Antihypertensive effects of dietary protein and its mechanism
Bibliographic record
Abstract
Hypertension is a leading cause of morbidity and mortality worldwide. Individuals with hypertension are at increased risk of stroke, heart disease and kidney failure. Both genetic and lifestyle factors, particularly diet, have been attributed an important role in the development of hypertension. Reducing dietary sugar and salt intake can help lower blood pressure; similarly, adequate protein intake may also attenuate hypertension. Observational, cross-sectional and longitudinal epidemiological studies, and controlled clinical trials, have documented significant inverse associations between protein intake and blood pressure. Human and animal studies have shown that specific amino acids within proteins may have antihypertensive effects. Cysteine, glutathione (a tripeptide), glutamate and arginine attenuate and prevent alterations that cause hypertension including insulin resistance, decreased nitric oxide bioavailability, altered renin angiotensin system function, increased oxidative stress and formation of advanced glycation end products. Leucine increases protein synthesis in skeletal muscle and improves insulin resistance by modulating hepatic gluconeogenesis. Taurine and tryptophan attenuate sympathetic nervous system activity. Soy protein helps lower blood pressure through its high arginine content and antioxidant activity exhibited by isoflavones. A diet containing an ample amount of protein may be a beneficial lifestyle choice for individuals with hypertension; one example is the Dietary Approaches to Stop Hypertension (DASH) diet, which is low in salt and saturated fat; includes whole grains, lean meat, poultry, fish and nuts; and is rich in vegetables, fruits and low-fat dairy products, which are good sources of antioxidant vitamins, minerals and fibre. Including an adequate supply of soy in the diet should also be encouraged.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".