Characterization of the Human Exposome by a Comprehensive and Quantitative Large-Scale Multianalyte Metabolomics Platform

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.

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.

Knowledge translation and knowledge mobilization from the FoodBAll project

This report describes the knowledge mobilization and translation outcomes of the Canadian-funded portion of a large, international project called the Food Biomarker Alliance (FoodBAll), which ran from 2015 to 2019. This remarkably successful project led to a large number of important findings, outputs, and impacts. In particular, FoodBAll unequivocally demonstrated that metabolomics could be used to not only discover biomarkers of food intake (BFIs), but also to measure diet in a more objective manner. FoodBAll also created standards for assessing and validating BFIs, papers and databases describing BFIs, and kits for measuring BFIs and it laid the groundwork for many global studies exploring food composition and precision nutrition.

Towards nutrition with precision: unlocking biomarkers as dietary assessment tools

Precision nutrition requires precise tools to monitor dietary habits. Yet current dietary assessment instruments are subjective, limiting our understanding of the causal relationships between diet and health. Biomarkers of food intake (BFIs) hold promise to increase the objectivity and accuracy of dietary assessment, enabling adjustment for compliance and misreporting. Here, we update current concepts and provide a comprehensive overview of BFIs measured in urine and blood. We rank BFIs based on a four-level utility scale to guide selection and identify combinations of BFIs that specifically reflect complex food intakes, making them applicable as dietary instruments. We discuss the main challenges in biomarker development and illustrate key solutions for the application of BFIs in human studies, highlighting different strategies for selecting and combining BFIs to support specific study designs. Finally, we present a roadmap for BFI development and implementation to leverage current knowledge and enable precision in nutrition research.

A Mediterranean Diet-Based Metabolomic Score and Cognitive Decline in Older Adults: A Case-Control Analysis Nested within the Three-City Cohort Study

SCOPE

Evidence on the Mediterranean diet (MD) and age-related cognitive decline (CD) is still inconclusive partly due to self-reported dietary assessment. The aim of the current study is to develop an MD- metabolomic score (MDMS) and investigate its association with CD in community-dwelling older adults.

METHODS AND RESULTS

This study includes participants from the Three-City Study from the Bordeaux (n = 418) and Dijon (n = 422) cohorts who are free of dementia at baseline. Repeated measures of cognition over 12 years are collected. An MDMS is designed based on serum biomarkers related to MD key food groups and using a targeted metabolomics platform. Associations with CD are investigated through conditional logistic regression (matched on age, sex, and education level) in both sample sets. The MDMS is found to be inversely associated with CD (odds ratio [OR] [95% confidence interval (CI)] = 0.90 [0.80-1.00]; p = 0.048) in the Bordeaux (discovery) cohort. Results are comparable in the Dijon (validation) cohort, with a trend toward significance (OR [95% CI] = 0.91 [0.83-1.01]; p = 0.084).

CONCLUSIONS

A greater adherence to the MD, here assessed by a serum MDMS, is associated with lower odds of CD in older adults.

Proteomics, Human Environmental Exposure, and Cardiometabolic Risk

BACKGROUND

The biological mechanisms linking environmental exposures with cardiovascular disease pathobiology are incompletely understood. We sought to identify circulating proteomic signatures of environmental exposures and examine their associations with cardiometabolic and respiratory disease in observational cohort studies.

METHODS

We tested the relations of >6500 circulating proteins with 29 environmental exposures across the built environment, green space, air pollution, temperature, and social vulnerability indicators in ≈3000 participants of the CARDIA study (Coronary Artery Risk Development in Young Adults) across 4 centers using penalized and ordinary linear regression. In >3500 participants from FHS (Framingham Heart Study) and JHS (Jackson Heart Study), we evaluated the prospective relations of proteomic signatures of the envirome with cardiovascular disease and mortality using Cox models.

RESULTS

Proteomic signatures of the envirome identified novel/established cardiovascular disease-relevant pathways including DNA damage, fibrosis, inflammation, and mitochondrial function. The proteomic signatures of the envirome were broadly related to cardiometabolic disease and respiratory phenotypes (eg, body mass index, lipids, and left ventricular mass) in CARDIA, with replication in FHS/JHS. A proteomic signature of social vulnerability was associated with a composite of cardiovascular disease/mortality (1428 events; FHS: hazard ratio, 1.16 [95% CI, 1.08-1.24]; P=1.77×10-5; JHS: hazard ratio, 1.25 [95% CI, 1.14-1.38]; P=6.38×10-6; hazard ratio expressed as per 1 SD increase in proteomic signature), robust to adjustment for known clinical risk factors.

CONCLUSIONS

Environmental exposures are related to an inflammatory-metabolic proteome, which identifies individuals with cardiometabolic disease and respiratory phenotypes and outcomes. Future work examining the dynamic impact of the environment on human cardiometabolic health is warranted.

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

Xenobiotic metabolites modify immune responses of the cervicovaginal epithelium: potential mechanisms underlying barrier disruption

OBJECTIVE

Xenobiotic metabolites are exogenous biochemicals that can adversely impact reproductive health. We previously identified xenobiotics in cervicovaginal fluid during pregnancy in association with short cervix. In other organ systems, xenobiotics can modify epithelial barrier function. We hypothesise that xenobiotics dysregulate epithelial cell and macrophage immune responses as a mechanism to disrupt the cervicovaginal barrier.

DESIGN

In vitro cell culture system.

SETTING

Laboratory within academic institution.

SAMPLE

Vaginal, ectocervical and endocervical epithelial cell lines and primary macrophages.

METHODS

Cells were treated with diethanolamine (2.5 mM), ethyl glucoside (5 mM) or tartrate (2.5 mM) for 24 h.

