Abstract
Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease of the nervous system and a main cause of neurological disability in young adults. Most disease-modifying therapies are administrated as long-term maintenance therapies and may, thereby, increase the risk of infections and other immune-mediated side effects. In the last years, several cerebrospinal fluid and soluble blood biomarkers have been suggested as potential key tools for diagnosis, prognosis, and treatment monitoring of MS. Recently, the specific ability of brain-derived blood extracellular vesicles (EVs) that cross the blood-brain barrier into the bloodstream, reflecting the current immune status of the central nervous system, has kindled interest as potential biomarkers. In this review, we discuss the current trends of clinical brain-derived blood biomarkers, with a special focus on the emerging role of brain-derived blood EVs in MS.
1 Introduction
Almost 3 million people worldwide are affected by multiple sclerosis (MS), an immune-mediated inflammatory and degenerative disease of the central nervous system (CNS) (). From a clinical perspective, MS is highly heterogeneous with most patients (85%–90%) experiencing an initial relapsing-remitting course (RRMS) marked by episodic inflammation and, if not treated effectively, followed by a secondary progressive (SPMS) phase, associated with gradual increasing disability (). Epidemiological data suggest that Epstein–Barr virus is a prerequisite for developing MS, but the underlying pathogenic mechanisms are still unclear (, ).
The MS diagnosis relies on the combination of clinical and paraclinical findings, with no single definitive diagnostic test available (). Currently, it is essential to determine inflammatory immune-mediated damage affecting at least two distinct regions (dissemination in space) of the CNS at varied time points (dissemination in time) to establish an MS diagnosis. Since the incorporation in the diagnostic criteria (1983), magnetic resonance imaging (MRI) of the brain and spinal cord holds a pivotal role in the diagnostic process. In addition, cerebrospinal fluid (CSF) analysis detecting intrathecal immunoglobulin G (IgG) synthesis was highlighted in the update of the diagnostic criteria of MS in 2017 ().
Recent advancements have shed light on detecting brain-derived proteins at remarkably low concentrations in blood, paving the way for the exploration of early blood-based biomarkers in MS (). Specific markers of immunopathological processes including neuroaxonal damage [neurofilament light chain (NfL)] and astrocyte activation [glial fibrillary acidic protein (GFAP)] are already rapidly emerging (, ). Extracellular vesicles (EVs) are defined as membrane-bound particles, released from virtually all cell types, with a sophisticated sorting mechanism of their cargo inclusive of lipids, proteins, and nucleic acids, in addition to carrying specific membrane proteins, mainly reflecting their donor cell. This peculiarity, plus their ability to cross the blood-brain barrier (BBB) into the blood stream, increased stability, and involvement in the regulation of both the immune system and CNS homeostasis, features brain-derived blood EVs, as improved biomarkers in CNS diseases, including MS (–). This review aims to summarize the current CSF and blood biomarkers in MS, discussing the unmet needs and future perspectives.
2 MS pathogenesis and fluid biomarkers
In the early stages of MS, the recurrent invasion of T and B cells in the brain and spinal cord drives a cascade of pathophysiological processes within the CNS (). Several fluid biomarkers have emerged as effective indicators of this complex interaction, which contributes to the diverse clinical manifestations observed in the disease (). Early episodes of acute focal inflammation, demyelination, and axonal damage, driven by infiltrating immune cells (macrophages, CD8+ T cells, CD4+ T cells, B cells, and plasma cells), could be typically detected through conventional MRI, showing new lesions in T2-weighted and/or T1-weighted gadolinium enhancing lesions (, ). Infiltrating immune cells are attracted to the CNS by several chemotactic factors such as chemokine (C-X-C motif) ligand 13 (CXCL13) for B cells (Figure 1) ().
Figure 1
Invading T and B cells closely interact within the CNS (, ). In contrast to T cells, the immune pathways involving B cell activation have, so far, served as the most robust fluid biomarkers for MS. Mature plasma cells secrete IgG and IgM antibodies intrathecally, also leading to release of free light chains (due to a mismatch between immunoglobulin light- and heavy-chain synthesis) (, ). This inflammatory process results in axonal damage and release of neuronal markers like NfL (). Over time, there is worsening of disability and accumulation of neurological deficits in the absence of concurrent relapses defined as “progression independent of relapse activity” (PIRA) (). Underlying mechanism driving PIRA is increasingly understood as a pathophysiological continuum of the early “relapsing” phase driven by a chronic “smouldering” inflammatory process compartmentalized within the CNS, characterized by innate immune cells and astrocytes (). Recent studies on positron emission tomography (PET) employing radioligands for innate immunity activation assessment have revealed an interestingly high prevalence of smouldering component in MS lesions (). Chronically active MS lesions are slowly expanding over time or as paramagnetic rim lesions, expressing a dense network of activated iron-laden microglia/macrophages (). Activated microglia and astrocytes release various mediators into the CSF, such as soluble triggering receptor expressed on myeloid cells 2 (sTREM2), chitinase 1 (CHIT1), chitinase-3–like protein 1 (CHI3L1), and GFAP, impacting axon, synaptic integrity, and function (–).
The critical role of the complement system in MS is underlined with the complement and Ig deposition across all areas of demyelination regardless of the plaque subtype, including complement-mediated myelin phagocytosis implying its importance once the disease is established. In progressive MS and long-standing disease patients, white matter plaques were consistently positive for complement proteins (C3, factor B, and C1q), regulators (factor H, C1inh, and clusterin) and activation products [C3b, iC3b, C4d, and terminal complement complex (TCC)] providing evidence that, once established, progression of inflammation in MS may not rely on infiltrating cells but rather on innate immune mechanisms including complement activation (, ).
