Contribution of Metabolomics to Multiple Sclerosis Diagnosis, Prognosis and Treatment

Tear Fluid-Based Metabolomics Profiling in Chronic Dacryocystitis Patients

Chronic dacryocystitis (CD) can result in severe complications and vision impairment due to ongoing microbial infections and persistent tearing. Tear fluid, which contains essential components vital for maintaining ocular surface health, has been investigated for its potential in the noninvasive identification of ocular biomarkers through metabolomics analysis. In this study, we employed UHPLC-MS/MS to analyze the tear metabolome of CD patients. UHPLC-MS/MS analysis of tear samples from CD patients revealed significant metabolic alterations. Compared with the control group, 298 metabolites were elevated, while 142 were decreased. KEGG pathway analysis suggested that these changes primarily affected arginine and proline metabolism, biosynthesis of amino acids, and phenylalanine biosynthesis in CD. Notably, 3-dehydroquinic acid, anthranilic acid, citric acid, and l-isoleucine emerged as potential biomarker candidates of CD with high diagnostic accuracy (AUC = 0.94). These findings suggest that tear fluid metabolism, particularly amino acid biosynthesis, plays a significant role in the pathogenesis of CD. Uncovering these metabolic products and pathways provides valuable insights into the mechanisms underlying CD and paves the way for the development of diagnostic tools and targeted therapies.

Artificial neural network-based prediction of multiple sclerosis using blood-based metabolomics data

Multiple sclerosis (MS) remains a challenging neurological condition for diagnosis and management and is often detected in late stages, delaying treatment. Artificial intelligence (AI) is emerging as a promising approach to extracting MS information when applied to different patient datasets. Given the critical role of metabolites in MS profiling, metabolomics data may be an ideal platform for the application of AI to predict disease. In the present study, a machine-learning (ML) approach was used for a detailed analysis of metabolite profiles and related pathways in patients with MS and healthy controls (HC). This approach identified unique alterations in biochemical metabolites and their correlation with disease severity parameters. To enhance the efficiency of using metabolic profiles to determine disease severity or the presence of MS, we trained an AI model on a large volume of blood-based metabolomics datasets. We constructed this model using an artificial neural network (ANN) architecture with perceptrons. Data were divided into training, validation, and testing sets to determine model accuracy. After training, accuracy reached 87 %, sensitivity was 82.5 %, specificity was 89 %, and precision was 77.3 %. Thus, the developed model seems highly robust, generalizable with a wide scope and can handle large amounts of data, which could potentially assist neurologists. However, a large multicenter cohort study is necessary for further validation of large-scale datasets to allow the integration of AI in clinical settings for accurate diagnosis and improved MS management.

Nuclear-Magnetic-Resonance-Spectroscopy-Derived Serum Biomarkers of Metabolic Vulnerability Are Associated with Disability and Neurodegeneration in Multiple Sclerosis

PURPOSE

Metabolic vulnerabilities can exacerbate inflammatory injury and inhibit repair in multiple sclerosis (MS). The purpose was to evaluate whether blood biomarkers of inflammatory and metabolic vulnerability are associated with MS disability and neurodegeneration.

METHODS

Proton nuclear magnetic resonance spectra were obtained from serum samples from 153 healthy controls, 187 relapsing-remitting, and 91 progressive MS patients. The spectra were analyzed to obtain concentrations of lipoprotein sub-classes, glycated acute-phase proteins, and small-molecule metabolites, including leucine, valine, isoleucine, alanine, and citrate. Composite indices for inflammatory vulnerability, metabolic malnutrition, and metabolic vulnerability were computed. MS disability was measured on the Expanded Disability Status Scale. MRI measures of lesions and whole-brain and tissue-specific volumes were acquired.

RESULTS

Valine, leucine, isoleucine, alanine, the Inflammatory Vulnerability Index, the Metabolic Malnutrition Index, and the Metabolic Vulnerability Index differed between healthy control and MS groups in regression analyses adjusted for age, sex, and body mass index. The Expanded Disability Status Scale was associated with small HDL particle levels, inflammatory vulnerability, and metabolic vulnerability. Timed ambulation was associated with inflammatory vulnerability and metabolic vulnerability. Greater metabolic vulnerability and inflammatory vulnerability were associated with lower gray matter, deep gray matter volumes, and greater lateral ventricle volume.

CONCLUSIONS

Serum-biomarker-derived indices of inflammatory and metabolic vulnerability are associated with disability and neurodegeneration in MS.

