A distinct serum metabolic signature outperforms C-reactive protein as a non-invasive marker for monitoring disease activity in Inflammatory Bowel Disease
Bibliographic record
Abstract
Abstract Background Achieving deep remission, such as e.g. histo-endoscopic remission, is a key objective in inflammatory bowel disease (IBD). However, this metric depends on invasive procedures that are impractical in routine care, whereas current non-invasive blood biomarkers demonstrate only limited correlation with disease activity. Objective We used serum metabolomics to identify blood-based signatures that comprehensively assess disease activity. Design Serum was collected from HC (n=195), UC (n=183; Kiel, discovery), UC (n=52; Leuven, validation) and CD (n=98; Kiel) cohorts alongside composite assessment of disease activity using endoscopic, clinical, biochemical, and histopathological criteria. Targeted metabolomics was performed with the Biocrates MxP Quant 500(XL) kit. Statistical analysis involved principal component analysis (PCA), linear mixed models (LMM), generalised estimating equation (GEE) and ML (logistic, LASSO regression). Results We identified numerous serum metabolites predominantly related to amino acid, bile acid and lipid metabolism that differed significantly between HC and inactive or active UC/CD. Among those, increased beta-alanine and Hex2Cer(d18:1/16:0) accompanied by diminished tryptophan and histidine distinguished active from inactive UC as well as HC from active IBD across several disease activity definitions. Combining those metabolites better estimated disease activity than C-reactive protein (CRP). Lastly, prior to initiation of advanced therapy, baseline metabolites and LASSO-selected combinations thereof exhibited limited ability to predict remission. Conclusion Serum metabolomics distinguished IBD from HC and inactive from active disease, with a four-metabolite panel outperforming CRP in comprehensively assessing disease activity. Despite restricted predictive performance, our findings underscore the value of serum metabolomics in elucidating IBD pathophysiology and improving disease monitoring. What is already known on this topic Serum metabolomics reveal systemic immunometabolic alterations in IBD, but prior studies lack composite disease activity measures and independent validation. What this study adds Cross-cohort analysis of comprehensively assessed disease activity revealed metabolic signatures that distinguish IBD from HC and inactive from active disease states, with superior discriminatory power compared to CRP. However, baseline metabolites exhibit limited ability to predict outcomes. How this study might affect research, practice or policy Our findings highlight both the potential and current constraints of serum metabolomics for clinical application, emphasizing the need for standardized, longitudinal, and multi-cohort studies to enable reproducible biomarker discovery in IBD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".