Nitrate consumption preserves HFD-induced skeletal muscle mitochondrial ADP sensitivity and lysine acetylation: A potential role for SIRT1
Bibliographic record
Abstract
Dietary nitrate supplementation, and the subsequent serial reduction to nitric oxide, has been shown to improve glucose homeostasis in several pre-clinical models of obesity and insulin resistance. While the mechanisms remain poorly defined, the beneficial effects of nitrate appear to be partially dependent on AMPK-mediated signaling events, a central regulator of metabolism and mitochondrial bioenergetics. Since AMPK can activate SIRT1, we aimed to determine if nitrate supplementation (4 mM sodium nitrate via drinking water) improved skeletal muscle mitochondrial bioenergetics and acetylation status in mice fed a high-fat diet (HFD: 60% fat). Consumption of HFD induced whole-body glucose intolerance, and within muscle attenuated insulin-induced Akt phosphorylation, mitochondrial ADP sensitivity (higher apparent Km), submaximal ADP-supported respiration, mitochondrial hydrogen peroxide (mtH2O2) production in the presence of ADP and increased cellular protein carbonylation alongside mitochondrial-specific acetylation. Consumption of nitrate partially preserved glucose tolerance and, within skeletal muscle, normalized insulin-induced Akt phosphorylation, mitochondrial ADP sensitivity, mtH2O2, protein carbonylation and global mitochondrial acetylation status. Nitrate also prevented the HFD-mediated reduction in SIRT1 protein, and interestingly, the positive effects of nitrate ingestion on glucose homeostasis and mitochondrial acetylation levels were abolished in SIRT1 inducible knock-out mice, suggesting SIRT1 is required for the beneficial effects of dietary nitrate. Altogether, dietary nitrate preserves mitochondrial ADP sensitivity and global lysine acetylation in HFD-fed mice, while in the absence of SIRT1, the effects of nitrate on glucose tolerance and mitochondrial acetylation were abrogated.
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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) | 0.001 | 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 | 0.001 |
| 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".