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Review

Refining Prognostic Factors in Adult-Onset Multiple Sclerosis: A Narrative Review of Current Insights

by
Tommaso Guerra
1,
Massimiliano Copetti
2,
Mariaclara Achille
1,3,
Caterina Ferri
4,
Marta Simone
3,
Sandra D’Alfonso
5,
Maura Pugliatti
4,6 and
Pietro Iaffaldano
1,*
1
Department of Translational Biomedicine and Neurosciences-DiBraiN, University of Bari “Aldo Moro”, Azienda Ospedaliero-Universitaria Consorziale Policlinico di Bari, 70100 Bari, Italy
2
Unit of Biostatistics, Fondazione IRCCS “Casa Sollievo della Sofferenza”, 71013 San Giovanni Rotondo, Italy
3
Child Neuropsychiatry Unit, Department of Precision and Regenerative Medicine, Jonic Area University of Bari “Aldo Moro”, 70100 Bari, Italy
4
Department of Neuroscience, S. Anna University Hospital, 44100 Ferrara, Italy
5
Department of Health Sciences, University of Piemonte Orientale, 28100 Novara, Italy
6
Department of Neuroscience and Rehabilitation, University of Ferrara, 44100 Ferrara, Italy
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2025, 26(16), 7756; https://doi.org/10.3390/ijms26167756
Submission received: 30 June 2025 / Revised: 28 July 2025 / Accepted: 1 August 2025 / Published: 11 August 2025
(This article belongs to the Special Issue Progression of Multiple Sclerosis: Environmental Factors, Genetics, Therapeutics and Immunopathogenesis)
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Abstract

Multiple sclerosis (MS) is characterized by a continuum of diverse neuroinflammatory and neurodegenerative processes that contribute to disease progression from the earliest stages. This leads to a highly heterogeneous clinical course, requiring early and accurate prognostic assessment: the identification of reliable prognostic biomarkers is crucial to support therapeutic decision-making and guide personalized disease management. In this narrative review, we critically examined the current MS literature, investigating prognostic factors associated with disease progression and irreversible disability in adult-onset MS, with a focus on different clinical, radiological, and molecular biomarkers. Particular attention is directed toward the prognostic value of baseline clinical and neuroimaging factors, emerging biomarkers of smoldering disease, and progression independent of relapse activity (PIRA) events. Additionally, we discussed the role of integrated prognostic tools and risk scores, as well as their potential impact on clinical practice. We aim to provide a comprehensive and clinically oriented synthesis of available evidence in the MS biomarkers field, supporting multifaceted prognostication strategies to improve long-term outcomes in people with MS.

1. Introduction

Neurologists have long pursued the identification of robust prognostic factors to anticipate the variable course of multiple sclerosis (MS), both in clinical care and research contexts [1,2,3]. Comparable to a complex maze that defies linear solutions, this challenge requires an integrated and multi-pronged approach. To enable early diagnosis and accurately identify MS patients at elevated risk of disease progression, precise diagnostic criteria and prognostic markers are both essential [4,5]. The predictive value of single clinical biomarkers remains moderate when considered alone, emphasizing the need for a comprehensive prognostic model incorporating imaging, molecular, clinical biomarkers, and genetic data. This is prodromal to the tailoring of individual treatment strategies to maximize clinical outcomes in the wide and constantly expanding field of disease-modifying therapies (DMTs) for MS [6]. According to the initial course of the disease, MS is typically categorized as either relapsing–remitting (RR) or primary progressive (PP); when disability accumulates, a secondary progressive (SP) phenotype is diagnosed [7]. Individuals with MS often experience a deterioration of their motor and cognitive performance, even when their inflammatory parameters remain stable: progression independent of relapse activity (PIRA) is now recognized as the epiphenomenon of smoldering MS disease. Since the disease’s onset and throughout its progression, PIRA and relapse-related worsening (RAW) episodes both contribute to irreversible neurological impairment [8,9]. In this continuum, the assessment of prognostic factors, including the newest outcomes, becomes crucial to capturing the evolution of the disease and guiding patient management and appropriate therapeutic choices. Potential predictors of MS progression have been the subject of numerous investigations [10,11,12]; however, clinical applications and widely accepted scores have been limited due to the lack of a systematic synthesis of evidence and the heterogeneity of findings [11,12]. The purpose of this study is to present an extensive and updated narrative review of evidence related to identified prognostic factors for adult-onset MS. The aim is to gather and thematically organize prognostic factors in MS, providing a synthesis that is relevant both from a clinical and research perspective.

2. Methods

2.1. Aims and Research Planning

This narrative review aims to provide a comprehensive and up-to-date overview of prognostic factors in adult-onset MS. Starting from a scrutiny of MS literature, we examined and discussed numerous topics in the different thematic sections of this review. A narrative review format was adopted to provide a broad overview of the heterogeneous literature on prognostic factors in MS, integrating evidence across various clinical, molecular, and radiological domains. Due to the wide variability in study designs, outcomes, and cohorts, the narrative approach allowed a wider interpretative perspective, essential to contextualize the clinical implications of this review.

2.2. Study Selection

A specific approach for selecting literature sources has been applied in the two primary academic databases, PubMed and Google Scholar. Our search strategy comprised pertinent keywords related to prognosis in MS. During the selection procedure, particular inclusion and exclusion criteria were applied to guarantee the high standard and applicability of this review. The main focus was about the prognostic role of different biomarkers in MS: studies investigating demographic, clinical, magnetic resonance imaging (MRI), laboratoristic biomarkers, and their association with various outcomes of clinical and radiological disease activity were included. Factors associated with DMTs exposure were also considered. Environmental exposures and genetic predispositions were not taken into consideration in this review.
Each identified published study was classified as high or low quality, depending on study design, population size, risk of bias, and assessment of outcomes [13]. Following this search path, some papers were eliminated since they were either unpublished manuscripts or non-peer-reviewed materials.
Specifying the time frame of the search, no restrictions were placed on publication dates, but we focused mainly on studies published in the last years. The time span of the references included in this review ranges from 2003 to 2025. Notably, the majority of the cited literature (approximately 70%) was published from 2020 onward, reflecting the most recent advancements in MS research field. All of the prognostic factors that were taken into consideration were listed in a summary table (Table 1) reporting the main studies cited in the text along with their key characteristics. Notably, we extracted the study-specific relative risk estimates (risk ratio, odds ratio [OR], hazard ratio [HR], or other measures of statistical association) together with the corresponding confidence intervals. Table 1 reports the details of the studies considered, including the year of publication, the study design, the population size, the outcomes investigated, the effect measures (with statistical specifications), the evidence of relevance to MS progression/worsening, and the reference in AMA format. Only prognostic factors with a higher strength of evidence were included and discussed in this narrative review. A flowchart detailing the selection and screening process is provided in Figure 1.

2.3. Role of the Funding Source

This study falls into the framework of “PROMISING study” (Next Generation EU—NRRP M6C2—Investment 2.1 Enhancement and Strengthening of Biomedical Research in the NHS—PNRR-MAD-2022-12376868), which aims to predict MS disease progression through the development of a prognostic score. Environmental and genetic prognostic factors, as well as those specifically related to pediatric-onset MS, will be thoroughly addressed in other studies that are part of the network of this project.

3. Demographic and Clinical Prognostic Factors

Demographic characteristics have been defined as classic predictors of disease course in MS, widely used by neurologists in clinical practice to orient treatment choices [82]. Age constitutes a multifaceted element in prognosis: age at MS onset, in the first place, may impact the disease course. Older age (>40 years) at onset was associated with a higher risk of SPMS conversion [22] and PIRA events [23,26]. This demographic factor resulted in a significant risk of SPMS, especially in men [20]. Considering disability milestones, age at onset greater than 50 years was significantly associated with a higher risk of reaching an irreversible Expanded Disability Status Scale (EDSS) 6.0 [24,27] in a shorter time [25] compared to patients with younger age at onset. Similarly, lower age at treatment initiation has been linked to an enhanced treatment effect on annualized relapse rate (ARR) and disability progression [26], as demonstrated in a meta-analysis of six randomized clinical trials [16].
The dual role of aging in MS as a prognostic factor has to be thoroughly evaluated: reduced inflammatory activity goes in parallel with an increase in the risk of irreversible disability accumulation linked to PIRA [26]. Age is relevant in determining disability progression in MS, with pediatric-onset MS characterized by a less steep increase in EDSS scores over time than older patients and a less pronounced effect of PIRA in accelerating EDSS progression [83]. Considering inflammatory activity, patient age is the most important determinant of decline in relapse incidence [14,19]. Therefore, knowing how aging phenomena impact immune and brain cell activity may help reduce non-relapses-related progression in MS patients [84].
Male sex has been linked to poor long-term outcomes in MS, according to numerous studies [82,85,86]. Specifically, male sex has been associated with a higher risk of reaching EDSS 6.0 and 7.0 [57]; conversely, female sex appeared to display a lower risk of reaching EDSS 3.0 [33] and exerted a protective role in the late-onset cohort for the risk of a 12-month confirmed disability worsening [43]. Female sex, younger age, and a higher EDSS during relapse were considered factors associated with a higher chance of EDSS improvement after relapse treatment [17]. However, some cohort studies have reported contradictory results, with no difference in prognosis between males and females [70,87].
Early clinical characteristics such as relapse frequency, recovery from relapses, and onset symptoms have also been recognized as crucial prognostic indicators [28]. A brainstem, cerebellar, or spinal cord syndrome was associated with poor recovery from the initial relapse [58], in parallel with the solid association of multifocal onset with a higher risk of SPMS [22]. The presence of motor, especially spinal, and brainstem symptoms at onset were associated with a shorter time to irreversible EDSS 6.0 [25,57], while patients presenting clinical isolated syndrome (CIS) with optic neuritis appeared to display a lower risk of reaching an EDSS score of 3.0 [33]. In addition, an incomplete recovery from the first attack was significantly associated with a higher hazard ratio to reach an EDSS of 6.0 [27,35]. A higher baseline EDSS score was associated with a higher risk of SPMS and was a predictor of EDSS worsening in numerous studies [19,22,29,30]. The early years of disease are considered crucial, both for prognosis assessment and—as discussed below—to exploit the most optimal therapeutic response. Change in EDSS from baseline to 24 months was a strong predictor of disability outcomes over 15 years [88]. A higher number of early PIRA and RAW events led to a higher risk of SPMS in a recent MSBase observational cohort study [89]. Frequent relapses in the first two years from the onset and shorter first inter-attack intervals predicted also a shorter time to reach disability endpoints EDSS 6.0, 8.0, and 10 [27,57,65]. Relapse frequency during the RRMS phase was strongly associated with a higher risk of progression [22].
This discussion ought to cover cognitive impairment as predictors of the worst outcomes. Chronic depression and cognitive dysfunction were associated with adverse long-term outcomes in MS [35]. Cognitive impairment was associated with higher odds of transitioning from a relapsing–remitting course to a progressive disease course, and, interestingly, it was also associated with a higher mortality risk [34]. An Italian 10-year retrospective longitudinal study outlined that cognitive impairment at diagnosis, with particular involvement of memory and processing speed, was associated with a more than threefold risk of reaching EDSS 4.0 and a twofold risk of SPMS conversion [90]. Cognitive impairment can occur independently of other neurological symptoms and is linked to a higher risk of future neurological disability [91].
People of all ethnicities are affected by MS, and the ethnic background must be included in the discussion about prognosis. A recent review outlined that Black, Latino/Hispanic, and South Asian individuals with MS in North America and the United Kingdom appear to have an earlier age of onset. Furthermore, compared to white MS patients, Black and Latino/Hispanic MS patients in the USA were more likely to have severe symptoms at disease onset and an earlier disability accrual [92]. Notable differences in MS-specific mortality trends by age and race/ethnicity were also highlighted, indicating an unequal burden of disease and a complex balance of environmental and social differences influencing disease variability [93].
These data underline the complexity of demographic influences and the necessity of integrating multiple clinical and biological variables when estimating prognosis (Figure 2).

