Metabolomics tools for identifying biomarkers for neuropsychiatric diseases

Nucleic acid-based wearable and implantable electrochemical sensors

The rapid advancements in nucleic acid-based electrochemical sensors for implantable and wearable applications have marked a significant leap forward in the domain of personal healthcare over the last decade. This technology promises to revolutionize personalized healthcare by facilitating the early diagnosis of diseases, monitoring of disease progression, and tailoring of individual treatment plans. This review navigates through the latest developments in this field, focusing on the strategies for nucleic acid sensing that enable real-time and continuous biomarker analysis directly in various biofluids, such as blood, interstitial fluid, sweat, and saliva. The review delves into various nucleic acid sensing strategies, emphasizing the innovative designs of biorecognition elements and signal transduction mechanisms that enable implantable and wearable applications. Special perspective is given to enhance nucleic acid-based sensor selectivity and sensitivity, which are crucial for the accurate detection of low-level biomarkers. The integration of such sensors into implantable and wearable platforms, including microneedle arrays and flexible electronic systems, actualizes their use in on-body devices for health monitoring. We also tackle the technical challenges encountered in the development of these sensors, such as ensuring long-term stability, managing the complexity of biofluid dynamics, and fulfilling the need for real-time, continuous, and reagentless detection. In conclusion, the review highlights the importance of these sensors in the future of medical engineering, offering insights into design considerations and future research directions to overcome existing limitations and fully realize the potential of nucleic acid-based electrochemical sensors for healthcare applications.

Metabolomic analysis of swainsonine poisoning in renal tubular epithelial cells

Locoweed is a poisonous plant widely present in grasslands around the world. Swainsonine (SW), an indole alkaloid that, is the main toxic component of the locoweed. To understand the mechanism of SW-induced toxicity and to delineate the metabolic profile of locoweed poisoning we performed the LC-MS/MS untargeted metabolomic study to analyze metabolites in SW-treated renal tubular epithelial cells (0.8 mg/mL, 12 h) and in order to identify the SW-induced metabolomic changes. The analysis identified 2,563 metabolites in positive ion mode and 1,990 metabolites in negative ion mode. Our results showed that the metabolites were mainly benzenoids, lipids and lipid-like molecules, nucleosides, nucleotides, and analogs, organic acids, and derivatives. The differential metabolites were primarily enriched in pathways involving bile secretion, primary bile acid biosynthesis, riboflavin metabolism, ferroptosis, drug metabolism-cytochrome P450, and primidine metabolism. We have screened out substances such as swainsonine, 3alpha,7alpha-Dihydroxy-5beta-cholestanate, 2-Hydroxyiminostilbene, and glycochenodeoxycholate, which may have the potential to serve as biomarkers for swainsonine poisoning. This study provides insights into the types of metabolomic alteration in renal tubular epithelial cells induced by swainsonine.

Non-targeted metabolomics revealed novel links between serum metabolites and primary ovarian insufficiency: a Mendelian randomization study

Background

Primary ovarian insufficiency (POI) is a common clinical endocrine disorder with a high heterogeneity in both endocrine hormones and etiological phenotypes. However, the etiology of POI remains unclear. Herein, we unraveled the causality of genetically determined metabolites (GDMs) on POI through Mendelian randomization (MR) study with the overarching goal of disclosing underlying mechanisms.

Methods

Genetic links with 486 metabolites were retrieved from GWAS data of 7824 European participants as exposures, while GWAS data concerning POI were utilized as the outcome. Via MR analysis, we selected inverse-variance weighted (IVW) method for primary analysis and several additional MR methods (MR-Egger, weighted median, and MR-PRESSO) for sensitivity analyses. MR-Egger intercept and Cochran's Q statistical analysis were conducted to assess potential heterogeneity and pleiotropy. In addition, genetic variations in the key target metabolite were scrutinized further. We conducted replication, meta-analysis, and linkage disequilibrium score regression (LDSC) to reinforce our findings. The MR Steiger test and reverse MR analysis were utilized to assess the robustness of genetic directionality. Furthermore, to deeply explore causality, we performed colocalization analysis and metabolic pathway analysis.

Results

Via IVW methods, our study identified 33 metabolites that might exert a causal effect on POI development. X-11437 showed a robustly significant relationship with POI in four MR analysis methods (P IVW=0.0119; P weighted-median =0.0145; PMR-Egger =0.0499; PMR-PRESSO =0.0248). Among the identified metabolites, N-acetylalanine emerged as the most significant in the primary MR analysis using IVW method, reinforcing its pivotal status as a serum biomarker indicative of an elevated POI risk with the most notable P-value (P IVW=0.0007; PMR-PRESSO =0.0022). Multiple analyses were implemented to further demonstrate the reliability and stability of our deduction of causality. Reverse MR analysis did not provide evidence for the causal effects of POI on 33 metabolites. Colocalization analysis revealed that some causal associations between metabolites and POI might be driven by shared genetic variants.

Conclusion

By incorporating genomics with metabolomics, this study sought to offer a comprehensive analysis in causal impact of serum metabolome phenotypes on risks of POI with implications for underlying mechanisms, disease screening and prevention.

Alterations of the gut microbiota in patients with schizophrenia

Introduction

Schizophrenia is a complex psychiatric disorder, of which molecular pathogenesis remains largely unknown. Accumulating evidence suggest that gut microbiota may affect brain function via the complex gut-brain axis, which may be a potential contributor to schizophrenia. However, the alteration of gut microbiota showed high heterogeneity across different studies. Therefore, this study aims to identify the consistently altered gut microbial taxa associated with schizophrenia.

Methods

We conducted a systematic search and synthesis of the up-to-date human gut microbiome studies on schizophrenia, and performed vote counting analyses to identify consistently changed microbiota. Further, we investigated the effects of potential confounders on the alteration of gut microbiota.

Results

We obtained 30 available clinical studies, and found that there was no strong evidence to support significant differences in α-diversity and β-diversity between schizophrenic patients and healthy controls. Among 428 differential gut microbial taxa collected from original studies, we found that 8 gut microbial taxa were consistently up-regulated in schizophrenic patients, including Proteobacteria, Gammaproteobacteria, Lactobacillaceae, Enterobacteriaceae, Lactobacillus, Succinivibrio, Prevotella and Acidaminococcus. While 5 taxa were consistently down-regulated in schizophrenia, including Fusicatenibacter, Faecalibacterium, Roseburia, Coprococcus and Anaerostipes.

Discussion

These findings suggested that gut microbial changes in patients with schizophrenia were characterized by the depletion of anti-inflammatory butyrate-producing genera, and the enrichment of certain opportunistic bacteria genera and probiotics. This study contributes to further understanding the role of gut microbiota in schizophrenia, and developing microbiota-based diagnosis and therapy for schizophrenia.

The relationship between alterations in plasma metabolites and treatment responses in antipsychotic-naïve female patients with schizophrenia

This study aimed to explore the relationship between alterations in plasma metabolites and treatment responses amongst antipsychotic-naïve female patients with schizophrenia. A total of 38 antipsychotic-naïve female schizophrenia patients (ANS) and 19 healthy female controls (HC) were recruited. Plasma samples were obtained from all participants, and targeted metabolomics were measured with FIA-MS/MS and LC-MS/MS. The positive and negative syndrome scale (PANSS) was used to assess the severity of psychotic symptoms before and after eight weeks of treatment. Receiver operator characteristics (ROC) curves were used to predict diagnostic and therapeutic responses. A total of 186 metabolites passed quality control procedures and were used in statistical analysis to identify potential biomarkers. Before treatment, the ANS patients had lower levels of γ -Aminobutyric Acid (GABA) and higher levels of Cholesteryl esters (CE) (20:3), Cholic Acid (CA) and Glycocholic Acid (GCA) compared to the HCs. These four differential metabonomic markers were synthesised into a combinatorial biomarker panel. This panel significantly distinguished ANS from HC. Moreover, this biomarker panel was able to effectively predict therapeutic responses. Our results suggest that plasma CE (20:3), CA, GCA, and GABA levels may be useful for diagnosing and predicting antipsychotic efficacy amongst female schizophrenia patients.

