Metabolome-wide association study of anti-epileptic drug treatment during pregnancy

Lipidome Changes Associated with a Diet-Induced Reduction in Hepatic Fat among Adolescent Boys with Metabolic Dysfunction-Associated Steatotic Liver Disease

Little is known about lipid changes that occur in the setting of metabolic-dysfunction-associated steatotic liver disease (MASLD) regression. We previously reported improvements in hepatic steatosis, de novo lipogenesis (DNL), and metabolomic profiles associated with oxidative stress, inflammation, and selected lipid metabolism in 40 adolescent boys (11-16 y) with hepatic steatosis ≥5% (98% meeting the definition of MASLD). Participants were randomized to a low-free-sugar diet (LFSD) (n = 20) or usual diet (n = 20) for 8 weeks. Here, we employed untargeted/targeted lipidomics to examine lipid adaptations associated with the LFSD and improvement of hepatic steatosis. Our LC-MS/MS analysis revealed decreased triglycerides (TGs), diacylglycerols (DGs), cholesteryl esters (ChE), lysophosphatidylcholine (LPC), and phosphatidylcholine (PC) species with the diet intervention (p < 0.05). Network analysis demonstrated significantly lower levels of palmitate-enriched TG species post-intervention, mirroring the previously shown reduction in DNL in response to the LFSD. Targeted oxylipins analysis revealed a decrease in the abundance of 8-isoprostane and 14,15-DiHET and an increase in 8,9-DiHET (p < 0.05). Overall, we observed reductions in TGs, DGs, ChE, PC, and LPC species among participants in the LFSD group. These same lipids have been associated with MASLD progression; therefore, our findings may indicate normalization of key biological processes, including lipid metabolism, insulin resistance, and lipotoxicity. Additionally, our targeted oxylipins assay revealed novel changes in eicosanoids, suggesting improvements in oxidative stress. Future studies are needed to elucidate the mechanisms of these findings and prospects of these lipids as biomarkers of MASLD regression.

Validation of a screening panel for pediatric metabolic dysfunction-associated steatotic liver disease using metabolomics

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as NAFLD, is the most common liver disease in children. Liver biopsy remains the gold standard for diagnosis, although more efficient screening methods are needed. We previously developed a novel NAFLD screening panel in youth using machine learning applied to high-resolution metabolomics and clinical phenotype data. Our objective was to validate this panel in a separate cohort, which consisted of a combined cross-sectional sample of 161 children with stored frozen samples (75% male, 12.8±2.6 years of age, body mass index 31.0±7.0 kg/m2, 81% with MASLD, 58% Hispanic race/ethnicity).

METHODS

Clinical data were collected from all children, and high-resolution metabolomics was performed using their fasting serum samples. MASLD was assessed by MRI-proton density fat fraction or liver biopsy and cardiometabolic factors. Our previously developed panel included waist circumference, triglycerides, whole-body insulin sensitivity index, 3 amino acids, 2 phospholipids, dihydrothymine, and 2 unknowns. To improve feasibility, a simplified version without the unknowns was utilized in the present study. Since the panel was modified, the data were split into training (67%) and test (33%) sets to assess the validity of the panel.

RESULTS

Our present highest-performing modified model, with 4 clinical variables and 8 metabolomics features, achieved an AUROC of 0.92, 95% sensitivity, and 80% specificity for detecting MASLD in the test set.

CONCLUSIONS

Therefore, this panel has promising potential for use as a screening tool for MASLD in youth.

Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication

Indiscriminate drug administration may lead to drug therapy results with varying effects on patients, and the proposal of personalized medication can help patients to receive effective drug therapy. Conventional ways of personalized medication, such as pharmacogenomics and therapeutic drug monitoring (TDM), can only be implemented from a single perspective. The development of pharmacometabolomics provides a research method for the realization of precise drug administration, which integrates the environmental and genetic factors, and applies metabolomics technology to study how to predict different drug therapeutic responses of organisms based on baseline metabolic levels. The published research on pharmacometabolomics has achieved satisfactory results in predicting the pharmacokinetics, pharmacodynamics, and the discovery of biomarkers of drugs. Among them, the pharmacokinetics related to pharmacometabolomics are used to explore individual variability in drug metabolism from the level of metabolism of the drugs in vivo and the level of endogenous metabolite changes. By searching for relevant literature with the keyword "pharmacometabolomics" on the two major literature retrieval websites, PubMed and Web of Science, from 2006 to 2023, we reviewed articles in the field of pharmacometabolomics that incorporated pharmacokinetics into their research. This review explains the therapeutic effects of drugs on the body from the perspective of endogenous metabolites and pharmacokinetic principles, and reports the latest advances in pharmacometabolomics related to pharmacokinetics to provide research ideas and methods for advancing the implementation of personalized medication.