4. Radiological Predictors

The prognosis and treatment choices of patients may be improved by considering all available conventional and advanced MRI measures [4]. In individuals with CIS and RMS, a greater number of brain T2-hyperintense white matter (WM) lesions at baseline raises the probability of disability accrual, MS progression [33,74,94], and RAW events [15]. Baseline gadolinium (Gd)-enhancing lesions were also independently associated with SPMS conversion at 15 years [32]. T1-hypointense lesions (“black holes”) primarily indicate axonal degeneration, white matter disruption, and are typically linked to irreversible clinical outcomes [95,96,97]. A more intriguing prognostic factor turned out to be lesion topography. The primary predictor of progressive disease and physical impairment in a group of MS patients with very long follow-up and uniform disease duration was cortical involvement, both in terms of lesions and atrophy [36]. Cortical lesion accrual was greater in SPMS than RMS and cortical lesion volume independently predicted EDSS changes throughout the disease course [37]. The number of spinal cord (SC) lesions on MRI was associated with future accumulation of disability largely independent of relapses [15,72]. A significant association between new SC lesions and clinical relapses within 3 months was found, turning this prognostic factor into a major driver of treatment change [61]. SC lesions also showed consistent association with EDSS and MS progression at 15 years [32]. The role of the optic nerve, recently included as the fifth topography for MS diagnostic criteria fulfillment [4] is triggering. The length of optic nerve lesions at onset correlated with the extent of retinal damage, as assessed by optical coherence tomography (OCT) parameters, and was associated with poorer visual recovery 12 months after disease onset [98].
A recent consensus established biomarkers of chronic active lesions (CAL), crucial signs of chronic inflammation: paramagnetic rim lesions (PRL) identified on susceptibility-sensitive MRI, MRI-defined slowly expanding lesions (SELs), and 18-kDa translocator protein (TSPO)-positive lesions on positron emission tomography (PET) [99]. PRLs have been linked to a more severe course of the disease [100], without a clear correlation with their topographical distribution. The number of PRL was also associated with the number of leptomeningeal contrast enhancement foci, linking leptomeninges to mechanisms related to sustaining chronic inflammation [101]. Additionally, PRLs have been connected to increased rates of atrophy in the brain and spinal cord [102]. The amount of SELs has been correlated with MS progression after 9 years, and severe SEL microstructural abnormalities were a predictor of EDSS worsening and SPMS conversion [29]. A higher definite SEL volume was associated with increasing disability, assessed by EDSS, z scores of the Multiple Sclerosis Functional Composite, Timed 25-Foot Walk Test and Paced Auditory Serial Addition Task, and increased risk of clinically defined progression [103]. In a recent study, the proportion of persisting black holes was higher in SELs compared to non-SELs, and within-patient SEL and persisting black holes volumes were positively correlated [104].
In addition to the prognostic value of MRI, neuroimaging may also aid in predicting treatment response [105]. New T2 lesions, an increase in T2 lesion volume, and Gd+ lesions on MRI are considered, isolated or combined, predictors of disability progression and treatment effectiveness [106,107,108]. It is applicable to both asymptomatic and symptomatic lesions, as demonstrated by a study of the MSBase analyzing the probability of treatment change among patients with clinically silent new MRI lesions [109]. Nowadays, it is crucial to take into account an integrated approach that supports the care of MS patients [110], given the strong predictive significance of the several radiological indicators mentioned above.
Molecular mechanisms of progression phenomena may be related to the concept that iron-positive rim lesions are characterized by the presence of iron-laden activated myeloid cells and the activation of related molecular pathways: an upregulation of the CD163–HMOX1–HAMP axis at the rims of chronic active lesions was reported, suggesting that haptoglobin-bound hemoglobin represents the key source of iron uptake and indicating a pro-inflammatory transcriptional profile [111,112]. This finding is further supported by the strong association between PRL levels and CSF concentrations of sCD163 in MS patients, whereas elevated IL10 mRNA expression was observed in perilesional myeloid cells [112]. A recent review [113] highlighted the involvement of ectopic lymphoid follicles in MS, underscoring their prognostic association with both cortical [114] and spinal cord [115] pathology. The molecular profile of inflammatory meningeal and perivascular infiltrates was defined by a high density of CXCR5+ cells, cytoplasmic NFATc1+ cells, enriched populations of CD3+CD27+ memory T cells, and CD4+CD69+ tissue-resident cells [116].

5. Fluid Biomarkers

Immune-related biomarkers can predict future impairment and correlate with MS severity, as well as imaging and clinical outcomes [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,19,20,22,23,24,25,26,27,28,29,30,32,33,34,35,36,37,43,57,58,61,65,70,72,74,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117]. Cerebrospinal fluid (CSF) biomarkers are useful not only in the diagnostic process: CSF samples can also enhance the biological profiling of the disease, thereby determining its long-term prognosis [118]. Historically, CSF-specific oligoclonal band (OCB) testing has been the most generally accessible laboratory test recognized as the cornerstone of MS diagnosis [119]. The presence of CSF-specific OCBs significantly doubled the risk of attaining disability milestones EDSS 4.0 or 6.0 in a meta-analysis including data from 1918 patients [62]. CSF-OCB presence was associated with a higher risk of relapses [41], accumulation of disability [33], and SPMS conversion [63]. In addition, an increased number of cortical lesions was found in OCB-positive compared to OCB-negative patients [38]. In this recent study, OCB presence at MS onset was associated with more severe gray matter pathology and with worse physical and cognitive impairment after 10 years, stressing the link with B cell activation, lymphoid-neogenesis, and pro-inflammatory immune response in the CSF of OCB+ patients [38].
The role of neurofilament light chains (NfL) elevation as a predictor of confirmed disability worsening independent of clinical relapses has been recently documented [120]. Considering neuroinflammatory activity, NfL levels were an independent factor for the occurrence of at least one relapse during the first two years after MS diagnosis and for the occurrence of Gd+ lesions during the first 2 years from diagnosis at brain and spine MRI scans [121]. Subjects with higher serum NfL Z scores showed a greater probability of relapses, EDSS worsening, and EDA in the following years [67]. Serum GFAP concentration, in contrast to serum levels of NfL, does not usually increase during acute inflammation; instead, it indicates faster grey matter (GM) brain volume loss and may serve as a predictive biomarker for subsequent PIRA [122]. Levels of GFAP correlated also with SEL count [123].
A possible prognostic neurodegenerative biomarker of GM dysfunction was suggested to be parvalbumin levels in the CSF at the time of MS diagnosis, highlighting a correlation with physical disability, fatigue, and MRI brain volume of strategic regions related to cognitive impairment [124]. The kappa free light chain (KFLC) index has recently been recognized as a diagnostic biomarker, but its prognostic role is also relevant [125]. KFLC index was an independent risk factor for PIRA [126] and was also predictive of disease activity in the first year after diagnosis [127] and during follow-up [126,127,128]. Stressing the combined use of different biomarkers, further stratification of MS disease activity risk in OCB-positive patients was possible using the KFLC index [128]. Considering the evidence of compartmentalized inflammation, a score was recently calculated based on glial and axonal markers (CHI3L1*GFAP/NfL), known as “Glia score” and related to progressive MS [129]. A detailed molecular CSF profiling, combined with clinical and radiological assessment, could serve as a prognostic marker for aggressive MS [130]. The CSF of MS patients with higher levels of GM damage at diagnosis showed a proinflammatory pattern of elevated levels of molecules linked to sustained B-cell activity and lymphoid neogenesis, such as CXCL13, IL6, IL8, and IL10; proinflammatory cytokines, such as TNF and IFNγ; and high levels of BAFF, APRIL, LIGHT, TWEAK, sTNFR1, sCD163, MMP2, and pentraxin III [130]. While levels of TNF-α exhibited a positive correlation with post-contrast-enhancing cerebral lesions and T2 cervical SC lesions, IL-6 rates were linked with post-contrast-enhancing thoracic SC lesions and IL-15 levels negatively correlated with T2 and Gd-positive lesions in cervical SC [40]. Free-circulating mitochondrial DNA (mtDNA) levels could also play a role in prognosis. A larger T2 lesion burden and EDSS worsening were positively connected with higher CSF quantities of mtDNA copies in progressive MS patients [131]. Moreover, CSF lactate levels have been connected to increased neurological impairment, and molecular biomarkers of neurodegeneration [132].

