'Omics in environmental epidemiological studies of chemical exposures: A systematic evidence map

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

A systematic review and meta-analysis of human population studies on the association between exposure to toxic environmental chemicals and left ventricular dysfunction (LVD)

BACKGROUND

Exposure to environmental chemicals has been associated with an elevated risk of heart failure (HF). However, the impact on early markers of HF, such as left ventricular dysfunction (LVD), remains limited.

OBJECTIVE

To establish a foundation of evidence regarding early HF markers and their association with environmental pollutants, a systematic review and meta-analysis was conducted.

METHODS

The search, conducted on October 13th, 2023, encompassed PubMed, Embase, and Web of Science without filters, focusing on observational studies reporting myocardial geometrical, structural, or functional alterations in individuals without a history of heart disease. This included the general adult population, workers, young people, and the elderly. The risk of bias was assessed using the ROBINS-I tool at both study and item levels.

RESULTS

The systematic review included 17 studies involving 43.358 individuals exposed to air pollution and 2038 exposed to heavy metals. Approximately 41% of the effect measures of associations reported significant abnormalities in myocardial structure or function. The metanalyses by pollutants categories indicated positive associations between LV systolic and diastolic abnormalities and exposure to PM2.5 [-0.069 (-0.104, -0.033); -0.044 (-0.062, -0.025)] and PM10 [-0.055 (-0.087, -0.022); -0.030 (-0.050, -0.010)] and NO2 [-0.042 (-0.071, -0.013); -0.021 (-0.037, -0.004)], as well as positive associations between lead exposure and LV systolic abnormalities [-0.033 (-0.051, -0.016)].

CONCLUSIONS

Existing evidence shows that specific early markers of HF may be associated with exposure to chemical pollutants. It is recommended to include such endpoints in new longitudinal and case-control studies to confirm further risk associations. These studies should consider co-exposures, account for vulnerable groups, and identify cardiotoxic compounds that may require regulation. When examining the link between myocardial abnormalities and environmental exposure, it is also advisable to explore the supportive use of Adverse Outcome Pathway (AOP) approaches to confirm a causal relationship.

20 years of polybrominated diphenyl ethers on toxicity assessments

Polybrominated diphenyl ethers (PBDEs) serve as brominated flame retardants which continue to receive considerable attention because of their persistence, bioaccumulation, and potential toxicity. Although PBDEs have been restricted and phased out, large amounts of commercial products containing PBDEs are still in use and discarded annually. Consequently, PBDEs added to products can be released into our surrounding environments, particularly in aquatic systems, thus posing great risks to human health. Many studies and reviews have described the possible toxic effects of PBDEs, while few studies have comprehensively summarized and analyzed the global trends of their toxicity assessment. Therefore, this study utilizes bibliometrics to evaluate the worldwide scientific output of PBDE toxicity and analyze the hotspots and future trends of this field. Firstly, the basic information including the most contributing countries/institutions, journals, co-citations, influential authors, and keywords involved in PBDE toxicity assessment will be visualized. Subsequently, the potential toxicity of PBDE exposure to diverse systems, such as endocrine, reproductive, neural, and gastrointestinal tract systems, and related toxic mechanisms will be discussed. Finally, we conclude this review by outlining the current challenges and future perspectives in environmentally relevant PBDE exposure, potential carriers for PBDE transport, the fate of PBDEs in the environment and human bodies, advanced stem cell-derived organoid models for toxicity assessment, and promising omics technologies for obtaining toxic mechanisms. This review is expected to offer systematical insights into PBDE toxicity assessments and facilitate the development of PBDE-based research.