Metabolite Stability in Archived Neonatal Dried Blood Spots Used for Epidemiologic Research

Epidemiologic studies of low-frequency exposures or outcomes using metabolomics analyses of neonatal dried blood spots (DBS) often require assembly of samples with substantial differences in duration of storage. Independent assessment of stability of metabolites in archived DBS will enable improved design and interpretation of epidemiologic research utilizing DBS. Neonatal DBS routinely collected and stored as part of the California Genetic Disease Screening Program between 1983 and 2011 were used. The study population included 899 children without cancer before age 6 years, born in California. High-resolution metabolomics with liquid-chromatography mass spectrometry was performed, and the relative ion intensities of common metabolites and selected xenobiotic metabolites of nicotine (cotinine and hydroxycotinine) were evaluated. In total, we detected 26,235 mass spectral features across 2 separate chromatography methods (C18 hydrophobic reversed-phase chromatography and hydrophilic-interaction liquid chromatography). For most of the 39 metabolites related to nutrition and health status, we found no statistically significant annual trends across the years of storage. Nicotine metabolites were captured in the DBS with relatively stable intensities. This study supports the usefulness of DBS stored long-term for epidemiologic studies of the metabolome. -Omics-based information gained from DBS may also provide a valuable tool for assessing prenatal environmental exposures in child health research.

Study protocol for a randomized, controlled trial using a novel, family-centered diet treatment to prevent nonalcoholic fatty liver disease in Hispanic children

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is the leading liver disorder among U.S. children and is most prevalent among Hispanic children with obesity. Previous research has shown that reducing the consumption of free sugars (added sugars + naturally occurring sugars in fruit juice) can reverse liver steatosis in adolescents with NAFLD. This study aims to determine if a low-free sugar diet (LFSD) can prevent liver fat accumulation and NAFLD in high-risk children.

METHODS

In this randomized controlled trial, we will enroll 140 Hispanic children aged 6 to 9 years who are ≥50th percentile BMI and without a previous diagnosis of NAFLD. Participants will be randomly assigned to either an experimental (LFSD) or a control (usual diet + educational materials) group. The one-year intervention includes removal of foods high in free sugars from the home at baseline, provision of LFSD household groceries for the entire family (weeks 1-4, 12, 24, and 36), dietitian-guided family grocery shopping sessions (weeks 12, 24, and 36), and ongoing education and motivational interviewing to promote LFSD. Both groups complete assessment measures at baseline, 6, 12, 18, and 24 months. Primary study outcomes are percent hepatic fat at 12 months and incidence of clinically significant hepatic steatosis (>5%) + elevated liver enzymes at 24 months. Secondary outcomes include metabolic markers potentially mediating or moderating NAFLD pathogenesis.

DISCUSSION

This protocol describes the rationale, eligibility criteria, recruitment strategies, analysis plan as well as a novel dietary intervention design. Study results will inform future dietary guidelines for pediatric NAFLD prevention.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT05292352.

Metabolome × Microbiome Changes Associated with a Diet-Induced Reduction in Hepatic Fat among Adolescent Boys

Dietary sugar reduction is one therapeutic strategy for improving nonalcoholic fatty liver disease (NAFLD), and the underlying mechanisms for this effect warrant further investigation. Here, we employed metabolomics and metagenomics to examine systemic biological adaptations associated with dietary sugar restriction and (subsequent) hepatic fat reductions in youth with NAFLD. Data/samples were from a randomized controlled trial in adolescent boys (11-16 years, mean ± SD: 13.0 ± 1.9 years) with biopsy-proven NAFLD who were either provided a low free-sugar diet (LFSD) (n = 20) or consumed their usual diet (n = 20) for 8 weeks. Plasma metabolomics was performed on samples from all 40 participants by coupling hydrophilic interaction liquid chromatography (HILIC) and C18 chromatography with mass spectrometry. In a sub-sample (n = 8 LFSD group and n = 10 usual diet group), 16S ribosomal RNA (rRNA) sequencing was performed on stool to examine changes in microbial composition/diversity. The diet treatment was associated with differential expression of 419 HILIC and 205 C18 metabolite features (p < 0.05), which were enriched in amino acid pathways, including methionine/cysteine and serine/glycine/alanine metabolism (p < 0.05), and lipid pathways, including omega-3 and linoleate metabolism (p < 0.05). Quantified metabolites that were differentially changed in the LFSD group, compared to usual diet group, and representative of these enriched metabolic pathways included increased serine (p = 0.001), glycine (p = 0.004), 2-aminobutyric acid (p = 0.012), and 3-hydroxybutyric acid (p = 0.005), and decreased linolenic acid (p = 0.006). Microbiome changes included an increase in richness at the phylum level and changes in a few genera within Firmicutes. In conclusion, the LFSD treatment, compared to usual diet, was associated with metabolome and microbiome changes that may reflect biological mechanisms linking dietary sugar restriction to a therapeutic decrease in hepatic fat. Studies are needed to validate our findings and test the utility of these "omics" changes as response biomarkers.