Association of metabolic dysfunction with cognitive decline and Alzheimer's disease: A review of metabolomic evidence

The discovery of new biomarkers that can distinguish Alzheimer's disease (AD) from mild cognitive impairment (MCI) in the early stages will help to provide new diagnostic and therapeutic strategies and slow the transition from MCI to AD. Patients with AD may present with a concomitant metabolic disorder, such as diabetes, obesity, and dyslipidemia, as a risk factor for AD that may be involved in the onset of both AD pathology and cognitive impairment. Therefore, metabolite profiling, or metabolomics, can be very useful in diagnosing AD, developing new therapeutic targets, and evaluating both the course of treatment and the clinical course of the disease. In addition, studying the relationship between nutritional behavior and AD requires investigation of the role of conditions such as obesity, hypertension, dyslipidemia, and elevated glucose level. Based on this literature review, nutritional recommendations, including weight loss by reducing calorie and cholesterol intake and omega-3 fatty acid supplementation can prevent cognitive decline and dementia in the elderly. The underlying metabolic causes of the pathology and cognitive decline caused by AD and MCI are not well understood. In this review article, metabolomics biomarkers for diagnosis of AD and MCI and metabolic risk factors for cognitive decline in AD were evaluated.

Analysis of Metabolic Changes in Endogenous Metabolites and Diagnostic Biomarkers for Various Diseases Using Liquid Chromatography and Mass Spectrometry

Analysis of endogenous metabolites in various diseases is useful for searching diagnostic biomarkers and elucidating the molecular mechanisms of pathophysiology. The author and collaborators have developed some LC/tandem mass spectrometry (LC/MS/MS) methods for metabolites and applied them to disease-related samples. First, we identified urinary conjugated cholesterol metabolites and serum N-palmitoyl-O-phosphocholine serine as useful biomarkers for Niemann-Pick disease type C (NPC). For the purpose of intraoperative diagnosis of glioma patients, we developed the LC/MS/MS analysis methods for 2-hydroxyglutaric acid or cystine and found that they could be good differential biomarkers. For renal cell carcinoma, we searched for various biomarkers for early diagnosis, malignancy evaluation and recurrence prediction by global metabolome analysis and targeted LC/MS/MS analysis. In pathological analysis, we developed a simultaneous LC/MS/MS analysis method for 13 steroid hormones and applied it to NPC cells, we found 6 types of reductions in NPC model cells. For non-alcoholic steatohepatitis (NASH), model mice were prepared with special diet and plasma bile acids were measured, and as a result, hydrophilic bile acids were significantly increased. In addition, we developed an LC/MS/MS method for 17 sterols and analyzed liver cholesterol metabolites and found a decrease in phytosterols and cholesterol synthetic markers and an increase in non-enzymatic oxidative sterols in the pre-onset stage of NASH. We will continue to challenge themselves to add value to clinical practice based on cutting-edge analytical chemistry methodology.

Metabolome subtyping reveals multi-omics characteristics and biological heterogeneity in major psychiatric disorders

Growing evidence suggests that major psychiatric disorders (MPDs) share common etiologies and pathological processes. However, the diagnosis is currently based on descriptive symptoms, which ignores the underlying pathogenesis and hinders the development of clinical treatments. This highlights the urgency of characterizing molecular biomarkers and establishing objective diagnoses of MPDs. Here, we collected untargeted metabolomics, proteomics and DNA methylation data of 327 patients with MPDs, 131 individuals with genetic high risk and 146 healthy controls to explore the multi-omics characteristics of MPDs. First, differential metabolites (DMs) were identified and we classified MPD patients into 3 subtypes based on DMs. The subtypes showed distinct metabolomics, proteomics and DNA methylation signatures. Specifically, one subtype showed dysregulation of complement and coagulation proteins, while the DNA methylation showed abnormalities in chemical synapses and autophagy. Integrative analysis in metabolic pathways identified the important roles of the citrate cycle, sphingolipid metabolism and amino acid metabolism. Finally, we constructed prediction models based on the metabolites and proteomics that successfully captured the risks of MPD patients. Our study established molecular subtypes of MPDs and elucidated their biological heterogeneity through a multi-omics investigation. These results facilitate the understanding of pathological mechanisms and promote the diagnosis and prevention of MPDs.

Evaluating changes in firefighter urinary metabolomes after structural fires: an untargeted, high resolution approach

Firefighters have elevated rates of urinary tract cancers and other adverse health outcomes, which may be attributable to environmental occupational exposures. Untargeted metabolomics was applied to characterize this suite of environmental exposures and biological changes in response to occupational firefighting. 200 urine samples from 100 firefighters collected at baseline and two to four hours post-fire were analyzed using untargeted liquid-chromatography and high-resolution mass spectrometry. Changes in metabolite abundance after a fire were estimated with fixed effects linear regression, with false discovery rate (FDR) adjustment. Partial least squares discriminant analysis (PLS-DA) was also used, and variable important projection (VIP) scores were extracted. Systemic changes were evaluated using pathway enrichment for highly discriminating metabolites. Metabolome-wide-association-study (MWAS) identified 268 metabolites associated with firefighting activity at FDR q < 0.05. Of these, 20 were annotated with high confidence, including the amino acids taurine, proline, and betaine; the indoles kynurenic acid and indole-3-acetic acid; the known uremic toxins trimethylamine n-oxide and hippuric acid; and the hormone 7a-hydroxytestosterone. Partial least squares discriminant analysis (PLS-DA) additionally implicated choline, cortisol, and other hormones. Significant pathways included metabolism of urea cycle/amino group, alanine and aspartate, aspartate and asparagine, vitamin b3 (nicotinate and nicotinamide), and arginine and proline. Firefighters show a broad metabolic response to fires, including altered excretion of indole compounds and uremic toxins. Implicated pathways and features, particularly uremic toxins, may be important regulators of firefighter's increased risk for urinary tract cancers.

Circulating metabolites modulated by diet are associated with depression

Metabolome reflects the interplay of genome and exposome at molecular level and thus can provide deep insights into the pathogenesis of a complex disease like major depression. To identify metabolites associated with depression we performed a metabolome-wide association analysis in 13,596 participants from five European population-based cohorts characterized for depression, and circulating metabolites using ultra high-performance liquid chromatography/tandem accurate mass spectrometry (UHPLC/MS/MS) based Metabolon platform. We tested 806 metabolites covering a wide range of biochemical processes including those involved in lipid, amino-acid, energy, carbohydrate, xenobiotic and vitamin metabolism for their association with depression. In a conservative model adjusting for life style factors and cardiovascular and antidepressant medication use we identified 8 metabolites, including 6 novel, significantly associated with depression. In individuals with depression, increased levels of retinol (vitamin A), 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1) (lecithin) and mannitol/sorbitol and lower levels of hippurate, 4-hydroxycoumarin, 2-aminooctanoate (alpha-aminocaprylic acid), 10-undecenoate (11:1n1) (undecylenic acid), 1-linoleoyl-GPA (18:2) (lysophosphatidic acid; LPA 18:2) are observed. These metabolites are either directly food derived or are products of host and gut microbial metabolism of food-derived products. Our Mendelian randomization analysis suggests that low hippurate levels may be in the causal pathway leading towards depression. Our findings highlight putative actionable targets for depression prevention that are easily modifiable through diet interventions.

Building Predictive Models for Schizophrenia Diagnosis with Peripheral Inflammatory Biomarkers

Machine learning and artificial intelligence technologies are known to be a convenient tool for analyzing multi-domain data in precision psychiatry. In the case of schizophrenia, the most commonly used data sources for such purposes are neuroimaging, voice and language patterns, and mobile phone data. Data on peripheral markers can also be useful for building predictive models. Here, we have developed five predictive models for the binary classification of schizophrenia patients and healthy individuals. Data on serum concentrations of cytokines, chemokines, growth factors, and age were among 38 parameters used to build these models. The sample consisted of 217 schizophrenia patients and 90 healthy individuals. The models architecture was involved logistic regression, deep neural networks, decision trees, support vector machine, and k-nearest neighbors algorithms. It was shown that the algorithm based on a deep neural network (consisting of five layers) showed a slightly higher sensitivity (0.87 ± 0.04) and specificity (0.52 ± 0.06) than other algorithms. Combining all variables into a single classifier showed a cumulative effect that exceeded the effectiveness of individual variables, indicating the need to use multiple biomarkers to diagnose schizophrenia. Thus, the data obtained showed the promise of using data on peripheral biomarkers and machine learning methods for diagnosing schizophrenia.

