Use of Glucagon-Like-Peptide 1 Receptor Agonists and Risk of Fracture as Compared to Use of Other Anti-hyperglycemic Drugs
Bibliographic record
Abstract
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are a new class of drugs that might have a potential beneficial effect on bone metabolism. Data on the effect of GLP-1 RAs and fracture risk are lacking. The aim of the present study was to investigate the association between the use of GLP-1 and the risk of fracture. A case-control study was performed using Danish National Health Service data. Cases were those who sustained a fracture and controls were those without a fracture during the study period (2007-2011), all aged 18 years and above. Conditional logistic regression estimated the odds ratios (OR) of fracture with current use of DPP4-I use. Analyses were adjusted for comorbidities and recent drug use. Among cases (n = 229,114), there were 6993 current non-insulin anti-diabetic drug (NIAD) users (excluding incretin users) and 255 GLP-1 RA users. Similarly, among controls (n = 229,114), 7209 were NIAD users (excluding incretin users) and 220 were GLP-1 RA users. Current GLP-1 RA use was not associated with a decreased risk of fracture [adjusted (adj.) OR 1.16; 95% CI 0.83-1.63]. Osteoporotic fracture risk was also not associated with current GLP-1 RA use (adj. OR 0.78; 95% CI 0.44-1.39). In our nation-wide case-control study, we identified that the use of GLP-1 RA was not associated with fracture risk as compared to the use of other anti-hyperglycemic drugs. Additionally, current GLP-1 RA use, stratified by cumulative or average daily dose, is not associated with fracture risk. Further research should focus on long-term use of GLP-1 RA and fracture risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".