Untargeted, High-Resolution Metabolomics in Pediatric Eosinophilic Esophagitis

BACKGROUND/OBJECTIVES

Eosinophilic esophagitis (EoE) is an inflammatory disease of unclear etiology. The aim of this study was to use untargeted plasma metabolomics to identify metabolic pathway alterations associated with EoE to better understand the pathophysiology.

METHODS

This prospective, case-control study included 72 children, aged 1-17 years, undergoing clinically indicated upper endoscopy (14 diagnosed with EoE and 58 controls). Fasting plasma samples were analyzed for metabolomics by high-resolution dual-chromatography mass spectrometry. Analysis was performed on sex-matched groups at a 2:1 ratio. Significant differences among the plasma metabolite features between children with and without EoE were determined using multivariate regression analysis and were annotated with a network-based algorithm. Subsequent pathway enrichment analysis was performed.

RESULTS

Patients with EoE had a higher proportion of atopic disease (85.7% vs 50%, P = 0.019) and any allergies (100% vs 57.1%, P = 0.0005). Analysis of the dual chromatography features resulted in a total of 918 metabolites that differentiated EoE and controls. Glycerophospholipid metabolism was significantly enriched with the greatest number of differentiating metabolites and overall pathway enrichment ( P < 0.01). Multiple amino and fatty acid pathways including linoleic acid were also enriched, as well as pyridoxine metabolism ( P < 0.01).

CONCLUSIONS

In this pilot study, we found differences in metabolites involved in glycerophospholipid and inflammation pathways in pediatric patients with EoE using untargeted metabolomics, as well as overlap with amino acid metabolome alterations found in atopic disease.

Sphinganine is associated with 24-h MAP in the non-sleepy with OSA

INTRODUCTION

Excessive daytime sleepiness is a debilitating symptom of obstructive sleep apnea (OSA) linked to cardiovascular disease, and metabolomic mechanisms underlying this relationship remain unknown. We examine whether metabolites from inflammatory and oxidative stress-related pathways that were identified in our prior work could be involved in connecting the two phenomena.

METHODS

This study included 57 sleepy (Epworth Sleepiness Scale (ESS) ≥ 10) and 37 non-sleepy (ESS < 10) participants newly diagnosed and untreated for OSA that completed an overnight in-lab or at home sleep study who were recruited from the Emory Mechanisms of Sleepiness Symptoms Study (EMOSS). Differences in fasting blood samples of metabolites were explored in participants with sleepiness versus those without and multiple linear regression models were utilized to examine the association between metabolites and mean arterial pressure (MAP).

RESULTS

The 24-h MAP was higher in sleepy 92.8 mmHg (8.4) as compared to non-sleepy 88.8 mmHg (8.1) individuals (P = 0.03). Although targeted metabolites were not significantly associated with MAP, when we stratified by sleepiness group, we found that sphinganine is significantly associated with MAP (Estimate = 8.7, SE = 3.7, P = 0.045) in non-sleepy patients when controlling for age, BMI, smoking status, and apnea-hypopnea index (AHI).

CONCLUSION

This is the first study to evaluate the relationship of inflammation and oxidative stress related metabolites in sleepy versus non-sleepy participants with newly diagnosed OSA and their association with 24-h MAP. Our study suggests that Sphinganine is associated with 24 hour MAP in the non-sleepy participants with OSA.

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.

Metabolomics Provides Novel Insights into Epilepsy Diagnosis and Treatment: A Review

