Metabolomics in Multiple Sclerosis: Advances, Challenges, and Clinical Perspectives—A Systematic Review
Department of Neurology, University Clinical Hospital in Bialystok, Marii Skłodowskiej-Curie 24A, 15-276 Bialystok, Poland; jansmusz3@gmail.com (J.S.);
Metabolomics and Proteomics Laboratory, Clinical Research Centre, Medical University of Bialystok, 15-276 Bialystok, Poland
Department of Regenerative Medicine and Immune Regulation, Medical University of Bialystok, Waszyngtona 13, 15-269 Bialystok, Poland
Klinika Bocian Fertility Clinic, ul. Akademicka 26, 15-267 Bialystok, Poland
Abstract
Multiple sclerosis (MS) is a chronic, immune-mediated neurodegenerative disorder marked by inflammation, demyelination, and neuronal loss within the central nervous system. Despite advances in diagnostics, current tools remain insufficiently sensitive and specific. Metabolomics has emerged as a promising approach to explore MS pathophysiology and discover novel biomarkers. This PRISMA-guided systematic review included 29 original studies using validated metabolomic techniques in adult patients with MS. Biological samples analyzed included serum, cerebrospinal fluid, and feces. Consistent metabolic alterations were identified across several pathways. The kynurenine pathway demonstrated a shift toward neurotoxic metabolites, alongside reductions in microbial-derived indoles, indicating inflammation and gut dysbiosis. Energy metabolism was impaired, with changes in glycolysis, tricarboxylic acid (TCA) cycle, and mitochondrial function. Lipid metabolism showed widespread dysregulation involving phospholipids, sphingolipids, endocannabinoids, and polyunsaturated fatty acids, some modulated by treatments such as ocrelizumab and interferon-β. Nitrogen metabolism was also affected, including amino acids, peptides, and nucleotides. Non-classical and xenobiotic metabolites, such as myo-inositol, further reflected host–microbiome–environment interactions. Several studies demonstrated the potential of metabolomics-based machine learning to distinguish MS subtypes. These findings highlight the value of metabolomics for biomarker discovery and support its integration into personalized therapeutic strategies in MS.
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Keywords: multiple sclerosis, metabolomics, immunometabolism
Article notes
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Received 2025 Jul 30; Revised 2025 Sep 11; Accepted 2025 Sep 17; Collection date 2025 Sep.
1. Introduction
Multiple sclerosis (MS) is a progressive, demyelinating autoimmune disease of the central nervous system. It is characterized by multifocal inflammatory lesions caused by T-lymphocytic and macrophage infiltrations as well as oligodendrocyte death. These pathological processes ultimately lead to the destruction of the myelin sheath and damage to nerve cells. B lymphocytes also play a significant role in the development of MS, as evidenced by increased intrathecal immunoglobulin synthesis, reflected by the presence of oligoclonal bands (OCB) in the cerebrospinal fluid (CSF) [1,2]. Pathologically, acute MS lesions display blood–brain barrier disruption, perivascular immune cell accumulation, and widespread demyelination accompanied by early axonal transection. Over time, these lesions evolve into either inactive gliotic scars or slowly expanding plaques with rims of activated microglia, w3hich are now recognized as drivers of disease progression. Importantly, the pathological burden extends beyond focal white matter lesions: cortical demyelination, diffuse microglial activation, mitochondrial dysfunction, and widespread axonal loss all contribute to irreversible neurodegeneration [3]. Clinically, these processes give rise to distinct phenotypes. Relapsing–remitting MS (RRMS) is characterized by episodes of acute inflammation and new lesion formation followed by partial remyelination. Secondary progressive MS (SPMS) emerges after years of RRMS, marked by diffuse neurodegeneration and gradual disability accumulation with fewer relapses. Primary progressive MS (PPMS) presents from onset with insidious neurological decline, often with spinal cord-predominant involvement. Clinically isolated syndrome (CIS) represents a first demyelinating event that may convert to definite MS, providing a unique window into the earliest stages of disease pathology [3,4]. The clinical presentation of MS is heterogeneous and reflects the multifocal distribution of demyelinating lesions within the CNS. The most common initial symptoms include limb weakness, optic neuritis with painful visual loss, paresthesias, diplopia and bladder dysfunction. Other frequent manifestations encompass impaired visual acuity, internuclear ophthalmoplegia, sensory loss with impaired vibration sense, spasticity, neuropathic pain, tremor, and ataxia. Cognitive deficits and depression are also common, affecting up to half of patients over the disease course, and contributing substantially to long-term disability and reduced quality of life [5].
MS is a global health issue, affecting an estimated 2.9 million people worldwide as of 2023. The disease is most common in high-income countries, particularly in North America and Western Europe [6]. Regardless of geographic location, women are approximately twice as likely as men to be affected by MS, exhibiting both higher prevalence and incidence [7,8]. MS is one of the most common diseases among young and middle-aged individuals and is a leading non-traumatic cause of disability in young adults [9,10].
The exact cause of MS remains unknown, although it likely involves a complex interplay of genetic predisposition, environmental triggers, and immune system dysregulation. Despite progress in diagnostic methods, challenges in early and accurate diagnosis still exist. Current approaches depend on clinical presentation, magnetic resonance imaging (MRI) findings, and CSF analysis, especially the detection of OCB [11]. However, these methods have limitations and may result in misdiagnosis, particularly in early or atypical cases. This underscores the ongoing need for new biomarkers that can enable earlier and more accurate diagnosis of MS. Additionally, there is a crucial demand for biomarkers that can monitor disease progression and assess therapeutic response in individuals receiving disease-modifying treatment (DMT).
In this context, metabolomics has emerged as a promising approach to explore the complex biochemical alterations associated with MS. This review aims to synthesize current knowledge by characterizing alterations in the levels of specific metabolites involved in key metabolic pathways whose disturbances contribute to the pathogenesis of MS, focusing on research published within the last five years. Additionally, it highlights recent advances in metabolomics technologies and discusses their potential for biomarker discovery in MS.
Added Value Compared with Prior Reviews
Recent syntheses have either provided broad overviews of metabolomic alterations in MS [12], spotlighted arachidonic-acid-derived lipid mediators [13], or focused on NMR-based CSF/serum profiles across neurological diseases [14]. More recently, a review covered both human and animal studies with a strong emphasis on lipidomics, but without systematic methodology or integration with clinical outcomes [15]. Our review advances the field by updating the evidence base through August 2025; delivering a phenotype-stratified, multi-matrix integration (serum, CSF, feces, brain tissue) that captures directional trajectories across RRMS → SPMS → PPMS as detailed later in Tables and Figures of the Results and Discussion sections; explicitly linking metabolites to disability and neuroaxonal/astroglial injury metrics (EDSS/MSSS, sNfL, sGFAP) and to neuroinflammation quantified by TSPO-PET; synthesizing therapy- and lifestyle-induced metabolic shifts (ocrelizumab, fingolimod, IFNβ, progressive resistance training) [16,17,18,19,20,21,22,23,24,25]; and outlining translational applications, including candidate markers and machine-learning classifiers for clinical staging [26,27]. This integrative, clinically anchored synthesis provides decision-oriented perspectives that extend beyond compilation and are intended to support biomarker validation and translational application.
2. Methods
This systematic review was conducted in accordance with the PRISMA 2020 guidelines [28] as illustrated in Figure 1. A comprehensive literature search was performed to identify original studies reporting metabolomic alterations in patients with MS.
2.1. Search Strategy
A systematic literature search was conducted using keyword-based queries. The search terms included “metabolome in multiple sclerosis” and “metabolomics of multiple sclerosis”. The search was performed in the following databases: PubMed (54 articles retrieved) and Google Scholar (23 articles retrieved).
2.2. Eligibility Criteria
Only studies published within the last five years were included to focus on the most recent advances in the field. Studies were eligible if they met the following criteria: original, peer-reviewed research articles conducted in adult human populations (≥18 years of age), involving patients with clinically defined MS, reporting metabolomic outcomes based on biological samples (e.g., serum, CSF, feces), using validated analytical techniques (e.g., NMR, LC-MS, GC-MS), and published in English. Animal studies, in vitro studies, and research focusing exclusively on pediatric populations were excluded.
2.3. Information Sources
A comprehensive literature search of electronic databases was conducted using the US National Library of Medicine, PubMed (https://pubmed.ncbi.nlm.nih.gov/, accessed between 1 April and 1 June 2025) and Google Scholar (https://scholar.google.com/, accessed between 1 April and 1 June 2025).
2.4. Quality and Risk of Bias Assessment
Risk of bias and methodological quality of the included studies were not formally assessed, consistent with the objective of this review, which was to perform a descriptive synthesis of recent metabolomic studies in multiple sclerosis. Significant heterogeneity in study design, sample type (serum, CSF, feces, tissue), and metabolomic techniques (LC-MS, NMR, GC-MS) limited the feasibility of applying a unified risk-of-bias tool such as ROBINS-I or QUADAS-2. Therefore, the synthesis relied on qualitative integration rather than quantitative meta-analysis. Several recurrent methodological limitations should nonetheless be acknowledged. Some studies were based on very small patient groups, often enrolling fewer than 20 individuals [29,30,31], which limits statistical power and generalizability. In addition, demographic reporting was frequently incomplete: in some cases, age or sex distribution was not specified or was only partially reported [30,31,32]. Finally, variability in biological samples (serum, CSF, urine, feces, brain tissue) and analytical platforms (LC-MS, GC-MS, NMR, shotgun lipidomics) hampered direct comparisons across studies. These issues introduce a risk of bias and highlight that the observed metabolic alterations, although promising, should be interpreted as preliminary until validated in larger, standardized, multicenter cohorts.
2.5. Technical Validation and Quality Control
As part of our systematic assessment, we evaluated whether the included metabolomics studies reported details of technical validation and quality control (QC) procedures. We specifically screened for information on instrument calibration, the use of pooled QC samples or replicate injections, the addition of isotopically labeled internal standards, and adherence to the Metabolomics Standards Initiative (MSI) reporting framework.
Reporting was highly heterogeneous across studies. Several reports provided detailed procedures, including multi-point calibration curves with isotopically labeled internal standards and systematic QC injections [22,23,33,34], consistent with best practice, corresponding to MSI Level 1 identification. In contrast, many studies employed commercial platforms (e.g., Metabolon Inc., Nightingale NMR) that rely on validated compound libraries and recovery standards but did not disclose details of QC design or internal standards [21,35,36]. Finally, some untargeted NMR studies reported only chemical shift referencing and metabolite annotation based on HMDB or the literature, without the spiking of authentic standards, corresponding to MSI Level 2 identification [20,37].
Overall, while validated analytical platforms (LC-MS, GC-MS, NMR) were consistently used, the level of technical validation reporting varied substantially. Only a minority of studies explicitly described calibration protocols, QC sample design, or isotopically labeled standards, which limits reproducibility and cross-study comparability. This highlights the urgent need for harmonized standard operating procedures (SOPs) and transparent reporting of technical validation in future metabolomics research on multiple sclerosis.
2.6. Study Selection
A total of 76 records were identified through database searching. After removing 4 duplicates, 72 unique articles were screened based on their title and abstract. Of these, 11 were excluded due to irrelevance. Full-text retrieval was attempted for 61 articles, but 14 were not accesible.