Comparative Analysis of Serum Amino Acid Profiles in Patients with Myasthenia Gravis and Multiple Sclerosis

Background: Multiple sclerosis (MS) and myasthenia gravis (MG) are autoimmune diseases that attack the central nervous system (CNS) and the neuromuscular junction, respectively. As the common pathogenesis of both diseases is associated with an autoimmune background and the involvement of T and B lymphocytes, the overlapping of selected clinical symptoms may cause difficulties in the differential diagnosis of both diseases. Methods: The aim of the study was to use Liquid Chromatography-Electrospray Ionization-Mass Spectrometry (LC-ESI-MS/MS) in conjunction with multivariate statistical analyses to examine the changes in amino acid metabolic profiles between patients with MG, MS, and a control group. Results: Comparative analysis of amino acids (AA) between patients with MG, MS, and within the control group allowed for the identification of statistically significant differences in the amino acid profile. Comparing the patients (patients with MS and MG) with the control group, and after taking the results of multiple tests into account, it was observed that amino acids such as ARG, PRO, TRP, CIT were significantly different between the groups. When considering the comparison between the AA concentrations in MS and MG patients, we found three AAs that were significantly different in the MS and MG groups, after correcting for multiple testing (CIT, GABA, and AAA). Higher concentrations of amino acids that showed significant differences were observed in patients with myasthenia gravis. Conclusions: Our results have indicated AAs that may prove valuable for improving the diagnostics of MS and MG patients. To better assess the potential utility of these markers, their performance requires further validation in a larger study group and limitation of possible confounding factors, e.g., medications and diet.

NMR-based metabolomics identification of potential serum biomarkers of disease progression in patients with multiple sclerosis

Multiple sclerosis (MS) is a chronic and progressive neurological disorder, characterized by neuroinflammation and demyelination within the central nervous system (CNS). The etiology and the pathogenesis of MS are still unknown. Till now, no satisfactory treatments, diagnostic and prognostic biomarkers are available for MS. Therefore, we aimed to investigate metabolic alterations in patients with MS compared to controls and across MS subtypes. Metabolic profiles of serum samples from patients with MS (n = 90) and healthy control (n = 30) were determined by Nuclear Magnetic Resonance (1H-NMR) Spectroscopy using cryogenic probe. This approach was also utilized to identify significant differences between the metabolite profiles of the MS groups (primary progressive, secondary progressive, and relapsing-remitting) and the healthy controls. Concentrations of nine serum metabolites (adenosine triphosphate (ATP), tryptophan, formate, succinate, glutathione, inosine, histidine, pantothenate, and nicotinamide adenine dinucleotide (NAD)) were significantly higher in patients with MS compared to control. SPMS serum exhibited increased pantothenate and tryptophan than in PPMS. In addition, lysine, myo-inositol, and glutamate exhibited the highest discriminatory power (0.93, 95% CI 0.869-0.981; 0.92, 95% CI 0.859-0.969; 0.91, 95% CI 0.843-0.968 respectively) between healthy control and MS. Using NMR- based metabolomics, we identified a set of metabolites capable of classifying MS patients and controls. These findings confirmed untargeted metabolomics as a useful approach for the discovery of possible novel biomarkers that could aid in the diagnosis of the disease.

Association between human blood metabolome and the risk of gastrointestinal tumors

BACKGROUND

The prevalence of gastrointestinal tumors continues to be significant. To uncover promising therapeutic targets for these tumors, we rigorously executed a Mendelian randomization (MR) study to comprehensively screen the blood metabolomes for potential causal mediators of five frequently encountered gastrointestinal tumors (Liver Cancer, Colorectal Cancer, Esophageal Cancer, Gastric Cancer and Pancreatic Cancer).

METHODS

We selected a comprehensive set of 137 distinct blood metabolites derived from three large-scale genome-wide association studies (GWASs) involving a total of 147827 participants of European ancestry. The gastrointestinal tumors-related data were obtained from a GWAS conducted within the Finnish study. Through meticulous MR analyses, we thoroughly assessed the associations between blood metabolites and gastrointestinal tumors. Additionally, a phenome-wide MR (Phe-MR) analysis was employed to investigate the potential on-target side effects of metabolite interventions.

RESULTS

We have identified 1 blood metabolites, namely isovalerylcarnitine (ORlog10: 1.01; 95%CI, 1.01-1.02; P = 1.81×10-7), as the potential causal mediators for liver cancer. However, no potential pathogenic mediators were detected for the other four tumors.

CONCLUSIONS

The current systematic MR analysis elucidated the potential role of isovalerylcarnitine as a causal mediator in the development of liver cancer. Leveraging the power of Phe-MR study facilitated the identification of potential adverse effects associated with drug targets for liver cancer prevention. Considering the weighing of pros and cons, isovalerylcarnitine emerges as a promising candidate for targeted drug interventions in the realm of liver cancer prevention.

