The blood exposome and its role in discovering causes of disease

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

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

Multi-adductomics: Advancing mass spectrometry techniques for comprehensive exposome characterization

Adductomics, an emerging field within the 'omics sciences, focuses on the formation and prevalence of DNA, RNA, and protein adducts induced by endogenous and exogenous agents in biological systems. These modifications often result from exposure to environmental pollutants, dietary components, and xenobiotics, impacting cellular functions and potentially leading to diseases such as cancer. This review highlights advances in mass spectrometry (MS) that enhance the detection of these critical modifications and discusses current and emerging trends in adductomics, including developments in MS instrument use, screening techniques, and the study of various biomolecular modifications from mono-adducts to complex hybrid crosslinks between different types of biomolecules. The review also considers challenges, including the need for specialized MS spectra databases and multi-omics integration, while emphasizing techniques to distinguish between exogenous and endogenous modifications. The future of adductomics possesses significant potential for enhancing our understanding of health in relation to environmental exposures and precision medicine.

Chemical Exposomics in Human Plasma by Lipid Removal and Large-Volume Injection Gas Chromatography-High-Resolution Mass Spectrometry

For comprehensive chemical exposomics in blood, analytical workflows are evolving through advances in sample preparation and instrumental methods. We hypothesized that gas chromatography-high-resolution mass spectrometry (GC-HRMS) workflows could be enhanced by minimizing lipid coextractives, thereby enabling larger injection volumes and lower matrix interference for improved target sensitivity and nontarget molecular discovery. A simple protocol was developed for small plasma volumes (100-200 μL) by using isohexane (H) to extract supernatants of acetonitrile-plasma (A-P). The HA-P method was quantitative for a wide range of hydrophobic multiclass target analytes (i.e., log Kow > 3.0), and the extracts were free of major lipids, thereby enabling robust large-volume injections (LVIs; 25 μL) in long sequences (60-70 h, 70-80 injections) to a GC-Orbitrap HRMS. Without lipid removal, LVI was counterproductive because method sensitivity suffered from the abundant matrix signal, resulting in low ion injection times to the Orbitrap. The median method quantification limit was 0.09 ng/mL (range 0.005-4.83 ng/mL), and good accuracy was shown for a certified reference serum. Applying the method to plasma from a Swedish cohort (n = 32; 100 μL), 51 of 103 target analytes were detected. Simultaneous nontarget analysis resulted in 112 structural annotations (12.8% annotation rate), and Level 1 identification was achieved for 7 of 8 substances in follow-up confirmations. The HA-P method is potentially scalable for application in cohort studies and is also compatible with many liquid-chromatography-based exposomics workflows.

Longitudinal Exposomics in a Multiomic Wellness Cohort Reveals Distinctive and Dynamic Environmental Chemical Mixtures in Blood

Chemical exposomes can now be comprehensively measured in human blood, but knowledge of their variability and longitudinal stability is required for robust application in cohort studies. Here, we applied high-resolution chemical exposomics to plasma of 46 adults, each sampled 6 times over 2 years in a multiomic cohort, resulting in 276 individual exposomes. In addition to quantitative analysis of 83 priority target analytes, we discovered and semiquantified substances that have rarely or never been reported in humans, including personal care products, pesticide transformation products, and polymer additives. Hierarchical cluster analysis for 519 confidently annotated substances revealed unique and distinctive coexposures, including clustered pesticides, poly(ethylene glycols), chlorinated phenols, or natural substances from tea and coffee; interactive heatmaps were publicly deposited to support open exploration of the complex (meta)data. Intraclass correlation coefficients (ICC) for all annotated substances demonstrated the relatively low stability of the exposome compared to that of proteome, microbiome, and endogenous small molecules. Implications are that the chemical exposome must be measured more frequently than other omics in longitudinal studies and four longitudinal exposure types are defined that can be considered in study design. In this small cohort, mixed-effect models nevertheless revealed significant associations between testosterone and perfluoroalkyl substances, demonstrating great potential for longitudinal exposomics in precision health research.

Personalized mixture toxicity testing: A proof-of-principle in vitro study evaluating the steroidogenic effects of reconstructed contaminant mixtures measured in blood of individual adults

Chemical risk assessments typically focus on single substances, often overlooking real-world co-exposures to chemical mixtures. Mixture toxicology studies using representative mixtures can reveal potential chemical interactions, but these do not account for the unique chemical profiles that occur in the blood of diverse individuals. Here we used the H295R steroidogenesis assay to screen personalized mixtures of 24 persistent organic pollutants (POPs) for cytotoxicity and endocrine disruption. Each mixture was reconstructed at a human exposure relevant concentration (1×), as well as at 10- and 100-fold higher concentration (10×, 100×) by acoustic liquid handling based on measured blood concentrations in a Swedish cohort. Among the twelve mixtures tested, nine mixtures decreased the cell viability by 4-18%, primarily at the highest concentration. While the median and maximum mixtures based on the whole study population induced no measurable effects on steroidogenesis at any concentration, the personalized mixture from an individual with the lowest total POPs concentration was the only mixture that affected estradiol synthesis (35% increase at the 100× concentration). Mixtures reconstructed from blood levels of three different individuals stimulated testosterone synthesis at the 1× (11-15%) and 10× concentrations (12-16%), but not at the 100× concentration. This proof-of-principle personalized toxicity study illustrates that population-based representative chemical mixtures may not adequately account for the toxicological risks posed to individuals. It highlights the importance of testing a range of real-world mixtures at relevant concentrations to explore potential interactions and non-monotonic effects. Further toxicological studies of personalized contaminant mixtures could improve chemical risk assessment and advance the understanding of human health, as chemical exposome data become increasingly available.

Screening for compounds with bioaccumulation potential in breast milk using their retention behavior in two-dimensional gas chromatography

Discovery of emerging pollutants in breast milk will be helpful for understanding the hazards to human health. However, it is difficult to identify key compounds among thousands present in complex samples. In this study, a method for screening compounds with bioaccumulation potential was developed. The method can decrease the number of compounds needing structural identification because the partitioning properties of bioaccumulative compounds can be mapped onto GC×GC chromatograms through their retention behaviors. Twenty pooled samples from seven provinces in China were analyzed. 1,286 compounds with bioaccumulation potential were selected from over 3,000 compounds. Sixty-two compounds, including aromatic compounds, phthalates, and phenolics etc., were identified with a high level of confidence and then quantified. Among them, twenty-seven compounds were found for the first time in breast milk. Three phthalate plasticizers and two phenolic antioxidants were found in significantly higher concentrations than other compounds. A toxicological priority index approach was applied to prioritize the compounds considering their concentrations, detection frequencies and eight toxic effects. The prioritization indicated that 13 compounds, including bis(2-ethylhexyl) phthalate, dibutyl phthalate, 1,3-di-tert-butylbenzene, phenanthrene, 2,6-di-tert-butyl-1,4-benzoquinone, 2,4-di-tert-butylphenol, and others, showed higher health risks. Meanwhile, some compounds with high risk for a particular toxic effect, such as benzothiazole and geranylacetone, were still noteworthy. This study is important for assessing the risks of human exposure to organic compounds.

Denoising Search doubles the number of metabolite and exposome annotations in human plasma using an Orbitrap Astral mass spectrometer

Chemical exposures may impact human metabolism and contribute to the etiology of neurodegenerative disorders like Alzheimer's Disease (AD). Identifying these small metabolites involves matching experimental spectra to reference spectra in databases. However, environmental chemicals or physiologically active metabolites are usually present at low concentrations in human specimens. The presence of noise ions can significantly degrade spectral quality, leading to false negatives and reduced identification rates. In response to this challenge, the Spectral Denoising algorithm removes both chemical and electronic noise. Spectral Denoising outperformed alternative methods in benchmarking studies on 240 tested metabolites. It improved high confident compound identifications at an average 35-fold lower concentrations than previously achievable. Spectral Denoising proved highly robust against varying levels of both chemical and electronic noise even with >150-fold higher intensity of noise ions than true fragment ions. For human plasma samples of AD patients that were analyzed on the Orbitrap Astral mass spectrometer, Denoising Search detected 2.3-fold more annotated compounds compared to the Exploris 240 Orbitrap instrument, including drug metabolites, household and industrial chemicals, and pesticides. This combination of advanced instrumentation with a superior denoising algorithm opens the door for precision medicine in exposome research.

