Kantarci, Orhun H.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system in which environmental factors play an important role in shaping disease risk, activity, and progression. Over the past decade, human and experimental studies have consistently shown alterations in the gut microbiome across the phenotypic spectrum of MS and have linked these changes to immune dysregulation, barrier dysfunction, neuroinflammation, and demyelination. Additionally, emerging evidence indicates that microbial function, particularly metabolite production plays a more direct role in shaping immune responses and associated neuropathology. Evidence from both human studies and experimental autoimmune encephalomyelitis models supports a functional role for microbial metabolites in shaping neuroimmune responses. Bacterially derived metabolites such as short-chain fatty acids, bile acids, polyamines, phytoestrogen metabolites, and tryptophan-derived compounds can influence T-cell differentiation, glial activation, epithelial integrity, and neuroimmune communication. Recent longitudinal studies also show associations between metabolite profiles and disability worsening. Because disease-modifying therapies, diet, and microbiome-directed interventions can reshape microbial metabolism, microbial metabolites may represent promising therapeutic targets in the gut-immune-brain axis. In this Review , we integrate current evidence to propose a mechanistic framework in which microbial metabolites act as central regulators of mucosal and systemic immunity that influence different aspects of MS biology. We discuss how this perspective shifts gut microbiome research from descriptive associations to biological mechanisms that more directly link the gut to immune responses and downstream neuropathology. We then evaluate therapeutic strategies that target microbial metabolism and outline key priorities for longitudinal, multi-omics, and interventional studies that are needed to enable microbiome-informed precision therapies in MS.

ABSTRACT Objectives We aimed to determine the frequency of subclinical optic nerve (ON) lesions using MRI, optical coherence tomography (OCT), and visual evoked potentials (VEP) in radiologically isolated syndrome (RIS), and to assess their diagnostic and prognostic significance. Methods We conducted a retrospective, multicenter study of 179 RIS individuals followed in clinical practice who met the 2023 RISC criteria. The diagnostic performance of the 2024 McDonald criteria, with and without optic nerve assessment, was evaluated and compared with that of the 2017 criteria. Associations with clinical conversion, comorbidities, and MRI disease activity were analyzed using multivariate models. Results Silent ON lesions appeared in 107/179 (59.8%) individuals, mainly detected by VEP (100/164; 61.0%) and OCT (69/118; 58.5%), with routine MRI identifying 31/166 (18.7%). During follow‐up, 71 (39.7%) had a first clinical event. The presence, laterality, or number of silent ON lesions did not correlate with clinical conversion, event type, or MRI activity. Younger age and the absence of comorbidities associated with other MRI lesions, rather than with ON lesions, were associated with clinical conversion. Including ON in the 2024 McDonald criteria increased sensitivity but decreased specificity, with 17/179 (9.5%) meeting dissemination‐in‐space criteria solely due to ON lesions. Interpretation Subclinical ON lesions are common in RIS and are mainly found by OCT and VEP, not routine MRI. Including ON increases sensitivity but does not predict clinical conversion and may lower specificity. These findings suggest cautious interpretation of ON and support a multimodal assessment approach in RIS.

Background: There is growing support for high-efficacy disease-modifying therapy (DMT) in multiple sclerosis (MS), but escalation (ESC) approaches remain common. Objective: To describe decision-making in a pragmatic trial of early high-efficacy treatment (EHT) versus ESC. Methods: DELIVER-MS is a multi-center, pragmatic, randomized controlled trial (RCT) with a parallel observational study (OBS), which enrolled treatment-naïve people with RRMS in 31 UK/US sites. Primary outcome was as follows: 36-month brain volume loss according to initial treatment approach (EHT vs. ESC). Stepwise multivariable logistic regression was used to predict participation in RCT versus OBS and choice of EHT versus ESC within the OBS cohort. Results: In total, 816 people with MS were enrolled. Participants declined randomization due to preference for a particular DMT (85%), efficacy concerns (20%), and safety concerns (9%). RCT versus OBS participation was associated with lower relapse rate ( p = 0.043) and greater brain parenchymal fraction ( p = 0.002). Among 374 in the OBS cohort, 125 (33%) chose ESC and 249 (67%) chose EHT. People commencing EHT had higher education attainment ( p < 0.001) and relapse rate ( p = 0.025). Conclusion: Baseline DELIVER-MS data demonstrate that participants with milder disease are more likely to participate in RCT. The choice of EHT versus ESC was associated with demographic factors and disease activity. Clinical trial registration: NCT03535298

