Metabolomics: a global biochemical approach to the study of central nervous system diseases

Metabolomics in Depression: What We Learn from Preclinical and Clinical Evidences

Depression is one of the predominant common mental illnesses that affects millions of people of all ages worldwide. Random mood changes, loss of interest in routine activities, and prevalent unpleasant senses often characterize this common depreciated mental illness. Subjects with depressive disorders have a likelihood of developing cardiovascular complications, diabesity, and stroke. The exact genesis and pathogenesis of this disease are still questionable. A significant proportion of subjects with clinical depression display inadequate response to antidepressant therapies. Hence, clinicians often face challenges in predicting the treatment response. Emerging reports have indicated the association of depression with metabolic alterations. Metabolomics is one of the promising approaches that can offer fresh perspectives into the diagnosis, treatment, and prognosis of depression at the metabolic level. Despite numerous studies exploring metabolite profiles post-pharmacological interventions, a quantitative understanding of consistently altered metabolites is not yet established. The article gives a brief discussion on different biomarkers in depression and the degree to which biomarkers can improve treatment outcomes. In this review article, we have systemically reviewed the role of metabolomics in depression along with current challenges and future perspectives.

α-Synuclein Overexpression and the Microbiome Shape the Gut and Brain Metabolome in Mice

Pathological forms of the protein α-synuclein contribute to a family of disorders termed synucleinopathies, which includes Parkinson's disease (PD). Most cases of PD are believed to arise from gene-environment interactions. Microbiome composition is altered in PD, and gut bacteria are causal to symptoms and pathology in animal models. To explore how the microbiome may impact PD-associated genetic risks, we quantitatively profiled nearly 630 metabolites from 26 biochemical classes in the gut, plasma, and brain of α-synuclein-overexpressing (ASO) mice with or without microbiota. We observe tissue-specific changes driven by genotype, microbiome, and their interaction. Many differentially expressed metabolites in ASO mice are also dysregulated in human PD patients, including amine oxides, bile acids and indoles. Notably, levels of the microbial metabolite trimethylamine N-oxide (TMAO) strongly correlate from the gut to the plasma to the brain, identifying a product of gene-environment interactions that may influence PD-like outcomes in mice. TMAO is elevated in the blood and cerebral spinal fluid of PD patients. These findings uncover broad metabolomic changes that are influenced by the intersection of host genetics and the microbiome in a mouse model of PD.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Association between cheese and fish consumption and the occurrence of depression based on European population: mediating role of metabolites

Background

The consumption of cheese and fish has been linked to the onset of depression. However, the connection between consuming cheese, consuming fish, experiencing depression, and the pathways that mediate this relationship remains unclear. The purpose of this research was to investigate the potential association between the consumption of cheese and fish and the occurrence of depression. Moreover, it is important to identify any metabolites that might be involved and understand their respective roles and functions.

Methods

A two-step, two-sample Mendelian randomization (MR) study was conducted using genome-wide association study (GWAS) data on cheese, non-oily fish, and oily fish consumption and depression, along with 12 alternate mediators. The study included a total of 451,486 participants in the cheese consumption group, 460,880 in the non-oily fish consumption group, 460,443 in the oily fish consumption group, and 322,580 with a diagnosis of depression. The single nucleotide polymorphism (SNP) estimates were pooled using inverse-variance weighted, weighted median, MR-Egger, simple mode, and weighted mode.

Results

The data we collected suggested that consuming more cheese correlated with a lower likelihood of experiencing depression (OR: 0.95; 95% CI: 0.92 to 0.98). Neither non-oily fish nor oily fish consumption was directly linked to depression onset (p = 0.08, p = 0.78, respectively). Although there was a direct causal relationship with depression, the mediating relationship of triglycerides (TG), total cholesterol in large HDL, cholesterol to total lipids ratio in large HDL, free cholesterol to total lipids ratio in large HDL, glycine, and phospholipids to total lipids ratio in very large HDL of cheese intake on depression risk were - 0.002 (95% CI: -0.023 - 0.020), -0.002 (95% CI: -0.049 - 0.045), -0.001 (95% CI: -0.033 - 0.031), -0.001 (95% CI: -0.018 - 0.015), 0.001 (95% CI: -0.035 - 0.037), and - 0.001 (95% CI: -0.024 - 0.021), respectively. The mediating relationship of uridine, free cholesterol to total lipids ratio in large HDL, total cholesterol in large HDL, acetoacetate, and 3-hydroxybutyrate (3-HB) between non-oily fish consumption and depression risk were 0.016 (95% CI: -0.008 - 0.040), 0.011 (95% CI: -1.269 - 1.290), 0.010 (95% CI: -1.316 - 1.335), 0.011 (95% CI: -0.089 - 0.110), and 0.008 (95% CI: -0.051 - 0.068), respectively. The mediation effect of uridine and free cholesterol to total lipids ratio in large HDL between intake of oily fish and the risk of depression was found to be 0.006 (95% CI: -0.015 - 0.028) and - 0.002 (95% CI: -0.020 - 0.017), respectively. The correlation between eating cheese and experiencing depression persisted even when adjusting for other variables like Indian snacks, mango consumption, sushi consumption, and unsalted peanuts using multivariable MR.

Conclusion

The consumption of cheese and fish influenced the likelihood of experiencing depression, and this may be mediated by certain metabolites in the body. Our study provided a new perspective on the clinical treatment of depression.

Metabolomics of cerebrospinal fluid reveals candidate diagnostic biomarkers to distinguish between spinal muscular atrophy type II and type III

AIMS

Classification of spinal muscular atrophy (SMA) is associated with the clinical prognosis; however, objective classification markers are scarce. This study aimed to identify metabolic markers in the cerebrospinal fluid (CSF) of children with SMA types II and III.

METHODS

CSF samples were collected from 40 patients with SMA (27 with type II and 13 with type III) and analyzed for metabolites.

RESULTS

We identified 135 metabolites associated with SMA types II and III. These were associated with lysine degradation and arginine, proline, and tyrosine metabolism. We identified seven metabolites associated with the Hammersmith Functional Motor Scale: 4-chlorophenylacetic acid, adb-chminaca,(+/-)-, dodecyl benzenesulfonic acid, norethindrone acetate, 4-(undecan-5-yl) benzene-1-sulfonic acid, dihydromaleimide beta-d-glucoside, and cinobufagin. Potential typing biomarkers, N-cyclohexylformamide, cinobufagin, cotinine glucuronide, N-myristoyl arginine, 4-chlorophenylacetic acid, geranic acid, 4-(undecan-5-yl) benzene, and 7,8-diamino pelargonate, showed good predictive performance. Among these, N-myristoyl arginine was unaffected by the gene phenotype.

CONCLUSION

This study identified metabolic markers are promising candidate prognostic factors for SMA. We also identified the metabolic pathways associated with the severity of SMA. These assessments can help predict the outcomes of screening SMA classification biomarkers.

Inhibition of Potato Fusarium Wilt by Bacillus subtilis ZWZ-19 and Trichoderma asperellum PT-29: A Comparative Analysis of Non-Targeted Metabolomics

Potato Fusarium Wilt is a soil-borne fungal disease that can seriously harm potatoes throughout their growth period and occurs at different degrees in major potato-producing areas in China. To reduce the use of chemical agents and improve the effect of biocontrol agents, the inhibitory effects of the fermentation broth of Bacillus subtilis ZWZ-19 (B) and Trichoderma asperellum PT-29 (T) on Fusarium oxysporum were compared under single-culture and co-culture conditions. Furthermore, metabolomic analysis of the fermentation broths was conducted. The results showed that the inhibitory effect of the co-culture fermentation broth with an inoculation ratio of 1:1 (B1T1) was better than that of the separately cultured fermentation broths and had the best control effect in a potted experiment. Using LC-MS analysis, 134 metabolites were determined and classified into different types of amino acids. Furthermore, 10 metabolic pathways had the most significant variations, and 12 were related to amino acid metabolism in the KEGG analysis. A correlation analysis of the 79 differential metabolites generated through the comprehensive comparison between B, T, and B1T1 was conducted, and the results showed that highly abundant amino acids in B1T1 were correlated with amino acids in B, but not in T.

