The Metabolome: a Key Measure for Exposome Research in Epidemiology

Tear Fluid as a Matrix for Biomonitoring Environmental and Chemical Exposures

PURPOSE

Exposures to hazardous chemicals have been linked to many detrimental health effects and it is therefore critical to have effective biomonitoring methods to better evaluate key environmental exposures that increase the risk of chronic disease and death. Traditional biomonitoring utilizing blood and urine is limited due to the specialized skills and invasiveness of collecting these fluid samples. This systematic review focuses on tear fluid, which is largely under-researched, as a promising complementary matrix to the traditional fluids used for biomonitoring. The objective is to evaluate the practicability of using human tear fluid for biomonitoring environmental exposures, highlighting potential pitfalls and opportunities.

RECENT FINDING

Tear fluid biomonitoring represents a promising method for assessing exposures because it can be collected with minimal invasiveness and tears contain exposure markers from both the external and internal environments. Tear fluid uniquely interfaces with the external environment at the air-tear interface, providing a surface for airborne chemicals to diffuse into the ocular environment and interact with biomolecules. Tear fluid also contains molecules from the internal environment that have travelled from the blood to tears by crossing the blood-tear barrier. This review demonstrates that tear fluid can be used to identify hazardous chemicals from the external environment and differentiate exposure groups.

Metabolomics-based search for lung cancer markers among patients with different smoking status

Tobacco smoking is the main etiological factor of lung cancer (LC), which can also cause metabolome disruption. This study aimed to investigate whether the observed metabolic shift in LC patients was also associated with their smoking status. Untargeted metabolomics profiling was applied for the initial screening of changes in serum metabolic profile between LC and chronic obstructive pulmonary disease (COPD) patients, selected as a non-cancer group. Differences in metabolite profiles between current and former smokers were also tested. Then, targeted metabolomics methods were applied to verify and validate the proposed LC biomarkers. For untargeted metabolomics, a single extraction-dual separation workflow was applied. The samples were analyzed using a liquid chromatograph-high resolution quadrupole time-of-flight mass spectrometer. Next, the selected metabolites were quantified using liquid chromatography-triple-quadrupole mass spectrometry. The acquired data confirmed that patients' stratification based on smoking status impacted the discriminating ability of the identified LC marker candidates. Analyzing a validation set of samples enabled us to determine if the putative LC markers were truly robust. It demonstrated significant differences in the case of four metabolites: allantoin, glutamic acid, succinic acid, and sphingosine-1-phosphate. Our research showed that studying the influence of strong environmental factors, such as tobacco smoking, should be considered in cancer marker research since it reduces the risk of false positives and improves understanding of the metabolite shifts in cancer patients.

Metabolomics in Preclinical Drug Safety Assessment: Current Status and Future Trends

Metabolomics is emerging as a powerful systems biology approach for improving preclinical drug safety assessment. This review discusses current applications and future trends of metabolomics in toxicology and drug development. Metabolomics can elucidate adverse outcome pathways by detecting endogenous biochemical alterations underlying toxicity mechanisms. Furthermore, metabolomics enables better characterization of human environmental exposures and their influence on disease pathogenesis. Metabolomics approaches are being increasingly incorporated into toxicology studies and safety pharmacology evaluations to gain mechanistic insights and identify early biomarkers of toxicity. However, realizing the full potential of metabolomics in regulatory decision making requires a robust demonstration of reliability through quality assurance practices, reference materials, and interlaboratory studies. Overall, metabolomics shows great promise in strengthening the mechanistic understanding of toxicity, enhancing routine safety screening, and transforming exposure and risk assessment paradigms. Integration of metabolomics with computational, in vitro, and personalized medicine innovations will shape future applications in predictive toxicology.

A population-based urinary and plasma metabolomics study of environmental exposure to cadmium

BACKGROUND

The application of metabolomics-based profiles in environmental epidemiological studies is a promising approach to refine the process of health risk assessment. We aimed to identify potential metabolomics-based profiles in urine and plasma for the detection of relatively low-level cadmium (Cd) exposure in large population-based studies.

METHOD

We analyzed 123 urinary metabolites and 94 plasma metabolites detected in fasting urine and plasma samples collected from 1,412 men and 2,022 women involved in the Tsuruoka Metabolomics Cohort Study. Regression analysis was performed for urinary N-acetyl-beta-D-glucosaminidase (NAG), plasma, and urinary metabolites as dependent variables, and urinary Cd (U-Cd, quartile) as an independent variable. The multivariable regression model included age, gender, systolic blood pressure, smoking, rice intake, BMI, glycated hemoglobin, low-density lipoprotein cholesterol, alcohol consumption, physical activity, educational history, dietary energy intake, urinary Na/K ratio, and uric acid. Pathway-network analysis was carried out to visualize the metabolite networks linked to Cd exposure.

RESULT

Urinary NAG was positively associated with U-Cd, but not at lower concentrations (Q2). Among urinary metabolites in the total population, 45 metabolites showed associations with U-Cd in the unadjusted and adjusted models after adjusting for the multiplicity of comparison with FDR. There were 12 urinary metabolites which showed consistent associations between Cd exposure from Q2 to Q4. Among plasma metabolites, six cations and one anion were positively associated with U-Cd, whereas alanine, creatinine, and isoleucine were negatively associated with U-Cd. Our results were robust by statistical adjustment of various confounders. Pathway-network analysis revealed metabolites and upstream regulator changes associated with mitochondria (ACACB, UCP2, and metabolites related to the TCA cycle).

CONCLUSION

These results suggested that U-Cd was associated with metabolites related to upstream mitochondrial dysfunction in a dose-dependent manner. Our data will help develop environmental Cd exposure profiles for human populations.

Changing epidemiology of colorectal cancer - birth cohort effects and emerging risk factors

Incidence and mortality of colorectal cancer (CRC) are increasing worldwide, suggesting broad changes in the epidemiology of CRC. In this Review, we discuss the changes that are becoming evident, including trends in CRC incidence and mortality by age and birth cohort, and consider the contributions of early-life exposures and emerging risk factors to these changes. Importantly, incidence of CRC has increased among people born since the early 1950s in nearly all regions of the world. These so-called birth cohort effects imply the involvement of factors that influence the earliest stages of carcinogenesis and have effects across the life course. Accumulating evidence supports the idea that early-life exposures are important risk factors for CRC, including exposures during fetal development, childhood, adolescence and young adulthood. Environmental chemicals could also have a role because the introduction of many in the 1950s and 1960s coincides with increasing incidence of CRC among people born during those years. To reverse the expected increases in the global burden of CRC, participation in average-risk screening programmes needs to be increased by scaling up and implementing evidence-based screening strategies, and emerging risk factors responsible for these increases need to be identified.

Assessing Metabolic Differences Associated with Exposure to Polybrominated Biphenyl and Polychlorinated Biphenyls in the Michigan PBB Registry

BACKGROUND

Polybrominated biphenyls (PBB) and polychlorinated biphenyls (PCB) are persistent organic pollutants with potential endocrine-disrupting effects linked to adverse health outcomes.