Serum Metabolic Profile in Schizophrenia Patients With Antipsychotic-Induced Constipation and Its relationship With Gut Microbiome

BACKGROUND AND HYPOTHESIS

Antipsychotics (APs), the cornerstone of schizophrenia treatment, confer a relatively high risk of constipation. However, the mechanisms underpinning AP-induced constipation are poorly understood. Thus, we hypothesized that (1) schizophrenia patients with AP-induced constipation have distinct metabolic patterns; (2) there is more than one mechanism at play in producing this adverse drug effect; and (3) AP-associated changes in the gut microbiome are related to the altered metabolic profiles.

STUDY DESIGN

Eighty-eight schizophrenia patients, including 44 with constipation (C) and 44 matched patients without constipation (NC), were enrolled in this study. Constipation was diagnosed by Rome IV criteria for constipation and colonic transit time using radiopaque markers (ROMs) while severity was evaluated with the Bristol Stool Form Scale (BSS) and Constipation Assessment Scale (CAS). Fasting blood samples were drawn from all participants and were subjected to non-targeted liquid chromatography-mass spectrometry (LC-MS) metabolomic analysis.

STUDY RESULTS

Eleven metabolites were significantly altered in AP-induced constipation which primarily disturbed sphingolipid metabolism, choline metabolism, and sphingolipid signaling pathway (P value < .05, FDR < 0.05). In the C group, changes in the gut bacteria showed a certain degree of correlation with 2 of the significantly altered serum metabolites and were associated with alterations in choline metabolism.

CONCLUSIONS

Our findings indicated that there were disturbances in distinct metabolic pathways that were associated with AP-induced constipation. In addition, this study presents evidence of a link between alterations in the gut microbiome and host metabolism which provides additional mechanistic insights on AP-induced constipation.

Metabolomic Profiling of Bipolar Disorder by 1H-NMR in Serbian Patients

Bipolar disorder (BD) is a brain disorder that causes changes in a person's mood, energy, and ability to function. It has a prevalence of 60 million people worldwide, and it is among the top 20 diseases with the highest global burden. The complexity of this disease, including diverse genetic, environmental, and biochemical factors, and diagnoses based on the subjective recognition of symptoms without any clinical test of biomarker identification create significant difficulties in understanding and diagnosing BD. A ¹H-NMR-based metabolomic study applying chemometrics of serum samples of Serbian patients with BD (33) and healthy controls (39) was explored, providing the identification of 22 metabolites for this disease. A biomarker set including threonine, aspartate, gamma-aminobutyric acid, 2-hydroxybutyric acid, serine, and mannose was established for the first time in BD serum samples by an NMR-based metabolomics study. Six identified metabolites (3-hydroxybutyric acid, arginine, lysine, tyrosine, phenylalanine, and glycerol) are in agreement with the previously determined NMR-based sets of serum biomarkers in Brazilian and/or Chinese patient samples. The same established metabolites (lactate, alanine, valine, leucine, isoleucine, glutamine, glutamate, glucose, and choline) in three different ethnic and geographic origins (Serbia, Brazil, and China) might have a crucial role in the realization of a universal set of NMR biomarkers for BD.

Plasma sphingolipid abnormalities in neurodegenerative diseases

BACKGROUND

In recent years, there has been increasing evidence that several lipid metabolism abnormalities play an important role in the pathogenesis of neurodegenerative diseases. However, it is still unclear which lipid metabolism abnormalities play the most important role in neurodegenerative diseases. Plasma lipid metabolomics (lipidomics) has been shown to be an unbiased method that can be used to explore lipid metabolism abnormalities in neurodegenerative diseases. Plasma lipidomics in neurodegenerative diseases has been performed only in idiopathic Parkinson's disease (IPD) and Alzheimer's disease (AD), and comprehensive studies are needed to clarify the pathogenesis.

METHODS

In this study, we investigated plasma lipids using lipidomics in individuals with neurodegenerative diseases and healthy controls (CNs). Plasma lipidomics was evaluated by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in those with IPD, dementia with Lewy bodies (DLB), multiple system atrophy (MSA), AD, and progressive supranuclear palsy (PSP) and CNs.

RESULTS

The results showed that (1) plasma sphingosine-1-phosphate (S1P) was significantly lower in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group. (2) Plasma monohexylceramide (MonCer) and lactosylceramide (LacCer) were significantly higher in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group. (3) Plasma MonCer levels were significantly positively correlated with plasma LacCer levels in all enrolled groups.

CONCLUSION

S1P, Glucosylceramide (GlcCer), the main component of MonCer, and LacCer are sphingolipids that are biosynthesized from ceramide. Recent studies have suggested that elevated GlcCer and decreased S1P levels in neurons are related to neuronal cell death and that elevated LacCer levels induce neurodegeneration by neuroinflammation. In the present study, we found decreased plasma S1P levels and elevated plasma MonCer and LacCer levels in those with neurodegenerative diseases, which is a new finding indicating the importance of abnormal sphingolipid metabolism in neurodegeneration.

Saliva metabolome alterations after acute stress

Major stress has systemic effects on the body that can have adverse consequences for physical and mental health. However, the molecular basis of these damaging effects remains incompletely understood. Here we use a longitudinal approach to characterise the acute systemic impact of major psychological stress in a pig model. We perform untargeted metabolomics on non-invasively obtained saliva samples from pigs before and 24 h after transfer to the novel physical and social environment of a slaughterhouse. The main molecular changes occurring include decreases in amino acids, B-vitamins, and amino acid-derived metabolites synthesized in B-vitamin-dependent reactions, as well as yet-unidentified metabolite features. Decreased levels of several of the identified metabolites are implicated in the pathology of human psychological disorders and neurodegenerative disease, suggesting a possible neuroprotective function. Our results provide a fingerprint of the acute effect of psychological stress on the metabolome and suggest candidate biomarkers with potential roles in stress-related disorders.

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

Dance movement therapy for neurodegenerative diseases: A systematic review

Background

The proportion of the world's elderly population continues to rise, and the treatment and improvement of neurodegenerative diseases have become issue of public health importance as people live longer and many countries have aging populations. This systematic review aims to discuss the effects of dance movement therapy (DMT) on motor function, cognitive deficit, mood, and quality of life in people with neurodegenerative diseases, such as Parkinson's disease (PD), mild cognitive impairment (MCI), Alzheimer's disease (AD).

Methods

Two reviewers independently conducted systematic search on the Cochrane library, PubMed database, Web of Science Core Collection database, and Physiotherapy Evidence database until February 1, 2022. Only systematic analyses and randomized controlled trials were included and further analyzed.

Results

Thirty-three studies on PD, 16 studies on MCI, 4 studies on AD were obtained. This systematic review found that DMT substantially improved the global cognitive function, memory, and executive function on the population with MCI. Compared with the non-dance group, DMT remarkably improved general disease condition, balance, and gait for individuals with PD. The evidence of the efficacy of DMT on AD is insufficient, and further research is needed.

Conclusion

DMT can effectively improve the motor function and cognitive deficits in neurodegenerative diseases. Positive effects of DMT on the mood and quality of life in ND patients are controversial and require further evidence. Future research on the effects of DMT on AD requires scientific design, large sample size, long-term comprehensive intervention, and clear reporting standards.

Systematic review registration

www.osf.io/wktez, identifier: 10.17605/OSF.IO/UYBKT.

NMR-Based Metabolomics of Rat Hippocampus, Serum, and Urine in Two Models of Autism

Autism spectrum disorders (ASDs) are increasingly diagnosed as developmental disabilities of unclear etiology related to genetic, epigenetic, or environmental factors. The diagnosis of ASD in children is based on the recognition of typical behavioral symptoms, while no reliable biomarkers are available. Rats in whom ASD-like symptoms are due to maternal administration of the teratogenic drugs valproate or thalidomide on critical day 11 of pregnancy are widely used models in autism research. The present studies, aimed at detecting changes in the levels of hydrophilic and hydrophobic metabolites, were carried out on 1-month-old rats belonging to the abovementioned two ASD models and on a control group. Analysis of both hydrophilic and hydrophobic metabolite levels gives a broader view of possible mechanisms involved in the pathogenesis of autism. Hippocampal proton magnetic resonance (MRS) spectroscopy and ex vivo nuclear magnetic resonance (NMR) analysis of serum and urine samples were used. The results were analyzed using advanced statistical tests. Both the results of our present MRS studies of the hippocampus and of the NMR studies of body fluids in both ASD models, particularly from the THAL model, appeared to be consistent with previously published NMR results of hippocampal homogenates and data from the literature on autistic children. We detected symptoms of disturbances in neurotransmitter metabolism, energy deficit, and oxidative stress, as well as intestinal malfunction, which shed light on the pathogenesis of ASD and could be used for diagnostic purposes. These results confirm the usefulness of the noninvasive techniques used in ASD studies.