EVs are pivotal in the intricate communication of neurons and glial cells of the CNS system holding neuroprotective and homeostatic effects but may have detrimental effects under pathological conditions (, ). EVs derived from T cells containing chemokine CCL5 and arachidonic acid can increase the expression of intercellular adhesion molecule 1 (ICAM-1) on endothelial cells and of Mac-1 on monocytes, contributing to the dysfunction of the BBB, leading to immune infiltration, a characteristic of MS pathogenesis (–). Dendritic cell (DCs) derived EVs carry cell surface molecules like major histocompatibility complex (MHC), ICAM-1, and other costimulatory molecules, which could aid in T-cell activation (38). EVs from activated microglia express pro-inflammatory mediators (Tumor Necrosis Factor-alpha (TNF-α) and Interleukin-1 (IL-1)) exhibiting a distinct proteomic profile enforcing inflammatory stimuli throughout the CNS (39). Recent studies show the role of astrocyte-derived EVs in the regulation of T-cell secretion and biomarker utility of myelin basic protein (MBP) and myelin oligodendrocyte glycoprotein (MOG) content in oligodendrocytes-derived EVs (40). Most immune cell–derived EVs seem to be significantly higher in treatment naïve relapsing MS patients with low disability, and their functions might depend on the physiological environment, despite limited changes in circulating immune cells ().
3 MS fluid biomarkers—current trends and beyond
The diagnostic criterion for MS underscores the importance of both MRI and biofluid biomarkers emphasizing the pivotal role of accurate diagnosis, prognosis, and treatment response in the management of the disease (). In addition to advancements in MRI techniques (7-T MRI, PET, magnetization transfer imaging, diffusion tensor imaging, and myelin water imaging), integrating biofluid biomarkers would be beneficial because of their ability to directly reflect the pathophysiological processes involved in the MS disease course (41). Cumulative evidence shows that the blood-based biomarker sNfL can predict relapses in relapsing MS patients, whereas CSF IgM oligoclonal bands, CHI3L1, and GFAP seem to be associated with a more progressive phenotype. Different aspects of microglial involvement (CHIT1 and sTREM2), astroglia pathology (CHI3L1 and GFAP), B-cell–related pathology (CXCL13), and neuroaxonal damage (sNfL) have been evaluated in several studies aiding in classifying MS disease activity (Table 1) (–). Brain-derived blood EVs (L1CAM, MOG, and GLAST) serve as potential windows into the CNS reflecting the underlying MS-related pathophysiology (Table 1) ().
Table 1
| Marker | Source | Measurement methods | Clinical significance and utility | Prognostic potential | Specificity to MS | Limitations | References |
|---|---|---|---|---|---|---|---|
| Validated and completely introduced into clinical practice | |||||||
| IgG OCB | CSF | Isoelectric focusing with specific IgG staining | Indicates intrathecal IgG synthesis; evidence of CNS immune activity; high sensitivity for MS diagnosis and validated biomarker in clinical utility | Predicts CIS to MS conversion; linked to disability progression | Limitation: present in other inflammatory/infectious neurological conditions | Time-consuming, qualitative method | (43–47) |
| IgG index | CSF and serum | Calculated as (IgG in CSF/IgG in serum)/(albumin in CSF/albumin in serum) | Measures intrathecal IgG synthesis; assesses blood-CSF barrier function | Limited; weak correlation with MS severity but linked to future disability worsening | Limitation: affected by other CNS conditions | Low sensitivity for MS diagnosis | (43–46) |
| κ-FLC | CSF and serum | Nephelometry, turbidimetry, κ-FLC index | Less expensive, faster quantitative alternative to OCB; detects intrathecal inflammation | Limited; predicts early relapses and disease activity in MS and enables risk stratification of disease activity in OCB-positive MS patients but still not widely validated in clinical practice | Moderate; approximately 90% diagnostic sensitivity and specificity for distinguishing MS from other neurological disorders; not exclusive to MS | Elevated in other conditions with intrathecal Ig synthesis; includes IgA and IgM (not limited to IgG) | (48–50) |
| Validated and not completely introduced into clinical practice | |||||||
| NfL | CSF and blood (serum, plasma) | Immunoassays (e.g., ELISA); ultrasensitive immunoassays (e.g., Simoa); automated assays (e.g., Lumipulse®) | Reflect severity of axonal damage; elevated in RRMS and progressive MS; normalizes post-treatment | Predicts CIS to MS conversion, relapses, EDSS worsening, and brain atrophy; elevated in serum before the onset of clinical symptoms; strong marker for tissue destruction and treatment efficacy | Moderate; specific for neuronal damage but not for a disease; elevated in other neurodegenerative disorders (e.g., Alzheimer’s, traumatic brain injury) | Serum levels influenced by age and weight (can be corrected by z-score normalization); threshold values for treatment success and disease reactivation need standardization | (51–56) |
| Partially validated and not introduced into clinical practice | |||||||
| IgM OCB | CSF | IgM index or non-linear formulas; immunoblotting; isoelectric focusing | Detects intrathecal IgM synthesis; linked to highly inflammatory RRMS and a subset of PPMS patients | Predicts shorter time to relapse and higher relapse rates; associated with disability progression and more aggressive PPMS | Moderate; found in approximately 40% of MS cases and also in other CNS conditions | Technical challenges in detection due to the high molecular weight of IgM; limited data compared to IgG OCB | (57–61) |