OBJECTIVES

In this study, we utilize high-resolution metabolomics (HRM) to identify internal exposure and biological responses underlying PCB and multigenerational PBB exposure for participants enrolled in the Michigan PBB Registry.

METHODS

HRM profiling was conducted on plasma samples collected from 2013 to 2014 from a subset of participants enrolled in the Michigan PBB Registry, including 369 directly exposed individuals (F0) who were alive when PBB mixtures were accidentally introduced into the food chain and 129 participants exposed to PBB in utero or through breastfeeding, if applicable (F1). Metabolome-wide association studies were performed for PBB-153 separately for each generation and Σ PCB (PCB-118, PCB-138, PCB-153, and PCB-180) in the two generations combined, as both had direct PCB exposure. Metabolite and metabolic pathway alterations were evaluated following a well-established untargeted HRM workflow.

RESULTS

Mean levels were 1.75  ng / mL [standard deviation (SD): 13.9] for PBB-153 and 1.04  ng / mL (SD: 0.788) for Σ PCB . Sixty-two and 26 metabolic features were significantly associated with PBB-153 in F0 and F1 [false discovery rate (FDR) p < 0.2 ], respectively. There were 2,861 features associated with Σ PCB (FDR p < 0.2 ). Metabolic pathway enrichment analysis using a bioinformatics tool revealed perturbations associated with Σ PCB in numerous oxidative stress and inflammation pathways (e.g., carnitine shuttle, glycosphingolipid, and vitamin B9 metabolism). Metabolic perturbations associated with PBB-153 in F0 were related to oxidative stress (e.g., pentose phosphate and vitamin C metabolism) and in F1 were related to energy production (e.g., pyrimidine, amino sugars, and lysine metabolism). Using authentic chemical standards, we confirmed the chemical identity of 29 metabolites associated with Σ PCB levels (level 1 evidence).

CONCLUSIONS

Our results demonstrate that serum PBB-153 is associated with alterations in inflammation and oxidative stress-related pathways, which differed when stratified by generation. We also found that Σ PCB was associated with the downregulation of important neurotransmitters, serotonin, and 4-aminobutanoate. These findings provide novel insights for future investigations of molecular mechanisms underlying PBB and PCB exposure on health. https://doi.org/10.1289/EHP12657.

Examination of Serum Metabolome Altered by Dietary Carbohydrate, Milk Protein, and Soy Protein Interventions Identified Novel Metabolites Associated with Blood Pressure: The ProBP Trial

SCOPE

This study aims to discover metabolites of dietary carbohydrate, soy and milk protein supplements and evaluate their roles in blood pressure (BP) regulation in the protein and blood pressure (ProBP), a cross-over trial.

METHODS AND RESULTS

Plasma metabolites are profiled at pre-trial baseline and after 8 weeks of supplementation with carbohydrate, soy protein, and milk protein, respectively, among 80 ProBP participants. After Bonferroni correction (α = 6.49 × 10-4 ), dietary interventions significantly changed 40 metabolites. Changes of erucate (22:1n9), an omega-9 fatty acid, are positively associated with systolic BP changes (Beta = 1.90, p = 6·27 × 10-4 ). This metabolite is also associated with higher odds of hypertension among 1261 participants of an independent cohort (odds ratio per unit increase = 1.34; 95% confidence interval: 1.07-1.68). High levels of acylcholines dihomo-linolenoyl-choline (p = 4.71E-04) and oleoylcholine (p = 3.48E-04) at baseline predicted larger BP lowering effects of soy protein. Increasing cheese intake during the trial, as reflected by isobutyrylglycine and isovalerylglycine, reduces the BP lowering effect of soy protein.

CONCLUSIONS

The study identifies molecular signatures of dietary interventions. Erucate (22:1n9) increases systolic BP. Acylcholine enhances and cheese intake reduces the BP lowering effect of soy protein supplement.

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.

Optimization of Mass Spectrometric Parameters in Data Dependent Acquisition for Untargeted Metabolomics on the Basis of Putative Assignments

Optimization of mass spectrometric parameters for a data dependent acquisition (DDA) experiment is essential to increase the MS/MS coverage and hence increase metabolite identifications in untargeted metabolomics. We explored the influence of mass spectrometric parameters including mass resolution, radio frequency (RF) level, signal intensity threshold, number of MS/MS events, cycle time, collision energy, maximum ion injection time (MIT), dynamic exclusion, and automatic gain control (AGC) target value on metabolite annotations on an Exploris 480-Orbitrap mass spectrometer. Optimal annotation results were obtained by performing ten data dependent MS/MS scans with a mass isolation window of 2.0 m/z and a minimum signal intensity threshold of 1 × 104 at a mass resolution of 180,000 for MS and 30,000 for MS/MS, while maintaining the RF level at 70%. Furthermore, combining an AGC target value of 5 × 106 and MIT of 100 ms for MS and an AGC target value of 1 × 105 and an MIT of 50 ms for MS/MS scans provided an improved number of annotated metabolites. A 10 s exclusion duration and a two stepped collision energy were optimal for higher spectral quality. These findings confirm that MS parameters do influence metabolomics results, and propose strategies for increasing metabolite coverage in untargeted metabolomics. A limitation of this work is that our parameters were only optimized for one RPLC method on single matrix and may be different for other protocols. Additionally, no metabolites were identified at level 1 confidence. The results presented here are based on metabolite annotations and need to be validated with authentic standards.

Phenotypically driven subgroups of ASD display distinct metabolomic profiles

Autism Spectrum Disorder (ASD) is a heterogeneous condition that includes a broad range of characteristics and associated comorbidities; however, the biology underlying the variability in phenotypes is not well understood. As ASD impacts approximately 1 in 100 children globally, there is an urgent need to better understand the biological mechanisms that contribute to features of ASD. In this study, we leveraged rich phenotypic and diagnostic information related to ASD in 2001 individuals aged 4 to 17 years from the Simons Simplex Collection to derive phenotypically driven subgroups and investigate their respective metabolomes. We performed hierarchical clustering on 40 phenotypes spanning four ASD clinical domains, resulting in three subgroups with distinct phenotype patterns. Using global plasma metabolomic profiling generated by ultrahigh-performance liquid chromatography mass spectrometry, we characterized the metabolome of individuals in each subgroup to interrogate underlying biology related to the subgroups. Subgroup 1 included children with the least maladaptive behavioral traits (N = 862); global decreases in lipid metabolites and concomitant increases in amino acid and nucleotide pathways were observed for children in this subgroup. Subgroup 2 included children with the highest degree of challenges across all phenotype domains (N = 631), and their metabolome profiles demonstrated aberrant metabolism of membrane lipids and increases in lipid oxidation products. Subgroup 3 included children with maladaptive behaviors and co-occurring conditions that showed the highest IQ scores (N = 508); these individuals had increases in sphingolipid metabolites and fatty acid byproducts. Overall, these findings indicated distinct metabolic patterns within ASD subgroups, which may reflect the biological mechanisms giving rise to specific patterns of ASD characteristics. Our results may have important clinical applications relevant to personalized medicine approaches towards managing ASD symptoms.