6. Therapies and Prognosis

DMTs have significantly improved the natural history of the disease, modifying its prognosis [1,2,3,4,5,6]. The assessment of therapeutic prognostic value is developed through four distinct dimensions of analysis: time from disease onset/diagnosis to treatment initiation, total exposure, type of treatment, and discontinuation. A delayed DMT initiation was associated with a higher risk of PIRA and RAW events in a cohort of adult-onset and pediatric-onset MS patients [26]. The time interval between disease onset and the first DMT start was a strong predictor of disability accumulation, independent of relapse activity, over the long term; in addition, an increased risk of disability accumulation was underlined for patients who started the treatment after 1.2 years from the onset [74]. A recent consensus has combined all references in favor of early intervention with high-efficacy disease-modifying therapies (HE-DMTs), representing the best window of opportunity to delay irreversible CNS damage and MS-related disability progression [133]. HE-DMTs commenced within 2 years of disease onset were associated with less disability after 6–10 years than when commenced later in the disease course [75]. Compared to patients who began treatment with DMT earlier, those who started later achieved an EDSS score of 6 faster, and their mortality rate was 38% higher [76]. Better patient-reported physical symptoms were also reported [77]. The Early Intensive Therapy (EIT) strategy was more effective than the escalation (ESC) strategy in controlling disability progression over time [78]. EIT strategy refers to the approach whereby patients are treated with HE-DMTs, including alemtuzumab, cladribine, fingolimod, natalizumab, ocrelizumab, ozanimod, and ponesimod as first therapy. Conversely, the ESC group included patients initially treated with ME-DMTs (azathioprine, interferon-beta products, glatiramer acetate, teriflunomide, dimethyl fumarate) and then escalated to HE-DMTs [133]. The risk of reaching EDSS 4.0 was reduced by 26% in patients starting with HE DMTs, in parallel with a reduced risk of relapses by 66% and a three times higher probability of confirmed disability improvement [79]. A lower probability of a first relapse and 6-month confirmed EDSS score worsening was found in patients starting an HE DMT as first therapy, compared to subjects starting moderate-efficacy DMTs (ME DMTs) [81]. Therefore, the median time to sustained accumulation of disability was longer for the EIT group [80]. A shorter DMT exposure is associated with a higher risk of PIRA event [26] and a higher risk of SPMS conversion [74]. This concept is reinforced by another study, which highlights that the occurrence of RAW events was predicted by the temporary or permanent discontinuation of the initial DMT [15]. The mechanisms linked to “silent progression” may be biologically impacted by the various modes of action of DMT utilized in clinical practice. The use of DMTs has been demonstrated to be crucial in reducing PIRA occurrence in numerous recent studies, significantly impacting prognosis [134,135,136].

7. Other Biomarkers with a Prognostic Value

Progressive neuronal and axonal loss is thought to be one of the primary mechanisms sustaining MS-related impairment, and this neurodegenerative process frequently involves the visual system. Consequently, orbital ultrasonography [59] and optical coherence tomography [137] became valuable non-invasive tools in the field of MS biomarkers. Combined macular ganglion cell and inner plexiform layers (mGCIPL) atrophy correlated with brain atrophy [137], and a significant thinning of the mGCIP was observed also in MS patients without a history of optic neuritis, highlighting a subclinical optic nerve involvement [138]. According to a recent review, cross-sectional measurement of peripapillary retinal nerve fiber layer (pRNFL) and mGCIPL thickness (≤88 µm and <77 µm, respectively) and longitudinal measurement of pRNFL thinning and mGCIPL thinning (1.5 µm/year and ≥1.0 µm/year, respectively) were associated with an increased risk of disability progression in subsequent years [139]. Optic nerve diameter (OND) in ultrasonography and RNFL thickness were significantly lower in patients with an EDSS score > 2 than in those with a score ≤ 2, indicating that OND was an independent predictor of EDSS > 2 [60]. Ultrasound findings and disease progression showed a substantial correlation, although there were no statistically significant changes related to relapses or other clinical factors [59].

8. The Strategic Role of Prognostic Algorithms in Clinical Decision-Making and Research

Combining various biomarker and treatment response measures with demographic and clinical prognostic factors has a more significant clinical impact on long-term prognosis than considering factors individually. The demonstration is the application of the Risk of Ambulatory Disability (RoAD) score, built on demographic, clinical baseline factors, and 1-year assessment of treatment response combined [31,140]. A common indicator of treatment failure is the occurrence of new T2 lesions on serial MRI. Particular thresholds in assessing lesion burden are linked to the progression of disability over time: the Canadian MS Working Group Treatment Optimization Recommendations and the modified Rio score are two examples of treatment algorithms that are utilized in clinical practice to support the clinical decision-making process [141,142,143]. These grading systems also consider both MRI results and clinical characteristics. In patients treated with teriflunomide, the MAGNIMS score predicted a 7-year probability of disability worsening, while in individuals treated with interferon beta-1a, it predicted long-term disability progression for up to 15 years [144,145]. The Multiple Sclerosis Treatment Decision Score (MS-TDS) is another example of a prediction model to assist with treatment decision-making. Combining different prognostic factors, it can identify patients who benefit from early platform medicine by estimating tailored therapy success probabilities [146]. No evidence of disease activity (NEDA) has been considered in recent years a therapeutic goal and measure of individual treatment response: in particular, NEDA-3 status requires the absence of relapses, EDSS progression, and inflammatory MRI activity, and NEDA-4 expanded this definition by adding the absence of increased brain atrophy [147,148]. A recent metanalysis highlighted that NEDA-3 and NEDA-4 at 1–2 years were associated with two times higher odds of no long-term disability progression at 6 years [149]. Considering all algorithms and scores proposed, MRI has the role of a fundamental prognostic factor for the monitoring of MS disease and treatment [150]. Using a machine learning technique, algorithms have recently been developed to predict confirmed disability accumulation, NEDA status, immunotherapy initiation, and the escalation from low- to high-efficacy therapy with intermediate to high accuracy [151,152,153].
A very recent prognostic tool is the Barcelona Risk Score (BRS), a validated algorithm that incorporates several biomarkers to classify each patient into four data-driven groups according to the risk of moderate long-term disability, considering different outcomes: RAW, PIRA, SPMS conversion, MRI features, and patient-reported scores. The BRS offers a versatile framework, designed to support clinical decision-making in everyday practice and across heterogeneous settings, and it is applicable even with limited data availability [154].
In conclusion, our practical recommendation is to adopt integrative algorithms capable of a comprehensive assessment of patients, by combining biomarkers that reflect the disease from multiple angles, ranging from clinical presentation [155] to molecular indicators of progression, and neuroimaging findings.

9. Conclusions

This review outlines key research findings regarding the prognostic role of diverse biomarkers in MS. Some limitations need to be acknowledged. As a narrative review, this study lacks the methodological rigor of a systematic review and is therefore potentially subject to selection bias and other inherent limitations related to the non-systematic inclusion of studies. Generalizability may also be impacted by variations in study designs, populations, and prognostic factor definitions. Furthermore, the capacity to assess prognostic importance or provide pooled estimates is restricted by the lack of quantitative synthesis. Additionally, it should be noted that many of the prognostic factors addressed in this review are not mutually independent: this reflects the intrinsic complexity of prognostication in MS, where different biomarkers often overlap and interact. Considering strengths and limitations of this narrative review, an up-to-date overview of the long-term prognostic value of different biomarkers could aid clinicians in better considering certain aspects of clinical practice, starting from demographic features to MRI and molecular biomarkers. Expanding disease registries to incorporate as many biomarkers of disease progression as possible would promote the idea of merging datasets to provide a multifaceted picture of MS patients [156]. In this perspective, our work could provide a comprehensive overview of prognostic factors in MS research in an integrated way, constituting a roadmap for future researchers in their efforts to contribute to existing studies aimed at improving MS care.

Author Contributions

Conceptualization, writing—original draft preparation, writing—review and editing, T.G. and P.I.; supervision, P.I.; Writing a significant part of the paper, M.C., C.F., M.P., M.S., M.A. and S.D. All authors have read and agreed to the published version of the manuscript.

Funding

The authors declare that financial support was received for the research and publication of this article. This study falls into the framework of “PROMISING study” (Next Generation EU–NRRP M6C2–Investment 2.1 Enhancement and Strengthening of Biomedical Research in the NHS–PNRR-MAD-2022-12376868). The funds came from the Italian National Recovery and Resilience Plan, known as “Italia Domani”, provided by the European Union (Next Generation EU) and the Ministry of Health of the Italian Republic. Soggetto Attuatore: Azienda Ospedaliero-Universitaria Policlinico di Bari.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors report no conflicts of interest with respect to the contents of the current review, but note that the authors have received advisory board, membership, speakers honoraria, travel support, research grants, consulting fees, or clinical trial support from the manufacturers of those drugs, including Actelion, Allergan, Almirall, Alexion, Bayer Schering, Biogen, Bristol Myers Squibb Celgene, Excemed, Genzyme, Forward Pharma, Ipsen, Medday, Merck, Mylan, Novartis, Sanofi, Roche, Teva, and their local affiliates.

Abbreviations

aHRadjusted Hazard Ratio
APRILA proliferation-inducing ligand
AUCArea Under the Curve
BAFFB-cell activating factor
CALchronic active lesions
CISclinical isolated syndrome
CSFcerebrospinal fluid
CXCLchemokine ligand
DMTsdisease-modifying therapies
EDSSExpanded Disability Status Scale
EITearly intensive therapy
ESCescalation strategy
Gdgadolinium
mGCIPLmacular ganglion cell and inner plexiform layers
GMgrey matter
HAMPhepcidin antimicrobial peptide
HMOX1heme oxygenase 1
HE DMTshigh-efficacy DMTs
ILinterleukin
KFLCkappa free light chain
MAGNIMSmagnetic resonance imaging in MS
MMP-2matrix metalloproteinase-2
ME DMTsmoderate-efficacy DMTs
MRImagnetic resonance imaging
mRNAmessenger RNA
mtDNAmitochondrial DNA
NEDAno evidence of disease activity
NfLneurofilament light chains
OCBsoligoclonal bands
ONDoptic nerve diameter
PRLparamagnetic rim lesions
pRNFLperipapillary retinal nerve fiber layer
PIRAprogression independent of relapse activity
PPMSprimary progressive multiple sclerosis
RAWrelapse-related worsening
RRMSrelapsing–remitting multiple sclerosis
SPMSsecondary progressive multiple sclerosis
SCspinal cord
SELsslowly expanding lesions
TNFtumor necrosis factor
TWEAKTNF-related weak inducer of apoptosis
TSPO18-kDa translocator protein
WMwhite matter

