Metabolome-wide Association Studies Research Articles

Identifying the metabolomics signatures of genetic variants identified for Alzheimer’s disease

AbstractBackgroundIntegrating genomics and metabolomics data in humans has the potential to unravel mechanistic insights about and look for evidence of metabolite causality in complex disorders.MethodWe conducted metabolome‐wide association studies (MWAS) on Alzheimer’s disease (AD) associated loci using ultraperformance liquid chromatography–mass spectrometry (UPLC‐MS) in two cohorts of the Airwave Study (1). Expanding our previous study focusing on ABCA7 (2), current MWAS investigated genetic variants in all other loci based on the largest and most recent GWAS on AD (3) to identify metabolic features associated with AD. We performed genome‐wide association studies in the Airwave Study (N = 3,708) to obtain genetic instruments of those features to run Mendelian randomisation (MR) and colocalization analyses.ResultOur MWAS identified 104 features associated with AD loci at metabolome‐wide significance level. MR analysis was conducted on 29 out of 104 features with at least three genetic instruments, showing the potentially causal effects of four metabolic features associated with rs1800978 (ABCA1) and rs587709 (LILRB2). Our in‐house annotation pipeline mapped those features as sphingomyelin (SM)(18:1/20:1), putative sphingolipid, and N‐Acylphosphatidylethanolamines (NAPE). We conducted pairwise colocalisation and HyPrColoc analysis, supporting evidence of common causal variants between SM(18:1/20:1), putative sphingolipid, and NAPE with AD (posterior probability of shared causal variant >0.8).ConclusionThis analysis has broadened our insight into the metabolic signatures of AD, with further study needed to dissect the underlying molecular mechanisms.

Metabolic Perturbations Associated with an Exposure Mixture of Per- and Polyfluoroalkyl Substances in the Atlanta African American Maternal-Child Cohort.

Prenatal exposure to single chemicals belonging to the per- and polyfluoroalkyl substances (PFAS) family is associated with biological perturbations in the mother, fetus, and placenta, plus adverse health outcomes. Despite our knowledge that humans are exposed to multiple PFAS, the potential joint effects of PFAS on the metabolome remain largely unknown. Here, we leveraged high-resolution metabolomics to identify metabolites and metabolic pathways perturbed by exposure to a PFAS mixture during pregnancy. Targeted assessment of perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorooctanesulfonic acid (PFOS), and perfluorohexanesulfonic acid (PFHxS), along with untargeted metabolomics profiling, were conducted on nonfasting serum samples collected from pregnant African Americans at 6-17 weeks gestation. We estimated the overall mixture effect and partial effects using quantile g-computation and single-chemical effects using linear regression. All models were adjusted for maternal age, education, parity, early pregnancy body mass index, substance use, and gestational weeks at sample collection. Our analytic sample included 268 participants and was socioeconomically diverse, with the majority receiving public health insurance (78%). We observed 13.3% of the detected metabolic features were associated with the PFAS mixture (n = 1705, p < 0.05), which was more than any of the single PFAS chemicals. There was a consistent association with metabolic pathways indicative of systemic inflammation and oxidative stress (e.g., glutathione, histidine, leukotriene, linoleic acid, prostaglandins, and vitamins A, C, D, and E metabolism) across all metabolome-wide association studies. Twenty-six metabolites were validated against authenticated compounds and associated with the PFAS mixture (p < 0.05). Based on quantile g-computation weights, PFNA contributed the most to the overall mixture effect for γ-aminobutyric acid (GABA), tyrosine, and uracil. In one of the first studies of its kind, we demonstrate the feasibility and utility of using methods designed for exposure mixtures in conjunction with metabolomics to assess the potential joint effects of multiple PFAS chemicals on the human metabolome. We identified more pronounced metabolic perturbations associated with the PFAS mixture than for single PFAS chemicals. Taken together, our findings illustrate the potential for integrating environmental mixture analyses and high-throughput metabolomics to elucidate the molecular mechanisms underlying human health.