| CXCL13 | CSF, serum | Immunoassays (e.g., ELISA), CXCL13 index | Elevated in early active and progressive MS; correlates with gadolinium-enhancing lesions, B-cell counts, IgG levels, κ-FLC index, relapse rate, and disease activity | Predicts CIS to MS conversion; monitors response to corticosteroids and long-term DMTs | High in CSF; independent of BBB dysfunction; undetectable in non-inflammatory controls; limitation in serum; elevated in other conditions like systemic autoimmune, inflammatory, infectious, and neoplastic diseases | Limited utility in serum—diagnostically irrelevant due to lack of CSF correlation and low specificity | (, 62–65) |
| CHI3L1 | CSF, serum | Immunoassays (e.g., ELISA) | Elevated in progressive MS; decreased during acute relapses compared to remission; unrelated to gadolinium lesions | Predicts CIS to MS conversion; correlates with disease progression in PPMS | Limitation: serum levels not significantly different between MS and healthy controls | Poor CSF-serum correlation; lacks specificity due to broad expression in other tissues beyond the CNS | (62, 66–69) |
| CHIT1 | CSF, brain tissue (post-mortem) | Immunoassays (e.g., ELISA); RNA analysis in white matter tissue | Specific to microglial activation; correlates with neuronal injury (NfL) and disease activity at follow-up (up to 6 years post-diagnosis); upregulated in chronic active lesions of MS | Predicts long-term disease activity and progression; CHIT1 RNA expression differentiates chronic active lesions from chronic inactive lesions | High specificity for chronic active lesions in MS | Limited longitudinal data; needs further validation | (70–73) |
| sTREM2 | CSF and blood (serum, plasma) | Immunoassays (e.g., ELISA) | Elevated in MS; linked to microglial activity; normalizes with natalizumab; partially reduced by mitoxantrone | Moderate correlation with EDSS and MS severity score; lack of strong correlation with other clinical measures | Limitation: elevated in other inflammatory neurological conditions | Insufficient data; weak serum-CSF correlation; not reliable as a blood biomarker | (74–78) |
| GFAP | CSF and blood (serum) | Immunoassays (e.g., ELISA) | Indicates astrocyte activity; reflects neuroinflammation; elevated in RRMS relapses, progressive MS; correlates with brain atrophy | Predicts disability progression in both active and non-active MS; elevated levels post-treatment indicate progression | Moderate; elevated in MS and NMOSD (predicts activity in NMOSD remission) | Labile in CSF; highly sensitive to freeze-thaw cycles; serum levels influenced by age; requires standardization for comparisons across MS subtypes | (79–83) |
| Not validated and not introduced into clinical practice | |||||||
| CD62p+ EVs | Plasma | Flow cytometry | Elevated in MS vs. HC | Reflects platelet activation and monocyte interaction with damaged endothelium | Low; common in other thrombosis-related or inflammatory conditions | Overlap with other conditions | (84) |
| CD61+ EVs, CD14+ EVs, CD45+ EVs | Plasma | Flow cytometry | Elevated CD61+ EVs in untreated MS vs. HC Elevated CD61+, CD14+, and CD45+ EVs in RRMS vs. HC and SPMS | Indicates platelet activation, monocyte, and leukocyte interaction with damaged endothelium | Low; signify broader immune activation rather than MS-specific inflammation | Limited specificity for MS pathology | (85) |
| MOG | Serum EVs | Western blotting, ELISA | Elevated MOG EV content in RRMS patients in relapse and SPMS vs. HC | Monitors disease activity | Moderate; marker implicated in other CNS autoimmune disorders | Cross-reactivity in assays | (86, 87) |
| TLR3 and TLR4 | Serum EVs | ELISA | Decreased TLR3 and elevated TLR4 in RRMS EVs vs. HC | Suggests altered innate immune signaling | Low; TLR expression changes occur in other autoimmune and inflammatory conditions | Requires further validation in larger cohorts | (88, 89) |
| Synaptopodin and synaptophysin (NEVs), complement components (AEVs) | Plasma L1CAM+ NEVs, plasma GLAST+ AEVs | ELISA, Luminex® | Decreased synaptopodin and synaptophysin in NEVs in MS vs. HC Elevated C1q, C3, C3b/iC3b, C5, C5a, factor H in AEVs in MS vs. HC Strong inverse correlations between both types of biomarkers in MS patients | Indicates synaptic loss and complement activation | Moderate; synaptic and complement markers are also observed in neurodegenerative diseases | Complexity in distinguishing source biomarkers | (90–92) |
| Absence of CD3 and CD41; presence of CD31, CD105, and CD144 | Plasma EVs | Flow cytometry | Elevated concentration of CNS endothelial-derived EV in active vs. stable MS and HC | Reflects BBB permeability and active disease | Moderate; endothelial-derived markers are seen in broader CNS pathologies, reducing specificity | Limited application outside severe cases | (93) |
| MBP | Serum EVs | ELISA | Elevated in CIS, RRMS, and PPMS vs. HC Elevated in PPMS vs. RRMS and CIS | Correlates with EDSS and MSSS Predicts disease subtype | High; marker strongly linked to demyelination, which is a hallmark of MS | Might be not cost-effective | (40, 86, 94) |
| EVs concentration | Plasma EVs | NTA | Increased after 5 h of treatment with fingolimod vs. pre-treatment | Monitors treatment response | Low; observed in other conditions involving immune activation | Requires specific equipment | (95) |
| IB4+ EVs concentration | CSF | Flow cytometry | Increased in RRMS and CIS vs. HC | Reflects microglia/macrophage activation | Moderate; microglial activation is a common feature in other neuroinflammatory conditions | Limited EVs concentration in CSF | (96) |
| EVs concentration, CCR3+/CCR5+ EVs, CD4+/CCR3+ EVs, CD4+/CCR5+ | CSF | Flow cytometry | Increased EVs in clinical relapse vs. remission Increased CCR3+/CCR5+ EVs, CD4+/CCR3+ EVs, and CD4+/CCR5+ EVs in patients with gadolinium-enhanced MR lesions | Identifies different MS phases | High; associated with active MS lesion pathology | Requires specialized equipment | (97) |
Summary of fluid biomarkers in multiple sclerosis.