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Figure 1. Flowchart of the methodology for identification, screening, and inclusion of studies in this narrative review.
Figure 1. Flowchart of the methodology for identification, screening, and inclusion of studies in this narrative review.
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Figure 2. Graphical overview of prognostic factors in multiple sclerosis.
Figure 2. Graphical overview of prognostic factors in multiple sclerosis.
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Table 1. Overview of the literature cited in the text and reviewed as part of the research for this narrative review, focusing on key clinical, radiological, and molecular prognostic factors in multiple sclerosis.
Table 1. Overview of the literature cited in the text and reviewed as part of the research for this narrative review, focusing on key clinical, radiological, and molecular prognostic factors in multiple sclerosis.
Prognostic FactorYear of PublicationStudy DesignPopulation SizeOutcome(s)Effect MeasureEvidence of Relevance to MS Progression/WorseningReferences
Demographic and clinical prognostic factorsAge2013Retrospective multi-center cohort study 12,570 relapse-onset patients and 881 patients with PPMSRelapse incidenceHR = 0.95, 95% CI = 0.949–0.953, p < 10−12. Tweedie model: rate ratio = 0.98, 95% CI = 0.982–0.985, p < 10−12Patient age is the most important determinant of decline in relapse incidence.[14]
Demographic and clinical prognostic factorsAge2021Retrospective 2-center cohort study687 RRMS patientsPIRASub distribution HR = 1.05 (1.01–1.10) for each year increase, p = 0.036The risk of PIRA was associated with increasing age.[15]
Demographic and clinical prognostic factorsAge2015Metanalysis: 6 trials 6693 RRMS Treatment effectiveness: ARR; disability progression (EDSS worsening sustained for 12 or 24 weeks). Relative effect (RE)Treatment effects on ARR (RE = 0.83 vs. RE = 1.30, p < 0.001) and on disability progression (RE = 0.82 vs. RE = 1.28, p = 0.017) were significantly higher in younger subjects. In RRMS, lower age is associated with higher treatment effects. [16]
Demographic and clinical prognostic factorsAge2024Retrospective cohort114 RMSChange in EDSS from first assessment at relapse to EDSS after the last relapse treatmentRegression coefficient (95% CI): 0.04 (0.02, 0.06) p < 0.001Female sex, younger age, and a higher EDSS during relapse as factors associated with a higher chance of EDSS improvement after relapse treatment.[17]
Demographic and clinical prognostic factorsAge2019Retrospective cohort2083 RRMSGlobal disability (eight Performance Scales (PSS-8) and the PHQ-9)PSS-8: 0.65 (0.49, 0.82) < 0.001; PHQ-9: 0.39 (−0.58, −0.19) < 0.001Older age is associated with higher global disability. [18]
Demographic and clinical prognostic factorsAge2018Retrospective multi-center cohort study4842 MS patients Risk of relapses after DMT discontinuationHR (95% CI) p-value: 0.97 (0.97, 0.98) < 0.001In younger patients the risk of relapses after DMT suspension is higher.[19]
Demographic and clinical prognostic factorsAge at onset2015Prospective study305 MS patientsSPMS conversion HR: 1.049; p = 0.00426The factor “age at onset” was significant for risk of SP in men.[20]
Demographic and clinical prognostic factorsAge at onset2015Prospective study305 MS patientsDeath (EDSS 10)HR: 1.061; p = 0.0135In men, age at onset remained a significant predictor of EDSS10. [20]
Demographic and clinical prognostic factorsAge at onset2022Retrospective cohort study661 MS patientsEDSS worsening95% CI: 0.04 to 0.40; p = 0.015For every 5 years earlier, the EDSS was 0.22 points worse. [21]
Demographic and clinical prognostic factorsAge at onset2022Retrospective cohort study 661 MS patientsSPMS conversion95% CI: 1.08 to 1.64; p = 0.008For every 5 years earlier, odds of SPMS 1.33 times higher. [21]
Demographic and clinical prognostic factorsAge at onset2022Retrospective cohort study 661 MS patientsBrain T2-lesion volume (T2LV)95% CI: 1.02 to 2.70; p < 0.001For every 5 years earlier, odds of T2LV 1.86 mL higher.[21]
Demographic and clinical prognostic factorsAge at onset2020Retrospective multicenter cohort study19,318 RRMS; 2343 SPMS identified with the DDA definition and 3868 identified with the neurologist definition (ND) SPMS conversion (SPMS definition according to ND and DDA)DDA group: HR (95% CI): 2.26 (1.92–2.67), p < 0.0001;
ND group: 1.85 (1.63–2.09), p < 0.0001		Age at onset > 40 years is associated with higher risk of SPMS [22]
Demographic and clinical prognostic factorsAge at onset2023Retrospective analysis of data from patients prospectively included (patients with a first demyelinating attack)1128 patientsPIRAHR, 1.43; 95% CI, 1.23–1.65; p < 0.001 for each older decadeOlder age at the first attack is a predictor of PIRA. [23]
Demographic and clinical prognostic factorsAge at onset2017Monocentric retrospective study3597 pMSTime to EDSS 4.0 and 6.0HR 2.0 [95% CI 1.7–2.4] and 2.3 [1.9–2.9]Worst outcomes with LOMS (≥50 years) (independent of PP course or male gender). [24]
Demographic and clinical prognostic factorsAge at onset2020Retrospective monocentric study157 PPMS patientsTime to EDSS 6.0 HR (95% CI): 1.03 (1.006–1.053); p = 0.012Older age of onset was associated with a shorter time to EDSS6[25]
Demographic and clinical prognostic factorsAge at onset2023Retrospective analysis of data from patients prospectively included (patients with a first demyelinating attack)1128 patientsAdjusted yearly EDSS increase ratesHR 0.18; 95% CI, 0.16–0.20 vs. 0.04; 95% CI, 0.02–0.05; p < 0.001Older age at the first attack is a predictor of PIRA. [23]
Demographic and clinical prognostic factorsAge at onset2023Retrospective multicenter cohort study16,130 MS patients PIRAAOMS vs. POMS HR, 1.42; 95% CI, 1.30–1.55; LOMS vs. POMS HR, 2.98; 95% CI, 2.60–3.41; p < 0.001. Older age at onset was associated with a higher risk of PIRA events. [26]
Demographic and clinical prognostic factorsAge at onset2020Prospective study415 MS patientsRisk of EDSS 6.0HR 3.846, 95% CI 1.240–11.932, p = 0.020Age at disease onset greater than 50 years was significantly associated with a higher HR to reach an EDSS of 6.0.[27]
Demographic and clinical prognostic factorsBaseline EDSS score2020Systematic review (30 studies). Data collection was guided by the checklist CHARMS and PROBAST. N/AN/AN/AThe single most common clinical predictor was baseline EDSS (n = 11). [28]
Demographic and clinical prognostic factorsBaseline EDSS score2020Retrospective multicenter cohort study (RISM) 19,318 RRMS; 2343 SPMS identified with the DDA definition and 3868 identified with the neurologist definition (ND) SPMS conversion (SPMS definition according to ND and DDA)DDA group: HR (95% CI) 1.41 (1.38–1.44),
p < 0.0001; ND group: 1.50 (1.48–1.53), p < 0.0001		A higher baseline EDSS score is associated with higher risk of SPMS [22]
Demographic and clinical prognostic factorsBaseline EDSS score2022Prospective, longitudinal cohort study53 RRMSEDSS score increase of ≥1.5, 1.0, or 0.5, confirmed after a 3-month relapse-free period, when the baseline EDSS score was 0, ≤5.5, or ≥6.0, respectivelyA higher baseline EDSS score (OR = 3.15 [95% CI = 1.61; 8.38], p = 0.003) is a significant independent predictor of EDSS score worsening at follow-up (C-index = 0.892) A higher baseline EDSS score is a predictor of EDSS worsening. [29]
Demographic and clinical prognostic factorsBaseline EDSS score2022Prospective, longitudinal cohort study53 RRMSSPMS conversionA higher baseline EDSS score (for each point higher: OR = 6.37 [1.98; 20.53], p = 0.002) independently predicted SPMS conversion (C-index = 0.947).A higher baseline EDSS score is predictor of SPMS conversion[29]
Demographic and clinical prognostic factorsBaseline EDSS score2016Post hoc analysis PRISMS long-term follow-up382 patientsRisk of EDSS 6.0 and time to EDSS 6.0R2 1.4125, 1.0862There is an association between EDSS at baseline and EDSS 6.0 and time to EDSS 6.0.[30]
Demographic and clinical prognostic factorsBaseline EDSS score2016Post hoc analysis PRISMS long-term follow-up382 patientsSPMS conversion and time to SPMSR2 0.8634; 0.6477There is an association between EDSS at baseline and SPMS conversion and time to SPMS conversion[30]
Demographic and clinical prognostic factorsBaseline EDSS score2018Retrospective multi-center cohort study 4842 MS patients CDPHR (95% CI) p-value: EDSS 2–3.5 1.79 (1.47, 2.17) < 0.001; EDSS 4.0–5.5 2.20 (1.77, 2.75) < 0.001; EDSS 6 + 2.62 (2.09, 3.28) < 0.001Hazard of CDP increased with increasing disability at baseline.[19]
Demographic and clinical prognostic factorsBaseline EDSS score2021Observational cohort study2649 MS patients MSIS physical score and psychological score worsening Each year of treatment delay was associated with a worse MSIS physical score by 2.75 points (95% CI 1.29 to 4.20), and worse MSIS psychological score by 2.02 points (95% CI 0.03 to 3.78)Earlier commencement of DMT was associated with better patient-reported physical symptoms.[31]
Radiological predictorsBaseline gadolinium-enhancing lesions2019Prospective study180 MS patientsEDSS correlation(≥1) β = 1.32, p < 0.01; (≥2) OR: 3.16, 1.08, 9.23; p = 0.035Baseline gadolinium-enhancing showed a consistent association with Expanded Disability Status Scale at 15 years. [32]
Radiological predictorsBaseline gadolinium-enhancing lesions2021Retrospective 2-center cohort study687 RRMS patientsRAWSub distribution HR = 2.38 (1.01–5.63), p = 0.047RAW was predicted by the presence of contrast-enhancing lesions on baseline MRI [15]
Radiological predictorsBrain T2 lesions at baseline MRI2015Observational study based on a prospective, open cohort1018 CISRisk of reaching EDSS score of at least 3.0 in 2 evaluations (defined “disability accumulation”)Adjusted HR scores of 2.9 (95% CI 1.4–6.0)The presence ≥10 brain T2 lesions on the baseline MRI was associated with a higher risk of the accumulation of disability[33]
