Pharmacological inhibition of PPARγ increases osteoblastogenesis and bone mass in male C57BL/6 mice
Bibliographic record
Abstract
Infiltration of bone marrow with fat is a prevalent feature in people with age-related bone loss and osteoporosis, which correlates inversely with bone formation and positively with high expression levels of peroxisomal proliferator-activated receptor gamma (PPARγ). Inhibition of PPARγ thus represents a potential therapeutic approach for age-related bone loss. In this study, we examined the effect of PPARγ inhibition on bone in skeletally mature C57BL/6 male mice. Nine-month-old mice were treated with a PPARγ antagonist, bisphenol-A-diglycidyl ether (BADGE), alone or in combination with active vitamin D (1,25[OH](2) D(3) ) for 6 weeks. Micro-computed tomography and bone histomorphometry indicated that mice treated with either BADGE or BADGE + 1,25(OH)(2) D(3) had significantly increased bone volume and improved bone quality compared with vehicle-treated mice. This phenotype occurred in the absence of alterations in osteoclast number. Furthermore, the BADGE + 1,25(OH)(2) D(3) -treated mice exhibited higher levels of unmineralized osteoid. All of the treated groups showed a significant increase in circulating levels of bone formation markers without changes in bone resorption markers, while blood glucose, parathyroid hormone, and Ca(+) remained normal. Furthermore, treatment with BADGE induced higher levels of expression of vitamin D receptor within the bone marrow. Overall, treated mice showed higher levels of osteoblastogenesis and bone formation concomitant with decreased marrow adiposity and ex vivo adipogenesis. Taken together, these observations demonstrate that pharmacological inhibition of PPARγ may represent an effective anabolic therapy for osteoporosis in the near future.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".