The remaining 47 full-text articles were assessed for eligibility. After applying inclusion and exclusion criteria, 18 studies were excluded for the following reasons: review articles (n = 8), non-human or in vitro studies (n = 4), not related to MS (n = 2), not reporting metabolomic data (n = 3), and pediatric-only populations (n = 1).
Finally, 29 studies met the predefined criteria and were included in the qualitative synthesis.
2.7. Data Synthesis
The extracted data were synthesized qualitatively. Studies were grouped by MS phenotype (RRMS, SPMS, PPMS, CIS) and biological sample type (serum, CSF, feces, brain tissue). Both nominally significant alterations (p < 0.05) and those surviving multiple testing correction (FDR < 0.05) were reported; however, only FDR-significant findings were interpreted as robust. Table 1 summarizes the study characteristics, whereas Table 2 presents significantly altered metabolites together with reported p-values and FDR-adjusted values. Findings are further described narratively in the Results section. In this review, statistical significance was defined as p < 0.05 unless otherwise specified in the original study (e.g., when authors reported more stringent thresholds or applied multiple testing corrections such as FDR), as summarized in Table 2. No meta-analysis or quantitative pooling was conducted due to methodological heterogeneity.
| Author (Year) | MS Phenotype | Number of Participants | Mean Age ± SD | Sex (F/M) | Biological Sample | Analysis Type | Analytical Technique |
|---|---|---|---|---|---|---|---|
| Staats Pires (2025) [38] | RRMS | RRMS—98 HC—39 | RRMS: 42.3 (10.8) HC: 36 (6.3) | RRMS 72F/26M HC 28F/11M | serum | targeted | HPLC/GCLC |
| Wicks (2025) [17] | RRMS, PMS | RRMS—184 PMS—91 HC—152 | RRMS: 44.2 (9.7) PMS: 54.1 (8.82) HC: 45.7 (13.9) | RRMS 139F/45M PMS 66F/25M HC 85F/67M | serum | targeted | NMR |
| Radford-Smith (2025) [20] | RRMS (progressors vs. non-progressors) | Non-progressors—52 Progressors—23 | Non-progressors: 46.4 Progressors: 48.9 | Non-progressors 43F/9M Progressors 16F/7M | serum | untargeted | NMR |
| Schwerdtfeger (2025) [25] | RRMS (progressors vs. non-progressors), | RRMS, stable—124 RRMS progression—68 | RRMS, stable: 51.5 (9.8) RRMS progression: 54.1 (8.5) | RRMS stable 94F/30M RRMS progression 52F/16M | serum + feces | untargeted | HPLC |
| Alwahsh (2024) [16] | RRMS, PPMS, SPMS | RRMS—30 PPMS—30 SPMS—30 HC—30 | RRMS: 37.0 (8.6) PPMS: 33.4 (7.0) SPMS: 37.9 (10.5) HC: 35.6 (2.5) | RRMS 19F/11M PPMS 15F/15M SPMS 15F/15M HC 15F/15M | serum | untargeted | NMR |
| Siavoshi (2024) [18] | RRMS (pre/post-ocrelizumab) | RRMS—31 | 40.84 (10.34) | 21F/10M | serum | untargeted | LC-MS/CG-MS |
| Ladakis (2024) [30] | MS lesions | SPMS—5 Controls—6 | Not reported | Not reported | brain tissue | untargeted | LC-MS + WGCNA |
| Fung (2024) [39] | RRMS | RRMS—28 HC—31 | RRMS: 41.4 (9.1) HC: 41.9 (9.5) | RRMS 21F/7M HC 23F/8M | serum | targeted | HPLC-MS/MS |
| Datta (2024) [26] | RRMS, PPMS | RRMS—41 PMS—31 RRMS-HC—44 PPMS-HC—47 | RRMS median: 39 (range 34–48) PPMS median: 49 (range 46–58) RRMS-HC median: 39.5 (range 33.45–39) PPMS-HC median: 53 (range 46.5–60.5) | RRMS 29F/12M PMS 22F/9M RRMS-HC 31F/13M PPMS-HC 35F/12M | serum | untargeted | 2D GC × GC-MS |
| Meier (2024) [23] | RRMS, PPMS, SPMS | RRMS—58 SPMS—9 PPMS—4 Undefined—3 HC—80 | MS: 43.6 (14.1) HC: 44.7 (16.1) | RRMS 42F/16M SPMS 6F/3M PPMS 2F/2M Undefined: 2F/1M HC 48F/32M | serum and CSF | targeted | LC-ESI-MS/MS |
| Murgia (2023) [40] | RRMS (fingolimod treatment) | RRMS—42 HC—22 | MS: 39.0 (8.7) HC: 40.8 (13.8) | MS 23F/19M HC 17F/5M | plasma | untargeted | NMR |
| Broos (2023) [22] | RRMS, PMS | RRMS—170 PMS—115 HC—125 | RRMS: 52.9 (0.9) PMS: 53.0 (0.9) HC: 52.9 (1.2) | RRMS 139F/31M PMS 65F/50M HC 92F/33M | serum | targeted | HPLC-MS/MS |
| Židó (2023) [32] | Early MS | MS—40 HC—33 | MS median: 34 (range 18–54) HC: not reported | MS 31F/9M HC 26F/7M | CSF | untargeted/targeted | HPLC-MS/MS |
| Židó (2022) [41] | Early SM | MS—19 HC—19 | MS: 36 HC: 35 | MS 16F/3M HC 16F/3M | CSF | untargeted/targeted | HPLC-MS/MS |
| Zahoor (2022) [35] | RRMS | RRMS—35 HC—14 | RRMS: 45 HC: 40 | RRMS 22F/13M HC 9F/5M | serum | untargeted | UPLC-MS/GCLC/MS |
| Yang (2021) [33] | RRMS, PPMS | RRMS—20 PPMS—2 HC—21 | MS: 34.8 (7.5) HC: 33.3 (8.5) | MS 14F/8M HC 13F/8M | plasma | untargeted | LC-MS/MS |
| Olsson (2021) [42] | RRMS/CIS | RRMS/CIS—58 HC—50 | RRMS/CIS median: 34 (range 27–40) HC median: 33 (range 28–39) | RRMS/CIS 44F/14M HC 34F/16M | serum | targeted | UPLC-MS/MS, |
| Fitzgerald (2021) [43] | MS | MS—514 HC—241 | MS: 42.54 (14.91) HC: 35.85 (15.71) | MS 468F/47M HC 224F/17M | plasma | untargeted | GC/MS, LC/MS/MS |
| Levi (2021) [36] | MS | MS—129 HC—58 | MS: 38.3 (11.8) HC: 45.8 (12.5) | MS 93F/36M HC 29F/29M | serum | untargeted | LC/MS |
| Probert (2021) [19] | CIS converters vs. non-converters | Converters—22 Non-converters—32 | Converters: 31.3 (9.9) Non-converters: 36.4 (11.2) | Converters 17F/5M Non-converters 21F/11M | CSF | untargeted | NMR, |
| Keller (2021) [21] | MS (PRT treatment) | MS—14 HC—13 | MS: 42 (13) HC: 39 (14) | MS 12F/2M HC 11F/2M | serum | untargeted | UPLC-MS/MS |
| Yeo (2021) [37] | RRMS | RRMS relapse—28 RRMS last relapse in 1–6 months—28 RRMS last relapse in 6–24 months—34 RRMS last relapse in over 24 months—101 | RRMS relapse: 38.3 (9.5) RRMS last relapse in 1–6 months: 38.7 (7.0) RRMS last relapse in 6–24 months: 43.5 (9.7) RRMS last relapse in over 24 months: 44.2 (9.9) | RRMS relapse 27F/11M RRMS last relapse in 1–6 months 23F/5M RRMS last relapse in 6–24 months 22F/12M RRMS last relapse in over 24 months 73F/28M | serum | untargeted/targeted | NRM |
| Yeo (2020) [27] | RRMS, SPSM | RRMS—31 SPMS—28 | RRMS: 43.5 (9.7) SPMS: 58.1 (9.6) | RRMS 23F/8M SPMS 20F/8M | serum | untargeted | NMR |
| Gaetani (2020) [34] | RRMS | RRMS—47 HC—43 | RRMS: 31.8 (9.7) HC: 32.7 (10.6) | RRMS 40F/7M HC 27F/16M | urine | targeted | HPLC-MS/MS |
| Carlsson (2020) [29] | SPMS | SPMS—12 HC—12 | SPMS: 58.7 (7.5) HC: 54.2 (6.1) | SPMS 7F/5M HC: 7F/5M | CSF | targeted | LC-HRMS, FIA-HRMS |
| Duscha (2020) [24] | MS | RRMS—161 SPMS—103 PPMS—39 HC—68 NMO—1 | RRMS: 45.8 (12.7) SPMS: 57.8 (9.4) PPMS: 60 (11.4) HC: 48.5 (14.5) NMO: 55 | RRMS 102F/59M SPMS 62F/39M PPMS 16F/23M HC 32F/36M NMO 1M | serum, feces | targeted | LC-MS/MS |
| Penkert (2020) [44] | HC vs. MS/CIS (Monozygotic twins) | MS—73 HC—73 | 40.8 (12.1) | 110F/36M | plasma | untargeted | shotgun lipidomics (LC-MS/MS) |
| Amatruda (2020) [31] | PPMS | PPMS—19 HC—8 Validation cohort: SPMS—11 RRMS—24 | PPMS: 49.8 (11.1) HC: 42.1 (8.4) validation cohort: not reported | PPMS 11F/8M HC 4F/4M validation cohort-not reported | plasma | untargeted | LC-MS/MS, |
| Waddington (2020) [45] | RRMS (IFNβ-treated), CIS | ADA (−)—52 ADA (+)—30 | ADA (−): 34.6 (9.3) ADA (+): 37.9 (9.8) | ADA (−) 37F/15M ADA (+) 19F/11M | serum, PBMCs | untargeted | NMR |
| Author (Year) | Clinical Groups | Metabolite | Direction | Fold-Change | Comparison | p-Value | FDR |
|---|---|---|---|---|---|---|---|
| Staats Pires (2025) [38] | RRMS, HC | KYNA | ↓ | 1.2-fold (19%) | RRMS vs. HC | p < 0.05 | FDR = 0.0394 |
| 3HK | ↓ | 1.5-fold (32%) | RRMS vs. HC | p < 0.05 | FDR = 0.0008 | ||
| AA | ↑ | 3.1-fold (212%) | RRMS vs. HC | p < 0.0001 | FDR < 0.0001 | ||
| KYN/TRP | ↑ | 1.15-fold (15%) | RRMS vs. HC | p < 0.01 | FDR = 0.0187 | ||
| 3HK/KYN | ↓ | 1.5-fold (33%) | RRMS vs. HC | p < 0.001 | FDR < 0.0001 | ||
| QUIN/KYNA | ↑ | 1.27-fold (27%) | RRMS vs. HC | p < 0.05 | NR | ||
| 3HAA/AA | ↓ | 1.8-fold (44%) | RRMS vs. HC | p < 0.0001 | FDR < 0.0001 | ||
| Wicks (2025) [17] | RRMS, PMS and HC | β-hydroxybutyrate | ↑ | 1.24-fold (24%) | RRMS vs. HC | NR | NR |
| β-hydroxybutyrate | ↓ | 0.87-fold (13%) | RRMS vs. PMS | NR | NR | ||
| β-hydroxybutyrate | ↑ | 1.43-fold (43%) | PMS vs. HC | p = 0.005 | NR | ||
| β-hydroxybutyrate | ↑ | 1.15-fold (15%) | RRMS vs. RRMS | NR | NR | ||