Causal associations of genetically predicted gut microbiota and blood metabolites with inflammatory states and risk of infections: a Mendelian randomization analysis

Background

Inflammation serves as a key pathologic mediator in the progression of infections and various diseases, involving significant alterations in the gut microbiome and metabolism. This study aims to probe into the potential causal relationships between gut microbial taxa and human blood metabolites with various serum inflammatory markers (CRP, SAA1, IL-6, TNF-α, WBC, and GlycA) and the risks of seven common infections (gastrointestinal infections, dysentery, pneumonia, bacterial pneumonia, bronchopneumonia and lung abscess, pneumococcal pneumonia, and urinary tract infections).

Methods

Two-sample Mendelian randomization (MR) analysis was performed using inverse variance weighted (IVW), maximum likelihood, MR-Egger, weighted median, and MR-PRESSO.

Results

After adding other MR models and sensitivity analyses, genus Roseburia was simultaneously associated adversely with CRP (Beta IVW = -0.040) and SAA1 (Beta IVW = -0.280), and family Bifidobacteriaceae was negatively associated with both CRP (Beta IVW = -0.034) and pneumonia risk (Beta IVW = -0.391). After correction by FDR, only glutaroyl carnitine remained significantly associated with elevated CRP levels (Beta IVW = 0.112). Additionally, threonine (Beta IVW = 0.200) and 1-heptadecanoylglycerophosphocholine (Beta IVW = -0.246) were found to be significantly associated with WBC levels. Three metabolites showed similar causal effects on different inflammatory markers or infectious phenotypes, stearidonate (18:4n3) was negatively related to SAA1 and urinary tract infections, and 5-oxoproline contributed to elevated IL-6 and SAA1 levels. In addition, 7-methylguanine showed a positive correlation with dysentery and bacterial pneumonia.

Conclusion

This study provides novel evidence confirming the causal effects of the gut microbiome and the plasma metabolite profile on inflammation and the risk of infection. These potential molecular alterations may aid in the development of new targets for the intervention and management of disorders associated with inflammation and infections.

Beneficial effects of short-term exposure to indoor biophilic environments on psychophysiological health: Evidence from electrophysiological activity and salivary metabolomics

BACKGROUND

The utilization of short-term natural exposure as a health intervention has great potential in the field of public health. However, previous studies have mostly focused on outdoor urban green spaces, with limited research on indoor biophilic environments, and the physiological regulatory mechanisms involved remain unclear.

OBJECTIVES

To explore the affective and physiological impact of short-term exposure to indoor biophilic environments and their potential regulatory mechanisms.

METHODS

A between-group design experiment was conducted, and the psychophysiological responses of participants to the indoor plants (Vicks Plant) were measured by a method combined the subjective survey, electrophysiological measurements, and salivary biochemical analysis. Volatile organic compounds (VOCs) from plants were also detected to analyze the main substances that caused olfactory stimuli.

RESULTS

Compared with the non-biophilic environment, short-term exposure to the indoor biophilic environment was associated with psychological and physiological relaxation, including reduced negative emotions, improved positive emotions, lower heart rate, skin conductance level, salivary cortisol and pro-inflammatory cytokines, and increased alpha brainwave power. Salivary metabolomics analysis revealed that the differential metabolites observed between the groups exhibited enrichment in two metabolic pathways related to neural function and immune response: phenylalanine, tyrosine and tryptophan biosynthesis, and ubiquinone and other terpenoid-quinone biosynthesis. These changes may be associated with the combined visual and olfactory stimuli of the biophilic environment, in which D-limonene was the dominant substance in plant-derived VOCs.

CONCLUSION

This research demonstrated the benefits of short-term exposure to indoor biophilic environments on psychophysiological health through evidence from both the nervous and endocrine systems.

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.

CSF metabolomics alterations after aneurysmal subarachnoid hemorrhage: what do we know?

PURPOSE

The purpose of this mini review is to describe metabolomics in cerebrospinal fluid (CSF) and its potential in aneurysmal subarachnoid hemorrhage (aSAH). In brain injury, patients' micro dialysis enables detecting biochemical change in brain tissue. Indicators for ischemia were detected such as lactate, pyruvate, glucose, and glutamate. In aSAH patients, the pathophysiology and the factor for poor outcome are not completely understood yet. Routine use of biomarkers in CSF, particularly in aSAH patients, is still lacking.

METHODS

This mini review was performed on the role of metabolomics alterations after aneurysmal subarachnoid hemorrhage.

RESULTS

We identified five clinical studies that addressed metabolomics in patients with aneurysmal subarachnoid hemorrhage.

CONCLUSION

There is increasing evidence suggesting that biomarkers can give insight in the pathogenesis and can serve as an outcome predictor. In this mini review, we present a brief overview of metabolomics profiling in neuroscience and wish to discuss the predictive and therapeutic value in aSAH patients.

Serum 3-Hydroxybutyrate is Expected to Serve as One of the Supportive Diagnostic Markers of Persistent Idiopathic Dentoalveolar Pain (PDAP)

Background

Persistent idiopathic dentoalveolar pain (PDAP), previously referred to as atypical odontalgia, is a chronic dental pain that occurs without signs of pathology. PDAP is considered a diagnosis of exclusion, and its definition is currently under refinement and remains ambiguous. The metabolite known as 3-hydroxybutyrate (3HB) has garnered significant interest as a potential indicator for both depression and chronic psychogenic pain. We investigated the characteristics of patients with PDAP and hypothesized that serum 3HB could support the diagnosis of PDAP.

Subjects and Methods

Forty-one patients with PDAP and 167 patients with odontogenic toothache were investigated regarding depression and anxiety scales in addition to the general dental evaluation. Blood tests including high-sensitivity CRP, HbA1c, and 3HB were performed for all patients. Associations between PDAP and patients' varying characteristics were investigated using hierarchical multivariate logistic regression analyses.

Results

There were more females, current smokers, patients with orofacial pain (such as temporomandibular joint pain, glossalgia, and headache), and people with elevated 3HB levels among patients with PDAP than among control participants. Multivariate logistic regression analyses predicting patients with PDAP identified the female sex (odds ratio [OR]: 4.16), current smoking (OR: 14.9), glossalgia (OR: 19.8) a high CES-D score (≥16) (OR: 5.98), and elevated serum 3HB (≥80 μmol/L) (OR: 18.4) factors significantly associated with PDAP.

Conclusion

Our results demonstrated that serum 3HB levels could be elevated in patients with PDAP compared to other types of odontogenic pain, although 3HB was not specific to PDAP. Based on our findings, five factors - female sex, current smoking, depressive tendencies, chronic orofacial pains, and high serum 3HB levels - could be useful for diagnosing PDAP.

A large-scale, targeted metabolomics method for the analysis and quantification of metabolites in human plasma via liquid chromatography-mass spectrometry

Metabolomics is the study of small molecules, primarily metabolites, that are produced during metabolic processes. Analysis of the composition of an organism's metabolome can yield useful information about an individual's health status at any given time. In recent years, the development of large-scale, targeted metabolomic methods has allowed for the analysis of biological samples using analytical techniques such as LC-MS/MS. This paper presents a large-scale metabolomics method for analysis of biological samples, with a focus on quantification of metabolites found in blood plasma. The method comprises a 10-min chromatographic separation using HILIC and RP stationary phases combined with positive and negative electrospray ionization in order to maximize metabolome coverage. Complete analysis of a single sample can be achieved in as little as 40 min using the two columns and dual modes of ionization. With 540 metabolites and the inclusion of over 200 analytical standards, this method is comprehensive and quantitatively robust when compared to current targeted metabolomics methods. This study uses a large-scale evaluation of metabolite recovery from plasma that enables absolute quantification of metabolites by correcting for analyte loss throughout processes such as extraction, handling, or storage. In addition, the method was applied to plasma collected from adjuvant breast cancer patients to confirm the suitability of the method to clinical samples.

MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data

Mass spectrometry technology can realize dynamic detection of many complex matrix samples in a simple, rapid, compassionate, precise, and high-throughput manner and has become an indispensable tool in accurate diagnosis. The mass spectrometry data analysis is mainly to analyze all metabolites in the organism quantitatively and to find the relative relationship between metabolites and physiological and pathological changes. A feature construction of mass spectrometry data (MSFS) method is proposed to construct the features of the original mass spectrometry data, so as to reduce the noise in the mass spectrometry data, reduce the redundancy of the original data and improve the information content of the data. Chi-square test is used to select the optimal non-redundant feature subset from high-dimensional features. And the optimal feature subset is visually analyzed and corresponds to the original mass spectrum interval. Training in 10 kinds of supervised learning models, and evaluating the classification effect of the models through various evaluation indexes. Taking two public mass spectrometry datasets as examples, the feasibility of the method proposed in this paper is verified. In the coronary heart disease dataset, during the identification process of mixed batch samples, the classification accuracy on the test set reached 1.000; During the recognition process, the classification accuracy on the test set advanced to 0.979. On the colorectal liver metastases data set, the classification accuracy on the test set reached 1.000. This paper attempts to use a new raw mass spectrometry data preprocessing method to realize the alignment operation of the raw mass spectrometry data, which significantly improves the classification accuracy and provides another new idea for mass spectrometry data analysis. Compared with MetaboAnalyst software and existing experimental results, the method proposed in this paper has obtained better classification results.

Pioneering Changes in Psychiatry: Biomarkers, Psychedelics, and AI

This viewpoint discusses integrating biomarkers, psychedelics, and AI into psychiatry for enhanced diagnostics, prognosis, and treatment. It highlights the potential of psychedelics in therapy, AI's role in predicting treatment response, and the challenges that must be addressed. The aim is to encourage research for more precise, personalized psychiatric care.

Serum amino acid profiles in patients with myasthenia gravis

Myasthenia gravis (MG) is an autoimmune disease characterized by weakness and rapid fatigue. Diagnostic methods used for myasthenia gravis are not conclusive and satisfactory, therefore it is necessary to develop reliable tools to help diagnose myasthenia gravis as early as possible. The aim of the study was to use HPLC-MS in conjunction with multivariate statistical analyses to investigate changes in the amino acid metabolic profiles between myasthenia gravis patients compared and controls. In addition, the effect of treatment regimens and myasthenia gravis type, on the observed changes in amino acid metabolic profiles were assessed. Serum levels of 29 amino acids were determined in 2 groups of individuals-28 patients with myasthenia gravis and 53 control subjects (CS). The results of our study indicate that serum levels of several amino acids in patients with myasthenia gravis changed significantly compared to the control group. Statistical analysis revealed differences between amino acids concentration in patients with different therapeutic scheme. In conclusion, amino acids may be involved in mechanisms underlying myasthenia gravis pathogenesis as well as may be potential biomarkers in MG patients diagnosis. However, considering the multifactorial, heterogenous and complex nature of this disease, validation on a larger study sample in further research is required before application into diagnostic practice.

Association between Plasma Metabolic Profiles of the Antidepressant Escitalopram and Clinical Response in Subjects with Depression

Liquid chromatography/electrospray ionization-mass spectrometry revealed plasma metabolic profiles for the antidepressant drug escitalopram (ECTP) and associated clinical responses in subjects with major depressive disorder (MDD). Metabolic profiles contribute to variations in responses to drug treatment of depression. To assess clinical responses and treatment outcomes, we quantified the levels of metabolites, including those of the parent drug, in plasma samples collected at different time points (days 0, 7, 14, and 42) during treatment of seven patients with MDD. Results showed that mean plasma levels of key metabolites and ECTP at day 7 were significantly associated with the clinical response at 42 days after treatment. Statistical analyses, including principal component analysis, of key metabolites and ECTP samples at different time points clearly distinguished the clinical responders from non-responder subjects. Although further validation with a larger cohort is necessary, our results indicate that early improvement and plasma levels of key metabolites and ECTP are predictive of therapeutic treatment outcomes and thus can be used to guide the use of ECTP.

Metabolomic Profiling in Children with Celiac Disease: Beyond the Gluten-Free Diet

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors. Despite a growing understanding of the pathophysiological mechanisms of the disease, diagnosis is still often delayed and there are no effective biomarkers for early diagnosis. The only current treatment, a gluten-free diet (GFD), can alleviate symptoms and restore intestinal villi, but its cellular effects remain poorly understood. To gain a comprehensive understanding of CD's progression, it is crucial to advance knowledge across various scientific disciplines and explore what transpires after disease onset. Metabolomics studies hold particular significance in unravelling the complexities of multifactorial and multisystemic disorders, where environmental factors play a significant role in disease manifestation and progression. By analyzing metabolites, we can gain insights into the reasons behind CD's occurrence, as well as better comprehend the impact of treatment initiation on patients. In this review, we present a collection of articles that showcase the latest breakthroughs in the field of metabolomics in pediatric CD, with the aim of trying to identify CD biomarkers for both early diagnosis and treatment monitoring. These advancements shed light on the potential of metabolomic analysis in enhancing our understanding of the disease and improving diagnostic and therapeutic strategies. More studies need to be designed to cover metabolic profiles in subjects at risk of developing the disease, as well as those analyzing biomarkers for follow-up treatment with a GFD.

Eight key rules for successful data-dependent acquisition in mass spectrometry-based metabolomics

In recent years, metabolomics has emerged as a pivotal approach for the holistic analysis of metabolites in biological systems. The rapid progress in analytical equipment, coupled to the rise of powerful data processing tools, now provides unprecedented opportunities to deepen our understanding of the relationships between biochemical processes and physiological or phenotypic conditions in living organisms. However, to obtain unbiased data coverage of hundreds or thousands of metabolites remains a challenging task. Among the panel of available analytical methods, targeted and untargeted mass spectrometry approaches are among the most commonly used. While targeted metabolomics usually relies on multiple-reaction monitoring acquisition, untargeted metabolomics use either data-independent acquisition (DIA) or data-dependent acquisition (DDA) methods. Unlike DIA, DDA offers the possibility to get real, selective MS/MS spectra and thus to improve metabolite assignment when performing untargeted metabolomics. Yet, DDA settings are more complex to establish than DIA settings, and as a result, DDA is more prone to errors in method development and application. Here, we present a tutorial which provides guidelines on how to optimize the technical parameters essential for proper DDA experiments in metabolomics applications. This tutorial is organized as a series of rules describing the impact of the different parameters on data acquisition and data quality. It is primarily intended to metabolomics users and mass spectrometrists that wish to acquire both theoretical background and practical tips for developing effective DDA methods.

Serum metabolomics analysis in patients with alcohol dependence

Objective

Alcohol dependence (AD) is a chronic recurrent mental disease caused by long-term drinking. It is one of the most prevalent public health problems. However, AD diagnosis lacks objective biomarkers. This study was aimed to shed some light on potential biomarkers of AD patients by investigating the serum metabolomics profiles of AD patients and the controls.

Methods

Liquid chromatography-mass spectrometry (LC-MS) was used to detect the serum metabolites of 29 AD patients (AD) and 28 controls. Six samples were set aside as the validation set (Control: n = 3; AD group: n = 3), and the remaining were used as the training set (Control: n = 26; AD group: n = 25). Principal component analysis (PCA) and partial least squares discriminant analysis (PCA-DA) were performed to analyze the training set samples. The metabolic pathways were analyzed using the MetPA database. The signal pathways with pathway impact >0.2, value of p <0.05, and FDR < 0.05 were selected. From the screened pathways, the metabolites whose levels changed by at least 3-fold were screened. The metabolites with no numerical overlap in their concentrations in the AD and the control groups were screened out and verified with the validation set.

Results

The serum metabolomic profiles of the control and the AD groups were significantly different. We identified six significantly altered metabolic signal pathways, including protein digestion and absorption; alanine, aspartate, and glutamate metabolism; arginine biosynthesis; linoleic acid metabolism; butanoate metabolism; and GABAergic synapse. In these six signal pathways, the levels of 28 metabolites were found to be significantly altered. Of these, the alterations of 11 metabolites changed by at least 3-fold compared to the control group. Of these 11 metabolites, those with no numerical overlap in their concentrations between the AD and the control groups were GABA, 4-hydroxybutanoic acid, L-glutamic acid, citric acid and L-glutamine.

