Protective effects of apple polyphenols on bone loss in mice with high fat diet-induced obesity
Bibliographic record
Abstract
Obesity-induced inflammation can lead to an imbalance in bone formation and resorption. Our previous studies have demonstrated that apple polyphenols (APs) can reduce body weight and inflammation. But their effect on bone is still unclear. In this study, we investigated the protective effects of APs on bone loss in mice with high-fat-diet (HFD)-induced obesity. Forty male C57BL/6J mice were divided into control group (10% fat diet), HFD group (60% fat diet), resveratrol group (60% fat diet), and AP group (60% fat diet). Micro-computed tomography revealed a significant increase in bone volume fraction and bone mineral density, and more trabecular bone and less trabecular bone separation in the AP group compared with the HFD group. In addition, serum tumor necrosis factor (TNF)-α, interleukin-6 (IL-6), and tartrate-resistant acid phosphatase (TRACP) levels were decreased; runt-related transcription factor 2 (Runx2) levels were increased; the collagen area was enlarged; and femur biomechanical property was enhanced in the AP group compared with the HFD group. APs significantly increased the ratio of osteoprotegerin to the receptor activator for the nuclear factor-κB ligand (OPG/RANKL) compared with the HFD group. Resveratrol could also improve the glucolipid regulation, but poorer osteogenic promotion was found compared to APs. The present study demonstrated that APs prevent loss of bone mass induced by obesity, which has potential implications for the prevention and treatment of obesity-related osteoporosis.
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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 | 0.000 |
| 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".