Urinary NMR metabolomic profiles discriminate inflammatory bowel disease from healthy
Bibliographic record
Abstract
BACKGROUND AND AIMS: Inflammatory bowel disease, a chronic inflammation of the intestinal tract, presents in two variations, Ulcerative Colitis (UC) and Crohn's disease (CD). Given that treatment of CD differs from UC, a single test that provided strong diagnostic ability would offer great clinical value. Two previous studies have indicated that CD can be distinguished from UC, and that both can be distinguished from non-IBD-type gastrointestinal disease, based on urinary and faecal metabolite profiling. METHODS: Analysis of healthy as well as CD and UC patients attending an IBD clinic was performed. IBD patients were classified into two groups (CD or UC) based on chart review of clinical, endoscopic, and histological assessment. Urine samples were obtained and analyzed using nuclear magnetic resonance (NMR) spectroscopy combined with targeted profiling techniques, followed by univariate and multivariate statistical analysis. RESULTS: Based on urinary metabolomics, individuals with IBD could be differentiated from healthy. Major differences between IBD and healthy included TCA cycle intermediates, amino acids, and gut microflora metabolites. Comparison of CD and UC patients revealed discrimination, but removal of patients with the surgical intervention confounder revealed that CD could not be discriminated from UC. CONCLUSIONS: This study highlights the potential for metabolomics to distinguish IBD from the healthy state but shows that careful consideration must be given to establishing disease-representative cohorts that are free of confounding factors.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".