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

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

Optimal transport for automatic alignment of untargeted metabolomic data

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

Metabolic signature of follicular fluid in infertility-related diseases: a narrative review

Metabolomics offers new methods to identify biomarkers for oocyte and embryo quality, and for a better understanding of the physiopathology of infertility. This review investigated the latest findings regarding metabolome-derived biomarkers in follicular fluid of women with the most common types of infertility, and the potential impact on reproductive medicine outcomes. PubMed was searched for publications on metabolomics and human follicular fluid, and key biomarkers, kinetics and relationships with infertility diseases were identified. A reduced concentration of glucose and increased concentrations of lactate and pyruvate were found in follicular fluid of patients with endometriosis and diminished ovarian reserve, and the opposite was found in patients with polycystic ovary syndrome. These signatures may lead to the hypothesis of changed metabolite concentrations in patients with endometriosis and diminished ovarian reserve, and a metabolic pathway alteration with decreased aerobic glycolysis in patients with polycystic ovary syndrome. However, the pattern found in patients with endometriosis and low responders may also be expected in follicular fluid of fertile women. Larger studies are needed to confirm the results. An international database may help to highlight follicular fluid biomarkers in order to improve the selection of cryopreserved oocytes, and to enrich culture medium to restore normal metabolism and improve reproductive treatment outcomes.

Challenges in the Metabolomics-Based Biomarker Validation Pipeline

As end-products of the intersection between the genome and environmental influences, metabolites represent a promising approach to the discovery of novel biomarkers for diseases. However, many potential biomarker candidates identified by metabolomics studies fail to progress beyond analytical validation for routine implementation in clinics. Awareness of the challenges present can facilitate the development and advancement of innovative strategies that allow improved and more efficient applications of metabolite-based markers in clinical settings. This minireview provides a comprehensive summary of the pre-analytical factors, required analytical validation studies, and kit development challenges that must be resolved before the successful translation of novel metabolite biomarkers originating from research. We discuss the necessity for strict protocols for sample collection, storage, and the regulatory requirements to be fulfilled for a bioanalytical method to be considered as analytically validated. We focus especially on the blood as a biological matrix and liquid chromatography coupled with tandem mass spectrometry as the analytical platform for biomarker validation. Furthermore, we examine the challenges of developing a commercially viable metabolomics kit for distribution. To bridge the gap between the research lab and clinical implementation and utility of relevant metabolites, the understanding of the translational challenges for a biomarker panel is crucial for more efficient development of metabolomics-based precision medicine.

An exposome atlas of serum reveals the risk of chronic diseases in the Chinese population

Although adverse environmental exposures are considered a major cause of chronic diseases, current studies provide limited information on real-world chemical exposures and related risks. For this study, we collected serum samples from 5696 healthy people and patients, including those with 12 chronic diseases, in China and completed serum biomonitoring including 267 chemicals via gas and liquid chromatography-tandem mass spectrometry. Seventy-four highly frequently detected exposures were used for exposure characterization and risk analysis. The results show that region is the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances are associated with multiple chronic diseases, and some of them exceed safe ranges. Multi-exposure models reveal significant risk effects of exposure on hyperlipidemia, metabolic syndrome and hyperuricemia. Overall, this study provides a comprehensive human serum exposome atlas and disease risk information, which can guide subsequent in-depth cause-and-effect studies between environmental exposures and human health.

Assessing human exposure to pesticides and mycotoxins: optimization and validation of a method for multianalyte determination in urine samples

Humans are exposed to an increasing number of contaminants, with diet being one of the most important exposure routes. In this framework, human biomonitoring is considered the gold standard for evaluating human exposure to chemicals. Pesticides and mycotoxins are chemicals of special concern due to their health implications. They constitute the predominant border rejection notifications for food and feed in Europe and the USA. However, current biomonitoring studies are focused on a limited number of compounds and do not evaluate mycotoxins and pesticides together. In this study, an analytical method has been developed for the determination of 30 pesticides and 23 mycotoxins of concern in urine samples. A salting-out liquid-liquid extraction (SALLE) procedure was optimized achieving recoveries between 70 and 120% for almost all the compounds and limits as lower as when QuEChERS was applied. The compounds were then determined by liquid chromatography coupled to triple quadrupole mass spectrometry. Different chromatographic conditions and analytical columns were tested, selecting a Hypersild gold aQ column as the best option. Finally, the method was applied to the analysis of 45 urine samples, in which organophosphate and pyrethroid pesticides (detection rates (DR) of 82% and 42%, respectively) and ochratoxin A and deoxynivalenol (DR of 51% and 33%, respectively) were the most detected compounds. The proposed analytical method involves the simultaneous determination of a diverse set of pesticides and mycotoxins, including their most relevant metabolites, in human urine. It serves as an essential tool for biomonitoring the presence of highly prevalent contaminants in modern society.

[Application of multiomics mass spectrometry in the research of chemical exposome]

Environmental factors, such as environmental pollutants, behaviors, and lifestyles, are the leading causes of chronic noncommunicable diseases. Estimates indicate that approximately 50% of all deaths worldwide can be attributed to environmental factors. The exposome is defined as the totality of human environmental (i.e., all nongenetic) exposures from conception, including general external exposure (e.g., climate, education, and urban environment), specific external exposure (e.g., pollution, physical activity, and diet), and internal exposure (e.g., metabolic factors, oxidative stress, inflammation, and protein modification). As a new paradigm, this concept aims to comprehensively understand the link between human health and environmental factors. Therefore, a comprehensive measurement of the exposome, including accurate and reliable measurements of exposure to the external environment and a wide range of biological responses to the internal environment, is of great significance. The measurement of the general external exposome depends on advances in environmental sensors, personal-sensing technologies, and geographical information systems. The determination of exogenous chemicals to which individuals are exposed and endogenous chemicals that are produced or modified by external stressors relies on improvements in methodology and the development of instrumental approaches, including colorimetric, chromatographic, spectral, and mass-spectrometric methods. This article reviews the research strategies for chemical exposomes and summarizes existing exposome-measurement methods, focusing on mass spectrometry (MS)-based methods. The top-down and bottom-up approaches are commonly used in exposome studies. The bottom-up approach focuses on the identification of chemicals in the external environment (e.g., soil, water, diet, and air), whereas the top-down approach focuses on the evaluation of endogenous chemicals and biological processes in biological samples (e.g., blood, urine, and serum). Low- and high-resolution MS (LRMS and HRMS, respectively) have become the most popular methods for the direct measurement of exogenous and endogenous chemicals owing to their superior sensitivity, specificity, and dynamic range. LRMS has been widely applied in the targeted analysis of expected chemicals, whereas HRMS is a promising technique for the suspect and unknown screening of unexpected chemicals. The development of MS-based multiomics, including proteomics, metabolomics, epigenomics, and spatial omics, provides new opportunities to understand the effects of environmental exposure on human health. Metabolomics involves the sum of all low-molecular-weight metabolites in a living system. Nontargeted metabolomics can measure both endogenous and exogenous chemicals, which would directly link exposure to biological effects, internal dose, and disease pathobiology, whereas proteomics could play an important role in predicting potential adverse health outcomes and uncovering molecular mechanisms. MS imaging (MSI) is an emerging technique that provides unlabeled in-depth measurements of endogenous and exogenous molecules directly from tissue and cell sections without changing their spatial information. MSI-based spatial omics, which has been widely applied in biomarker discovery for clinical diagnosis, as well as drug and pollutant monitoring, is expected to become an effective method for exposome measurement. Integrating these response measurements from metabolomics, proteomics, spatial omics, and epigenomics will enable the generation of new hypotheses to discover the etiology of diseases caused by chemical exposure. Finally, we highlight the major challenges in achieving chemical exposome measurements.

