Antiresorptive therapy and newly diagnosed diabetes in women: a historical cohort study
Bibliographic record
Abstract
AIMS: Undercarboxylated osteocalcin (ucOC) promotes increased insulin sensitivity and increased secretion. Since antiresorptive therapy (AT) decreases ucOC levels, AT could increase the risk of diabetes and this would have serious clinical ramifications. We sought to test this hypothesis by examining the association between new use of AT and newly-diagnosed diabetes. METHODS: Using a bone mineral density (BMD) registry for Manitoba, Canada, we identified 33 640 women aged ≥50 years without diabetes at their first BMD test for 1998-2013. We linked these women to a province-wide retail pharmacy database to identify new AT exposure each year for up to 5 years after a BMD test. Time-dependent analysis was used to test the independent association between new use of AT and newly diagnosed diabetes. RESULTS: This cohort had a mean age of 65 years, a mean body mass index of 26.8 kg/m(2) , and 12% were receiving glucocorticoid and 13% hormone replacement therapy at BMD test. In the first year after BMD test, 29% of women started AT (bisphosphonates, 92%). Over a mean 4.2 years of follow-up, 3.7% new AT users and 4.2% non-users had diabetes (adjusted hazard ratio 1.01, 95% confidence interval 0.87-1.16). Sensitivity analyses using AT dose-response gradients also found no significant associations with diabetes. CONCLUSIONS: Despite the plausible biological mechanisms related to ucOC, new use of AT was not a risk factor for diabetes in this cohort. The clinical implications of these findings are reassuring, as AT is widely prescribed for treating osteoporosis in older women who are also at high risk of developing diabetes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".