| Acetoacetate | ↑ | 1.15-fold (15%) | RRMS vs. HC | NR | NR | ||
| Acetoacetate | ↓ | 0.88-fold (12%) | RRMS vs. PMS | p = NR | NR | ||
| Acetoacetate | ↑ | 1.31-fold (31%) | PMS vs. HC | p = 0.013 | NR | ||
| Acetoacetate | ↑ | 1.13-fold (13%) | PMS vs. RRMS | p = NR | NR | ||
| Radford-Smith (2025) [20] | RRMS (progressors vs. non-progressors) | Glucose | ↑ | - | progressors vs. non-progressors | p = 0.0021 (discovery cohort); p = 0.0027 (validation cohort) | NR |
| Glutamate | ↑ | - | progressors vs. non-progressors | p = 0.0006 (discovery cohort); p = 0.0409 (validation cohort) | NR | ||
| Glutamine | ↑ | - | progressors vs. non-progressors | p = 0.0052 | NR | ||
| Schwerdtfeger (2025) [25] | RRMS (progressors vs. non-progressors) | Spermidine (serum) | ↑ | - | progressors vs. non-progressors | p < 0.05 | NR |
| p-Cresol sulfate (serum) | ↑ | - | progressors vs. non-progressors | p < 0.05 | NR | ||
| Nicotinate (feces) | ↓ | - | progressors vs. non-progressors | p < 0.05 | NR | ||
| Phenyllactate (feces) | ↓ | - | progressors vs. non-progressors | p < 0.05 | NR | ||
| Protoporphyrin IX (feces) | ↓ | - | progressors vs. non-progressors | p < 0.05 | NR | ||
| N-Oleoyltaurine (feces) | ↓ | - | progressors vs. non-progressors | p < 0.05 | NR | ||
| Alwahsh (2024) [16] | RRMS, PPMS, SPMS and HC | Tryptophan | ↑ | 3.34-fold | RRMS vs. HC | p = 4.39 × 10−7 | FDR < 0.05 |
| Succinate | ↑ | 1.58-fold | RRMS vs. HC | p = 2.50 × 10−6 | FDR < 0.05 | ||
| ATP | ↑ | 1.98-fold | RRMS vs. HC | p = 1.11 × 10−4 | FDR < 0.05 | ||
| Formate | ↑ | 2.47-fold | RRMS vs. HC | p = 5.65 × 10−4 | FDR < 0.05 | ||
| Inosine | ↑ | 1.61-fold | RRMS vs. HC | p = 1.24 × 10−3 | FDR < 0.05 | ||
| Histidine | ↑ | 1.90-fold | RRMS vs. HC | p = 2.90 × 10−3 | FDR < 0.05 | ||
| Glutathione | ↑ | 1.47-fold | RRMS vs. HC | p = 3.29 × 10−3 | FDR < 0.05 | ||
| Pantothenate | ↑ | 1.37-fold | RRMS vs. HC | p = 5.54 × 10−3 | FDR < 0.05 | ||
| Lysine | ↓ | 0.48-fold | RRMS vs. HC | p = 3.11 × 10−19 | FDR < 0.001 | ||
| Myo-inositol | ↓ | 0.36-fold | RRMS vs. HC | p = 6.13 × 10−18 | FDR < 0.001 | ||
| Glutamate | ↓ | 0.57-fold | RRMS vs. HC | p = 1.56 × 10−16 | FDR < 0.001 | ||
| Threonine | ↓ | 0.38-fold | RRMS vs. HC | p = 7.64 × 10−16 | FDR < 0.001 | ||
| Glycine | ↓ | 0.45-fold | RRMS vs. HC | p = 9.51 × 10−15 | FDR < 0.001 | ||
| Tyrosine | ↓ | 0.61-fold | RRMS vs. HC | p = 3.33 × 10−12 | FDR < 0.001 | ||
| Choline | ↓ | 0.50-fold | RRMS vs. HC | p = 5.18 × 10−12 | FDR < 0.001 | ||
| O-phosphocholine | ↓ | 0.54-fold | RRMS vs. HC | p = 1.79 × 10−9 | FDR < 0.001 | ||
| Serine | ↓ | 0.71-fold | RRMS vs. HC | p = 7.47 × 10−8 | FDR < 0.001 | ||
| Cysteine | ↓ | 0.65-fold | RRMS vs. HC | p = 1.67 × 10−3 | FDR < 0.001 | ||
| sn-Glycero-3-phosphocholine | ↓ | 0.77-fold | RRMS vs. HC | p = 1.10 × 10−6 | FDR < 0.05 | ||
| Creatine | ↓ | 0.46-fold | RRMS vs. HC | p = 1.64 × 10−5 | FDR < 0.001 | ||
| Tryptophan | ↑ | - | RRMS vs. PPMS | p = 1.42 × 10−5 | FDR < 0.05 | ||
| Ascorbate | ↑ | - | RRMS vs. PPMS | p < 0.05 | FDR < 0.01 | ||
| Histidine | ↓ | - | RRMS vs. PPMS | p = 5.48 × 10−5 | FDR < 0.05 | ||
| Proline | ↓ | - | RRMS vs. PPMS | p = 8.03 × 10−7 | FDR < 0.001 | ||
| Phenylalanine | ↓ | - | RRMS vs. PPMS | p = 1.24 × 10−6 | FDR < 0.001 | ||
| Cysteine | ↓ | - | RRMS vs. PPMS | p = 3.93 × 10−5 | FDR < 0.001 | ||
| Formate | ↓ | - | RRMS vs. PPMS | p = 1.49 × 10−3 | FDR < 0.05 | ||
| Fumarate | ↓ | - | RRMS vs. PPMS | p = 2.33 × 10−5 | FDR < 0.001 | ||
| Inosine | ↑ | - | RRMS vs. SPMS | p = 5.23 × 10−3 | FDR < 0.05 | ||
| NAD+ | ↑ | - | RRMS vs. SPMS | p = 9.88 × 10−3 | FDR < 0.05 | ||
| Siavoshi (2024) [18] | RRMS (pre/post—ocrelizumab) | Pregnenediol disulfate | ↓ | - | pre- vs. post-ocrelizumab | p = 1.78 × 10−7 | FDR = 1.90 × 10−4 |
| Pregnenetriol disulfate | ↓ | - | pre- vs. post-ocrelizumab | p = 5.29 × 10−5 | FDR = 1.13 × 10−2 | ||
| Androstene-diol disulfate | ↓ | - | pre- vs. post-ocrelizumab | p = 1.15 × 10−4 | FDR = 1.76 × 10−2 | ||
| Taurocholenate sulfate | ↓ | - | pre- vs. post-ocrelizumab | p = 2.43 × 10−4 | FDR = 2.16 × 10−2 | ||
| 1-Linoleoyl-GPA (18:2) | ↓ | - | pre- vs. post-ocrelizumab | p = 2.39 × 10−4 | FDR = 2.16 × 10−2 | ||
| 2′-Deoxyuridine | ↓ | - | pre- vs. post-ocrelizumab | p = 1.64 × 10−5 | FDR = 4.38 × 10−3 | ||
| PFOS | ↓ | - | pre- vs. post-ocrelizumab | p = 1.17 × 10−6 | FDR = 6.23 × 10−4 | ||
| PFOA | ↓ | - | pre- vs. post-ocrelizumab | p = 2.20 × 10−4 | FDR = 2.16 × 10−2 | ||
| Serine | ↓ | - | pre- vs. post-ocrelizumab | p = 1.43 × 10−4 | FDR = 1.92 | ||
| Lactate | ↓ | - | pre- vs. post-ocrelizumab | p = 1.87 × 10−4 | FDR = 2.16 × 10−2 | ||
| N-acetyl-β-alanine | ↑ | - | pre- vs. post-ocrelizumab | p = 6.85 × 10−5 | FDR = 1.22 × 10−2 | ||
| Propyl | ↑ | - | pre- vs. post-ocrelizumab | p = 1.02 × 10−5 | FDR = 3.64 × 10−3 | ||
| Propyl 4-hydroxybenzoate sulfate | ↑ | - | pre- vs. post-ocrelizumab | p = 1.02 × 10−5 | FDR = 3.64 × 10−3 | ||
| Ladakis (2024) [30] | MS lesions (SPMS) and HC | Sphingomyelins | ↑ | - | MS lesions vs. control | p = 1.26 × 10−8 | FDR = 2.15 × 10−7 |
| Ceramides | ↑ | - | MS lesions vs. control | p = 1.26 × 10−8 | FDR = 2.15 × 10−7 | ||
| Sphingosine | ↑ | - | MS lesions vs. control | p = 2.54 × 10−6 | FDR = 2.88 × 10−5 | ||
| Dipeptides | ↑ | - | MS lesions vs. control | p = 0.04 | FDR = 0.10 | ||
| Lysophospholipids | ↓ | - | MS lesions vs. control | p = 5.48 × 10−9 | FDR = 1.86 × 10−7 | ||
| Monoacylglycerols | ↓ | - | MS lesions vs. control | p = 0.008 | FDR = 0.04 | ||
| Hexosylceramides | ↓ | - | MS lesions vs. control | p = 0.002 | FDR = 0.06 | ||
| Guanosine | ↓ | - | MS lesions vs. control | p = 0.00014 | FDR = 0.001 | ||
| Pyridoxamine phosphate | ↓ | - | MS lesions vs. control | p = 0.00014 | FDR = 0.001 | ||
| Glutamate-γ-methyl ester | ↓ | - | MS lesions vs. control | p = 0.00014 | FDR = 0.001 | ||
| Nucleotides | ↓ | - | MS lesions vs. control | p = 0.01 | FDR = 0.05 | ||
| UFAs | ↓ | - | MS lesions vs. control | p = 0.02 | FDR = 0.06 | ||
| Endocannabinoids | ↓ | - | MS lesions vs. control | p < 0.001 | FDR = 5.2 × 10−9 | ||
| Fung (2024) [39] | RRMS and HC | 9-HODE | ↑ | 2.6-fold | preserved white matter integrity vs. white matter injury | p = 0.008 | FDR ≈ 0.05 |
| ALA/GLA | ↑ | 1.17-fold | preserved white matter integrity vs. white matter injury | p = 0.04 | FDR ≈ 0.05 | ||
| 9-HOTrE | ↑ | 4.2-fold | preserved white matter integrity vs. white matter injury | p = 0.003 | FDR ≈ 0.05 | ||
| 9,10-EpOME | ↑ | - | white matter injury vs. preserved white matter | p = 0.01 | FDR ≈ 0.05 | ||
| 9-HOTrE | ↓ | - | white matter injury vs. preserved white matter | p = 0.003 | FDR ≈ 0.05 | ||
| Datta (2024) [26] | RRMS, PPMS and HS | 2-Ethylhexanoic acid | ↑ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.3 (ns) |
| Ribose | ↑ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.3 (ns) | ||
| Erythrose | ↑ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.3 (ns) | ||
| 3-Indolepropionic acid | ↓ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.3 (ns) | ||
| α-D-Glucopyranose | ↑ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.3 (ns) | ||
| D-Glucuronic acid lactone | ↑ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.3 (ns) | ||
| Heptanoic acid | ↑ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.3 (ns) | ||
| L-Threose | ↑ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.3 (ns) | ||
| Lanthionine | ↑ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.3 (ns) | ||
| Linoleic acid | ↑ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.3 (ns) | ||
| 11,14-Eicosadienoic acid | ↑ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.3 (ns) | ||
| Succinic acid | ↓ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.3 (ns) | ||
| Meier (2024) [23] | RRMS, PPMS, SPMS and HC | 2-AG (CSF) | ↑ | - | Male RRMS vs. female RRMS and female HC | p < 0.05 | FDR ≈ 0.05 |