Metabolomic approach for obstructive sleep apnea in adults: a systematic review

Obstructive Sleep Apnea (OSA) corresponds to episodes of complete or partial upper airway obstruction during sleep. The gold standard for diagnosing OSA is polysomnography; however, metabolomics is an innovative and highly sensitive method that seeks to identify and quantify small molecules in biological systems. Identify the metabolites most frequently associated with obstructive sleep apnea in adults. The search for articles was conducted between October 2020 and August 2021, in electronic databases, such as MEDLINE/PubMed, Scielo, Embase, and Cochrane, through the combination of descriptors: obstructive sleep apnea, metabolomic, adult. This systematic review included all cross-sectional studies published, including human patients aged 18 years or older, of both genders who underwent type I or II polysomnography and metabolomics study. The search strategy selected 3697 surveys, and 4 of them were selected to be a part of this systematic review. Based on the analyzed surveys, it was found that all of them were able to diagnose OSA, reaching a sensitivity of 75-97%, and specificity that ranged from 72 to 100%; besides differentiating patients with OSA (severe, moderate, and mild) from simple snorers with a mean sensitivity of 77.2% and specificity of 66.25%. These findings suggest that, in addition to being used as a screening and diagnostic strategy for OSA, metabolomics has the potential to be used for severity stratification and to monitor the disease's progression.

Urinary Metabolomic Profile of Youth at Risk of Chronic Kidney Disease in Nicaragua

Key Points

Urinary concentrations of glycine, a molecule associated with thermoregulation, were elevated among youth from a high-risk region for chronic kidney disease of non-traditional etiology (CKDnt). Urinary concentrations of pyruvate, citric acid, and inosine were lower among youth at higher risk of CKDnt, suggesting renal stress. Metabolomic analyses may shed light on early disease processes or profiles or risk in the context of CKDnt.

Background

CKD of a nontraditional etiology (CKDnt) is responsible for high mortality in Central America, although its causes remain unclear. Evidence of kidney dysfunction has been observed among youth, suggesting that early kidney damage contributing to CKDnt may initiate in childhood.

Methods

Urine specimens of young Nicaraguan participants 12–23 years without CKDnt (n =136) were analyzed by proton nuclear magnetic resonance spectroscopy for 50 metabolites associated with kidney dysfunction. Urinary metabolite levels were compared by, regional CKDnt prevalence, sex, age, and family history of CKDnt using supervised statistical methods and pathway analysis in MetaboAnalyst. Magnitude of associations and changes over time were assessed through multivariable linear regression.

Results

In adjusted analyses, glycine concentrations were higher among youth from high-risk regions (β =0.82, [95% confidence interval, 0.16 to 1.85]; P = 0.01). Pyruvate concentrations were lower among youth with low eGFR (β = −0.36 [95% confidence interval, −0.57 to −0.04]; P = 0.03), and concentrations of other citric acid cycle metabolites differed by key risk factors. Over four years, participants with low eGFR experienced greater declines in 1-methylnicotinamide and 2-oxoglutarate and greater increases in citrate and guanidinoacetate concentrations.

Conclusion

Urinary concentration of glycine, a molecule associated with thermoregulation and kidney function preservation, was higher among youth in high-risk CKDnt regions, suggestive of greater heat exposure or renal stress. Lower pyruvate concentrations were associated with low eGFR, and citric acid cycle metabolites, such as pyruvate, likely relate to mitochondrial respiration rates in the kidneys. Participants with low eGFR experienced longitudinal declines in concentrations of 1-methylnicotinamide, an anti-inflammatory metabolite associated with anti-fibrosis in tubule cells. These findings merit further consideration in research on the origins of CKDnt.

A precision environmental health approach to prevention of human disease

Human health is determined by the interaction of our environment with the genome, epigenome, and microbiome, which shape the transcriptomic, proteomic, and metabolomic landscape of cells and tissues. Precision environmental health is an emerging field leveraging environmental and system-level ('omic) data to understand underlying environmental causes of disease, identify biomarkers of exposure and response, and develop new prevention and intervention strategies. In this article we provide real-life illustrations of the utility of precision environmental health approaches, identify current challenges in the field, and outline new opportunities to promote health through a precision environmental health framework.

Untargeted Metabolomics by Liquid Chromatography-Mass Spectrometry in Biomedical Research

Metabolomics, alone or in combination with other omics sciences, has shown great relevance in a large number of investigations in different branches of biomedicine, often providing novel discoveries and helping to expand the knowledge. Metabolomics analyses are carried out using different techniques, but in this chapter, we focus on liquid chromatography coupled to high-resolution mass spectrometry. The designated methodology consists of an untargeted approach for the analysis of plasma samples. The use of this method, with a reverse-phase column and electrospray ionization in positive mode, covers the detection of a broad range of metabolites, mainly of nonpolar and of intermediate polarity. This chapter also reviews the mass fragmentation spectra for the identification of bile acids, acylcarnitines, and glycerophospholipids.

Thyroid Carcinoma: A Review for 25 Years of Environmental Risk Factors Studies

Environmental factors are established contributors to thyroid carcinomas. Due to their known ability to cause cancer, exposure to several organic and inorganic chemical toxicants and radiation from nuclear weapons, fallout, or medical radiation poses a threat to global public health. Halogenated substances like organochlorines and pesticides can interfere with thyroid function. Like phthalates and bisphenolates, polychlorinated biphenyls and their metabolites, along with polybrominated diethyl ethers, impact thyroid hormones biosynthesis, transport, binding to target organs, and impair thyroid function. A deeper understanding of environmental exposure is crucial for managing and preventing thyroid cancer. This review aims to investigate the relationship between environmental factors and the development of thyroid cancer.

Multi-omics signatures of the human early life exposome

Environmental exposures during early life play a critical role in life-course health, yet the molecular phenotypes underlying environmental effects on health are poorly understood. In the Human Early Life Exposome (HELIX) project, a multi-centre cohort of 1301 mother-child pairs, we associate individual exposomes consisting of >100 chemical, outdoor, social and lifestyle exposures assessed in pregnancy and childhood, with multi-omics profiles (methylome, transcriptome, proteins and metabolites) in childhood. We identify 1170 associations, 249 in pregnancy and 921 in childhood, which reveal potential biological responses and sources of exposure. Pregnancy exposures, including maternal smoking, cadmium and molybdenum, are predominantly associated with child DNA methylation changes. In contrast, childhood exposures are associated with features across all omics layers, most frequently the serum metabolome, revealing signatures for diet, toxic chemical compounds, essential trace elements, and weather conditions, among others. Our comprehensive and unique resource of all associations ( https://helixomics.isglobal.org/ ) will serve to guide future investigation into the biological imprints of the early life exposome.