MS, multiple sclerosis; HC, healthy controls; EV, extracellular vesicles; RRMS, relapsing-remitting MS; SPMS, secondary progressive MS; PPMS, primary progressive MS; CIS, clinically isolated syndrome; BBB, blood-brain barrier; EDSS, expanded disability status scale; MSSS, MS severity score; CSF, cerebrospinal fluid; CNS, central nervous system; OCB, oligoclonal bands; κ-FLC, kappa-free light chains; NGS, next-generation sequencing; NMOSD, neuromyelitis optica spectrum disorder; TLR, Toll-like receptor; NTA, nanoparticle tracking analysis; NEVs, neuron-derived extracellular vesicles; AEVs, astrocyte-derived extracellular vesicles; MOG, myelin oligodendrocyte glycoprotein; MBP, myelin basic protein; IFN-β, interferon-beta.
Certain limitations of the emerging fluid biomarkers intrude their clinical transition. For example, NfL is a promising biomarker but with limited diagnostic use due to its unspecific increase in the blood connected to several neurological conditions (42). EVs hold potential as biomarkers; however, existing knowledge gaps in terms of EVs biology, biodistribution, and assay standardization are yet to be fully elucidated (). Although MS fluid biomarkers hold a promising frontier, addressing standardization, data validation, and accessibility are key in resolving ongoing challenges. Composite scoring with integrated clinical and MRI metrics [e.g., the MAGNIMS score or no evidence of disease activity 3 (NEDA-3) and NEDA-4] and multimodal biomarker profiling (CSF and blood-based biomarkers with neuroimaging) may be a way forward in MS management (41). Furthermore, artificial intelligence (automated lesion detection and improved diagnostic accuracy) holds transformative potential in enhancing clinical decision-making.
In conclusion, despite the limitations, the recent advances within the field hold a promising frontier, giving a paradigm shift from the conventional CSF (oligoclonal banding) analysis to a new era of brain-derived blood biomarkers (NfL, GFAP, and EVs), enabling improved longitudinal disease monitoring and personalized treatment.
Statements
Author contributions
SA: Conceptualization, Formal Analysis, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing. KM: Formal Analysis, Investigation, Writing – original draft, Writing – review & editing. RB: Investigation, Writing – review & editing. SM: Investigation, Writing – review & editing. CK: Investigation, Writing – review & editing. OT: Investigation, Project administration, Writing – review & editing. KMM: Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This research was funded by and the Research Council of Norway through its Centers of Excellence funding scheme (Grant Number 288164).
Acknowledgments
The figure was created with BioRender (BioRender.com).
Conflict of interest
K-MM has received speaker honoraria from Biogen, Novartis, or Sanofi and has participated in clinical trials organized by Biogen, Merck, Novartis, Roche, and Sanofi. OT has participated in advisory boards and received speaker honoraria from Biogen, Merck, Novartis, Teva, Roche, Sanofi, and Bristol Myers Squibb and has participated in clinical trials organized by Merck, Novartis, Roche, and Sanofi.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Abbreviations
MS, multiple sclerosis; CNS, central nervous system; BBB, blood-brain barrier; CXCL13, chemokine (C-X-C motif) ligand 13; APCs, antigen-presenting cells; κ-FLCs, kappa-free light chains; NfL, neurofilament light chain; CSF, cerebrospinal fluid; sTREM2, soluble triggering receptor expressed on myeloid cells 2; CHIT1, chitotriosidase 1; CHI3L1, chitinase-3–like protein 1; GFAP, glial fibrillary acidic protein; EVs, extracellular vesicles; miRNA, microRNA; lncRNA, long non-coding RNA; MOG, myelin oligodendrocyte glycoprotein; MBP, myelin basic protein.
References
1
WaltonCKingRRechtmanLKayeWLerayEMarrieRAet al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition. Mult Scler. (2020) 26:1816–21.
2
ThompsonAJBaranziniSEGeurtsJHemmerBCiccarelliO. Multiple sclerosis. Lancet. (2018) 391:1622–36.
3
Fernández-FournierMLópez-MolinaMTorres IglesiasGBotellaLChamorroBLaso-GarcíaFet al. Antibody content against epstein–barr virus in blood extracellular vesicles correlates with disease activity and brain volume in patients with relapsing–remitting multiple sclerosis. Int J Mol Sci. (2023) 24:14192.
4
MradMFSabaESNakibLKhourySJ. Exosomes from subjects with multiple sclerosis express EBV-derived proteins and activate monocyte-derived macrophages. Neurol Neuroimmunology Neuroinflammation. (2021) 8:e1004.
5
ThompsonAJBanwellBLBarkhofFCarrollWMCoetzeeTComiGet al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurology. (2018) 17:162–73.
6
TeunissenCEKimbleLBayoumySBolsewigKBurtscherFCoppensSet al. Methods to discover and validate biofluid-based biomarkers in neurodegenerative dementias. Mol Cell Proteomics. (2023) 22:100629.
7
MeierSWillemseEAJSchaedelinSOechteringJLorscheiderJMelie-GarciaLet al. Serum glial fibrillary acidic protein compared with neurofilament light chain as a biomarker for disease progression in multiple sclerosis. JAMA Neurology. (2023) 80:287–97.