| AEA (CSF) | ↑ | - | RRMS < 39 yrs vs. age/sex-matched HC | p < 0.05 | FDR ≈ 0.05 | ||
| 2-AG (CSF) | ↑ | - | RRMS < 39 yrs vs. age/sex-matched HC | p < 0.05 | FDR ≈ 0.05 | ||
| AA (CSF) | ↑ | - | male vs. female RRMS | p < 0.05 | FDR ≈ 0.05 | ||
| Cortisol (CSF) | ↑ | - | PMS (PPMS and SPMS) vs. RRMS and HC | p < 0.05 | FDR ≈ 0.05 | ||
| Corticosterone (CSF) | ↑ | - | PMS (PPMS and SPMS) vs. HC | p < 0.05 | FDR ≈ 0.05 | ||
| 2-OG (CSF) | ↑ | - | RRMS ≥39 yrs vs. HC | p < 0.05 | FDR ≈ 0.05 | ||
| PGE2 (serum) | ↑ | - | RRMS vs. HC | p < 0.05 | FDR ≈ 0.05 | ||
| SEA (serum) | ↑ | - | MS (all subtypes) vs. HC | p < 0.05 | FDR ≈ 0.05 | ||
| Murgia (2023) [40] | RRMS (after fingolimod treatment) and HC | Alanine | ↑ | - | RRMS after fingolimod treatment vs. baseline | p < 0.05 | FDR ≈ 0.05 |
| Phenylalanine | ↑ | - | RRMS after fingolimod treatment vs. baseline | p < 0.05 | FDR ≈ 0.05 | ||
| Glycine | ↑ | - | RRMS after fingolimod treatment vs. baseline | p < 0.05 | FDR ≈ 0.05 | ||
| Pyroglutamic acid | ↑ | - | RRMS after fingolimod treatment vs. baseline | p < 0.05 | FDR ≈ 0.05 | ||
| Tryptophan | ↑ | - | RRMS after fingolimod treatment vs. baseline | p < 0.05 | FDR ≈ 0.05 | ||
| Fructose | ↑ | - | RRMS after fingolimod treatment vs. baseline | p < 0.05 | FDR ≈ 0.05 | ||
| Glucose | ↑ | - | RRMS after fingolimod treatment vs. baseline | p < 0.05 | FDR ≈ 0.05 | ||
| 2-Hydroxyisovalerate | ↑ | - | RRMS after fingolimod treatment vs. baseline | p < 0.05 | FDR ≈ 0.05 | ||
| Creatinine | ↑ | - | RRMS after fingolimod treatment vs. baseline | p < 0.05 | FDR ≈ 0.05 | ||
| Lactate | ↓ | - | RRMS after fingolimod treatment vs. baseline | p < 0.05 | FDR ≈ 0.05 | ||
| Isoleucine | ↓ | - | RRMS after fingolimod treatment vs. baseline | p < 0.05 | FDR ≈ 0.05 | ||
| Glutamate | ↓ | - | RRMS after fingolimod treatment vs. baseline | p < 0.05 | FDR ≈ 0.05 | ||
| Lysine | ↑ | - | responders vs. non-responders | p < 0.05 | FDR ≈ 0.05 | ||
| Lactate | ↑ | - | responders vs. non-responders | p < 0.05 | FDR ≈ 0.05 | ||
| Glucose | ↑ | - | non-responders vs. responders | p < 0.05 | FDR ≈ 0.05 | ||
| Broos (2023) [22] | RRMS, PMS, and HC | 11,12-DiHET | ↑ | - | RRMS vs. HC | p < 0.05 | FDR < 0.05 |
| DPAn-3 | ↑ | - | RRMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| 15-HETE | ↑ | - | PMS vs. HC | p < 0.05 | FDR (ns) | ||
| 8-HETE | ↑ | - | PMS vs. HC | p < 0.05 | FDR (ns) | ||
| 5-HETE | ↑ | - | PMS vs. HC | p < 0.05 | FDR (ns) | ||
| 11,12-DiHETE | ↑ | - | PMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| 20-HETE | ↑ | - | PMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| ARA | ↑ | - | PMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| DGLA | ↑ | - | PMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| AdA | ↑ | - | PMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| 9-HODE | ↓ | - | PMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| 13-HODE | ↓ | - | PMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| 14,15-DiHETE | ↓ | - | PMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| 19,20-DiHDPA | ↓ | - | PMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| Židó (2023) [32] | Early MS and HC | Arginine | ↓ | - | Early MS vs. HC | p = 0.0037 | FDR < 0.05 |
| Histidine | ↓ | - | Early MS vs. HC | p = 0.0058 | FDR < 0.05 | ||
| Glutamate | ↓ | - | Early MS vs. HC | p = 0.0145 | FDR < 0.05 | ||
| Choline | ↓ | - | Early MS vs. HC | p = 0.0233 | FDR < 0.05 | ||
| Tyrosine | ↓ | - | Early MS vs. HC | p = 0.0313 | FDR < 0.05 | ||
| Serine | ↓ | - | Early MS vs. HC | p = 0.0473 | FDR < 0.05 | ||
| Methionine | ↑ | - | Early MS vs. HC | p < 0.001 | FDR < 0.05 | ||
| Linoleic acid | ↓ | - | Early MS vs. HC | p = 0.001 | FDR < 0.05 | ||
| Stearic acid | ↓ | - | Early MS vs. HC | p = 0.029 | FDR < 0.05 | ||
| Spermidine | ↑ | - | Early MS vs. HC | p = 0.0124 | FDR < 0.05 | ||
| Oleic acid | ↑ | - | Early MS vs. HC | p = 0.015 | FDR < 0.05 | ||
| Židó (2022) [41] | Early MS and HC | Arginine | ↓ | - | Early MS vs. HC | p = 0.007 | FDR < 0.05 |
| Histidine | ↓ | - | Early MS vs. HC | p = 0.012 | FDR < 0.05 | ||
| Palmitic acid | ↑ | - | Early MS vs. HC | p = 0.039 | FDR ≈ 0.05 | ||
| Zahoor (2022) [35] | RRMS and HC | Phosphoethanolamine | ↑ | - | RRMS vs. HC | p < 0.05 | FDR < 0.10 (ns) |
| Lactic acid | ↑ | - | RRMS vs. HC | p < 0.05 | FDR < 0.10 (ns) | ||
| Fumaric acid | ↑ | - | RRMS vs. HC | p < 0.05 | FDR < 0.10 (ns) | ||
| 3-Hydroxybutyrate | ↑ | - | RRMS vs. HC | p < 0.05 | FDR < 0.10 (ns) | ||
| Oleoylethanolamide (OEA) | ↑ | - | RRMS vs. HC | p < 0.05 | FDR < 0.10 (ns) | ||
| Sphingosine-1-phosphate (S1P) | ↑ | - | RRMS vs. HC | p < 0.05 | FDR < 0.10 (ns) | ||
| Yang (2021) [33] | RRMS, PPMS and HC | L-Tyrosine | ↓ | - | RRMS and PPMS vs. HC | p < 0.001 | FDR < 0.05 |
| L-Tryptophan | ↓ | - | RRMS and PPMS vs. HC | p = 0.015 | FDR < 0.05 | ||
| L-Phenylalanine | ↓ | - | RRMS and PPMS vs. HC | p = 0.033 | FDR < 0.05 | ||
| L-Leucine | ↓ | - | RRMS and PPMS vs. HC | p = 0.0049 | FDR < 0.05 | ||
| L-Isoleucine | ↓ | - | RRMS and PPMS vs. HC | p < 0.001 | FDR < 0.05 | ||
| Sphingosine-1-phosphate | ↓ | - | RRMS and PPMS vs. HC | p < 0.001 | FDR < 0.05 | ||
| Sphinganine-1-phosphate | ↓ | - | RRMS and PPMS vs. HC | p < 0.001 | FDR < 0.05 | ||
| Phytosphingosine | ↓ | - | RRMS and PPMS vs. HC | p < 0.01 | FDR < 0.05 | ||
| 17α-Estradiol | ↓ | - | RRMS and PPMS vs. HC | p < 0.001 | FDR < 0.05 | ||
| Methyl jasmonate | ↑ | - | RRMS and PPMS vs. HC | p < 0.001 | FDR < 0.05 | ||
| Myo-inositol | ↓ | - | RRMS and PPMS vs. HC | p < 0.001 | FDR < 0.05 | ||
| Oleic acid | ↑ | - | RRMS and PPMS vs. HC | p = 0.046 | FDR < 0.05 | ||
| Palmitic acid | ↑ | - | RRMS and PPMS vs. HC | p < 0.01 | FDR < 0.05 | ||
| Arachidonic acid | ↑ | - | RRMS and PPMS vs. HC | p = 0.015 | FDR < 0.05 | ||
| β-Alanyl-L-arginine | ↑ | - | RRMS and PPMS vs. HC | p < 0.01 | FDR < 0.05 | ||
| 4-Oxoglutaramate | ↑ | - | RRMS and PPMS vs. HC | p < 0.01 | FDR < 0.05 | ||
| Isocitric acid | ↑ | - | RRMS and PPMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| O-Phosphoethanolamine | ↑ | - | RRMS and PPMS vs. HC | p < 0.01 | FDR < 0.05 | ||
| Sorbitol | ↑ | - | RRMS and PPMS vs. HC | p < 0.01 | FDR < 0.05 | ||
| Spermidine | ↑ | - | RRMS and PPMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| Homovanillic acid | ↓ | - | RRMS and PPMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| Deoxyuridine | ↓ | - | RRMS and PPMS vs. HC | p < 0.01 | FDR < 0.0 | ||
| L-Arogenate | ↓ | - | RRMS and PPMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| Pseudouridine | ↓ | - | RRMS and PPMS vs. HC | p < 0.01 | FDR < 0.05 | ||
| Uridine | ↓ | - | RRMS and PPMS vs. HC | p < 0.001 | FDR < 0.05 | ||
| Dodecanoic acid | ↓ | - | RRMS and PPMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| Niacinamide | ↓ | - | RRMS and PPMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| α-Dimorphecolic acid | ↓ | - | RRMS and PPMS vs. HC | p < 0.01 | FDR < 0.05 | ||
| cis-4-Hydroxy-D-proline | ↓ | - | RRMS and PPMS vs. HC | p = 0.004 | FDR < 0.05 | ||
| N-Acetyl-L-asparagine | ↓ | - | RRMS and PPMS vs. HC | p < 0.01 | FDR < 0.05 | ||
| L-Valine | ↑ | - | RRMS and PPMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| Glutamic acid | ↑ | - | RRMS and PPMS vs. HC | p < 0.001 | FDR < 0.05 | ||
| Myristic acid | ↓ | - | RRMS and PPMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| DHEA | ↓ | - | RRMS and PPMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| Creatinine | ↓ | - | RRMS and PPMS vs. HC | p < 0.01 | FDR < 0.05 | ||
| Nicotinuric acid | ↓ | - | RRMS and PPMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| Trans-cinnamic acid | ↓ | - | RRMS and PPMS vs. HC | p < 0.01 | FDR < 0.05 | ||
| 2-Dehydropantoate | ↓ | - | RRMS and PPMS vs. HC | p < 0.05 | FDR < 0.05 | ||
| Olsson (2021) [42] | RRMS, CIS and HC | Acetate | ↓ | - | RRMS and CIS vs. HC | p = 0.021 | FDR = 0.067 (ns) |
| Valine | ↓ | - | RRMS and CIS vs. HC | p = 0.005 | FDR > 0.05 (ns) | ||