Mechanistic insights into the health benefits of fish-oil supplementation against fine particulate matter air pollution: a randomized controlled trial

BACKGROUND

Dietary fish-oil supplementation might attenuate the associations between fine particulate matter (PM_2.5) and subclinical biomarkers. However, the molecular mechanisms remain to be elucidated. This study aimed to explore the molecular mechanisms of fish-oil supplementation against the PM_2.5-induced health effects.

METHODS

We conducted a randomized, double-blinded, and placebo-controlled trial among healthy college students in Shanghai, China, from September 2017 to January 2018. A total of 70 participants from the Fenglin campus of Fudan University were included. We randomly assigned participants to either supplementation of 2.5-gram fish oil (n = 35) or sunflower-seed oil (placebo) (n = 35) per day and conducted four rounds of health measurements in the last two months of the trial. As a post hoc exploratory study, the present untargeted metabolomics analysis used remaining blood samples collected in the previous trial and applied a Metabolome-Wide Association Study framework to compare the effects of PM_2.5 on the metabolic profile between the sunflower-seed oil and fish oil groups.

RESULTS

A total of 65 participants completed the trial (34 of the fish oil group and 31 of the sunflower-seed oil group). On average, ambient PM_2.5 concentration on the day of health measurements was 34.9 µg/m³ in the sunflower-seed oil group and 34.5 µg/m³ in the fish oil group, respectively. A total of 3833 metabolites were significantly associated with PM_2.5 in the sunflower-seed oil group and 1757 in the fish oil group. Of these, 1752 metabolites showed significant between-group differences. The identified differential metabolites included arachidonic acid derivatives, omega-3 fatty acids, omega-6 fatty acids, and omega-9 fatty acids that were related to unsaturated fatty acid metabolism, which plays a role in the inflammatory responses.

CONCLUSION

This trial suggests fish-oil supplementation could mitigate the PM_2.5-induced inflammatory responses via modulating fatty acid metabolism, providing biological plausibility for the health benefits of fish-oil supplementation against PM_2.5 exposure.

TRIAL REGISTRATION

This study is registered at ClinicalTrails.gov (NCT03255187).

Untargeted metabolomics reveals that multiple reproductive toxicants are present at the endometrium

BACKGROUND

Recent epidemiologic research shows many environmental chemicals exhibit endocrine disrupting effects on the female reproductive system. Few studies have examined exposure at reproductive organs. Our aim was to perform a preliminary untargeted metabolomic characterization of menstrual blood, a novel biofluid, to identify environmental toxins present in the endometrium and evaluate the suitability of this sample type for exposome research.

METHODS

Whole blood menstrual samples were collected from four women using a menstrual cup. Samples were analyzed for small molecules that include both environmental chemicals and endogenous metabolites using untargeted liquid chromatography with high-resolution mass spectrometry (LC-HRMS). Principal component analysis (PCA) and ANOVA was used to identify differences within and between individuals' menstrual blood metabolomic profiles, and the influence of the sample processing method. To assess the presence of environmental exposures, LC-HRMS chemical profiles were matched to the ToxCast chemical database, which includes 4557 commonly used commercial chemicals. Select compounds were confirmed by comparison to reference standards.

RESULTS

PCA of metabolome profiles showed analysis of menstrual blood samples were highly reproducible, with high variability in detected metabolites between participants and low variability between analytical replicates of an individual's sample. Endogenous metabolites detected in menstrual blood samples achieved good coverage of the human blood metabolome. We found 1748 annotations for environmental chemicals, including suspected reproductive toxicants such as phenols, parabens, phthalates, and organochlorines. Storage temperature for the first 24 h did not significantly influence global metabolomic profiles.

CONCLUSION

Our results show chemical exposures linked to reproductive toxicity and endocrine disruption are present in menstrual blood, a sampling medium for the endometrium.

Integrative exposomic, transcriptomic, epigenomic analyses of human placental samples links understudied chemicals to preeclampsia

BACKGROUND

Environmental health research has recently undergone a dramatic shift, with ongoing technological advancements allowing for broader coverage of exposure and molecular biology signatures. Approaches to integrate such measures are still needed to increase understanding between systems-level exposure and biology.

OBJECTIVES

We address this gap by evaluating placental tissues to identify novel chemical-biological interactions associated with preeclampsia. This study tests the hypothesis that understudied chemicals are present in the human placenta and associated with preeclampsia-relevant disruptions, including overall case status (preeclamptic vs. normotensive patients) and underlying transcriptomic/epigenomic signatures.

METHODS

A non-targeted analysis based on high-resolution mass spectrometry was used to analyze placental tissues from a cohort of 35 patients with preeclampsia (n = 18) and normotensive (n = 17) pregnancies. Molecular feature data were prioritized for confirmation based on association with preeclampsia case status and confidence of chemical identification. All molecular features were evaluated for relationships to mRNA, microRNA, and CpG methylation (i.e., multi-omic) signature alterations involved in preeclampsia.

RESULTS

A total of 183 molecular features were identified with significantly differentiated abundance in placental extracts of preeclamptic patients; these features clustered into distinct chemical groupings using unsupervised methods. Of these features, 53 were identified (mapping to 40 distinct chemicals) using chemical standards, fragmentation spectra, and chemical metadata. In general, human metabolites had the largest feature intensities and strongest associations with preeclampsia-relevant multi-omic changes. Exogenous drugs were second most abundant and had fewer associations with multi-omic changes. Other exogenous chemicals (non-drugs) were least abundant and had the fewest associations with multi-omic changes.

CONCLUSIONS

These global data trends suggest that human metabolites are heavily intertwined with biological processes involved in preeclampsia etiology, while exogenous chemicals may still impact select transcriptomic/epigenomic processes. This study serves as a demonstration of merging systems exposures with systems biology to better understand chemical-disease relationships.

Addressing big data challenges in mass spectrometry-based metabolomics

Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.

Precision environmental health monitoring by longitudinal exposome and multi-omics profiling

Conventional environmental health studies have primarily focused on limited environmental stressors at the population level, which lacks the power to dissect the complexity and heterogeneity of individualized environmental exposures. Here, as a pilot case study, we integrated deep-profiled longitudinal personal exposome and internal multi-omics to systematically investigate how the exposome shapes a single individual's phenome. We annotated thousands of chemical and biological components in the personal exposome cloud and found they were significantly correlated with thousands of internal biomolecules, which was further cross-validated using corresponding clinical data. Our results showed that agrochemicals and fungi predominated in the highly diverse and dynamic personal exposome, and the biomolecules and pathways related to the individual's immune system, kidney, and liver were highly associated with the personal external exposome. Overall, this data-driven longitudinal monitoring study shows the potential dynamic interactions between the personal exposome and internal multi-omics, as well as the impact of the exposome on precision health by producing abundant testable hypotheses.