Conclusion

The metabolite profile of the AD group was significantly different from that of the control group. GABA, 4-hydroxybutanoic acid, L-glutamic acid, citric acid, and L-glutamine could be used as potential diagnostic markers for AD.

Systematic metabolic characterization of mental disorders reveals age‐related metabolic disturbances as potential risk factors for depression in older adults

Mental disorders are associated with dysregulated metabolism, but comprehensive investigations of their metabolic similarities and differences and their clinical relevance are few. Here, based on the plasma metabolome and lipidome of subcohort1, comprising 100 healthy participants, 55 cases with anxiety, 52 persons with depression, and 41 individuals with comorbidity, which are from WCHAT, a perspective cohort study of community‐dwelling older adults aged over 50, multiple metabolites as potential risk factors of mental disorders were identified. Furthermore, participants with mental illnesses were classified into three subtypes (S1, S2, and S3) by unsupervised classification with lipidomic data. Among them, S1 showed higher triacylglycerol and lower sphingomyelin, while S2 displayed opposite features. The metabolic profile of S3 was like that of the normal group. Compared with S3, individuals in S1 and S2 had worse quality of life, and suffered more from sleep and cognitive disorders. Notably, an assessment of 6,467 individuals from the WCHAT showed an age‐related increase in the incidence of depression. Seventeen depression‐related metabolites were significantly correlated with age, which were validated in an independent subcohort2. Collectively, this work highlights the clinical relevance of metabolic perturbation in mental disorders, and age‐related metabolic disturbances may be a bridge‐linking aging and depressive.

Potential Plasma Metabolic Biomarkers of Tourette Syndrome Discovery Based on Integrated Nontargeted and Targeted Metabolomics Screening Plasma Metabolic Biomarkers of TS

Objective

Tourette syndrome (TS) is a chronic neuropsychiatric disorder characterized by abnormal movements, phonations, and tics, but an accurate TS diagnosis remains challenging and indeed depends on its description of clinical symptoms. Our study was conducted to discover and verify some metabolite biomarkers based on nontargeted and targeted metabolomics.

Methods

We conducted untargeted ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) for preliminary screening of potential biomarkers on 30 TS patients and 10 healthy controls and then performed validation experiments based on targeted ultrahigh-performance liquid chromatography triple quadrupole-MS (UHPLC/MS/MS) on 35 TS patients and 14 healthy controls.

Results

1775 differentially expressed metabolites were identified by partial least squares discriminant analysis (PLS-DA), fold-change analysis, T-test, and hierarchical clustering analysis (adjusted p value <0.05 and |logFC| > 1). TS plasma samples were found to be differentiated from healthy samples in our approach. Furthermore, aspartate and asparagine metabolism pathways were considered to be a significant enrichment pathway in TS progression based on metabolite pathway enrichment analysis. For the 8 metabolites involved in this pathway that we detected, we then performed validation experiments based on targeted UHPLC/MS/MS. The t-test, Mann-Whitney U test, and receiver operating characteristic (ROC) curve analysis were used to determine potential biomarkers. Ultimately, L-arginine and L-pipecolic acid were validated as significantly differentiated metabolites (p < 0.05), with an AUC of 70.0% and 80.3%, respectively.

Conclusion

L-pipecolic acid was defined as a potential biomarker for TS diagnosis by the combined application of nontargeted and targeted metabolomic analysis.

The role of the ZEB1–neuroinflammation axis in CNS disorders

Zinc finger E-box binding homeobox 1 (ZEB1) is a master modulator of the epithelial–mesenchymal transition (EMT), a process whereby epithelial cells undergo a series of molecular changes and express certain characteristics of mesenchymal cells. ZEB1, in association with other EMT transcription factors, promotes neuroinflammation through changes in the production of inflammatory mediators, the morphology and function of immune cells, and multiple signaling pathways that mediate the inflammatory response. The ZEB1–neuroinflammation axis plays a pivotal role in the pathogenesis of different CNS disorders, such as brain tumors, multiple sclerosis, cerebrovascular diseases, and neuropathic pain, by promoting tumor cell proliferation and invasiveness, formation of the hostile inflammatory micromilieu surrounding neuronal tissues, dysfunction of microglia and astrocytes, impairment of angiogenesis, and dysfunction of the blood–brain barrier. Future studies are needed to elucidate whether the ZEB1–neuroinflammation axis could serve as a diagnostic, prognostic, and/or therapeutic target for CNS disorders.

Insights into the development of pentylenetetrazole-induced epileptic seizures from dynamic metabolomic changes

Epilepsy is often considered to be a progressive neurological disease, and the nature of this progression remains unclear. Understanding the overall and common metabolic changes of epileptic seizures can provide novel clues for their control and prevention. Herein, a chronic kindling animal model was established to obtain generalized tonic-clonic seizures via the repeated injections of pentylenetetrazole (PTZ) at subconvulsive dose. Dynamic metabolomic changes in plasma and urine from PTZ-kindled rats at the different kindling phases were explored using NMR-based metabolomics, in combination with behavioral assessment, brain neurotransmitter measurement, electroencephalography and histopathology. The increased levels of glucose, lactate, glutamate, creatine and creatinine, together with the decreased levels of pyruvate, citrate and succinate, ketone bodies, asparagine, alanine, leucine, valine and isoleucine in plasma and/or urine were involved in the development and progression of seizures. These altered metabolites reflected the pathophysiological processes including the compromised energy metabolism, the disturbed amino acid metabolism, the peripheral inflammation and changes in gut microbiota functions. NMR-based metabolomics could provide brain disease information by the dynamic plasma and urinary metabolic changes during chronic epileptic seizures, yielding classification of seizure stages and profound insights into controlling epilepsy via targeting deficient energy metabolism.

Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study

BACKGROUND

Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach and determined whether the selected metabolites improved prediction accuracy beyond the effect of age.

METHODS

Five thousand three hundred seventy-four participants from the Whitehall II study, mean age 55.8 (standard deviation (SD) 6.0) years in 1997-1999 when 233 metabolites were quantified using nuclear magnetic resonance metabolomics. Participants were followed for a median 21.0 (IQR 20.4, 21.7) years for clinically-diagnosed dementia (N=329). Elastic net penalized Cox regression with 100 repetitions of nested cross-validation was used to select models that improved prediction accuracy for incident dementia compared to an age-only model. Risk scores reflecting the frequency with which predictors appeared in the selected models were constructed, and their predictive accuracy was examined using Royston's R², Akaike's information criterion, sensitivity, specificity, C-statistic and calibration.

RESULTS

Sixteen of the 100 models had a better c-statistic compared to an age-only model and 15 metabolites were selected at least once in all 16 models with glucose present in all models. Five risk scores, reflecting the frequency of selection of metabolites, and a 1-SD increment in all five risk scores was associated with higher dementia risk (HR between 3.13 and 3.26). Three of these, constituted of 4, 5 and 15 metabolites, had better prediction accuracy (c-statistic from 0.788 to 0.796) compared to an age-only model (c-statistic 0.780), all p<0.05.

CONCLUSIONS

Although there was robust evidence for the role of glucose in dementia, metabolites measured in midlife made only a modest contribution to dementia prediction once age was taken into account.

Metabolite Signature of Simvastatin Treatment Involves Multiple Metabolic Pathways

Statins inhibit the 3-hydroxy-3-methylglutaryl-CoA reductase enzyme and are the most widely used medication for hypercholesterolemia. Previous studies on the metabolite signature of simvastatin treatment have included only a small number of metabolites. We performed a high-throughput liquid chromatography–tandem mass spectroscopy profiling on the effects of simvastatin treatment on 1098 metabolite concentrations in the participants of the METSIM (Metabolic Syndrome In Men) study including 1332 participants with simvastatin treatment and 6200 participants without statin treatment. We found that simvastatin exerts profound pleiotropic effects on different metabolite pathways, affecting not only lipids, but also amino acids, peptides, nucleotides, carbohydrates, co-factors, vitamins, and xenobiotics. We identified 321 metabolites significantly associated with simvastatin treatment, and 313 of these metabolites were novel. Our study is the first comprehensive evaluation of the metabolic signature of simvastatin treatment in a large population-based study.