Epilepsy is one of the most common diseases of the central nervous system. The diagnosis of epilepsy mainly depends on electroencephalograms and symptomatology, while diagnostic biofluid markers are still lacking. In addition, approximately 30% of patients with epilepsy (PWE) show a poor response to the currently available anti-seizure medicines. An increasing number of studies have reported alterations in the blood, brain tissue, cerebrospinal fluid and urine metabolome in PWE and animal models of epilepsy. The aim of this review was to identify potential metabolic biomarkers and pathways that might facilitate diagnostic, therapeutic and prognostic determination in PWE and the understanding of the pathogenesis of the disease. The PubMed and Embase databases were searched for metabolomic studies of PWE and epileptic models published before December 2020. The study objectives, types of models and reported differentially altered metabolites were examined and compared. Pathway analyses were performed using MetaboAnalyst 5.0 online software. Thirty-five studies were included in this review. Metabolites such as glutamate, lactate and citrate were disturbed in both PWE and epileptic models, which might be potential biomarkers of epilepsy. Metabolic pathways including alanine, aspartate and glutamate metabolism; glycine, serine and threonine metabolism; glycerophospholipid metabolism; glyoxylate and dicarboxylate metabolism; and arginine and proline metabolism were involved in epilepsy. These pathways might play important roles in the pathogenesis of the disease. This review summarizes metabolites and metabolic pathways related to epilepsy and provides a novel perspective for the identification of potential biomarkers and therapeutic targets for epilepsy.

Plasma metabolomics of autism spectrum disorder and influence of shared components in proband families

Prevalence of autism spectrum disorder (ASD) has been increasing in the United States in the past decades. The exact mechanisms remain enigmatic, and diagnosis of the disease still relies primarily on assessment of behavior. We first used a case-control design (75 idiopathic cases and 29 controls, enrolled at Boston Children's Hospital from 2007-2012) to identify plasma biomarkers of ASD through a metabolome-wide association study approach. Then we leveraged a family-based design (31 families) to investigate the influence of shared genetic and environmental components on the autism-associated features. Using untargeted high-resolution mass spectrometry metabolomics platforms, we detected 19 184 features. Of these, 191 were associated with ASD (false discovery rate < 0.05). We putatively annotated 30 features that had an odds ratio (OR) between <0.01 and 5.84. An identified endogenous metabolite, O-phosphotyrosine, was associated with an extremely low autism odds (OR 0.17; 95% confidence interval 0.06-0.39). We also found that glutathione metabolism was associated with ASD (P = 0.048). Correlations of the significant features between proband and parents were low (median = 0.09). Of the 30 annotated features, the median correlations within families (proband-parents) were -0.15 and 0.24 for the endogenous and exogenous metabolites, respectively. We hypothesize that, without feature identification, family-based correlation analysis of autism-associated features can be an alternative way to assist the prioritization of potentially diagnostic features. A panel of ASD diagnostic metabolic markers with high specificity could be derived upon further studies.

Vitamin B status and association with antiseizure medication in pregnant women with epilepsy

OBJECTIVE

Antiseizure medication (ASM) use interacts with vitamin B status in nonpregnant epilepsy populations. We aimed to examine the association between ASM and vitamin B status in pregnant women with epilepsy.

METHODS

We performed a cross-sectional study of pregnancies in women with epilepsy enrolled in the Norwegian Mother, Father and Child Cohort Study from 1999 to 2008. Data on ASM and vitamin supplement use were collected from questionnaires. We analyzed maternal plasma concentrations of ASM and metabolites of folate, including unmetabolized folic acid (UMFA), riboflavin (vitamin B2), pyridoxine (vitamin B6), and niacin (vitamin B3) during gestational weeks 17-19.

RESULTS

We included 227 singleton pregnancies exposed to ASM with available plasma samples (median maternal age 29 years, range 18 to 41 years). From the preconception period to gestational week 20, any supplement of folic acid was reported in 208 of pregnancies (94%), riboflavin in 72 (33%), pyridoxine in 77 (35%), and niacin in 45 (20%). High ASM concentrations correlated with high concentrations of UMFA and inactive folate metabolites, and with low concentrations of riboflavin and metabolically active pyridoxine. There was no association between ASM and niacin status.

SIGNIFICANCE

ASM concentrations during pregnancy were associated with vitamin B status in pregnant women with epilepsy. Additional studies are needed to determine the clinical impact of these findings, and to define the optimal vitamin doses that should be recommended to improve pregnancy outcomes.

Metabolomics as a Truly Translational Tool for Precision Medicine

Metabolomics is unique among omics technologies in being applicable to metabolism and toxicity studies broadly across organisms (e.g., humans, other mammals, model organisms, and even bacteria) and across biological materials (e.g., blood, urine, saliva, biopsy, and stool), including cultured cells and subcellular fractions. Metabolomics can be used to characterize biologic response patterns in humans as well as to support mechanistic studies in model systems and ex vivo studies. A broad range of resources are available, including publicly accessible data repositories (e.g., Metabolomics Workbench), tools for biostatistics and bioinformatics (e.g., MetaboAnalyst), metabolite identification (e.g., Metlin), and pathway analysis (e.g., Kyoto Encyclopedia of Genes and Genomes). Thus, metabolomics is more than a promise of the future; metabolomics is already available as a translational approach to facilitate precision medicine. This ACT Symposium review will contain an introduction to metabolomics in toxicity studies followed by sections on translational metabolic networks, translational metabolite biomarkers of acetaminophen-induced acute liver injury, translational framework using high-resolution metabolomics for integrated pharmacokinetics and pharmacodynamics, and precision medicine applications: extracting actionable targets from untargeted metabolomics data following one year in space.