Concentration-dependent alterations in the human plasma proteome following controlled exposure to diesel exhaust

Traffic-related air pollution (TRAP) exposure is associated with systemic health effects, which can be studied using blood-based markers. Although we have previously shown that high TRAP concentrations alter the plasma proteome, the concentration-response relationship between blood proteins and TRAP is unexplored in controlled human exposure studies. We aimed to identify concentration-dependent plasma markers of diesel exhaust (DE), a model of TRAP. Fifteen healthy non-smokers were enrolled into a double-blinded, crossover study where they were exposed to filtered air (FA) and DE at 20, 50 and 150 μg/m3 PM2.5 for 4h, separated by ≥ 4-week washouts. We collected blood at 24h post-exposure and used label-free mass spectrometry to quantify proteins in plasma. Proteins exhibiting a concentration-response, as determined by linear mixed effects models (LMEMs), were assessed for pathway enrichment using WebGestalt. Top candidates, identified by sparse partial least squares discriminant analysis and LMEMs, were confirmed using enzyme-linked immunoassays. Thereafter, we assessed correlations between proteins that showed a DE concentration-response and acute inflammatory endpoints, forced expiratory volume in 1 s (FEV1) and methacholine provocation concentration causing a 20% drop in FEV1 (PC20). DE exposure was associated with concentration-dependent alterations in 45 proteins, which were enriched in complement pathways. Of the 9 proteins selected for confirmatory immunoassays, based on complementary bioinformatic approaches to narrow targets and availability of high-quality assays, complement factor I (CFI) exhibited a significant concentration-dependent decrease (-0.02 μg/mL per μg/m3 of PM2.5, p = 0.04). Comparing to FA at discrete concentrations, CFI trended downward at 50 (-2.14 ± 1.18, p = 0.08) and significantly decreased at 150 μg/m3 PM2.5 (-2.93 ± 1.18, p = 0.02). CFI levels were correlated with FEV1, PC20 and nasal interleukin (IL)-6 and IL-1β. This study details concentration-dependent alterations in the plasma proteome following DE exposure at concentrations relevant to occupational and community settings. CFI shows a robust concentration-response and association with established measures of airway function and inflammation.

Exposure to a Mixture of Endocrine-Disrupting Chemicals and Metabolic Outcomes in Belgian Adolescents

Childhood exposure to endocrine-disrupting chemicals (EDCs), either alone or in mixtures, may affect metabolic outcomes, yet existing evidence remains inconclusive. In our study of 372 adolescents from the Flemish Environment and Health Study (FLEHS IV, 2017-2018), we measured 40 known and suspected EDCs and assessed metabolic outcomes, including body mass index z-score (zBMI), abdominal obesity (AO), total cholesterol (TC), and triglycerides (TG). We applied Bayesian kernel machine regression (BKMR) and Bayesian penalized horseshoe regression for variable selection and then built multivariate generalized propensity score (mvGPS) models to provide an overview of the effects of selected EDCs on metabolic outcomes. As a result, BKMR and horseshoe together identified five EDCs associated with zBMI, three with AO, three with TC, and five with TG. Through mvGPS analysis, monoiso-butyl phthalate (MIBP), polychlorinated biphenyl (PCB-170), and hexachlorobenzene (HCB) each showed an inverse association with zBMI, as did PCB-170 with AO. Copper (Cu) was associated with higher TC and TG, except in boys where it was linked to lower TG. Additionally, monoethyl phthalate (MEP) and monobenzyl phthalate (MBzP) were associated with higher TG. To conclude, our findings support the association between certain chemicals (Cu, MEP, and MBzP) and elevated lipid levels, aligning with prior studies. Further investigation is needed for sex-specific effects.

Advances and applications of machine learning and deep learning in environmental ecology and health

Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.

Integrating -omics approaches into population-based studies of endocrine disrupting chemicals: A scoping review