Left DLPFC activity is associated with plasma kynurenine levels and can predict treatment response to escitalopram in major depressive disorder

AIM

To establish treatment response biomarkers that reflect the pathophysiology of depression, it is important to use an integrated set of features. This study aimed to determine the relationship between regional brain activity at rest and blood metabolites related to treatment response to escitalopram to identify the characteristics of depression that respond to treatment.

METHODS

Blood metabolite levels and resting-state brain activity were measured in patients with moderate to severe depression (n = 65) before and after 6-8 weeks of treatment with escitalopram, and these were compared between Responders and Nonresponders to treatment. We then examined the relationship between blood metabolites and brain activity related to treatment responsiveness in patients and healthy controls (n = 36).

RESULTS

Thirty-two patients (49.2%) showed a clinical response (>50% reduction in the Hamilton Rating Scale for Depression score) and were classified as Responders, and the remaining 33 patients were classified as Nonresponders. The pretreatment fractional amplitude of low-frequency fluctuation (fALFF) value of the left dorsolateral prefrontal cortex (DLPFC) and plasma kynurenine levels were lower in Responders, and the rate of increase of both after treatment was correlated with an improvement in symptoms. Moreover, the fALFF value of the left DLPFC was significantly correlated with plasma kynurenine levels in pretreatment patients with depression and healthy controls.

CONCLUSION

Decreased resting-state regional activity of the left DLPFC and decreased plasma kynurenine levels may predict treatment response to escitalopram, suggesting that it may be involved in the pathophysiology of major depressive disorder in response to escitalopram treatment.

New quantification method for monitoring eighteen L-amino acids levels in schizophrenic patients by high-performance liquid chromatography coupled to tandem quadrupole mass spectrometer

A fast, uncomplicated, sensitive, and fully validated high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method has been developed for estimating L-amino acids in the plasma of schizophrenic patients. The gradient-elution chromatographic method was implemented with the Luna® PFP column (50 × 2.0 mm, 5-μm), and a mobile phase of 0.1% formic acid in water and methanol was used. The intraday and interday variability of the L-amino acids were less than 13.11%, and their accuracy ranged from 85.14 - 116.75% at the quality control levels and the lower limit of quantification (LLOQ) ranged from 2.5 - 15 nM. The extraction efficiency (apparent recovery) of amino acids from healthy plasma was employed by spiking the plasma with standard amino acids at the quality control levels. Their percentage recoveries ranged from 80.4% to 119.94%. Our method has a short run time and fast sample preparation compared with existing methods, which are suffered from long preparative steps and/or time-consuming analysis, restricted reagents, and suboptimal performance characteristics presently available technologies. Therefore, the proposed HPLC-MS/MS method was effectively applied for monitoring the L-amino acids in the plasma of schizophrenic patients and healthy volunteers.

[Blood Based Biomarker for Optimization of Early and Differential Diagnosis of Alzheimer's Dementia]

AIM

Dementia in Alzheimer´s disease is a global challenge. There is growing evidence that investigating blood biomarkers to diagnose Alzheimer´s disease is a promising fast, minimally invasive, and less costly method. The aim of this study was to review available studies on promising biomarkers for Alzheimer´s disease.

METHOD

The latest studies were collated for this review.

RESULTS

Immunoassays followed by mass spectrometry and immunomagnetic reduction were reported to be highly relevant methods for detecting amyloid-ß 42 (Aß42) and amyloid-ß 40 (Aß40) to calculate the Aß42/Aß40 ratio, thereby improving the early diagnosis of Alzheimer´s disease. Amyloid-ß (Aß) peptides in blood plasma were considered as potential markers, as they correlated with the brain's Aß pathology. Phosphorylated tau protein 181 (p-tau181), phosphorylated tau protein 217 (p-tau217) and phosphorylated tau protein 231 (p-tau231) in blood samples assessed via Simoa technology served as parameters for the early and differential diagnosis of AD, and were markers of tau pathology in the brain. Neurofilament light chain (Nfl) and glial fibrillary acid protein (GFAP) were additional markers possibly facilitating the assessment of axonal and astroglial brain damage in Alzheimer´s disease. GFAP in blood was useful as an additional marker to detect early and to predict the time course of Alzheimer´s disease.

CONCLUSIONS

Determining blood biomarkers represents less invasive and less costly diagnostics for Alzheimer´s disease. The investigation of blood biomarkers such as the Aß42/Aß40 ratio, p-tau217, p-tau231, Nfl and GFAP have been promising in establishing the AT(N) classification for Alzheimer´s disease. High-throughput methods should be evaluated in large patient cohort studies and via meta-analyses of studies. Consensus criteria with standard protocols for measuring these biomarkers while considering ethical issues and Alzheimer´s phenotype should unify normative values from different laboratories. The AT(N) classification of Alzheimer´s disease in blood would be a key element towards the implementation of minimally-invasive precision medicine.

Gut microbiome and neurocritically ill patients

Since the times of Rokitansky and Cushing, we have been fascinated by the connections between the gut and the brain. Recent advances in next-generation sequencing techniques have shown that this relationship is even more complex and integral to our sense of self than previously imagined. As these techniques refine our understanding of the abundance and diversity of the gut bacterial microbiome, the relationship between the gut and the brain has been redefined. Now, this is understood as a complex symbiotic network with bidirectional communication, the gut-brain axis. The implication of this communication involves an intense focus of research on a variety of chronic psychiatric, neurological, neurodegenerative, and neuro-oncological diseases. Recently, the gut-brain axis has been studied in neurologically ill patients requiring intensive care. Preliminary studies have shown that acute brain injury changes the bacterial phenotype from one that is symbiotic with the host human to one that is pathologic, termed the “pathobiome.” This can contribute to nosocomial pneumonia and sepsis. The first studies in neurologically ill patients in the neurointensive care unit (NeuroICU) demonstrated changes in the gut microbiome between neuroICU patients and healthy matched subjects. Specifically, a decrease in short-chain fatty acid-producing bacteria and increase in harmful gut microbes have been associated with mortality and decreased function at discharge. Although these preliminary findings are exciting and have opened a new field of research in the complex NeuroICU population, there are several limitations and challenges. Further investigation is needed to confirm these correlations and understand their implications on patients in a complex intensive care environment.

Integrated Metabolomics and Proteomics Analysis of Urine in a Mouse Model of Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is a serious stress disorder that occurs in individuals who have experienced major traumatic events. The underlying pathological mechanisms of PTSD are complex, and the related predisposing factors are still not fully understood. In this study, label-free quantitative proteomics and untargeted metabolomics were used to comprehensively characterize changes in a PTSD mice model. Differential expression analysis showed that 12 metabolites and 27 proteins were significantly differentially expressed between the two groups. Bioinformatics analysis revealed that the differentiated proteins were mostly enriched in: small molecule binding, transporter activity, extracellular region, extracellular space, endopeptidase activity, zymogen activation, hydrolase activity, proteolysis, peptidase activity, sodium channel regulator activity. The differentially expressed metabolites were mainly enriched in Pyrimidine metabolism, D-Glutamine and D-glutamate metabolism, Alanine, aspartate and glutamate metabolism, Arginine biosynthesis, Glutathione metabolism, Arginine, and proline metabolism. These results expand the existing understanding of the molecular basis of the pathogenesis and progression of PTSD, and also suggest a new direction for potential therapeutic targets of PTSD. Therefore, the combination of urine proteomics and metabolomics explores a new approach for the study of the underlying pathological mechanisms of PTSD.