Amino acids in inflammatory bowel diseases: Modern diagnostic tools and methodologies

Amino acids are crucial building blocks of living organisms. Together with their derivatives, they participate in many intracellular processes to act as hormones, neuromodulators, and neurotransmitters. For several decades amino acids have been studied for their potential as markers of various diseases, including inflammatory bowel diseases. Subsequent improvements in sample pretreatment, separation, and detection methods have enabled the specific and very sensitive determination of these molecules in multicomponent matrices-biological fluids and tissues. The information obtained from targeted amino acid analysis (biomarker-based analytical strategy) can be further used for early diagnostics, to monitor the course of the disease or compliance of the patients. This review will provide an insight into current knowledge about inflammatory bowel diseases, the role of proteinogenic amino acids in intestinal inflammation and modern analytical techniques used in its diagnosis and disease activity monitoring. Current advances in the analysis of amino acids focused on sample pretreatment, separation strategy, or detection methods are highlighted, and their potential in clinical laboratories is discussed. In addition, the latest clinical data obtained from the metabolomic profiling of patients suffering from inflammatory bowel diseases are summarized with a focus on proteinogenic amino acids.

Therapeutic Approaches to Amyotrophic Lateral Sclerosis from the Lab to the Clinic

Amyotrophic Lateral Sclerosis (ALS) is a terminal neuro-degenerative disorder that is clinically recognized as a gradual degeneration of the upper and lower motor neurons, with an average duration of 3 to 5 years from initiation of symptoms to death. The mechanisms underlying the pathogenesis and progression of the disease are multifactorial. Therefore, to find effective treatments, it is necessary to understand this heterogeneity underlying the progression of ALS. Recent developments in gene therapy have opened a new avenue to treat this condition, especially for the characterized genetic types. Gene therapy methods have been studied in a variety of pre-clinical settings and clinical trials, and they may be a promising path for developing an effective and safe ALS cure. A growing body of evidence demonstrates abnormalities in energy metabolism at the cellular and whole-body level in animal models and in people living with ALS. The use and incorporation of high-throughput "omics" methods has radically transformed our thought about ALS, strengthening our understanding of the disease's dynamic molecular architecture, differentiating distinct patient subtypes, and creating a reasonable basis for the identification of biomarkers and novel individualised treatments. Future clinical and laboratory trials would also focus on the diverse relationships between metabolism and ALS to address the issue of whether targeting deficient metabolism in ALS is an effective way to change disease progression. In this review, we focus on the detailed pathogenesis of ALS and highlight principal genes, i.e., SOD1, TDP-43, C9orf72, and FUS, targeted therapeutic approaches of ALS. An attempt is made to provide up-to-date information on clinical outcomes, including various biomarkers which are thought to be important players in early ALS detection.

Blood glutamine synthetase signaling in alcohol use disorder and racial disparity

As of 2018, 14.4 million adults ages 18 and older in the U.S had alcohol use disorder (AUD). However, only about 8% of adults who had AUD in the past year received treatment. Surveys have also shown racial disparities regarding AUD treatments. Thus, it is imperative to identify racial disparities in AUD patients, as it may indicate a specific underlying pathophysiology in an AUD subpopulation. To identify racial disparity in AUD, we enrolled 64 cohorts, including 26 AUD participants and 38 healthy controls, from Northwest Louisiana using community-based enrollment. Then, we used psychometric scales to assess alcohol drinking patterns and measured blood metabolites change using LC-MS/MS. Alcohol-related scales from the questionnaires did not differ between the Caucasian AUD participants and African-American AUD participants. From blood metabolomics analyses, we identified that 6 amino acids were significantly different by AUD status and or race. Interestingly, Caucasian AUD participants had a higher glutamate metabolism mediated by glutamine synthetase (GS). The correlation between blood glutamate/glutamine ratio and GS activity was only significant in the Caucasian AUD group whereas no changes were observed in African-American AUD group or controls. Taken together, our findings from this sample population demonstrate that blood GS is a potential biomarker associated with Caucasian AUD, which is an important step towards the application of a new pharmacological treatment for AUD.

A metabolic biomarker predicts Parkinson's disease at the early stages in patients and animal models

BackgroundCare management of Parkinson's disease (PD) patients currently remains symptomatic, mainly because diagnosis relying on the expression of the cardinal motor symptoms is made too late. Earlier detection of PD therefore represents a key step for developing therapies able to delay or slow down its progression.MethodsWe investigated metabolic markers in 3 different animal models of PD, mimicking different phases of the disease assessed by behavioral and histological evaluation, and in 3 cohorts of de novo PD patients and matched controls (n = 129). Serum and brain tissue samples were analyzed by nuclear magnetic resonance spectroscopy and data submitted to advanced multivariate statistics.ResultsOur translational strategy reveals common metabolic dysregulations in serum of the different animal models and PD patients. Some of them were mirrored in the tissue samples, possibly reflecting pathophysiological mechanisms associated with PD development. Interestingly, some metabolic dysregulations appeared before motor symptom emergence and could represent early biomarkers of PD. Finally, we built a composite biomarker with a combination of 6 metabolites. This biomarker discriminated animals mimicking PD from controls, even from the first, nonmotor signs and, very interestingly, also discriminated PD patients from healthy subjects.ConclusionFrom our translational study, which included 3 animal models and 3 de novo PD patient cohorts, we propose a promising biomarker exhibiting a high accuracy for de novo PD diagnosis that may possibly predict early PD development, before motor symptoms appear.FundingFrench National Research Agency (ANR), DOPALCOMP, Institut National de la Santé et de la Recherche Médicale, Université Grenoble Alpes, Association France Parkinson.

Metabolomics as a promising tool for improving understanding of Multiple Sclerosis: a review of recent advances

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system that usually affects young adults. The development of MS is closely related to the changes in the metabolome. Metabolomics studies have been performed using biofluids or tissue samples from rodent models and human patients to reveal metabolic alterations associated with MS progression. This review aims to provide an overview of the applications of metabolomics that for the investigations of the perturbed metabolic pathways in MS and to reveal the potential of metabolomics in personalizing treatments. In conclusion, informative variations of metabolites can be potential biomarkers in advancing our understanding of MS pathogenesis for MS diagnosis, predicting the progression of the disease, and estimating drug effects. Metabolomics will be a promising technique for improving clinical care in MS.

Screening of SERT and p11 mRNA Levels in Airline Pilots: A Translational Approach

Airline pilots are frequently exposed to numerous flights per week, changes in their circadian rhythms, and extended periods away from home. All these stressors make pilots susceptible to developing psychiatric disorders. Recently, emphasis has been placed on the need for molecular tests that help in the diagnosis of depression. The genes SLC6A4 and S100A10 encode serotonin transporter (SERT) and p11 protein, respectively. Their expression has been frequently associated with stress and depression. In this work, we quantified, by quantitative PCR, the expression of SERT and p11 in peripheral mononuclear cells of airline pilots compared to patients with depression and healthy volunteers. Moreover, by mass spectrometry, we quantified the serum serotonin levels in the same three groups. We found that SERT and p11 were overexpressed in the mononuclear cells of airline pilots and depressed patients compared to healthy volunteers. Although serum serotonin was not different between healthy volunteers and airline pilots, a decreasing trend was observed in the latter. As expected, serum serotonin in the patients was significantly lower. Alterations in SERT and p11 in airline pilots could be related to professional stress, a condition that could potentially affect their long-term mental health.