Radiological predictorsBrain T2 lesions at baseline MRI2021Retrospective 2-center cohort study687 RRMS patientsRAWsHR = 3.92 (1.36–11.29), p = 0.012RAW was predicted by the presence of >9 T2 lesions on baseline MRI [15]
Radiological predictorsCALs2022Prospective, longitudinal cohort study54 RRMSEDSS score increase of ≥1.5, 1.0, or 0.5, confirmed after a 3-month relapse-free period, when the baseline EDSS score was 0, ≤5.5, or ≥6.0, respectivelyA lower baseline MTR values of SELs (for each % higher: OR = 0.66 [0.41; 0.92], p = 0.033) is a significant independent predictor of EDSS score worsening at follow-up (C-index = 0.892) Lower baseline MTR values of SELs are predictor of EDSS worsening. [29]
Radiological predictorsCALs2022Prospective, longitudinal cohort study56 RRMSSPMS conversionA lower baseline MTR values of SELs (for each % higher: OR = 0.48 [0.25; 0.89], p = 0.02) independently predicted SPMS conversion (C-index = 0.947).A lower baseline MTR values of SELs are predictor of SPMS conversion[29]
Radiological predictorsCALs2022Prospective, longitudinal cohort study52 RRMSEDSS score increase of ≥1.5, 1.0, or 0.5, confirmed after a 3-month relapse-free period, when the baseline EDSS score was 0, ≤5.5, or ≥6.0, respectivelyA higher proportion of SELs among baseline lesions (OR = 1.22 [95% CI = 1.04; 1.58], p = 0.04) is a significant independent predictor of EDSS score worsening at follow-up (C-index = 0.892) A higher proportion of SELs among baseline lesions is a predictor of EDSS worsening. [29]
Demographic and clinical prognostic factorsCognitive disfunction2022Retrospective study408 MS patientsSPMS conversion OR = 2.29, p = 0.043Cognitive dysfunction was associated with higher odds of transitioning from relapsing–remitting course to a progressive disease course[34]
Demographic and clinical prognostic factorsCognitive disfunction2022Retrospective study409 MS patientsMortality aHR = 3.07, p = 0.006Cognitive dysfunction was associated with higher hazard of death in the total sample[34]
Demographic and clinical prognostic factorsCognitive disfunction2016Prospective observational study793 MS patientsReaching severe disability: EDSS 6.0 and higher HR 4.64; CI 1.11–19.50; p = 0.036Cognitive dysfunction 10 years after disease onset was associated with severe disability. [35]
Radiological predictorsCortical lesions2021Prospective study63 MS patientsEDSS 0.37 (0.23 to 0.508); cortical lesions were higher in SPMS (100% sensitivity and 88% specificity)Cortical lesions, grey matter volume and cervical cord volume explained 60% of the variance of EDSS; cortical lesions alone explained 43%. [36]
Radiological predictorsCortical lesions2022Prospective study20 RRMS patients, 13 SPMS patients, along with 10 age-matched healthy controlsMS progression 3.6 lesions/year ± 4.2 vs. 1.1 lesions/year ± 0.9, respectively; p = 0.03Cortical lesion accrual was greater in participants with SPMS than with RRMS. [37]
Radiological predictorsCortical lesions2022Prospective study20 RRMS patients, 13 SPMS patients, along with 10 age-matched healthy controlsEDSS changes β = 0.5, p = 0.003Total cortical lesion volume independently predicted baseline EDSS and EDSS changes at follow-up[37]
Radiological predictorsCortical lesions201710-year observational, cross-sectional study40 OCB-negative and 50 OCB-positive MS Presence of cytokinesCXCL13 (r = 0.922; p < 0.001), CXCL12 (r = 0.678; p = 0.022), OPN (r = 0.692; p = 0.018), IL6 (r = 0.628; p = 0.039), TWEAK (r = 0.629; p = 0.038)CL load significantly correlated with levels of several molecules linked to the B cell immune response.[38]
Fluid biomarkersCSF biomarkers2020Longitudinal 4-yearprospective study99 RRMS patients (treatment-naive)Risk of EDA (measures of disease activity: (1) evidence of relapses; (2) confirmed disability progression as assessed by an increase of the EDSS score by at least 1 point sustained over 6 months; and (3) evidence of new or newly enlarging WMT2 lesions). HR = 1.78; p = 0.0001CXCL13, LIGHT and APRIL were the CSF molecules more strongly associated with the risk of EDA.[39]
Fluid biomarkersCSF biomarkers2020Longitudinal 4-year prospective study99 RRMS patients (treatment-naive)Risk of cortical thinning. β = 4.7 × 10−4; p < 0.001Higher CSF levels of CXCL13 were associated with more severe cortical thinning.[39]
Fluid biomarkersCSF biomarkers2024Prospective study118 de novo diagnosed RRMS patients and 112 controlsCorrelation with number of T2 and Gd(+) lesions on head MRI in patients with newly diagnosed RRMS.R Spearman: 0.2434; t (N-2) 2.2165; p = 0.0296TNF-α levels positively correlated with post-contrast-enhancing brain lesions.[40]
Fluid biomarkersCSF biomarkers2024Prospective study118 de novo diagnosed RRMS patients and 112 controlsCorrelation with number of T2 and Gd (+) lesions on C-spine MRI in patients with newly diagnosed RRMS.R Spearman: −0.2730; t (N-2) −2.5058; p = 0.0143IL-15 levels in CSF correlated negatively with both the number of T2 lesions in C spine MRI and the number of Gd(+) lesions in C spine MRI[40]
Fluid biomarkersCSF biomarkers2021Meta-analysisSix longitudinal studies, 1221 CIS/early RRMS patients Risk of a second clinical relapse. HR = 3.62, 95% CI 1.75–7.48, I2 = 88%, p = 0.0005The pooled analysis confirmed that the presence of intrathecal IgM synthesis is a risk factor for a second clinical relapse.[41]
Demographic and clinical prognostic factorsDisease duration2023Retrospective multicenter cohort study (RISM) 16,130 MS patients PIRAHR, 1.04; 95% CI, 1.04–1.05; p < 0.001A longer disease duration was associated with a higher risk of PIRA events. [26]
Demographic and clinical prognostic factorsDisease duration2019Retrospective cohort2083 RRMSWalking speed (T25FW speed)HR −0.05 95% CI (−0.08, −0.02) < 0.001Walking speed is slower in patients with a longer disease duration (per 5 years).[18]
Demographic and clinical prognostic factorsDisease duration2013Retrospective study (London Multiple Sclerosis Clinic database)730 MS patientsDSS 6OR, 0.76 [95% CI, 0.69–0.84] and 0.44 [95% CI, 0.37–0.52] for 5- and 15-year latency, respectively)Longer latency to progression was associated with lower probability of attaining DSS 6. [42]
TreatmentDMT exposure2023Retrospective multicenter cohort study (RISM) 16,130 MS patients PIRAHR, 0.69; 95% CI, 0.64–0.74; p < 0.001A shorter DMT exposure as associated with a higher risk of PIRA events. [26]
TreatmentDMT exposure2021Retrospective 2-center cohort study687 RRMS patientsRAWsHR = 1.11 (1.02–1.21), p = 0.015RAW was predicted by the temporary or permanent discontinuation of the initial DMT [15]
TreatmentDMT exposure2020Retrospective multicenter cohort study (RISM) 646 POMS, 8473 AOMS and 382 LOMS patients
at the first demyelinating event		Risk of 12-month confirmed disability worseningaHR in non-exposed versus exposed: 6.3 (4.9–8.0) for adult-onset, p < 0.0001; LOMS 1.9 (0.9–4.1), p = 0.07. DMT exposure reduced the risk of 12-month CDW, with a progressive risk reduction in different
quartiles of exposure in paediatric-onset and adult-onset patients.		[43]
TreatmentDMT exposure2020Retrospective multicenter cohort study (RISM) 646 POMS, 8473 AOMS and 382 LOMS patients
at the first demyelinating event		Risk of sustained EDSS 4.0aHR in non-exposed versus exposed: 6.3 (4.9–8.0) for adult-onset, p < 0.0001; LOMS 1.9 (0.9–4.1), p = 0.07. DMT exposure reduced the risk of sustained EDSS score of 4.0[43]
TreatmentDMT exposure2016Retrospective multi-center cohort study2466 MS patientsEDSS at 10 yearsCoeff = −0.86, p = 1.3 × 10−9.Cumulative treatment exposure was independently associated with lower EDSS at 10 years.[44]
TreatmentDMT exposure2020Retrospective multi-center cohort study (MSBase registry)1085 patients with ≥15-year follow-upRisk of relapsesHR 0.59, 95% CI 0.50–0.70, p = 10−9Treated patients were less likely to experience relapses (0.59, 0.50–0.70, p = 10−9) and worsening of disability[45]
TreatmentDMT exposure2020Retrospective multi-center cohort study (MSBase registry)1085 patients with ≥15-year follow-upRisk of EDSS worseningHR 0.81, 95% CI 0.67–0.99, p = 0.043Treated patients were less likely to experience relapses (0.59, 0.50–0.70, p = 10−9) and worsening of disability[45]
TreatmentDMT exposure2020Retrospective multicenter cohort study (RISM) 19,318 RRMS; 2343 SPMS identified with the DDA definition and 3868 identified with the neurologist SPMS conversion (SPMS definition according to ND and DDA)DDA group: HR (95% CI) 0.43 (0.36–0.50) p < 0.0001A longer exposure to DMT is associated with lower risk of SPMS [22]
Other biomarkersEvoked potentials2011Retrospective monocentric study80 MS patientsRisk of EDSS 4.0 and 6.0log-rank test: p < 0.001Increased risk of disability in patients with EP score higher than the median value. EP score of 8 or 9 showed the highest sensitivity and specificity in predicting EDSS 4.0 and 6.0[46]
Other biomarkersEvoked potentials2023Prospective monocentric study181 MS patientsRisk of MSSS worseningOR 0.04; IC 95% 0.01–0.06; p-Value 0.002P100 latency resulted in a predictor for disability over time (MSSS). [47]
Other biomarkersEvoked potentials2016Retrospective monocentric study100 MS patients EDSS worsening from baseline dataOR = 1.2; 95 % CI 1.1–1.3; p = 0.0012Baseline global EP score was a highly significant predictor of EDSS progression 6 years later.[48]
Fluid biomarkersGFAP2022Prospective cohort study (Comprehensive Longitudinal Investigation of MS at the Brigham and Women’s Hospital -climbstudy.org)257 MS patients6-months confirmed disability progression (6mCDP). EDSS progression was defined as an increase in the EDSS score since the previous visit of ≥1.0 point from an EDSS score of 1.0–5.0 or ≥0.5 point from an EDSS score of ≥5.5.6mCDP was defined as EDSS progression that was sustained for at least 180 days.HR = 1.71; 95% CI = 1.19–2.45; p = 0.004Higher sGFAP levels were associated with higher risk of 6mCDP. The association was stronger in patients with low sNfL (aHR = 2.44; 95% CI 1.32–4.52; p = 0.005) and patients who were nonactive in the 2 years prior or after the sample.[49]