| L-Methionine | ↓ | - | RRMS and CIS vs. HC | p = 0.025 | FDR > 0.05 (ns) | ||
| Kynurenic acid (KYNA) | ↓ | - | RRMS and CIS vs. HC | p = 0.019 | FDR > 0.05 (ns) | ||
| Creatine | ↑ | - | RRMS and CIS vs. HC | p = 0.022 | FDR > 0.05 (ns) | ||
| Pantothenic acid | ↑ | - | RRMS and CIS vs. HC | p = 0.021 | FDR > 0.05 (ns) | ||
| D-Glucuronic acid | ↑ | - | RRMS and CIS vs. HC | p = 0.031 | FDR > 0.05 (ns) | ||
| 3-Hydroxyanthranilic acid (3HAA) | ↑ | - | RRMS and CIS vs. HC | p = 0.040 | FDR > 0.05 (ns) | ||
| Acetate/Butyrate ratio | ↓ | - | RRMS and CIS vs. HC | p = 0.005 | FDR = 0.06 (ns) | ||
| Acetate/(Propionate + Butyrate) ratio | ↓ | - | RRMS and CIS vs. HC | p = 0.010 | FDR = 0.06 (ns) | ||
| Fitzgerald (2021) [43] | MS and HC | Phenyllactate (PLA) | ↓ | - | MS vs. HC | p < 1.0 × 10−19 | FDR < 0.001 |
| 3-(4-Hydroxyphenyl)lactate | ↓ | - | MS vs. HC | p < 1.0 × 10−17 | FDR < 0.001 | ||
| Indolelactate | ↓ | - | MS vs. HC | p < 1.0 × 10−15 | FDR < 0.001 | ||
| Imidazole lactate | ↓ | - | MS vs. HC | p < 1.0 × 10−10 | FDR < 0.001 | ||
| Tyrosine | ↓ | - | MS vs. HC | p < 1.0 × 10−6 | FDR < 0.001 | ||
| Tryptophan | ↓ | - | MS vs. HC | p < 1.0 × 10−10 | FDR < 0.001 | ||
| Phenylpyruvate | ↓ | - | MS vs. HC | p < 1.0 × 10−6 | FDR < 0.001 | ||
| Kynurenine | ↓ | - | MS vs. HC | p < 0.05 | FDR ≈ 0.05 | ||
| Phenylacetylglutamine | ↑ | - | MS vs. HC | p < 1.0 × 10−4 | FDR < 0.05 | ||
| p-Cresol glucuronide | ↑ | - | MS vs. HC | p < 1.0 × 10−4 | FDR < 0.05 | ||
| p-Cresol sulfate | ↑ | - | MS vs. HC | p < 0.001 | FDR < 0.05 | ||
| 4-Hydroxyphenylpyruvate | ↓ | - | MS vs. HC | p < 1.0 × 10−9 | FDR < 0.001 | ||
| Xanthurenate | ↓ | - | MS vs. HC | p < 1.0 × 10−4 | FDR < 0.05 | ||
| Levi (2021) [36] | MS and HC | β-Hydroxyasparagine | ↑ | - | MS vs. HC | p < 0.004 | FDR < 0.05 |
| Sphingosine-1-phosphate (S1P) | ↓ | - | MS vs. HC | p < 0.007 | FDR < 0.05 | ||
| Carnitine | ↓ | - | MS vs. HC | p < 0.007 | FDR < 0.05 | ||
| Indolepropionate | ↓ | - | MS vs. HC | p < 0.03 | FDR < 0.05 | ||
| Indolelactate | ↓ | - | MS vs. HC | p < 0.03 | FDR < 0.05 | ||
| p-Cresol sulfate | ↑ | - | MS vs. HC | p = 0.12 | FDR (ns) | ||
| Stachydrine | ↑ | - | MS vs. HC | NR | FDR < 0.05 | ||
| 3-Hydroxyhippurate | ↑ | - | MS vs. HC | NR | FDR < 0.05 | ||
| Probert (2021) [19] | CIS converters and non-converters | Glucose | ↑ | - | CIS converters vs. non-converters | p < 0.05 | FDR (ns) |
| Lactate | ↑ | - | CIS converters vs. non-converters | p < 0.05 | FDR (ns) | ||
| Myo-inositol | ↓ | - | CIS converters vs. non-converters | p < 0.05 | FDR (ns) | ||
| Creatine | ↓ | - | CIS converters vs. non-converters | p < 0.05 | FDR (ns) | ||
| Keller (2021) [21] | MS and HC (after PRT treatment) | Myristate | ↑ | - | MS vs. HC after PRT | p < 0.001 | FDR (ns) |
| Palmitate | ↑ | - | MS vs. HC after PRT | p < 0.001 | FDR (ns) | ||
| Arachidonate | ↑ | - | MS vs. HC after PRT | p = 0.0012 | FDR (ns) | ||
| Oleate/Vaccinate | ↑ | - | MS vs. HC after PRT | p = 0.0012 | FDR (ns) | ||
| Linoleate | ↑ | - | MS vs. HC after PRT | p = 0.0017 | FDR (ns) | ||
| 5-Dodecenoate | ↑ | - | MS vs. HC after PRT | p = 0.003 | FDR (ns) | ||
| 1-Palmitoleoyl-GPC | ↑ | - | MS vs. HC after PRT | p = 0.00012 | FDR (ns) | ||
| 1-Stearoyl-GPC | ↑ | - | MS vs. HC after PRT | p = 0.00061 | FDR (ns) | ||
| 1-1-Enyl-palmitoyl-GPC | ↑ | - | MS vs. HC after PRT | p = 0.0012 | FDR (ns) | ||
| Margarate | ↑ | - | MS vs. HC after PRT | p = 0.00061 | FDR (ns) | ||
| Dihomolinoleate | ↑ | - | MS vs. HC after PRT | p = 0.0017 | FDR (ns) | ||
| Stearidonate | ↑ | - | MS vs. HC after PRT | p = 0.0017 | FDR (ns) | ||
| 3-Hydroxyisobutyrate | ↑ | - | MS vs. HC after PRT | p = 0.00037 | FDR (ns) | ||
| Glycerol | ↑ | - | MS vs. HC after PRT | p = 0.0031 | FDR (ns) | ||
| 3-Aminoisobutyrate | ↑ | - | MS vs. HC after PRT | p = 0.0052 | FDR (ns) | ||
| Hydantoin-5-propionate | ↑ | - | MS vs. HC after PRT | p = 0.00061 | FDR (ns) | ||
| DHEAS | ↑ | - | MS vs. HC after PRT | p = 0.03 | FDR (ns) | ||
| Acylcarnitines | ↑ | - | MS vs. HC after PRT | p = 0.018 | FDR (ns) | ||
| Stearate | ↑ | - | MS vs. HC after PRT | p = 0.0040 | FDR (ns) | ||
| Yeo (2021) [37] | RRMS | Lysine | ↑ | - | RRMS, relapse vs. long remission (LR ≥ 24 M) | p < 0.05 | FDR (ns) |
| Asparagine | ↑ | - | RRMS, relapse vs. long remission (LR ≥ 24 M) | p < 0.05 | FDR (ns) | ||
| Isoleucine | ↓ | - | RRMS, relapse vs. long remission (LR ≥ 24 M) | p < 0.01 | FDR (ns) | ||
| Leucine | ↓ | - | RRMS, relapse vs. long remission (LR ≥ 24 M) | p < 0.01 | FDR (ns) | ||
| Yeo (2020) [27] | RRMS and SPMS | Lipoproteins | ↓ | - | SPMS vs. RRMS | NR | FDR < 0.05 |
| Choline | ↓ | - | SPMS vs. RRMS | NR | FDR < 0.05 | ||
| 3-Hydroxybutyrate | ↓ | - | SPMS vs. RRMS | NR | FDR < 0.05 | ||
| Glucose | ↑ | - | SPMS vs. RRMS | NR | FDR < 0.05 | ||
| N-acetylated glycoproteins | ↑ | - | SPMS vs. RRMS | NR | FDR < 0.05 | ||
| Gaetani (2020) [34] | RRMS and HC | Kynurenine | ↓ | - | RRMS vs. HC | p = 0.01 | NR |
| K/T ratio | ↓ | - | RRMS vs. HC | p = 0.04 | NR | ||
| Tryptophan | ↑ | - | RRMS vs. HC | p = 0.001 | NR | ||
| Indole-3-propionic acid | ↑ | - | RRMS vs. HC | p < 0.001 | NR | ||
| Indole-3-propionic acid | ↑ | - | RRMS with recent relapse (<30 days) vs. stable RRMS | p = 0.04 | NR | ||
| K/A ratio | ↓ | - | RRMS with recent relapse (<30 days) vs. stable RRMS | p = 0.03 | NR | ||
| Anthranilate | ↑ | - | RRMS with recent relapse (<30 days) vs. stable RRMS | p = 0.02 | NR | ||
| Carlsson (2020) [29] | SPMS and HC | Glycine | ↑ | - | SPMS vs. HC | p = 0.016 | FDR < 0.05 |
| ADMA | ↑ | - | SPMS vs. HC | p = 0.009 | FDR < 0.05 | ||
| PC-O (34:0) | ↑ | - | SPMS vs. HC | p = 0.046 | FDR < 0.05 | ||
| Hexoses | ↑ | - | SPMS vs. HC | p = 0.010 | FDR < 0.05 | ||
| Duscha (2020) [24] | MS | Propionic acid | ↓ | - | MS vs. HC | p = 0.0016 | FDR < 0.05 |
| Penkert (2020) [44] | HC and MS/CIS (Monozygotic twins) | Ether phosphatidylcholines (PC O-) | ↓ | - | MS vs. HC co-twins | p = 0.00081 | FDR = 0.0095 |
| Ether phosphatidylethanolamines (PE O-) | ↓ | - | MS vs. HC co-twins | p = 0.0015 | FDR = 0.0095 | ||
| Phosphatidylcholines (PC) | ↓ | - | MS vs. HC co-twins | p = 0.017 | FDR = 0.074 | ||
| PC O- species with DPA (C22:5) | ↓ | - | MS vs. HC co-twins | p = 0.00047 | FDR = 0.011 | ||
| PC O- species with other PUFA acyl chains (C22:4, C20:3, C20:4) | ↓ | - | MS vs. HC co-twins | p < 0.05 | FDR (ns) | ||
| PC O- species with ether-bound alkyl chains (O-16:0;0, O-16:1;0, O-18:1;0) | ↓ | - | MS vs. HC co-twins | p < 0.05 | FDR (ns) | ||
| PC O-16:1;0/20:3;0 | ↓ | - | MS vs. HC co-twins | p = 0.00006 | FDR = 0.015 | ||
| Multiple PC O- species with PUFA side chains | ↓ | - | MS vs. HC co-twins | p < 0.05 | FDR (ns) | ||
| Amatruda (2020) [31] | PPMS, HC, validation cohort: SPMS and RRMS | DiHexCer(d18:1/18:2) | ↓ | - | PPMS vs. HC | p < 0.05 | FDR (ns) |
| DiHexCer(d18:1/18:3) | ↓ | - | PPMS vs. HC | p < 0.05 | FDR (ns) | ||
| SM(d18:1/14:0) | ↓ | - | PPMS vs. HC | p < 0.01 | FDR (ns) | ||
| MonoHexCer(d18:1/20:0) | ↑ | - | PPMS vs. HC | p < 0.01 | FDR (ns) | ||
| LPA-18:2 | ↓ | - | PPMS progressors vs. PPMS non-progressors and HC | p < 0.05 | FDR (ns) | ||
| LPA-18:2 | ↓ | - | SPMS progressors vs. SPMS non-progressors | p < 0.05 | FDR (ns) | ||
| Waddington (2020) [45] | RRMS (IFNβtreated), CIS | VLDL-PA | ↑ | - | ADA+ vs. ADA− | p < 0.05 | FDR (ns) |
| Cholesteryl esters in VLDL | ↑ | - | ADA+ vs. ADA− | p < 0.05 | FDR (ns) | ||
| TG/PG ratio | ↑ | - | ADA+ vs. ADA− | p < 0.05 | FDR (ns) | ||
| Free cholesterol | ↑ | - | ADA+ vs. ADA− | p < 0.05 | FDR (ns) |
3. Results
Metabolomics is an emerging data-driven field ofresearch that focuses on the comprehensive analysis of low-molecular-weight metabolites (<1.5 kDa) within biological systems. These molecules are end-products of numerous interconnected pathways, and their analysis provides a dynamic snapshot of both physiological and pathological states [46].
