Altered tryptophan metabolism is associated with pediatric multiple sclerosis risk and course
Bibliographic record
Abstract
OBJECTIVE: To determine if altered tryptophan (Trp) metabolism is associated with MS risk or disease severity in children. METHODS: Participants with pediatric-onset MS and clinically isolated syndrome (CIS) within 4 years of disease onset and healthy controls underwent collection of serum. Longitudinal disability and processing speed measures and relapse data were collected in cases. Global metabolomics were conducted in 69/67 cases/controls. Targeted Trp measurement was performed in a discovery group (82 cases, 50 controls) and a validation group (92 cases, 50 controls), while functional gut microbiome analysis was done in 17 cases. Adjusted logistic, linear and negative binomial regression and Cox-proportional hazard models were used. RESULTS: Using global metabolomics data, higher relative abundances of Trp and indole lactate, a known gut microbiota-derived Trp metabolite, were associated with lower risk of MS. In cases, higher relative abundances of gut microbiota-derived Trp metabolites were associated with lower disability and higher processing speed scores and higher relative abundance of kynurenine was associated with higher relapse rate. Using targeted tryptophan measures, in the discovery and validation groups, each 1 mcg/mL increase in serum Trp level was associated with 20% (95% CI: 4-34%) and 32% (95% CI: 16-44%) decrease in adjusted odds of having MS, respectively. A lower relative abundance of gut microbial genes involved in Trp catabolism was associated with higher relapse risk. INTERPRETATION: Trp metabolism by the gut microbiota and the kynurenine pathway may be relevant to the risk of MS in children as well as MS activity and severity.
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 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 | 0.001 |
| 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 | 0.001 |
| 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".