Effects of antiseizure medications on placental cells: Focus on heterodimeric placental carriers

OBJECTIVE

Appropriate placental nutrient transfer is essential for optimal fetal development. We have previously shown that antiseizure medications (ASMs) can alter the expression of placental carriers for folate and thyroid hormones. Here we extended our analysis to heterodimeric carriers that mediate the placental uptake of amino acids and antioxidant precursors. We focused on the L-type amino acid transporter (LAT)2/SLC7A8, the cystine/glutamate antiporter xCT/SLC7A11, and their chaperone 4F2hc/SLC3A2.

METHODS

BeWo cells were exposed for two or five days to therapeutic concentrations of valproate, levetiracetam, carbamazepine, lamotrigine, or lacosamide. Transcript levels were measured by quantitative PCR. Levetiracetam effects on placental carriers were further explored using a tailored gene array.

RESULTS

At five days, 30 μg/mL levetiracetam (high therapeutic concentrations) significantly reduced the expression of all studied genes (p < 0.05). Carbamazepine treatment was associated with lower SLC7A8 (LAT2) expression (p < 0.05), whereas valproate increased the transcript levels of this transporter by up to 2.0-fold (p < 0.01). Some of these effects were already observed after two incubation days. Lamotrigine did not alter gene expression, and lacosamide slightly elevated SLC3A2 levels (p < 0.05). The array analysis confirmed the trends observed for levetiracetam and identified additional affected genes.

SIGNIFICANCE

Altered expression of placental heterodimeric transporters may represent a mechanism by which ASM affect fetal development. The placental effects are differential, with valproate, carbamazepine and levetiracetam as the more active compounds. The concentration-dependence of those ASM effects are in line with established dose-dependent teratogenicity implying that ASM doses should be adjusted during pregnancy with caution.

Precision medicine in women with epilepsy: The challenge, systematic review, and future direction

Epilepsy is one of the most prevalent neurologic conditions, affecting almost 70 million people worldwide. In the United States, 1.3 million women with epilepsy (WWE) are in their active reproductive years. Women with epilepsy (WWE) face gender-specific challenges such as pregnancy, seizure exacerbation with hormonal pattern fluctuations, contraception, fertility, and menopause. Precision medicine, which applies state-of-the art molecular profiling to diagnostic, prognostic, and therapeutic problems, has the potential to advance the care of WWE by precisely tailoring individualized management to each patient's needs. For example, antiseizure medications (ASMs) are among the most common teratogens prescribed to women of childbearing potential. Teratogens act in a dose-dependent manner on a susceptible genotype. However, the genotypes at risk for ASM-induced teratogenic deficits are unknown. Here we summarize current challenging issues for WWE, review the state-of-art tools for clinical precision medicine approaches, perform a systematic review of pharmacogenomic approaches in management for WWE, and discuss potential future directions in this field. We envision a future in which precision medicine enables a new practice style that puts focus on early detection, prediction, and targeted therapies for WWE.

Genetic or Toxicant-Induced Disruption of Vesicular Monoamine Storage and Global Metabolic Profiling in Caenorhabditis elegans

The proper storage and release of monoamines contributes to a wide range of neuronal activity. Here, we examine the effects of altered vesicular monoamine transport in the nematode Caenorhabditis elegans. The gene cat-1 is responsible for the encoding of the vesicular monoamine transporter (VMAT) in C. elegans and is analogous to the mammalian vesicular monoamine transporter 2 (VMAT2). Our laboratory has previously shown that reduced VMAT2 activity confers vulnerability on catecholamine neurons in mice. The purpose of this article was to determine whether this function is conserved and to determine the impact of reduced VMAT activity in C. elegans. Here we show that deletion of cat-1/VMAT increases sensitivity to the neurotoxicant 1-methyl-4-phenylpyridinium (MPP+) as measured by enhanced degeneration of dopamine neurons. Reduced cat-1/VMAT also induces changes in dopamine-mediated behaviors. High-resolution mass spectrometry-based metabolomics in the whole organism reveals changes in amino acid metabolism, including tyrosine metabolism in the cat-1/VMAT mutants. Treatment with MPP+ disrupted tryptophan metabolism. Both conditions altered glycerophospholipid metabolism, suggesting a convergent pathway of neuronal dysfunction. Our results demonstrate the evolutionarily conserved nature of monoamine function in C. elegans and further suggest that high-resolution mass spectrometry-based metabolomics can be used in this model to study environmental and genetic contributors to complex human disease.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Circulating Metabolites as Potential Biomarkers for Neurological Disorders-Metabolites in Neurological Disorders

There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed.