MAIN OUTCOME MEASURES

Cytokines and matrix metalloproteinases were measured in cell supernatants (n = 3 per condition). One-way analysis of variance (ANOVA) with Dunnett's test for multiple comparisons was performed.

RESULTS

Diethanolamine induces inflammatory cytokines, whereas ethyl glucoside and tartrate generally exert anti-inflammatory effects across all cells. Diethanolamine increases interleukin 6 (IL-6), IL-8, interferon γ-induced protein 10 kDa (IP-10), growth-regulated oncogene (GRO), fractalkine, matrix metalloproteinase 1 (MMP-1), MMP-9 and MMP-10 (p < 0.05 for all), factors involved in acute inflammation and recruitment of monocytes, neutrophils and lymphocytes. Ethyl glucoside and tartrate decrease multiple cytokines, including RANTES and MCP-1 (p < 0.05 for all), which serve as chemotactic factors. Vaginal cells exhibit heightened inflammatory tone compared with cervical cells and macrophages, with a greater number of differentially expressed analytes after xenobiotic exposure.

CONCLUSIONS

Xenobiotic metabolites present in the cervicovaginal space during pregnancy modify immune responses, unveiling potential pathways through which environmental exposures may contribute to the pathogenesis of cervical remodelling preceding preterm birth. Future work identifying xenobiotic sources and routes of exposure offers the potential to modify environmental risks to improve pregnancy outcomes.

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

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

QComics: Recommendations and Guidelines for Robust, Easily Implementable and Reportable Quality Control of Metabolomics Data

The implementation of quality control strategies is crucial to ensure the reproducibility, accuracy, and meaningfulness of metabolomics data. However, this pivotal step is often overlooked within the metabolomics workflow and frequently relies on the use of nonstandardized and poorly reported protocols. To address current limitations in this respect, we have developed QComics, a robust, easily implementable and reportable method for monitoring and controlling data quality. The protocol operates in various sequential steps aimed to (i) correct for background noise and carryover, (ii) detect signal drifts and "out-of-control" observations, (iii) deal with missing data, (iv) remove outliers, (v) monitor quality markers to identify samples affected by improper collection, preprocessing, or storage, and (vi) assess overall data quality in terms of precision and accuracy. Notably, this tool considers important issues often neglected along quality control, such as the need of separately handling missing values and truly absent data to avoid losing relevant biological information, as well as the large impact that preanalytical factors may elicit on metabolomics results. Altogether, the guidelines compiled in QComics might contribute to establishing gold standard recommendations and best practices for quality control within the metabolomics community.

GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review

Exhaled breath analysis, with particular emphasis on volatile organic compounds, represents a growing area of clinical research due to its obvious advantages over other diagnostic tests. Numerous pathologies have been extensively investigated for the identification of specific biomarkers in exhalates through metabolomics. However, the transference of breath tests to clinics remains limited, mainly due to deficiency in methodological standardization. Critical steps include the selection of breath sample types, collection devices, and enrichment techniques. GC-MS is the reference analytical technique for the analysis of volatile organic compounds in exhalates, especially during the biomarker discovery phase in metabolomics. This review comprehensively examines and compares metabolomic studies focusing on cancer, lung diseases, and infectious diseases. In addition to delving into the experimental designs reported, it also provides a critical discussion of the methodological aspects, ranging from the experimental design and sample collection to the identification of potential pathology-specific biomarkers.

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.

Mass Spectrometry-Based Metabolomics Multi-platform for Alzheimer's Disease Research

The integration of complementary analytical platforms is nowadays the most common strategy for comprehensive metabolomics analysis of complex biological systems. In this chapter, we describe methods and tips for the application of a mass spectrometry multi-platform in Alzheimer's disease research, based on the combination of direct mass spectrometry and orthogonal hyphenated approaches, namely, reversed-phase ultrahigh-performance liquid chromatography and gas chromatography. These procedures have been optimized for the analysis of multiple biological samples from human patients and transgenic animal models, including blood serum, various brain regions (e.g., hippocampus, cortex, cerebellum, striatum, olfactory bulbs), and other peripheral organs (e.g., liver, kidney, spleen, thymus).

How far are we from reliable metabolomics-based biomarkers? The often-overlooked importance of addressing inter-individual variability factors

Metabolomics has proven great potential to unravel the molecular basis of diseases. However, most attempts aimed at identifying reliable metabolomics-based biomarkers for diagnosis, prediction, and prognosis of diseases have repeatedly failed because of inconsistent results and unsatisfactory replication in independent cohorts. This review article explores the possible causes behind this reproducibility crisis, with special focus on the role that inter-individual variability factors play in modulating the susceptibility to disease development. Furthermore, we provide future perspectives on the applicability of metabolomics in biomedical research and its translatability into clinical practice.

Dilute and shoot approach for toxicology testing

Toxicology testing is performed in clinical settings, forensic settings, and for controlling doping. Drug screening is a toxicology test to determine if drugs are present in biological samples. The most common specimen type for drug testing is urine, as drugs and/or their metabolites are often more concentrated in the urine, extending the detection window of drugs. The dilute-and-shoot method is a simple procedure used in toxicology testing, where a sample is diluted before being directly injected into the liquid chromatography-mass spectrometry (LC-MS) system. This method is easy, quick, and cost-saving, and can be used for protein-poor liquid specimens such as urine. Thus, it is reasonable and attractive for busy toxicology laboratories to combine the dilute-and-shoot method with high-resolution hyphenated-MS for urine drug screening. This method has several disadvantages, including a suboptimal detection capability for certain analytes, as well as interference from co-eluting matrix components called matrix effects, in which co-eluting matrix molecules alter the ionization efficiency of the analyte molecules at the ionization source in LC-MS, altering (mostly reducing) the analyte detection capability. The matrix effect testing is essential for the validation of LC-MS-based assays. A reasonable approach to addressing these undesirable effects would be to minimize these components. The most straightforward approach is to reduce the amounts of matrix components by using a higher dilution of the specimen and a lower volume for specimen injection. Optimization of the chromatographic separation is another reasonable approach for reducing co-eluting matrix components with the analyte.

