The effect of a weight reducing low carbohydrate vegan diet on apolipoproteins and blood pressure
Bibliographic record
Abstract
Objective To determine the effect of a weight reducing low carbohydrate vegan diet, high in soy protein and vegetable oil, on apolipoproteins (apo) and blood pressure (BP) under metabolic conditions. Method Forty‐seven hypercholesterolemic subjects (19M, 28F; 56.9±7.5y; 30.9±2.7kg/m2) were instructed to take a low carbohydrate (26%) high protein vegan diet (test) or a low saturated fat National Cholesterol Education Program ATP III diet (control). For one month, with all food provided, subjects consumed 60% of their estimated energy requirements. Results Forty‐four subjects completed the study. Body weight fell equally by 3.5% on both diets. LDL‐C was reduced more on the test than the control diet (‐20.4±2.8% vs. ‐12.3±2.6%, P=0.002). The corresponding values for apoB and the apoB:apoA‐1 ratio were ‐21.1±2.8% vs. ‐13.2±2.2% (P=0.001) and ‐13.8±3.4% vs. ‐4.2±2.1% (P=0.001). Blood pressure was also reduced on the test than the control diet (systolic BP: ‐4.6±1.0% vs. ‐2.7±1.2%, P=0.046; diastolic BP: ‐5.8±1.0% vs. ‐3.1±1.4, P=0.014). Conclusion Weight loss reduces apoB, the apoB:apoA‐1 ratio and blood pressure more on a low carbohydrate vegan diet, high in vegetable protein and oil, than on a standard therapeutic diet. Funding: The Solae Company, Loblaw Companies Limited Grant Funding Source Canadian Institutes of Health Research
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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 | 0.001 |
| 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) | 0.001 | 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".