Apple Flavonols Mitigate Adipocyte Inflammation and Promote Angiogenic Factors in LPS- and Cobalt Chloride-Stimulated Adipocytes, in Part by a Peroxisome Proliferator-Activated Receptor-γ-Dependent Mechanism
Bibliographic record
Abstract
Adipose tissue (AT) expansion induces local hypoxia, a key contributor to the chronic low-grade inflammation that drives obesity-associated disease. Apple flavonols phloretin (PT) and phlorizin (PZ) are suggested anti-inflammatory molecules but their effectiveness in obese AT is inadequately understood. Using in vitro models designed to reproduce the obese AT microenvironment, 3T3-L1 adipocytes were cultured for 24 h with PT or PZ (100 μM) concurrent with the inflammatory stimulus lipopolysaccharide (LPS; 10 ng/mL) and/or the hypoxia mimetic cobalt chloride (CoCl2; 100 μM). Within each condition, PT was more potent than PZ and its effects were partially mediated by peroxisome proliferator-activated receptor (PPAR)-γ (p < 0.05), as tested using the PPAR-γ antagonist bisphenol A diglycidyl ether (BADGE). In LPS-, CoCl2-, or LPS + CoCl2-stimulated adipocytes, PT reduced mRNA expression and/or secreted protein levels of inflammatory and macrophage chemotactic adipokines, and increased that of anti-inflammatory and angiogenic adipokines, which was consistent with reduced mRNA expression of M1 polarization markers and increased M2 markers in RAW 264.7 macrophages cultured in media collected from LPS + CoCl2-simulated adipocytes (p < 0.05). Further, within LPS + CoCl2-stimulated adipocytes, PT reduced reactive oxygen species accumulation, nuclear factor-κB activation, and apoptotic protein expression (p < 0.05). Overall, apple flavonols attenuate critical aspects of the obese AT phenotype.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".