Total analysis system for the determination of uremic toxins in human plasma based on bead injection solid phase extraction hyphenated to mass spectrometry

Indoxyl sulfate (INDS) and p-cresol sulfate (pCS) are two of the most relevant uremic toxins that are recognized to have an essential role in chronic kidney disease (CKD) progression and associated cardiovascular risk. Thus, it is crucial to accurately assess their circulating levels in the body. Aiming at establishing an analytical strategy for quantification of INDS and pCS in human plasma, an automatic on-line micro-solid-phase extraction (μSPE) procedure hyphenated to tandem mass spectrometry (MS/MS) detection without previous chromatographic separation was herein developed. The bead injection (BI) concept was used to implement the μSPE procedure in the lab-on-valve (LOV) format. After studying the extraction conditions, the anion-exchange OASIS WAX sorbent beads (10 mg) and 99% ACN-H2O (15:85, v/v)-1% (v/v) NH4OH were chosen as sorbent and eluent, respectively, as they provided the highest analyte recoveries. Subsequently, the μSPE-BI-LOV system was hyphenated on-line to a MS/MS detector and the full analytical cycle, comprising sample preparation and analytes detection, was completed in <20 min. The developed μSPE-BI-LOV-MS methodology presented good linearity (r2 > 0.999) for quantification of the target analytes at concentrations ranging from 18 to 360 μg mL-1 in plasma. LOQ values were 2 μg mL-1 for INDS and 7 μg mL-1 for pCS in plasma. Human plasma samples from healthy subjects and individuals with CKD were successfully analyzed using the developed approach. The proposed automatic methodology can be described as an eco-friendly strategy, with a favorable score of 0.64 after greenness evaluation using the AGREE metric.

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.

Plasma metabolomic profiles of plant-based dietary indices reveal potential pathways for metabolic syndrome associations

BACKGROUND AND AIMS

Plant-based dietary patterns have been associated with improved health outcomes. This study aims to describe the metabolomic fingerprints of plant-based diet indices (PDI) and examine their association with metabolic syndrome (MetS) and its components in a Danish population.

METHODS

The MAX study comprised 676 participants (55% women, aged 18-67 y) from Copenhagen. Sociodemographic and dietary data were collected using questionnaires and three 24-h dietary recalls over one year (at baseline, and at 6 and 12 months). Mean dietary intakes were computed, as well as overall PDI, healthful (hPDI) and unhealthful (uPDI) scores, according to food groups for each plant-based index. Clinical variables were also collected at the same time points in a health examination that included complete blood tests. MetS was defined according to the International Diabetes Federation criteria. Plasma metabolites were measured using a targeted metabolomics approach. Metabolites associated with PDI were selected using random forest models and their relationships with PDIs and MetS were analyzed using generalized linear mixed models.

RESULTS

The mean prevalence of MetS was 10.8%. High, compared to low, hPDI and uPDI scores were associated with a lower and higher odd of MetS, respectively [odds ratio (95%CI); hPDI: 0.56 (0.43-0.74); uPDI: 1.61 (1.26-2.05)]. Out of 411 quantified plasma metabolites, machine-learning metabolomics fingerprinting revealed 13 metabolites, including food and food-related microbial metabolites, like hypaphorine, indolepropionic acid and lignan-derived enterolactones. These metabolites were associated with all PDIs and were inversely correlated with MetS components (p < 0.05). Furthermore, they had an explainable contribution of 12% and 14% for the association between hPDI or uPDI, respectively, and MetS only among participants with overweight/obesity.

CONCLUSIONS

Metabolites associated with PDIs were inversely associated with MetS and its components, and may partially explain the effects of plant-based diets on cardiometabolic risk factors.

Dilute-and-shoot coupled to mixed mode liquid chromatography-tandem mass spectrometry for the analysis of persistent and mobile organic compounds in human urine

In this work, a comprehensive method for the simultaneous determination of 33 diverse persistent and mobile organic compounds (PMOCs) in human urine was developed by dilute-and-shoot (DS) followed by mixed-mode liquid chromatography coupled with tandem mass spectrometry (MMLC-MS/MS). In the sample preparation step, DS was chosen since it allowed the quantification of all targets in comparison to lyophilization. For the chromatographic separation, Acclaim Trinity P1 and P2 trimodal columns provided greater capacity for retaining PMOCs than reverse phase and hydrophilic interaction liquid chromatography. Therefore, DS was validated at 5 and 50 ng/mL in urine with both mixed mode columns at pH = 3 and 7. Regarding figures of merit, linear calibration curves (r2 > 0.999) built between instrumental quantification limits (mostly below 5 ng/mL) and 500 ng/mL were achieved. Despite only 60% of the targets were recovered at 5 ng/mL because of the dilution, all PMOCs were quantified at 50 ng/mL. Using surrogate correction, apparent recoveries in the 70-130% range were obtained for 91% of the targets. To analyse human urine samples, the Acclaim Trinity P1 column at pH = 3 and 7 was selected as a consensus between analytical coverage (i.e. 94% of the targets) and chromatographic runs. In a pooled urine sample, industrial chemicals (acrylamide and bisphenol S), biocides and their metabolites (2-methyl-4-isothiazolin-3-one, dimethyl phosphate, 6-chloropyridine-3-carboxylic acid, and ammonium glufosinate) and an artificial sweetener (aspartame) were determined at ng/mL levels. The outcomes of this work showed that humans are also exposed to PMOCs due to their persistence and mobility, and therefore, further human risk assessment is needed.

