Multiple sclerosis (MS) is a chronic autoimmune disorder characterized by inflammation, demyelination, and neurodegeneration within the central nervous system (CNS). While the inflammatory components of MS have been extensively studied, the progressive neurodegenerative aspect remains a critical factor contributing to long-term disability. Therefore, the identification and validation of biomarkers associated with neurodegenerative processes are essential for improved diagnosis, prognosis, and treatment monitoring. This review explores cerebrospinal fluid (CSF) and blood-based biomarkers, including neurofilaments, lipid markers, kynurenines, and other molecular indicators that provide insights into neurodegeneration in MS.

Multiple sclerosis (MS) is a chronic, immune-mediated neurological disorder with increasing global prevalence. Emerging evidence underscores the role of lifestyle interventions (particularly diet and physical activity) in modulating disease progression and improving quality of life. This narrative review synthesizes current scientific literature on the effects of dietary interventions, including the Mediterranean, ketogenic, Swank, Wahls, gluten-free, and fasting-based diets, alongside various physical activity regimens. The Mediterranean and ketogenic diets show promise in reducing inflammation, enhancing neuroprotection, and improving metabolic health. Similarly, structured physical activity (including aerobic, resistance, sensorimotor, and mind–body exercises) demonstrates benefits in mobility, fatigue, and mental well-being. The review highlights the need for personalized, sustainable approaches that integrate nutritional and exercise-based strategies for optimal MS management in the long term.

The gut microbiota has emerged as a central regulator of the gut–brain axis, profoundly influencing neural, immune, and metabolic homeostasis. Increasing evidence indicates that disturbances in microbial composition and function contribute to the onset and progression of neurodegenerative diseases (NDs) through mechanisms involving neuroinflammation, oxidative stress, and impaired neurotransmission. Gut dysbiosis is characterized by a loss of microbial diversity, a reduction in beneficial commensals, and an enrichment of pro-inflammatory taxa. These shifts alter intestinal permeability and systemic immune tone, allowing microbial metabolites and immune mediators to affect central nervous system (CNS) integrity. Metabolites such as short-chain fatty acids (SCFAs), tryptophan derivatives, lipopolysaccharides (LPS), and trimethylamine N-oxide (TMAO) modulate blood–brain barrier (BBB) function, microglial activation, and neurotransmitter synthesis, linking intestinal imbalance to neuronal dysfunction and cognitive decline. Disruption of this gut–brain communication network promotes chronic inflammation and metabolic dysregulation, key features of neurodegenerative pathology. SCFA-producing and tryptophan-metabolizing bacteria appear to exert neuroprotective effects by modulating immune responses, epigenetic regulation, and neuronal resilience. The aim of this work was to comprehensively explore the current evidence on the bidirectional communication between the gut microbiota and the CNS, with a focus on identifying the principal molecular, immune, and metabolic mechanisms supported by the strongest and most consistent data. By integrating findings from recent human studies, this review sought to clarify how microbial composition and function influence neurochemical balance, immune activation, and BBB integrity, ultimately contributing to the onset and progression of neurodegenerative processes. Collectively, these findings position the gut microbiota as a dynamic interface between the enteric and CNS, capable of influencing neurodegenerative processes through immune and metabolic signaling.

Secondary Progressive Multiple Sclerosis (SPMS) represents a challenging phase of multiple sclerosis, marked by gradual neurological decline and reduced inflammatory activity. In recent years, magnetic resonance imaging (MRI) has become essential for characterizing the neurodegenerative changes underlying SPMS, including white and gray matter damage, brain atrophy, slowly expanding lesions, and iron rim lesions. This narrative review aims to synthesize the current knowledge on established and emerging MRI biomarkers relevant to SPMS, with a particular focus on their diagnostic, prognostic, and therapeutic implications. This review discusses key themes, such as the shift from inflammatory to neurodegenerative mechanisms, the role of advanced imaging techniques, and the limitations of conventional MRI in detecting smoldering disease. In doing so, it identifies current gaps in evidence, including the need for standardized imaging protocols and large-scale longitudinal studies. A clearer understanding and application of MRI biomarkers may facilitate earlier diagnosis, more tailored treatment strategies, and improved outcomes in patients with SPMS.

