Propranolol Promotes Bone Formation and Limits Resorption Through Novel Mechanisms During Anabolic Parathyroid Hormone Treatment in Female C57BL/6J Mice
Bibliographic record
Abstract
ABSTRACT Although the nonselective β-blocker, propranolol, improves bone density with parathyroid hormone (PTH) treatment in mice, the mechanism of this effect is unclear. To address this, we used a combination of in vitro and in vivo approaches to address how propranolol influences bone remodeling in the context of PTH treatment. In female C57BL/6J mice, intermittent PTH and propranolol administration had complementary effects in the trabecular bone of the distal femur and fifth lumbar vertebra (L5), with combination treatment achieving microarchitectural parameters beyond that of PTH alone. Combined treatment improved the serum bone formation marker, procollagen type 1 N propeptide (P1NP), but did not impact other histomorphometric parameters relating to osteoblast function at the L5. In vitro, propranolol amplified the acute, PTH-induced, intracellular calcium signal in osteoblast-like cells. The most striking finding, however, was suppression of PTH-induced bone resorption. Despite this, PTH-induced receptor activator of nuclear factor κ-B ligand (RANKL) mRNA and protein levels were unaltered by propranolol, which led us to hypothesize that propranolol could act directly on osteoclasts. Using in situ methods, we found Adrb2 expression in osteoclasts in vivo, suggesting β-blockers may directly impact osteoclasts. Consistent with this, we found propranolol directly suppresses osteoclast differentiation in vitro. Taken together, this work suggests a strong anti-osteoclastic effect of nonselective β-blockers in vivo, indicating that combining propranolol with PTH could be beneficial to patients with extremely low bone density. © 2022 American Society for Bone and Mineral Research (ASBMR).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 |
| 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".