Dipeptidyl Peptidase 4 Inhibitors and the Risk of Bullous Pemphigoid Among Patients With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: There are uncertainties regarding the association between dipeptidyl peptidase 4 (DPP-4) inhibitors and bullous pemphigoid (BP), a potentially severe autoimmune skin disease. Thus, we conducted a population-based study to determine whether use of DPP-4 inhibitors, when compared with other second- to third-line antidiabetic drugs, is associated with an increased risk of BP in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: Using the U.K. Clinical Practice Research Datalink, we conducted a cohort study among 168,774 patients initiating antidiabetic drugs between January 2007 and March 2018. Using time-dependent Cox proportional hazards models, we estimated adjusted hazard ratios (HRs) with 95% CIs of incident BP associated with current use of DPP-4 inhibitors, compared with current use of other second- to third-line antidiabetic drugs. We also conducted a propensity score-matched analysis to assess the impact of residual confounding. RESULTS: During 711,311 person-years of follow-up, 150 patients were newly diagnosed with BP (crude incidence rate, 21.1 per 100,000 person-years). Current use of DPP-4 inhibitors was associated with an increased risk of BP (47.3 vs. 20.0 per 100,000 person-years; HR 2.21 [95% CI 1.45-3.38]). HRs gradually increased with longer durations of use, reaching a peak after 20 months (HR 3.60 [95% CI 2.11-6.16]). Similar results were obtained in the propensity score-matched analysis (HR 2.40 [95% CI 1.13-4.66]). CONCLUSIONS: In this large population-based study, use of DPP-4 inhibitors was associated with an at least doubling of the risk of BP in patients with type 2 diabetes, albeit the absolute risk was low.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".