Discovery and validation of acetyl-L-carnitine in serum for diagnosis of major depressive disorder and remission status through metabolomic approach

Major depressive disorder (MDD) is one of the most common psychiatric disorders that accompany psychophysiological and mood changes. However, the pathophysiology-based disease mechanism of MDD is not yet fully understood, and diagnosis is also conducted through interviews with clinicians and patients. Diagnosis and treatment of MDD are limited due to the absence of biomarkers underlying the pathophysiological mechanisms of MDD. Although various attempts have been made to discover metabolite biomarkers for the diagnosis and treatment response of MDD, problems with sample size and consistency of results have limited clinical application. In addition, it was reported that future biomarker studies must consider exposure to antidepressants, which is the main cause of heterogeneity in depression subgroups. Therefore, the purpose of this study is to discover and validate biomarkers for the diagnosis of depression in consideration of exposure to drug treatment including antidepressants that contribute to the heterogeneity of the MDD subgroup. In the biomarker discovery and validation set, the disease group consisted of a mixture of patients exposed and unexposed to drug treatment including antidepressants for the treatment of MDD. The serum metabolites that differed between the MDD patients and the control group were profiled using mass spectrometry. The validation set including the remission group was used to verify the effectiveness as a biomarker for the diagnosis of depression and determination of remission status. The presence of different metabolites between the two groups was confirmed through serum metabolite profiling between the MDD patient group and the control group. Finally, Acetylcarnitine was selected as a biomarker. In validation, acetylcarnitine was significantly decreased in MDD and was distinguished from remission status. This study confirmed that the discovered acetylcarnitine has potential as a biomarker for diagnosing depression and determining remission status, regardless of exposure to drug treatment including antidepressants.

Searching for biomarkers in schizophrenia and psychosis: Case-control study using capillary electrophoresis and liquid chromatography time-of-flight mass spectrometry and systematic review for biofluid metabolites

Metabolomics has been attracting attention in recent years as an objective method for diagnosing schizophrenia. In this study, we analyzed 378 metabolites in the serum of schizophrenia patients using capillary electrophoresis‐ and liquid chromatography‐time‐of‐flight mass spectrometry. Using multivariate analysis with the orthogonal partial least squares method, we observed significantly higher levels of alanine, glutamate, lactic acid, ornithine, and serine and significantly lower levels of urea, in patients with chronic schizophrenia compared to healthy controls. Additionally, levels of fatty acids (15:0), (17:0), and (19:1), cis‐11‐eicosenoic acid, and thyroxine were significantly higher in patients with acute psychosis than in those in remission. Moreover, we conducted a systematic review of comprehensive metabolomics studies on schizophrenia over the last 20 years and observed consistent trends of increase in some metabolites such as glutamate and glucose, and decrease in citrate in schizophrenia patients across several studies. Hence, we provide substantial evidence for metabolic biomarkers in schizophrenia patients through our metabolomics study.

In this study, we analyzed 378 metabolites in the serum of schizophrenia patients using CE and LC‐TOFMS. With a systematic review, here we provide substantial evidence for metabolic biomarkers in schizophrenia patients through our metabolomics study.

Review of Metabolomics-Based Biomarker Research for Parkinson's Disease

Parkinson's disease (PD), as the second most common neurodegenerative disease, is seriously affecting the life quality of the elderly. However, there is still a lack of efficient medical methods to diagnosis PD before apparent symptoms occur. In recent years, clinical biomarkers including genetic, imaging, and tissue markers have exhibited remarkable benefits in assisting PD diagnoses. Due to the advantages of high-throughput detection of metabolites and almost non-invasive sample collection, metabolomics research of PD is widely used for diagnostic biomarker discovery. However, there are also a few shortages for those identified biomarkers, such as the scarcity of verifications regarding the sensitivity and specificity. Thus, reviewing the research progress of PD biomarkers based on metabolomics techniques is of great significance for developing PD diagnosis. To comprehensively clarify the progress of current metabolic biomarker studies in PD, we reviewed 20 research articles regarding the discovery and validation of biomarkers for PD diagnosis from three mainstream academic databases (NIH PubMed, ISI Web of Science, and Elsevier ScienceDirect). By analyzing those materials, we summarized the metabolic biomarkers identified by those metabolomics studies and discussed the potential approaches used for biomarker verifications. In conclusion, this review provides a comprehensive and updated overview of PD metabolomics research in the past two decades and particularly discusses the validation of disease biomarkers. We hope those discussions might provide inspiration for PD biomarker discovery and verification in the future.

Exploring the metabolomic profile of cerebellum after exposure to acute stress

Psychological stress and stress-related disorders constitute a major health problem in modern societies. Although the brain circuits involved in emotional processing are intensively studied, little is known about the implication of cerebellum in stress responses whereas the molecular changes induced by stress exposure in cerebellum remain largely unexplored. Here, we investigated the effects of acute stress exposure on mouse cerebellum. We used a forced swim test (FST) paradigm as an acute stressor. We then analyzed the cerebellar metabolomic profiles of stressed (n = 11) versus control (n = 11) male CD1 mice by a Nuclear Magnetic Resonance (NMR)-based, untargeted metabolomics approach. Our results showed altered levels of 19 out of the 47 annotated metabolites, which are implicated in neurotransmission and N-acetylaspartic acid (NAA) turnover, as well as in energy and purine/pyrimidine metabolism. We also correlated individual metabolite levels with FST behavioral parameters, and reported associations between FST readouts and levels of 4 metabolites. This work indicates an altered metabolomic signature after acute stress in the cerebellum and highlights a previously unexplored involvement of cerebellum in stress responses.

Characteristics of serum metabolites in sporadic amyotrophic lateral sclerosis patients based on gas chromatography-mass spectrometry

To identify differential metabolites and metabolic pathways and provide guidance for the novel biomarkers for diagnosis and prognosis of amyotrophic lateral sclerosis (ALS). ALS patients and people without nervous diseases were recruited. Metabolomic analysis was performed using gas chromatography-mass spectrometry (GC/MS). The orthogonal projections to latent structures discriminant analysis (OPLS-DA) were used to identify differential metabolites. Kyoto Encyclopedia of Genes and Genomes and MetaboAnalyst were used to identify metabolic pathways. 75 metabolites were detected and aligned. The OPLS-DA showed the metabolomic profile of ALS patients and those in the fast-progression and slow-progression ALS groups differed from that of CTRL (p < 0.05). The levels of maltose, glyceric acid, lactic acid, beta-alanine, phosphoric acid, glutamic acid, ethanolamine and glycine in ALS were significantly higher, while 2,4,6-tri-tert-butylbenzenethiol was lower. Glycine, serine and threonine metabolism, D-glutamine and D-glutamate metabolism, alanine, aspartate, and glutamate metabolism, beta-alanine metabolism, and pyruvate metabolism were significantly altered metabolic pathways in ALS. ROC was used to discriminate ALS from CTRL with an AUC of 0.898 (p < 0.001) using 2,4,6-tri-tert-butylbenzenethiol, beta-alanine, glycine, and ethanolamine. The serum metabolites and metabolic pathways in ALS patients are significantly altered compared with CTRL. These findings may contribute to the early diagnosis of ALS.

NMR- and MS-Based Untargeted Metabolomic Study of Stool and Serum Samples from Patients with Anorexia Nervosa

Anorexia nervosa (AN), a pathological restriction of food intake, leads to metabolic dysregulation. We conducted a metabolomics study to reveal changes caused by AN and the effect of hospital realimentation on metabolism. Both stool and serum from patients with AN and healthy controls were analyzed by NMR and MS. Statistical analysis revealed several altered biochemical and anthropometric parameters and 50 changed metabolites, including phospholipids, acylcarnitines, amino acids, derivatives of nicotinic acid, nucleotides, and energy metabolism intermediates. Biochemical and anthropometric parameters were correlated with metabolomic data. Metabolic changes in patients with AN described in our study imply serious system disruption defects, such as the development of inflammation and oxidative stress, changed free thyroxine (fT4) and thyroid-stimulating hormone (TSH) levels, a deficit of vitamins, muscle mass breakdown, and a decrease in ketone bodies as an important source of energy for the brain and heart. Furthermore, our data indicate only a very slight improvement after treatment. However, correlations of metabolomic results with body weight, interleukin 6, tumor necrosis factor α, fT4, and TSH might entail better prognoses and treatment effectiveness in patients with better system parameter status. Data sets are deposited in MassIVE: MSV000087713, DOI: 10.25345/C57R7X.

Metabolomics signatures of acutely ill and short-term weight recovered women with anorexia nervosa

Although metabolomics studies are recently spreading and have allowed the characterization of putative biomarkers in many diseases, they are relatively scanty in anorexia nervosa (AN). In this explorative study we analyzed the fecal metabolomics profiles of women with AN in the underweight phase (n = 24) and after short-term weight restoration (n = 16) and compared them with 20 healthy women. An untargeted metabolomic procedure allowed the characterization of 224 metabolites involved in energy, lipid, and amino acid metabolism. A partial least square discriminant analysis identified 14 metabolites with a variable importance in projection score >1.5 that clearly differentiated underweight from weight-restored patients from healthy women. Compared with healthy women, fecal concentrations of valeric acid and 3-methyl,2-ketobutyric acid were increased in both underweight and weight-restored patients; fecal concentrations of propionic acid, stearic acid, linolenic acid, methyl-galactoside, coprosterol, cycloserine, and lauric acid were increased while fecal levels of xylose, fucose, and rhamnose were decreased in underweight patients and normalized after weight-restoration; fecal concentrations of piperine, phenylalanine, butyric acid, and meso-erythritol-1 were decreased while fecal levels of hydroxystearic acid were increased in weight-restored but normal in underweight AN patients. All these changes point to peculiar fecal metabolomics profiles of acute and short-term weight restored AN patients. The value of these changes to improve our understanding of the pathophysiology of AN and to characterize potential biomarker targets for developing new treatment strategies needs further studies to be clarified.

