Dietary Curcumin Intervention Targets Mouse White Adipose Tissue Inflammation and Brown Adipose Tissue UCP1 Expression
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine whether dietary curcumin intervention targets both white adipose tissue (WAT) inflammation and brown adipose tissue (BAT)-mediated energy expenditure. METHODS: C57BL/6J mice were fed with a low-fat diet, high-fat diet (HFD), or HFD plus curcumin. In addition to assessing the effect of curcumin intervention on metabolic profiles, this study assessed WAT macrophage infiltration and composition and inflammatory cytokine production. Metabolic cages were applied for determining energy expenditure. Raw264.7 (ATCC, Manassas, Virginia) and other cell models were utilized to test the in vitro effect of curcumin treatment. RESULTS: Curcumin intervention reduced WAT macrophage infiltration and altered macrophage functional polarity, as the ratio of M2-like versus M1-like macrophages increased after curcumin intervention. Curcumin treatment reduced M1-like macrophage markers or proinflammation cytokine expression in both macrophages and adipocytes. Curcumin intervention also increased energy expenditure and body temperature in response to a cold challenge. Finally, the in vivo and in vitro investigations suggested that curcumin increased expression of uncoupling protein 1 (UCP1), possibly involving PPAR-dependent and -independent mechanisms. CONCLUSIONS: Curcumin intervention targets both WAT inflammation and BAT UCP1 expression. These observations advanced our knowledge on the metabolic beneficial effects of the curry compound curcumin, bringing us a novel perspective on dietary polyphenol research.
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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 | 0.000 |
| 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) | 0.002 | 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".