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

Polybrominated biphenyls (PBB) and polychlorinated biphenyls (PCB) are persistent organic pollutants with potential endocrine-disrupting effects linked to adverse health outcomes. In this study, we utilize high-resolution metabolomics (HRM) to identify internal exposure and biological responses underlying PCB and multigenerational PBB exposure for participants enrolled in the Michigan PBB Registry. HRM profiling was conducted on plasma samples collected from 2013 to 2014 from a subset of participants enrolled in the Michigan PBB Registry, including 369 directly exposed individuals (F0) who were alive when PBB mixtures were accidentally introduced into the food chain and 129 participants exposed to PBB in utero or through breastfeeding, if applicable (F1). Metabolome-wide association studies were performed for PBB-153 separately for each generation and (PCB-118, PCB-138, PCB-153, and PCB-180) in the two generations combined, as both had direct PCB exposure. Metabolite and metabolic pathway alterations were evaluated following a well-established untargeted HRM workflow. Mean levels were [standard deviation (SD): 13.9] for PBB-153 and (SD: 0.788) for . Sixty-two and 26 metabolic features were significantly associated with PBB-153 in F0 and F1 [false discovery rate (FDR) ], respectively. There were 2,861 features associated with (FDR ). Metabolic pathway enrichment analysis using a bioinformatics tool revealed perturbations associated with in numerous oxidative stress and inflammation pathways (e.g., carnitine shuttle, glycosphingolipid, and vitamin B9 metabolism). Metabolic perturbations associated with PBB-153 in F0 were related to oxidative stress (e.g., pentose phosphate and vitamin C metabolism) and in F1 were related to energy production (e.g., pyrimidine, amino sugars, and lysine metabolism). Using authentic chemical standards, we confirmed the chemical identity of 29 metabolites associated with levels (level 1 evidence). Our results demonstrate that serum PBB-153 is associated with alterations in inflammation and oxidative stress-related pathways, which differed when stratified by generation. We also found that was associated with the downregulation of important neurotransmitters, serotonin, and 4-aminobutanoate. These findings provide novel insights for future investigations of molecular mechanisms underlying PBB and PCB exposure on health. https://doi.org/10.1289/EHP12657.

The impact of heat exposures on biomarkers of AKI and plasma metabolome among agricultural and non-agricultural workers

BackgroundAgricultural workers are consistently exposed to elevated heat exposures and vulnerable to acute kidney injury. The underlying pathophysiology and detailed molecular mechanisms of AKI among agricultural workers, and the disproportionate burden of HRI and heat stress exposure are not well understood, especially at the level of cellular metabolism. ObjectiveThe aim of this study was to examine the impact of heat exposures on renal biomarkers and on the human metabolome via untargeted high-resolution metabolomics among agricultural and non-agricultural workers. MethodsBlood and urine samples were collected pre- and post-work shift from 63 agricultural workers and 27 non– agricultural workers. We evaluated pre- and post-work shift renal biomarkers and completed untargeted metabolomics using high-resolution mass spectrometry with liquid chromatography. Metabolome-wide association studies (MWAS) models identified the metabolic features differentially expressed between agricultural workers and non-agricultural workers. ResultsMedian values of pre-shift creatinine and osteopontin (p < 0.05) were higher for agricultural workers than non-agricultural workers. Metabolic pathway enrichment analyses revealed 27 diverse pathways differed between agricultural workers and non-agricultural workers (p < 0.05) including TCA cycle and urea cycle, carbohydrate metabolism, histidine metabolism and evidence for altered microbiome shikimate pathway. ConclusionThis is the first investigation on the metabolic pathways that are affected among agricultural workers who are exposed to heat compared to non-heat exposed workers. This study shows extensive responses of central metabolic systems to heat exposures that impact human health.

The role of metabolic perturbations in mediating the effects of ambient air pollution on lung cancer in the cancer prevention studies.