Development and Application of an LC-MS/MS Untargeted Exposomics Method with a Separated Pooled Quality Control Strategy

Pooled quality controls (QCs) are usually implemented within untargeted methods to improve the quality of datasets by removing features either not detected or not reproducible. However, this approach can be limiting in exposomics studies conducted on groups of exposed and nonexposed subjects, as compounds present at low levels only in exposed subjects can be diluted and thus not detected in the pooled QC. The aim of this work is to develop and apply an untargeted workflow for human biomonitoring in urine samples, implementing a novel separated approach for preparing pooled quality controls. An LC-MS/MS workflow was developed and applied to a case study of smoking and non-smoking subjects. Three different pooled quality controls were prepared: mixing an aliquot from every sample (QC-T), only from non-smokers (QC-NS), and only from smokers (QC-S). The feature tables were filtered using QC-T (T-feature list), QC-S, and QC-NS, separately. The last two feature lists were merged (SNS-feature list). A higher number of features was obtained with the SNS-feature list than the T-feature list, resulting in identification of a higher number of biologically significant compounds. The separated pooled QC strategy implemented can improve the nontargeted human biomonitoring for groups of exposed and nonexposed subjects.

Integrating Environment and Aging Research: Opportunities for Synergy and Acceleration

Despite significant overlaps in mission, the fields of environmental health sciences and aging biology are just beginning to intersect. It is increasingly clear that genetics alone does not predict an individual’s neurological aging and sensitivity to disease. Accordingly, aging neuroscience is a growing area of mutual interest within environmental health sciences. The impetus for this review came from a workshop hosted by the National Academies of Sciences, Engineering, and Medicine in June of 2020, which focused on integrating the science of aging and environmental health research. It is critical to bridge disciplines with multidisciplinary collaborations across toxicology, comparative biology, epidemiology to understand the impacts of environmental toxicant exposures and age-related outcomes. This scoping review aims to highlight overlaps and gaps in existing knowledge and identify essential research initiatives. It begins with an overview of aging biology and biomarkers, followed by examples of synergy with environmental health sciences. New areas for synergistic research and policy development are also discussed. Technological advances including next-generation sequencing and other-omics tools now offer new opportunities, including exposomic research, to integrate aging biomarkers into environmental health assessments and bridge disciplinary gaps. This is necessary to advance a more complete mechanistic understanding of how life-time exposures to toxicants and other physical and social stressors alter biological aging. New cumulative risk frameworks in environmental health sciences acknowledge that exposures and other external stressors can accumulate across the life course and the advancement of new biomarkers of exposure and response grounded in aging biology can support increased understanding of population vulnerability. Identifying the role of environmental stressors, broadly defined, on aging biology and neuroscience can similarly advance opportunities for intervention and translational research. Several areas of growing research interest include expanding exposomics and use of multi-omics, the microbiome as a mediator of environmental stressors, toxicant mixtures and neurobiology, and the role of structural and historical marginalization and racism in shaping persistent disparities in population aging and outcomes. Integrated foundational and translational aging biology research in environmental health sciences is needed to improve policy, reduce disparities, and enhance the quality of life for older individuals.

The Potential of Metabolomics in Biomedical Applications

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

Tailoring Multi-omics to Inflammatory Bowel Diseases: All for One and One for All

Inflammatory bowel disease [IBD] has a multifactorial origin and originates from a complex interplay of environmental factors with the innate immune system at the intestinal epithelial interface in a genetically susceptible individual. All these factors make its aetiology intricate and largely unknown. Multi-omic datasets obtained from IBD patients are required to gain further insights into IBD biology. We here review the landscape of multi-omic data availability in IBD and identify barriers and gaps for future research. We also outline the various technical and non-technical factors that influence the utility and interpretability of multi-omic datasets and thereby the study design of any research project generating such datasets. Coordinated generation of multi-omic datasets and their systemic integration with clinical phenotypes and environmental exposures will not only enhance understanding of the fundamental mechanisms of IBD but also improve therapeutic strategies. Finally, we provide recommendations to enable and facilitate generation of multi-omic datasets.

Advances in Proteomic and Metabolomic Profiling of Neurodegenerative Diseases

Proteomics and metabolomics are two emerging fields that hold promise to shine light on the molecular mechanisms causing neurodegenerative diseases. Research in this area may reveal and quantify specific metabolites and proteins that can be targeted by therapeutic interventions intended at halting or reversing the neurodegenerative process. This review aims at providing a general overview on the current status of proteomic and metabolomic profiling in neurodegenerative diseases. We focus on the most common neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. We discuss the relevance of state-of-the-art metabolomics and proteomics approaches and their potential for biomarker discovery. We critically review advancements made so far, highlighting how metabolomics and proteomics may have a significant impact in future therapeutic and biomarker development. Finally, we further outline technologies used so far as well as challenges and limitations, placing the current information in a future-facing context.

Capturing the microbial volatilome: an oft overlooked 'ome'

Among the diverse metabolites produced by microbial communities, some are volatile. Volatile organic compounds (VOCs) are vigorously cycled by microbes as metabolic substrates and products and as signaling molecules. Yet, current microbial metabolomic studies predominantly focus on nonvolatile metabolites and overlook VOCs, which therefore represent a missing component of the metabolome. Advances in VOC detection now allow simultaneous observation of the numerous VOCs constituting the 'volatilome' of microbial systems. We present a roadmap for integrating and advancing VOC and other 'omics approaches and highlight the potential for realtime VOC measurements to help overcome limitations in discrete 'omics sampling. Including volatile metabolites in metabolomics, both conceptually and in practice, will build a more comprehensive understanding of microbial processes across ecological communities.

Operationalizing the Exposome Using Passive Silicone Samplers

The exposome, which is defined as the cumulative effect of environmental exposures and corresponding biological responses, aims to provide a comprehensive measure for evaluating non-genetic causes of disease. Operationalization of the exposome for environmental health and precision medicine has been limited by the lack of a universal approach for characterizing complex exposures, particularly as they vary temporally and geographically. To overcome these challenges, passive sampling devices (PSDs) provide a key measurement strategy for deep exposome phenotyping, which aims to provide comprehensive chemical assessment using untargeted high-resolution mass spectrometry for exposome-wide association studies. To highlight the advantages of silicone PSDs, we review their use in population studies and evaluate the broad range of applications and chemical classes characterized using these samplers. We assess key aspects of incorporating PSDs within observational studies, including the need to preclean samplers prior to use to remove impurities that interfere with compound detection, analytical considerations, and cost. We close with strategies on how to incorporate measures of the external exposome using PSDs, and their advantages for reducing variability in exposure measures and providing a more thorough accounting of the exposome. Continued development and application of silicone PSDs will facilitate greater understanding of how environmental exposures drive disease risk, while providing a feasible strategy for incorporating untargeted, high-resolution characterization of the external exposome in human studies.