Biomarkers of Berry Intake: Systematic Review Update

Berries are rich in (poly)phenols, and these compounds may be beneficial to human health. Estimating berry consumption through self-reported questionnaires has been challenging due to compliance issues and a lack of precision. Estimation via food-derived biomarkers in biofluids was proposed as a complementary alternative. We aimed to review and update the existing evidence on biomarkers of intake for six different types of berries. A systematic literature search was performed to update a previous systematic review on PubMed, Web of Science, and Scopus from January 2020 until December 2022. Out of 42 papers, only 18 studies were eligible. A multimetabolite panel is suggested for blueberry and cranberry intake. Proposed biomarkers for blueberries include hippuric acid and malvidin glycosides. For cranberries, suggested biomarkers are glycosides of peonidin and cyanidin together with sulfate and glucuronide conjugates of phenyl-γ-valerolactone derivatives. No new metabolite candidates have been found for raspberries, strawberries, blackcurrants, and blackberries. Further studies are encouraged to validate these multimetabolite panels for improving the estimation of berry consumption.

Scout triggered multiple reaction monitoring mass spectrometry for the rapid transfer of large multiplexed targeted methods in metabolomics

The field of metabolomics based on mass spectrometry has grown considerably in recent years due to the need to detect and, above all, quantify a very large number of metabolites, simultaneously. Up to now, targeted multiplexed analysis on complex samples by Liquid Chromatography coupled with tandem Mass Spectrometry (LC-MS/MS) has relied almost exclusively on compound detection based on absolute retention times, as in the Scheduled-MRM (sMRM) approach. Those methods turn out to be poorly transferable from one instrument to another and result in a time-consuming and tedious method development involving a significant number of critical parameters that need specific re-optimisation. To address this challenge, we introduce a novel acquisition mode called scout-triggered MRM (stMRM). In stMRM, a marker transition is used to trigger MS analysis for a group of dependent target analytes. These marker transitions are strategically distributed throughout the chromatographic run, and the dependent analytes are associated based on their retention times. The result is a targeted assay that remains robust even in the presence of retention time shifts. A 3 to 5-fold increase in the number of detected transitions associated to plasma metabolites was obtained when transferring from a direct application of a published sMRM to a stMRM method. This significant improvement highlights the universal applicability of the stMRM method, as it can be implemented on any LC system without the need for extensive method development. We subsequently illustrate the robustness of stMRM in modified chromatographic elution conditions. Despite a large change in metabolite's selectivity, the multiplexed assay successfully recovered 70% of the monitored transitions when consequently modifying the gradient method. These findings demonstrate the versatility and adaptability of stMRM, opening new avenues for the development of highly multiplexed LC-MS/MS methods in metabolomics. These methods are characterized by their analytical transparency and straightforward implementation using existing literature data.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy.

Quantification of 10 B vitamins in mouse colon by LC-MS/MS: Application on breast cancer mice treated with doxorubicin

B vitamins play important roles in various physiological processes, including cell metabolism and DNA synthesis. The intestine is critical for the absorption and utilization of B vitamins, but few analytical methods for detecting intestinal B vitamins are currently available. In this study, we developed a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the simultaneous quantification of 10 B vitamins in mouse colon tissue, including thiamin (B1), riboflavin (B2), nicotinic acid (B3), niacinamide (B3-AM), pantothenic acid (B5), pyridoxine (B6), pyridoxal 5'-phosphate (B6-5P), biotin (B7), folic acid (B9), and cyanocobalamin (B12). The method was thoroughly validated following the U.S. Food and Drug Administration (FDA) guidelines and yielded good results in terms of linearity (r² > 0.9928), lower limit of quantification (40-600 ng/g), accuracy (88.9-119.80 %) and precision (relative standard deviation ≤ 19.71 %), recovery (87.95-113.79 %), matrix effect (91.26-113.78 %), and stability (85.65-114.05 %). Furthermore, we applied our method to profile B vitamins in the colons of mice with breast cancer after doxorubicin chemotherapy treatment, which revealed that the doxorubicin treatment led to significant colon damage and accumulation of several B vitamins including B1, B2 and B5. We also confirmed the capability of this method for quantifying B vitamins in other intestinal tissues like the ileum, jejunum, and duodenum. The newly developed method is simple, specific, and useful for targeted profiling of B vitamins in mouse colon, with a potential for future studies on the role of these micronutrients in healthy and diseased states.

Evaluation of aristolochic acid Ι nephrotoxicity in mice via 1H NMR quantitative metabolomics and network pharmacology approaches

Background

Although many studies have shown that herbs containing aristolochic acids can treat various human diseases, AAΙ in particular has been implicated as a nephrotoxic agent.

Methods and results

Here, we detail the nephrotoxic effect of AAΙ via an approach that integrated 1H NMR-based metabonomics and network pharmacology. Our findings revealed renal injury in mice after the administration of AAΙ. Metabolomic data confirmed significant differences among the renal metabolic profiles of control and model groups, with significant reductions in 12 differential metabolites relevant to 23 metabolic pathways. Among them, there were seven important metabolic pathways: arginine and proline metabolism; glycine, serine, and threonine metabolism; taurine and hypotaurine metabolism; ascorbate and aldehyde glycolate metabolism; pentose and glucosinolate interconversion; alanine, aspartate, and glutamate metabolism; and glyoxylate and dicarboxylic acid metabolism. Relevant genes, namely, nitric oxide synthase 1 (NOS1), pyrroline-5-carboxylate reductase 1 (PYCR1), nitric oxide synthase 3 (NOS3) and glutamic oxaloacetic transaminase 2 (GOT2), were highlighted via network pharmacology and molecular docking techniques. Quantitative real-time PCR findings revealed that AAI administration significantly downregulated GOT2 and NOS3 and significantly upregulated NOS1 and PYCR1 expression and thus influenced the metabolism of arginine and proline.

Conclusion

This work provides a meaningful insight for the mechanism of AAΙ renal injury.