10541 Background: Exposure to ambient air pollution is an established risk factor for lung cancer. Investigation on the underlying mechanisms of air pollution carcinogenicity remains challenging due to the complex composition of air pollution and a lack of sensitive biomarkers to accurately measure exposure and corresponding response. To address the knowledge gaps, we applied high-resolution metabolomics to identify metabolic signatures of exogenous air pollution exposures and endogenous processes involved in lung carcinogenesis. Methods: A total of 683 matched lung cancer and control pairs within the established Cancer Prevention Study-II (CPS-II) Nutrition and CPS-3 cohorts were included in this study. The plasma metabolome was profiled with ultrahigh-performance liquid chromatography-tandem mass spectrometry. Assessment of exposure to six ambient air pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM10), fine particulate matter (PM2.5), sulfur dioxide (SO2), and ozone (O3), was conducted using spatiotemporally-resolved models based on residential address at blood draw and calculated based on yearly average levels. We conducted metabolome-wide association studies with multiple linear regression models to assess associations of ambient air pollution and lung cancer with a meet-in-the-middle approach. Models controlled for potential confounders and covariates including age at blood draw, sex, race, body mass index, alcohol consumption, smoking status, passive smoke exposure, vegetable and fruit intake, and educational level. Metabolites significant at the FDR &lt; 0.2 level in the air pollution model were analyzed in the lung cancer model. High-dimensional mediation analysis was used as a secondary analysis to compare results from the meet-in-the-middle analysis. Results: Among 1,204 metabolic features extracted from the blood samples, seven features were significantly associated with air pollution exposure and lung cancer incidence at the FDR &lt; 0.2 level in the meet-in-the-middle analysis. Six features were significant at the p &lt; 0.01 level through high-dimensional mediation analysis. All confirmed metabolites are enriched within peptide, lipid, and amino acid pathways. Gamma-glutamylglutamine and gamma-glutamylmethionine were statistically significantly associated with CO, NO2, and PM10 exposure and lung cancer incidence in both analyses. Conclusions: This is the largest prospective metabolomics study examining biological perturbations associated with air pollution exposure and lung cancer incidence. Peptide metabolism may play an important role in mediating the association between air pollution and lung cancer. Findings from this study will lay the foundation for future studies to further understand the biological mechanisms underlying the carcinogenicity of air pollution.

Abstract P206: Later Age at Menarche is Associated With Metabolite Signatures for Cardiovascular Diseases: A Metabolome-Wide Association Study of Racially and Ethnically Diverse Women From the Boston Birth Cohort

Introduction: Later menarche onset portends lower risk of cardiometabolic outcomes in adults (e.g., obesity, diabetes, hypertension, and myocardial infarction) and in pregnancy (e.g., preterm birth). However, the underlying mechanisms underlying these associations are unclear. Metabolome-wide association studies afford an opportunity to identify mechanistic pathways. Objective: To identify metabolome signatures of age at menarche in a diverse sample of women. Methods: This analysis includes 1301 women from the Boston Birth Cohort who were enrolled 24-72 hrs. after delivery, when we collected maternal plasma sample for metabolome and information on maternal age at menarche (range 7-18y) via a standard questionnaire interview. We used linear regression and restricted cubic splines to examine the association between age at menarche and metabolites. We adjusted for maternal year of birth, place of birth, education, smoking status, Mediterranean diet score, pre-pregnancy BMI, hypertensive disorders, and diabetes. Results: Of the 1301 study women, 63.7% identified as Black and 21.3% as Hispanic. Maternal age at delivery was 28.3y ±6.6y and age at menarche was 12.87y ±1.95y. Of the 389 metabolites we examined, 63 were associated with late menarche (&gt;15y vs. menarche 12-13y; Fig., A ). A 1-yr increment in menarche age was associated with 7 metabolites ( Fig., B ); of these, imidazole propionate and C18:2 SM were (inverse) linear dose-response ( Fig., C ). Other metabolites, including some implicated in cardiovascular disease (e.g., dimethylguanidino valeric acid), showed a threshold effect starting at menarche &gt;14 years (Fig., C). Conclusion: In this racially and ethnically diverse cohort of women from Boston, later age at menarche is associated with many metabolites, including inverse associations with metabolites that have been previously implicated in cardiovascular disease. Our findings help inform mechanistic pathways and that, if replicated, could be targeted for cardiometabolic prediction and prevention.