Fluid biomarkersGFAP2017Retrospective monocentric studyGFAP levels in the CSF from 18 patients with RRMS, 8 patients with CIS and 35 healthy controlsInfratentorial chronic inflammatory lesion loadr = 0.55, p = 0.004GFAP concentrations significantly correlated with infratentorial chronic, post-inflammatory lesion load [50]
Fluid biomarkersGFAP2017Retrospective monocentric studyGFAP levels in the CSF from 18 patients with RRMS, 8 patients with CIS and 35 healthy controlsInfratentorial chronic inflammatory lesion loadr = 0.71, p = 0.0002GFAP concentrations significantly correlated with the intensity of gadolinium-enhancement as a parameter for the acute activity of inflammatory processes.[50]
Fluid biomarkersGFAP2024Retrospective study (but with prospective data collection)133 RRMS patientsSPMS conversionc β: 0.34
[−0.78;1.46]; p = 0.555		GFAP was not associated with conversion to SPMS. [51]
Fluid biomarkersGFAP2024Retrospective study (but with prospective data collection)133 RRMS patientsEDSS score worseningc β: 0.34
[−0.78;1.46]; p = 0.556		GFAP was not associated with disability progression. [51]
Radiological predictorsGray matter pathology2014Prospective cohort study73 MS patientsEDSS worseningOR = 0.79, p = 0.01; C-index = 0.69Baseline GMF is predictor of worsening of disability in the long term. [52]
Radiological predictorsGray matter pathology2021Prospective study 332 MS patients, 96 healthy controls Cognitive decline (test-defined assessment)Nagelkerke R2 = 0.22, p < 0.001A prediction model that included only whole-brain MRI measures showed cortical grey matter volume as the only significant MRI predictor of cognitive decline.[53]
Radiological predictorsGray matter pathology2021Prospective study63 MS patientsEDSS−0.26 (−0.444 to −0.074)Across all subjects, cortical lesions, grey matter volume and cervical cord volume explained 60% of the variance of the Expanded Disability Status Scale. [36]
Radiological predictorsGray matter pathology2022Retrospective multi-center cohort study373 MS patientsDifference in mean EDSS score over the years of follow-upA deep learning architecture based on convolutional neural networks was implemented to predict: (1) clinical worsening (EDSS-based model), (2) cognitive deterioration (SDMT-based model), or (3) both (EDSS + SDMT-based model). The convolutional neural network model showed high predictive accuracy for clinical (83.3%) and cognitive (67.7%) worsening, although the highest accuracy was reached when training the algorithm using both EDSS and SDMT information (85.7%).[54]
Radiological predictorsGray matter pathology2014Prospective study81 MS patients Disease progressionPatients with disability Progression showed significantly increased loss of whole brain (−3.8% vs. −2.0%, p < 0.001), cortical (−3.4% vs. −1.8%, p = 0.009) compared to patients with no progression.GM atrophy showed association with disease progression[55]
Demographic and clinical prognostic factorsOnset type2022Retrospective study21 RRMS patients, 13 SPMS patients, along with 10 age-matched healthy controlsSPMS conversionp > 0.05Affected bowel and bladder functions
during the first relapse were ineffective in predicting the transition to the SPMS course. 		[56]
Demographic and clinical prognostic factorsOnset type2015Observational study based on a prospective, open cohort1016 CISRisk of reaching EDSS score of at least 3.0 in 2 evaluations (defined “disability accumulation”)HR 0.5; 95% CI 0.3–0.8 Patients presenting CIS with optic neuritis appeared to display a lower risk of reaching an EDSS score of 3.0. [33]
Demographic and clinical prognostic factorsOnset type2016Prospective observational study793 MS patientsReaching moderate disability: EDSS 3.0–5.5HR 0.42; CI 0.23–0.77; p = 0.005Complete remission of neurological symptoms at onset reduced the risk of moderate disability.[35]
Demographic and clinical prognostic factorsOnset type2020Retrospective multicenter cohort study (RISM) 19,318 RRMS; 2343 SPMS identified with dhe DDA definition and 3868 identified with the neurologist definition (ND) SPMS conversion (SPMS definition according to ND and DDA)DDA group: HR (95% CI) 1.26 (1.12–1.40), p < 0.0001;
ND group: 1.13 (1.03–1.23), p = 0.011		Multifocal onset is associeted with higher risk of SPMS [22]
Demographic and clinical prognostic factorsOnset type2020Retrospective monocentric study157 PPMS patientsTime to EDSS 6.0 HR (95% CI): 2.13 (1.24–3.63); p= 0.006The presence of spinal motor symptoms at onset were associated with a shorter time to EDSS6[25]
Demographic and clinical prognostic factorsOnset type2013Retrospective monocentric study197 MS patientsRisk of EDSS 6.08.1 and 13.1 fold increased risk to EDSS 6, respectively (p = 0.04 and p = 0.01). Motor and brainstem symptoms at onset were also associated with higher risk of EDSS 6.0[57]
Demographic and clinical prognostic factorsOnset type2020Prospective study415 MS patientsRisk of EDSS 6.0HR 2.107, 95% CI 1.168–3.800, p = 0.013An incomplete recovery from first attack was significantly associated with a higher HR to reach an EDSS of 6.0.[27]
Demographic and clinical prognostic factorsOnset type2015Population-based cohort (retrospective - prospective)Population-based cohort (105 patients with relapsing-remitting MS, 86 with bout-onset progressive MS) and a clinic-based cohort (415 patients with bout-onset progressive MS)Recovery from first relapsep = 0.001A brainstem, cerebellar, or spinal cord syndrome was associated with a poor recovery from the initial relapse.[58]
Other biomarkersOptic nerve diameter2021Prospective study63 MS patients Disease progressionp = 0.041 for the right eye and p = 0.037 for the left eyeSmaller diameters of optic nerve are associated with poor clinical progression and greater disability (measured by EDSS).[59]
Other biomarkersOptic nerve diameter2021Prospective study63 MS patients Sustained increase (>3 months) of over 0.5 points on the EDSS.p = 0.07 for the right eye and p = 0.043 for the left eyeSmaller diameters of optic nerve are associated with poor clinical progression and greater disability (measured by EDSS).[59]
Other biomarkersOptic nerve diameter2019Prospective study49 RRMS patients, 50 matched healthy controlsSustained EDSS > 2p = 0.044, OR = 0.000, 95% CI = 0.000–0.589Optic nerve diameter was an independent predictor of EDSS > 2[60]
Demographic and clinical prognostic factorsPIRA2023Retrospective analysis of data from patients prospectively included (patients with a first demyelinating attack)1128 patientsAdjusted yearly EDSS increase rates0.31; 95% CI, 0.26–0.35 vs. 0.13; 95% CI, 0.10–0.16; p < 0.001Early PIRA had steeper EDSS yearly increase rates than late PIRA.[23]
Demographic and clinical prognostic factorsPIRA2023Retrospective analysis of data from patients prospectively included (patients with a first demyelinating attack)1128 patientsRisk of reaching EDSS 6.0HR, 26.21; 95% CI, 2.26–303.95; p = 0.009Early PIRA had a 26-fold greater risk of reaching EDSS 6.0 from the first attack (HR, 26.21; 95% CI, 2.26–303.95; p = 0.009).[23]
Demographic and clinical prognostic factorsPIRA2023Retrospective analysis of data from patients prospectively included (patients with a first demyelinating attack)1128 patientsRisk of reaching EDSS 6.0HR, 7.93; 95% CI, 2.25–27.96; p = 0.001Patients with PIRA had an 8-fold greater risk of reaching EDSS 6.0.[23]
Radiological predictorsPresence of new Gd + SC lesions2018Single-centre retrospective study201 RRMS patients Relapse occurrence (clinical relapses within 3 months)B 1.113, Exp (B), 95% CI for EXP(B) 3.042, 1.158–7.995; p = 0.024A significant association between new Gd + SC lesions and clinical relapses within 3 months was found. [61]
Radiological predictorsPresence of new Gd + SC lesions2018Single-centre retrospective study201 RRMS patients DMT changes within 3 monthsB 1.482, Exp (B), 95% CI for EXP(B) 4.402, 1.642–11.799; p = 0.003Even without clinical symptoms, worsening SC findings significantly predicted treatment changes. [61]
Fluid biomarkersPresence of OCBs2013Meta analysis: 71 studies12,253 MS patients,EDSS worsening, EDSS disability milestones1.96 (95% CI 1.31 to 2.94; p = 0.001) with no between-study heterogeneity (I2 = 0%; X2 = 2.95, df = 3, p = 0.40) OCB-positive MS patients had an OR of 1.96 of reaching disability outcomes. [62]
Fluid biomarkersPresence of OCBs2021Retrospective registry-based study7322 patients, 6494 OCB+Risk of reaching sustained EDSS score milestones 3.0, 4.0 and 6.0EDSS 3.0 (HR = 1.29, 95% CI 1.12 to 1.48, p < 0.001) and 4.0 (HR = 1.38, 95% CI 1.17 to 1.63, p < 0.001).CSF-OCB presence is associated with higher risk of reaching EDSS milestones 3.0 anf 4.0.[63]
Fluid biomarkersPresence of OCBs2021Retrospective registry-based study7322 patients, 6494 OCB+SPMS conversion.HR: 1.20, 95% CI 1.02 to 1.41, p = 0.03, n = 5721OCB positivity IS associated with increased risk of conversion to SPMS.[63]
Fluid biomarkersPresence of OCBs2015Observational study based on a prospective, open cohort1017 CISRisk of reaching EDSS score of at least 3.0 in 2 evaluations (defined “disability accumulation”)adjusted HR scores of 2.0 (95% CI 1.2–3.6)The presence of OCBs was associated with a higher risk of the accumulation of disability[33]
Fluid biomarkersPresence of OCBs2021Meta-analysisSix longitudinal studies, 1221 CIS/early RRMS patients Risk of a second clinical relapse. HR = 2.18, 95% CI 1.24–3.82, I2 = 73%, p = 0.007The pooled analysis confirmed that the presence of OCBs (IgG) is a risk factor for a second clinical relapse.[41]
Fluid biomarkersPresence of OCBs2021Retrospective monocentric study358 patients, 287 OCB positiveMSSS OCB + vs. OCB - (2.10 vs. 0.94, p value = 0.023)Median MSSS was significantly higher in the OCB positive group (2.10 vs. 0.94, p value = 0.023) and remained significant when controlling for age at EDSS.[64]
Fluid biomarkersPresence of OCBs201710-year observational, cross-sectional study40 OCB-negative and 50 OCB-positive MS Presence of cortical lesionsmean ± standard deviation: OCB + 6.1 ± 6.1 (0–24), ocb − 2.2 ± 2.8 (0–11), p < 0.0001Increased number of CLs was found in OCB+ compared to OCB− patients.[38]
