Effectiveness of a vegan based high soy protein diet on weight loss and serum lipids
Bibliographic record
Abstract
Background: High protein, low carbohydrate diets have focused on animal protein sources, but their associated saturated fat may result in undesirable blood lipid effects. Objective: To assess the effectiveness of a vegan based high soy protein diet on body weight and blood lipids, under metabolic and real‐world conditions. Method: 44 overweight hyperlipidemic subjects (18M, 26F; 56.2±7.5y; 31.1±2.6kg/m2; LDL 4.07±1.21mmol/L) took either a vegan based high protein diet (vegan) or a low fat (NCEP Step 2) control. Subjects consumed 60% of their estimated energy requirements during the 1‐month metabolic phase and were advised to follow their respective diet for an additional 6 months ad libitum. 23 participants completed both phases. Results: On the metabolic phase, both NCEP and vegan diets resulted in weight loss (−5.1±0.2%, P<0.005; −4.9±0.4%, p<0.005; respectively) and total:HDL‐C reductions (−6.4±2.8%, P=0.046; −19.2±6.5%, P=0.002; respectively). At the end of the ad libitum phase, body weight reduction on the NCEP and vegan diets, compared to baseline, were −6.6±1.0% (P<0.005) and −7.3±1.2% (P<0.005) and total:HDL‐C were −3.4±2.6% (P=0.127) and −10.1±.3.2% (P=0.005), respectively. Conclusion: Under real‐world conditions, a high soy protein vegan diet appears to improve the blood lipid profile compared to a NCEP diet despite similar weight reductions. Research support: The Solae Company
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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 | 0.000 |
| 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) | 0.001 | 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".