Sitagliptin and risk of fractures in type 2 diabetes: <scp>R</scp> esults from the <scp>TECOS</scp> trial
Bibliographic record
Abstract
AIM: To examine fracture incidence among participants in the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS). RESEARCH DESIGN AND METHODS: We used data from 14 671 participants in the TECOS study who were randomized double-blind to sitagliptin (n = 7332) or placebo (n = 7339). Cumulative fracture incidence rates were calculated and their association with study treatment assignment was examined using multivariable Cox proportional hazards regression. RESULTS: The baseline mean (standard deviation) participant age was 65.5 (8.0) years, diabetes duration was 11.6 (8.1) years and glycated haemoglobin level was 7.2 (0.5)% [55.2 (5.5) mmol/mol], and 29.3% of participants were women and 32.1% were non-white. During 43 222 person-years' follow-up, 375 (2.6%; 8.7 per 1000 person-years) had a fracture; 146 were major osteoporotic fractures (hip, n = 34; upper extremity, n = 81; and clinical spine, n = 31). Adjusted analyses showed fracture risk increased independently with older age (P < .001), female sex (P < .001), white race (P < .001), lower diastolic blood pressure (P < .001) and diabetic neuropathy (P = .003). Sitagliptin, compared with placebo, was not associated with a higher fracture risk [189 vs 186 incident fractures: unadjusted hazard ratio (HR) 1.01, 95% confidence interval (CI) 0.82 to 1.23, P = .944; adjusted HR 1.03, P = .745], major osteoporotic fractures (P = .673) or hip fractures (P = .761). Insulin therapy was associated with a higher fracture risk (HR 1.40, 95% CI 1.02-1.91; P = .035), and metformin with a lower risk (HR 0.76, 95% CI 0.59-0.98; P = .035). CONCLUSION: Fractures were common among people with diabetes in the TECOS study, but were not related to sitagliptin therapy. Insulin and metformin treatment were associated with higher and lower fracture risks, respectively.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".