzheimer's disease (AD), in this article, we propose a mediation analysis approach with high‐dimensional exposures and high‐dimensional mediators to integrate data collected from multiple platforms. The proposed method combines principal component analysis with penalized least squares estimation for a set of linear structural equation models. The former reduces the dimensionality and produces uncorrelated linear combinations of the exposure variables, whereas the latter achieves simultaneous path selection and effect estimation while allowing the mediators to be correlated. Applying the method to the AD data identifies numerous interesting protein peptides, brain regions, and protein–structure–memory paths, which are in accordance with and also supplement existing findings of AD research. Additional simulations further demonstrate the effective empirical performance of the method.

Identifying active xenobiotics in humans by use of a suspect screening technique coupled with lipidomic analysis

Lipidomic analysis has been proven to be a powerful technique to explore the underlying associations between xenobiotics and health status of organisms. Here, we established a strategy that combined the lipidomic analysis with high-throughput suspect contaminant screening technique with an aim to efficiently identify active xenobiotics in humans. Firstly, in the light of single liquid phase equilibrium of chloroform-methanol-water (15:14:2, v/v/v), we developed an efficient method that was able to simultaneously extract both polar and nonpolar lipids in serum samples. By use of this method, targeted and non-targeted lipid analyses were conducted for n = 120 serum samples collected from Wuxi city, China. Secondly, we established a suspect database containing 1450 contaminants that have been previously reported in human samples, and contaminants in this database were screened in the same batch of serum samples by use of high-resolution mass spectrometry (HR-MS). Thirdly, the underlying associations between suspect contaminants and lipids were explored and discussed, and we observed that levels of some lipids were statistically correlated with concentrations of numerous contaminants. Among these active contaminants, 23 ones were identified on the basis of HR MS¹ and MS² characteristics, and these contaminants belonged to the classes of phthalates, phenols, parabens, or perfluorinated compounds (PFCs). Three active xenobiotics were fully validated by comparison with authentic standards, and they were perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and diethyl phthalate (DEP). There were statistically significant changes in levels of triglyceride (TG), lysophosphocholine (LPC), and sphingomyelin (SM) as peak areas of xenobiotics increase. We also observed that, among target lipid molecules, 18:0 lysophosphatidylethanolamine (LPE(18:0)) was very sensitive, and this lipid responded to exposure of various contaminants. Our present study provides novel knowledge on potential alteration of lipid metabolism in humans following exposure to xenobiotics, and provides an efficient strategy for efficiently identifying active xenobiotics in humans.

Longitudinal exposure to consumer product chemicals and changes in plasma oxylipins in pregnant women

BACKGROUND

Exposure to consumer product chemicals during pregnancy may increase susceptibility to pregnancy disorders by influencing maternal inflammation. However, effects on specific inflammatory pathways have not been well characterized. Oxylipins are a diverse class of lipids that act as important mediators and biomarkers of several biological pathways that regulate inflammation. Adverse pregnancy outcomes have been associated with circulating oxylipin levels in pregnancy. In this study, we aimed to determine the longitudinal associations between plasma oxylipins and urinary biomarkers of three classes of consumer product chemicals among pregnant women.

METHODS

Data come from a study of 90 pregnant women nested within the LIFECODES cohort. Maternal plasma and urine were collected at three prenatal visits. Plasma was analyzed for 61 oxylipins, which were grouped according to biosynthetic pathways that we defined by upstream: 1) fatty acid precursor, including linoleic, arachidonic, docosahexaenoic, or eicosapentaenoic acid; and 2) enzyme pathway, including cyclooxygenase (COX), lipoxygenase (LOX), or cytochrome P450 (CYP). Urine was analyzed for 12 phenol, 12 phthalate, and 9 organophosphate ester (OPE) biomarkers. Linear mixed effect models were used for single-pollutant analyses. We implemented a novel extension of quantile g-computation for longitudinal data to examine the joint effect of class-specific chemical mixtures on individual plasma oxylipin concentrations.

RESULTS

We found that urinary biomarkers of consumer product chemicals were positively associated with pro-inflammatory oxylipins from several biosynthetic pathways. Importantly, these associations depended upon the chemical class of exposure biomarker. We estimated positive associations between urinary phenol biomarkers and oxylipins produced from arachidonic acid by LOX enzymes, including several important pro-inflammatory hydroxyeicosatetraenoic acids (HETEs). On average, mean concentrations of oxylipin produced from the arachidonic acid/LOX pathway were 48%-71% higher per quartile increase in the phenol biomarker mixture. For example, a simultaneous quartile increase in all urinary phenols was associated with 53% higher (95% confidence interval [CI]: 11%, 111%) concentrations of 12-HETE. The positive associations among phenols were primarily driven by methyl paraben, 2,5-dichlorophenol, and triclosan. Additionally, we observed that phthalate and OPE metabolites were associated with higher concentrations of oxylipins produced from linoleic acid by CYP enzymes, including the pro-inflammatory dihydroxy-octadecenoic acids (DiHOMEs). Associations among DiHOME oxylipins were driven by metabolites of benzylbutyl and di-isodecyl phthalate, and by the metabolite of tris(1,3-dichloro-2-propyl) phosphate among OPEs. We also observed inverse associations between phthalate and OPE metabolites and oxylipins produced from other pathways; however, adjusting for a plasma indicator of dietary fatty acid intake attenuated those results.

