Effect of a vegan based high protein, low carbohydrate diet on weight loss and serum lipids
Bibliographic record
Abstract
Background High protein, low carbohydrate diets, such as the Atkins’ diet, have gained popularity in recent years as weight loss strategies. However, high amounts of animal protein and its associated saturated fat, in the absence of weight loss, may result in undesirable effects on the lipid profile and increase coronary heart disease risk. Objective To determine if exchange of saturated fat and animal protein for monounsaturated fat and vegetable protein, mainly from soy, will result in a significant effect on blood lipids while still encouraging weight loss. Method Thirty overweight hyperlipidemic subjects will each undergo 1 of 2 interventions for 1 month: a diet high in vegetable proteins and vegetable fats (26% carbohydrate, 30% protein, 44% fat) or a diet very low in saturated fat, based on milled whole‐wheat cereals and low‐fat dairy foods (58% carbohydrate, 16% protein, 26% fat). Subjects will consume 70% of their estimated energy requirements and all study foods will be provided. Fasting blood lipids and glucose, blood pressure and body weight will be measured at weeks 0, 2 and 4, with body composition (Bioelectrical Impedance Analysis) measured at baseline and at the end of the treatment. Results Twenty‐five hyperlipidemic subjects have been recruited thus far. Preliminary data will be presented. Conclusion A diet where carbohydrates are exchanged for unsaturated fats, such as vegetable oils and nuts, and high animal protein for vegetable proteins, such as soy, may result in significant weight loss and significant improvements in metabolic risk factors for CHD. Research support: 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".