Associations between persistent organic pollutants and endometriosis: A multiblock approach integrating metabolic and cytokine profiling

Humans are exposed daily to complex mixtures of chemical pollutants through their environment and diet, some of which have the potential to disrupt the bodies' natural endocrine functions and contribute to reproductive diseases like endometriosis. Increasing epidemiological and experimental evidence supports the association between endometriosis and certain persistent organic pollutants (POPs) like dioxins; however, little is known about the underlying linking mechanisms. The main objective of this study is to proof the methodological applicability and discovery potential of integrating ultra-trace mass spectrometry (MS) profiling of POP biomarkers and endogenous biomarker profiling (MS metabolomics and cytokines) in a case-control study for the etiological research of endometriosis. The approach is applied in a pilot clinical-based study conducted in France where women with and without surgically confirmed endometriosis were recruited. Serum samples were analysed with high-resolution MS for about 30 polychlorinated biphenyls (PCBs), organochlorinated pesticides and perfluoroalkyl substances (PFAS). About 600 serum metabolites and lipids were identified with targeted metabolomics using tandem MS with the Biocrates MxP® Quant 500 Kit. A panel of 4 pro-inflammatory cytokines were analysed using ELISA-based 4-PLEX analyser. Statistical analysis included a battery of variable selection approaches, multivariate logistic regression for single-chemical associations, Bayesian kernel machine regressions (BKMR) to identify mixture effects of POPs and a multiblock approach to identify shared biomarker signatures among high risk clusters. The results showed the positive associations between some POPs and endometriosis risk, including the pesticide trans-nonachlor Odds Ratio (95% Confidence Interval) 3.38 (2.06-5.98), p < 0.0001 and PCB 114 OR (95% CI) 1.83 (1.17-2.93), p = 0.009. The BKMR approach showed a tendency of a positive cumulative effect of the mixture, however trans-nonachlor exhibited significant associations within the mixture and interacted with other PCBs, strengthening the effects at highest concentrations. Finally, the multiblock analysis, relating the various blocks of data, revealed a latent cluster of women with higher risk of endometrioma presenting higher concentrations of trans-nonachlor, PCB 114 and dioxin-like toxic equivalents from PCBs, together with an increased inflammatory profile (i.e. elevated interleukin-8 and monocyte chemoattractant protein-1). It was also highlighted a specific metabolic pattern characterized by dysregulation of bile acid homeostasis and lipase activity. Further research will be required with larger sample size to confirm these findings and gain insight on the underlying mechanisms between POPs and endometriosis.

Mass Spectrometry as a Crucial Analytical Basis for Omics Sciences

This review is devoted to the consideration of mass spectrometric platforms as applied to omics sciences. The most significant attention is paid to omics related to life sciences (genomics, proteomics, meta-bolomics, lipidomics, glycomics, plantomics, etc.). Mass spectrometric approaches to solving the problems of petroleomics, polymeromics, foodomics, humeomics, and exosomics, related to inorganic sciences, are also discussed. The review comparatively presents the advantages of various principles of separation and mass spectral techniques, complementary derivatization, used to obtain large arrays of various structural and quantitative information in the mentioned omics sciences.

Periconception air pollution, metabolomic biomarkers, and fertility among women undergoing assisted reproduction

BACKGROUND

Air pollution exposure has been linked with diminished fertility. Identifying the metabolic changes induced by periconception air pollution exposure among women could enhance our understanding of the potential biological pathways underlying air pollution's reproductive toxicity.

OBJECTIVE

To identify serum metabolites associated with periconception air pollution exposure and evaluate the extent to which these metabolites mediate the association between air pollution and live birth.

METHODS

We included 200 women undergoing a fresh assisted reproductive technology (ART) cycle at Massachusetts General Hospital Fertility Center (2005-2015). A serum sample was collected during stimulation, and untargeted metabolic profiling was conducted using liquid chromatography with ultra-high-resolution mass spectrometry. Exposure to nitrogen dioxide (NO₂), ozone (O₃), fine particulate matter <2.5 µm (PM_2.5), and black carbon (BC) was estimated using validated spatiotemporal models. Multivariable linear regression models were used to evaluate the associations between the air pollutants, live birth, and metabolic feature intensities. A meet in the middle approach was used to identify overlapping features and metabolic pathways.

RESULTS

From the C18 and HILIC chromatography columns, 10,803 and 12,968 metabolic features were extracted. There were 190 metabolic features and 18 pathways that were significantly associated with both air pollution and live birth (P < 0.05) across chromatography columns. Eight features were confirmed metabolites implicated in amino acid and nutrient metabolism with downstream effects on oxidative stress and inflammation. Six confirmed metabolites fell into two intuitive clusters - "antioxidants" and "oxidants"- which could potentially mediate some of the association between air pollution and lower odds of live birth. Tryptophan and vitamin B3 metabolism were common pathways linking air pollution exposure to decreased probability of live birth.

CONCLUSION

Higher periconception air pollution exposure was associated with metabolites and biologic pathways involved in inflammation and oxidative stress that may mediate the observed associations with lower probability of live birth following ART.

Precision Medicine on the Fly: Using Drosophila to Decipher Gene-Environment Interactions in Parkinson's Disease

Big data approaches have profoundly influenced state-of-the-art in many fields of research, with toxicology being no exception. Here, we use Parkinson's disease as a window through which to explore the challenges of a dual explosion of metabolomic data addressing the myriad environmental exposures individuals experience and genetic analyses implicating many different loci as risk factors for disease. We argue that new experimental approaches are needed to convert the growing body of omics data into molecular mechanisms of disease that can be therapeutically targeted in specific patients. We outline one attractive strategy, which capitalizes on the rapid generation time and advanced molecular tools available in the fruit fly, Drosophila, to provide a platform for mechanistic dissection and drug discovery.

Analytical aspects of meet-in-metabolite analysis for molecular pathway reconstitution from exposure to adverse outcome

To explore the etiology of diseases is one of the major goals in epidemiological study. Meet-in-metabolite analysis reconstitutes biomonitoring-based adverse outcome (AO) pathways from environmental exposure to a disease, in which the chemical exposome-related metabolism responses are transmitted to incur the AO-related metabolism phenotypes. However, the ongoing data-dependent acquisition of non-targeted biomonitoring by high-resolution mass spectrometry (HRMS) is biased against the low abundance molecules, which forms the major of molecular internal exposome, i.e., the totality of trace levels of environmental pollutants and/or their metabolites in human samples. The recent development of data-independent acquisition protocols for HRMS screening has opened new opportunities to enhance unbiased measurement of the extremely low abundance molecules, which can encompass a wide range of analytes and has been applied in metabolomics, DNA, and protein adductomics. In addition, computational MS for small molecules is urgently required for the top-down exposome databases. Although a holistic analysis of the exposome and endogenous metabolites is plausible, multiple and flexible strategies, instead of "putting one thing above all" are proposed.

