Bone turnover markers predict changes in bone mineral density in men treated with abaloparatide: results from the abaloparatide for the treatment of men with osteoporosis (ATOM) study
Bibliographic record
Abstract
Early increases in bone turnover markers (BTMs) in response to anabolic therapy correlate with 18-mo BMD increases in postmenopausal women with osteoporosis; however, this relationship has not been assessed in men. In this analysis, the correlation between changes from baseline in fasting intact serum procollagen type I N propeptide (PINP) and serum CTX at 1, 3, 6, and 12 mo and percent increase from baseline in BMD at 12 mo in men from the randomized phase 3 ATOM study (NCT03512262) were evaluated using Pearson's correlation coefficients. The uncoupling index (UI), a measure of the balance between markers of bone formation (PINP) and bone resorption (CTX), with positive UI favoring bone formation, was calculated. Results in men were compared to 12-mo results for women from the ACTIVE study using the z score test after Fisher's Z transformation. In abaloparatide-treated men, PINP increases at 1 mo (r = 0.485), 3 mo (r = 0.614), 6 mo (r = 0.632), and 12 mo (r = 0.521) were highly correlated (p < .0001) with 12-mo LS BMD increases. The mean UI for abaloparatide-treated men was greater than placebo as early as 1 mo (2.26 vs -0.25). At month 3, the mean UI for men was greater (1.32) than for women (0.88) (p < .001). There was a significant correlation between 3-mo UI and LS BMD at 12 mo in both men (r = 0.453; p < .001) and women (r = 0.252; p < .01). UI at months 6 and 12 were also significantly correlated with 12-mo LS BMD in men and women, but the correlation was stronger in men than women. These data support that early changes in BTMs in men treated with abaloparatide are associated with subsequent changes in BMD similar to what has been reported in women.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".