Dipeptidyl peptidase-4 inhibitors and incidence of inflammatory bowel disease among patients with type 2 diabetes: population based cohort study
Bibliographic record
Abstract
OBJECTIVE: To assess whether the use of dipeptidyl peptidase-4 inhibitors is associated with the incidence of inflammatory bowel disease in patients with type 2 diabetes. DESIGN: Population based cohort study. SETTING: More than 700 general practices contributing data to the United Kingdom Clinical Practice Research Datalink. PARTICIPANTS: A cohort of 141 170 patients, at least 18 years of age, starting antidiabetic drugs between 1 January 2007 and 31 December 2016, with follow-up until 30 June 2017. MAIN OUTCOME MEASURES: Adjusted hazard ratios for incident inflammatory bowel disease associated with use of dipeptidyl peptidase-4 inhibitors overall, by cumulative duration of use, and by time since initiation, estimated using time dependent Cox proportional hazards models. Use of dipeptidyl peptidase-4 inhibitors was modelled as a time varying variable and compared with use of other antidiabetic drugs, with exposures lagged by six months to account for latency and diagnostic delays. RESULTS: 34.5 per 100 000 person years; hazard ratio 1.75, 95% confidence interval 1.22 to 2.49). Hazard ratios gradually increased with longer durations of use, reaching a peak after three to four years of use (hazard ratio 2.90, 1.31 to 6.41) and decreasing after more than four years of use (1.45, 0.44 to 4.76). A similar pattern was observed with time since starting dipeptidyl peptidase-4 inhibitors. These findings remained consistent in several sensitivity analyses. CONCLUSIONS: In this first population based study, the use of dipeptidyl peptidase-4 inhibitors was associated with an increased risk of inflammatory bowel disease. Although these findings need to be replicated, physicians should be aware of this possible association.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".