Soy Flour Muffins Do Not Improve Risk Factors for Cardiovascular Disease in Adults with Hypercholesterolemia
Bibliographic record
Abstract
Soy may reduce cardiovascular disease (CVD) risk by lowering LDL cholesterol (LDL‐C), but it is not known whether this health benefit is related to the matrix of soy foods. This study assessed the dose‐dependent LDL‐C lowering effect of muffins made with whole de‐fatted soy flour in addition to other CVD risk factors. In a double‐blind, parallel, multicentre clinical trial, healthy adults (n = 243) with elevated LDL‐C (蠅 3.0 and 蠄 5.0 mmol/L) were randomly assigned to consume daily for 6 weeks either: (1) two soy muffins (25g soy protein); (2) one soy and one wheat muffin (12.5g soy protein and 12.5g whey protein); or (3) two wheat muffins (25g whey protein, control). A one‐way ANOVA determined the effect of treatment on the net change in fasting plasma lipids, C‐reactive protein (CRP), glucose, insulin, insulin resistance (HOMA‐IR), and blood pressure. Analyses were performed by intention‐to‐treat. Participants had a mean (±SD) age of 55.0±8.8 years and were overweight (BMI: 28.0±4.6 kg/m 2 ). No significant net change in LDL‐C was found in the groups assigned to soy compared to control (0.01±0.05 mmol/L vs. ‐0.04±0.05 mmol/L vs. ‐0.04±0.05 mmol/L; p = 0.718). Further, there were no significant intervention effects on other CVD risk factors. Whole de‐fatted soy flour muffins do not lower LDL‐C and other CVD risk factors in hypercholesterolemic adults. Funded by the Government of Canada Growing Forward I Science Substantiation Program (RBPI#1746).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 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".