Risk of Low Bone Mineral Density Associated With Psychotropic Medications and Mental Disorders in Postmenopausal Women
Bibliographic record
Abstract
BACKGROUND: Independent reports suggest that various psychotropic medications and psychiatric disorders are associated with changes in bone mineral density (BMD). The objective of this study was to clarify the independent effects of a range of mental illnesses and psychotropic medications on BMD among postmenopausal women. METHODS: Women 50 years or older with baseline BMD measured by dual-energy x-ray absorptiometry were identified in a database containing all clinical dual-energy x-ray absorptiometry test results for the Province of Manitoba, Canada. Records were linked with population-based administrative health databases to provide detailed information on sociodemographic factors, mental and physical health diagnoses, and prescription medication usage. Osteoporotic cases (n = 6820) were matched on age, sex, and ethnicity to 3 control subjects with normal BMD (n = 20,247). Multivariable conditional logistic regression compared cases and control subjects on diagnosed mental illnesses and use of psychotropic medications. RESULTS: Selective serotonin reuptake inhibitors (adjusted odds ratios, 1.46; 95% confidence interval [CI], 1.25-1.69), atypical antipsychotics (AOR, 1.55; 95% CI, 1.06-2.28), and benzodiazepines (AOR, 1.17; 95% CI, 1.06-1.29) were associated with higher risk of osteoporosis. Tricyclic antidepressants were associated with lower odds of osteoporosis (AOR, 0.57; 95% CI, 0.49-0.65). These drug effects were independent of mental illness diagnoses including depression (AOR, 0.86; 95% CI, 0.75-0.98) and schizophrenia (AOR, 1.98; 95% CI, 1.04-3.77). CONCLUSIONS: Some psychotropic medications are associated with an increased risk of osteoporotic BMD, whereas tricyclic antidepressants may be protective against osteoporosis, and these effects are independent of mental illness diagnoses. Clinicians should consider these effects when prescribing psychotropic medications in postmenopausal women.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".