Urine and Serum Metabolomic Profiles Differ by Disease Activity in Pregnant Women With Inflammatory Bowel Diseases
Bibliographic record
Abstract
Background and Aims: Inflammatory bowel disease (IBD), inclusive of ulcerative colitis and Crohn's disease, are chronic inflammatory conditions that impact women of childbearing age. It has been previously shown that IBD is associated with altered metabolomic profiles, but whether metabolomic changes also affect pregnant patients with IBD is completely unknown. Methods: This was a prospective cohort study comprised of 48 pregnant women with IBD who were followed throughout preconception and pregnancy. IBD disease activity was measured using biochemical markers C-reactive protein or fecal calprotectin using enzyme-linked immunosorbent assay and clinical disease activity using Harvey-Bradshaw Index or partial Mayo scores. Serum and urine samples were collected from preconception, trimester 1, and trimester 2 and analyzed using nuclear magnetic resonance spectroscopy combined with metabolomics set enrichment analysis. Results: We identified a total of 24 urine metabolites and 17 serum metabolites which were altered by active disease across pregnancy. First trimester (T1) active disease-associated metabolites were enriched in "amino acid metabolism" and "fatty-acid β-oxidation." The leading urine metabolites at T1 were trimethyl-N-oxide (TMAO), succinic acid, and 3-hydroxy-2-methylbutyric acid, and leading serum metabolites were TMAO, glucose, and acetic acid. Multivariate modeling using serum TMAO, glucose, and acetic acid predicts T1 disease activity and correlated with mode of delivery and infant weights at delivery. Moreover, cross-time point modeling using metabolomes predicted future disease flare-up during pregnancy. Conclusion: These results suggest select host metabolites may be able to discriminate and predict disease activity and are correlated with pregnancy outcomes at delivery. This warrants further validation of metabolomics to monitor IBD in pregnancy.
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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".