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.

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, and their combined utility for patient stratification and prediction, remains underexplored. Objective: 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 gray matter atrophy mediates the EDSS–sNfL 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 gray matter volume (r = -0.449, BF₁₀ = 0.022) and lateral ventricular volume (r = 0.349, BF₁₀ = 0.285). Mediation confirmed that gray 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 gray matter volume, ventricular volume, and age as top predictors. Conclusions: This integrative multi-method analysis demonstrates that sNfL is robustly associated with global gray 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 personalized therapeutic strategies.

Background: Diabetic peripheral neuropathy (DPN) affects up to 50% of diabetes patients and is driven by hyperglycemia-induced oxidative stress, mitochondrial dysfunction, polyol pathway activation, advanced glycation end-product formation, and inflammation. Current management is largely symptomatic, prompting interest in metabolic/nutritional therapies. This review critically evaluates the mechanistic rationale and clinical evidence for alpha-lipoic acid (ALA) and benfotiamine as adjunctive treatments for DPN. Methods: A structured narrative review of PubMed/MEDLINE was conducted using predefined keywords for DPN, oxidative stress, metabolic therapy, and thiamine derivatives. Randomized controlled trials, clinical studies, systematic reviews, and relevant experimental studies were included. Evidence was synthesized qualitatively with emphasis on mechanistic plausibility, clinical efficacy, intervention duration, and methodological rigor. Results: ALA consistently improves short-term symptoms across multiple randomized trials. The long-term NATHAN 1 trial reported a marginal, borderline significant effect on the primary composite endpoint (NIS-LL, p = 0.05) without significant improvements in nerve conduction studies; therefore, evidence for functional stabilization is very limited and inconclusive. ALA’s effects are attributed to antioxidant activity, mitochondrial protection, and improved microvascular function. Benfotiamine has a strong biochemical rationale (transketolase activation, diversion of glycolytic intermediates from damaging pathways), but clinical evidence remains limited to short-duration, symptom-based studies, with no large-scale, long-term trials published. Conclusions: Both agents target key pathways in DPN pathogenesis. ALA is the most established adjunctive metabolic therapy for symptomatic DPN, although no study has demonstrated structural nerve regeneration or a definitive disease-modifying effect. Benfotiamine is biologically plausible but requires further validation in long-term randomized trials with structural and biomarker-based endpoints. Outside of documented thiamine deficiency, its routine use cannot be recommended based on current evidence.

Background: Anti-amyloid monoclonal antibodies represent the first disease-modifying therapeutic strategy targeting amyloid-β pathology in early Alzheimer’s disease (AD). Although several agents have demonstrated the ability to reduce cerebral amyloid burden, their clinical efficacy and safety remain subjects of substantial scientific and regulatory debate. This study aimed to synthesize randomized evidence evaluating the benefit–risk profile of anti-amyloid monoclonal antibodies in biomarker-confirmed early AD. Methods: A systematic review and classical pairwise meta-analysis of randomized controlled trials (RCTs) was conducted following the PRISMA 2020 guidelines. PubMed/MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were searched for phase III placebo-controlled trials evaluating lecanemab, donanemab, aducanumab, and gantenerumab in patients with mild cognitive impairment due to AD or mild AD dementia with biomarker confirmation of amyloid pathology. The primary outcome was change from baseline in the Clinical Dementia Rating–Sum of Boxes (CDR-SB) at the longest available follow-up. Safety outcomes included amyloid-related imaging abnormalities with edema or effusion (ARIA-E), amyloid-related imaging abnormalities with hemorrhage (ARIA-H), serious adverse events, and treatment discontinuation. Random-effects meta-analyses were performed. Results: Six randomized comparisons derived from four phase III trials involving 7695 participants met the eligibility criteria. Anti-amyloid monoclonal antibodies were associated with a statistically significant slowing of clinical progression compared with placebo (pooled mean difference in CDR-SB: −0.42 points; 95% CI −0.59 to −0.25; I² = 78%). The observed effect was primarily driven by trials of lecanemab and donanemab, whereas aducanumab demonstrated discordant results across trials and gantenerumab showed no clinically meaningful benefit. Despite statistical significance, the magnitude of the pooled effect approached the lower boundary of the minimal clinically important difference reported for CDR-SB in early AD. Treatment was associated with a markedly increased risk of ARIA-E (pooled risk ratio 10.1; 95% CI 7.8–13.0), with moderate heterogeneity across studies. Most ARIA-E events were asymptomatic and detected through protocol-mandated MRI monitoring. Conclusions: In biomarker-confirmed early Alzheimer’s disease, anti-amyloid monoclonal antibodies produce a statistically significant but modest slowing of clinical decline accompanied by a substantially increased risk of ARIA. The benefit–risk profile appears heterogeneous across individual antibodies and may reflect pharmacological differences in amyloid targeting and clearance mechanisms. These findings support cautious, individualized use of anti-amyloid therapies and highlight the need for longer-term studies to determine whether short-term slowing of decline translates into clinically meaningful disease modification.