8
Di FilippoMGaetaniLCentonzeDHegenHKuhleJTeunissenCEet al. Fluid biomarkers in multiple sclerosis: from current to future applications. Lancet Regional Health – Europe. (2024) 44.
9
GurunathanSKangMHJeyarajMQasimMKimJH. Review of the isolation, characterization, biological function, and multifarious therapeutic approaches of exosomes. Cells. (2019) 8.
10
HuoLDuXLiXLiuSXuY. The emerging role of neural cell-derived exosomes in intercellular communication in health and neurodegenerative diseases. Front Neurosci. (2021) 15.
11
MyckoMPBaranziniSE. microRNA and exosome profiling in multiple sclerosis. Mult Scler. (2020) 26:599–604.
12
HornungSDuttaSBitanG. CNS-derived blood exosomes as a promising source of biomarkers: opportunities and challenges. Front Mol Neurosci. (2020) 13.
13
FilippiMBar-OrAPiehlFPreziosaPSolariAVukusicSet al. Multiple sclerosis. Nat Rev Dis Primers. (2018) 4:43.
14
EngelhardtBComabellaMChanA. Multiple sclerosis: Immunopathological heterogeneity and its implications. Eur J Immunol. (2022) 52:869–81.
15
RoccaMAPreziosaPBarkhofFBrownleeWCalabreseMDe StefanoNet al. Current and future role of MRI in the diagnosis and prognosis of multiple sclerosis. Lancet Regional Health – Europe. (2024) 44.
16
DendrouCAFuggerLFrieseMA. Immunopathology of multiple sclerosis. Nat Rev Immunol. (2015) 15:545–58.
17
NovakovaLAxelssonMMalmeströmCZetterbergHBlennowKSvenningssonAet al. NFL and CXCL13 may reveal disease activity in clinically and radiologically stable MS. Multiple Sclerosis Related Disord. (2020) 46:102463.
18
HegenHWaldeJBerekKArrambideGGnanapavanSKaplanBet al. Cerebrospinal fluid kappa free light chains for the diagnosis of multiple sclerosis: A systematic review and meta-analysis. Multiple Sclerosis J. (2022) 29:169–81.
19
HegenHArrambideGGnanapavanSKaplanBKhalilMSaadehRet al. Cerebrospinal fluid kappa free light chains for the diagnosis of multiple sclerosis: A consensus statement. Multiple Sclerosis J. (2022) 29:182–95.
20
GaetaniLBlennowKCalabresiPDi FilippoMParnettiLZetterbergH. Neurofilament light chain as a biomarker in neurological disorders. J Neurology Neurosurg Psychiatry. (2019) 90:870–81.
21
KapposLWolinskyJSGiovannoniGArnoldDLWangQBernasconiCet al. Contribution of relapse-independent progression vs relapse-associated worsening to overall confirmed disability accumulation in typical relapsing multiple sclerosis in a pooled analysis of 2 randomized clinical trials. JAMA Neurology. (2020) 77:1132–40.
22
TurCCarbonell-MirabentPCobo-CalvoÁOtero-RomeroSArrambideGMidagliaLet al. Association of early progression independent of relapse activity with long-term disability after a first demyelinating event in multiple sclerosis. JAMA Neurology. (2023) 80:151–60.
23
HamzaouiMGarciaJBoffaGLazzarottoAAbsintaMRiciglianoVAGet al. Positron emission tomography with [F]-DPA-714 unveils a smoldering component in most multiple sclerosis lesions which drives disease progression. Ann Neurology. (2023) 94:366–83.
24
JäckleKZeisTSchaeren-WiemersNJunkerAvan der MeerFKramannNet al. Molecular signature of slowly expanding lesions in progressive multiple sclerosis. Brain. (2020) 143:2073–88.
25
HinsingerGGaléottiNNabholzNUrbachSRigauVDematteiCet al. Chitinase 3-like proteins as diagnostic and prognostic biomarkers of multiple sclerosis. Multiple Sclerosis J. (2015) 21:1251–61.
26
CantóETintoréMVillarLMCostaCNurtdinovRÁlvarez-CermeñoJCet al. Chitinase 3-like 1: prognostic biomarker in clinically isolated syndromes. Brain. (2015) 138:918–31.
27
SteinackerPVerdeFFangLFenebergEOecklPRoeberSet al. Chitotriosidase (CHIT1) is increased in microglia and macrophages in spinal cord of amyotrophic lateral sclerosis and cerebrospinal fluid levels correlate with disease severity and progression. J Neurology Neurosurg Psychiatry. (2018) 89:239–47.
28
FilipelloFGoldsburyCYouSFLoccaAKarchCMPiccioL. Soluble TREM2: Innocent bystander or active player in neurological diseases? Neurobiol Dis. (2022) 165:105630.
29
HögelHRissanenEBarroCMatilainenMNylundMKuhleJet al. Serum glial fibrillary acidic protein correlates with multiple sclerosis disease severity. Multiple Sclerosis J. (2018) 26:210–9.
30
AbdelhakAFoschiMAbu-RumeilehSYueJKD’AnnaLHussAet al. Blood GFAP as an emerging biomarker in brain and spinal cord disorders. Nat Rev Neurology. (2022) 18:158–72.
31
BreijECBrinkBPVeerhuisRVan den BergCVloetRYanRet al. Homogeneity of active demyelinating lesions in established multiple sclerosis. Ann Neurology: Off J Am Neurological Assoc Child Neurol Society. (2008) 63:16–25.
32
IngramGLovelessSHowellOWHakobyanSDanceyBHarrisCLet al. Complement activation in multiple sclerosis plaques: an immunohistochemical analysis. Acta neuropathologica Commun. (2014) 2:1–15.
