Blood proteomic signatures associated with disease activity in inflammatory bowel diseases
Bibliographic record
Abstract
BACKGROUND AND AIMS: Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), remain heterogeneous disorders with variable response to biologics. Post-operative recurrence in CD is common despite surgery and prophylactic biotherapies. Understanding the inflammatory mediators associated with recurrence and treatment response could pave the way for personalized strategies. METHODS: We analyzed serum inflammatory protein signatures using proteomics in two prospective cohorts. The REMIND cohort included post-operative CD patients undergoing ileocecal resection with endoscopic assessment at 6 months (M6). Serum samples were collected at surgery and 6 months later. The ELYP cohort consisted of active IBD patients starting new biotherapies (anti-tumor necrosis factor [anti-TNF], ustekinumab, or vedolizumab). Serum samples were collected pre- and post-treatment (Weeks 14 and 52). RESULTS: In the REMIND cohort, proteomic analysis revealed elevated levels of IFN-γ, CXCL9, and MMP-10 in patients with recurrence, with concentrations associated with recurrence severity. Preoperative MMP-10 levels predicted severe recurrence (AUC = 0.70). Under biotherapies, treatment-specific proteins were associated with recurrence: CXCL9 for anti-TNF and OSM/TGFα modules for ustekinumab. In the ELYP cohort, IFN-γ and CXCL9 were significantly elevated in CD compared to UC and associated with disease activity. Early response to anti-TNF treatment (Week 14) was associated with reductions in CXCL9, MMP-10, and OSM, while deep remission (Week 52) correlated with decreases in CXCL9 and OSM. CONCLUSION: Our findings reveal inflammatory blood proteomic signatures associated with post-operative recurrence and biologic treatment failure in IBD. Several key biomarkers were identified. These results support the rationale for personalized approaches, including combination therapies targeting multiple pathways.ClincialTrials.gov number, NCT02693340 and NCT02693340.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".