Urinary Metabolic Profiles of Inflammatory Bowel Disease in Interleukin-10 Gene-Deficient Mice
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a chronic debilitating disorder that is thought to have both genetic and environmental contributors. Commensal microflora have been shown to play a key part in the disease process. Metabolomics, the study of large numbers of small molecule metabolites, has demonstrated that disease and/or changes in gut microbial composition modulate mammalian urine metabolite fingerprints. The aim of this project was to associate the development of IBD with specific changes in a mouse urinary metabolic fingerprint. Interleukin-10 (IL-10) gene-deficient mice were raised alongside age-matched 129/SvEv controls in conventional housing. Urine samples (22 h) were collected at ages 4, 6, 8, 12, 16, and 20 weeks. Metabolite concentrations were derived from analysis of nuclear magnetic resonance spectra, and both multivariate and two-way analysis of variance (ANOVA) statistical techniques were applied to the resulting data. Principal component analysis and partial least-squares-discriminant analysis of urine derived from the control and IL-10 gene-deficient mice revealed that while both groups initially had similar metabolic profiles, they diverged substantially with the onset of IBD as assessed through external phenotypic changes. Several metabolites, including trimethylamine (TMA) and fucose, changed dramatically in the IL-10 gene-deficient mice following 8 weeks of age, concomitant with the known timeline for development of severe histological injury. This study illustrates that metabolomics is effective at distinguishing IBD using urinary metabolite profiles.
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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".