The role of PPARγ in childhood obesity-induced fractures
Bibliographic record
Abstract
Globally, obesity is on the rise with ~ 30% of the world's population now obese, and childhood obesity is following similar trends. Childhood obesity has been associated with numerous chronic conditions, including musculoskeletal disorders. This review highlights the effects of childhood adiposity on bone density by way of analyzing clinical studies and further describing two severe skeletal conditions, slipped capital femoral epiphysis and Blount's disease. The latter half of this review discusses bone remodeling and cell types that mediate bone growth and strength, including key growth factors and transcription factors that help orchestrate this complex pathology. In particular, the transcriptional factor peroxisome proliferator-activated receptor gamma (PPARγ) is examined as it is a master regulator of adipocyte differentiation in mesenchymal stem cells (MSCs) that can also influence osteoblast populations. Obese individuals are known to have higher levels of PPARγ expression which contributes to their increased adipocyte numbers and decreased bone density. Modulating PPAR*gamma* signaling can have significant effects on adipogenesis, thereby directing MSCs down the osteoblastogenesis pathway and in turn increasing bone mineral density. Lastly, we explore the potential of PPARγ as a druggable target to decrease adiposity, increase bone density, and be a treatment for children with obesity-induced bone fractures.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".