[New advances in exposomics-analysis methods and research paradigms based on chromatography-mass spectrometry]

The occurrence and development of human diseases are influenced by both genetic and environmental factors. Research models that describe disease occurrence only from the perspective of genetics present certain limitations. In recent years, effects of environment factors on the occurrence and development of diseases have attracted extensive attentions. Exposomics focuses on the measurement of all exposure factors in an individual's life and how these factors are related to disease development. Exposomics provides new ideas to promote studies on the relationship between human health and environmental factors. Environmental exposures are characterized with different physical and chemical properties, as well as very low concentrations in vivo, which contribute great challenges in the comprehensive measurement of chemical residues in the human body. Chromatography-mass spectrometry-based technologies combine the high-efficiency separation ability of chromatography with the high resolution and sensitive detection characteristics of mass spectrometry; the combination of these techniques can achieve the high-coverage, high-throughput, and sensitive detection of environmental exposures, thus providing a powerful tool for measuring chemical exposures. Exposomics-analysis methods based on chromatography-mass spectrometry mainly include targeted quantitative analysis, suspect screening, and non-targeted screening. To explore the relationship between environmental exposure and the occurrence and development of diseases, researchers have developed research paradigms, including exposome wide association study, mixed-exposure study, exposomics and multi-omics (genome, transcriptome, proteome, metabolome)-association study, and so on. The emergence of these methods has brought about unprecedented developments in exposomics studies. In this manuscript, analytical methods based on chromatography-mass spectrometry, exposomics research paradigms, and their relevant prospects are reviewed.

Health position paper and redox perspectives - Disease burden by transportation noise

Transportation noise is a ubiquitous urban exposure. In 2018, the World Health Organization concluded that chronic exposure to road traffic noise is a risk factor for ischemic heart disease. In contrast, they concluded that the quality of evidence for a link to other diseases was very low to moderate. Since then, several studies on the impact of noise on various diseases have been published. Also, studies investigating the mechanistic pathways underlying noise-induced health effects are emerging. We review the current evidence regarding effects of noise on health and the related disease-mechanisms. Several high-quality cohort studies consistently found road traffic noise to be associated with a higher risk of ischemic heart disease, heart failure, diabetes, and all-cause mortality. Furthermore, recent studies have indicated that road traffic and railway noise may increase the risk of diseases not commonly investigated in an environmental noise context, including breast cancer, dementia, and tinnitus. The harmful effects of noise are related to activation of a physiological stress response and nighttime sleep disturbance. Oxidative stress and inflammation downstream of stress hormone signaling and dysregulated circadian rhythms are identified as major disease-relevant pathomechanistic drivers. We discuss the role of reactive oxygen species and present results from antioxidant interventions. Lastly, we provide an overview of oxidative stress markers and adverse redox processes reported for noise-exposed animals and humans. This position paper summarizes all available epidemiological, clinical, and preclinical evidence of transportation noise as an important environmental risk factor for public health and discusses its implications on the population level.

Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS

Humans interact with thousands of chemicals. This study aims to identify substances of emerging concern and in need of human health risk evaluations. Sixteen pooled human serum samples were constructed from 25 individual samples each from the National Institute of Environmental Health Sciences' Clinical Research Unit. Samples were analyzed using gas chromatography (GC) × GC/time-of-flight (TOF)-mass spectrometry (MS) in a suspect screening analysis, with follow-up confirmation analysis of 19 substances. A standard reference material blood sample was also analyzed through the confirmation process for comparison. The pools were stratified by sex (female and male) and by age (≤45 and >45). Publicly available information on potential exposure sources was aggregated to annotate presence in serum as either endogenous, food/nutrient, drug, commerce, or contaminant. Of the 544 unique substances tentatively identified by spectral matching, 472 were identified in females, while only 271 were identified in males. Surprisingly, 273 of the identified substances were found only in females. It is known that behavior and near-field environments can drive exposures, and this work demonstrates the existence of exposure sources uniquely relevant to females.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Supporting and implementing the beneficial parts of the exposome: The environment can be the problem, but it can also be the solution

In 2005, Christopher P. Wild introduced the exposome concept, encompassing the biochemical changes in the organism in response to the totality of all environmental exposures throughout the entire lifespan and their association with health outcomes. The exposome concept also aimed at to completing the genome, that describes the genetic predisposition as a determinant of disease and death as well as potential targets of intervention. The exposome can be subdivided into multiple pollutomes related to specific chemical and physical pollutants (or other forms of environmental risks), periods of life (infancy, childhood, adolescence, adulthood, and old age) or geographical locations. While exposome research and, in general, health research of the last decades has predominantly focused on what factors contribute to and initiate morbidity and mortality, little is done on factors that will help to develop, maintain, or even increase human health. We want to contribute to this reorientation by supporting and implementing the beneficial exposome, comprising all environmental exposures with the potential to promote health.

Evaluating the causal relationship between human blood metabolites and gastroesophageal reflux disease

BACKGROUND

Gastroesophageal reflux disease (GERD) affects approximately 13% of the global population. However, the pathogenesis of GERD has not been fully elucidated. The development of metabolomics as a branch of systems biology in recent years has opened up new avenues for the investigation of disease processes. As a powerful statistical tool, Mendelian randomization (MR) is widely used to explore the causal relationship between exposure and outcome.

AIM

To analyze of the relationship between 486 blood metabolites and GERD.

METHODS

Two-sample MR analysis was used to assess the causal relationship between blood metabolites and GERD. A genome-wide association study (GWAS) of 486 metabolites was the exposure, and two different GWAS datasets of GERD were used as endpoints for the base analysis and replication and meta-analysis. Bonferroni correction is used to determine causal correlation features (P < 1.03 × 10-4). The results were subjected to sensitivity analysis to assess heterogeneity and pleiotropy. Using the MR Steiger filtration method to detect whether there is a reverse causal relationship between metabolites and GERD. In addition, metabolic pathway analysis was conducted using the online database based MetaboAnalyst 5.0 software.

RESULTS

In MR analysis, four blood metabolites are negatively correlated with GERD: Levulinate (4-oxovalerate), stearate (18:0), adrenate (22:4n6) and p-acetamidophenylglucuronide. However, we also found a positive correlation between four blood metabolites and GERD: Kynurenine, 1-linoleoylglycerophosphoethanolamine, butyrylcarnitine and guanosine. And bonferroni correction showed that butyrylcarnitine (odd ratio 1.10, 95% confidence interval: 1.05-1.16, P = 7.71 × 10-5) was the most reliable causal metabolite. In addition, one significant pathways, the "glycerophospholipid metabolism" pathway, can be involved in the pathogenesis of GERD.

CONCLUSION

Our study found through the integration of genomics and metabolomics that butyrylcarnitine may be a potential biomarker for GERD, which will help further elucidate the pathogenesis of GERD and better guide its treatment. At the same time, this also contributes to early screening and prevention of GERD. However, the results of this study require further confirmation from both basic and clinical real-world studies.

