Metabolomic Biomarkers of Multiple Sclerosis: A Systematic Review

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.

Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity

There are no blood-based biomarkers distinguishing patients with relapsing-remitting (RRMS) from secondary progressive multiple sclerosis (SPMS) although evidence supports metabolomic changes according to MS disease severity. Here machine learning analysis of serum metabolomic data stratified patients with RRMS from SPMS with high accuracy and a putative score was developed that stratified MS patient subsets. The top differentially expressed metabolites between SPMS versus patients with RRMS included lipids and fatty acids, metabolites enriched in pathways related to cellular respiration, notably, elevated lactate and glutamine (gluconeogenesis-related) and acetoacetate and bOHbutyrate (ketone bodies), and reduced alanine and pyruvate (glycolysis-related). Serum metabolomic changes were recapitulated in the whole blood transcriptome, whereby differentially expressed genes were also enriched in cellular respiration pathways in patients with SPMS. The final gene-metabolite interaction network demonstrated a potential metabolic shift from glycolysis toward increased gluconeogenesis and ketogenesis in SPMS, indicating metabolic stress which may trigger stress response pathways and subsequent neurodegeneration.

NOX-induced oxidative stress is a primary trigger of major neurodegenerative disorders

Neurodegenerative diseases (NDDs) causing cognitive impairment and dementia are difficult to treat due to the lack of understanding of primary initiating factors. Meanwhile, major sporadic NDDs share many risk factors and exhibit similar pathologies in their early stages, indicating the existence of common initiation pathways. Glucose hypometabolism associated with oxidative stress is one such primary, early and shared pathology, and a likely major cause of detrimental disease-associated cascades; targeting this common pathology may therefore be an effective preventative strategy for most sporadic NDDs. However, its exact cause and trigger remain unclear. Recent research suggests that early oxidative stress caused by NADPH oxidase (NOX) activation is a shared initiating mechanism among major sporadic NDDs and could prove to be the long-sought ubiquitous NDD trigger. We focus on two major NDDs - Alzheimer's disease (AD) and Parkinson's disease (PD), as well as on acquired epilepsy which is an increasingly recognized comorbidity in NDDs. We also discuss available data suggesting the relevance of the proposed mechanisms to other NDDs. We delve into the commonalities among these NDDs in neuroinflammation and NOX involvement to identify potential therapeutic targets and gain a deeper understanding of the underlying causes of NDDs.

Metabolite Alterations in Autoimmune Diseases: A Systematic Review of Metabolomics Studies

Autoimmune diseases, characterized by the immune system's loss of self-tolerance, lack definitive diagnostic tests, necessitating the search for reliable biomarkers. This systematic review aims to identify common metabolite changes across multiple autoimmune diseases. Following PRISMA guidelines, we conducted a systematic literature review by searching MEDLINE, ScienceDirect, Google Scholar, PubMed, and Scopus (Elsevier) using keywords "Metabolomics", "Autoimmune diseases", and "Metabolic changes". Articles published in English up to March 2023 were included without a specific start date filter. Among 257 studies searched, 88 full-text articles met the inclusion criteria. The included articles were categorized based on analyzed biological fluids: 33 on serum, 21 on plasma, 15 on feces, 7 on urine, and 12 on other biological fluids. Each study presented different metabolites with indications of up-regulation or down-regulation when available. The current study's findings suggest that amino acid metabolism may serve as a diagnostic biomarker for autoimmune diseases, particularly in systemic lupus erythematosus (SLE), multiple sclerosis (MS), and Crohn's disease (CD). While other metabolic alterations were reported, it implies that autoimmune disorders trigger multi-metabolite changes rather than singular alterations. These shifts could be consequential outcomes of autoimmune disorders, representing a more complex interplay. Further studies are needed to validate the metabolomics findings associated with autoimmune diseases.

Studies of Genetic and Proteomic Risk Factors of Amyotrophic Lateral Sclerosis Inspire Biomarker Development and Gene Therapy

