The Role of Gut Microbiome in the Pathogenesis of Multiple Sclerosis and Related Disorders
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic, progressive neuroinflammatory disease with a complex pathophysiological background. A variety of diverse factors have been attributed to the propagation of inflammation and neurodegeneration in MS, mainly genetic, immunological, and environmental factors such as vitamin D deficiency, infections, or hormonal disbalance. Recently, the importance of the gut-brain axis for the development of many neurological conditions, including stroke, movement disorders, and neuroinflammatory disorders, has been postulated. The purpose of our paper was to summarize current evidence confirming the role of the gut microbiome in the pathophysiology of MS and related disorders, such as neuromyelitis optica spectrum disorder (NMO-SD). For this aim, we conducted a systematic review of the literature listed in the following databases: Medline, Pubmed, and Scopus, and were able to identify several studies demonstrating the involvement of the gut microbiome in the pathophysiology of MS and NMO-SD. It seems that the most relevant bacteria for the pathophysiology of MS are those belonging to Pseudomonas, Mycoplasma, Haemophilus, Blautia, Dorea, Faecalibacterium, Methanobrevibacter, Akkermansia, and Desulfovibrionaceae genera, while Clostridium perfringens and Streptoccocus have been demonstrated to play a role in the pathophysiology of NMO-SD. Following this line of evidence, there is also some preliminary data supporting the use of probiotics or other agents affecting the microbiome that could potentially have a beneficial effect on MS/NMO-SD symptoms and prognosis. The topic of the gut microbiome in the pathophysiology of MS is therefore relevant since it could be used as a biomarker of disease development and progression as well as a potential disease-modifying therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".