Tobin, W Oliver

Background: Smokers with multiple sclerosis (MS) experience worse disease, yet underlying mechanisms remain unknown. Smoking disrupts bile acid and tryptophan metabolism in non-MS populations; both pathways involve host–microbiome co-metabolism and have been linked to MS. Objective: Determine whether smoking perturbs these metabolic pathways in MS and whether such alterations statistically mediate smoking’s effect on MS severity. Methods: We analyzed serum bile acid, tryptophan, and tobacco-related metabolites across four independent MS cohorts ( N = 266) using discovery-replication analyses. Mixed-effects regression assessed replicating associations with current smoking and nicotine exposure. Mediation analyses tested if replicating metabolites were potential mediators between smoking and MS severity. Hypothesis-generating metagenomic analyses explored smoking-associated gut–microbial shifts and metabolite correlations. Results: Current smokers and nicotine-exposed MS subjects had reductions in bile acids and tryptophan metabolites, notably indolepropionate, a neuroprotective, anti-inflammatory gut–microbial metabolite. Lower indolepropionate statistically mediated ~20% of smoking’s adverse effect on MS severity. Metagenomic analyses identified potential smoking-enriched MS-linked taxa, and that indolepropionate broadly co-occurs with microbial networks (e.g. Lachnoclostridium appeared inversely associated with indolepropionate in smokers with MS). Conclusion: Tobacco exposure disrupts host–microbiome tryptophan and bile acid metabolism in persons with multiple sclerosis, with indolepropionate depletion partially mediating disease severity, highlighting a potential mechanistic pathway warranting further investigation in MS smokers.

Abstract Glioblastoma (GBM), isocitrate dehydrogenase wild-type (IDHwt) and central nervous system diffuse large B-cell lymphoma (CNS-DLBCL) are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. Deep learning on T1 postcontrast and T2-weighted MRI sequences were used to differentiate GBM and CNS-DLBCL. A three-stage temporal study design was utilized. Model development was performed on 146 patients with CNS-DLBCL and 146 age-matched, sex-matched, and MRI year–matched patients with GBM diagnosed at Mayo Clinic between 1998 and 2019. Models were tested on independent temporal test cohorts. Initial testing included 240 independent GBM diagnosed at Mayo Clinic between 1998 and 2019. The prospective test cohort included 37 patients with CNS-DLBCL and 256 patients with GBM diagnosed at Mayo Clinic after January 1, 2020, and 36 patients with CNS-DLBCL diagnosed at an external institution. Of the patients diagnosed at Mayo Clinic, 47% had MRIs generated from non-Mayo institutions. Two different model approaches were compared: (i) ensemble approach using area under the receiver operating characteristic curve (AUC) and cross-validation for model selection and (ii) loss approach minimizing cross-entropy loss and cross-validation to evaluate prediction performance. The AUCs on the prospective test cohort were 0.84 [95% confidence interval (CI), 0.78–0.90] and 0.83 (95% CI, 0.77–0.88) for the ensemble and loss approaches, respectively. Stability of ensemble prediction improved with the increasing number of models. Stratified AUC analysis demonstrated consistent performance across sex and age. We utilized a robust temporal study design and applied 2 different analytic approaches to develop a classification model. The findings confirm the feasibility of using MRI-based deep learning models to differentiate GBM from CNS-DLBCL. Significance: GBM, IDHwt and CNS-DLBCL are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. We developed MRI-based deep learning models to differentiate GBM, IDHwt from CNS-DLBCL using a rigorous three-stage temporal design that included prospective validation. The model AUC on a prospective cohort was 0.84.

