Bone Fracture Risk is Not Associated with the Use of Glucagon-Like Peptide-1 Receptor Agonists: A Population-Based Cohort Analysis
Bibliographic record
Abstract
Glucagon-like Peptide-1 receptor agonists (GLP1-ra) are a relatively new class of anti-hyperglycemic drugs which may positively affect bone metabolism and thereby decrease (osteoporotic) bone fracture risk. Data on the effect of GLP1-ra on fracture risk are scarce and limited to clinical trial data only. The aim of this study was to investigate, in a population-based cohort, the association between the use of GLP1-ra and bone fracture risk. We conducted a population-based cohort study, with the use of data from the Clinical Practice Research Datalink (CPRD) database (2007-2012). The study population (N = 216,816) consisted of all individuals with type 2 diabetes patients with at least one prescription for a non-insulin anti-diabetic drug and were over 18 years of age. Cox proportional hazards models were used to estimate the hazard ratio of fracture in GLP1-ra users versus never-GLP1-ra users. Time-dependent adjustments were made for age, sex, lifestyle, comorbidity and the use of other drugs. There was no decreased risk of fracture with current use of GLP1-ra compared to never-GLP1-ra use (adjusted HR 0.99, 95 % CI 0.82-1.19). Osteoporotic fracture risk was also not decreased by current GLP1-ra use (adjusted HR 0.97; 95 % CI 0.72-1.32). In addition, stratification according to cumulative dose did not show a decreased bone fracture risk with increasing cumulative GLP1-ra dose. We showed in a population-based cohort study that GLP1-ra use is not associated with a decreased bone fracture risk compared to users of other anti-hyperglycemic drugs. Future research is needed to elucidate the potential working mechanisms of GLP1-ra on bone.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".