Metabolomic-Based Studies of the Intake of Virgin Olive Oil: A Comprehensive Review

Virgin olive oil (VOO) is a high-value product from the Mediterranean diet. Some health and nutritional benefits have been associated with its consumption, not only because of its monounsaturated-rich triacylglycerols but also due to its minor bioactive components. The search for specific metabolites related to VOO consumption may provide valuable information to identify the specific bioactive components and to understand possible molecular and metabolic mechanisms implicated in those health effects. In this regard, metabolomics, considered a key analytical tool in nutritional studies, offers a better understanding of the regulatory functions of food components on human nutrition, well-being, and health. For that reason, the aim of the present review is to summarize the available scientific evidence related to the metabolic effects of VOO or its minor bioactive compounds in human, animal, and in vitro studies using metabolomics approaches.

Plasma metabolomics study in screening and differential diagnosis of multiple primary lung cancer

BACKGROUND

Multiple primary lung cancer (MPLC) is becoming increasingly common in clinical practice. Imaging examination is sometimes difficult to differentiate from intrapulmonary metastasis (IM) or single primary lung cancer (SPLC) before surgery. There is a lack of effective blood biomarkers as an auxiliary diagnostic method.

PARTICIPANTS AND METHODS

A total of 179 patients who were hospitalized and operated in our department from January to June 2019 were collected, and they were divided into SPLC with 136 patients, MPLC with 24 patients, and IM with 19 patients. In total, 96 healthy people without lung cancer were enrolled. Medical history, imaging, and pathology data were assembled from all participants. Plasma metabolomics analysis was performed by quadrupole time-of-flight tandem mass spectrometry, and data were analyzed using SPSS19.0/Simca 14.1/MetaboAnalyst5.0 software. Significant metabolites were selected by variable importance in projection, P value, and fold change. The area under the receiver operating characteristic curve was used to evaluate their diagnostic ability.

RESULTS

There were significant differences in plasma metabolite profiles between IM and MPLC. Seven metabolites were screened out. Two metabolites had higher levels in IM, and five metabolites had higher levels in MPLC. All had favorable discriminating capacity. Phosphatidyl ethanolamine (38:5) showed the highest sensitivity (0.95) and specificity (0.92). It was followed by l -histidine with sensitivity 0.92 and specificity 0.84. l -tyrosine can be used to identify SPLC and MPLC. The panel composed of related metabolites exhibited higher diagnostic ability. Eight principal metabolites caused remarkable differences between healthy people and MPLC, and five of them had area under the curves greater than 0.85, showing good discriminating power.

CONCLUSION

Through the study of plasma metabolomics, it was found that there were obvious differences in the metabolite profiles of MPLC, IM, SPLC, and the healthy population. Some discovered metabolites possessed excellent diagnostic competence with high sensitivity and specificity. They had the potential to act as biomarkers for the screening and differential diagnosis of MPLCs.

Dietary Sources of Anthocyanins and Their Association with Metabolome Biomarkers and Cardiometabolic Risk Factors in an Observational Study

Anthocyanins (ACNs) are (poly)phenols associated with reduced cardiometabolic risk. Associations between dietary intake, microbial metabolism, and cardiometabolic health benefits of ACNs have not been fully characterized. Our aims were to study the association between ACN intake, considering its dietary sources, and plasma metabolites, and to relate them with cardiometabolic risk factors in an observational study. A total of 1351 samples from 624 participants (55% female, mean age: 45 ± 12 years old) enrolled in the DCH-NG MAX study were studied using a targeted metabolomic analysis. Twenty-four-hour dietary recalls were used to collect dietary data at baseline, six, and twelve months. ACN content of foods was calculated using Phenol Explorer and foods were categorized into food groups. The median intake of total ACNs was 1.6mg/day. Using mixed graphical models, ACNs from different foods showed specific associations with plasma metabolome biomarkers. Combining these results with censored regression analysis, metabolites associated with ACNs intake were: salsolinol sulfate, 4-methylcatechol sulfate, linoleoyl carnitine, 3,4-dihydroxyphenylacetic acid, and one valerolactone. Salsolinol sulfate and 4-methylcatechol sulfate, both related to the intake of ACNs mainly from berries, were inversely associated with visceral adipose tissue. In conclusion, plasma metabolome biomarkers of dietary ACNs depended on the dietary source and some of them, such as salsolinol sulfate and 4-methylcatechol sulfate may link berry intake with cardiometabolic health benefits.

Plasma anthocyanins and their metabolites reduce in vitro migration of pancreatic cancer cells, PANC-1, in a FAK- and NF-kB dependent manner: Results from the ATTACH-study a randomized, controlled, crossover trial in healthy subjects

Pancreatic cancer is primarily considered to be a metastatic disease with a low 5-year survival rate. We aimed to detect if plasma-isolated anthocyanins and their metabolites (PAMs) modulate pancreatic cancer cells migration and to describe molecular targets of PAMs in this process. Plasma metabolites were isolated by solid-phase extraction before and after a 28-days intervention trial involving 35 healthy subjects comparing effects of a daily anthocyanin-rich juice intake vs. placebo. Plasma extracts were used for migration and mechanistic in vitro studies as well as for metabolomic analysis. Pancreatic PANC-1 and AsPC-1 were used for migration studies in a Boyden chamber co-cultured with endothelial cells. Expression of adhesion molecules on cancer and endothelial cells were determined by flow cytometry and NF-kB (nuclear factor-kappa B) p65 and focal adhesion kinase activation were measured by immunoassays. UHPLC-MS/MS metabolomics was done in plasma and urine samples. Plasma extracts isolated after the intake of the anthocyanin-rich juice significantly reduced PANC-1 migration, but not AsPC-1 migration. In PANC-1, and to a lower extent in endothelial cells, plasma extracts after juice intake decreased the expression of ß1- and ß4-integrins and intercellular adhesion molecule-1. Pooled plasma from volunteers with the highest inhibition of PANC-1 migration (n = 10) induced a reduction of NF-kB-p65 and FAK-phosphorylation in cancer and in endothelial cells. Concerning metabolites, 14 were significantly altered by juice intervention and PANC-1 migration was inversely associated with the increase of o-coumaric acid and peonidin-3-galactoside. PAMs were associated with lower PANC-1 cell migration opening new strategies for metastatic pancreatic cancer treatment.