High temperature exacerbates ozone-induced airway inflammation: Implication of airway microbiota and metabolites

Short-term exposure to ozone (O3) has been associated with airway inflammation. Given that high temperature (HT) accelerates O3 production, it is of significance to determine whether co-exposure to HT exacerbates O3-induced airway inflammation. The aim of this study was to examine the possible promotive effect of HT on O3-induced airway inflammation and underlying mechanisms. Forty-eight C57BL/6 N male mice were randomly divided into four groups: filtered air (control), O3, HT, and HT + O3 (co-exposure) groups. Mice in control and O3 groups were exposed to filtered air or 1 ppm O3 at 24 °C, respectively, while mice in HT and co-exposure groups were exposed to filtered air or 1 ppm O3 at 36 °C, respectively. The exposure scenario for four groups was 4 h/d for 5 consecutive days. Bronchoalveolar lavage fluids (BALF) were collected 24 h after the last exposure and subjected to examinations of oxidative stress and inflammation biomarkers, 16S rRNA sequencing, and metabolic profiling. Lung tissues were processed for H&E histological staining. The results showed that O3 inhalation triggered oxidative stress and inflammation in the airways, which was worsen by co-exposure to HT. Further studies revealed that co-exposure to HT strengthened O3-induced decline in Firmicutes and Allobaculum in airways. Moreover, co-exposure to HT promoted O3-induced airway metabolic disorder. Spearman correlation analysis revealed correlations among microbiota dysbiosis, metabolic disorder, oxidative stress and inflammation induced by co-exposure to HT and O3. Taken together, HT exposure aggravates O3-induced airway oxidative stress and inflammation, possibly through modulation of microbiota and metabolism of the airways.

Invisible Hand behind Female Reproductive Disorders: Bisphenols, Recent Evidence and Future Perspectives

Reproductive disorders are considered a global health problem influenced by physiological, genetic, environmental, and lifestyle factors. The increased exposure to bisphenols, a chemical used in large quantities for the production of polycarbonate plastics, has raised concerns regarding health risks in humans, particularly their endocrine-disrupting effects on female reproductive health. To provide a basis for future research on environmental interference and reproductive health, we reviewed relevant studies on the exposure patterns and levels of bisphenols in environmental matrices and humans (including susceptible populations such as pregnant women and children). In addition, we focused on in vivo, in vitro, and epidemiological studies evaluating the effects of bisphenols on the female reproductive system (the uterus, ovaries, fallopian tubes, and vagina). The results indicate that bisphenols cause structural and functional damage to the female reproductive system by interfering with hormones; activating receptors; inducing oxidative stress, DNA damage, and carcinogenesis; and triggering epigenetic changes, with the damaging effects being intergenerational. Epidemiological studies support the association between bisphenols and diseases such as cancer of the female reproductive system, reproductive dysfunction, and miscarriage, which may negatively affect the establishment and maintenance of pregnancy. Altogether, this review provides a reference for assessing the adverse effects of bisphenols on female reproductive health.

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

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

Exploring the interplay between metabolomics and genetics in Parkinson's disease: Insights from ongoing research and future avenues

Parkinson's disease (PD) is a widespread neurodegenerative disorder, whose complex aetiology remains under construction. While rare variants have been associated with the monogenic PD form, most PD cases are influenced by multiple genetic and environmental aspects. Nonetheless, the pathophysiological pathways and molecular networks involved in monogenic/idiopathic PD overlap, and genetic variants are decisive in elucidating the convergent underlying mechanisms of PD. In this scenario, metabolomics has furnished a dynamic and systematic picture of the synergy between the genetic background and environmental influences that impact PD, making it a valuable tool for investigating PD-related metabolic dysfunctions. In this review, we performed a brief overview of metabolomics current research in PD, focusing on significant metabolic alterations observed in idiopathic PD from different biofluids and strata and exploring how they relate to genetic factors associated with monogenic PD. Dysregulated amino acid metabolism, lipid metabolism, and oxidative stress are the critical metabolic pathways implicated in both genetic and idiopathic PD. By merging metabolomics and genetics data, it is possible to distinguish metabolic signatures of specific genetic backgrounds and to pinpoint subgroups of PD patients who could derive personalized therapeutic benefits. This approach holds great promise for advancing PD research and developing innovative, cost-effective treatments.

EISA-EXPOSOME: One Highly Sensitive and Autonomous Exposomic Platform with Enhanced in-Source Fragmentation/Annotation

Lacking a highly sensitive exposome screening technique is one of the biggest challenges in moving exposomic research forward. Enhanced in-source fragmentation/annotation (EISA) has been developed to facilitate molecular identification in untargeted metabolomics and proteomics. In this work, with a mixture of 50 pesticides at three concentration levels (20, 4, and 0.8 ppb), we investigated the analytical performance of the EISA technique over the well-accepted targeted MS/MS mode (TMM) in the detection and identification of chemicals at low levels using a quadrupole time-of-flight (qTOF) instrument. Compared with the TMM method, the EISA technique can recognize additional 1, 20, and 23 chemicals, respectively, at the three concentration levels (20, 4, and 0.8 ppb, respectively) investigated. At the 0.8 ppb level, intensities of precursor ions and fragments observed using the EISA technique are 30-1,154 and 3-80 times higher, respectively, than those observed at the TMM mode. A higher matched fragment ratio (MFR) between the EISA technique and the TMM method was recognized for most chemicals. We further developed a chemical annotation informatics algorithm, EISA-EXPOSOME, which can automatically search each precursor ion (m/z) in the MS/MS library against the EISA MS1 spectra. This algorithm then calculated a weighted score to rank the candidate features by comparing the experimental fragment spectra to those in the library. The peak intensity, zigzag index, and retention time prediction model as well as the peak correlation coefficient were further adopted in the algorithm to filter false positives. The performance of EISA-EXPOSOME was demonstrated using a pooled dust extract with a pesticide mixture (n = 200) spiked at 5 ppb. One urine sample spiked with a contaminant mixture (n = 50) at the 5 ppb level was also used for the validation of the pipeline. Proof-of-principal application of EISA-EXPOSOME in the real sample was further evaluated on the pooled dust sample with a modified T3DB database (n = 1650). Our results show that the EISA-EXPOSOME algorithm can remarkably improve the detection and annotation coverage at trace levels beyond the traditional approach as well as facilitate the high throughput screening of suspected chemicals.

Longitudinal associations between metabolites and immediate, short- and medium-term exposure to ambient air pollution: Results from the KORA cohort study

BACKGROUND

Short-term exposure to air pollution has been reported to be associated with cardiopulmonary diseases, but the underlying mechanisms remain unclear. This study aimed to investigate changes in serum metabolites associated with immediate, short- and medium-term exposures to ambient air pollution.

METHODS

We used data from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999-2001) and two follow-up examinations (F4: 2006-08 and FF4: 2013-14). Mass-spectrometry-based targeted metabolomics was used to quantify metabolites among serum samples. Only participants with repeated metabolites measurements were included in this analysis. We collected daily averages of fine particles (PM2.5), coarse particles (PMcoarse), nitrogen dioxide (NO2), and ozone (O3) at urban background monitors located in Augsburg, Germany. Covariate-adjusted generalized additive mixed-effects models were used to examine the associations between immediate (2-day average of same day and previous day as individual's blood withdrawal), short- (2-week moving average), and medium-term exposures (8-week moving average) to air pollution and metabolites. We further performed pathway analysis for the metabolites significantly associated with air pollutants in each exposure window.

RESULTS

Of 9,620 observations from 4,261 study participants, we included 5,772 (60.0%) observations from 2,583 (60.6%) participants in this analysis. Out of 108 metabolites that passed quality control, multiple significant associations between metabolites and air pollutants with several exposure windows were identified at a Bonferroni corrected p-value threshold (p < 3.9 × 10-5). We found the highest number of associations for NO2, particularly at the medium-term exposure windows. Among the identified metabolic pathways based on the metabolites significantly associated with air pollutants, the glycerophospholipid metabolism was the most robust pathway in different air pollutants exposures.

CONCLUSIONS

Our study suggested that short- and medium-term exposure to air pollution might induce alterations of serum metabolites, particularly in metabolites involved in metabolic pathways related to inflammatory response and oxidative stress.