Metabolomic signatures associated with depression and predictors of antidepressant response in humans: A CAN-BIND-1 report

One of the biggest challenges in treating depression is the heterogeneous and qualitative nature of its clinical presentations. This highlights the need to find quantitative molecular markers to tailor existing treatment strategies to the individual's biological system. In this study, high-resolution metabolic phenotyping of urine and plasma samples from the CAN-BIND study collected before treatment with two common pharmacological strategies, escitalopram and aripiprazole, was performed. Here we show that a panel of LDL and HDL subfractions were negatively correlated with depression in males. For treatment response, lower baseline concentrations of apolipoprotein A1 and HDL were predictive of escitalopram response in males, while higher baseline concentrations of apolipoprotein A2, HDL and VLDL subfractions were predictive of aripiprazole response in females. These findings support the potential of metabolomics in precision medicine and the possibility of identifying personalized interventions for depression.

Untargeted Plasma Metabolomic Profiling in Patients with Major Depressive Disorder Using Ultra-High Performance Liquid Chromatography Coupled with Mass Spectrometry

Major depressive disorder (MDD) is a neuropsychiatric illness with an increasing incidence and a shortfall of efficient diagnostic tools. Interview-based diagnostic tools and clinical examination often lead to misdiagnosis and inefficient systematic treatment selection. Diagnostic and treatment monitoring biomarkers are warranted for MDD. Thus, the emerging field of metabolomics is a promising tool capable of portraying the metabolic repertoire of biomolecules from biological samples in a minimally invasive fashion. Herein, we report an untargeted metabolomic profiling performed in plasma samples of 11 MDD patients, at baseline (MDD1) and at 12 weeks following antidepressant therapy with escitalopram (MDD2), and in 11 healthy controls (C), using ultra-high performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-(ESI+)-MS). We found two putative metabolites ((phosphatidylserine PS (16:0/16:1) and phosphatidic acid PA (18:1/18:0)) as having statistically significant increased levels in plasma samples of MDD1 patients compared to healthy subjects. ROC analysis revealed an AUC value of 0.876 for PS (16:0/16:1), suggesting a potential diagnostic biomarker role. In addition, PS (18:3/20:4) was significantly decreased in MDD2 group compared to MDD1, with AUC value of 0.785.

Towards a biochemical approach to occupational stress management

Given the immense and growing cost of occupational stress to society through lost productivity and the burden to healthcare systems, current best practices for detecting, managing and reducing stress in the workplace are clearly sub-optimal and substantially better methods are required. Subjective, self-reported psychology and psychiatry-based instruments are prone to biases whereas current objective, biology-based measures produce conflicting results and are far from reliable. A multivariate approach to occupational stress research is required that reflects the broad, coordinated, physiological response to demands placed on the body by exposure to diverse occupational stressors. A literature review was conducted to determine the extent of application of the emerging multivariate technology of metabolomics to occupational stress research. Of 170 articles meeting the search criteria, three were identified that specifically studied occupational stressors using metabolomics. A further ten studies were not specifically occupational or were of indirect or peripheral relevance. The occupational studies, although limited in number highlight the technological challenges associated with the application of metabolomics to investigate occupational stress. They also demonstrate the utility to evaluate stress more comprehensively than univariate biomarker studies. The potential of this multivariate approach to enhance our understanding of occupational stress has yet to be established. This will require more studies with broader analytical coverage of the metabolome, longitudinal sampling, combination with experience sampling methods and comparison with psychometric models of occupational stress. Progress will likely involve combining multi-omic data into a holistic, systems biology approach to detecting, managing and reducing occupational stress and optimizing workplace performance.

Metabolomic Characterization of Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS)

Introduction

PANS is a controversial clinical entity, consisting of a complex constellation of psychiatric symptoms, adventitious changes, and expression of various serological alterations, likely sustained by an autoimmune/inflammatory disease. Detection of novel biomarkers of PANS is highly desirable for both diagnostic and therapeutic management of affected patients. Analysis of metabolites has proven useful in detecting biomarkers for other neuroimmune-psychiatric diseases. Here, we utilize the metabolomics approach to determine whether it is possible to define a specific metabolic pattern in patients affected by PANS compared to healthy subjects.

Design

This observational case-control study tested consecutive patients referred for PANS between June 2019 to May 2020. A PANS diagnosis was confirmed according to the PANS working criteria (National Institute of Mental Health [NIMH], 2010). Healthy age and sex-matched subjects were recruited as controls.

Methods

Thirty-four outpatients referred for PANS (mean age 9.5 years; SD 2.9, 71% male) and 25 neurotypical subjects matched for age and gender, were subjected to metabolite analysis. Serum samples were obtained from each participant and were analyzed using Nuclear Magnetic Resonance (NMR) spectroscopy. Subsequently, multivariate and univariate statistical analyses and Receiver Operator Curves (ROC) were performed.

Results

Separation of the samples, in line with the presence of PANS diagnosis, was observed by applying a supervised model (R²X = 0.44, R²Y = 0.54, Q² = 0.44, p-value < 0.0001). The significantly altered variables were 2-Hydroxybutyrate, glycine, glutamine, histidine, tryptophan. Pathway analysis indicated that phenylalanine, tyrosine, and tryptophan metabolism, as well as glutamine and glutamate metabolism, exhibited the largest deviations from neurotypical controls.

Conclusion

We found a unique plasma metabolic profile in PANS patients, significantly differing from that of healthy children, that suggests the involvement of specific patterns of neurotransmission (tryptophan, glycine, histamine/histidine) as well as a more general state of neuroinflammation and oxidative stress (glutamine, 2-Hydroxybutyrate, and tryptophan-kynurenine pathway) in the disorder. This metabolomics study offers new insights into biological mechanisms underpinning the disorder and supports research of other potential biomarkers implicated in PANS.

Identification of Plasmatic Biomarkers of Foie Gras Qualities in Duck by Metabolomics

The foie gras is an emblematic product of French gastronomy composed of waterfowl fatty liver. The organoleptic qualities of this product depend on the liver characteristics such as liver weight (LW) and technological yield (TY) at cooking. One of the main issues for producers is to classify the foie gras with high or low technological quality before cooking them. Thus the study aims at identifying biomarkers of these characteristics with non-invasive biomarkers in duck. ¹H-NMR (nuclear magnetic resonance of the proton) analyses were performed on plasma of male mule ducks at different time points during the overfeeding period to obtain a large range of liver characteristics so as to identify plasmatic biomarkers of foie gras. We used two methods, one based on bucket data from the ¹H-NMR spectra and another one based on the fingerprints of several metabolites. PLS analyses and Linear models were performed to identify biomarkers. We identified 18 biomarkers of liver weight and 15 biomarkers of technological yield. As these two quality parameters were strongly correlated (−0.82), 13 biomarkers were common. The lactate was the most important biomarker, the other were mainly amino acids. Contrary to the amino acids, the lactate increased with the liver weight and decreased with the technological yield. We also identified 5 biomarkers specific to LW (3 carbohydrates: glucuronic acid, mannose, sorbitol and 2 amino acids: glutamic acid and methionine) that were negatively correlated to liver weight. It was of main interest to identify 2 biomarkers specific to the technological yield. Contrary to the isovaleric acid, the valine was negatively correlated to the technological yield.