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.

Emerging trends and applications of metabolomics in food science and nutrition.

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

Metabolomics insights into the prenatal exposure effects of polybrominated diphenyl ethers on neonatal birth outcomes.

Effects of polybrominated diphenyl ethers (PBDEs) on neonatal birth outcomes vary across previous studies, and the related mechanism investigation remains poorly understood, especially at the metabolic level. To evaluate the associations between prenatal PBDEs exposure and neonatal birth outcomes including gestational age, neonatal weight, birth length, head circumference (HC), Apgar score at 1 min (Apgar1) and 5 min, and further reveal the underlying metabolic disorders in a population-based birth cohort study. Gas chromatography-triple quadrupole tandem mass spectrometry (GC-MS/MS) based targeted method and GC-MS based untargeted method were respectively conducted to obtain PBDE levels and metabolic profiles of 200 placental tissue samples from a typical e-waste recycling area (Guiyu) and reference area (Haojiang) in China. Spearman correlation and regression analyses were applied to assess the associations between the placental PBDE levels and birth outcomes. Metabolome-wide association studies and the meet-in-the-middle approach were employed to explore disruptions linking PBDE exposures and the corresponding adverse birth outcomes. Eight out of 27 PBDE congeners were detected in placenta with more than 50% frequency in at least one district and significantly higher in Guiyu than those in Haojiang. The lower HC and Apgar1 had significant associations with PBDE exposures after adjustment for potential confounders. A total of 66, 16 and 14 metabolites were significantly correlated with PBDE exposures, HC and Apgar1, respectively. 4 and 12 PBDE-related metabolites were significantly associated with the risks of decreasing neonatal HC and Apgar1. The disrupted metabolites were mainly involved in the pentose phosphate pathway, ascorbate metabolism, threonine metabolism, butanoate metabolism, lipid metabolism, and arginine biosynthesis. In this birth cohort, higher placental PBDE levels were significantly associated with the lower HC and Apgar1. The associations might be modified by multiple metabolic disturbances through increasing oxidative stress, mediating neurotoxicity, maternal gut microbiota dysbiosis and vasodilatation regulation.

Statistical power of transcriptome-wide association studies.

Transcriptome-Wide Association Studies (TWASs) have become increasingly popular in identifying genes (or other endophenotypes or exposures) associated with complex traits. In TWAS, one first builds a predictive model for gene expressions using an expression quantitative trait loci (eQTL) data set in stage 1, then tests the association between the predicted gene expression and a trait based on a large, independent genome-wide association study (GWAS) data set in stage 2. However, since the sample size of the eQTL data set is usually small and the coefficient of multiple determination (i.e., ) of the model for many genes is also small, a question of interest is to what extent these factors affect the statistical power of TWAS. In addition, in contrast to a standard (univariate) TWAS (UV-TWAS) considering only a single gene at a time, multivariate TWAS (MV-TWAS) methods have recently emerged to account for the effects of multiple genes, or a gene's nonlinear effects, simultaneously. With the absence of the power analysis for these MV-TWAS methods, it would be of interest to investigate whether one can gain or lose power by using the newly proposed MV-TWAS instead of UV-TWAS. In this paper, we first outline a general method for sample size/power calculations for two-sample TWAS, then use real data-the Alzheimer's Disease Neuroimaging Initiative (ADNI) expression quantitative trait loci (eQTL) data and the Genotype-Tissue Expression (GTEx) eQTL data for stage 1, the International Genomics of Alzheimer's Project Alzheimer's disease (AD) GWAS summary data and UK Biobank (UKB) individual-level data for stage 2-to empirically address these questions. Our most important conclusions are the following. First, a sample size of a few thousands (~8000) would suffice in stage 1, where the power of TWAS would be more determined by cis-heritability of gene expression. Second, as in the general case of simple regression versus multiple regression, the power of MV-TWAS may be higher or lower than that of UV-TWAS, depending on the specific relationships among the GWAS trait and multiple genes (or linear and nonlinear terms of the same gene's expression levels), such as their correlations and effect sizes. Interestingly, several top genes with large power gains in MV-TWAS (over that in UV-TWAS)were known to be (and in our data more significantly) associated with AD. We also reached similar conclusions in an application to the GTEx whole blood gene expression data and UKB GWAS data of high-density lipoprotein cholesterol. The proposed method and the conclusions are expected to be useful in planning and designing future TWAS and other related studies (e.g., Proteome- or Metabolome-Wide Association Studies) when determining the sample sizes for the two stages.

