The gut microbiome in pediatric-onset multiple sclerosis
Bibliographic record
Abstract
A growing body of evidence points to the gut microbiota playing a role in immune-mediated, neurological disease, such as multiple sclerosis (MS). An important initial step to understanding the relationship between the gut microbiota and MS is to compare the gut microbial community composition and metagenome in individuals with and without MS. In order to investigate the differences between the gut microbiome of MS patients and unaffected individuals, I conducted a systematic review of ten published articles from 2008-2018. The majority of studies found no difference in gut microbiota diversity, but consistent taxonomic differences were observed. To further investigate the MS microbiome, I conducted a case-control study using stool samples from individuals under the age of 22 with and without pediatric-onset MS who were enrolled in the Canadian Pediatric Demyelinating Disease Network. The study compared microbial taxonomy, gene annotations, metabolic pathways, and predicted metabolites in stool samples derived from metagenomic reads between MS cases and controls. I also assessed the relationship between diet, gut microbiota composition, and MS risk in the same population. The results of this dissertation showed subtle differences in individual taxa and functions between the gut microbiota of MS cases and controls. Additionally, a healthier diet rich in fiber was associated with a lower risk of MS and the MS-associated microbiota composition was also associated with aspects of diet. Overall, this research suggests that the gut microbiome plays a role in MS and that the potential interaction between diet and the gut microbiota is relevant in the development of MS. While gut microbiota diversity didn't significantly differ between MS cases and controls, we observed subtle differences in the relative abundance of individual taxa and functions. Future studies with larger sample sizes and longitudinal data collection are needed to both confirm findings and to verify interpretation of these findings. Understanding the role of the gut microbiota in MS may lead to the development of novel prevention and treatment strategies for this debilitating disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".