A missing jigsaw within the hygiene hypothesis: Low-dose bisphenol A exposure attenuates lipopolysaccharide-induced asthma protection

The rising occurrence of allergic asthma in early life across industrialized countries suggests that environmental factors play a crucial role in determining asthma susceptibility and severity. While prior exposure to microbial lipopolysaccharides (LPSs) has been found to offer protection against allergic asthma, infants residing in urban environments are increasingly exposed to environmental pollutants. Utilizing limulus lysate test screens and virtual screening models, we identified pollutants that can modulate LPS bioactivity. This investigation revealed that bisphenol A (BPA), a chemical commonly used in numerous household items and previously implicated in obesity and cancer, effectively neutralizes LPS. In-depth mechanistic analyses showed that BPA specifically binds to the lipid A component of LPS, leading to inactivation. This interaction eliminates the immunostimulatory activity of LPS, making mice more susceptible to house dust mite (HDM)-induced allergic asthma. BPA reactivates lung epithelial cells, consequently amplifying type 2 responses to HDMs in dendritic cells. This chemical interplay provides new insights into the pathophysiology of asthma in relation to human exposure. Understanding the intricate relationships between environmental chemicals and microbial antigens, as well as their impacts on innate immunity, is critical for the development of intervention strategies to address immune disorders resulting from urbanization.

Comparing the sensitivity of a low- and a high-resolution mass spectrometry approach for xenobiotic trace analysis: An exposome-type case study

The chemical exposome consists of environmental exposures experienced throughout a lifetime but to date analytical approaches to investigate the plethora of low-abundance chemicals remain very limited. Liquid chromatography high-resolution mass spectrometry (HRMS) is commonly applied in untargeted exposome-wide analyses of xenobiotics in biological samples; however, human biomonitoring approaches usually utilize targeted low-resolution triple quadrupole (QQQ) mass spectrometry tailored to a small number of chemicals. HRMS can cover a broader chemical space but the detection of molecules from low-level exposure amidst a background of highly-abundant endogenous molecules has proven to be difficult. In this study, a triple quadrupole (QQQ) and a high-resolution mass spectrometer (HRMS) with identical chromatography were utilized to determine the limits of quantitation (LOQ) of >100 xenobiotics and estrogenic hormones in pure solvent and human urine. Both instrumental platforms are currently applied in exposure assessment studies and were operated in their most frequently used acquisition mode (full scan for HRMS and multiple reaction monitoring for QQQ) to mimic typical applications. For HRMS analyses, the median LOQ was 0.9 and 1.2 ng/mL in solvent and urine, respectively, while for low-resolution QQQ measurements, the median LOQ was 0.1 and 0.2 ng/mL in solvent and urine, respectively. To evaluate the calculated LOQs in complex biological samples, spot urine samples from 24 Nigerian female volunteers were investigated. The higher LOQ values for HRMS resulted in less quantified low-abundance analytes and decreased the number of compounds detected below the LOQ. Even at chronic low-dose exposure, such compounds might be relevant for human health because of high individual toxicity or potential mixture effects. Nevertheless, HRMS enabled the additional screening for exposure to unexpected/unknown analytes, including emerging compounds and biotransformation products. Therefore, a synergy between high- and low-resolution mass spectrometry may currently be the best option to elucidate and quantify xenobiotics in comprehensive exposome-wide association studies (ExWAS).

The contribution of the exposome to the burden of cardiovascular disease

Large epidemiological and health impact assessment studies at the global scale, such as the Global Burden of Disease project, indicate that chronic non-communicable diseases, such as atherosclerosis and diabetes mellitus, caused almost two-thirds of the annual global deaths in 2020. By 2030, 77% of all deaths are expected to be caused by non-communicable diseases. Although this increase is mainly due to the ageing of the general population in Western societies, other reasons include the increasing effects of soil, water, air and noise pollution on health, together with the effects of other environmental risk factors such as climate change, unhealthy city designs (including lack of green spaces), unhealthy lifestyle habits and psychosocial stress. The exposome concept was established in 2005 as a new strategy to study the effect of the environment on health. The exposome describes the harmful biochemical and metabolic changes that occur in our body owing to the totality of different environmental exposures throughout the life course, which ultimately lead to adverse health effects and premature deaths. In this Review, we describe the exposome concept with a focus on environmental physical and chemical exposures and their effects on the burden of cardiovascular disease. We discuss selected exposome studies and highlight the relevance of the exposome concept for future health research as well as preventive medicine. We also discuss the challenges and limitations of exposome studies.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

CIL-ExPMRM: An Ultrasensitive Chemical Isotope Labeling Assisted Pseudo-MRM Platform to Accelerate Exposomic Suspect Screening

Exposome is the future of next-generation environmental health to establish the association between environmental exposure and diseases. However, due to low concentrations of exposure chemicals, exposome has been hampered by lacking an effective analytical platform to characterize its composition. In this study, by combining the benefit of chemical isotope labeling and pseudo-multiple reaction monitoring (CIL-pseudo-MRM), we have developed one highly sensitive and high-throughput platform (CIL-ExPMRM) by isotope labeling urinary exposure biomarkers. Dansyl chloride (DnsCl), N-methylphenylethylamine (MPEA), and their isotope-labeled forms were used to derivatize polar hydroxyl and carboxyl compounds, respectively. We have programmed a series of scripts to optimize MRM transition parameters, curate the MRM database (>70,000 compounds), predict accurate retention time (RT), and automize dynamic MRMs. This was followed by an automated MRM peak assignment, peak alignment, and statistical analysis. A computational pipeline was eventually incorporated into a user-friendly website interface, named CIL-ExPMRM (http://www.exposomemrm.com/). The performance of this platform has been validated with a relatively low false positive rate (10.7%) across instrumental platforms. CIL-ExPMRM has systematically overcome key bottlenecks of exposome studies to some extent and outperforms previous methods due to its independence of MS/MS availability, accurate RT prediction, and collision energy optimization, as well as the ultrasensitivity and automated robust intensity-based quantification. Overall, CIL-ExPMRM has great potential to advance the exposomic studies based on urinary biomarkers.

Environment-wide association study of elevated liver enzymes: results from the Korean National Environmental Health Survey 2018-2022

Background

Environmental exposure is characterized by low concentration, chronic, and complex exposure. Traditional epidemiological studies show limitations in reflecting these characteristics since they usually focus on a single or very limited number of exposure factors at a time. In this study, we adopted the methodology of environment-wide association study (EWAS) to figure out the association of human liver function with various environmentally hazardous substances.

Methods

We analyzed 2,961 participants from the Korean National Environmental Health Survey Cycle 4 (2018-2020). Using generalized linear model (GLM) analysis, we analyzed the association of 72 variables with 3 liver function indices (aspartate aminotransferase [AST], alanine aminotransferase [ALT], and gamma glutamyl transferase [GGT]). Finally, we visualized our results with Manhattan plot.

Results

In GLM analysis, perfluorooctanesulfonate were positively associated with ALT (odds ratio [OR]: 2.2; 95% confidence interval [CI]: 1.39-3.46; p adjusted = 0.0147) and perfluorodecanoic acid showed positive association with GGT (OR: 2.73; 95% CI: 1.36-5.5; p adjusted = 0.0256). Plasma mercury showed positive association with GGT (OR: 1.45; 95% CI: 1.14-1.84; p adjusted = 0.0315). Using a plastic container while keeping food in the refrigerator was associated with elevated GGT compared to using a glass container (OR: 1.51; 95% CI: 1.16-1.95; p adjusted = 0.0153). 2-ethyl-5-oxohexyl phthalate, showed a negative trend with all 3 indices, with AST (OR: 0.54; 95% CI: 0.39-0.73; p adjusted = 0.00357), ALT (OR: 0.5; 95% CI: 0.34-0.75; p adjusted = 0.036), GGT (OR: 0.55; 95% CI: 0.4-0.76; p adjusted = 0.00697). Bisphenol S and frequent use of sunblock cream showed negative association with ALT (OR: 0.77; 95% CI: 0.66-0.89), and GGT (OR: 0.25; 95% CI: 0.11-0.55), respectively.

