Impact of type 2 diabetes on clinical outcomes and advanced therapy use in patients with Crohn’s disease: a real-world propensity score-matched analysis
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes mellitus (T2DM) may adversely affect the course and treatment outcomes of Crohn's disease (CD). However, data remain inconsistent. AIMS: To evaluate the impact of T2DM on clinical outcomes and advanced therapy use in patients with CD using real-world electronic health record data. METHODS: We conducted a retrospective cohort study using the TriNetX Global Collaborative Network. Adult patients with CD were stratified by the presence of T2DM. Propensity score matching was used to balance demographic and clinical characteristics. Primary outcomes included corticosteroid use, abdominal surgery, drug-related adverse events, and the initiation of advanced therapies. Secondary outcomes included the incidence of neoplasms and mental disorders. Time-to-event outcomes were analyzed using Kaplan-Meier curves and hazard ratios. RESULTS: After matching, 7182 patients were included in each cohort. Corticosteroid use was higher in diabetic patients (61.6% vs 55.2%; P < .001), as were abdominal surgery (32.8% vs 31.1%; P = .010) and drug-related adverse events (4.3% vs 2.4%; P < .001). Use of anti-TNF (10.4% vs 12.2%; P < .001) and IL-23 inhibitors (4.6% vs 5.4%; P = .018) was lower in diabetic patients. Use of vedolizumab (2.4% vs 2.6%; P = .401) and JAK inhibitors (0.6% in both; P = .955) was similar. Neoplasm rates were comparable (3.5% vs 3.2%; P = .386), while mental disorders were more common in the diabetic cohort (51.5% vs 41.2%; P < .001). CONCLUSIONS: Patients with CD and coexisting T2DM experience a more severe disease course yet appear to be undertreated with advanced therapies. These findings highlight the need for tailored, multidisciplinary management strategies in this high-risk population.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".