Oxytocin, a New Determinant of Bone Mineral Density in Post-Menopausal Women: Analysis of the OPUS Cohort
Bibliographic record
Abstract
INTRODUCTION: Oxytocin (OT), a neurohypophysial hormone regulated by estrogen and leptin, may play a role in bone metabolism in humans as suggested by animal studies. This study assessed the relationship between OT and bone status in a large population of postmenopausal women. SUBJECTS AND METHODS: Subjects were included in the Osteoporosis and Ultrasound study, a 6-year prospective study in a population-based cohort. Final visit data were used for this cross-sectional study. OT, leptin, and estradiol serum levels were measured in 1097 postmenopausal women and compared with bone mineral density (BMD), fractures, and the bone turnover markers (BTMs) procollagen type 1 N-terminal propeptide, bone alkaline phosphatase, and C-telopeptide of type 1 collagen. RESULTS: The median age was 70.8 years, 16% were osteoporotic, 48% were osteopenic, and 29% had at least one fracture. The OT serum level was related to spine (r = +0.12, P = .0002) and total hip BMD (r = +0.21, P < .0001) and with BTM (procollagen type 1 N-terminal propeptide: r = -0.13, P < .0001, bone alkaline phosphatase: r = -0.07, P = .02, C-telopeptide of type 1 collagen: r = -0.18, P < .0001). The relationship of OT with BMD was independent of BTM. After adjustment for confounding factors, the correlation between OT serum level and BMD remains significant at the hip in women with unmeasurable estradiol or leptin above the median value. There was no significant relationship between OT serum levels and fractures. CONCLUSION: High OT levels are associated with high BMD, especially at the hip in women with low estradiol or high leptin serum levels. The mechanism may be explained by the effect of OT on bone turnover.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".