Conclusions

We conducted an exploratory study on environmental exposure and human liver function. By using EWAS methodology, we identified 7 factors that could have potential association with liver function.

Linking Transthyretin-Binding Chemicals and Free Thyroid Hormones: In Vitro to In Vivo Extrapolation Based on a Competitive Binding Model

Large numbers of pollutants competitively inhibit the binding between thyroid hormones and transthyretin (TTR) in vitro. However, the impact of this unintended binding on free thyroid hormones in vivo has not yet been characterized. Herein, we established a quantitative in vitro to in vivo extrapolation (QIVIVE) method based on a competitive binding model to quantify the effect of TTR-binding chemicals on free thyroid hormones in human blood. Twenty-five TTR-binding chemicals including 6 hydroxyl polybromodiphenyl ethers (OH-PDBEs), 6 hydroxyl polychlorobiphenyls (OH-PCBs), 4 halogenphenols, 5 per- and polyfluorinated substances (PFASs), and 4 phenols were selected for investigation. Incorporating the in vitro binding parameters and human exposure data, the QIVIVE model could well predict the in vivo effect on free thyroid hormones. Co-exposure to twenty-five typical TTR-binding chemicals resulted in median increases of 0.080 and 0.060% in circulating levels of free thyroxine (FT4) and free triiodothyronine (FT3) in the general population. Individuals with occupational exposure to TTR-binding chemicals suffered 1.88-32.2% increases in free thyroid hormone levels. This study provides a quantitative tool to evaluate the thyroid-disrupting risks of TTR-binding chemicals and proposes a new framework for assessing the in vivo effects of chemical exposures on endogenous molecules.

Screening strategy for 1210 exogenous chemicals in serum by two-dimensional liquid chromatography-mass spectrometry

Humans are at risk of exogenous exposure to exogenous chemicals. Challenges exist for the comprehensive monitoring of residues with different physical and chemical properties in serum. Here, an on-line two-dimensional liquid chromatography (2D-LC) - high resolution mass spectrometry system (HRMS) was developed, expanding the range of the partition coefficient in octanol/water of the residue analysis from -8 to 12. A high-coverage serum residue screening strategy was further designed by integrating 2D-LC system with HRMS full MS/data independent acquisition and automatic spectral library searching. This strategy enables to simultaneously screen 1210 pesticides, veterinary/human drugs, other chemical pollutants and their metabolites in serum with a single analysis. Method validation showed 92% and 81% of 1022 residues spiked in serum could be detected at 50 ng/mL and 5 ng/mL, respectively. The developed method was applied to the analysis of 24 separately pooled serum samples, 58 suspect residues were found, some of them were detected at high frequencies over than 50%. Among them, 4,6-Dinitro-O-cresol and probable carcinogenic folpet are highly toxic, and cimaterol is banned in China. Collectively, this study developed a 2D-LC-HRMS -based screening strategy for screening pesticides, veterinary/human drugs, and other chemical pollutants in serum, it is helpful for studying the effect of exogenous exposures on human health.

Monitoring long-term chemical exposome by characterizing the hair metabolome using a high-resolution mass spectrometry-based suspect screening approach

Hair has recently emerged as a biospecimen for characterizing the long-term chemical exposome in biomonitoring investigations spanning several months, as chemical compounds circulating in the bloodstream accumulate in hair. Although there has been interest in using human hair as a biospecimen for exposome studies, it has yet to be widely adopted compared to blood and urine. Here, we applied a high-resolution mass spectrometry (HRMS)-based suspect screening strategy to characterize the long-term chemical exposome in human hair. Hair samples were collected from 70 subjects and cut into 3 cm segments, which were then mixed to prepare pooled samples. The pooled hair samples underwent a sample preparation procedure, and the hair extracts were further analyzed using an HRMS-based suspect screening approach. An in-house chemical suspect list containing 1227 chemical entries from National Report on Human Exposure to Environmental Chemicals (Report) published by the U.S. CDC and the Exposome-Explorer 3.0 database developed by the WHO was subsequently used to screen and filter the suspect features against the HRMS dataset. Overall, we matched 587 suspect features in the HRMS dataset to 246 unique chemical formulas in the suspect list, and the structures of 167 chemicals were further identified through a fragmentation analysis. Among these, chemicals such as mono-2-ethylhexyl phthalate, methyl paraben, and 1-naphthol, which have been detected in the urine or blood for exposure assessment, were also identified in human hair. This suggests that hair reflects the accumulation of environmental compounds to which an individual is exposed. Exposure to exogenous chemicals may exert adverse effects on cognitive function, and we discovered 15 chemicals in human hair that may contribute to the pathogenesis of Alzheimer's disease. This finding suggests that human hair may be a promising biospecimen for monitoring long-term exposure to multiple environmental chemicals and perturbations in endogenous chemicals in biomonitoring investigations.

HExpPredict: In Vivo Exposure Prediction of Human Blood Exposome Using a Random Forest Model and Its Application in Chemical Risk Prioritization

BACKGROUND

Due to many substances in the human exposome, there is a dearth of exposure and toxicity information available to assess potential health risks. Quantification of all trace organics in the biological fluids seems impossible and costly, regardless of the high individual exposure variability. We hypothesized that the blood concentration (CB) of organic pollutants could be predicted via their exposure and chemical properties. Developing a prediction model on the annotation of chemicals in human blood can provide new insight into the distribution and extent of exposures to a wide range of chemicals in humans.

OBJECTIVES

Our objective was to develop a machine learning (ML) model to predict blood concentrations (CBs) of chemicals and prioritize chemicals of health concern.

METHODS

We curated the CBs of compounds mostly measured at population levels and developed an ML model for chemical CB predictions by considering chemical daily exposure (DE) and exposure pathway indicators (δij), half-lives (t1/2), and volume of distribution (Vd). Three ML models, including random forest (RF), artificial neural network (ANN) and support vector regression (SVR) were compared. The toxicity potential or prioritization of each chemical was represented as a bioanalytical equivalency (BEQ) and its percentage (BEQ%) estimated based on the predicted CB and ToxCast bioactivity data. We also retrieved the top 25 most active chemicals in each assay to further observe changes in the BEQ% after the exclusion of the drugs and endogenous substances.

RESULTS

We curated the CBs of 216 compounds primarily measured at population levels. RF outperformed the ANN and SVF models with the root mean square error (RMSE) of 1.66 and 2.07μM, the mean absolute error (MAE) values of 1.28 and 1.56μM, the mean absolute percentage error (MAPE) of 0.29 and 0.23, and R2 of 0.80 and 0.72 across test and testing sets. Subsequently, the human CBs of 7,858 ToxCast chemicals were successfully predicted, ranging from 1.29×10-6 to 1.79×10-2 μM. The predicted CBs were then combined with ToxCast in vitro bioassays to prioritize the ToxCast chemicals across 12 in vitro assays with important toxicological end points. It is interesting that we found the most active compounds to be food additives and pesticides rather than widely monitored environmental pollutants.

DISCUSSION

We have shown that the accurate prediction of "internal exposure" from "external exposure" is possible, and this result can be quite useful in the risk prioritization. https://doi.org/10.1289/EHP11305.

Discovering Hair Biomarkers of Alzheimer's Disease Using High Resolution Mass Spectrometry-Based Untargeted Metabolomics

Hair may be a potential biospecimen to discover biomarkers for Alzheimer's disease (AD) since it reflects the integral metabolic profiles of body burden over several months. Here, we described the AD biomarker discovery in the hair using a high-resolution mass spectrometry (HRMS)-based untargeted metabolomics approach. A total of 24 patients with AD and 24 age- and sex-matched cognitively healthy controls were recruited. The hair samples were collected 0.1-cm away from the scalp and further cut into 3-cm segments. Hair metabolites were extracted by ultrasonication with methanol/phosphate-buffered saline 50/50 (v/v) for 4 h. A total of 25 discriminatory chemicals in hair between the patients with AD and controls were discovered and identified. The AUC value achieved 0.85 (95% CI: 0.72~0.97) in patients with very mild AD compared to healthy controls using a composite panel of the 9 biomarker candidates, indicating high potential for the initiation or promotion phase of AD dementia in the early stage. A metabolic panel combined with the nine metabolites may be used as biomarkers for the early detection of AD. The hair metabolome can be used to reveal metabolic perturbations for biomarker discovery. Investigating perturbations of the metabolites will offer insight into the pathogenesis of AD.