Advanced MRI Techniques: Diagnosis and Follow-Up of Multiple Sclerosis

Brain and spinal cord imaging plays a pivotal role in aiding clinicians with the diagnosis and monitoring of multiple sclerosis. Nevertheless, the significance of magnetic resonance imaging in MS extends beyond its clinical utility. Advanced imaging modalities have facilitated the in vivo detection of various components of MS pathogenesis, and, in recent years, MRI biomarkers have been utilized to assess the response of patients with relapsing-remitting MS to the available treatments. Similarly, MRI indicators of neurodegeneration demonstrate potential as primary and secondary endpoints in clinical trials targeting progressive phenotypes. This review aims to provide an overview of the latest advancements in brain and spinal cord neuroimaging in MS.

Biomarkers of bipolar disorder based on metabolomics: A systematic review

Bipolar disorder (BD) is a severe affective disorder characterized by recurrent episodes of depression or mania/hypomania, which significantly impair cognitive function, life skills, and social abilities of patients. There is little understanding of the neurobiological mechanisms of BD. The diagnosis of BD is primarily based on clinical assessment and psychiatric examination, highlighting the urgent need for objective markers to facilitate the diagnosis of BD. Metabolomics can be used as a diagnostic tool for disease identification and evaluation. This study summarized the altered metabolites in BD and analyzed aberrant metabolic pathways, which might contribute to the diagnosis of BD. Search of PubMed and Web of science for human BD studies related to metabolism to identify articles published up to November 19, 2022 yielded 987 articles. After screening and applying the inclusion and exclusion criteria, 16 untargeted and 11 targeted metabolomics studies were included. Pathway analysis of the potential differential biometabolic markers was performed using the Kyoto encyclopedia of genes and genomes (KEGG). There were 72 upregulated and 134 downregulated biomarkers in the untargeted metabolomics studies using blood samples. Untargeted metabolomics studies utilizing urine specimens revealed the presence of 78 upregulated and 54 downregulated metabolites. The targeted metabolomics studies revealed abnormalities in the metabolism of glutamate and tryptophan. Enrichment analysis revealed that the differential metabolic pathways were mainly involved in the metabolism of glucose, amino acid and fatty acid. These findings suggested that certain metabolic biomarkers or metabolic biomarker panels might serve as a reference for the diagnosis of BD.

Potential Metabolite Biomarkers of Multiple Sclerosis from Multiple Biofluids

Multiple sclerosis (MS) is a chronic and progressive neurological disorder without a cure, but early intervention can slow disease progression and improve the quality of life for MS patients. Obtaining an accurate diagnosis for MS is an arduous and error-prone task that requires a combination of a detailed medical history, a comprehensive neurological exam, clinical tests such as magnetic resonance imaging, and the exclusion of other possible diseases. A simple and definitive biofluid test for MS does not exist, but is highly desirable. To address this need, we employed NMR-based metabolomics to identify potentially unique metabolite biomarkers of MS from a cohort of age and sex-matched samples of cerebrospinal fluid (CSF), serum, and urine from 206 progressive MS (PMS) patients, 46 relapsing-remitting MS (RRMS) patients, and 99 healthy volunteers without a MS diagnosis. We identified 32 metabolites in CSF that varied between the control and PMS patients. Utilizing patient-matched serum samples, we were able to further identify 31 serum metabolites that may serve as biomarkers for PMS patients. Lastly, we identified 14 urine metabolites associated with PMS. All potential biomarkers are associated with metabolic processes linked to the pathology of MS, such as demyelination and neuronal damage. Four metabolites with identical profiles across all three biofluids were discovered, which demonstrate their potential value as cross-biofluid markers of PMS. We further present a case for using metabolic profiles from PMS patients to delineate biomarkers of RRMS. Specifically, three metabolites exhibited a variation from healthy volunteers without MS through RRMS and PMS patients. The consistency of metabolite changes across multiple biofluids, combined with the reliability of a receiver operating characteristic classification, may provide a rapid diagnostic test for MS.

Alterations in Cerebrospinal Fluid Urea Occur in Late Manifest Huntington's Disease

Background

Huntington's disease (HD) is a neurodegenerative disorder caused by expanded cytosine-adenine-guanine (CAG) repeats in the Huntingtin gene, resulting in the production of mutant huntingtin proteins (mHTT). Previous research has identified urea as a key metabolite elevated in HD animal models and postmortem tissues of HD patients. However, the relationship between disease course and urea elevations, along with the molecular mechanisms responsible for these disturbances remain unknown.

Objective

To better understand the molecular disturbances and timing of urea cycle metabolism across different stages in HD.

Methods

We completed a global metabolomic profile of cerebrospinal fluid (CSF) from individuals who were at several stages of disease: pre-manifest (PRE), manifest (MAN), and late manifest (LATE) HD participants, and compared to controls.