Data-Driven Cluster Analysis of Oxidative Stress Indexes in relation to Vitamin D Level, Age, and Metabolic Control in Patients with Type 2 Diabetes on Metformin Therapy

Recent advances in vitamin D research indicate that patients with type 2 diabetes mellitus (T2DM) are suffering from vitamin D deficiency and increased oxidative stress to a variable extent, which could produce different health impacts for each individual. The novel multivariate statistical method applied in the present study allows metabolic phenotyping of T2DM individuals based on vitamin D status, metabolic control, and oxidative stress status in order to identify effectively different subtypes in our type 2 DM study population. Data-driven statistical cluster analysis was performed with 95 patients with T2DM, treated with metformin. Clusters were based on 12 variables-age, disease duration, vitamin D level, insulin, fasting glycemia (FG), glycated hemoglobin (HbA1c), high-density and low-density lipoprotein, total cholesterol (TC), triglycerides (TG), body mass index (BMI), and triglycerides/glucose index (TYG). The analysis revealed four unique clusters which differed significantly in terms of vitamin D status, with a mean 25 (OH) D level in cluster 1 (57.84 ± 11.46 nmol/L) and cluster 4 (53.78 ± 22.36 nmol/L), falling within the insufficiency range. Cluster 2 had the highest mean level of 25 (OH) D (84.55 ± 22.66 nmol/L), indicative of vitamin D sufficiency. Cluster 3 had a mean vitamin D level below 50 nmol/L (49.27 ± 16.95), which is considered deficient. Patients in the vitamin D sufficient cluster had a significantly better glycemic and metabolic control as well as a lower level of lipid peroxidation compared to other clusters. The patients from the vitamin D sufficient cluster also had a significantly higher level of vitamin D/MPO, vitamin D/XO, vitamin D/MDA, vitamin D/CAT, and vitamin D/TRC than that in the vitamin deficient and insufficient clusters. The vitamin D deficient cluster included significantly younger patients and had a significantly lower level of AOPP/TRC and albumin/TRC than the vitamin D sufficient cluster. The evidence from our cluster analysis in the context of separated T2DM demonstrates beneficial effects of optimal vitamin D status on metabolic control and oxidative stress in T2DM patients. Older T2DM patients require higher vitamin D levels in order to achieve good metabolic control and favorable antioxidant protection. Since protein damage is more pronounced in these patients, adding water-soluble antioxidant in addition to higher doses of vitamin D should be considered.

Suspect Screening, Prioritization, and Confirmation of Environmental Chemicals in Maternal-Newborn Pairs from San Francisco

Our proof-of-concept study develops a suspect screening workflow to identify and prioritize potentially ubiquitous chemical exposures in matched maternal/cord blood samples, a critical period of development for future health risks. We applied liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-QTOF/MS) to perform suspect screening for ∼3500 industrial chemicals on pilot data from 30 paired maternal and cord serum samples (n = 60). We matched 662 suspect features in positive ionization mode and 788 in negative ionization mode (557 unique formulas overall) to compounds in our database, and selected 208 of these for fragmentation analysis based on detection frequency, correlation in feature intensity between maternal and cord samples, and peak area differences by demographic characteristics. We tentatively identified 73 suspects through fragmentation spectra matching and confirmed 17 chemical features (15 unique compounds) using analytical standards. We tentatively identified 55 compounds not previously reported in the literature, the majority which have limited to no information about their sources or uses. Examples include (i) 1-(1-acetyl-2,2,6,6-tetramethylpiperidin-4-yl)-3-dodecylpyrrolidine-2,5-dione (known high production volume chemical) (ii) methyl perfluoroundecanoate and 2-perfluorooctyl ethanoic acid (two PFAS compounds); and (iii) Sumilizer GA 80 (plasticizer). Thus, our workflow demonstrates an approach to evaluating the chemical exposome to identify and prioritize chemical exposures during a critical period of development.

Approaches in metabolomics for regulatory toxicology applications

Innovative methodological approaches are needed to conduct human health and environmental risk assessments on a growing number of marketed chemicals. Metabolomics is progressively proving its value as an efficient strategy to perform toxicological evaluations of new and existing substances, and it will likely become a key tool to accelerate chemical risk assessments. However, additional guidance with widely accepted and harmonized procedures is needed before metabolomics can be routinely incorporated in decision-making for regulatory purposes. The aim of this review is to provide an overview of metabolomic strategies that have been successfully employed in toxicity assessment as well as the most promising workflows in a regulatory context. First, we provide a general view of the different steps of regulatory toxicology-oriented metabolomics. Emphasis is put on three key elements: robustness of experimental design, choice of analytical platform, and use of adapted data treatment tools. Then, examples in which metabolomics supported regulatory toxicology outputs in different scenarios are reviewed, including chemical grouping, elucidation of mechanisms of toxicity, and determination of points of departure. The overall intention is to provide insights into why and how to plan and conduct metabolomic studies for regulatory toxicology purposes.

The Exposome in the Era of One Health

Current studies on environmental chemistry mainly focus on a single stressor or single group of stressors, which does not reflect the multiple stressors in the dynamic exposome we are facing. Similarly, current studies on environmental toxicology mostly target humans, animals, or the environment separately, which are inadequate to solve the grand challenge of multiple receptors in One Health. Though chemical, biological, and physical stressors all pose health threats, the susceptibilities of different organisms are different. As such, significant relationships and interactions of the chemical, biological, and physical stressors in the environment and their holistic environmental and biological consequences remain unclear. Fortunately, the rapid developments in various techniques, as well as the concepts of multistressors in the exposome and multireceptor in One Health provide the possibilities to understand our environment better. Since the combined stressor is location-specific and mixture toxicity is species-specific, more comprehensive frameworks to guide risk assessment and environmental treatment are urgently needed. Here, three conceptual frameworks to categorize unknown stressors, spatially visualize the riskiest stressors, and investigate the combined effects of multiple stressors across multiple species within the concepts of the exposome and One Health are proposed for the first time.

Plasma metabolites associated with exposure to perfluoroalkyl substances and risk of type 2 diabetes - A nested case-control study

Perfluoroalkyl substances (PFAS) are widespread persistent environmental pollutants. There is evidence that PFAS induce metabolic perturbations in humans, but underlying mechanisms are still unknown. In this exploratory study, we investigated PFAS-related plasma metabolites for their associations with type 2 diabetes (T2D) to gain potential mechanistic insight in these perturbations. We used untargeted LC-MS metabolomics to find metabolites related to PFAS exposures in a case-control study on T2D (n = 187 matched pairs) nested within the Västerbotten Intervention Programme cohort. Following principal component analysis (PCA), six PFAS measured in plasma appeared in two groups: 1) perfluorononanoic acid, perfluorodecanoic acid and perfluoroundecanoic acid and 2) perfluorohexane sulfonic acid, perfluorooctane sulfonic acid and perfluorooctanoic acid. Using a random forest algorithm, we discovered metabolite features associated with individual PFAS and PFAS exposure groups which were subsequently investigated for associations with risk of T2D. PFAS levels correlated with 171 metabolite features (0.16 ≤ |r| ≤ 0.37, false discovery rate (FDR) adjusted p < 0.05). Out of these, 35 associated with T2D (p < 0.05), with 7 remaining after multiple testing adjustment (FDR < 0.05). PCA of the 35 PFAS- and T2D-related metabolite features revealed two patterns, dominated by glycerophospholipids and diacylglycerols, with opposite T2D associations. The glycerophospholipids correlated positively with PFAS and associated inversely with risk for T2D (Odds Ratio (OR) per 1 standard deviation (1-SD) increase in metabolite PCA pattern score = 0.2; 95% Confidence Interval (CI) = 0.1-0.4). The diacylglycerols also correlated positively with PFAS, but they associated with increased risk for T2D (OR per 1-SD = 1.9; 95% CI = 1.3-2.7). These results suggest that PFAS associate with two groups of lipid species with opposite relations to T2D risk.