Health effects of endocrine disrupting chemicals (EDCs) are challenging to detect in the general population. Omics technologies become increasingly common to identify early biological changes before the apparition of clinical symptoms, to explore toxic mechanisms and to increase biological plausibility of epidemiological associations. This scoping review systematically summarises the application of omics in epidemiological studies assessing EDCs-associated biological effects to identify potential gaps and priorities for future research. Ninety-eight human studies (2004-2021) were identified through database searches (PubMed, Scopus) and citation chaining and focused on phthalates (34 studies), phenols (19) and PFASs (17), while PAHs (12) and recently-used pesticides (3) were less studied. The sample sizes ranged from 10 to 12,476 (median = 159), involving non-pregnant adults (38), pregnant women (11), children/adolescents (15) or both latter populations studied together (23). Several studies included occupational workers (10) and/or highly exposed groups (11) focusing on PAHs, PFASs and pesticides, while studies on phenols and phthalates were performed in the general population only. Analysed omics layers included metabolic profiles (30, including 14 targeted analyses), miRNA (13), gene expression (11), DNA methylation (8), microbiome (5) and proteins (3). Twenty-one studies implemented targeted multi-assays focusing on clinical routine blood lipid traits, oxidative stress or hormones. Overall, DNA methylation and gene expression associations with EDCs did not overlap across studies, while some EDC-associated metabolite groups, such as carnitines, nucleotides and amino acids in untargeted metabolomic studies, and oxidative stress markers in targeted studies, were consistent across studies. Studies had common limitations such as small sample sizes, cross-sectional designs and single sampling for exposure biomonitoring. In conclusion, there is a growing body of evidence evaluating the early biological responses to exposure to EDCs. This review points to a need for larger longitudinal studies, wider coverage of exposures and biomarkers, replication studies and standardisation of research methods and reporting.

Polycyclic aromatic hydrocarbons exposure and plasma lncRNA signature: A profile and functional analysis

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental pollutants that pose detrimental effects on human health, and the exploration of the associations of PAHs exposure with long non-coding RNA (lncRNA) may provide novel clues to the underlying mechanisms. In the present study, we detected 10 urinary PAHs metabolites by GC-MS and plasma lncRNAs levels by Human LncRNA Array v4 among 230 participants from two panels (160 in the Shiyan panel and 70 in the Wuhan-Zhuhai panel). We applied linear regression models to assess the associations between PAHs metabolites and lncRNAs separately in each panel and combined the results using fixed-effect meta-analysis. To explore the potential origin of PAHs-related lncRNAs in plasma, we estimated their tissue-specificity and associations between lncRNAs levels in plasma and leukocytes. Leukocytes mRNA sequencing data and RNA binding proteins were utilized to explore implicated pathways of identified lncRNAs. We found that urinary 1-hydroxyphenanthrene (1-OH-Phe) was inversely associated with 8 lncRNAs and positively associated with 1 lncRNA, as well as 9-hydroxyphenanthrene (9-OH-Phe) was inversely associated with 11 lncRNAs (FDR < 0.1). Tissue specificity analysis using Genome Tissue Expression database suggested that several identified lncRNAs might specifically express in organs targeted by PAHs exposure (lung, liver, heart, kidney, and brain). Besides, plasma levels of 1-OH-Phe related ENSG00000260616 and 9-OH-Phe related STARD4-AS1 were inversely associated with their intra-leukocytes levels (P value < 0.05). Notably, STARD4-AS1 was positively associated with the expression levels of its neighboring protein-coding gene (CAMK4 and STARD4) in leukocytes and were involved in pathways related to cellular response to DNA damage, which we further confirmed using DNA damage biomarker, 8-hydroxydeoxyguanosine. Functional analysis also revealed vital pathways related to cytokine-mediated signaling and glucose homeostasis. Our findings provided novel insights into plausible biological mechanisms underlying the adverse effects of PAHs exposure.

A Case for Accelerating Standards to Achieve the FAIR Principles of Environmental Health Research Experimental Data

BACKGROUND

Funding agencies, publishers, and other stakeholders are pushing environmental health science investigators to improve data sharing; to promote the findable, accessible, interoperable, and reusable (FAIR) principles; and to increase the rigor and reproducibility of the data collected. Accomplishing these goals will require significant cultural shifts surrounding data management and strategies to develop robust and reliable resources that bridge the technical challenges and gaps in expertise.

OBJECTIVE

In this commentary, we examine the current state of managing data and metadata-referred to collectively as (meta)data-in the experimental environmental health sciences. We introduce new tools and resources based on in vivo experiments to serve as examples for the broader field.