Background: Real-world evidence regarding the effectiveness and safety of anti-calcitonin gene-related peptide (CGRP) monoclonal antibodies in Eastern European populations remains limited. We aimed to evaluate treatment outcomes and safety in Romanian patients with high-frequency episodic migraine (HFEM) and chronic migraine (CM). Methods: We conducted a multicenter retrospective observational cohort study including 113 adults with HFEM (n = 38) or CM (n = 75) who initiated treatment with galcanezumab, erenumab, fremanezumab, or eptinezumab at specialized headache centers in Romania. Monthly migraine days (MMDs) in HFEM and monthly headache days (MHDs) in CM were assessed at the baseline and during follow-up. Treatment outcomes, discontinuation, persistence, and adverse events were evaluated during routine clinical follow-up. Results: At month 6, headache frequency was substantially reduced in both groups, with median MMDs decreasing from 10 to 2 in patients with HFEM and median MHDs decreasing from 25 to 6 in patients with CM. A ≥50% reduction in headache frequency was achieved by 78.9% of patients with HFEM and 64.0% of those with CM. Among the patients with CM, 70.7% met the predefined criterion for conversion to episodic migraine at month 6. During follow-up, 22 patients discontinued treatment. Adverse events were reported in 28 patients (24.8%), were predominantly non-serious, and resulted in treatment discontinuation in 3 patients (2.7%). No serious adverse events were documented. Treatment persistence at 12 months was 82.3%. Conclusions: In this Romanian real-world cohort, anti-CGRP monoclonal antibody treatment was associated with substantial reductions in migraine/headache frequency in patients with HFEM and CM. The treatment was generally well-tolerated, with predominantly non-serious adverse events and a low rate of discontinuation due to adverse events. These findings provide real-world evidence from Romania and support further prospective evaluation of the long-term effectiveness and safety of anti-CGRP monoclonal antibodies.

The Background: monoclonal antibody therapies represent high-efficacy treatment options for relapsing forms of multiple sclerosis (MS). However, the absence of direct head-to-head randomized trials and the use of heterogeneous comparators across pivotal studies complicate comparative effectiveness assessments. While network meta-analysis (NMA) offers a framework to integrate evidence, the fragmented structure of the available evidence base precludes a conventional NMA with global indirect comparisons and treatment ranking. Methods: A systematic review with qualitative assessment of treatment effects of randomized controlled trials evaluating monoclonal antibody therapies in relapsing forms of multiple sclerosis was conducted. Annualized relapse rate (ARR) was analyzed as the primary outcome, and six-month confirmed disability progression (CDP) as the key secondary outcome. Network geometry and connectivity were explicitly assessed for each outcome prior to quantitative synthesis. Analyses were restricted to comparator-defined connected components of the evidence base, and indirect comparisons across disconnected components were not performed. Sensitivity analyses, including descriptive analyses in progressive multiple sclerosis, were conducted where appropriate. Results: nine randomized controlled trials involving 6762 patients were included. For ARR, the evidence network was fragmented into three disconnected components defined by placebo-, interferon beta-1a-, and teriflunomide-controlled trials. Within connected sub-networks, monoclonal antibody therapies consistently demonstrated substantial reductions in ARR relative to their respective comparators, with overlapping confidence intervals suggesting broadly comparable relapse suppression among high-efficacy agents. For CDP, network connectivity was more limited, and treatment effects were more heterogeneous. Significant reductions in disability progression were observed for some agents within comparator-specific networks, while uncertainty remained for others. Due to network disconnection, no global treatment ranking was performed. Conclusions: this study provides a transparent synthesis of randomized evidence on monoclonal antibody therapies in relapsing MS. By explicitly accounting for network connectivity and comparator heterogeneity, the analysis avoids unsupported indirect comparisons and global treatment hierarchies. The findings support robust relapse suppression across monoclonal antibody therapies within comparable trial frameworks, while highlighting heterogeneity in disability outcomes. These results illustrate the importance of contextual interpretation in comparative effectiveness research in MS.