Revealing the Antiepileptic Effect of α-Asaronol on Pentylenetetrazole-Induced Seizure Rats Using NMR-Based Metabolomics

α-Asaronol from Acorus tatarinowii (known as "Shichangpu" in Traditional Chinese medicine) has been proved to possess more efficient antiepileptic activity and lower toxicity than α-asarone (namely "Xixinnaojiaonang" as an antiepileptic drug in China) in our previous study. However, the molecular mechanism of α-asaronol against epilepsy needs to be known if to become a novel antiepileptic medicine. Nuclear magnetic resonance (NMR)-based metabolomics was applied to investigate the metabolic patterns of plasma and the brain tissue extract from pentylenetetrazole (PTZ)-induced seizure rats when treated with α-asaronol or α-asarone. The results showed that α-asaronol can regulate the metabolomic level of epileptic rats to normal to some extent, and four metabolic pathways were associated with the antiepileptic effect of α-asaronol, including alanine, aspartate, and glutamate metabolism; synthesis and degradation of ketone bodies; glutamine and glutamate metabolism; and glycine, serine, and threonine metabolism. It was concluded that α-asaronol plays a vital role in enhancing energy metabolism, regulating the balance of excitatory and inhibitory neurotransmitters, and inhibiting cell membrane damage to prevent the occurrence of epilepsy. These findings are of great significance in developing α-asaronol into a promising antiepileptic drug derived from Traditional Chinese medicine.

NMR-based Metabolomics and Fatty Acid Profiles to Unravel Biomarkers in Preclinical Animal Models of Compulsive Behavior

Compulsivity is a key manifestation of inhibitory control deficit and a cardinal symptom of psychopathological conditions such as obsessive-compulsive and attention-deficit hyperactivity disorders, in which metabolic alterations have raised attention as putative biomarkers for early identification. The present study assessed the metabolic profile in a preclinical model of a compulsive phenotype of rats. We used the schedule-induced polydipsia (SIP) method to classify male Wistar rats into high drinkers (HDs) or low drinkers (LDs) according to their compulsive drinking rate developed by exposure to a fixed-time 60 s (FT-60) schedule of reinforcement with water available ad libitum during 20 sessions. Before and after SIP, blood samples were collected for subsequent serum analysis by nuclear magnetic resonance spectroscopy coupled to multivariate analysis. Although no differences existed in the pre-SIP set, the compulsive drinking behavior induced remarkable metabolic alterations: HD rats selected by SIP exhibited a hyperlipidemic, hypoglycemic, and hyperglutaminergic profile compared with their low-compulsive counterparts. Interestingly, these alterations were not attributable to the mere exposure to reward pellets because a control experiment did not show differences between HDs and LDs after 20 sessions of pellet consumption without intermittent reinforcement. Our results shed light toward the implication of dietary and metabolic factors underpinning the vulnerability to compulsive behaviors.

Seasonal variation in serum metabolites of northern European dogs

Background

Metabolic profiling identifies seasonal variance of serum metabolites in humans. Despite the presence of seasonal disease patterns, no studies have assessed whether serum metabolites vary seasonally in dogs.

Hypothesis

There is seasonal variation in the serum metabolite profiles of healthy dogs.

Animals

Eighteen healthy, client‐owned dogs.

Methods

A prospective cohort study. Serum metabolomic profiles were assessed monthly in 18 healthy dogs over a 12‐month period. Metabolic profiling was conducted using a canine‐specific proton nuclear magnetic resonance spectroscopy platform, and the effects of seasonality were studied for 98 metabolites using a cosinor model. Seasonal component was calculated, which describes the seasonal variation of each metabolite.

Results

We found no evidence of seasonal variation in 93 of 98 metabolites. Six metabolites had statistically significant seasonal variance, including cholesterol (mean 249 mg/dL [6.47 mmol/L] with a seasonal component amplitude of 9 mg/dL [0.23 mmol/L]; 95% confidence interval [CI] 6‐13 mg/dL [0.14‐0.33 mmol/L], P < .008), with a peak concentration of 264 mg/dL (6.83 mmol/L) in June and trough concentration of 236 mg/dL (6.12 mmol/L) in December. In contrast, there was a significantly lower concentration of lactate (mean 20 mg/dL [2.27 mmol/L] with a seasonal component amplitude of 4 mg/dL [0.42 mmol/L]; 95% CI 2‐6 mg/dL [0.22‐0.62 mmol/L], P < .001) during the summer months compared to the winter months, with a peak concentration of 26 mg/dL (2.9 mmol/L) in February and trough concentration of 14 mg/dL (1.57 mmol/L) in July.

Conclusions and Clinical Importance

We found no clear evidence that seasonal reference ranges need to be established for serum metabolites of dogs.

Metabolomics of neurological disorders in India

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

A urine and serum metabolomics study of gastroesophageal reflux disease in TCM syndrome differentiation using UPLC-Q-TOF/MS

Gastroesophageal reflux disease (GERD) is a common, chronic and complex upper gastrointestinal disease. In Traditional Chinese medicine (TCM) theory, GERD is classified into two main types: stagnant heat of liver and stomach (SHLS) and deficient cold of spleen and stomach (DCSS). The discovery and evaluation of potential biomarkers for different syndrome types of GERD may contribute to comprehend specific molecular mechanism and identify new targets for diagnosis and appropriate management. In our study, 60 subjects including 40 GERD patients (20 SHLS and 20 DCSS) and 20 healthy controls were recruited, and the serum and urine metabolic profiles from untargeted liquid chromatography coupled to mass spectrometry (LC-MS) metabolomics approach were obtained. Finally 38 biomarkers associated with disease were identified and 9 metabolic pathways were enriched. The most enriched pathways were amino acid metabolism, steroid hormone biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism and TCA cycle. According to the area under curve (AUC) value, we propose a cohort of three metabolites from urine and serum samples as promising biomarkers for TCM syndrome differentiation of GERD, which are prolylhydroxyproline, glycitein-4'-O-glucuronide, capsianoside I in urine and neuAcalpha2-3Galbeta-Cer (d18:1/16:0), sphinganine, arachidonic acid in serum. The cumulative AUC value of merged biomarkers in urine and serum was 0.979 (95%CI 0.927-1) and 0.842 (95%CI 0.704-0.980), respectively. The results indicated that LC-MS based metabolomic profiling method might be an effective and promising tool on further pathogenesis discovering of GERD. The findings provided new strategy for the diagnosis of GERD TCM syndrome differentiation in clinic.

Metabolic profile changes in serum of migraine patients detected using 1H-NMR spectroscopy

Background

Migraine is a common brain disorder but reliable diagnostic biomarkers in blood are still lacking. Our aim was to identify, using proton nuclear magnetic resonance (¹H-NMR) spectroscopy, metabolites in serum that are associated with lifetime and active migraine by comparing metabolic profiles of patients and controls.

Methods

Fasting serum samples from 313 migraine patients and 1512 controls from the Erasmus Rucphen Family (ERF) study were available for ¹H-NMR spectroscopy. Data was analysed using elastic net regression analysis.

Results

A total of 100 signals representing 49 different metabolites were detected in 289 cases (of which 150 active migraine patients) and 1360 controls. We were able to identify profiles consisting of 6 metabolites predictive for lifetime migraine status and 22 metabolites predictive for active migraine status. We estimated with subsequent regression models that after correction for age, sex, BMI and smoking, the association with the metabolite profile in active migraine remained. Several of the metabolites in this profile are involved in lipid, glucose and amino acid metabolism.

Conclusion

This study indicates that metabolic profiles, based on serum concentrations of several metabolites, including lipids, amino acids and metabolites of glucose metabolism, can distinguish active migraine patients from controls.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10194-021-01357-w.

Omics sciences for systems biology in Alzheimer's disease: State-of-the-art of the evidence

Alzheimer's disease (AD) is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in biological alterations and disease spatial-temporal progression. Human in-vivo and post-mortem studies point out a failure of multi-level biological networks underlying AD pathophysiology, including proteostasis (amyloid-β and tau), synaptic homeostasis, inflammatory and immune responses, lipid and energy metabolism, oxidative stress. Therefore, a holistic, systems-level approach is needed to fully capture AD multi-faceted pathophysiology. Omics sciences - genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics - embedded in the systems biology (SB) theoretical and computational framework can generate explainable readouts describing the entire biological continuum of a disease. Such path in Neurology is encouraged by the promising results of omics sciences and SB approaches in Oncology, where stage-driven pathway-based therapies have been developed in line with the precision medicine paradigm. Multi-omics data integrated in SB network approaches will help detect and chart AD upstream pathomechanistic alterations and downstream molecular effects occurring in preclinical stages. Finally, integrating omics and neuroimaging data - i.e., neuroimaging-omics - will identify multi-dimensional biological signatures essential to track the clinical-biological trajectories, at the subpopulation or even individual level.

