A low-carbohydrate/high-fat diet reduces blood pressure in spontaneously hypertensive rats without deleterious changes in insulin resistance
Bibliographic record
Abstract
Previous studies reported that diets high in simple carbohydrates could increase blood pressure in rodents. We hypothesized that the converse, a low-carbohydrate/high-fat diet, might reduce blood pressure. Six-week-old spontaneously hypertensive rats (SHR; n = 54) and Wistar-Kyoto rats (WKY; n = 53, normotensive control) were fed either a control diet (C; 10% fat, 70% carbohydrate, 20% protein) or a low-carbohydrate/high-fat diet (HF; 20% carbohydrate, 60% fat, 20% protein). After 10 wk, SHR-HF had lower (P < 0.05) mean arterial pressure than SHR-C (148 ± 3 vs. 159 ± 3 mmHg) but a similar degree of cardiac hypertrophy (33.4 ± 0.4 vs. 33.1 ± 0.4 heart weight/tibia length, mg/mm). Mesenteric arteries and the entire aorta were used to assess vascular function and endothelial nitric oxide synthase (eNOS) signaling, respectively. Endothelium-dependent (acetylcholine) relaxation of mesenteric arteries was improved (P < 0.05) in SHR-HF vs. SHR-C, whereas contraction (potassium chloride, phenylephrine) was reduced (P < 0.05). Phosphorylation of eNOSSer1177 increased (P < 0.05) in arteries from SHR-HF vs. SHR-C. Plasma glucose, insulin, and homoeostatic model of insulin assessment were lower (P < 0.05) in SHR-HF vs. SHR-C, whereas peripheral insulin sensitivity (insulin tolerance test) was similar. After a 10-h fast, insulin stimulation (2 U/kg ip) increased (P < 0.05) phosphorylation of AktSer473 and S6 in heart and gastrocnemius similarly in SHR-C vs. SHR-HF. In conclusion, a low-carbohydrate/high-fat diet reduced blood pressure and improved arterial function in SHR without producing signs of insulin resistance or altering insulin-mediated signaling in the heart, skeletal muscle, or vasculature.
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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".