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.

An Overview on the Application of Chemometrics Tools in Food Authenticity and Traceability

The use of advanced chemometrics tools in food authenticity research is crucial for managing the huge amount of data that is generated by applying state-of-the-art analytical methods such as chromatographic, spectroscopic, and non-targeted fingerprinting approaches. Thus, this review article provides description, classification, and comparison of the most important statistical techniques that are commonly employed in food authentication and traceability, including methods for exploratory data analysis, discrimination, and classification, as well as for regression and prediction. This literature revision is not intended to be exhaustive, but rather to provide a general overview to non-expert readers in the use of chemometrics in food science. Overall, the available literature suggests that the selection of the most appropriate statistical technique is dependent on the characteristics of the data matrix, but combining complementary tools is usually needed for properly handling data complexity. In that way, chemometrics has become a powerful ally in facilitating the detection of frauds and ensuring the authenticity and traceability of foods.

Exploration of the Gut-Brain Axis through Metabolomics Identifies Serum Propionic Acid Associated with Higher Cognitive Decline in Older Persons

The gut microbiome is involved in nutrient metabolism and produces metabolites that, via the gut−brain axis, signal to the brain and influence cognition. Human studies have so far had limited success in identifying early metabolic alterations linked to cognitive aging, likely due to limitations in metabolite coverage or follow-ups. Older persons from the Three-City population-based cohort who had not been diagnosed with dementia at the time of blood sampling were included, and repeated measures of cognition over 12 subsequent years were collected. Using a targeted metabolomics platform, we identified 72 circulating gut-derived metabolites in a case−control study on cognitive decline, nested within the cohort (discovery n = 418; validation n = 420). Higher serum levels of propionic acid, a short-chain fatty acid, were associated with increased odds of cognitive decline (OR for 1 SD = 1.40 (95% CI 1.11, 1.75) for discovery and 1.26 (1.02, 1.55) for validation). Additional analyses suggested mediation by hypercholesterolemia and diabetes. Propionic acid strongly correlated with blood glucose (r = 0.79) and with intakes of meat and cheese (r > 0.15), but not fiber (r = 0.04), suggesting a minor role of prebiotic foods per se, but a possible link to processed foods, in which propionic acid is a common preservative. The adverse impact of propionic acid on metabolism and cognition deserves further investigation.

Integrated Metabonomics and Network Pharmacology to Reveal the Action Mechanism Effect of Shaoyao Decoction on Ulcerative Colitis

Background

Traditional Chinese medicine (TCM) has the advantage of multi-component and multi-target, which becomes a hot spot in the treatment of numerous diseases. Shaoyao decoction (SYD) is a TCM prescription, which is mainly used to treat damp-heat dysentery clinically, with small side effects and low cost. However, its mechanism remains elusive. The purpose of this study is to explore the mechanism of SYD in the treatment of mice with ulcerative colitis (UC) induced by dextran sulfate sodium (DSS) through metabolomics and network pharmacology, and verify through molecular docking and immunohistochemistry, so as to provide a scientific basis for the role of SYD in the treatment of UC.

Materials and Methods

Firstly, DSS-induced UC models were established and then untargeted metabolomics analysis of feces, livers, serum and urine was performed to determine biomarkers and metabolic pathways closely related to the role of SYD. Besides, network pharmacology was applied to screen the active components and UC-related targets, which was verified by molecular docking. Finally, metabonomics and network pharmacology were combined to draw the metabolite-pathway-target network and verified by immunohistochemistry.

Results

Metabolomics results showed that a total of 61 differential metabolites were discovered in SYD-treated UC with 3 main metabolic pathways containing glycerophospholipid metabolism, sphingolipid metabolism and biosynthesis of unsaturated fatty acids, as well as 8 core targets involving STAT3, IL1B, IL6, IL2, AKT1, IL4, ICAM1 and CCND1. Molecular docking demonstrated that the first five targets had strong affinity with quercetin, wogonin, kaempferol and baicalein. Combined with metabolomics and network pharmacology, sphingolipid signaling pathway, PI3K/AKT-mTOR signaling pathway and S1P3 pathway were identified as the main pathways.

Conclusion

SYD can effectively ameliorate various symptoms and alleviate intestinal mucosal damage and metabolic disorder in DSS induced UC mice. Its effect is mainly related to sphingolipid metabolism, PI3K/AKT-mTOR signaling pathway and S1P3 pathway.

Exploring Next Generation Probiotics for Metabolic and Microbiota Dysbiosis Linked to Xenobiotic Exposure: Holistic Approach

Variation of gut microbiota in metabolic diseases seems to be related to dysbiosis induced by exposure to multiple substances called Microbiota Disrupting Chemicals (MDCs), which are present as environmental and dietary contaminants. Some recent studies have focused on elucidating the alterations of gut microbiota taxa and their metabolites as a consequence of xenobiotic exposures to find possible key targets involved in the severity of the host disease triggered. Compilation of data supporting the triad of xenobiotic-microbiota-metabolic diseases would subsequently allow such health misbalances to be prevented or treated by identifying beneficial microbe taxa that could be Next Generation Probiotics (NGPs) with metabolic enzymes for MDC neutralisation and mitigation strategies. In this review, we aim to compile the available information and reports focused on variations of the main gut microbiota taxa in metabolic diseases associated with xenobiotic exposure and related microbial metabolite profiles impacting the host health status. We performed an extensive literature search using SCOPUS, Web of Science, and PubMed databases. The data retrieval and thorough analyses highlight the need for more combined metagenomic and metabolomic studies revealing signatures for xenobiotics and triggered metabolic diseases. Moreover, metabolome and microbiome compositional taxa analyses allow further exploration of how to target beneficial NGP candidates according to their alleged variability abundance and potential therapeutic significance. Furthermore, this holistic approach has identified limitations and the need of future directions to expand and integrate key knowledge to design appropriate clinical and interventional studies with NGPs. Apart from human health, the beneficial microbes and metabolites identified could also be proposed for various applications under One Health, such as probiotics for animals, plants and environmental bioremediation.

Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations.

METHODS

We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty.

RESULTS

Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk.

CONCLUSIONS

These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.

IDSL.UFA Assigns High-Confidence Molecular Formula Annotations for Untargeted LC/HRMS Data Sets in Metabolomics and Exposomics

Untargeted liquid chromatography/high-resolution mass spectrometry (LC/HRMS) assays in metabolomics and exposomics aim to characterize the small molecule chemical space in a biospecimen. To gain maximum biological insights from these data sets, LC/HRMS peaks should be annotated with chemical and functional information including molecular formula, structure, chemical class, and metabolic pathways. Among these, molecular formulas may be assigned to LC/HRMS peaks through matching theoretical and observed isotopic profiles (MS1) of the underlying ionized compound. For this, we have developed the Integrated Data Science Laboratory for Metabolomics and Exposomics-United Formula Annotation (IDSL.UFA) R package. In the untargeted metabolomics validation tests, IDSL.UFA assigned 54.31-85.51% molecular formula for true positive annotations as the top hit and 90.58-100% within the top five hits. Molecular formula annotations were also supported by tandem mass spectrometry data. We have implemented new strategies to (1) generate formula sources and their theoretical isotopic profiles, (2) optimize the formula hits ranking for the individual and aligned peak lists, and (3) scale IDSL.UFA-based workflows for studies with larger sample sizes. Annotating the raw data for a publicly available pregnancy metabolome study using IDSL.UFA highlighted hundreds of new pregnancy-related compounds and also suggested the presence of chlorinated perfluorotriether alcohols (Cl-PFTrEAs) in human specimens. IDSL.UFA is useful for human metabolomics and exposomics studies where we need to minimize the loss of biological insights in untargeted LC/HRMS data sets. The IDSL.UFA package is available in the R CRAN repository https://cran.r-project.org/package=IDSL.UFA. Detailed documentation and tutorials are also provided at www.ufa.idsl.me.

Integration of Omics Approaches Enhances the Impact of Scientific Research in Environmental Applications

In the original article [...]

Assessing Adherence to Healthy Dietary Habits Through the Urinary Food Metabolome: Results From a European Two-Center Study

Background

Diet is one of the most important modifiable lifestyle factors in human health and in chronic disease prevention. Thus, accurate dietary assessment is essential for reliably evaluating adherence to healthy habits.

Objectives

The aim of this study was to identify urinary metabolites that could serve as robust biomarkers of diet quality, as assessed through the Alternative Healthy Eating Index (AHEI-2010).

Design

We set up two-center samples of 160 healthy volunteers, aged between 25 and 50, living as a couple or family, with repeated urine sampling and dietary assessment at baseline, and 6 and 12 months over a year. Urine samples were subjected to large-scale metabolomics analysis for comprehensive quantitative characterization of the food-related metabolome. Then, lasso regularized regression analysis and limma univariate analysis were applied to identify those metabolites associated with the AHEI-2010, and to investigate the reproducibility of these associations over time.

Results

Several polyphenol microbial metabolites were found to be positively associated with the AHEI-2010 score; urinary enterolactone glucuronide showed a reproducible association at the three study time points [false discovery rate (FDR): 0.016, 0.014, 0.016]. Furthermore, other associations were found between the AHEI-2010 and various metabolites related to the intake of coffee, red meat and fish, whereas other polyphenol phase II metabolites were associated with higher AHEI-2010 scores at one of the three time points investigated (FDR < 0.05 or β ≠ 0).

Conclusion

We have demonstrated that urinary metabolites, and particularly microbiota-derived metabolites, could serve as reliable indicators of adherence to healthy dietary habits.

Clinical Trail Registration

www.ClinicalTrials.gov, Identifier: NCT03169088.

Screening of Serum Biomarkers of Coal Workers' Pneumoconiosis by Metabolomics Combined with Machine Learning Strategy

Pneumoconiosis remains one of the most serious global occupational diseases. However, effective treatments are lacking, and early detection is crucial for disease prevention. This study aimed to explore serum biomarkers of occupational coal workers’ pneumoconiosis (CWP) by high-throughput metabolomics, combining with machine learning strategy for precision screening. A case–control study was conducted in Beijing, China, involving 150 pneumoconiosis patients with different stages and 120 healthy controls. Metabolomics found a total of 68 differential metabolites between the CWP group and the control group. Then, potential biomarkers of CWP were screened from these differential metabolites by three machine learning methods. The four most important differential metabolites were identified as benzamide, terazosin, propylparaben and N-methyl-2-pyrrolidone. However, after adjusting for the influence of confounding factors, including age, smoking, drinking and chronic diseases, only one metabolite, propylparaben, was significantly correlated with CWP. The more severe CWP was, the higher the content of propylparaben in serum. Moreover, the receiver operating characteristic curve (ROC) of propylparaben showed good sensitivity and specificity as a biomarker of CWP. Therefore, it was demonstrated that the serum metabolite profiles in CWP patients changed significantly and that the serum metabolites represented by propylparaben were good biomarkers of CWP.

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.