Introduction Neurological post-acute sequelae of COVID-19 (neuroPASC) are associated with persistent cognitive dysfunction and quality-of-life decline. We aimed to identify clinical, behavioral and sociodemographic factors associated with neuroPASC symptom burden two years after COVID-19 among individuals without prior neurological disease. Methods In this prospective, observational study, individuals with neuroPASC ( n = 102) and controls without symptomatic COVID-19 ( n = 74), all without prior neurological, psychiatric, or post-viral conditions, were enrolled between February 2022 and June 2024 across five academic sites. An unsupervised algorithm identified clusters with differing self-reported neurological symptom burden within the neuroPASC group. Functional differences between clusters were evaluated using quality-of-life, neurological and cognitive evaluations. Demographics, behavioral history, comorbidities, and blood biomarkers were compared across clusters and controls. Multivariable logistic regression assessed predictors of neuroPASC severity, including demographics, body-mass-index, Charlson Comorbidity Index, Framingham Risk Score, pre-existing endocrine/metabolic and/or gastrointestinal/hepatobiliary conditions, COVID-19 vaccination prior to infection, hospitalization during acute infection, and cumulative alcohol use. Results Two clusters emerged based on neurological symptom burden, labeled “high-burden” and “low-burden” neuroPASC, reflecting differences in the number and frequency of symptoms. Both clusters had deficits in quality-of-life and cognitive function compared to controls, with greater impairment in high-burden than low-burden neuroPASC. The clusters did not differ by sex, education, tobacco and cannabis use, blood pressure, body-mass-index, HbA1C, days since infection, hospitalization during COVID-19, pre-COVID vaccination rate, antibody-positivity, inflammation, and neurodegeneration biomarkers. The high-burden cluster was older and exhibited higher comorbidity burden and greater cumulative alcohol use compared with the low-burden cluster and controls. Pre-existing endocrine/metabolic and gastrointestinal/hepatobiliary conditions were more common in high-burden (63%) than in low-burden neuroPASC (35%). After adjusting for clinical and demographic factors, these pre-existing conditions remained the only independent predictor of severity, conferring a 3.5-fold increase in the odds of high-burden versus low-burden neuroPASC. Discussion Older age, higher comorbidity burden, greater cumulative alcohol use, and endocrine/metabolic and gastrointestinal conditions, rather than acute COVID-19 severity, were observed in the high-burden neuroPASC cluster. After multivariable adjustment, only pre-existing endocrine/metabolic and/or gastrointestinal/hepatobiliary conditions remained independently associated with high-burden neuroPASC, conferring a 3.5-fold increase in odds and highlighting the need for targeted post-infection monitoring in at-risk patients.

Abstract Background Neuroimaging data typically arrives as Digital Imaging and Communications in Medicine (DICOM) files, batched by exam. We use exam or study to denote a single imaging session of a participant, containing up to ∼40 series (the generic term for scans and derived products). Different series have different properties and applications, making their labeling complex. Filenames often change in transit, and Series Description tags (0008, 103E) are arbitrary, operator‐entered, and unreliably coupled to the series type. Furthermore, label preferences vary from site to site, making the application of a common classification system mandatory for multisite studies. Here we describe the system that we have developed and applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) and SCAN. Method Although Series Description is unreliable, DICOM includes other tags that parameterize MR pulse sequences and suggest their purpose. The mapping from these features to types is difficult to explicitly specify, especially since different manufacturers and protocols use different conventions. Thus we developed a machine learning classifier using 4060 semiautomatically labeled head and spinal series from 1.5 to 7T MR scanners, including both site‐specific and standard protocols designed to study a wide range of neurological conditions, including dementia, hydrocephalus, and multiple sclerosis. An 80%/20% split, stratified by type, was used for the training/test sets. After initial training, a few cases in the training set with high errors due to rarity were augmented and the model retrained. Performance was evaluated on the clean test set. Result The taxonomy is presented in Table 1. The classifier correctly identified 807 out of 812 (99.4%) series, with the error cases coming from outside the ADNI or SCAN protocols (Table 2). The confusion matrix is shown in Figure 1. Conclusion We implemented an automatic series type classifier with labels chosen to be generally useful for the brain MRI community. Some errors were noted, but the test set was deliberately challenging and the errors appear to be ones that could easily be made by a human. The classifier is expected to have good tolerance for as yet unseen head MR protocols but is likely unsatisfactory for body MRI.