Ischemic stroke remains a leading cause of mortality and disability worldwide, with current therapies such as intravenous thrombolysis and mechanical thrombectomy benefiting only a limited proportion of patients. Neuroinflammation is a key contributor to secondary brain injury, creating a strong rationale for adjunctive therapies targeting immune modulation. Fingolimod, a sphingosine-1-phosphate receptor (S1PR) modulator originally approved for multiple sclerosis, has shown promising effects in both preclinical and early clinical studies of acute ischemic stroke. Methods: We conducted a structured narrative review of preclinical and clinical studies published between 2015 and 2024, using PubMed, Scopus, and Web of Science databases. Inclusion criteria were original studies evaluating fingolimod in ischemic stroke models or human patients, either as monotherapy or in combination with reperfusion therapies. Exclusion criteria included conference abstracts without peer review, studies lacking mechanistic insight, and non-English publications. Results: Preclinical evidence demonstrates that fingolimod reduces infarct size, preserves blood–brain barrier integrity, and modulates neuroinflammation through multiple mechanisms, including T cell sequestration, microglial polarization, and mitochondrial protection. Clinical trials, though limited in size, suggest improved short- and long-term outcomes when fingolimod is used in combination with intravenous thrombolysis or endovascular therapy, with a manageable safety profile. Novel nanotechnology-based delivery systems further enhance central nervous system (CNS) targeting and reduce systemic side effects. Conclusions: Fingolimod represents a promising multi-targeted adjunctive strategy for ischemic stroke, acting at the intersection of immune modulation, vascular protection, and neuroprotection. While current findings are encouraging, larger randomized controlled trials and biomarker-driven patient selection are needed to validate its clinical utility. This review highlights the translational potential of fingolimod and outlines key directions for future research.

Background: The clinical–radiological paradox in multiple sclerosis (MS) underscores the need for biomarkers that better reflect neurodegenerative pathology. Serum neurofilament light chain (sNfL) is a dynamic marker of neuroaxonal injury, while brain volumetry provides structural assessment of disease impact. However, the precise link between sNfL and regional atrophy patterns, as well as their combined utility for patient stratification and prediction, remains underexplored. Objective: This study aimed to establish a multimodal biomarker framework by integrating sNfL with comprehensive volumetric MRI to define neurodegenerative endophenotypes and predict neuroaxonal injury using Bayesian inference and machine learning. Methods: In a cohort of 57 MS patients, sNfL levels were measured using single-molecule array (Simoa) technology. Brain volumes for 42 regions were quantified via automated deep learning segmentation (mdbrain software). We employed (1) Bayesian correlation to quantify evidence for sNfL–volumetric associations; (2) mediation analysis to test whether grey matter atrophy mediates the EDSS–sNfL (Expanded Disability Status Scale) relationship; (3) unsupervised K-means clustering to identify patient subtypes based on combined sNfL–volumetric profiles; and (4) supervised machine learning (Elastic Net and Random Forest regression) to predict sNfL from volumetric features. Results: Bayesian analysis revealed strong evidence linking sNfL to total grey matter volume (r = −0.449, BF₁₀ = 0.022) and lateral ventricular volume (r = 0.349, BF₁₀ = 0.285). Mediation confirmed that grey matter atrophy significantly mediates the relationship between EDSS and sNfL (indirect effect = 0.45, 95% CI [0.20, 0.75]). Unsupervised clustering identified three distinct endophenotypes: “High Neurodegeneration” (elevated sNfL, severe atrophy, high disability), “Moderate Injury,” and “Benign Volumetry” (low sNfL, preserved volumes, mild disability). Supervised models predicted sNfL with high accuracy (R² = 0.65), identifying total grey matter volume, ventricular volume, and age as top predictors. Conclusions: This integrative multi-method analysis demonstrates that sNfL is robustly associated with global grey matter and ventricular volumes, and that these measures define clinically meaningful neurodegenerative subtypes in MS. Machine learning confirms that a concise set of volumetric features can effectively predict neuroaxonal injury. These findings advance a pathobiology-driven subtyping framework and provide a validated model for using routine MRI volumetry to assess neuroaxonal health, with implications for prognosis and personalised therapeutic strategies.