ArticleNature communications2022
Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Current Status and Future Opportunities in Modeling Clinical Characteristics of Multiple Sclerosis.Frontiers in neurology · 2022Pooled it
- Clemastine fumarate accelerates accumulation of disability in progressive multiple sclerosis by enhancing pyroptosis.The Journal of clinical investigation · 2025Trial
- CSF Proteins Associated With Neuroaxonal Damage in Early Active Multiple Sclerosis.Neurology(R) neuroimmunology & neuroinflammation · 2026Article
- Rethinking MS Therapeutics: From Disease Pathogenesis Mechanisms to AI-Driven Drug Discovery.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2026Review
- From Diagnosis to Disease Staging: Multisite Validation of Cerebrospinal Fluid Molecular Tests in Multiple Sclerosis.Annals of neurology · 2026Article
- Modulation of the immunological and neuroinflammatory microenvironment in older people with multiple sclerosis.Frontiers in immunology · 2026Review
- Bridging Inflammation and Neurodegeneration in Multiple Sclerosis: Mechanisms and Emerging Therapies.Cureus · 2026Review
- Refining Prognostic Factors in Adult-Onset Multiple Sclerosis: A Narrative Review of Current Insights.International journal of molecular sciences · 2025Review
- A Window into New Insights on Progression Independent of Relapse Activity in Multiple Sclerosis: Role of Therapies and Current Perspective.International journal of molecular sciences · 2025Review
- The contribution of tumor necrosis factor to multiple sclerosis: a possible role in progression independent of relapse?Journal of neuroinflammation · 2024Review
- Article
- Predicting disease severity in multiple sclerosis using multimodal data and machine learning.Journal of neurology · 2024Article
- Big data and artificial intelligence applied to blood and CSF fluid biomarkers in multiple sclerosis.Frontiers in immunology · 2024Review
- Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis.Nature communications · 2023Article
- Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.The Cochrane database of systematic reviews · 2023Review
- Drug library screen identifies inhibitors of toxic astrogliosis.Multiple sclerosis and related disorders · 2022Article
- Confounder-adjusted MRI-based predictors of multiple sclerosis disability.Frontiers in radiology · 2022Article
- Cerebrospinal Fluid Biomarkers of Myeloid and Glial Cell Activation Are Correlated With Multiple Sclerosis Lesional Inflammatory Activity.Frontiers in neuroscience · 2021Article
- Extensive Healthy Donor Age/Gender Adjustments and Propensity Score Matching Reveal Physiology of Multiple Sclerosis Through Immunophenotyping.Frontiers in neurology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While autopsy studies identify many abnormalities in the central nervous system (CNS) of subjects dying with neurological diseases, without their quantification in living subjects across the lifespan, pathogenic processes cannot be differentiated from epiphenomena. Using machine learning (ML), we searched for likely pathogenic mechanisms of multiple sclerosis (MS). We aggregated cerebrospinal fluid (CSF) biomarkers from 1305 proteins, measured blindly in the training dataset of untreated MS patients (N = 129), into models that predict past and future speed of disability accumulation across all MS phenotypes. Healthy volunteers (N = 24) data differentiated natural aging and sex effects from MS-related mechanisms. Resulting models, validated (Rho 0.40-0.51, p < 0.0001) in an independent longitudinal cohort (N = 98), uncovered intra-individual molecular heterogeneity. While candidate pathogenic processes must be validated in successful clinical trials, measuring them in living people will enable screening drugs for desired pharmacodynamic effects. This will facilitate drug development making, it hopefully more efficient and successful.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.