Objective To evaluate the feasibility and potential efficacy of percutaneous spinal stimulation (epidural stimulation, ES) combined with task-specific training to reduce spasticity and improve gait and balance in individuals with progressive multiple sclerosis (MS). Methods Two men with progressive MS (EDSS 6.5) underwent ES lead implantation targeting the lower spinal cord, followed by one month of rehabilitation involving 12 ES-assisted training sessions. Assessments included instrumented gait analysis, Modified Ashworth Scale (MAS), pendulum test, and static balance testing performed at baseline, and at end of the study with ES-Off and ES-On conditions. Results Participant 1, with spastic hemiparetic gait, demonstrated improved lower extremity joint kinematics and muscle activation during gait, reduced knee extensor spasticity, and enhanced functional movement patterns with ES-On. Participant 2, with significant paraparesis and minimal spasticity, showed limited gait changes but experienced marked improvements in static balance, particularly under eyes-closed conditions. No adverse events were reported. Conclusion This pilot study demonstrates the feasibility and tolerability of ES paired with task-specific training in progressive MS. Participant-specific responses were observed, including improvements in gait kinematics, neuromuscular activation, spasticity, or balance; however, spatiotemporal gait parameters did not improve. These preliminary findings highlight response heterogeneity and the need for individualized ES parameter tuning. Trial registry name and URL: https://clinicaltrials.gov/study/NCT06019611

Background Psychiatric comorbidities are increasingly recognised in demyelinating diseases, yet their frequency and timing in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) are not well defined. We aimed to characterise the spectrum and temporal pattern of psychiatric illness in MOGAD and compare to multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) with aquaporin-4 antibody (AQP4-IgG). Methods We conducted a cross-sectional study of patients with MOGAD (n=388), age-matched and sex-matched MS (n=257) and AQP4-IgG-positive NMOSD (n=58) evaluated at Mayo Clinic (1991–2025). Psychiatric diagnoses, timing relative to the first attack and patient-reported outcomes were extracted from electronic health records (EHRs). Complementary analyses were conducted using the NeuroBlu real-world EHR database. Results Among 388 patients with MOGAD, 54.8% had ≥1 psychiatric disorder, most commonly anxiety (47.7%), depression (36.6%) and attention-deficit/hyperactivity disorder (ADHD, 9%). After the first attack, 33.3% of patients with MOGAD developed a new psychiatric disorder, often within the first year. MOGAD showed a lower frequency of depression versus MS (36.6% vs 47.5%, p=0.02) but similar rates of anxiety and ADHD. Temporal patterns differed: psychiatric disorders often appeared years before onset in MS but clustered shortly after onset in MOGAD. NeuroBlu analyses supported high rates of psychiatric disorders across the three conditions. Conclusion Psychiatric comorbidities are common in MOGAD, particularly depression and anxiety, typically emerging close after onset, whereas in MS they often preceded onset by years. These findings underscore the need for routine psychiatric screening in MOGAD and highlight unresolved questions regarding whether, and to what extent, MOG-IgG-mediated inflammatory demyelination contributes to psychiatric vulnerability.

Background: Tremor in multiple sclerosis (MS) is often refractory to medications. Deep brain stimulation (DBS) may help, but possible side effects and disease exacerbation limit its use. Objectives: Evaluate outcomes of DBS for MS-associated tremor. Methods: Retrospective review of 18 patients with MS-associated tremor who underwent DBS, analyzing Expanded Disability Status Scale (EDSS) and Fahn–Tolosa–Marin (FTM) tremor scores. Results: Median age at surgery was 49.5 years (range, 28–75), with postoperative follow-up 2.5 years (range, 0–22). No relapses (0/18) or new lesions (0/8) occurred postoperatively. All had ventral intermediate nucleus leads; other targets were not used in programming. EDSS remained stable (median 6 to 6.25; p = 0.2). FTM scores improved (median 14.5 to 10, p = 0.008). Patients with EDSS 3.5–6 had variable outcomes ( p = 0.02). Conclusions: DBS is a potential treatment for severe MS tremor and did not provoke relapse. Targeting VIM alone may optimize tremor control and maintain magnetic resonance imaging (MRI) access.