Pretreatment with Anticatabolic Agents Blunts But Does Not Eliminate the Skeletal Anabolic Response to Parathyroid Hormone in Oophorectomized Mice
Bibliographic record
Abstract
Previous studies have indicated that bisphosphonate pretreatment can inhibit the anabolic actions of PTH. We examined the capacity of two anticatabolic agents with different mechanisms of action, alendronate and osteoprotegerin (OPG), to influence the anabolic activity of PTH. Oophorectomized mice were pretreated for 30 d with alendronate or OPG and then treated for 30 d with the respective anticatabolic alone or the respective anticatabolic plus PTH(1-34). Bones were analyzed by bone mineral density (BMD), microcomputed tomography, histology and histomorphometry, and biochemical bone markers. OPG pretreatment produced a greater inhibition of bone turnover and a greater increase in bone than alendronate. Increases in bone were sustained during subsequent treatment with vehicle or continued administration of the anticatabolic. Pretreatment with each anticatabolic blunted the capacity of PTH to increase BMD and bone volume and continued treatment with each anticatabolic agent also reduced the effectiveness of PTH. Although both anticatabolics decreased the maximal PTH effect, BMD and bone volume increased more when PTH was added than when only anticatabolics were used. These results demonstrate that mechanistically distinct anticatabolics may reduce PTH efficacy, that the characteristics of this inhibition may reflect the different modes of action of the anticatabolics, but that the addition of PTH still provides a skeletal benefit even if the anabolic effect is submaximal.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".