The multiple sclerosis gut microbiome and disease activity: A systematic review
Bibliographic record
Abstract
BACKGROUND: The gut microbiome is a potential therapeutic target for multiple sclerosis (MS), yet its association with disease activity remains unclear. We systematically reviewed the literature to investigate the relationship between the gut microbiome and MS disease activity, course, and disability progression. METHODS: We searched MEDLINE, EMBASE, Web of Science and Google Scholar (01/2011-02/2024) to identify relevant observational or interventional studies published in English. Case reports were ineligible. Outcomes included disease activity (e.g. relapses, MRI), course (e.g. relapsing-remitting/secondary-progressive [RR/SPMS]) and disability progression (e.g. using the Expanded Disability Status Scale [EDSS]). Study quality was evaluated using the Newcastle-Ottawa Scale. RESULTS: Four longitudinal and nineteen cross-sectional studies were included, totaling 1760 persons with MS. Most were female (1237/1760, 70 %) and had RRMS (1378/1760, 78 %). The majority of studies (67 %;10/15) examining gut diversity (alpha or beta) did not find an association with disease activity, course or progression. However, several gut taxa exhibited significant associations with study outcomes, including eight that varied in consistent directions: a higher abundance of Actinobacteria, Bacteroidota and Roseburia inulinivorans were associated with better MS outcomes (e.g. lower EDSS scores), while higher abundances of Streptococcus, Clostridium nexile, Clostridium scindens and Collinsella aerofaciens were associated with worse outcomes (e.g. higher MRI lesion volumes). CONCLUSIONS: Gut diversity was not associated with MS-related outcomes in most studies whereas several gut taxa were, including higher abundances of short chain-fatty acid producers (e.g. Bacteroidota) showing associations with lower EDSS scores. Most studies were cross-sectional, limiting interpretation of findings; longitudinal studies are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".