Curcumin Prevents High Fat Diet Induced Insulin Resistance and Obesity via Attenuating Lipogenesis in Liver and Inflammatory Pathway in Adipocytes
Bibliographic record
Abstract
BACKGROUND: Mechanisms underlying the attenuation of body weight gain and insulin resistance in response to high fat diet (HFD) by the curry compound curcumin need to be further explored. Although the attenuation of the inflammatory pathway is an accepted mechanism, a recent study suggested that curcumin stimulates Wnt signaling pathway and hence suppresses adipogenic differentiation. This is in contrast with the known repressive effect of curcumin on Wnt signaling in other cell lineages. METHODOLOGY AND PRINCIPAL FINDINGS: We conducted the examination on low fat diet, or HFD fed C57BL/6J mice with or without curcumin intervention for 28 weeks. Curcumin significantly attenuated the effect of HFD on glucose disposal, body weight/fat gain, as well as the development of insulin resistance. No stimulatory effect on Wnt activation was observed in the mature fat tissue. In addition, curcumin did not stimulate Wnt signaling in vitro in primary rat adipocytes. Furthermore, curcumin inhibited lipogenic gene expression in the liver and blocked the effects of HFD on macrophage infiltration and the inflammatory pathway in the adipose tissue. CONCLUSIONS AND SIGNIFICANCE: We conclude that the beneficial effect of curcumin during HFD consumption is mediated by attenuating lipogenic gene expression in the liver and the inflammatory response in the adipose tissue, in the absence of stimulation of Wnt signaling in mature adipocytes.
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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) | 0.002 | 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".