Skeletal Muscle Metabolomic Responses to Endurance and Resistance Training in Rats under Chronic Unpredictable Mild Stress

The objectives of this study were to compare the antidepressant effects between endurance and resistance exercise for optimizing interventions and examine the metabolomic changes in different types of skeletal muscles in response to the exercise, using a rat model of chronic unpredictable mild stress (CUMS)-induced depression. There were 32 male Sprague-Dawley rats randomly divided into a control group (C) and 3 experimental groups: CUMS control (D), endurance exercise (E), and resistance exercise (R). Group E underwent 30 min treadmill running, and group R performed 8 rounds of ladder climbing, 5 sessions per week for 4 weeks. Body weight, sucrose preference, and open field tests were performed pre and post the intervention period for changes in depressant symptoms, and the gastrocnemius and soleus muscles were sampled after the intervention for metabolomic analysis using the ¹H-NMR technique. The results showed that both types of exercise effectively improved the depression-like symptoms, and the endurance exercise appeared to have a better effect. The levels of 10 metabolites from the gastrocnemius and 13 metabolites from the soleus of group D were found to be significantly different from that of group C, and both types of exercise had a callback effect on these metabolites, indicating that a number of metabolic pathways were involved in the depression and responded to the exercise interventions.

Perturbations in Amino Acid Metabolism in Reserpine-Treated Zebrafish Brain Detected by 1H Nuclear Magnetic Resonance-Based Metabolomics

Depression is a complex and disabling psychiatric disorder, which is expected to be a leading cause for disability by 2030. According to World Health Organization, about 350 million people are suffering with mental health disorders around the globe, especially depression. However, the mechanisms involved in stress-induced depression have not been fully elucidated. In this study, a stress-like state was pharmacologically induced in zebrafish using reserpine, a drug widely used to mediate depression in experimental animal models. Zebrafish received single intraperitoneal (i.p.) injections of 20, 40, and 80 mg/kg body weight reserpine doses and were subjected to open-field test at 2, 24, 48, 72, and 96 h after the treatment. Along with observed changes in behavior and measurement of cortisol levels, the fish were further examined for perturbations in their brain metabolites by ¹H nuclear magnetic resonance (NMR)-based metabolomics. We found a significant increase in freezing duration, whereas total distance travelled was decreased 24 h after single intraperitoneal injection of reserpine. Cortisol level was also found to be higher after 48 h of reserpine treatment. The ¹H NMR data showed that the levels of metabolites such as glutamate, glutamine, histamine, valine, leucine and histidine, lactate, l-fucose, betaine and γ-amino butyric acid (GABA), β-hydroxyisovalerate, and glutathione were significantly decreased in the reserpine-treated group. This study provided some insights into the molecular nature of stress that could contribute toward a better understanding of depression disorder.

Genetically Determined Levels of Serum Metabolites and Risk of Neuroticism: A Mendelian Randomization Study

BACKGROUND

Neuroticism is a strong predictor for a variety of social and behavioral outcomes, but the etiology is still unknown. Our study aims to provide a comprehensive investigation of causal effects of serum metabolome phenotypes on risk of neuroticism using Mendelian randomization (MR) approaches.

METHODS

Genetic associations with 486 metabolic traits were utilized as exposures, and data from a large genome-wide association study of neuroticism were selected as outcome. For MR analysis, we used the standard inverse-variance weighted (IVW) method for primary MR analysis and 3 additional MR methods (MR-Egger, weighted median, and MR pleiotropy residual sum and outlier) for sensitivity analyses.

RESULTS

Our study identified 31 metabolites that might have causal effects on neuroticism. Of the 31 metabolites, uric acid and paraxanthine showed robustly significant association with neuroticism in all MR methods. Using single nucleotide polymorphisms as instrumental variables, a 1-SD increase in uric acid was associated with approximately 30% lower risk of neuroticism (OR: 0.77; 95% CI: 0.62-0.95; PIVW = 0.0145), whereas a 1-SD increase in paraxanthine was associated with a 7% higher risk of neuroticism (OR: 1.07; 95% CI: 1.01-1.12; PIVW = .0145).

DISCUSSION

Our study suggested an increased level of uric acid was associated with lower risk of neuroticism, whereas paraxanthine showed the contrary effect. Our study provided novel insight by combining metabolomics with genomics to help understand the pathogenesis of neuroticism.

An emerging potential of metabolomics in multiple sclerosis: a comprehensive overview

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the nervous system that primarily affects young adults. Although the exact etiology of the disease remains obscure, it is clear that alterations in the metabolome contribute to this process. As such, defining a reliable and disease-specific metabolome has tremendous potential as a diagnostic and therapeutic strategy for MS. Here, we provide an overview of studies aimed at identifying the role of metabolomics in MS. These offer new insights into disease pathophysiology and the contributions of metabolic pathways to this process, identify unique markers indicative of treatment responses, and demonstrate the therapeutic effects of drug-like metabolites in cellular and animal models of MS. By and large, the commonly perturbed pathways in MS and its preclinical model include lipid metabolism involving alpha-linoleic acid pathway, nucleotide metabolism, amino acid metabolism, tricarboxylic acid cycle, d-ornithine and d-arginine pathways with collective role in signaling and energy supply. The metabolomics studies suggest that metabolic profiling of MS patient samples may uncover biomarkers that will advance our understanding of disease pathogenesis and progression, reduce delays and mistakes in diagnosis, monitor the course of disease, and detect better drug targets, all of which will improve early therapeutic interventions and improve evaluation of response to these treatments.

A metabolome-wide association study in the general population reveals decreased levels of serum laurylcarnitine in people with depression

Depression constitutes a leading cause of disability worldwide. Despite extensive research on its interaction with psychobiological factors, associated pathways are far from being elucidated. Metabolomics, assessing the final products of complex biochemical reactions, has emerged as a valuable tool for exploring molecular pathways. We conducted a metabolome-wide association analysis to investigate the link between the serum metabolome and depressed mood (DM) in 1411 participants of the KORA (Cooperative Health Research in the Augsburg Region) F4 study (discovery cohort). Serum metabolomics data comprised 353 unique metabolites measured by Metabolon. We identified 72 (5.1%) KORA participants with DM. Linear regression tests were conducted modeling each metabolite value by DM status, adjusted for age, sex, body-mass index, antihypertensive, cardiovascular, antidiabetic, and thyroid gland hormone drugs, corticoids and antidepressants. Sensitivity analyses were performed in subcohorts stratified for sex, suicidal ideation, and use of antidepressants. We replicated our results in an independent sample of 968 participants of the SHIP-Trend (Study of Health in Pomerania) study including 52 (5.4%) individuals with DM (replication cohort). We found significantly lower laurylcarnitine levels in KORA F4 participants with DM after multiple testing correction according to Benjamini/Hochberg. This finding was replicated in the independent SHIP-Trend study. Laurylcarnitine remained significantly associated (p value < 0.05) with depression in samples stratified for sex, suicidal ideation, and antidepressant medication. Decreased blood laurylcarnitine levels in depressed individuals may point to impaired fatty acid oxidation and/or mitochondrial function in depressive disorders, possibly representing a novel therapeutic target.

An integrated meta-analysis of peripheral blood metabolites and biological functions in major depressive disorder

Major depressive disorder (MDD) is a serious mental illness, characterized by high morbidity, which has increased in recent decades. However, the molecular mechanisms underlying MDD remain unclear. Previous studies have identified altered metabolic profiles in peripheral tissues associated with MDD. Using curated metabolic characterization data from a large sample of MDD patients, we meta-analyzed the results of metabolites in peripheral blood. Pathway and network analyses were then performed to elucidate the biological themes within these altered metabolites. We identified 23 differentially expressed metabolites between MDD patients and controls from 46 studies. MDD patients were characterized by higher levels of asymmetric dimethylarginine, tyramine, 2-hydroxybutyric acid, phosphatidylcholine (32:1), and taurochenodesoxycholic acid and lower levels of L-acetylcarnitine, creatinine, L-asparagine, L-glutamine, linoleic acid, pyruvic acid, palmitoleic acid, L-serine, oleic acid, myo-inositol, dodecanoic acid, L-methionine, hypoxanthine, palmitic acid, L-tryptophan, kynurenic acid, taurine, and 25-hydroxyvitamin D compared with controls. L-tryptophan and kynurenic acid were consistently downregulated in MDD patients, regardless of antidepressant exposure. Depression rating scores were negatively associated with decreased levels of L-tryptophan. Pathway and network analyses revealed altered amino acid metabolism and lipid metabolism, especially for the tryptophan-kynurenine pathway and fatty acid metabolism, in the peripheral system of MDD patients. Taken together, our integrated results revealed that metabolic changes in the peripheral blood were associated with MDD, particularly decreased L-tryptophan and kynurenic acid levels, and alterations in the tryptophan-kynurenine and fatty acid metabolism pathways. Our findings may facilitate biomarker development and the elucidation of the molecular mechanisms that underly MDD.