Genetic Architecture of Untargeted Lipidomics in Cardiometabolic-Disease Patients Combines Strong Polygenic Control and Pleiotropy.

Analysis of the genetic control of small metabolites provides powerful information on the regulation of the endpoints of genome expression. We carried out untargeted liquid chromatography–high-resolution mass spectrometry in 273 individuals characterized for pathophysiological elements of the cardiometabolic syndrome. We quantified 3013 serum lipidomic features, which we used in both genome-wide association studies (GWAS), using a panel of over 2.5 M imputed single-nucleotide polymorphisms (SNPs), and metabolome-wide association studies (MWAS) with phenotypes. Genetic analyses showed that 926 SNPs at 551 genetic loci significantly (q-value < 10−8) regulate the abundance of 74 lipidomic features in the group, with evidence of monogenic control for only 22 of these. In addition to this strong polygenic control of serum lipids, our results underscore instances of pleiotropy, when a single genetic locus controls the abundance of several distinct lipid features. Using the LIPID MAPS database, we assigned putative lipids, predominantly fatty acyls and sterol lipids, to 77% of the lipidome signals mapped to the genome. We identified significant correlations between lipids and clinical and biochemical phenotypes. These results demonstrate the power of untargeted lipidomic profiling for high-density quantitative molecular phenotyping in human-genetic studies and illustrate the complex genetic control of lipid metabolism.

Abstract MP45: Metabolomic Profiling Provides Novel Insights Into The Biochemical Mechanism Of Mental Stress-induced Ischemia

Introduction: Mental stress-induced myocardial ischemia (MSI) is a marker of future cardiovascular events. The underlying biochemical mechanisms of this cardiovascular condition are poorly understood. Our objective was to use metabolic profiling to identify biochemical changes associated with mental stress (MS) in patients with stable coronary artery disease (CAD) and investigate differential pathways that are involved in MSI. Methods: A total of 110 Individuals with stable CAD who participated in the Mental Stress Ischemia Prognosis Study (MIPS) underwent a standardized mental stress to provoke of myocardial ischemia, detected with single-photon emission tomography. Plasma samples were obtained before and after MS at 45 and 90 minutes and metabolomic profiling was performed using liquid chromatography/mass spectrometry using a confirmed 500 compounds. Metabolomic features and pathways associated with ischemia using metabolome-wide association studies and Mummichog, respectively. Results: The sample involved a subset case-control for (MSI+ vs MSI-). At 45 minutes and 90 minutes, there were significant stress-induced changes in several compounds. There was a significant upregulation of metabolites associated with lipid signaling et ( p = 0.025, Inosine diphosphate). A comparison between the MSI+ and MS- groups showed differential upregulation of DNA methylation metabolites ( p = .036, 5-Methylcytosine hydrochloride) at 45 minutes and microbiome-related metabolites at 90 minutes (P=0.041, N, N-dimethyl-1,4-phenylenediamine). Pathway analyses were consistent with the aforementioned findings. Conclusion: Upregulation of metabolic pathways involved in DNA methylation and microbial metabolism is differentially associated with MSI. These findings provide novel mechanistic insights into the association between acute mental stress and myocardial ischemia as a marker for cardiovascular vulnerability in patients with CAD

The Oxidative Potential of Fine Particulate Matter and Biological Perturbations in Human Plasma and Saliva Metabolome.

