The risk of fragility fractures in new users of dipeptidyl peptidase-4 inhibitors compared to sulfonylureas and other anti-diabetic drugs: A cohort study
Bibliographic record
Abstract
AIMS: Mixed evidence exists for the effect of incretin-based therapies on osteoporosis in type-2 diabetes. Therefore, we conducted a cohort study to determine the association between dipeptidyl peptidase-4 (DPP-4) inhibitors and common osteoporotic "fragility fractures" (upper extremity, hip, spine). METHODS: The UK-based Clinical Practice Research Datalink was used to identify adults without prior fractures receiving a new anti-diabetic drug or a new type-2 diabetes diagnosis between 2007 and 2016. The primary aim was to compare new-users of DPP-4 inhibitors versus new-users of sulfonylureas (SU). The association between DPP-4 inhibitors and incident fractures was estimated using Cox proportional hazards models. Deciles of high-dimensional propensity scores and other anti-diabetic drugs were used as covariates. RESULTS: We identified 7993 and 26,636 new-users of DPP-4 inhibitors and SUs, respectively. At cohort entry, the mean age was 58.8, 40% were female, mean diabetes duration was 1.3 years, and 42% had A1c > 9%. Over 9 years (mean follow-up = 1.2 years), the incident rate of fragility fractures was lower among DPP-4 versus SU users (3.0/1000 vs. 5.2/1000 person-years; P-value = 0.007). After adjustment, there was no statistically significant difference in fracture risk (hazard ratio adjusted, aHR = 0.80, 95%CI 0.51-1.24; P-value = 0.3125). In a secondary analysis, DPP-4 inhibitors were not associated with a difference in fracture risk compared to insulin (aHR = 0.91, 95%CI 0.40-2.09); however were associated with a lower fracture risk versus thiazolidinediones (aHR = 0.47, 95%CI 0.26-0.83). Sensitivity analyses supported findings. CONCLUSIONS: DPP-4 inhibitors are not associated with an increased risk of fragility fractures compared with SUs or insulin; however, are associated with a lower risk versus thiazolidinediones.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".