Effect of barley protein supplementation on coronary heart disease risk reduction
Bibliographic record
Abstract
Objective: High animal protein weight loss diets have been associated with rises in LDL‐C. We wished to determine the effect of high barley protein intake on LDL‐C. Method: 23 hypercholesterolemic individuals (16F, 7M; 57±7y; LDL‐C, 3.90±0.21mmol/L) completed two 4‐week treatments of bread supplementation containing either 30g/d (based on a 2000 kcal diet) of barley protein (treatment group) or dairy protein (calcium caseinate placebo), in a randomized controlled crossover design. Serum lipids, body weight and blood pressure were measured biweekly on each treatment. Results: At week 4, changes expressed as the difference between baseline demonstrated no evidence that barley protein supplementation altered LDL‐C and TChol:HDL‐C (−0.09±0.11mmol/L, P=0.454 and 0.13±0.14mmol/L, P=0.364, respectively). Corresponding data for the dairy protein supplementation also showed no overall improvement (LDL‐C, 0.00±0.11mmol/L, P=0.989 and TChol:HDL‐C, 0.19±0.11mmol/L, P=0.088). Furthermore, the effect of barley protein on blood lipids was not significantly different from dairy protein (LDL‐C, P=0.836; TChol:HDL‐C, P=0.964). Conclusion: This study demonstrates that neither barley nor dairy protein appear to alter blood lipids and may be equally suitable for weight reduction diets. Funding: Natural Sciences and Engineering Research Council of Canada, Loblaws Ltd.
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 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) | 0.002 | 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".