Demographic and clinical prognostic factorsRelapses2015Population-based cohort (retrospective-prospective)Population-based cohort (105 patients with relapsing-remitting MS, 86 with bout-onset progressive MS) and a clinic-based cohort (415 patients with bout-onset progressive MS)SPMS conversionHalf of the good recoverers developed progressive MS by 30.2 years after MS onset, whereas half of the poor recoverers developed progressive MS by 8.3 years after MS onset (p = 0.001). Patients with MS with poor recovery from early relapses will develop progressive disease course earlier than those with good recovery. [58]
Demographic and clinical prognostic factorsRelapses2020Prospective study415 MS patientsRisk of EDSS 6.0HR 2.217, 95% CI 1.148–4.281, p = 0.018 ≥2 relapses during the first 2 years after onset were significantly associated with a higher HR to reach an EDSS of 6.0.[27]
Demographic and clinical prognostic factorsRelapses2015Population-based cohort (retrospective-prospective)Population-based cohort (105 patients with relapsing-remitting MS, 86 with bout-onset progressive MS) and a clinic-based cohort (415 patients with bout-onset progressive MS)Recovery from first relapsep = 0.001A fulminant relapse was associated with a poor recovery from the initial relapse.[58]
Demographic and clinical prognostic factorsRelapses2010Retrospective study (London Multiple Sclerosis Clinic database)806 RTMS patientsDSS 6, 8, 10various ORFrequent relapses in the first 2 years and shorter first inter-attack intervals predicted shorter times to reach hard disability endpoints. [65]
Demographic and clinical prognostic factorsRelapses2013Retrospective monocentric study197 MS patientsRisk of EDSS 8.01.28 (5 years) and 1.19 (10 years), respectively p = 0.032 and p = 0.015The number of relapses in five and ten years of disease onset was associated with a slightly increased risk to EDSS 8[57]
Demographic and clinical prognostic factorsRelapses2024Retrospective cohort115 RMSChange in EDSS from first assessment at relapse to EDSS after the last relapse treatment for all relapse events: EDSS improvement (after relapse treatment with steroid or PLEX)Regression coefficient (95% CI): −0.32 (−0.44, −0.19) p < 0.001Female sex, younger age, and a higher EDSS during relapse as factors associated with a higher chance of EDSS improvement after relapse treatment. [17]
Demographic and clinical prognostic factorsRelapses2020Retrospective multicenter cohort study (RISM) 19,318 RRMS; 2343 SPMS identified with the DDA definition and 3868 identified with the neurologist definition (ND) SPMS conversion (SPMS definition according to ND and DDA)DDA group: HR (95% CI) 2.90 (2.54–3.30),
p < 0.0001; ND group: 1.78 (1.64–1.94), p < 0.0001		A higher number of relapses during RRMS phase is associated with higher risk of SPMS [22]
Demographic and clinical prognostic factorsRelapses2020Retrospective multicenter cohort study (RISM) 646 POMS, 8473 AOMS and 382 LOMS patients
at the first demyelinating event		Risk of 12-month confirmed disability worseningaHR: AOMS 1.37 (1.36–1.39); LOMS 1.40 (1.31–1.49)Relapses were a risk factor for 12-month confirmed disability worsening in all three cohorts[43]
Fluid biomarkersSerum NfL level2020Prospective cohort study258 MS patientsConversion to clinically diagnosed progressive MSAUC of 0.744 (95% CI 0.61–0.88, p = 0.054).MS patients with low serum NfL values (<7.62 pg/mL) at the baseline were 7.1 times less likely to develop progressive MS. [66]
Fluid biomarkersSerum NfL level2020Prospective cohort study258 MS patientsAnnual rate of EDSS progression0.17 units/year, Kruskal–Wallis p = 0.020, df 2Patients with the highest NfL levels (>13.2 pg/mL) progressed most rapidly with an EDSS annual rate of 0.16 (p = 0.004), remaining significant after adjustment for sex, age, and disease-modifying treatment (p = 0.022)[66]
Fluid biomarkersSerum NfL level2022Case-control5390 control, 1313 MS patientsRisk of relapseOR 1.41, 95% CI
1.30–1.54; p < 0.0001		Patients with higher sNfL Z scores showed a
greater probability of relapses in the
following year, based on a model with Z score as a
continuous predictor. 		[67]
Fluid biomarkersSerum NfL level2022Case-control5390 control, 1313 MS patientsEDSS worseningOR 1.11, 1.03–1.21;
p = 0.0093		People with higher sNfL Z scores showed a
greater probability of EDSS worsening in the
following year, based on a model with Z score as a
continuous predictor. 		[67]
Fluid biomarkersSerum NfL level2022Case-control5390 control, 1313 MS patientsEDA 3OR 1.43, 1.31–1.57; p < 0.0001People with higher sNfL Z scores showed a
greater probability of EDA-3 in the following year, based on a model with Z score as a
continuous predictor		[67]
Fluid biomarkersSerum NfL level2022Case-control5390 control, 1313 MS patientsClinical or MRI disease
activity		All people with multiple sclerosis: OR 3.15,
95% CI 2.35–4.23; p < 0.0001); people
considered stable with no evidence of disease activity
(2.66, 1.08–6.55; p = 0.034)		A sNfL Z score above 1.5 was associated
with an increased risk of future clinical or MRI disease
activity in all people with multiple sclerosis and in people
considered stable with no evidence of disease activity.		[67]
Fluid biomarkersSerum NfL level2019Prospective study235 MS patients in a 2-year RCT of intramuscular interferon β-1a, and in serum (n = 164) from the extension study.Risk of EDSS ≥ 6.0 at Year 8 OR = 3.4; 95% CI = 1.2–9.9, p < 0.05Year 2 CSF levels were predictive of reaching EDSS ≥ 6.0 at Year 8[68]
Fluid biomarkersSerum NfL level2019Prospective study235 MS patients in a 2-year RCT of intramuscular interferon β-1a, and in serum (n = 164) from the extension study.Risk of EDSS ≥ 6.0 at Year 8 OR = 11.0, 95% CI = 2.0–114.6; p < 0.01Year 3 serum levels were predictive of reaching EDSS ≥ 6.0 at Year 9[68]
Fluid biomarkersSerum NfL level2019Prospective study235 MS patients in a 2-year RCT of intramuscular interferon β-1a, and in serum (n = 164) from the extension study.Risk of EDSS ≥ 6.0 at Year 15OR (upper vs. lower tertile) = 4.9; 95% CI = 1.4–20.4; p < 0.05Year 4 serum levels were predictive of reaching EDSS ≥ 6.0 at Year 15[68]
Fluid biomarkersSerum NfL level2021Prospective study369 blood samples from 155 early relapsing-remitting MS patients on interferon beta-1a. Odds of EDA-3upper vs. lower 86.5% vs. 57.9%; OR = 4.25, 95% CI: [2.02, 8.95]; p = 0.0001In patients with disease activity (EDA-3), those with higher sNFL had higher odds of EDA-3 in the following year than those with low sNFL.[69]
Fluid biomarkersSerum NfL level2021Prospective study369 blood samples from 155 early relapsing-remitting MS patients on interferon beta. BVL (brain volume loss)β = −0.36%; 95% CI = [−0.60, −0.13]; p = 0.002In patients with disease activity (EDA-3), those with higher sNFL had greater whole brain volume loss during the following year.[69]
Fluid biomarkersSerum NfL level2020Prospective cohort study258 MS patientsEDSS score of ≥ 4 2nd tertile (>7.62 pg/mL): HR = 5.5 (95% CI 1.4–21.0), p = 0.012; 3rd tertile (>13.2 pg/mL): HR = 5.2 (95% CI 1.5–18.6), p = 0.010. AUC of 0.734 (95% CI 0.63–0.84, p = 0.001).MS patients with higher serum NfL values (>7.62 pg/mL) at the baseline had a significantly higher risk of developing an EDSS ≥ 4, showing that they were on average > 5-times at higher risk of developing EDSS ≥ 4 over the follow-up. [66]
Fluid biomarkersSerum NfL level2024Retrospective study (but with prospective data collection)133 RRMS patientsSPMS conversionc β [95% CI]: 9.92 [0.62;19.21]; p = 0.037sNfL was associated with conversion to SPMS,[51]
Fluid biomarkersSerum NfL level2024Retrospective study (but with prospective data collection)133 RRMS patientsEDSS score worseningc β: 0.34
[−0.78;1.46]; p = 0.554		sNfL was not associated with disability progression. [51]
Demographic and clinical prognostic factorsSex2013Retrospective multi-center cohort study (MSBase registry)11,570 relapse-onset patients and 881 patients with PPMSRelapse incidenceRelapse frequency was 17.7% higher in females compared with males.Within the initial 5 years, the female-to-male ratio increased from 2.3:1 to 3.3:1 in patients with 0 versus ≥4 relapses per year, respectively. [14]
Demographic and clinical prognostic factorsSex2015Observational study based on a prospective, open cohort1015 CISRisk of reaching EDSS score of at least 3.0 in 2 evaluations (defined “disability accumulation”)HR 0.7; 95% CI 0.5–1.1 Female sex appeared to display a lower risk of reaching an EDSS score of 3.0. [33]
Demographic and clinical prognostic factorsSex2024Retrospective cohort113 RMSChange in EDSS from first assessment at relapse to EDSS after the last relapse treatment for all relapse events: EDSS improvement (after relapse treatment with steroid or PLEX)Regression coefficient (95% CI): −0.60 (−0.98, −0.21) p < 0.01Female sex, younger age, and a higher EDSS during relapse as factors associated with a higher chance of EDSS improvement after relapse treatment. [17]
Demographic and clinical prognostic factorsSex2019Retrospective cohort2083 RRMSWalking speed (T25FW speed)HR 0.28, 95% CI 0.20–0.36, <0.001Walking speed is slower in females. [18]
Demographic and clinical prognostic factorsSex2013Retrospective monocentric study197 MS patientsRisk of EDSS 6.0, 7.04.63-fold increased risk to EDSS 6 (p < 0.001); 4.69-fold increased risk to EDSS 7 (p = 0.006). Male sex was associated with a higher risk to EDSS 6 and 7. [57]
Demographic and clinical prognostic factorsSex2024Prospective study149 MS patientstime-to-relapseHR = 0.91; 95 %CI = 0.53–1.58No sex differences in time-to-relapse emerged. [70]
Demographic and clinical prognostic factorsSex2024Prospective study149 MS patientsEDSS worseningOR = 0.75; 95% CI = 0.21–2.35Males had no increased risk of EDSS worsening compared to females. [70]
Demographic and clinical prognostic factorsSex2020Retrospective multicenter cohort study (RISM) 646 POMS, 8473 AOMS and 382 LOMS patients
