Quantitative Metabolomic Profiling of Serum and Urine in DSS-Induced Ulcerative Colitis of Mice by <sup>1</sup>H NMR Spectroscopy
Bibliographic record
Abstract
Quantitative profiling of a large number of metabolic compounds is a promising method to detect biomarkers in inflammatory bowel diseases (IBD), such as ulcerative colitis (UC). We induced an experimental form of UC in mice by treatment with dextran sulfate sodium (DSS) and characterized 53 serum and 69 urine metabolites by use of (1)H NMR spectroscopy and quantitative ("targeted") analysis to distinguish between diseased and healthy animals. Hierarchical multivariate orthogonal partial least-squares (OPLS) models were developed to detect and predict separation of control and DSS-treated mice. DSS treatment resulted in weight loss, colonic inflammation, and increase in myeloperoxidase activity. Metabolomic patterns generated from the OPLS data clearly separated DSS-treated from control mice with a slightly higher predictive power (Q(2)) for serum (0.73) than urine (0.71). During DSS colitis, creatine, carnitine, and methylamines increased in urine while in serum, maximal increases were observed for ketone bodies, hypoxanthine, and tryptophan. Antioxidant metabolites decreased in urine whereas in serum, glucose and Krebs cycle intermediates decreased strongly. Quantitative metabolic profiling of serum and urine thus discriminates between healthy and DSS-treated mice. Analysis of serum or urine seems to be equally powerful for detecting experimental colitis, and a combined analysis offers only a minor improvement.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".