Hepatic fat is a stronger correlate of key clinical and molecular abnormalities than visceral and abdominal subcutaneous fat in youth

INTRODUCTION

Body fat distribution is strongly associated with cardiometabolic disease (CMD), but the relative importance of hepatic fat as an underlying driver remains unclear. Here, we applied a systems biology approach to compare the clinical and molecular subnetworks that correlate with hepatic fat, visceral fat, and abdominal subcutaneous fat distribution.

RESEARCH DESIGN AND METHODS

This was a cross-sectional sub-study of 283 children/adolescents (7-19 years) from the Yale Pediatric NAFLD Cohort. Untargeted, high-resolution metabolomics (HRM) was performed on plasma and combined with existing clinical variables including hepatic and abdominal fat measured by MRI. Integrative network analysis was coupled with pathway enrichment analysis and multivariable linear regression (MLR) to examine which metabolites and clinical variables associated with each fat depot.

RESULTS

The data divided into four communities of correlated variables (|r|>0.15, p<0.05) after integrative network analysis. In the largest community, hepatic fat was associated with eight clinical biomarkers, including measures of insulin resistance and dyslipidemia, and 878 metabolite features that were enriched predominantly in amino acid (AA) and lipid pathways in pathway enrichment analysis (p<0.05). Key metabolites associated with hepatic fat included branched-chain AAs (valine and isoleucine/leucine), aromatic AAs (tyrosine and tryptophan), serine, glycine, alanine, and pyruvate, as well as several acylcarnitines and glycerophospholipids (all q<0.05 in MLR adjusted for covariates). The other communities detected in integrative network analysis consisted of abdominal visceral, superficial subcutaneous, and deep subcutaneous fats, but no clinical variables, fewer metabolite features (280, 312, and 74, respectively), and limited findings in pathway analysis.

CONCLUSIONS

These data-driven findings show a stronger association of hepatic fat with key CMD risk factors compared with abdominal fats. The molecular network identified using HRM that associated with hepatic fat provides insight into potential mechanisms underlying the hepatic fat-insulin resistance interface in youth.

Untargeted Metabolomics Screen of Mid-pregnancy Maternal Serum and Autism in Offspring

Discovering pathophysiologic networks in a blood-based approach may help to generate valuable tools for early treatment or preventive measures in autism. To date targeted or untargeted metabolomics approaches to identify metabolic features and pathways affecting fetal neurodevelopment have rarely been applied to pregnancy samples, that is, an early period potentially relevant for the development of autism spectrum disorders (ASD). We conducted a population-based study relying on autism diagnoses retrieved from California Department of Developmental Services record. After linking cases to and sampling controls from birth certificates, we retrieved stored maternal mid-pregnancy serum samples collected as part of the California Prenatal Screening Program from the California Biobank for children born 2004 to 2010 in the central valley of California. We retrieved serum for 52 mothers whose children developed autism and 62 population controls originally selected from all eligible children matched by birth year and child's sex. Also, we required that these mothers were relatively low or unexposed to air pollution and select pesticides during early pregnancy. We identified differences in metabolite levels in several metabolic pathways, including glycosphingolipid biosynthesis and metabolism, N-glycan and pyrimidine metabolism, bile acid pathways and, importantly, C21-steroid hormone biosynthesis and metabolism. Disturbances in these pathways have been shown to be relevant for neurodevelopment in rare genetic syndromes or implicated in previous studies of autism. This study provides new insight into maternal mid-pregnancy metabolic features possibly related to the development of autism and an incentive to explore whether these pathways and metabolites are useful for early diagnosis, treatment, or prevention. LAY SUMMARY: This study found that in mid-pregnancy the blood of mothers who give birth to a child that develops autism has some characteristic features that are different from those of blood samples taken from control mothers. These features are related to biologic mechanisms that can affect fetal brain development. In the future, these insights may help identify biomarkers for early autism diagnosis and treatment or preventive measures. Autism Res 2020, 13: 1258-1269. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.