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease affecting the upper and lower motor neurons, leading to muscle weakness, motor impairments, disabilities and death. Approximately 5-10% of ALS cases are associated with positive family history (familial ALS or fALS), whilst the remainder are sporadic (sporadic ALS, sALS). At least 50 genes have been identified as causative or risk factors for ALS. Established pathogenic variants include superoxide dismutase type 1 (SOD1), chromosome 9 open reading frame 72 (c9orf72), TAR DNA Binding Protein (TARDBP), and Fused In Sarcoma (FUS); additional ALS-related genes including Charged Multivesicular Body Protein 2B (CHMP2B), Senataxin (SETX), Sequestosome 1 (SQSTM1), TANK Binding Kinase 1 (TBK1) and NIMA Related Kinase 1 (NEK1), have been identified. Mutations in these genes could impair different mechanisms, including vesicle transport, autophagy, and cytoskeletal or mitochondrial functions. So far, there is no effective therapy against ALS. Thus, early diagnosis and disease risk predictions remain one of the best options against ALS symptomologies. Proteomic biomarkers, microRNAs, and extracellular vehicles (EVs) serve as promising tools for disease diagnosis or progression assessment. These markers are relatively easy to obtain from blood or cerebrospinal fluids and can be used to identify potential genetic causative and risk factors even in the preclinical stage before symptoms appear. In addition, antisense oligonucleotides and RNA gene therapies have successfully been employed against other diseases, such as childhood-onset spinal muscular atrophy (SMA), which could also give hope to ALS patients. Therefore, an effective gene and biomarker panel should be generated for potentially "at risk" individuals to provide timely interventions and better treatment outcomes for ALS patients as soon as possible.

ATR-FTIR spectroscopy of plasma supported by multivariate analysis discriminates multiple sclerosis disease

Multiple sclerosis (MS) is one of the most common neurodegenerative diseases showing various symptoms both of physical and cognitive type. In this work, we used attenuated total reflection Fourier transformed infrared (ATR-FTIR) spectroscopy to analyze plasma samples for discriminating MS patients from healthy control individuals, and identifying potential spectral biomarkers helping the diagnosis through a quick non-invasive blood test. The cohort of the study consists of 85 subjects, including 45 MS patients and 40 healthy controls. The differences in the spectral features both in the fingerprint region (1800-900 cm^-1) and in the high region (3050-2800 cm^-1) of the infrared spectra were highlighted also with the support of different chemometric methods, to capture the most significant wavenumbers for the differentiation. The results show an increase in the lipid/protein ratio in MS patients, indicating changes in the level (metabolism) of these molecular components in the plasma. Moreover, the multivariate tools provided a promising rate of success in the diagnosis, with 78% sensitivity and 83% specificity obtained through the random forest model in the fingerprint region. The MS diagnostic tools based on biomarkers identification on blood (and blood component, like plasma or serum) are very challenging and the specificity and sensitivity values obtained in this work are very encouraging. Overall, the results obtained suggest that ATR-FTIR spectroscopy on plasma samples, requiring minimal or no manipulation, coupled with statistical multivariate approaches, is a promising analytical tool to support MS diagnosis through the identification of spectral biomarkers.

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

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

Blood-based biomarkers of inflammation in amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease in which many processes are detected including (neuro)inflammation. Many drugs have been tested for ALS in clinical trials but most have failed to reach their primary endpoints. The development and inclusion of different types of biomarkers in diagnosis and clinical trials can assist in determining target engagement of a drug, in distinguishing between ALS and other diseases, and in predicting disease progression rate, drug responsiveness, or an adverse event. Ideally, among other characteristics, a biomarker in ALS correlates highly with a disease process in the central nervous system or with disease progression and is conveniently obtained in a peripheral tissue. Here, we describe the state of biomarkers of inflammation in ALS by focusing on peripherally detectable and cellular responses from blood cells, and provide new (combinatorial) directions for exploration that are now feasible due to technological advancements.

Metabolomics in Autoimmune Diseases: Focus on Rheumatoid Arthritis, Systemic Lupus Erythematous, and Multiple Sclerosis

The metabolomics approach represents the last downstream phenotype and is widely used in clinical studies and drug discovery. In this paper, we outline recent advances in the metabolomics research of autoimmune diseases (ADs) such as rheumatoid arthritis (RA), multiple sclerosis (MuS), and systemic lupus erythematosus (SLE). The newly discovered biomarkers and the metabolic mechanism studies for these ADs are described here. In addition, studies elucidating the metabolic mechanisms underlying these ADs are presented. Metabolomics has the potential to contribute to pharmacotherapy personalization; thus, we summarize the biomarker studies performed to predict the personalization of medicine and drug response.

Laboratory biomarkers of Multiple Sclerosis (MS)

Multiple Sclerosis (MS) is a neurological disease that affects the central nervous system (CNS). The diagnosis of the disease is quite challenging due to its variation among patients. As a result, the need to enhance diagnostic procedures, evaluate objective prognostic markers and promote effective monitoring of patients' responses to treatment has prompted the identification of many biomarkers. To present up-to-date knowledge on potential biomarkers for MS used to assess disease activity, progression, and therapeutic responses. The search for articles was conducted in various databases, namely, PubMed, Cochrane Library, and CINAHL, using an identical search strategy and terms that included "Multiple Sclerosis," "MS," "biomarkers," "potential," "magnetic resonance spectroscopy," "progress," "marker," "predict," "disability," "indicator," and "mass spectrometry." Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed when scrutinizing the articles for inclusion in the study. The search process identified 75 articles that were used in this systematic review. MS biomarkers consisted of laboratory biomarkers, imaging biomarkers, and genetic and immunogenetic biomarkers. The efficacy, which leads to their potential classification, relies on numerous factors, such as sensitivity, specificity, clinical rationale, predictability, practicality, biological rationale, reproducibility, and correlations with prognosis and disability. Oligoclonal bands (OCBs) and magnetic resonance imaging (MRI) features are the most established biomarkers so far, although kappa free light chains (kFLCs), the measles-rubella-zoster (MRZ) reaction, and neurofilament light chains (NfLs) might show potential in the near future after more studies are conducted.

