The use of antidepressants is linked to bone loss: A systematic review and metanalysis
Bibliographic record
Abstract
Introduction: Depression and antidepressants are among risk factors for osteoporosis. However, there are still inconsistencies in literature regarding bone consequences of antidepressant drugs and the role of age and the natural decline of bone health in patients with depression. Objective: To investigate the relationship between antidepressant and bone mineral density (BMD). Methods: We conducted a systematic review and metanalysis according to PRISMA guidelines searching on PubMed/Medline, Cochrane Database, and Scopus libraries and registered with PROSPERO (registration number CRD42021254006) using generic terms for antidepressants and BMD. Search was restricted to English language only and without time restriction from inception up to June 2021. Methodological quality was assessed with the Newcastle-Ottawa scale. Results: Eighteen papers were included in the qualitative analysis and five in the quantitative analysis. A total of 42,656 participants affected by different subtypes of depression were identified. Among the included studies, 10 used serotonin reuptake inhibitors (SSRIs) only, 6 involved the use of SSRIs and tricyclic antidepressants, and 2 the combined use of more than two antidepressants. No significant studies meeting the inclusion criteria for other most recent categories of antidepressants, such as vortioxetine and esketamine. Overall, we observed a significant effect of SSRI on decrease of BMD with a mean effect of 0.28 (95% CI = 0.08, 0.39). Conclusion: Our data suggest that SSRIs are associated with a decrease of BMD. We aim to raise clinicians' awareness of the potential association between the use of antidepressants and bone fragility to increase monitoring of bone health.
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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 | 0.033 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.044 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".