Opportunities for Gene and Environment Research in Cancer: An Updated Review of NCI's Extramural Grant Portfolio

BACKGROUND

The study of gene-environment (GxE) interactions is a research priority for the NCI. Previously, our group analyzed NCI's extramural grant portfolio from fiscal years (FY) 2007 to 2009 to determine the state of the science in GxE research. This study builds upon our previous effort and examines changes in the landscape of GxE cancer research funded by NCI.

METHODS

The NCI grant portfolio was examined from FY 2010 to 2018 using the iSearch application. A time-trend analysis was conducted to explore changes over the study interval.

RESULTS

A total of 107 grants met the search criteria and were abstracted. The most common cancer types studied were breast (19.6%) and colorectal (18.7%). Most grants focused on GxE using specific candidate genes (69.2%) compared with agnostic approaches using genome-wide (26.2%) or whole-exome/whole-genome next-generation sequencing (NGS) approaches (19.6%); some grants used more than one approach to assess genetic variation. More funded grants incorporated NGS technologies in FY 2016-2018 compared with prior FYs. Environmental exposures most commonly examined were energy balance (46.7%) and drugs/treatment (40.2%). Over the time interval, we observed a decrease in energy balance applications with a concurrent increase in drug/treatment applications.

CONCLUSIONS

Research in GxE interactions has continued to concentrate on common cancers, while there have been some shifts in focus of genetic and environmental exposures. Opportunities exist to study less common cancers, apply new technologies, and increase racial/ethnic diversity.

IMPACT

This analysis of NCI's extramural grant portfolio updates previous efforts and provides a review of NCI grant support for GxE research.

Metabolic Signatures of the Exposome-Quantifying the Impact of Exposure to Environmental Chemicals on Human Health

Human health and well-being are intricately linked to environmental quality. Environmental exposures can have lifelong consequences. In particular, exposures during the vulnerable fetal or early development period can affect structure, physiology and metabolism, causing potential adverse, often permanent, health effects at any point in life. External exposures, such as the “chemical exposome” (exposures to environmental chemicals), affect the host’s metabolism and immune system, which, in turn, mediate the risk of various diseases. Linking such exposures to adverse outcomes, via intermediate phenotypes such as the metabolome, is one of the central themes of exposome research. Much progress has been made in this line of research, including addressing some key challenges such as analytical coverage of the exposome and metabolome, as well as the integration of heterogeneous, multi-omics data. There is strong evidence that chemical exposures have a marked impact on the metabolome, associating with specific disease risks. Herein, we review recent progress in the field of exposome research as related to human health as well as selected metabolic and autoimmune diseases, with specific emphasis on the impacts of chemical exposures on the host metabolome.

What is new in the exposome?

The exposome concept refers to the totality of exposures from a variety of external and internal sources including chemical agents, biological agents, or radiation, from conception onward, over a complete lifetime. It encompasses also "psychosocial components" including the impact of social relations and socio-economic position on health. In this review we provide examples of recent contributions from exposome research, where we believe their application will be of the greatest value for moving forward. So far, environmental epidemiology has mainly focused on hard outcomes, such as mortality, disease exacerbation and hospitalizations. However, there are many subtle outcomes that can be related to environmental exposures, and investigations can be facilitated by an improved understanding of internal biomarkers of exposure and response, through the application of omic technologies. Second, though we have a wealth of studies on environmental pollutants, the assessment of causality is often difficult because of confounding, reverse causation and other uncertainties. Biomarkers and omic technologies may allow better causal attribution, for example using instrumental variables in triangulation, as we discuss here. Even more complex is the understanding of how social relationships (in particular socio-economic differences) influence health and imprint on the fundamental biology of the individual. The identification of molecular changes that are intermediate between social determinants and disease status is a way to fill the gap. Another field in which biomarkers and omics are relevant is the study of mixtures. Epidemiology often deals with complex mixtures (e.g. ambient air pollution, food, smoking) without fully disentangling the compositional complexity of the mixture, or with rudimentary approaches to reflect the overall effect of multiple exposures or components. From the point of view of disease mechanisms, most models hypothesize that several stages need to be transitioned through health to the induction of disease, but very little is known about the characteristics and temporal sequence of such stages. Exposome models reinforce the idea of a biography-to-biology transition, in that everyone's disease is the product of the individual history of exposures, superimposed on their underlying genetic susceptibilities. Finally, exposome research is facilitated by technological developments that complement traditional epidemiological study designs. We describe in depth one such new tools, adductomics. In general, the development of high-resolution and high-throughput technologies interrogating multiple -omics (such as epigenomics, transcriptomics, proteomics, adductomics and metabolomics) yields an unprecedented perspective into the impact of the environment in its widest sense on disease. The world of the exposome is rapidly evolving, though a huge gap still needs to be filled between the original expectations and the concrete achievements. Perhaps the most urgent need is for the establishment of a new generation of cohort studies with appropriately specified biosample collection, improved questionnaire data (including social variables), and the deployment of novel technologies that allow better characterization of individual environmental exposures, ranging from personal monitoring to satellite based observations.

Reference Standardization for Quantification and Harmonization of Large-Scale Metabolomics

Reference standardization was developed to address quantification and harmonization challenges for high-resolution metabolomics (HRM) data collected across different studies or analytical methods. Reference standardization relies on the concurrent analysis of calibrated pooled reference samples at predefined intervals and enables a single-step batch correction and quantification for high-throughput metabolomics. Here, we provide quantitative measures of approximately 200 metabolites for each of three pooled reference materials (220 metabolites for Qstd3, 211 metabolites for CHEAR, 204 metabolites for NIST1950) and show application of this approach for quantification supports harmonization of metabolomics data collected from 3677 human samples in 17 separate studies analyzed by two complementary HRM methods over a 17-month period. The results establish reference standardization as a method suitable for harmonizing large-scale metabolomics data and extending capabilities to quantify large numbers of known and unidentified metabolites detected by high-resolution mass spectrometry methods.