The human microbial exposome: expanding the Exposome-Explorer database with gut microbial metabolites

Metabolites produced by the gut microbiota play an important role in the cross-talk with the human host. Many microbial metabolites are biologically active and can pass the gut barrier and make it into the systemic circulation, where they form the gut microbial exposome, i.e. the totality of gut microbial metabolites in body fluids or tissues of the host. A major difficulty faced when studying the microbial exposome and its role in health and diseases is to differentiate metabolites solely or partially derived from microbial metabolism from those produced by the host or coming from the diet. Our objective was to collect data from the scientific literature and build a database on gut microbial metabolites and on evidence of their microbial origin. Three types of evidence on the microbial origin of the gut microbial exposome were defined: (1) metabolites are produced in vitro by human faecal bacteria; (2) metabolites show reduced concentrations in humans or experimental animals upon treatment with antibiotics; (3) metabolites show reduced concentrations in germ-free animals when compared with conventional animals. Data was manually collected from peer-reviewed publications and inserted in the Exposome-Explorer database. Furthermore, to explore the chemical space of the microbial exposome and predict metabolites uniquely formed by the microbiota, genome-scale metabolic models (GSMMs) of gut bacterial strains and humans were compared. A total of 1848 records on one or more types of evidence on the gut microbial origin of 457 metabolites was collected in Exposome-Explorer. Data on their known precursors and concentrations in human blood, urine and faeces was also collected. About 66% of the predicted gut microbial metabolites (n = 1543) were found to be unique microbial metabolites not found in the human GSMM, neither in the list of 457 metabolites curated in Exposome-Explorer, and can be targets for new experimental studies. This new data on the gut microbial exposome, freely available in Exposome-Explorer ( http://exposome-explorer.iarc.fr/ ), will help researchers to identify poorly studied microbial metabolites to be considered in future studies on the gut microbiota, and study their functionalities and role in health and diseases.

Advancing Exposomic Research in Prenatal Respiratory Disease Programming

Disease programming reflects interactions between genes and the environment. Unlike the genome, environmental exposures and our response to exposures change over time. Starting in utero, the respiratory system and related processes develop sequentially in a carefully timed cascade, thus effects depend on both exposure dose and timing. A multitude of environmental and microbial exposures influence respiratory disease programming. Effects result from toxin-induced shifts in a host of molecular, cellular, and physiologic states and their interacting systems. Moreover, pregnant women and the developing child are not exposed to a single toxin, but to complex mixtures.

Integration of proteomic and metabolomic analyses: New insights for mapping informal workers exposed to potentially toxic elements

Occupational exposure to potentially toxic elements (PTEs) is a concerning reality of informal workers engaged in the jewelry production chain that can lead to adverse health effects. In this study, untargeted proteomic and metabolomic analyses were employed to assess the impact of these exposures on informal workers' exposome in Limeira city, São Paulo state, Brazil. PTE levels (Cr, Mn, Ni, Cu, Zn, As, Cd, Sn, Sb, Hg, and Pb) were determined in blood, proteomic analyses were performed for saliva samples (n = 26), and metabolomic analyses in plasma (n = 145) using ultra-high performance liquid chromatography (UHPLC) coupled with quadrupole-time-of-flight (Q-TOF) mass spectrometry. Blood PTE levels of workers, controls, and their family members were determined by inductively coupled plasma-mass spectrometry (ICP-MS). High concentration levels of Sn and Cu were detected in welders' blood (p < 0.001). Statistical analyses were performed using MetaboAnalyst 4.0. The results showed that 26 proteins were upregulated, and 14 proteins downregulated on the welder group, and thirty of these proteins were also correlated with blood Pb, Cu, Sb, and Sn blood levels in the welder group (p < 0.05). Using gene ontology analysis of these 40 proteins revealed the biological processes related to the upregulated proteins were translational initiation, SRP-dependent co-translational protein targeting to membrane, and viral transcription. A Metabolome-Wide Association Study (MWAS) was performed to search for associations between blood metabolites and exposure groups. A pathway enrichment analysis of significant features from the MWAS was then conducted with Mummichog. A total of 73 metabolomic compounds and 40 proteins up or down-regulated in welders were used to perform a multi-omics analysis, disclosing seven metabolic pathways potentially disturbed by the informal work: valine leucine and isoleucine biosynthesis, valine leucine and isoleucine degradation, arginine and proline metabolism, ABC transporters, central carbon metabolism in cancer, arachidonic acid metabolism and cysteine and methionine metabolism. The majority of the proteins found to be statistically up or downregulated in welders also correlated with at least one blood PTE level, providing insights into the biological responses to PTE exposures in the informal work exposure scenario. These findings shed new light on the effects of occupational activity on workers' exposome, underscoring the harmful effects of PTE.

A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species

Per- and polyfluoroalkyl substances (PFAS) are a diverse group of man-made chemicals that are commonly found in body tissues. The toxicokinetics of most PFAS are currently uncharacterized, but long half-lives (t½) have been observed in some cases. Knowledge of chemical-specific t½ is necessary for exposure reconstruction and extrapolation from toxicological studies. We used an ensemble machine learning method, random forest, to model the existing in vivo measured t½ across four species (human, monkey, rat, mouse) and eleven PFAS. Mechanistically motivated descriptors were examined, including two types of surrogates for renal transporters: (1) physiological descriptors, including kidney geometry, for renal transporter expression and (2) structural similarity of defluorinated PFAS to endogenous chemicals for transporter affinity. We developed a classification model for t½ (Bin 1: <12 h; Bin 2: <1 week; Bin 3: <2 months; Bin 4: >2 months). The model had an accuracy of 86.1% in contrast to 32.2% for a y-randomized null model. A total of 3890 compounds were within domain of the model, and t½ was predicted using the bin medians: 4.9 h, 2.2 days, 33 days, and 3.3 years. For human t½, 56% of PFAS were classified in Bin 4, 7% were classified in Bin 3, and 37% were classified in Bin 2. This model synthesizes the limited available data to allow tentative extrapolation and prioritization.

Environmental chemicals and endogenous metabolites in bile of USA and Norway patients with primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) is a complex bile duct disorder. Its etiology is incompletely understood, but environmental chemicals likely contribute to risk. Patients with PSC have an altered bile metabolome, which may be influenced by environmental chemicals. This novel study utilized state-of-the-art high-resolution mass spectrometry (HRMS) with bile samples to provide the first characterization of environmental chemicals and metabolomics (collectively, the exposome) in PSC patients located in the United States of America (USA) (n = 24) and Norway (n = 30). First, environmental chemical- and metabolome-wide association studies were conducted to assess geographic-based similarities and differences in the bile of PSC patients. Nine environmental chemicals (false discovery rate, FDR < 0.20) and 3143 metabolic features (FDR < 0.05) differed by site. Next, pathway analysis was performed to identify metabolomic pathways that were similarly and differentially enriched by the site. Fifteen pathways were differentially enriched (P < .05) in the categories of amino acid, glycan, carbohydrate, energy, and vitamin/cofactor metabolism. Finally, chemicals and pathways were integrated to derive exposure-effect correlation networks by site. These networks demonstrate the shared and differential chemical-metabolome associations by site and highlight important pathways that are likely relevant to PSC. The USA patients demonstrated higher environmental chemical bile content and increased associations between chemicals and metabolic pathways than those in Norway. Polychlorinated biphenyl (PCB)-118 and PCB-101 were identified as chemicals of interest for additional investigation in PSC given broad associations with metabolomic pathways in both the USA and Norway patients. Associated pathways include glycan degradation pathways, which play a key role in microbiome regulation and thus may be implicated in PSC pathophysiology.