at the first demyelinating event		Risk of 12-month confirmed disability worseningaHR: LOMS, female sex 0.74 (0.53–1.04)Female sex exerted a protective role in
the late-onset cohort for the risk of confirmed 12-months CDW.		[43]
Radiological predictorsSpinal cord atrophy2014Prospective study159 MS patients Risk of EDSS score of at least 6.0 (requirement of a walking aid)OR = 0.57 per 1 SD higher cord area; 95% CI 0.37, 0.86; p = 0.01Long-term physical disability was independently linked with atrophy of the spinal cord [71]
Radiological predictorsSpinal cord atrophy2021Prospective study63 MS patientsEDSS −0.27 (−0.421 to −0.109)Across all subjects, cortical lesions, grey matter volume and cervical cord volume explained 60% of the variance of the Expanded Disability Status Scale. [36]
Radiological predictorsSpinal cord lesions2024Monocentric retrospective study205 RRMSCDA occurrence. CDA was defined as an EDSS increase of 1.5 for baseline EDSS scores of 0, an increase of 1 for baseline EDSS scores between 1.0 and 5.0, and an increase of 0.5 for baseline EDSS scores of 5.5 or higher; the increase had to be confirmed on clinical follow-up over at least 6 months; PIRA and RAWSpearman’s rank correlation coefficient (rs): SCLN and SCLV were closely correlated (rs = 0.91, p < 0.001) and were both significantly associated with CDA on follow-up (p < 0.001). Subgroup analyses confirmed this association for patients with PIRA on CDA (34 events, p < 0.001)The number of SC lesions on MRI is associated with future accumulation of disability largely independent of relapses. [72]
Radiological predictorsSpinal cord lesions2019Prospective study178 MS patientsEDSS correlationβ = 1.53, p < 0.01Spinal cord lesions showed a consistent association with Expanded Disability Status Scale at 15 years. [32]
Radiological predictorsSpinal cord lesions2019Prospective study178 MS patientsSPMS conversion at 15 years follow-upOR 4.71, 1.72, 12.92; p = 0.003Spinal cord lesions were independently associated with secondary progressive multiple sclerosis at 15 years. [32]
Radiological predictorsSpinal cord lesions2021Retrospective 2-center cohort study687 RRMS patientsCDA occurrenceSub distribution HR = 4.08 (1.29–12.87), p = 0.016The risk of PIRA was associated with the presence of spinal cord lesions at baseline MRI scan. [15]
Radiological predictorsSpinal cord lesions2024Monocentric retrospective study204 RRMSCDA occurrenceOR 5.8, 95% CI 2.1 to 19.10The volume of SC lesions on MRI is associated with future accumulation of disability largely independent of relapses. [72]
Radiological predictorsSpinal cord lesions2010Retrospective cohort study25 RRMS patientsEDSS 4.0HR 7.2, 95% confidence interval 1.4–36.4The diffuse abnormality in cervical spinal cord at the beginning of the disease is persistent and predicts a worse prognosis in RRMS patients.[73]
Radiological predictorsSpinal cord lesions2024Monocentric retrospective study204 RRMSCDA occurrenceOR 5.8, 95% CI 2.1 to 19.8Patients without any SC lesions experienced significantly less CDA [72]
TreatmentTime to DMT initiation2023Retrospective multicenter cohort study (RISM) 16,130 MS patients Risk of PIRAHR, 1.16; 95% CI, 1.00–1.34; p = 0.04Delayed DMT initiation was associated with higher risk of PIRA events.[26]
TreatmentTime to DMT initiation2023Retrospective multicenter cohort study (RISM) 16,130 MS patients Risk of RAWHR, 1.75; 95% CI, 1.28–2.39; p = 0.001Delayed DMT initiation was associated with higher risk of RAW events. [26]
TreatmentTime to DMT initiation2021Retrospective multi-center cohort study (BMSD) 11,871 RRMS patients3-month CDWRetrospective 2-center cohort study+G91The time interval between disease onset and the first DMT start is a strong predictor of disability accumulation, independent of relapse activity, over the long-term.[74]
TreatmentTime to DMT initiation2021Retrospective multi-center cohort study (BMSD) 11,871 RRMS patients12-month CDWHR 95% CI 1.21 (1.09–1.35) p = 0.0004The time interval between disease onset and the first DMT start is a strong predictor of disability accumulation, independent of relapse activity, over the long-term.[74]
TreatmentTime to DMT initiation2020Retrospective multi-center cohort study (MSBase registry; Swedish MS registry) 308 in the MSBase registry and 236 in the Swedish MS registry Difference in mean EDSS score over the years of follow-upMean EDSS 2.2 (SD 1.6) in the early group compared with 2.9 (SD 1.8) in the late group (p < 0.0001). All follow-up years (mean EDSS score 2.3 [SD 1.8] vs. 3.5 [SD 2.1]; p < 0.0001), with a difference between groups of −0.98 (95% CI −1.51 to −0.45; p < 0.0001)High-efficacy therapy commenced within 2 years of disease onset is associated with less disability after 6–10 years than when commenced later in the disease course.[75]
TreatmentTime to DMT initiation2018Retrospective multi-center cohort study 3795 MS patients (Danish MS Register)Risk of reaching EDSS 6.0HR, 1.42; 95% confidence interval (CI), 1.18–1.70; p < 0.001Patients who started treatment with DMT later reached an EDSS score of 6 more quickly compared with patients who started early[76]
TreatmentTime to DMT initiation2018Retrospective multi-center cohort study 3795 MS patients (Danish MS Register)Mortality HR, 1.38; 95% CI, 0.96–1.99; p = 0.08Mortality increased by 38% in later DMT starters.[76]
TreatmentTime to DMT initiation2023Observational cohort study2648 MS patients MSIS physical score and psychological score worsening Worsening of MSIS physical score worsening by 2.75 points (95% CI 1.29 to 4.20), and MSIS psychological score by 2.02 points (95% CI 0.03 to 3.78)Earlier commencement of disease-modifying treatment was associated with better patient-reported physical symptoms[77]
TreatmentTreatment strategy2021Retrospective multicenter cohort study (RISM) 2702 RRMS patients (PS matching: 363 pairs)Mean annual delta-EDSSMean annual delta-EDSS values were all significantly (p < 0.02) higher in the ESC group compared with the EIT group. In particular, the mean delta-EDSS differences between the two groups tended to increase from 0.1 (0.01–0.19, p = 0.03) at 1 year to 0.30 (0.07–0.53, p = 0.009) at 5 years and to 0.67 (0.31–1.03, p = 0.0003) at 10 years.EIT strategy is more effective than ESC strategy in controlling disability progression over time.[78]
TreatmentTreatment strategy2023Retrospective multi-center cohort study (Swedish MS registry, Czech national MS registry)6410 MS patientsRisk of reaching EDSS 4 HR 0.74, 95% CI 0.6–0.91, p-value 0.0327)The risk of reaching EDSS 4.0 was reduced by 26% in patients starting with HE DMTs. [79]
TreatmentTreatment strategy2023Retrospective multi-center cohort study (Swedish MS registry, Czech national MS registry)6410 MS patientsRisk of relapsesHR 0.34, 95% CI 0.3–0.39, p-value < 0.001)The risk of relapses was reduced by 66% (HR 0.34, 95% CI 0.3–0.39, p-value < 0.001)[79]
TreatmentTreatment strategy2023Retrospective multi-center cohort study (Swedish MS registry, Czech national MS registry)6410 MS patientsProbability of confirmed disability improvement (CDI)HR 3.04, 95% CI 2.37–3.9, p-value < 0.001The probability of CDI was three times higher. [79]
TreatmentTreatment strategy2019Retrospective cohort study 592 MS patientsMean (SD) 5-year change in EDSS scoreEIT group vs. the ESC group (0.3 [1.5] vs. 1.2 [1.5]). β = −0.85; 95% CI, −1.38 to −0.32; p = 0.002) Mean (SD) 5-year change in EDSS score was lower in the EIT group than the ESC group[80]
TreatmentTreatment strategy2019Retrospective cohort study 593 MS patientsMedian time to sustained accumulation of disability (SAD)6.0 (3.17–9.16) years for EIT and 3.14 (2.77–4.00) years for ESC (p = 0.05). Median time to SAD was longer for the EIT group[80]
TreatmentTreatment strategy2020Retrospective cohort study (Danish MS Register)388 patients in the study: 194 starting initial therapy with heDMT matched to 194 patients starting meDMT.6-month confirmed EDSS score worsening16.7% (95% CI 10.4–23.0%) and 30.1% (95% CI 23.1–37.1%) for heDMT and meDMT initiators, respectively (HR 0.53, 95% CI 0.33–0.83, p = 0.006). A lower probability of 6-month confirmed EDSS score worsening was found in patients starting a heDMT as first therapy, compared to a matched sample starting meDMT.[81]
TreatmentTreatment strategy2020Retrospective cohort study (Danish MS Register)388 patients in the study: 194 starting initial therapy with heDMT matched to 194 patients starting meDMT.Risk of first relapse after treatment startHR 0.50, 95% CI 0.37–0.67A lower probability of a first relapse was found in patients starting a heDMT as first therapy, compared to a matched sample starting meDMT.[81]
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Guerra, T.; Copetti, M.; Achille, M.; Ferri, C.; Simone, M.; D’Alfonso, S.; Pugliatti, M.; Iaffaldano, P. Refining Prognostic Factors in Adult-Onset Multiple Sclerosis: A Narrative Review of Current Insights. Int. J. Mol. Sci. 2025, 26, 7756. https://doi.org/10.3390/ijms26167756

AMA Style

Guerra T, Copetti M, Achille M, Ferri C, Simone M, D’Alfonso S, Pugliatti M, Iaffaldano P. Refining Prognostic Factors in Adult-Onset Multiple Sclerosis: A Narrative Review of Current Insights. International Journal of Molecular Sciences. 2025; 26(16):7756. https://doi.org/10.3390/ijms26167756

Chicago/Turabian Style

Guerra, Tommaso, Massimiliano Copetti, Mariaclara Achille, Caterina Ferri, Marta Simone, Sandra D’Alfonso, Maura Pugliatti, and Pietro Iaffaldano. 2025. "Refining Prognostic Factors in Adult-Onset Multiple Sclerosis: A Narrative Review of Current Insights" International Journal of Molecular Sciences 26, no. 16: 7756. https://doi.org/10.3390/ijms26167756

APA Style

Guerra, T., Copetti, M., Achille, M., Ferri, C., Simone, M., D’Alfonso, S., Pugliatti, M., & Iaffaldano, P. (2025). Refining Prognostic Factors in Adult-Onset Multiple Sclerosis: A Narrative Review of Current Insights. International Journal of Molecular Sciences, 26(16), 7756. https://doi.org/10.3390/ijms26167756

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