Particulate oxidative potential may comprise a key health-relevant parameter of particulate matter (PM) toxicity. To identify biological perturbations associated with particulate oxidative potential and examine the underlying molecular mechanisms, we recruited 54 participants from two dormitories near and far from a congested highway in Atlanta, GA. Fine particulate matter oxidative potential ("FPMOP") levels at the dormitories were measured using dithiothreitol assay. Plasma and saliva samples were collected from participants four times for longitudinal high-resolution metabolic profiling. We conducted metabolome-wide association studies to identify metabolic signals with FPMOP. Leukotriene metabolism and galactose metabolism were top pathways associated with ≥5 FPMOP-related indicators in plasma, while vitamin E metabolism and leukotriene metabolism were found associated with most FPMOP indicators in saliva. We observed different patterns of perturbed pathways significantly associated with water-soluble and -insoluble FPMOPs, respectively. We confirmed five metabolites directly associated with FPMOP, including hypoxanthine, histidine, pyruvate, lactate/glyceraldehyde, and azelaic acid, which were implications of perturbations in acute inflammation, nucleic acid damage and repair, and energy perturbation. The unique metabolic signals were specific to FPMOP, but not PM mass, providing initial indication that FPMOP might constitute a more sensitive, health-relevant measure for elucidating etiologies related to PM2.5 exposures.

Metabolomics of neurological disorders in India.

Metabolomics is the comprehensive study of the metabolome and its alterations within biological fluids and tissues. Over the years, applications of metabolomics have been explored in several areas, including personalised medicine in diseases, metabolome-wide association studies (MWAS), pharmacometabolomics and in combination with other branches of omics such as proteomics, transcriptomics and genomics. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the major analytical techniques widely employed in metabolomics. In addition, MS is coupled with chromatography techniques like gas chromatography (GC) and liquid chromatography (LC) to separate metabolites before analysis. These analytical techniques have made possible identification and quantification of large numbers of metabolites, encompassing characterization of diseases and facilitating a systematic and rational therapeutic strategy based on metabolic patterns. In recent years, the metabolomics approach has been used to obtain a deeper insight into the underlying biochemistry of neurodegenerative disorders and the discovery of biomarkers of clinical implications. The current review mainly focuses on an Indian perspective of metabolomics for the identification of metabolites and metabolic alterations serving as potential diagnostic biomarkers for neurological diseases including acute spinal cord injury, amyotrophic lateral sclerosis, tethered cord syndrome, spina bifida, stroke, Parkinson's disease, glioblastoma and neurological disorders with inborn errors of metabolism.

High-Resolution Exposomics and Metabolomics Reveals Specific Associations in Cholestatic Liver Diseases.

