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Effect of Grape Seed Extract on Blood Pressure in Subjects with Pre-Hypertension

2012· article· en· W3145897914 on OpenAlexvenueno aff
Robinson, Bob Lu, Indika Edirisinghe, C. T. Kappagoda

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2012
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBlood pressurePrehypertensionMedicineAmbulatory blood pressurePlaceboGrape seed extractNutraceuticalInternal medicinePopulationDiastoleCardiologyTraditional medicine

Abstract

fetched live from OpenAlex

Pre-hypertension affects approximately 31% of the adult population of the United States over the age of 18 years. It is defined in the 7th report of the Joint National Committee (JNC - 7) on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure as a systolic blood pressure of 120-139 mmHg or a diastolic blood pressure of 80-89 mmHg. JNC-7 also recommended that individuals considered to be prehypertensive require health-promoting lifestyle modifications to prevent cardiovascular disease. This study was undertaken to determine whether a grape seed extract (GSE) which is a nutraceutical containing vasodilator phenolic compounds lowers blood pressure in subjects with pre-hypertension. The subjects were randomized into a placebo or an experimental group (GSE at a dose of 300 mg/day) and treated for 8 weeks. Serum lipids and blood glucose were measured at the beginning of the study and at the end. The blood pressure was recorded using an ambulatory monitoring device at the start of the treatment period and at the end. Both the systolic and diastolic blood pressures were significantly lower after treatment with GSE. Treatment with the placebo had no effect on blood pressure. There were no significant changes in serum lipids or blood glucose values. These findings suggest that GSE could be used as a nutraceutical in a lifestyle modification program for patients with pre-hypertension.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.335
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2012
Admission routes1
Has abstractyes

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