Two primary analytical approaches are commonly distinguished. Untargeted metabolomics aims to detect as many metabolites as possible in a biological sample, generating large datasets that capture global metabolic patterns. This strategy is particularly valuable in exploratory research, where it can reveal broad alterations and guide the focus of subsequent targeted investigations. In contrast, targeted metabolomics concentrates on a predefined set of compounds. It offers greater sensitivity and specificity, enabling detailed interrogation of selected pathways and serving as a tool for hypothesis validation [47].
Among analytical platforms, gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS) are the most frequently employed. GC-MS provides excellent resolution and well-established spectral libraries but requires chemical derivatization, which may introduce variability and limit the analysis of thermolabile compounds. LC-MS offers broader versatility, detecting polar, thermolabile, and higher-molecular-weight metabolites such as lipids, amino acids, and organic acids. Its strengths are high sensitivity and wide coverage, although inter-laboratory reproducibility can be more challenging due to variability in chromatographic conditions and data processing pipelines. Finally, nuclear magnetic resonance (NMR) spectroscopy provides a complementary, non-destructive approach. Although less sensitive than MS-based methods, NMR requires minimal sample preparation, delivers highly reproducible quantitative data, and allows direct analysis of intact biofluids, making it robust for comparative studies [48,49].
Metabolomic techniques provide a powerful window into the discovery of novel biomarkers for early pathological changes, disease progression, and therapeutic efficacy in disorders such as MS. Moreover, they provide insights into disease-altered metabolic processes, potentially revealing new targets for therapeutic intervention.
Studies analyzed in this review employed a range of analytical platforms, most commonly LC-MS-20, GC-MS-5, and NMR spectroscopy-8. The biological matrices analyzed included blood plasma or serum, CSF and others (urine, feces and, in a few cases, brain tissue). Detailed information on the biological samples analyzed in each study is presented in Table 1. Both untargeted and targeted metabolomics approaches were used across these studies.
Across the included studies, most cohorts were defined using the McDonald criteria, although different revisions were applied. Specifically, McDonald 2005 [33], McDonald 2010 [17,34,35,38,40,44,45], and McDonald 2017 [16,18,22,23,24,25,27,32,37,39,41,42,43] were reported. One study relied on the Poser criteria for CIS conversion [19]. Several reports did not specify diagnostic criteria and were therefore classified as not reported (NR) [20,21,29,30,36].
Additional diagnostic confirmation varied. MRI was used in multiple studies to assess demyelinating lesions, atrophy, or disease activity [17,22,24,25,31,32,37,39,40,41,42,43]. CSF analysis for oligoclonal bands (OCB) was performed in some cohorts [19,24,32,41]. More advanced modalities included PET (TSPO-PET) [20] and optical coherence tomography (OCT) [38,43]. A number of studies further strengthened diagnostic reliability by integrating biochemical biomarkers, such as serum neurofilament light chain (sNfL) [13,39], glial fibrillary acidic protein (GFAP) [13] and cytokine or immune profiling [18,20,44].
Overall, most studies consistently applied McDonald criteria (2010/2017 revisions), exceptions included the use of older criteria [19] or absence of clear reporting [20,21,29,30,36]. Diagnostic reliability was often reinforced through MRI, CSF analysis, and biochemical markers, and in selected studies by advanced imaging such as OCT or PET.
In the following sections, we discuss the main metabolic pathways found to be dysregulated in MS, including tryptophan–kynurenine metabolism, energy metabolism (glycolysis and the TCA cycle), lipid metabolism, amino acid and nitrogen metabolism, and xenobiotic metabolism. Each pathway is considered in terms of its biological relevance, the nature of the alterations observed, and the potential implications for biomarker discovery and therapeutic targeting in MS.
A comprehensive overview of significantly altered metabolites, with reported p-values and FDR-adjusted values where available, is presented in Table 2. This tabular synthesis complements the narrative description of pathway-level alterations provided in the main text.
3.1. The Kynurenine Pathway
Several studies [16,26,33,34,38,40,42,43] have reported significant alterations in the levels of kynurenine pathway (KP) metabolites, highlighting its involvement in the pathogenesis and progression of MS.
The KP is the primary route of tryptophan (TRP) catabolism and generates several bioactive metabolites with immunomodulatory and neuroactive properties. Among these, kynurenine (KYN), 3-hydroxykynurenine (3HK), anthranilic acid (AA), kynurenic acid (KYNA), 3-hydroxyanthranilic acid (3HAA), and quinolinic acid (QUIN) have been implicated in neurodegeneration. The rate-limiting step of TRP conversion to KYN in extrahepatic tissues is catalyzed by indoleamine 2,3-dioxygenase (IDO), whose expression is upregulated by proinflammatory cytokines such as interferon-gamma (IFN-γ). Several KP metabolites are neuroactive; for instance, QUIN is considered both neurotoxic and gliotoxic due to its excitotoxic effects on glutamatergic neurotransmission and its cytotoxicity toward neurons, astrocytes, and oligodendrocytes [50,51,52].
Targeted metabolomics has revealed altered circulating KP metabolites in MS: KYNA and 3HK are often decreased (↓ 1.2-fold and ↓ 1.5-fold, respectively [38]), while AA (↑ 3.1-fold [38]) and 3HAA [42] are elevated. Ratios reflecting enzyme activity (e.g., KYN:TRP, 3HK:KYN, QUIN:KYNA) are frequently disrupted, indicating shifts toward proinflammatory and neurotoxic states. Imaging studies further associate KYN and TRP levels with brain atrophy and choroid plexus volume [38].
Additional studies have shown altered TRP levels across MS subtypes. Serum TRP was elevated in RRMS compared to healthy controls (↑ 3.3-fold) and showed higher levels in RRMS than in progressive forms (SPMS and PPMS), with a trend toward stepwise decline across disease stages, reflecting progressive KP dysregulation [16]. In a Chinese cohort, L-TRP was decreased in both RRMS and PPMS and correlated negatively with tumor necrosis factor alpha (TNF-α) and positively with interleukins (IL-7, IL-12), Macrophage Inflammatory Protein-1 alpha (MIP-1α) and Monocyte Chemoattractant Protein-1 (MCP-1), suggesting compensatory immunoregulatory signaling [33]. Another study reported a similar pattern of reduced KYNA and elevated 3HAA, although findings did not reach statistical significance after correction for multiple comparisons [42]. Reduced KYN was associated with higher disability measured by the Expanded Disability Status Scale (EDSS), and other downstream metabolites, such as xanthurenic acid, correlated with retinal thinning in the GCIPL layer, linking KP alterations to neurodegenerative changes [44].
KP disturbances also extend to gut microbiota-derived TRP catabolites. Indolepropionate and its precursor indolelactate, produced by commensal bacteria, were reduced in MS serum, even though the abundance of indolepropionate-producing bacteria was unchanged. In contrast, indolelactate-producing taxa were markedly reduced, suggesting a microbial bottleneck that may impair neuroprotection and sustain inflammation [36]. Urine samples from patients with RRMS exhibited decreased KYN and KYN:TRP ratio, independent of treatment status or disease duration. Sex-specific patterns were also observed, with females exhibiting lower urinary TRP, KYN, AA, and serotonin. Elevated urinary TRP and indole-3-propionic acid (IPA) correlated positively with EDSS scores, whereas KYN:TRP correlated inversely. During relapse, increased AA and IPA, alongside a decreased KYN:AA ratio, indicated a dynamic metabolic response to inflammation [34]. Elevated L-TRP levels were also confirmed in RRMS compared to healthy controls, reinforcing the consistency of KP dysregulation across cohorts [26].
Recent evidence indicates that DMTs can modulate KP in MS, influencing both the balance between neurotoxic and neuroprotective metabolites and overall immune regulation. For example, fingolimod, a sphingosine-1-phosphate (S1P) receptor modulator, was associated with longitudinal changes in KP metabolism. Specifically, a gradual increase in serum tryptophan levels was observed over the two-year course of treatment, suggesting a shift in upstream TRP availability [40]. Collectively, these findings highlight a potential role of DMTs not only in immunoregulation but also in restoring metabolic homeostasis through modulation of the KP.
These findings collectively emphasize the kynurenine pathway as a crucial connection between immune dysregulation, neurodegeneration, and potential therapeutic targets in MS. Further targeted metabolomic studies are needed to clarify the clinical revelance of these effects across various MS treatments.
3.7. Subgroup Analysis by MS Phenotype
In CIS, changes included increased glucose and lactate, together with an early shift in the kynurenine pathway, marked by reduced KYNA [19,42].