Progress in development of prognostic and therapeutic options for the rare cholestatic liver diseases, primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC), is hampered by limited knowledge of their pathogeneses. In particular, the potential role of hepatotoxic and/or metabolism‐altering environmental chemicals in the pathogenesis of these diseases remains relatively unstudied. Moreover, the extent to which metabolic pathways are altered due to ongoing cholestasis and subsequent liver damage or possibly influenced by hepatotoxic chemicals is poorly understood. In this study, we applied a comprehensive exposomics‐metabolomics approach to uncover potential pathogenic contributors to PSC and PBC. We used untargeted high‐resolution mass spectrometry to characterize a wide range of exogenous chemicals and endogenous metabolites in plasma and tested them for association with disease. Exposome‐wide association studies (EWAS) identified environmental chemicals, including pesticides, additives and persistent pollutants, that were associated with PSC and/or PBC, suggesting potential roles for these compounds in disease pathogenesis. Metabolome‐wide association studies (MWAS) found disease‐associated alterations to amino acid, eicosanoid, lipid, co‐factor, nucleotide, mitochondrial and microbial metabolic pathways, many of which were shared between PSC and PBC. Notably, this analysis implicates a potential role of the 5‐lipoxygenase pathway in the pathogenesis of these diseases. Finally, EWAS × MWAS network analysis uncovered linkages between environmental agents and disrupted metabolic pathways that provide insight into potential mechanisms for PSC and PBC. Conclusion: This study establishes combined exposomics‐metabolomics as a generalizable approach to identify potentially pathogenic environmental agents and enumerate metabolic alterations that may impact PSC and PBC, providing a foundation for diagnostic and therapeutic strategies.

Prenatal per- and polyfluoroalkyl substance (PFAS) exposure, metabolomic perturbation, and lower birth weight in African American women: a meet-in-the-middle approach

BACKGROUND AND AIM: Prenatal exposures to per- and polyfluoroalkyl substances (PFAS) have been linked to restricted fetal growth. Pregnant African American (AA) women are disproportionately exposed to higher levels of select PFAS and experience higher rates of low birth weight than other races/ethnicities. However, the detailed molecular mechanisms underlying these disparities remain largely unknown. The purpose of this study is to investigate biological pathways and intermediate biomarkers underlying association between prenatal PFAS exposure and lower birth weight using high resolution plasma metabolomics among pregnant AA cohort. METHODS: Serum perfluorohexane sulfonic acid, perfluorooctane sulfonic acid, perfluorooctanoic acid, and perfluorononanoic acid meaurements, and untargeted plasma metabolomics profiling were conducted in 312 pregnant AA women in Atlanta, Georgia at 8-14 weeks of gestation using fasting blood samples. We utilized a high-resolution metabolomics workflow, including Metabolome-Wide Association Studies (MWAS), enriched pathway analyses, and chemical annotations, with the ‘meet-in-the-middle’ approach to identify the potential biological pathways and intermediate biomarkers of the PFAS-birth weight relationship. RESULTS:Among 25,516 metabolomic features extracted from the polar and nonpolar analytical columns, 320 features were significantly associated with serum PFAS and birth weight in the MWAS. After further validation using online databases or authentic chemical standards in the laboratory, we found 8 confirmed biomarkers including glycine, α-ketoisovalerate, uric acid, monoradylglycerol, and some polyunsaturated fatty acids and steroid hormones. In addition, 17 biological pathways associated with serum PFAS and birth weight were identified, including linoleate, vitamin D3, branched-chain amino acid, and fatty acid metabolism. The significant biomarkers and biological pathways identified were mostly involved in amino acid, lipid, and fatty acid metabolism or inflammation and oxidative stress. CONCLUSIONS:This study among pregnant AA found several maternal metabolic perturbations associated with serum PFAS concentrations and birth weight. Future studies are warranted to validate these findings and completely apprehend the underlying biological mechanisms. KEYWORDS: PFAS, metabolomics, birth weight

Exposure to legacy and novel perfluoroalkyl substance disturbs the metabolic homeostasis in pregnant women and fetuses: A metabolome-wide association study.