33
PistonoCOseraCCucciaMBergamaschiR. Roles of extracellular vesicles in multiple sclerosis: from pathogenesis to potential tools as biomarkers and therapeutics. Sclerosis. (2023) 1:91–112.
34
SchnatzAMüllerCBrahmerAKrämer-AlbersEM. Extracellular Vesicles in neural cell interaction and CNS homeostasis. FASEB Bioadv. (2021) 3:577–92.
35
Sáenz-CuestaMOsorio-QuerejetaIOtaeguiD. Extracellular vesicles in multiple sclerosis: what are they telling us? Front Cell Neurosci. (2014) 8:100.
36
BarryOPKazanietzMGPraticoDFitzGeraldGA. Arachidonic acid in platelet microparticles up-regulates cyclooxygenase-2-dependent prostaglandin formation via a protein kinase C/mitogen-activated protein kinase-dependent pathway. J Biol Chem. (1999) 274:7545–56.
37
QuandtJDorovini-ZisK. The beta chemokines CCL4 and CCL5 enhance adhesion of specific CD4+ T cell subsets to human brain endothelial cells. J Neuropathology Exp Neurology. (2004) 63:350–62.
38
SeguraENiccoCLombardBVéronPRaposoGBatteuxFet al. ICAM-1 on exosomes from mature dendritic cells is critical for efficient naive T-cell priming. Blood. (2005) 106:216–23.
39
AiresIDRibeiro-RodriguesTBoiaRFerreira-RodriguesMGirãoHAmbrósioAFet al. Microglial extracellular vesicles as vehicles for neurodegeneration spreading. Biomolecules. (2021) 11:770.
40
AgliardiCGueriniFRZanzotteraMBolognesiEPiccioliniSCaputoDet al. Myelin basic protein in oligodendrocyte-derived extracellular vesicles as a diagnostic and prognostic biomarker in multiple sclerosis: a pilot study. Int J Mol Sci. (2023) 24:894.
41
AnderhaltenLWohlrabFPaulF. Emerging MRI and biofluid biomarkers in the diagnosis and prognosis of multiple sclerosis. Lancet Regional Health – Europe. (2024) 44.
42
eBioMedicine. Blood biomarkers for multiple sclerosis: neurofilament light chain and beyond. eBioMedicine. (2024) 104.
43
LinkHHuangY-M. Oligoclonal bands in multiple sclerosis cerebrospinal fluid: An update on methodology and clinical usefulness. J Neuroimmunology. (2006) 180:17–28.
44
McLeanBNLuxtonRWThompsonEJ. A study of immunoglobulin G in the cerebrospinal fluid of 1007 patients with suspected neurological disease using isoelectric focusing and the Log IgG-Index. A comparison and diagnostic applications. Brain. (1990) 113:1269–89.
45
LundingJMidgardRVedelerCA. Oligoclonal bands in cerebrospinal fluid:a comparative study of isoelectric focusing, agarose gel electrophoresis and IgG index. Acta Neurologica Scandinavica. (2000) 102:322–5.
46
ArrambideGEspejoCCarbonell-MirabentPDieli-CrimiRRodríguez-BarrancoMCastilloMet al. The kappa free light chain index and oligoclonal bands have a similar role in the McDonald criteria. Brain. (2022) 145:3931–42.
47
Rojas JuanIPatruccoLCristianoE. Oligoclonal bands and MRI in clinically isolated syndromes: predicting conversion time to multiple sclerosis. J Neurol. (2010) 257:1188–91.
48
BerekKBstehGAuerMDi PauliFGramsAMilosavljevicDet al. Kappa-free light chains in CSF predict early multiple sclerosis disease activity. Neurol Neuroimmunology Neuroinflammation. (2021) 8:e1005.
49
DekeyserCDe KeselPCambronMVanopdenboschLVan HijfteLVercammenMet al. Inter-assay diagnostic accuracy of cerebrospinal fluid kappa free light chains for the diagnosis of multiple sclerosis. Front Immunol. (2024).
50
DuellFEvertssonBAl NimerFSandinÅOlssonDOlssonTet al. Diagnostic accuracy of intrathecal kappa free light chains compared with OCBs in MS. Neurol Neuroimmunology Neuroinflammation. (2020) 7:e775.
51
NingLWangB. Neurofilament light chain in blood as a diagnostic and predictive biomarker for multiple sclerosis: A systematic review and meta-analysis. PloS One. (2022) 17:e0274565.
52
FreedmanMSGnanapavanSBoothRACalabresiPAKhalilMKuhleJet al. Guidance for use of neurofilament light chain as a cerebrospinal fluid and blood biomarker in multiple sclerosis management. EBioMedicine. (2024) 101:104970.
53
BäckströmDLinderJJakobson MoSRiklundKZetterbergHBlennowKet al. NfL as a biomarker for neurodegeneration and survival in Parkinson disease. Neurology. (2020) 95:e827–e38.
54
SahraiHNorouziAHamzehzadehSMajdiAKahfi-GhanehRSadigh-EteghadS. SIMOA-based analysis of plasma NFL levels in MCI and AD patients: a systematic review and meta-analysis. BMC Neurology. (2023) 23:331.
55
UrbanoTMaramottiRTondelliMGallinganiCCarboneCIacovinoNet al. Comparison of serum and cerebrospinal fluid neurofilament light chain concentrations measured by ella™ and lumipulse™ in patients with cognitive impairment. Diagnostics. (2024) 14:2408.
56
VecchioDPuricelliCMalucchiSVirgilioEMartireSPergaSet al. Serum and cerebrospinal fluid neurofilament light chains measured by SIMOA™, Ella™, and Lumipulse™ in multiple sclerosis naïve patients. Mult Scler Relat Disord. (2024) 82:105412.