Potential Biomarkers Associated with Multiple Sclerosis Pathology

Multiple sclerosis (MS) is a complex disease of the central nervous system (CNS) that involves an intricate and aberrant interaction of immune cells leading to inflammation, demyelination, and neurodegeneration. Due to the heterogeneity of clinical subtypes, their diagnosis becomes challenging and the best treatment cannot be easily provided to patients. Biomarkers have been used to simplify the diagnosis and prognosis of MS, as well as to evaluate the results of clinical treatments. In recent years, research on biomarkers has advanced rapidly due to their ability to be easily and promptly measured, their specificity, and their reproducibility. Biomarkers are classified into several categories depending on whether they address personal or predictive susceptibility, diagnosis, prognosis, disease activity, or response to treatment in different clinical courses of MS. The identified members indicate a variety of pathological processes of MS, such as neuroaxonal damage, gliosis, demyelination, progression of disability, and remyelination, among others. The present review analyzes biomarkers in cerebrospinal fluid (CSF) and blood serum, the most promising imaging biomarkers used in clinical practice. Furthermore, it aims to shed light on the criteria and challenges that a biomarker must face to be considered as a standard in daily clinical practice.

The Impact of Excluding Nonrandomized Studies From Systematic Reviews in Rare Diseases: "The Example of Meta-Analyses Evaluating the Efficacy and Safety of Enzyme Replacement Therapy in Patients With Mucopolysaccharidosis"

Nonrandomized studies are usually excluded from systematic reviews. This could lead to loss of a considerable amount of information on rare diseases. In this article, we explore the impact of excluding nonrandomized studies on the generalizability of meta-analyses results on mucopolysaccharidosis (MPS) disease. A comprehensive search of systematic reviews on MPS patients up to May 2020 was carried out (CRD42020191217). The primary endpoint was the rate of patients excluded from systematic reviews if only randomized studies were considered. Secondary outcomes included the differences in patient and study characteristics between randomized and nonrandomized studies, the methods used to combine data from studies with different designs, and the number of patients excluded from systematic reviews if case reports were not considered. More than 50% of the patients analyzed have been recruited in nonrandomized studies. Patient characteristics, duration of follow-up, and the clinical outcomes evaluated differ between the randomized and nonrandomized studies. There are feasible strategies to combine the data from different randomized and nonrandomized designs. The analyses suggest the relevance of including case reports in the systematic reviews, since the smaller the number of patients in the reference population, the larger the selection bias associated to excluding case reports. Our results recommend including nonrandomized studies in the systematic reviews of MPS to increase the representativeness of the results and to avoid a selection bias. The recommendations obtained from this study should be considered when conducting systematic reviews on rare diseases.

Thermal Liquid Biopsy (TLB) of Blood Plasma as a Potential Tool to Help in the Early Diagnosis of Multiple Sclerosis

Background: Multiple sclerosis (MS) is frequently characterized by a variety of clinical signs, often exhibiting little specificity. The diagnosis requires a combination of medical observations and instrumental tests, and any support for its objective assessment is helpful. Objective: Herein, we describe the application of thermal liquid biopsy (TLB) of blood plasma samples, a methodology for predicting the occurrence of MS with a noninvasive, quick blood test. Methods: TLB allows one to define an index (TLB score), which provides information about overall real-time alterations in plasma proteome that may be indicative of MS. Results: This pilot study, based on 85 subjects (45 MS patients and 40 controls), showed good performance indexes (sensitivity and specificity both around 70%). The diagnostic methods better discriminate between early stage and low-burden MS patients, and it is not influenced by gender, age, or assumption of therapeutic drugs. TLB is more accurate for patients having low disability level (≤ 3.0, measured by the expanded disability status scale, EDSS) and a relapsing–remitting diagnosis. Conclusion: Our results suggest that TLB can be applied to MS, especially in an initial phase of the disease when diagnosis is difficult and yet more important (in such cases, accuracy of prediction is close to 80%), as well as in personalized patient periodic monitoring. The next step will be determining its utility in differentiating between MS and other disorders, in particular in inflammatory diseases.