Biomarkers for the Clinical Diagnosis of Alzheimer's Disease: Metabolomics Analysis of Brain Tissue and Blood

With an increase in aging populations worldwide, age-related diseases such as Alzheimer's disease (AD) have become a global concern. At present, a cure for neurodegenerative disease is lacking. There is an urgent need for a biomarker that can facilitate the diagnosis, classification, prognosis, and treatment response of AD. The recent emergence of highly sensitive mass-spectrometry platforms and high-throughput technology can be employed to discover and catalog vast datasets of small metabolites, which respond to changed status in the body. Metabolomics analysis provides hope for a better understanding of AD as well as the subsequent identification and analysis of metabolites. Here, we review the state-of-the-art emerging candidate biomarkers for AD.

Serum 3-Hydroxybutyrate in Patients with Psychogenic Somatoform Symptoms May Be a Predictor of the Effectiveness of Sertraline and Venlafaxine

Background

Selective serotonin reuptake inhibitors (SSRIs) and serotonin-noradrenaline reuptake inhibitors (SNRIs) are often used to treat outpatients with psychogenic somatoform symptoms but prove ineffective in some cases. The metabolite 3-hydroxybutyrate (3HB) is currently attracting attention as a marker of the severity of depression. We investigated whether serum 3HB levels in patients with psychogenic somatoform symptoms can predict the effectiveness of sertraline and venlafaxine.

Patients and Methods

Physical and psychiatric problems were assessed in 132 outpatients, and symptomatic response and serum 3HB concentrations were examined before and after treatment with sertraline (50 mg/day) or venlafaxine (75 mg/day).

Results

In 30.3% of patients with psychogenic symptoms, serum 3HB was above the upper limit of normal (<80 μmol/L). According to multiple logistic regression analysis, only episodes of suicidal ideation showed a significant positive association with elevated 3HB (odds ratio 10.2; 95% confidence interval (CI) 2.46–42.2). The sensitivity of 3HB for the effectiveness of sertraline or venlafaxine for psychosomatic symptoms was 44.6%, but specificity was 93.9%. Hierarchical multiple logistic regression analysis identified 3HB as a better predictor of the effectiveness of medication (odds ratio 10.0; 95% CI, 2.49–40.3) than episodes of suicidal ideation.

Conclusion

The present findings suggest that high serum 3HB levels in patients with psychogenic somatoform symptoms may be associated with suicidal ideation and the effectiveness of sertraline and venlafaxine at low to intermediate doses. The 3HB level may be a good predictor of the effectiveness of medication. Examination of serum 3HB levels may lead to earlier and more appropriate administration of sertraline and venlafaxine.

Integrated multi-omics uncovers reliable potential biomarkers and adverse effects of zinc deficiency

BACKGROUND

Zinc deficiency is a worldwide public health problem. Currently, there are no established biomarkers available for the accurate diagnosis of zinc-deficiency in individuals. Additionally, a comprehensive view of the adverse effects of zinc deficiency is lacking. Our aim was to identify superior biomarkers of zinc deficiency and uncover the adverse effects of zinc deficiency.

METHODS

We performed multi-omics analysis using serum proteomics-metabolomics and liver proteomics on zinc-deficient rats to identify candidate biomarkers and reveal the associated adverse effects of zinc deficiency. Secondly, the candidate biomarkers were validated in two zinc-deficient populations and an RCT zinc supplementation trial on a zinc-deficient population.

RESULTS

Our integrated multi-omics approach revealed numerous biomarkers (>2000) and glutathione metabolism as the most important changed pathway in zinc deficiency. Three candidate biomarkers from glutathione metabolism were validated in repeated zinc-deficient rats by quantitative analysis. Only glutathione sulfotransferase omega-1 (GSTO1) (among 3 candidate biomarkers) was validated in the two zinc-deficient populations and zinc-supplemented population. Compared with serum zinc, serum GSTO1 yielded a better response to zinc supplementation and a higher correlation coefficient with zinc intake and the AUC value and has the potential for diagnosing zinc deficiency. By integrated multi-omics, we identified both established and novel adverse effects of zinc deficiency.

CONCLUSIONS

Our integrated multi-omics analysis revealed more complete information about zinc deficiency; GSTO1 was found to be a reliable potential biomarker for diagnosis of zinc deficiency. This trial is registered at http://www.chictr.org.cn/registry.aspx as ChiCTR1900028162.

Alterations in acylcarnitines, amines, and lipids inform about the mechanism of action of citalopram/escitalopram in major depression

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), yet their mechanisms of action are not fully understood and their therapeutic benefit varies among individuals. We used a targeted metabolomics approach utilizing a panel of 180 metabolites to gain insights into mechanisms of action and response to citalopram/escitalopram. Plasma samples from 136 participants with MDD enrolled into the Mayo Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) were profiled at baseline and after 8 weeks of treatment. After treatment, we saw increased levels of short-chain acylcarnitines and decreased levels of medium-chain and long-chain acylcarnitines, suggesting an SSRI effect on β-oxidation and mitochondrial function. Amines-including arginine, proline, and methionine sulfoxide-were upregulated while serotonin and sarcosine were downregulated, suggesting an SSRI effect on urea cycle, one-carbon metabolism, and serotonin uptake. Eighteen lipids within the phosphatidylcholine (PC aa and ae) classes were upregulated. Changes in several lipid and amine levels correlated with changes in 17-item Hamilton Rating Scale for Depression scores (HRSD₁₇). Differences in metabolic profiles at baseline and post-treatment were noted between participants who remitted (HRSD₁₇≤ 7) and those who gained no meaningful benefits (<30% reduction in HRSD₁₇). Remitters exhibited (a) higher baseline levels of C3, C5, alpha-aminoadipic acid, sarcosine, and serotonin; and (b) higher week-8 levels of PC aa C34:1, PC aa C34:2, PC aa C36:2, and PC aa C36:4. These findings suggest that mitochondrial energetics-including acylcarnitine metabolism, transport, and its link to β-oxidation-and lipid membrane remodeling may play roles in SSRI treatment response.

Oxytocin-induced increase in N,N-dimethylglycine and time course of changes in oxytocin efficacy for autism social core symptoms

Background

Oxytocin is expected as a novel therapeutic agent for autism spectrum disorder (ASD) core symptoms. However, previous results on the efficacy of repeated administrations of oxytocin are controversial. Recently, we reported time-course changes in the efficacy of the neuropeptide underlying the controversial effects of repeated administration; however, the underlying mechanisms remained unknown.

Methods

The current study explored metabolites representing the molecular mechanisms of oxytocin’s efficacy using high-throughput metabolomics analysis on plasma collected before and after 6-week repeated intranasal administration of oxytocin (48 IU/day) or placebo in adult males with ASD (N = 106) who participated in a multi-center, parallel-group, double-blind, placebo-controlled, randomized controlled trial.

Results

Among the 35 metabolites measured, a significant increase in N,N-dimethylglycine was detected in the subjects administered oxytocin compared with those given placebo at a medium effect size (false discovery rate (FDR) corrected P = 0.043, d = 0.74, N = 83). Furthermore, subgroup analyses of the participants displaying a prominent time-course change in oxytocin efficacy revealed a significant effect of oxytocin on N,N-dimethylglycine levels with a large effect size (P_FDR = 0.004, d = 1.13, N = 60). The increase in N,N-dimethylglycine was significantly correlated with oxytocin-induced clinical changes, assessed as changes in quantifiable characteristics of autistic facial expression, including both of improvements between baseline and 2 weeks (P_FDR = 0.006, r = − 0.485, N = 43) and deteriorations between 2 and 4 weeks (P_FDR = 0.032, r = 0.415, N = 37).

Limitations

The metabolites changes caused by oxytocin administration were quantified using peripheral blood and therefore may not directly reflect central nervous system changes.