Apolipoprotein E and sex modulate fatty acid metabolism in a prospective observational study of cognitive decline

BACKGROUND

Fatty acids play prominent roles in brain function as they participate in structural, metabolic and signaling processes. The homeostasis of fatty acids and related pathways is known to be impaired in cognitive decline and dementia, but the relationship between these metabolic disturbances and common risk factors, namely the ɛ4 allele of the apolipoprotein E (ApoE-ɛ4) gene and sex, remains elusive.

METHODS

In order to investigate early alterations associated with cognitive decline in the fatty acid-related serum metabolome, we here applied targeted metabolomics analysis on a nested case-control study (N=368), part of a prospective population cohort on dementia.

RESULTS

When considering the entire study population, circulating levels of free fatty acids, acyl-carnitines and pantothenic acid were found to be increased among those participants who had greater odds of cognitive decline over a 12-year follow-up. Interestingly, stratified analyses indicated that these metabolomic alterations were specific for ApoE-ɛ4 non-carriers and women.

CONCLUSIONS

Altogether, our results highlight that the regulation of fatty acids and related metabolic pathways during ageing and cognitive decline depends on complex inter-relationships between the ApoE-ε4 genotype and sex. A better understanding of the ApoE-ɛ4 and sex dependent modulation of metabolism is essential to elucidate the individual variability in the onset of cognitive decline, which would help develop personalized therapeutic approaches.

Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives

The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental ætiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount.

Food and Microbiota Metabolites Associate with Cognitive Decline in Older Subjects: A 12-Year Prospective Study

SCOPE

Diet is considered an important modulator of cognitive decline and dementia, but the available evidence is, however, still fragmented and often inconsistent.

METHODS AND RESULTS

The article studies the long-term prospective Three-City Cohort, which consists of two separate nested case-control sample sets from different geographic regions (Bordeaux, n = 418; Dijon, n = 424). Cognitive decline is evaluated through five neuropsychological tests (Mini-Mental State Examination, Benton Visual Retention Test, Isaac's Set Test, Trail-Making Test part A, and Trail-Making Test part B). The food-related and microbiota-derived circulating metabolome is studied in participants free of dementia at baseline, by subjecting serum samples to large-scale quantitative metabolomics analysis. A protective association is found between metabolites derived from cocoa, coffee, mushrooms, red wine, the microbial metabolism of polyphenol-rich foods, and cognitive decline, as well as a negative association with metabolites related to unhealthy dietary components, such as artificial sweeteners and alcohol.

CONCLUSION

These results provide insight into the early metabolic events that are associated with the later risk to develop cognitive decline within the crosstalk between diet, gut microbiota and the endogenous metabolism, which can help identify potential targets for preventive and therapeutic strategies to preserve cognitive health.

Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.

Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.

High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology

Urine is a noninvasive biofluid that is rich in polar metabolites and well suited for metabolomic epidemiology. However, because of individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenges in untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Although numerous urine normalization methods have been implemented (e.g., creatinine, specific gravity-SG), most are manual and, therefore, not practical for population-based studies. To address this issue, we developed a method to measure SG in 96-well-plates using a refractive index detector (RID), which exhibited accuracy within 85-115% and <3.4% precision. Bland-Altman statistics showed a mean deviation of -0.0001 SG units (limits of agreement: -0.0014 to 0.0011) relative to a hand-held refractometer. Using this RID-based SG normalization, we developed an automated LC-MS workflow for untargeted urinary metabolomics in a 96-well-plate format. The workflow uses positive and negative ionization HILIC chromatography and acquires mass spectra in data-independent acquisition (DIA) mode at three collision energies. Five technical internal standards (tISs) were used to monitor data quality in each method, all of which demonstrated raw coefficients of variation (CVs) < 10% in the quality controls (QCs) and < 20% in the samples for a small cohort (n = 87 urine samples, n = 22 QCs). Application in a large cohort (n = 842 urine samples, n = 248 QCs) demonstrated CVQC < 5% and CVsamples < 16% for 4/5 tISs after signal drift correction by cubic spline regression. The workflow identified >540 urinary metabolites including endogenous and exogenous compounds. This platform is suitable for performing urinary untargeted metabolomic epidemiology and will be useful for applications in population-based molecular phenotyping.

Mechanistic Insights into Alzheimer's Disease Unveiled through the Investigation of Disturbances in Central Metabolites and Metabolic Pathways

Hydrophilic metabolites are closely involved in multiple primary metabolic pathways and, consequently, play an essential role in the onset and progression of multifactorial human disorders, such as Alzheimer’s disease. This review article provides a comprehensive revision of the literature published on the use of mass spectrometry-based metabolomics platforms for approaching the central metabolome in Alzheimer’s disease research, including direct mass spectrometry, gas chromatography-mass spectrometry, hydrophilic interaction liquid chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry. Overall, mounting evidence points to profound disturbances that affect a multitude of central metabolic pathways, such as the energy-related metabolism, the urea cycle, the homeostasis of amino acids, fatty acids and nucleotides, neurotransmission, and others.

Analytical challenges in human exposome analysis with focus on environmental analysis combined with metabolomics

Environmental factors, such as chemical exposures, are likely to play a crucial role in the development of several human chronic diseases. However, how the specific exposures contribute to the onset and progress of various diseases is still poorly understood. In part, this is because comprehensive characterization of the chemical exposome is a highly challenging task, both due to its complex dynamic nature as well as due to the analytical challenges. Herein, the analytical challenges in the field of exposome research are reviewed, with specific emphasis on the sampling, sample preparation, and analysis, as well as challenges in the compound identification. The primary focus is on the human chemical exposome, that is, exposures to mixtures of environmental chemicals and its impact on human metabolome. In order to highlight the recent progress in the exposome research in relation to human health and disease, selected examples of human exposome studies are presented.