In RRMS, consistent alterations were reported in energy metabolism. Reported changes included increases in succinate and formate [16], alongside broader enrichment of glycolysis, pyruvate metabolism, and the TCA cycle [26,35]. Lipid metabolism was also frequently implicated, particularly sphingolipids and phosphatidylcholines [16,33,35]. Dysregulation of SCFA, notably reduced propionic acid, was observed in serum of patients with RRMS [24]. Additionally, protective lipids, such as 9-HODE and ALA, were decreased [39]. RRMS cohorts also showed further changes in the kynurenine pathway, with decreased concentrations of KYN and KYNA and increased concentrations of tryptophan [16,40,46,47]. Perturbations in amino acid metabolism were repeatedly described, with decreases in arginine, histidine, and glutamate in CSF [32,41]. In RRMS groups, microbial indoles, including myo-inositol and indole-3-propionic, acid were decreased [16,19,26].
In SPMS, findings included elevated ketone bodies including BHB and AcAc [17], increased arachidonic acid cascade including HETE [22], and higher levels of spermidine [25] and ADMA [29]. Additionally, decreased levels of nucleotides including guanosine were reported in patients with SPMS.
In PPMS, reported changes involved osmolytes and microbial-derived metabolites, with lower myo-inositol and higher p-cresol sulfate [25,33]. Alterations in steroid hormones and neurosteroids (cortisol, corticosterone and related metabolites) were consistently elevated in CSF of PPMS compared RRMS and controls [23,33]. PPMS cohorts also showed shifts in lipid metabolism including decreased levels of specific dihydroxyceramides [31].
Overall, the available studies suggest that different MS phenotypes are associated with partially distinct metabolic changes. These observations underline the heterogeneity of reported findings and highlight the need for further work in larger, stratified cohorts.
4. Discussion
This systematic review integrates the most recent metabolomic studies in MS, highlighting convergent findings across heterogeneous designs and analytical platforms. Despite differences in cohorts, sample types, and methodologies, several metabolic domains show consistent alterations, which together delineate a trajectory of biochemical reprogramming as the disease progresses from CIS to RRMS and progressive forms (SPMS, PPMS). These consensus findings help refine the pathogenic interpretation of metabolomics in MS and support its potential as a tool for disease staging.
Across multiple independent cohorts, the kynurenine pathway consistently showed a shift towards neurotoxic metabolites, with reductions in KYN, KYNA, and 3HK, paralleled by increases in QUIN and 3HAA [16,34,38,43]. This imbalance reflects proinflammatory activation of indoleamine-2,3-dioxygenase and aligns with neurodegeneration via excitotoxic and gliotoxic mechanisms. Microbiota-derived tryptophan metabolites, including indole-3-propionate and indolelactate, were also decreased [36,50], reinforcing the concept of impaired gut–brain kynurenine metabolism. These alterations correlate with clinical disability and retinal thinning, supporting their utility as early indicators of neurodegenerative processes.
Perturbations in energy metabolism were among the most reproducible findings. CIS converters showed early elevations of glucose and lactate in CSF, surpassing oligoclonal bands in predictive value for MS conversion [19]. In RRMS, succinate, ATP, and formate were frequently elevated, although some cohorts reported reduced succinate, highlighting methodological heterogeneity [16,23]. In progressive MS, reliance on alternative substrates was evident: β-hydroxybutyrate and acetoacetate were consistently increased and correlated with EDSS and MSSS [17]. Reductions in acetate and carnitine further point to mitochondrial dysfunction and impaired fatty acid transport [18,20]. Altogether, these data outline a metabolic transition from glycolysis-driven immune activation in early stages to ketone body utilization and mitochondrial exhaustion in progression.
Lipidomic studies also revealed highly reproducible alterations. In RRMS, protective PUFA-derived mediators such as 9-HODE, gamma-linolenic acid, and alpha-linolenic acid were linked to preserved white matter integrity, whereas proinflammatory lipids such as 9,10-EpOME correlated with tissue injury [39]. In SPMS and PPMS, arachidonic acid-derived species (5-, 8-, and 15-HETE) were elevated and associated with EDSS, neurofilament light chain, and brain atrophy, while protective HODE and docosapentaenoic acid derivatives declined [13]. Sphingolipid abnormalities, including reduced sphingosine-1-phosphate and ether-linked phosphatidylcholines and phosphatidylethanolamines, were repeatedly reported in serum, CSF, and twin studies [18,34]. In brain tissue, lesion cores were enriched in ceramides and sphingomyelins, whereas lysophospholipids and endocannabinoids were depleted [30]. SCFAs, particularly propionate and acetate, were consistently reduced in serum and feces [24,42], with supplementation shown to restore Treg function and stabilize EDSS [24]. These convergent findings position lipid metabolism as a critical determinant of MS progression, integrating immune, mitochondrial, and gut–brain signaling.
Amino acid and nitrogen metabolism showed comparable convergence. Most studies identified reductions in essential and aromatic amino acids (lysine, tyrosine, phenylalanine, leucine, isoleucine, arginine, histidine, serine, glutamate), particularly in RRMS and newly diagnosed MS [16,32,33,34]. These deficits were linked to both immune dysregulation and retinal thinning [43]. Dynamic oscillations in amino acid levels correlated with disease activity: lysine and asparagine rose during relapses, while leucine and isoleucine increased during stability, outperforming neurofilament light chain as relapse biomarkers [37]. Progressive forms were characterized by elevations in spermidine, a polyamine associated with mitochondrial stress and proinflammatory cytokines IL-17 and TNF-α [25,33,41]. Elevated ADMA in SPMS further implicates impaired nitric oxide signaling and endothelial dysfunction [29]. Together, these patterns highlight amino acid metabolism as a sensitive marker of disease state and activity.
Nucleotide metabolism was also notably disrupted. In RRMS, inosine and NAD+ were elevated compared with SPMS and PPMS, reflecting compensatory redox and nucleotide turnover [16]. By contrast, uridine, deoxyuridine, pseudouridine, and guanosine were decreased across progressive phenotypes and in demyelinated brain tissue [30,33]. These findings point to nucleotide exhaustion as a feature of chronic neurodegeneration and loss of repair capacity.
Several studies further revealed convergent signatures of microbiota and xenobiotic metabolism. Elevated microbial fermentation products, including p-cresol sulfate, p-cresol glucuronide, and phenylacetylglutamine, were associated with disability and neuroinflammation [25,36,43]. Conversely, protective metabolites such as indolepropionate, skatole, and propionate were decreased [24,36,42]. Myo-inositol, a central polyol involved in osmotic and glial signaling, consistently declined in both serum and CSF, particularly in progressive subtypes [16,19,32], and outperformed oligoclonal bands in predicting conversion from CIS [19]. Treatment also influenced the xenobiotic profile, as ocrelizumab reduced circulating perfluorinated compounds (PFOS, PFOA) and altered bile acid and steroid conjugates [18]. These results underscore the host–microbiota–environment axis as a key regulator of MS progression.
The metabolomic alterations described in our review often parallel proteomic findings, underscoring shared pathophysiological mechanisms. For example, shifts in kynurenine pathway metabolites that correlate with proinflammatory cytokines such as IFN-γ and IL-17 [33,34,43] align with proteomic studies demonstrating elevated cytokine signatures in newly diagnosed RRMS [54]. Metabolomic changes in lipid mediators converge with proteomic markers of tissue damage: higher levels of arachidonic acid-derived HETEs reflect both increased disability and elevated sNfL [13,18], a well-established biomarker of axonal degeneration validated in large longitudinal cohorts [55], while protective ω-3 derivatives show negative correlations with sGFAP [13], recently shown to capture astroglial injury in MS [56] and further associated with disease subtype and MRI markers of severity [57]. Proteomics and metabolomics capture different but complementary dimensions of MS biology: whereas proteomics highlights immune- and neurodegeneration-related proteins, metabolomics reflects systemic biochemical shifts in energy and lipid metabolism. Combining these approaches not only broadens the scope of biomarker discovery but also increases the likelihood of identifying clinically relevant markers that can improve disease monitoring and therapeutic decision-making [58]. Recent multi-omics studies reinforce this convergence: one integrated biochemical, proteomic, and metabolomic analyses of CSF to predict clinical conversion from CIS to MS [19], while another combined metabolomic profiling with scRNA-seq to link altered aromatic amino acid metabolism to immune cell transcriptional programs [43]. Although not part of the present systematic review, additional work has integrated blood metabolomic and transcriptomic data to stratify patients with SM by disease severity, highlighting the broader potential of cross-layer omics approaches to expand mechanistic insight and biomarker discovery [59].
The included studies varied substantially in sample size, ranging from small exploratory cohorts (n < 20) to large multicenter analyses (n > 300), as summarized in Table 1. This heterogeneity inevitably affects the robustness and generalizability of the reported findings. Although we could not perform a formal sensitivity analysis given the qualitative design of this review, we observed that key metabolite alterations, such as shifts in energy metabolism and lipid pathways, were consistently replicated in larger cohorts [21,36,43], whereas some associations identified in smaller NMR-based studies may reflect preliminary signals requiring further validation.
Another limitation is the heterogeneity in reporting statistical significance across studies, with some reporting p-values at different thresholds and others applying FDR corrections. This inconsistency reduces direct comparability but was retained in our tables to ensure accuracy of the original reports.
Reporting of comorbidities and exclusion criteria was highly heterogeneous across the included studies. Only a minority provided clear information on how potential confounding conditions were managed. Notably, some investigations applied highly restrictive criteria by including only patients on a single therapy and excluding all chronic diseases and concomitant medications [40], or by excluding neuroinflammatory disorders and dementia from the control group [23]. Similarly, other studies explicitly excluded participants with autoimmune or neurological diseases and those on psychopharmacotherapy [32,41]. By contrast, most studies did not specify exclusion criteria beyond basic demographics or treatment status [16,17,21,22], although some adjusted for covariates such as BMI or smoking [25,45], or considered pseudo-relapses due to infections [37]. Overall, metabolic, psychiatric, and autoimmune comorbidities were rarely reported or systematically excluded, complicating the interpretation of metabolomic changes as disease-specific.
Taken together, the reviewed studies suggest that MS phenotypes may be associated with partly distinct metabolic alterations. CIS and early RRMS have most often been linked to changes in glycolysis, the kynurenine pathway, and amino acid availability. In RRMS, additional findings include reductions in protective lipid mediators and microbial metabolites. In SPMS, reported changes include elevations of ketone bodies, arachidonic acid derivatives, and polyamines, together with signs of nucleotide depletion. In PPMS, disturbances have been more frequently described in osmolytes and microbial-derived metabolites, reductions in specific ceramides, and increases in steroid hormones and neurosteroids. While these patterns point to possible phenotype-specific metabolic signatures, the evidence remains heterogeneous and limited, particularly for progressive forms. To illustrate these observations, we provide a schematic overview (Figure 2). Together, these additions clarify how metabolomics can not only deepen mechanistic understanding but also support staging diagnosis, complementing established biomarkers such as neurofilament light chain and MRI measures.
Although this review was focused on human metabolomic studies, several of the altered pathways we identified overlap with therapeutic targets under active investigation. For instance, mitochondrial dysfunction and impaired bioenergetics have motivated repurposing strategies such as metformin, which reduced inflammation, improved disease outcomes, and attenuated demyelination in EAE models [60]. Activation of Nrf2 signaling, mirrored in metabolomic data, underlies the therapeutic effect of dimethyl fumarate, which induces Nrf2 pathway activation in PBMCs and promotes regulatory immune phenotypes in patients with SM [61]. Similarly, an increase in astrocytic PGC-1α in active MS lesions may reflect an endogenous oxidative stress-mitigating response [62]. While a systematic evaluation of preclinical and clinical drug data exceeds this review’s scope, our findings provide mechanistic grounding that could guide translational research.