METHODS

We discuss previous and ongoing efforts to improve (meta)data collection and curation. These include global efforts by the Functional Genomics Data Society to develop metadata collection tools such as the Investigation, Study, Assay (ISA) framework, and the Center for Expanded Data Annotation and Retrieval. We also conduct a case study of in vivo data deposited in the Gene Expression Omnibus that demonstrates the current state of in vivo environmental health data and highlights the value of using the tools we propose to support data deposition.

DISCUSSION

The environmental health science community has played a key role in efforts to achieve the goals of the FAIR guiding principles and is well positioned to advance them further. We present a proposed framework to further promote these objectives and minimize the obstacles between data producers and data scientists to maximize the return on research investments. https://doi.org/10.1289/EHP11484.

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.

Prenatal Exposure to Metabolism-Disrupting Chemicals, Cord Blood Transcriptome Perturbations, and Birth Weight in a Belgian Birth Cohort

Prenatal exposure to metabolism-disrupting chemicals (MDCs) has been linked to birth weight, but the molecular mechanisms remain largely unknown. In this study, we investigated gene expressions and biological pathways underlying the associations between MDCs and birth weight, using microarray transcriptomics, in a Belgian birth cohort. Whole cord blood measurements of dichlorodiphenyldichloroethylene (p,p'-DDE), polychlorinated biphenyls 153 (PCB-153), perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), and transcriptome profiling were conducted in 192 mother-child pairs. A workflow including a transcriptome-wide association study, pathway enrichment analysis with a meet-in-the-middle approach, and mediation analysis was performed to characterize the biological pathways and intermediate gene expressions of the MDC-birth weight relationship. Among 26,170 transcriptomic features, we successfully annotated five overlapping metabolism-related gene expressions associated with both an MDC and birth weight, comprising BCAT2, IVD, SLC25a16, HAS3, and MBOAT2. We found 11 overlapping pathways, and they are mostly related to genetic information processing. We found no evidence of any significant mediating effect. In conclusion, this exploratory study provides insights into transcriptome perturbations that may be involved in MDC-induced altered birth weight.

The Impacts and Analysis of Individual and Social Risks of the Stochastic Emission of Benzene from Floating-Roof Tanks Using Response Surface Analysis and MPACT Model

In the present study, the researchers used an integrated approach composed of response surface analysis (RSM) and MPACT model to predict fatality rates caused by benzene emitted from floating-roof tanks. RSM scenarios were configured in Expert Design (version 7.0) software using the central composite design (CCD) method and five variables of wind speed, relative humidity, atmospheric temperature, failure diameter, and emission height were considered. Continuous Pasquill-Gifford Gaussian model was used to estimate the results of the RSM scenarios. The response values were considered for exposure concentrations above 50 ppm (slight damages), 150 ppm (moderate damage), and 1000 ppm (high damage). The analysis of individual and social risks for each scenario was done using the MPACT model in SAFETI program (version 8.22) by providing two variables of population characteristics and the frequency of tank wall failure. The results showed that atmospheric temperature, wind speed, failure diameter, and emission height have positive effects on the dispersion of the cloud of toxic benzene vapor with a concentration of 1000 ppm. Intolerable individual risk distances were estimated to be lower for indoor environments than for outdoor. Maximum distances of intolerable individual risks for the worst-case scenarios were estimated up to 2500 m from the emission point, which resulted from exposure to a concentration of 1000-ppm benzene. Results regarding the estimation of social risks showed that over 1600 fatalities should be expected under the worst-case scenarios. The three factors of high temperature, low wind speed, and low emission height play a major role in the occurrence of scenarios with the highest fatalities. High wind speed and high emission height were the most important factors in most scenarios with zero fatalities rate. Generally, the findings of this study show the necessity to provide an emergency response plan in the studied industry in both autumn and winter due to low wind speed. However, the coupling of the developed statistical models based on regional meteorological conditions with the MPACT model can help researchers to design an emergency response plan to deal with leakage incidents in petrochemical industries.