Pharmacologic or genetic activation of SIRT1 attenuates the fat-induced decrease in beta-cell function in vivo
Bibliographic record
Abstract
BACKGROUND: There is evidence that sirtuin 1 (SIRT1), a key regulator of nutrient metabolism, increases β-cell secretory function. Excess circulating fat, as seen in obesity, has been shown to decrease β-cell function, an effect that may involve decreased SIRT1 activity. Consequently, SIRT1 activation may increase β-cell function in conditions of elevated plasma-free fatty acid levels. Here we attempted to attenuate the lipid-induced decrease in β-cell function in vivo using pharmacological and genetic models of SIRT1 activation. METHODS: Our pharmacologic model involved 48 h intravenous infusion of Wistar rats with either saline or oleate with or without the SIRT1 activator resveratrol. Additionally, we used β-cell-specific SIRT1 overexpressing (BESTO) mice and wild-type littermates infused for 48 h intravenously with either saline or oleate. In both models, the infusion period was followed by assessment of β-cell function using the hyperglycemic clamp method. RESULTS: Lipid infusion resulted in a significant decrease in β-cell function as expected in both rats (p < 0.05) and mice (p < 0.001). Both models of SIRT1 activation, which did not alter β-cell function in the absence of fat, resulted in partial protection from the fat-induced decrease in β-cell function (NS vs. control). CONCLUSION: These results suggest that SIRT1 is a therapeutic target in decreased β-cell function specifically induced by fat.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".