Meta-analysis identifies common gut microbiota signatures in patients with multiple sclerosis
Bibliographic record
Abstract
Abstract BackgroundPrevious studies have identified a large number of distinct microbial taxa that are different between patients with multiple sclerosis (MS) and controls. However, interpretating findings on MS-associated microbiome is challenging as results do not completely concur and studies have included relatively few individuals. To date, it is unclear whether there is a common gut microbial signature in patients with MS across studies. To identity the most common compositional differences of the gut microbiome in MS versus healthy controls, we performed a meta-analysis. This was based on 16S rRNA gene sequences from seven published studies, comprising a total of 524 adult patients with MS and control subjects.ResultsWe found that although alpha and beta diversity did not differ between MS and controls, a lower relative abundance ofPrevotellaand a dysbiosis of numerous genera within theClostridiaclass were reproducibly associated with MS. Additionally, network analysis revealed that the recognized negativeBacteroides-Prevotellacorrelation in controls was disrupted in MS. immunosuppressive agents normalized MS-associated microbiomePrevotellato a similar level as healthy controls.ConclusionsOur meta-analysis revealed reproducible gut microbiome signatures in MS across geographically diverse studies. These findings form the basis for future novel therapeutic approaches and possibly enhanced MS recognition/diagnosis by targeting common microbiome signatures.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.021 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".