Evaluation of Metabolic Profiles of Patients with Anorexia Nervosa at Inpatient Admission, Short- and Long-Term Weight Regain-Descriptive and Pattern Analysis

Acute anorexia nervosa (AN) constitutes an extreme physiological state. We aimed to detect state related metabolic alterations during inpatient admission and upon short- and long-term weight regain. In addition, we tested the hypothesis that metabolite concentrations adapt to those of healthy controls (HC) after long-term weight regain. Thirty-five female adolescents with AN and 25 female HC were recruited. Based on a targeted approach 187 metabolite concentrations were detected at inpatient admission (T₀), after short-term weight recovery (T₁; half of target-weight) and close to target weight (T₂). Pattern hunter and time course analysis were performed. The highest number of significant differences in metabolite concentrations (N = 32) were observed between HC and T₁. According to the detected main pattern, metabolite concentrations at T₂ became more similar to those of HC. The course of single metabolite concentrations (e.g., glutamic acid) revealed different metabolic subtypes within the study sample. Patients with AN after short-term weight regain are in a greater “metabolic imbalance” than at starvation. After long-term weight regain, patients reach a metabolite profile similar to HC. Our results might be confounded by different metabolic subtypes of patients with AN.

Metabolomics study of cerebrospinal fluid from diabetic rats with cognitive impairment simultaneously treated with Panax quinquefolius and Acorus gramineus

A metabolomics approach was used to explore the effects of Panax quinquefolius (PQ) and Acorus gramineus (AG) on learning and memory in rats with diabetic-induced cognitive impairment. Thirty Wistar rats were divided into three groups, namely, the normal group, model group, and PQ-AG group (PQ-AG group, 1.80 g/kg/d). Diabetes was induced by intraperitoneal injection of streptozotocin (65 mg/kg). Cerebrospinal fluid (CSF) was collected via cisterna magna puncture, and the Morris water maze method was used to evaluate learning and memory in rats after 11 weeks of PQ-AG treatment. Metabolic profiling of CSF samples was performed by using UPLC-Q-TOF-MS. Compared with the normal group, the escape latency of the Morris water maze was significantly prolonged in model group rats after 12 weeks (p < 0.01). Compared with the model group, however, the escape latency was significantly shortened in PQ-AG group rats (p < 0.05). In multivariate statistical analysis, we identified 33 potential biomarkers, and six biomarkers were altered by PQ-AG. These biomarkers were involved in the metabolism of pyrimidine; nicotinate, and nicotinamide; glycine, serine, and threonine; and ascorbate and aldarate. Taken collectively, our results indicate that PQ-AG can attenuate diabetic-induced cognitive impairment by affecting a variety of metabolic pathways. Our results provide an experimental basis for studying the mechanism of action of PQ-AG.

Candidate metabolic biomarkers for schizophrenia in CNS and periphery: Do any possible associations exist?

Due to the limitations of analytical techniques and the complicity of schizophrenia, nowadays it is still a challenge to diagnose and stratify schizophrenia patients accurately. Many attempts have been made to identify and validate available biomarkers for schizophrenia from CSF and/or peripheral blood in clinical studies with consideration to disease stages, antipsychotic effects and even gender differences. However, conflicting results handicap the validation and application of biomarkers for schizophrenia. In view of availability and feasibility, peripheral biomarkers have superior advantages over biomarkers in CNS. Meanwhile, schizophrenia is considered to be a devastating neuropsychiatric disease mainly taking place in CNS featured by widespread defects in multiple metabolic pathways whose dynamic interactions, until recently, have been difficult to difficult to investigate. Evidence for these alterations has been collected piecemeal, limiting the potential to inform our understanding of the interactions among relevant biochemical pathways. Taken these points together, it will be interesting to investigate possible associations of biomarkers between CNS and periphery. Numerous studies have suggested putative correlations within peripheral and CNS systems especially for dopaminergic and glutamatergic metabolic biomarkers. In addition, it has been demonstrated that blood concentrations of BDNF protein can also reflect its changes in the nervous system. In turn, BDNF also interacts with glutamatergic, dopaminergic and serotonergic systems. Therefore, this review will summarize metabolic biomarkers identified both in the CNS (brain tissues and CSF) and peripheral blood. Further, more attentions will be paid to discussing possible physical and functional associations between CNS and periphery, especially with respect to BDNF.

Acyl-Carnitine plasma levels and their association with metabolic syndrome in individuals with schizophrenia

The metabolic syndrome (MetS) affects individuals with schizophrenia at a higher rate when compared to individuals in the general population. Accumulating evidence indicated that subjects with MetS generally manifest elevated levels of acyl-carnitines, which are important carriers for transporting fatty acyl group. Abnormalities of acyl-carnitines in individuals with schizophrenia with or without MetS had not been sufficiently characterized. We conducted this post-hoc analysis with our published data to further evaluate the differences of 29 acyl-carnitines in 46 individuals with schizophrenia with MetS and 123 without MetS. The rate of MetS was 27.2% (46/169) in the individuals with schizophrenia. After FDR correction, the individuals with schizophrenia and MetS showed significantly higher levels of 17 plasma acyl-carnitines, compared to individuals without MetS. Eight acyl-carnitines (i.e., C3, C4, C5, C6: 1, C10: 1, C10: 2, C14: 2-OH, C16: 2-OH) were significantly different between two groups after adjusting for age and sex. The correlation analysis reported that acyl-carnitine concentrations have potential correlations with certain metabolic parameters. Our findings provide valuable new clues for exploring the roles of acyl-carnitines in the diagnosis and treatment of schizophrenia. More data and molecular biology evidences are needed to replicate our findings and elucidate relevant mechanisms.

Links between central CB1-receptor availability and peripheral endocannabinoids in patients with first episode psychosis

There is an established, link between psychosis and metabolic abnormalities, such as altered glucose metabolism and dyslipidemia, which often precede the initiation of antipsychotic treatment. It is known that obesity-associated metabolic disorders are promoted by activation of specific cannabinoid targets (endocannabinoid system (ECS)). Our recent data suggest that there is a change in the circulating lipidome at the onset of first episode psychosis (FEP). With the aim of characterizing the involvement of the central and peripheral ECSs, and their mutual associations; here, we performed a combined neuroimaging and metabolomic study in patients with FEP and healthy controls (HC). Regional brain cannabinoid receptor type 1 (CB1R) availability was quantified in two, independent samples of patients with FEP (n = 20 and n = 8) and HC (n = 20 and n = 10), by applying three-dimensional positron emission tomography, using two radiotracers, [¹¹C]MePPEP and [¹⁸F]FMPEP-d2. Ten endogenous cannabinoids or related metabolites were quantified in serum, drawn from these individuals during the same imaging session. Circulating levels of arachidonic acid and oleoylethanolamide (OEA) were reduced in FEP individuals, but not in those who were predominantly medication free. In HC, there was an inverse association between levels of circulating arachidonoyl glycerol, anandamide, OEA, and palmitoyl ethanolamide, and CB1R availability in the posterior cingulate cortex. This phenomenon was, however, not observed in FEP patients. Our data thus provide evidence of cross talk, and dysregulation between peripheral endocannabinoids and central CB1R availability in FEP.

Genetic Influence on Efficacy of Pharmacotherapy for Pediatric Attention-Deficit/Hyperactivity Disorder: Overview and Current Status of Research

Multiple stimulant and non-stimulant medications are approved for the treatment of attention-deficit/hyperactivity disorder (ADHD), one of the most prevalent childhood neurodevelopmental disorders. Choosing among the available agents and determining the most effective ADHD medication for a given child can be a time-consuming process due to the high inter-individual variability in treatment efficacy. As a result, there is growing interest in identifying predictors of ADHD medication response in children through the burgeoning field of pharmacogenomics. This article reviews childhood ADHD pharmacogenomics efficacy studies published during the last decade (2009-2019), which have largely focused on pharmacodynamic candidate gene investigations of methylphenidate and atomoxetine response, with a smaller number investigating pharmacokinetic candidate genes and genome-wide approaches. Findings from studies which have advanced the field of ADHD pharmacogenomics through investigation of meta-analytic approaches and gene-gene interactions are also overviewed. Despite recent progress, no one genetic variant or currently available pharmacogenomics test has demonstrated clinical utility in pinpointing the optimal ADHD medication for a given individual patient, highlighting the need for further investigation.