CONCLUSIONS

Our findings support the hypothesis that consumer product chemicals may have diverse impacts on inflammation processes in pregnancy. Certain pro-inflammatory oxylipins were generally higher among participants with higher urinary chemical biomarker concentrations. Associations varied by class of chemical and by the biosynthetic pathway of oxylipin production, indicating potential specificity in the inflammatory effects of these environmental chemicals during pregnancy that warrant investigation in larger studies.

Exposome in human health: Utopia or wonderland?

The current human exposome primarily emphasizes the total environmental exposure during an entire life. The characteristics of "lifelong" and "all environmental factors" make it very challenging to bring the exposomic study into real-life applications. Herein, we mainly discuss the typical application scenarios of exposomics and how to conduct an exposomic study to establish relationships between the exposome and human health. To increase the feasibility and efficiency, we propose that (1) an exposomic study can start with health events during critical-window periods; (2) both data- and hypothesis-driven exposomics should be combined to prioritize the risk of environmental factors; and (3) reliable statistical analysis of high-dimensional data of external and internal exposure factors are urgently needed. With standardization of the exposomic study, it will be critical to build a "wonderland" for human health.

Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects

Causal mediation analysis aims to characterize an exposure's effect on an outcome and quantify the indirect effect that acts through a given mediator or a group of mediators of interest. With the increasing availability of measurements on a large number of potential mediators, like the epigenome or the microbiome, new statistical methods are needed to simultaneously accommodate high-dimensional mediators while directly target penalization of the natural indirect effect (NIE) for active mediator identification. Here, we develop two novel prior models for identification of active mediators in high-dimensional mediation analysis through penalizing NIEs in a Bayesian paradigm. Both methods specify a joint prior distribution on the exposure-mediator effect and mediator-outcome effect with either (a) a four-component Gaussian mixture prior or (b) a product threshold Gaussian prior. By jointly modeling the two parameters that contribute to the NIE, the proposed methods enable penalization on their product in a targeted way. Resultant inference can take into account the four-component composite structure underlying the NIE. We show through simulations that the proposed methods improve both selection and estimation accuracy compared to other competing methods. We applied our methods for an in-depth analysis of two ongoing epidemiologic studies: the Multi-Ethnic Study of Atherosclerosis (MESA) and the LIFECODES birth cohort. The identified active mediators in both studies reveal important biological pathways for understanding disease mechanisms.

DNA methylation at birth potentially mediates the association between prenatal lead (Pb) exposure and infant neurodevelopmental outcomes

Early-life lead (Pb) exposure has been linked to adverse neurodevelopmental outcomes. Recent evidence has indicated a critical role of DNA methylation (DNAm) in cognition, and Pb exposure has also been shown to alter DNAm. However, it is unknown whether DNAm is part of the mechanism of Pb neurotoxicity. This longitudinal study investigated the associations between trimester-specific (T1, T2, and T3) maternal blood Pb concentrations, gene-specific DNAm in umbilical cord blood, and infant neurodevelopmental outcomes at 12 and 24 months of age (mental development index, psychomotor development index, and behavioral rating scale of orientation/engagement and emotional regulation) among 85 mother-infant pairs from the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) study. In the mediation analysis for this pilot study, P < 0.1 was considered significant. DNAm at a locus in CCSER1 (probe ID cg02901723) mediated the association between T2 Pb on 24-month orientation/engagement [indirect effect estimate 4.44, 95% confidence interval (-0.09, 10.68), P = 0.06] and emotional regulation [3.62 (-0.05, 8.69), P = 0.05]. Cg18515027 (GCNT1) DNAm mediated the association of T1 Pb [-4.94 (-10.6, -0.77), P = 0.01] and T2 Pb [-3.52 (-8.09, -0.36), P = 0.02] with 24-month EMOCI, but there was a positive indirect effect estimate between T2 Pb and 24-month psychomotor development index [1.25 (-0.11, 3.32), P = 0.09]. The indirect effect was significant for cg19703494 (TRAPPC6A) DNAm in the association between T2 Pb and 24-month mental development index [1.54 (0, 3.87), P = 0.05]. There was also an indirect effect of cg23280166 (VPS11) DNAm on T3 Pb and 24-month EMOCI [2.43 (-0.16, 6.38), P = 0.08]. These associations provide preliminary evidence for gene-specific DNAm as mediators between prenatal Pb and adverse cognitive outcomes in offspring.

Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges

Mediation analysis investigates the intermediate mechanism through which an exposure exerts its influence on the outcome of interest. Mediation analysis is becoming increasingly popular in high-throughput genomics studies where a common goal is to identify molecular-level traits, such as gene expression or methylation, which actively mediate the genetic or environmental effects on the outcome. Mediation analysis in genomics studies is particularly challenging, however, thanks to the large number of potential mediators measured in these studies as well as the composite null nature of the mediation effect hypothesis. Indeed, while the standard univariate and multivariate mediation methods have been well-established for analyzing one or multiple mediators, they are not well-suited for genomics studies with a large number of mediators and often yield conservative p-values and limited power. Consequently, over the past few years many new high-dimensional mediation methods have been developed for analyzing the large number of potential mediators collected in high-throughput genomics studies. In this work, we present a thorough review of these important recent methodological advances in high-dimensional mediation analysis. Specifically, we describe in detail more than ten high-dimensional mediation methods, focusing on their motivations, basic modeling ideas, specific modeling assumptions, practical successes, methodological limitations, as well as future directions. We hope our review will serve as a useful guidance for statisticians and computational biologists who develop methods of high-dimensional mediation analysis as well as for analysts who apply mediation methods to high-throughput genomics studies.