57
MagliozziRMazziottiVMontibellerLPisaniAIMarastoniDTamantiAet al. Cerebrospinal fluid igM levels in association with inflammatory pathways in multiple sclerosis patients. Front Cell Neurosci. (2020) 14.
58
MandrioliJSolaPBedinRGambiniMMerelliE. A multifactorial prognostic index in multiple sclerosis. J Neurology. (2008) 255:1023–31.
59
VillarLMCasanovaBOuamaraNComabellaMJaliliFLeppertDet al. Immunoglobulin M oligoclonal bands: biomarker of targetable inflammation in primary progressive multiple sclerosis. Ann Neurol. (2014) 76:231–40.
60
VillarLMSádabaMCRoldánEMasjuanJGonzález-PorquéPVillarrubiaNet al. Intrathecal synthesis of oligoclonal IgM against myelin lipids predicts an aggressive disease course in MS. J Clin Invest. (2005) 115:187–94.
61
VillarLMGonzález-PorquéPMasjuánJAlvarez-CermeñoJCBootelloAKeirG. A sensitive and reproducible method for the detection of oligoclonal IgM bands. J Immunol Methods. (2001) 258:151–5.
62
PikeSCGilliFPachnerAR. The CXCL13 index as a predictive biomarker for activity in clinically isolated syndrome. Int J Mol Sci. (2023) 24.
63
LucchiniMDe ArcangelisVPiroGNocitiVBiancoADe FinoCet al. CSF CXCL13 and chitinase 3-like-1 levels predict disease course in relapsing multiple sclerosis. Mol Neurobiology. (2023) 60:36–50.
64
KhademiMKockumIAnderssonMLIacobaeusEBrundinLSellebjergFet al. Cerebrospinal fluid CXCL13 in multiple sclerosis: a suggestive prognostic marker for the disease course. Mult Scler. (2011) 17:335–43.
65
DiSanoKDGilliFPachnerAR. Intrathecally produced CXCL13: A predictive biomarker in multiple sclerosis. Mult Scler J Exp Transl Clin. (2020) 6:2055217320981396.
66
MohammedMSAl-Rubae'iSHNRheimaAMAl-KazazzFF. A novel sandwich ELISA method for quantifying CHI3L1 in blood serum and cerebrospinal fluid multiple sclerosis patients using sustainable photo-irradiated zero-valence gold nanoparticles. Results Chem. (2024) 11:101856.
67
Pérez-MirallesFPrefasiDGarcía-MerinoAGascón-GiménezFMedranoNCastillo-VillalbaJet al. CSF chitinase 3-like-1 association with disability of primary progressive MS. Neurol Neuroimmunol Neuroinflamm. (2020) 7.
68
FloroSCarandiniTPietroboniAMDe RizMAScarpiniEGalimbertiD. Role of chitinase 3–like 1 as a biomarker in multiple sclerosis. Neurol Neuroimmunology Neuroinflammation. (2022) 9:e1164.
69
CantóEReverterFMorcillo-SuárezCMatesanzFFernándezOIzquierdoGet al. Chitinase 3-like 1 plasma levels are increased in patients with progressive forms of multiple sclerosis. Mult Scler. (2012) 18:983–90.
70
OldoniESmetsIMallantsKVandeberghMVan HorebeekLPoesenKet al. CHIT1 at diagnosis reflects long-term multiple sclerosis disease activity. Ann Neurol. (2020) 87:633–45.
71
BeliënJSwinnenSD’hondtRVerdú de JuanLDedonckerNMatthysPet al. CHIT1 at diagnosis predicts faster disability progression and reflects early microglial activation in multiple sclerosis. Nat Commun. (2024) 15:5013.
72
RabinABelloEKumarSZekiDAAfshariKDeshpandeMet al. Targeted proteomics of cerebrospinal fluid in treatment naïve multiple sclerosis patients identifies immune biomarkers of clinical phenotypes. Sci Rep. (2024) 14:21793.
73
ComabellaMFernándezMMartinRRivera-VallvéSBorrásEChivaCet al. Cerebrospinal fluid chitinase 3-like 1 levels are associated with conversion to multiple sclerosis. Brain. (2010) 133:1082–93.
74
IoannidesZACsurhesPASwayneAFoubertPAftabBTPenderMP. Correlations between macrophage/microglial activation marker sTREM-2 and measures of T-cell activation, neuroaxonal damage and disease severity in multiple sclerosis. Mult Scler J Exp Transl Clin. (2021) 7:20552173211019772.
75
PiccioLBuonsantiCCellaMTassiISchmidtREFenoglioCet al. Identification of soluble TREM-2 in the cerebrospinal fluid and its association with multiple sclerosis and CNS inflammation. Brain. (2008) 131:3081–91.
76
ÖhrfeltAAxelssonMMalmeströmCNovakovaLHeslegraveABlennowKet al. Soluble TREM-2 in cerebrospinal fluid from patients with multiple sclerosis treated with natalizumab or mitoxantrone. Mult Scler. (2016) 22:1587–95.
77
AshtonNJSuárez-CalvetMHeslegraveAHyeARazquinCPastorPet al. Plasma levels of soluble TREM2 and neurofilament light chain in TREM2 rare variant carriers. Alzheimer’s Res Ther. (2019) 11:94.
78
CignarellaFFilipelloFBollmanBCantoniCLoccaAMikesellRet al. TREM2 activation on microglia promotes myelin debris clearance and remyelination in a model of multiple sclerosis. Acta Neuropathol. (2020) 140:513–34.