CE-MS metabolic profiling of volume-restricted plasma samples from an acute mouse model for epileptic seizures to discover potentially involved metabolomic features

Currently, a high variety of analytical techniques to perform metabolomics is available. One of these techniques is capillary electrophoresis coupled to mass spectrometry (CE-MS), which has emerged as a rather strong analytical technique for profiling polar and charged compounds. This work aims to discover with CE-MS potential metabolic consequences of evoked seizures in plasma by using a 6Hz acute corneal seizure mouse model. CE-MS is an appealing technique because of its capability to handle very small sample volumes, such as the 10 μL plasma samples obtained using capillary microsampling in this study. After liquid-liquid extraction, the samples were analyzed with CE-MS using low-pH separation conditions, followed by data analysis and biomarker identification. Both electrically induced seizures showed decreased values of methionine, lysine, glycine, phenylalanine, citrulline, 3-methyladenine and histidine in mice plasma. However, a second provoked seizure, 13 days later, showed a less pronounced decrease of the mean concentrations of these plasma metabolites, demonstrated by higher fold change ratios. Other obtained markers that can be related to seizure activities based on literature data, are isoleucine, serine, proline, tryptophan, alanine, arginine, valine and asparagine. Most amino acids showed relatively stable plasma concentrations between the basal levels (Time point 1) and after the 13-day wash-out period (Time point 3), which suggests its effectiveness. Overall, this work clearly demonstrated the possibility of profiling metabolite consequences related to seizure activities of an intrinsically low amount of body fluid using CE-MS. It would be useful to investigate and validate, in the future, the known and unknown metabolites in different animal models as well as in humans.

Metabolomics in pharmacology - a delve into the novel field of pharmacometabolomics

Introduction: Pharmacometabolomics is an emerging science pursuing the application of precision medicine. Combining both genetic and environmental factors, the so-called pharmacometabolomic approach guides patient selection and stratification in clinical trials and optimizes personalized drug dosage, improving efficacy and safety.Areas covered: This review illustrates the progressive introduction of pharmacometabolomics as an innovative solution for enhancing the discovery of novel drugs and improving research and development (R&D) productivity of the pharmaceutical industry. An extended analysis on published pharmacometabolomics studies both in animal models and humans includes results obtained in several areas such as hepatology, gastroenterology, nephrology, neuropsychiatry, oncology, drug addiction, embryonic cells, neonatology, and microbiomics.Expert opinion: a tailored, individualized therapy based on the optimization of pharmacokinetics and pharmacodynamics, the improvement of drug efficacy, and the abolition of drug toxicity and adverse drug reactions is a key issue in precision medicine. Genetics alone has become insufficient for deciphring intra- and inter-individual variations in drug-response, since they originate both from genetic and environmental factors, including human microbiota composition. The association between pharmacogenomics and pharmacometabolomics may be considered the new strategy for an in-deep knowledge on changes and alterations in human and microbial metabolic pathways due to the action of a drug.

Maternal serum metabolome and traffic-related air pollution exposure in pregnancy

BACKGROUND

Maternal exposure to traffic-related air pollution during pregnancy has been shown to increase the risk of adverse birth outcomes and neurodevelopmental disorders. By utilizing high-resolution metabolomics (HRM), we investigated perturbations of the maternal serum metabolome in response to traffic-related air pollution to identify biological mechanisms.

METHODS

We retrieved stored mid-pregnancy serum samples from 160 mothers who lived in the Central Valley of California known for high air particulate levels. We estimated prenatal traffic-related air pollution exposure (carbon monoxide, nitric oxides, and particulate matter <2.5 μm) during first-trimester using the California Line Source Dispersion Model, version 4 (CALINE4) based on residential addresses recorded at birth. We used liquid chromatography-high resolution mass spectrometry to obtain untargeted metabolic profiles and partial least squares discriminant analysis (PLS-DA) to select metabolic features associated with air pollution exposure. Pathway analyses were employed to identify biologic pathways related to air pollution exposure. As potential confounders we included maternal age, maternal race/ethnicity, and maternal education.

RESULTS

In total we extracted 4038 and 4957 metabolic features from maternal serum samples in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 (negative ion mode) columns, respectively. After controlling for confounding factors, PLS-DA (Variable Importance in Projection (VIP) ≥2) yielded 181 and 251 metabolic features (HILIC and C18, respectively) that discriminated between the high (n = 98) and low exposed (n = 62). Pathway enrichment analysis for discriminatory features associated with air pollution indicated that in maternal serum oxidative stress and inflammation related pathways were altered, including linoleate, leukotriene, and prostaglandin pathways.

CONCLUSION

The metabolomic features and pathways we found to be associated with air pollution exposure suggest that maternal exposure during pregnancy induces oxidative stress and inflammation pathways previously implicated in pregnancy complications and adverse outcomes.