Results

Approximately 500 metabolites were significantly altered in PRE participants compared to controls, although no significant differences in CSF urea or urea metabolites were observed. CSF urea was significantly elevated in LATE participants only. There were no changes in the urea metabolites citrulline, ornithine, and arginine.

Conclusions

Overall, our study confirms that CSF elevations occur late in the HD course, and these changes may reflect accumulating deficits in cellular energy metabolism.

Differential Metabolites in Osteoarthritis: A Systematic Review and Meta-Analysis

(1) Many studies have attempted to utilize metabolomic approaches to explore potential biomarkers for the early detection of osteoarthritis (OA), but consistent and high-level evidence is still lacking. In this study, we performed a systematic review and meta-analysis of differential small molecule metabolites between OA patients and healthy individuals to screen promising candidates from a large number of samples with the aim of informing future prospective studies. (2) Methods: We searched the EMBASE, the Cochrane Library, PubMed, Web of Science, Wan Fang Data, VIP Date, and CNKI up to 11 August 2022, and selected relevant records based on inclusion criteria. The risk of bias was assessed using the Newcastle-Ottawa quality assessment scale. We performed qualitative synthesis by counting the frequencies of changing directions and conducted meta-analyses using the random effects model and the fixed-effects model to calculate the mean difference and 95% confidence interval. (3) Results: A total of 3798 records were identified and 13 studies with 495 participants were included. In the 13 studies, 132 kinds of small molecule differential metabolites were extracted, 58 increased, 57 decreased and 17 had direction conflicts. Among them, 37 metabolites appeared more than twice. The results of meta-analyses among four studies showed that three metabolites increased, and eight metabolites decreased compared to healthy controls (HC). (4) Conclusions: The main differential metabolites between OA and healthy subjects were amino acids (AAs) and their derivatives, including tryptophan, lysine, leucine, proline, phenylalanine, glutamine, dimethylglycine, citrulline, asparagine, acetylcarnitine and creatinine (muscle metabolic products), which could be potential biomarkers for predicting OA.

The changes in metabolomics profile induced by intermittent theta burst stimulation in major depressive disorder: an exploratory study

BACKGROUND

Recently, there has been an ongoing interest in the mechanism of intermittent theta burst stimulation (iTBS) in major depressive disorder. Studying the metabolite changes induced by iTBS may help to understand the mechanism.

METHODS

Eleven participants with major depressive disorder received 10 days iTBS treatment. Magnetic resonance imaging (MRI) was used to target the region of the left dorsolateral prefrontal cortex (DLPFC) in each participant. We analyzed the effects of iTBS on metabolites using high-throughput profiling and assessed its impact on depressive symptoms. These analyses were considered exploratory, and no correction for multiple comparisons was applied.

RESULTS

Among the 318 measured metabolites, a significant increase in cystine, asymmetric dimethylarginine (ADMA), 1-methylhistidine, indoleacetic acid (IAA), diethanolamine (DEA), dopa, riboflavin-5'-monophosphate (FMN), and a significant decrease in alphalinolenic acid (ALA), gamma-linolenic acid (GLA), serotonin, linoleic acid (LA) (p < 0.05) were detected in the patients after iTBS treatment. In Pearson correlation analysis, the plasma levels of LA, FMN and ADMA at baseline were significantly related to the reduction rate of the 17-item Hamilton Depression Rating Scale and the Patient Health Questionnaire-9 scores (p < 0.05).

CONCLUSIONS

Our study highlights that LA, FMN, ADMA and their relationship with oxidative stress, may be key factors in the antidepressant efficacy of iTBS.

Lessons from immunotherapies in multiple sclerosis

The improved understanding of multiple sclerosis (MS) neurobiology alongside the development of novel markers of disease will allow precision medicine to be applied to MS patients, bringing the promise of improved care. Combinations of clinical and paraclinical data are currently used for diagnosis and prognosis. The addition of advanced magnetic resonance imaging and biofluid markers has been strongly encouraged, since classifying patients according to the underlying biology will improve monitoring and treatment strategies. For example, silent progression seems to contribute significantly more than relapses to overall disability accumulation, but currently approved treatments for MS act mainly on neuroinflammation and offer only a partial protection against neurodegeneration. Further research, involving traditional and adaptive trial designs, should strive to halt, repair or protect against central nervous system damage. To personalize new treatments, their selectivity, tolerability, ease of administration, and safety must be considered, while to personalize treatment approaches, patient preferences, risk-aversion, and lifestyle must be factored in, and patient feedback used to indicate real-world treatment efficacy. The use of biosensors and machine-learning approaches to integrate biological, anatomical, and physiological parameters will take personalized medicine a step closer toward the patient's virtual twin, in which treatments can be tried before they are applied.