79
SunMLiuNXieQLiXSunJWangHet al. A candidate biomarker of glial fibrillary acidic protein in CSF and blood in differentiating multiple sclerosis and its subtypes: A systematic review and meta-analysis. Mult Scler Relat Disord. (2021) 51:102870.
80
BarroCHealyBCLiuYSaxenaSPaulAPolgar-TurcsanyiMet al. Serum GFAP and nfL levels differentiate subsequent progression and disease activity in patients with progressive multiple sclerosis. Neurol Neuroimmunol Neuroinflamm. (2023) 10.
81
RosensteinINordinASabirHMalmeströmCBlennowKAxelssonMet al. Association of serum glial fibrillary acidic protein with progression independent of relapse activity in multiple sclerosis. J Neurol. (2024) 271:4412–22.
82
SchindlerPAktasORingelsteinMWildemannBJariusSPaulFet al. Glial fibrillary acidic protein as a biomarker in neuromyelitis optica spectrum disorder: a current review. Expert Rev Clin Immunol. (2023) 19:71–91.
83
SimrénJWeningerHBrumWSKhalilSBenedetALBlennowKet al. Differences between blood and cerebrospinal fluid glial fibrillary Acidic protein levels: The effect of sample stability. Alzheimers Dement. (2022) 18:1988–92.
84
SheremataWAJyWDelgadoSMinagarAMcLartyJAhnY. Interferon-beta1a reduces plasma CD31+ endothelial microparticles (CD31+EMP) in multiple sclerosis. J Neuroinflammation. (2006) 3:23.
85
Sáenz-CuestaMHaritzITamaraC-TMaiderM-CIñakiO-QAlvaroPet al. Circulating microparticles reflect treatment effects and clinical status in multiple sclerosis. Biomarkers Med. (2014) 8:653–61.
86
GalazkaGMyckoMPSelmajIRaineCSSelmajKW. Multiple sclerosis: Serum-derived exosomes express myelin proteins. Mult Scler. (2018) 24:449–58.
87
MoseleyCEVirupakshaiahAForsthuberTGSteinmanLWaubantEZamvilSS. MOG CNS autoimmunity and MOGAD. Neurol Neuroimmunol Neuroinflamm. (2024) 11:e200275.
88
D’AncaMFenoglioCBuccellatoFRVisconteCGalimbertiDScarpiniE. Extracellular vesicles in multiple sclerosis: role in the pathogenesis and potential usefulness as biomarkers and therapeutic tools. Cells. (2021) 10.
89
BhargavaPNogueras-OrtizCChawlaSBækRJørgensenMMKapogiannisD. Altered levels of toll-like receptors in circulating extracellular vesicles in multiple sclerosis. Cells. (2019) 8.
90
BhargavaPNogueras-OrtizCKimSDelgado-PerazaFCalabresiPAKapogiannisD. Synaptic and complement markers in extracellular vesicles in multiple sclerosis. Mult Scler. (2021) 27:509–18.
91
Nogueras-OrtizCJErenEYaoPCalzadaEDunnCVolpertOet al. Single-extracellular vesicle (EV) analyses validate the use of L1 Cell Adhesion Molecule (L1CAM) as a reliable biomarker of neuron-derived EVs. J Extracell Vesicles. (2024) 13:e12459.
92
LiDZouSHuangZSunCLiuG. Isolation and quantification of L1CAM-positive extracellular vesicles on a chip as a potential biomarker for Parkinson’s Disease. J Extracell Vesicles. (2024) 13:e12467.
93
MazzuccoMMannheimWShettySVLindenJR. CNS endothelial derived extracellular vesicles are biomarkers of active disease in multiple sclerosis. Fluids Barriers CNS. (2022) 19:13.
94
MartinsenVKursulaP. Multiple sclerosis and myelin basic protein: insights into protein disorder and disease. Amino Acids. (2022) 54:99–109.
95
Sáenz-CuestaMAlberroAMuñoz-CullaMOsorio-QuerejetaIFernandez-MercadoMLopeteguiIet al. The first dose of fingolimod affects circulating extracellular vesicles in multiple sclerosis patients. Int J Mol Sci. (2018) 19.
96
VerderioCMuzioLTurolaEBergamiANovellinoLRuffiniFet al. Myeloid microvesicles are a marker and therapeutic target for neuroinflammation. Ann Neurol. (2012) 72:610–24.
97
GeraciFRagonesePBarrecaMMAliottaEMazzolaMARealmutoSet al. Differences in intercellular communication during clinical relapse and gadolinium-enhanced MRI in patients with relapsing remitting multiple sclerosis: A study of the composition of extracellular vesicles in cerebrospinal fluid. Front Cell Neurosci. (2018) 12:418.
Summary
Keywords
multiple sclerosis (MS), cerebrospinal fluid (CSF), brain-derived blood biomarkers, extracellular vesicles (EVs), magnetic resonance imaging (MRI)
Citation
Anandan S, Maciak K, Breinbauer R, Mostafavi S, Kvistad CE, Torkildsen O and Myhr K-M (2025) Brain-derived blood biomarkers in multiple sclerosis—current trends and beyond. Front. Immunol. 16:1569503. doi: 10.3389/fimmu.2025.1569503
Received
31 January 2025
Accepted
26 May 2025
Published
16 June 2025
Volume
16 - 2025
Edited by
Stella E. Tsirka, Stony Brook University, United States
Reviewed by
Luisa María Villar, Ramón y Cajal University Hospital, Spain
Updates
Copyright
© 2025 Anandan, Maciak, Breinbauer, Mostafavi, Kvistad, Torkildsen and Myhr.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Shamundeeswari Anandan, Shamundeeswari.Anandan@uib.no; samanandhan@gmail.com
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.