A carbohydrate restricted – high fat diet reduces blood pressure in spontaneously hypertensive rats without causing insulin resistance
Bibliographic record
Abstract
We hypothesized that a low carbohydrate ‐ high fat (LC) diet will attenuate blood pressure (BP) and cardiac hypertrophy in spontaneously hypertensive rats (SHR). SHR (6 wk old, n=54) and Wistar Kyoto rats (WKY, 6 wk old, n=53; normotensive control) were fed control (C, 70% carbohydrate, 10% fat, 20% protein) or LC diet (20% carbohydrate, 60% fat, 20% protein). After 10 wk mean arterial BP was 11 mmHg lower (p<0.05) in SHR‐LC vs. SHR‐C, however, cardiac S6K phosphorylation was augmented (p<0.05) and heart:tibia length was unchanged. Compared to SHR‐C, mesenteric arteries of SHR‐LC had improved (p<0.05) endothelium dependent (acetylcholine) & independent (sodium nitroprusside) relaxation, reduced (p<0.05) contraction (potassium chloride, phenylephrine), and increased (p<0.05) eNOS Ser1177 phosphorylation. Plasma glucose was similar among all groups. Insulin was lower (p<0.05) in rats fed LC vs. C. SHR‐LC had greater (p<0.05) peripheral insulin sensitivity (insulin tolerance test) vs. WKY‐C. Insulin stimulated (10h fast, 2U/kg IP) phosphorylation of Akt Ser473 and S6 in heart and gastrocnemius was not altered SHR‐ LC vs. C. In conclusion, an LC diet reduced blood pressure and improved arterial function without producing insulin resistance or altering insulin mediated signaling in heart and skeletal muscle.
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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 | 0.000 |
| 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".