Perfluoroalkyl substances (PFASs) exist extensively and several of these have been verified to be toxic. Prenatal exposure to PFASs has attracted much attention. Metabolome-wide association analyses can be used to explore the toxicity mechanisms of PFASs by identifying associated biomarkers. To evaluate associations between the metabolites in maternal and cord serum and internal exposure to several common PFASs. Paired maternal and cord serum samples were collected from 84 pregnant women who gave birth between 2015 and 2016. Seven legacy and two novel PFASs were measured. A nontarget metabolomic method and an iterative metabolite annotation based on metabolic pathways were applied to characterize the metabolic profiles. Linear regression adjusted with the false discovery rate and covariates was used to indicate the associations. A total of 279 features in maternal serum and 338 features in cord serum were identified as metabolites associated with PFAS exposure. Perfluorooctanoic acid (PFOA) and perfluorohexane sulfonic acid (PFHxS) were two PFASs associated with more metabolites, while the two novel chlorinated polyfluorinated ether sulfonic acids (Cl-PFESAs) showed less relevance to the metabolome. With pathway enrichment analysis, we found that three fatty acid metabolisms and retinol metabolism were correlated with PFAS exposure in maternal blood, and that sterol metabolism showed the correlation in both maternal serum and cord serum. We identified metabolites and pathways in pregnant women and fetuses associated with the exposure to several PFAS, indicating a promising application for metabolome-wide association studies. Additional research is needed to confirm causation.

DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.

Multiple-testing correction in metabolome-wide association studies

BackgroundThe search for statistically significant relationships between molecular markers and outcomes is challenging when dealing with high-dimensional, noisy and collinear multivariate omics data, such as metabolomic profiles. Permutation procedures allow for the estimation of adjusted significance levels without assuming independence among metabolomic variables. Nevertheless, the complex non-normal structure of metabolic profiles and outcomes may bias the permutation results leading to overly conservative threshold estimates i.e. lower than those from a Bonferroni or Sidak correction.MethodsWithin a univariate permutation procedure we employ parametric simulation methods based on the multivariate (log-)Normal distribution to obtain adjusted significance levels which are consistent across different outcomes while effectively controlling the type I error rate. Next, we derive an alternative closed-form expression for the estimation of the number of non-redundant metabolic variates based on the spectral decomposition of their correlation matrix. The performance of the method is tested for different model parametrizations and across a wide range of correlation levels of the variates using synthetic and real data sets.ResultsBoth the permutation-based formulation and the more practical closed form expression are found to give an effective indication of the number of independent metabolic effects exhibited by the system, while guaranteeing that the derived adjusted threshold is stable across outcome measures with diverse properties.

Metabolites and metabolic pathways associated with glucocorticoid resistance in pregnant African-American women

Glucocorticoid resistance (GR) is associated with exposure to chronic stress and an increased risk of metabolic and inflammatory disorders in both animal and human populations. Studies on ethnic disparities highlight the African-American (AA) population as having a high propensity to both GR and chronic stress exposure. Glucocorticoids and inflammation play a very important role in pregnancy outcome and fetal development. To date, however, the metabolites and metabolic pathways associated with GR during pregnancy have not been identified, obscuring the mechanisms by which adverse health consequences arise, and thus impeding targeted therapeutic intervention. The objective of this study was to perform untargeted high-resolution metabolomics (HRM) profiling on 273 pregnant AA women, to identify metabolites and metabolic pathways associated with GR during the first trimester of pregnancy and to evaluate their cross-sectional association with birth outcomes and psychosocial variables related to chronic stress exposure. For this study, GR was determined by the concentration of dexamethasone required for 50% inhibition (Dex IC50) of the cytokine tumor-necrosis factor alpha (TNF-alpha) release in vitro in response to a standard dose of lipopolysaccharide. The results for Metabolome-Wide Association Studies (MWAS) and pathway enrichment analysis for serum metabolic associations with Dex IC50, showed energy (nicotinamide and TCA cycle), amino acid, and glycosphingolipid metabolism as top altered pathways. Bioinformatic analysis showed that GR, as indicated by elevated Dex IC50 in the pregnant women, was associated with increased inflammatory metabolites, oxidative stress related metabolites, increased demand for functional amino acids to support growth and development, and disruption in energy-related metabolites. If confirmed in future studies, targeting these physiologically significant metabolites and metabolic pathways may lead to future assessment and intervention strategies to prevent inflammatory and metabolic complications observed in pregnant populations.