Genetic variation in the serotonin transporter and HTR1B receptor predicts reduced bone formation during serotonin reuptake inhibitor treatment in older adults
Bibliographic record
Abstract
OBJECTIVES: Studies have reported an association between serotonin reuptake inhibitors (SRIs) and accelerated bone loss. Genetic variation in the serotonin system might modulate bone metabolism changes during SRI treatment. In a clinical trial we examined functional genetic polymorphisms of serotonin transporter and receptors involved in bone metabolism to determine whether they predict changes in bone metabolism during SRI treatment. METHODS: In 69 adults (age ≥ 60) participating in a 12-week, open-label trial of the SRI venlafaxine for major depression, serum markers of bone formation (P1NP) and resorption (β-CTX) were assayed before and after treatment. Participants were genotyped for putative high- versus low-expressing polymorphisms in the serotonin transporter (5HTTLPR) and 1B receptor (HTR1B) genes. RESULTS: Bone formation was significantly reduced with administration of venlafaxine in participants with the high-expressing 5HTTLPR genotype and those with the low-expressing HTR1B genotype. This primarily occurred in individuals with the combination of the high-expressing 5HTTLPR genotype and the low-expressing HTR1B genotype. CONCLUSIONS: These preliminary findings indicate that genetic variation in the serotonin receptors predicts changes in bone metabolism during SRI use. If these results are replicated and clinically confirmed, we will have identified a genetic subgroup at high risk for deleterious bone outcomes with the use of SRIs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".