Omics approaches: interactions at the maternal-fetal interface and origins of child health and disease

Immunoperinatology is an emerging field. Transdisciplinary efforts by physicians, physician-scientists, basic science researchers, and computational biologists have made substantial advancements by identifying unique immunologic signatures of specific diseases, discovering innovative preventative or treatment strategies, and establishing foundations for individualized neonatal intensive care of the most vulnerable neonates. In this review, we summarize the immunobiology and immunopathology of pregnancy, highlight omics approaches to study the maternal-fetal interface, and their contributions to pregnancy health. We examined the importance of transdisciplinary, multiomic (such as genomics, transcriptomics, proteomics, metabolomics, and immunomics) and machine-learning strategies in unraveling the mechanisms of adverse pregnancy, neonatal, and childhood outcomes and how they can guide the development of novel therapies to improve maternal and neonatal health. IMPACT: Discuss immunoperinatology research from the lens of omics and machine-learning approaches. Identify opportunities for omics-based approaches to delineate infection/inflammation-associated maternal, neonatal, and later life adverse outcomes (e.g., histologic chorioamnionitis [HCA]).

Pathways to Identify Electrophiles In Vivo Using Hemoglobin Adducts: Hydroxypropanoic Acid Valine Adduct and Its Possible Precursors

Analytical methods and tools for the characterization of the human exposome by untargeted mass spectrometry approaches are advancing rapidly. Adductomics methods have been developed for untargeted screening of short-lived electrophiles, in the form of adducts to proteins or DNA, in vivo. The identification of an adduct and its precursor electrophile in the blood is more complex than that of stable chemicals. The present work aims to illustrate procedures for the identification of an adduct to N-terminal valine in hemoglobin detected with adductomics, and pathways for the tracing of its precursor and possible exposure sources. Identification of the adduct proceeded via preparation and characterization of standards of adduct analytes. Possible precursor(s) and exposure sources were investigated by measurements in blood of adduct formation by precursors in vitro and adduct levels in vivo. The adduct was identified as hydroxypropanoic acid valine (HPA-Val) by verification with a synthesized reference. The HPA-Val was measured together with other adducts (from acrylamide, glycidamide, glycidol, and acrylic acid) in human blood (n = 51, schoolchildren). The HPA-Val levels ranged between 6 and 76 pmol/g hemoglobin. The analysis of reference samples from humans and rodents showed that the HPA-Val adduct was observed in all studied samples. No correlation of the HPA-Val level with the other studied adducts was observed in humans, nor was an increase in tobacco smokers observed. A small increase was observed in rodents exposed to glycidol. The formation of the HPA-Val adduct upon incubation of blood with glycidic acid (an epoxide) was shown. The relatively high adduct levels observed in vivo in relation to the measured reactivity of the epoxide, and the fact that the epoxide is not described as naturally occurring, suggest that glycidic acid is not the only precursor of the HPA-Val adduct identified in vivo. Another endogenous electrophile is suspected to contribute to the in vivo HPA-Val adduct level.

Unraveling Pathophysiology of Takotsubo Syndrome: The Emerging Role of the Oxidative Stress's Systemic Status

Takotsubo Syndrome (TTS) is usually triggered by emotional or physical stressors, thus suggesting that an increased sympathetic activity, leading to myocardial perfusion abnormalities and ventricular dysfunction, plays a major pathogenetic role. However, it remains to be elucidated why severe emotional and physical stress might trigger TTS in certain individuals but not others. Clinical research has been focused mainly on mechanisms underlying the activation of the sympathetic nervous system and the occurrence of myocardial ischemia in TTS. However, scientific evidence shows that additional factors might play a pathophysiologic role in the condition's occurrence. In this regard, a significant contribution arrived from metabolomics studies that followed the systemic response to TTS. Specifically, preliminary data clearly show that there is an interplay between inflammation, genetics, and oxidative status which might explain susceptibility to the condition. This review aims to sum up the established pathogenetic factors underlying TTS and to appraise emerging mechanisms, with particular emphasis on oxidative status, which might better explain susceptibility to the condition.

Integrating ion mobility into comprehensive multidimensional metabolomics workflows: critical considerations

BACKGROUND

Ion mobility (IM) separation capabilities are now widely available to researchers through several commercial vendors and are now being adopted into many metabolomics workflows. The added peak capacity that ion mobility offers with minimal compromise to other analytical figures-of-merit has provided real benefits to sensitivity and structural selectivity and have allowed more specific metabolite annotations to be assigned in untargeted workflows. One of the greatest promises of contemporary IM-enabled instrumentation is the capability of operating multiple analytical dimensions inline with minimal sample volumes, which has the potential to address many grand challenges currently faced in the omics fields. However, comprehensive operation of multidimensional mass spectrometry comes with its own inherent challenges that, beyond operational complexity, may not be immediately obvious to practitioners of these techniques.

AIM OF REVIEW

In this review, we outline the strengths and considerations for incorporating IM analysis in metabolomics workflows and provide a critical but forward-looking perspective on the contemporary challenges and prospects associated with interpreting IM data into chemical knowledge.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We outline a strategy for unifying IM-derived collision cross section (CCS) measurements obtained from different IM techniques and discuss the emerging field of high resolution ion mobility (HRIM) that is poised to address many of the contemporary challenges associated with ion mobility metabolomics. Whereas the LC step limits the throughput of comprehensive LC-IM-MS, the higher peak capacity of HRIM can allow fast LC gradients or rapid sample cleanup via solid-phase extraction (SPE) to be utilized, significantly improving the sample throughput.

Longitudinal associations between metabolites and long-term exposure to ambient air pollution: Results from the KORA cohort study

BACKGROUND

Long-term exposure to air pollution has been associated with cardiopulmonary diseases, while the underlying mechanisms remain unclear.

OBJECTIVES

To investigate changes in serum metabolites associated with long-term exposure to air pollution and explore the susceptibility characteristics.

METHODS

We used data from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999-2001) and two follow-up examinations (F4: 2006-08 and FF4: 2013-14). Mass-spectrometry-based targeted metabolomics was used to quantify metabolites among serum samples. Only participants with repeated metabolites measurements were included in the current analysis. Land-use regression (LUR) models were used to estimate annual average concentrations of ultrafine particles, particulate matter (PM) with an aerodynamic diameter less than 10 μm (PM₁₀), coarse particles (PM_coarse), fine particles, PM_2.5 absorbance (a proxy of elemental carbon related to traffic exhaust, PM_2.5abs), nitrogen oxides (NO₂, NO_x), and ozone at individuals' residences. We applied confounder-adjusted mixed-effects regression models to examine the associations between long-term exposure to air pollution and metabolites.

RESULTS

Among 9,620 observations from 4,261 KORA participants, we included 5,772 (60.0%) observations from 2,583 (60.6%) participants in this analysis. Out of 108 metabolites that passed stringent quality control across three study points in time, we identified nine significant negative associations between phosphatidylcholines (PCs) and ambient pollutants at a Benjamini-Hochberg false discovery rate (FDR) corrected p-value < 0.05. The strongest association was seen for an increase of 0.27 μg/m³ (interquartile range) in PM_2.5abs and decreased phosphatidylcholine acyl-alkyl C36:3 (PC ae C36:3) concentrations [percent change in the geometric mean: -2.5% (95% confidence interval: -3.6%, -1.5%)].

CONCLUSIONS

Our study suggested that long-term exposure to air pollution is associated with metabolic alterations, particularly in PCs with unsaturated long-chain fatty acids. These findings might provide new insights into potential mechanisms for air pollution-related adverse outcomes.