Development of a Plasma Screening Panel for Pediatric Nonalcoholic Fatty Liver Disease Using Metabolomics

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in children, but diagnosis is challenging due to limited availability of noninvasive biomarkers. Machine learning applied to high-resolution metabolomics and clinical phenotype data offers a novel framework for developing a NAFLD screening panel in youth. Here, untargeted metabolomics by liquid chromatography-mass spectrometry was performed on plasma samples from a combined cross-sectional sample of children and adolescents ages 2-25 years old with NAFLD (n = 222) and without NAFLD (n = 337), confirmed by liver biopsy or magnetic resonance imaging. Anthropometrics, blood lipids, liver enzymes, and glucose and insulin metabolism were also assessed. A machine learning approach was applied to the metabolomics and clinical phenotype data sets, which were split into training and test sets, and included dimension reduction, feature selection, and classification model development. The selected metabolite features were the amino acids serine, leucine/isoleucine, and tryptophan; three putatively annotated compounds (dihydrothymine and two phospholipids); and two unknowns. The selected clinical phenotype variables were waist circumference, whole-body insulin sensitivity index (WBISI) based on the oral glucose tolerance test, and blood triglycerides. The highest performing classification model was random forest, which had an area under the receiver operating characteristic curve (AUROC) of 0.94, sensitivity of 73%, and specificity of 97% for detecting NAFLD cases. A second classification model was developed using the homeostasis model assessment of insulin resistance substituted for the WBISI. Similarly, the highest performing classification model was random forest, which had an AUROC of 0.92, sensitivity of 73%, and specificity of 94%. Conclusion: The identified screening panel consisting of both metabolomics and clinical features has promising potential for screening for NAFLD in youth. Further development of this panel and independent validation testing in other cohorts are warranted.

Multigenerational metabolic profiling in the Michigan PBB registry

Although polychlorinated biphenyls and polybrominated biphenyls are no longer manufactured the United States, biomonitoring in human populations show that exposure to these pollutants persist in human tissues. The objective of this study was to identify metabolic variations associated with exposure to 2,2'4,4',5,5'-hexabromobiphenyl (PBB-153) and 2,2'4,4',5,5'-hexachlorobiphenyl (PCB-153) in two generations of participants enrolled in the Michigan PBB Registry (http://pbbregistry.emory.edu/). Untargeted, high-resolution metabolomic profiling of plasma collected from 156 individuals was completed using liquid chromatography with high-resolution mass spectrometry. PBB-153 and PCB-153 levels were measured in the same individuals using targeted gas chromatography-tandem mass spectrometry and tested for dose-dependent correlation with the metabolome. Biological response to these exposures were evaluated using identified endogenous metabolites and pathway enrichment. When compared to lipid-adjusted concentrations for adults in the National Health and Nutrition Examination Survey (NHANES) for years 2003-2004, PCB-153 levels were consistent with similarly aged individuals, whereas PBB-153 concentrations were elevated (p<0.0001) in participants enrolled in the Michigan PBB Registry. Metabolic alterations were correlated with PBB-153 and PCB-153 in both generations of participants, and included changes in pathways related to catecholamine metabolism, cellular respiration, essential fatty acids, lipids and polyamine metabolism. These pathways were consistent with pathophysiological changes observed in neurodegenerative disease and included previously identified metabolomic markers of Parkinson's disease. To determine if the metabolic alterations detected in this study are replicated other cohorts, we evaluated correlation of PBB-153 and PCB-153 with plasma fatty acids measured in NHANES. Both pollutants showed similar associations with fatty acids previously linked to PCB exposure. Thus, the results from this study show metabolic alterations correlated with PBB-153 and PCB-153 exposure can be detected in human populations and are consistent with health outcomes previously reported in epidemiological and mechanistic studies.

The metabolome: A key measure for exposome research in epidemiology

PURPOSE OF REVIEW

Application of omics to study human health has created a new era of opportunities for epidemiology research. However, approaches to characterize exogenous health triggers have largely not leveraged advances in analytical platforms and big data. In this review, we highlight the exposome, which is defined as the cumulative measure of exposure and biological responses across a lifetime as a cornerstone for new epidemiology approaches to study complex and preventable human diseases.

RECENT FINDINGS

While no universal approach exists to measure the entirety of the exposome, use of high-resolution mass spectrometry methods provide distinct advantages over traditional biomonitoring and have provided key advances necessary for exposome research. Application to different study designs and recommendations for combining exposome data with novel data analytic frameworks to study complex interactions of multiple stressors are also discussed.

SUMMARY

Even though challenges still need to be addressed, advances in methods to characterize the exposome provide exciting new opportunities for epidemiology to support fundamental discoveries to improve public health.