Gut microbiota in early pediatric multiple sclerosis: a case−control study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Alterations in the gut microbial community composition may be influential in neurological disease. Microbial community profiles were compared between early onset pediatric multiple sclerosis (MS) and control children similar for age and sex. METHODS: Children ≤18 years old within 2 years of MS onset or controls without autoimmune disorders attending a University of California, San Francisco, USA, pediatric clinic were examined for fecal bacterial community composition and predicted function by 16S ribosomal RNA sequencing and phylogenetic reconstruction of unobserved states (PICRUSt) analysis. Associations between subject characteristics and the microbiota, including beta diversity and taxa abundance, were identified using non-parametric tests, permutational multivariate analysis of variance and negative binomial regression. RESULTS: Eighteen relapsing-remitting MS cases and 17 controls (mean age 13 years; range 4-18) were studied. Cases had a short disease duration (mean 11 months; range 2-24) and half were immunomodulatory drug (IMD) naïve. Whilst overall gut bacterial beta diversity was not significantly related to MS status, IMD exposure was (Canberra, P < 0.02). However, relative to controls, MS cases had a significant enrichment in relative abundance for members of the Desulfovibrionaceae (Bilophila, Desulfovibrio and Christensenellaceae) and depletion in Lachnospiraceae and Ruminococcaceae (all P and q < 0.000005). Microbial genes predicted as enriched in MS versus controls included those involved in glutathione metabolism (Mann-Whitney, P = 0.017), findings that were consistent regardless of IMD exposure. CONCLUSIONS: In recent onset pediatric MS, perturbations in the gut microbiome composition were observed, in parallel with predicted enrichment of metabolic pathways associated with neurodegeneration. Findings were suggestive of a pro-inflammatory milieu.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".