Conclusion

Our findings demonstrate an association of N,N-dimethylglycine upregulation with the time-course change in the efficacy of oxytocin on autistic social deficits. Furthermore, the current findings support the involvement of the N-methyl-D-aspartate receptor and neural plasticity to the time-course change in oxytocin’s efficacy.

Trial registration: A multi-center, parallel-group, placebo-controlled, double-blind, confirmatory trial of intranasal oxytocin in participants with autism spectrum disorders (the date registered: 30 October 2014; UMIN Clinical Trials Registry: https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000017703) (UMIN000015264).

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.

A Load to Find Clinically Useful Biomarkers for Depression

Depression is heterogeneous and complex disease with diverse symptoms. Its neurobiological underpinning is still not completely understood. For now, there are still no validated, easy obtainable, clinically useful noninvasive biomarker(s) or biomarker panel that will be able to confirm a diagnosis of depression, its subtypes and improve diagnostic procedures. Future multimodal preclinical and clinical research that involves (epi)genetic, molecular, cellular, imaging, and other studies is necessary to advance our understanding of the role of monoamines, GABA, HPA axis, neurotrophins, metabolome, and glycome in the pathogenesis of depression and their potential as diagnostic, prognostic, and treatment response biomarkers. These studies should be focused to include the first-episode depression and antidepressant drug-naïve patients with large sample sizes to reduce variability in different biological and clinical parameters. At present, metabolomics study revealed with high precision that a neurometabolite panel consisting of plasma metabolite biomarkers (GABA, dopamine, tyramine, kynurenine) might represent clinically useful biomarkers of MDD.

Association Between Plasma Metabolites and Psychometric Scores Among Children With Developmental Disabilities: Investigating Sex-Differences

Background: Developmental disabilities are defined by delays in learning, language, and behavior, yet growing evidence has revealed disturbances in metabolic systems that may also be present. Little is known about whether these metabolic issues contribute to the symptoms or severity of these disabilities, or whether sex plays a role in these associations, given that boys are disproportionately affected by some developmental disabilities. Here we sought to investigate the correlation between psychometric scores, sex, and the plasma metabolome. Methods: The plasma metabolomes of children with autism spectrum disorder (ASD; n = 167), idiopathic developmental delay (i-DD; n = 51), Down syndrome (DS; n = 31), and typically developing controls (TD; n = 193) were investigated using NMR spectroscopy. Spearman rank correlations and multiple linear regression models (adjusted for child's neurodevelopmental diagnosis, child's sex, child's age, child's race/ethnicity, maternal age at child's birth, and parental homeownership) were used to examine the association between plasma metabolites and sex in relation to psychometric measures of cognitive skills, adaptive behavior, and maladaptive behavior in our study population. Results: Higher levels of metabolites involved in cellular energy and mitochondrial function among children with ASD (fumarate and cis-aconitate), DS (lactate), and TD (pyruvate) are associated with poorer cognitive and adaptive subscales. Similarly, higher o-acetylcarnitine associated with deficits in cognitive subscales among all DS cases and TD boys, and carnitine correlated with increased maladaptive behavior among girls with ASD and girls with DS. Among children with DS, elevated myo-inositol, ornithine, and creatine correlated with poorer scores across several subscales. Even among TD cases, elevated 3-hydroxybutyrate correlated with decreased receptive language. In contrast, higher levels of glutamate were associated with better socialization skills among ASD cases. Even after adjusting for the child's neurodevelopmental diagnosis, sex, and other possible confounders, key metabolites including glycolysis metabolites (lactate and pyruvate), ketone bodies (3-hydroxybutyrate and acetoacetate), TCA cycle metabolites (cis-aconitate and fumarate), as well as ornithine were associated with deficits in multiple domains of cognitive function, adaptive skills, and aberrant behaviors. Conclusions: Our results highlight that some plasma metabolites may relate to specific functional subdomains within cognitive, adaptive, and behavioral development with some variation by diagnosis and sex.

Metabolomics in chronic pain research

BACKGROUND AND OBJECTIVE

Metabolomics deals with the identification and quantification of small molecules (metabolites) in biological samples. As metabolite levels can reflect normal or altered metabolic pathways, their measurement provides information to improve the understanding, diagnosis and management of diseases. Despite its immense potential, metabolomics applications to pain research have been sparse. This paper describes current metabolomics techniques, reviews published human metabolomics pain research and compares successful metabolomics research in other areas of medicine with the goal of highlighting opportunities offered by metabolomics to advance pain medicine.

DATABASES AND DATA TREATMENT

Non-systematic review.

RESULTS

Our search identified 19 studies that adopted a metabolomics approach in: fibromyalgia (7), chronic widespread pain (4), other musculoskeletal pain conditions (5), neuropathic pain (1), complex regional pain syndrome (1) and pelvic pain (1). The studies used either mass spectrometry or nuclear magnetic resonance. Most are characterized by small sample sizes. Some consistency has been found for alterations in glutamate and testosterone metabolism, and metabolic imbalances caused by the gut microbiome.

CONCLUSIONS

Metabolomics research in chronic pain is in its infancy. Most studies are at the pilot stage. Metabolomics research has been successful in other areas of medicine. These achievements should motivate investigators to expand metabolomics research to improve the understanding of the basic mechanisms of human pain, as well as provide tools to diagnose, predict and monitor chronic pain conditions. Metabolomics research can lead to the identification of biomarkers to support the development and testing of treatments, thereby facilitating personalized pain medicine.

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

Tracking the potential involvement of metabolic disease in Alzheimer's disease-Biomarkers and beyond

There is a vast literature linking systemic metabolic conditions to dementia due to Alzheimer's disease (AD). Advances in in vivo measurements of AD neuropathology using brain imaging, cerebrospinal fluid (CSF), and/or blood biomarkers have led to research in AD that uses in vivo biomarkers as outcomes, focusing primarily on amyloid, tau, and neurodegeneration as constructs. Studies of Type 2 Diabetes Mellitus (T2DM) and AD biomarkers seem to show that T2DM is not related to amyloid deposition, but is related to neurodegeneration and tau deposition. There is a dearth of studies examining adiposity, insulin resistance, and metabolic syndrome in relation to AD biomarkers and the associations in these studies are inconsistent. Metabolomics studies have reported associations of unsaturated fatty acids with AD neuropathology at autopsy, and sphingolipids and glycerophospholipids in relation to neurodegeneration and amyloid and tau. There are other neurodegenerative diseases, such as Lewy body disease that may overlap with AD, and specific biomarkers for these pathologies are being developed and should be integrated into AD biomarker research. More longitudinal studies are needed with concurrent assessment of metabolic factors and AD biomarkers in order to improve the opportunity to assess causality. Ideally, AD biomarkers should be integrated into clinical trials of interventions that affect metabolic factors. Advances in blood-based AD biomarkers, which are less costly and invasive compared with CSF and brain imaging biomarkers, could facilitate widespread implementation of AD biomarkers in studies examining the metabolic contribution to AD.

Metabolomic Biomarkers in Anxiety Disorders

Anxiety disorders range among the most prevalent psychiatric disorders and belong to the leading disorders in the study of the total global burden of disease. Anxiety disorders are complex conditions, with not fully understood etiological mechanisms. Numerous factors, including psychological, genetic, biological, and chemical factors, are thought to be involved in their etiology. Although the diagnosis of anxiety disorders is constantly evolving, diagnostic manuals rely on symptom lists, not on objective biomarkers and treatment effects are small to moderate. The underlying biological factors that drive anxiety disorders may be better suited to serve as biomarkers for guiding personalized medicine, as they are objective and can be measured externally. Therefore, the incorporation of novel biomarkers into current clinical methods might help to generate a classification system for anxiety disorders that can be linked to the underlying dysfunctional pathways. The study of metabolites (metabolomics) in a large-scale manner shows potential for disease diagnosis, for stratification of patients in a heterogeneous patient population, for monitoring therapeutic efficacy and disease progression, and for defining therapeutic targets. All of these are important properties for anxiety disorders, which is a multifactorial condition not involving a single-gene mutation. This review summarizes recent investigations on metabolomics studies in anxiety disorders.