Bone Turnover with Venlafaxine Treatment in Older Adults with Depression
Bibliographic record
Abstract
OBJECTIVES: Epidemiologic data suggest older adults receiving serotonergic antidepressants may have accelerated bone loss. We examined bone turnover marker changes and patient-level variables associated with these changes in older adults receiving protocolized antidepressant treatment. DESIGN: Open-label, protocolized treatment study. SETTING: Medical centers in Pittsburgh, St Louis, and Toronto. PARTICIPANTS: Older adults with major depression (N = 168). MEASUREMENTS: Serum levels of the bone resorption marker C-terminal cross-linking telopeptide of type 1 collagen (CTX) and the bone formation marker procollagen type 1 N propeptide (P1NP) were assayed before and after 12 weeks of treatment with venlafaxine. Whether CTX and P1NP changes were associated with depression remission and duration of depression and genetic polymorphisms in the serotonin transporter (5HTTLPR) and 1B receptor (HTR1B) were also examined. RESULTS: CTX increased and P1NP decreased during venlafaxine treatment, a profile consistent with accelerated bone loss. Two individual-level clinical variables were correlated with bone turnover; participants whose depression did not go into remission had higher CTX levels, and those with chronic depression had lower P1NP levels. HTR1B genotype predicted P1NP change, whereas 5HTTLPR genotype was unrelated to either biomarker. CONCLUSION: Bone turnover markers change with antidepressant treatment in a pattern that suggests accelerated bone loss, although the clinical significance of these changes is unclear. These data are preliminary and argue for a larger, controlled study to confirm whether antidepressants are harmful to bone metabolism and whether certain individuals might be at increased 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".