In contrast to prior reviews [12,13,14], which were either pathway-centric, lipid-restricted, or method-limited, our work integrates findings from multiple analytical platforms and biofluids, restricted to adult human MS cohorts, and stratified by phenotype. By aggregating consensus metabolic signals across RRMS, SPMS, and PPMS, and embedding them within clinical and imaging contexts, this review delivers a systematic, translational perspective that was not achieved by earlier analyses [15].
Limitations
While this review provides a comprehensive synthesis of recent metabolomic findings in multiple sclerosis, several limitations must be acknowledged. Substantial heterogeneity in study designs, sample types (serum, CSF, feces, brain tissue), and metabolomic techniques (LC-MS, GC-MS, NMR) precluded direct cross-study comparisons and prevented a meta-analysis. The review was not registered in PROSPERO, and a formal risk of bias or quality assessment was not conducted, as the included studies were predominantly descriptive and methodologically diverse. Potential confounding factors such as diet, medications, and comorbidities were not consistently reported across studies and may have influenced metabolite levels. Given these constraints, a quantitative meta-analysis was not feasible; accordingly, we deliberately adopted a qualitative synthesis framework to integrate and interpret the metabolomic findings across studies. A further limitation is the lack of harmonized pre-analytical protocols across studies. Only a minority of reports explicitly described SOPs or provided details of sample handling (e.g., fasting state, time to processing, storage conditions, freeze–thaw cycles) [19,23,27,29,32,37,41]. The majority of studies reported metabolite data without specifying these critical pre-analytical steps, complicating direct cross-study comparability.
Pre-anal;itilac variability presents further limitation. Many included studies reported nominal p-values without systematic correction for multiple comparisons, which increases the risk of false positive results and limits the robustness of proposed biomarkers [16,18,25,27,31,32,33,38,39,40,41].
Technical validation reporting was similarly heterogeneous. Only a minority provided detailed descriptions of calibration protocols, internal standards, or QC procedures [22,23,33,34], while most offered limited or no information. This variability reduces reproducibility and hampers direct cross-study comparisons, highlighting the need for harmonized reporting standards in future metabolomics research in multiple sclerosis.
Sample size varied widely, ranging from very small exploratory cohorts to large multicenter analyses. Small sample sizes increase the risk of bias and reduce the reproducibility of findings, limiting the strength of cross-study comparisons.
Most included studies did not systematically report or exclude comorbidities, with only a few providing clear criteria [23,32,40,41]. This lack of standardization limits comparability and generalizability of the findings.
Another limitation is the absence of a formal quality assessment framework. Tools such as ROBINS-I or QUADAS-2 are not readily applicable to metabolomics, while adherence to the Metabolomics Standards Initiative (MSI) reporting guidelines was partial and inconsistent across studies. Some studies provided extensive QC and metabolite identification criteria [18,22,23,26,35,43,45], while the majority reported metabolite data without systematic reference to QC measures or MSI reporting levels. As a result, we did not implement a structured scoring system, but emphasize that future systematic reviews should incorporate tailored quality appraisal approaches to ensure reproducibility and minimize bias.
Finally the reproducibility of machine-learning-based biomarker discovery. Only two studies applied such approaches [26,27]. Reporting was heterogeneous: while one study employed rigorous validation (external tenfold cross-validation with repetition, permutation testing, and reporting of accuracy, sensitivity, and specificity) [27], most other attempts provided less detail on hyperparameter settings and external replication [26]. Table 3 summarizes these methodological aspects. These examples illustrate both the feasibility and the current variability of machine-learning applications in MS metabolomics, underscoring the need for standardized workflows, including transparent hyperparameter specification, validation schemes, and replication in independent cohorts.
| Author (Year) | MS Phenotype | Algorithm | Validation Strategy | Performance Metrics |
|---|---|---|---|---|
| Datta (2024) [26] | RRMS vs. Progressive | PLS-DA, SVM, Random Forest (biosigner feature selection/classification) | Train–test split; classifiers evaluated on independent test set; feature tiers assigned by biosigner. | Prediction accuracy reported; AUROC NR. |
| Yeo (2020) [27] | RRMS vs. SPMS | OPLS-DA | External 10-fold cross-validation with repetition and permutation testing; ensemble of 1000 models; independent test sets | Accuracy, sensitivity, specificity reported |
5. Conclusions
Metabolomics offers a comprehensive lens through which to understand the biochemical complexity of MS. The metabolic domains most consistently affected—kynurenine catabolism, mitochondrial energetics, lipid signaling, amino acid turnover and gut-derived metabolite processing—align closely with known pathological features of MS, including immune activation, demyelination, and neurodegeneration.
The reproducibility of findings across cohorts and analytical platforms remains an obstacle to clinical translation. Further challenges include the standardization of protocols for sample collection, storage, and data analysis, as well as controlling for confounding variables such as diet, medications, and comorbidities.
To fully harness the diagnostic and therapeutic potential of metabolomics in MS, future efforts should focus on
- Longitudinal, phenotype-stratified metabolomic studies that capture dynamic metabolic trajectories across disease stages, including conversion from CIS to MS and progression from RRMS to SPMS/PPMS;
- Validation of candidate biomarkers (e.g., myo-inositol, lysine, acetate) in multicenter cohorts using harmonized protocols;
- Integration of multi-omics platforms, including transcriptomics, microbiomics, and neuroimaging, to build mechanistic models of MS pathophysiology;
- While metabolomics-based machine-learning classifiers demonstrate promising discriminatory performance (typically 70–80% accuracy), these approaches are not yet sufficiently robust for clinical decision-making. Their current role should be regarded as exploratory, requiring validation in larger, multicenter cohorts before they can be translated into practice;
- Exploration of therapeutic modulation, assessing how metabolic pathways respond to DMTs (e.g., fingolimod, ocrelizumab, IFNβ) or interventions like resistance training and probiotic supplementation;
- To improve reproducibility and comparability of metabolomic findings, future guidelines should mandate transparent reporting of pre-analytical procedures and support the development of SOPs and pre-analytical validation studies;
- Future studies should adopt harmonized standard operating procedures and transparent reporting of calibration, QC design, and internal standards, ideally aligned with the Metabolomics Standards Initiative, to improve reproducibility and facilitate clinical translation in multiple sclerosis;
- Future studies should harmonize inclusion and exclusion criteria and systematically document comorbidities, medications, and lifestyle factors to minimize confounding and enhance reproducibility;
- With continued advances in analytical technologies and systems-level approaches, metabolomics shows considerable promise for advancing personalized medicine in MS, particularly through biomarker discovery that may eventually support diagnosis, monitoring of disease progression, and development of targeted therapeutic strategies. However, clinical validation in large, standardized cohorts is still required before translation into practice.
Abbreviations
↑—increased, ↓—decreased, 2-AG—2-arachidonoylglycerol, 3HAA—3-hydroxyanthranilic acid, 3HK—3-hydroxykynurenine, 5-HETE—5-hydroxyeicosatetraenoic acid, 8-HETE—8-hydroxyeicosatetraenoic acid, 9-HODE—9-hydroxyoctadecadienoic acid, 9,10-EpOME—9,10-epoxyoctadecenoic acid, 9-HOTrE—9-hydroxyoctadecatrienoic acid, 11,12-DiHET—11,12-dihydroxyeicosatrienoic acid, 11,12-DiHETE—11,12-dihydroxyeicosatetraenoic acid, 13-HODE—13-hydroxyoctadecadienoic acid, 14,15-DiHETE—14,15-dihydroxyeicosatetraenoic acid, 15-HETE—15-hydroxyeicosatetraenoic acid, 19,20-DiHDPA—19,20-dihydroxydocosapentaenoic acid, 20-HETE—20-hydroxyeicosatetraenoic acid, AA—anthranilic acid, AcAc—acetoacetate, ADA—anti-drug antibodies, ADMA—asymmetric dimethylarginine, AEA—anandamide, ALA—alpha-linolenic acid, ARA—arachidonic acid, ATP—adenosine triphosphate, BHB—beta-hydroxybutyrate, CIS—clinically isolated syndrome, CSF—cerebrospinal fluid, DGLA—dihomo-γ-linolenic acid, DHEAS—dehydroepiandrosterone sulfate, DiHexCer—dihexosylceramide, DPA—docosapentaenoic acid, DPAn-3—n-3 docosapentaenoic acid, DMT—disease-modifying treatment, EDSS—Expanded Disability Status Scale, eCBs—endocannabinoids, ESI—electrospray ionization, FA—fractional anisotropy, FDR—false discovery rate, FIA—flow injection analysis, GC-MS—gas chromatography–mass spectrometry, GFAP—glial fibrillary acidic protein, GLA—gamma-linolenic acid, HC—healthy controls, HETE—hydroxyeicosatetraenoic acid, HPLC—high-performance liquid chromatography, IFNβ—interferon-β, IL—interleukin, IPA—indolepropionic acid, KP—kynurenine pathway, KYN—kynurenine, KYNA—kynurenic acid, LC-MS—liquid chromatography–mass spectrometry, LDL—low-density lipoprotein, LPA—lysophosphatidic acid, MCP-1—monocyte chemoattractant protein-1, MRI—magnetic resonance imaging, MS—multiple sclerosis, MSSS—Multiple Sclerosis Severity Score, NAD+—nicotinamide adenine dinucleotide, NMR—nuclear magnetic resonance, NR—not reported, OCB—oligoclonal bands, OCT—optical coherence tomography, OEA—oleoylethanolamide, OPLS-DA—orthogonal partial least squares–discriminant analysis, PBMCs—peripheral blood mononuclear cells, PC—phosphatidylcholine, PC-O—ether-linked phosphatidylcholine, PE—phosphatidylethanolamine, PET—positron emission tomography, PFOA—perfluorooctanoate, PFOS—perfluorooctanesulfonate, PGE2—prostaglandin E2, PLS-DA—partial least squares–discriminant analysis, PPMS—primary progressive multiple sclerosis, PRISMA—Preferred Reporting Items for Systematic Reviews and Meta-Analyses, PRT—progressive resistance training, PUFA—polyunsaturated fatty acid, QUIN—quinolinic acid, RRMS—relapsing–remitting multiple sclerosis, S1P—sphingosine-1-phosphate, SCFA—short-chain fatty acid, SDMA—symmetric dimethylarginine, SEA—stearoylethanolamide, sGFAP—serum glial fibrillary acidic protein, sNfL—serum neurofilament light chain, SM—sphingomyelin, SOPs—standard operating procedures, SPMS—secondary progressive multiple sclerosis, SVM—support vector machine, TNF-α—tumor necrosis factor alpha, TRP—tryptophan, TSPO-PET—translocator protein positron emission tomography, UFAs—unsaturated fatty acids, UPLC-MS—ultra-performance liquid chromatography–mass spectrometry, VLDL—very-low-density lipoprotein, WGCNA—weighted gene co-expression network analysis.
Institutional Review Board Statement
Not applicable as this project did not involve humans or animals.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Funding Statement
This research received no external funding.
Footnotes
Footnote Group
References
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Associated Data
Data Availability Statement
Not applicable.