Effect of a plant-based low-carbohydrate diet on body weight and blood lipids in hyperlipidemic adults
Bibliographic record
Abstract
H protein, low-carbohydrate diets increase intake of animal protein and fat for weight loss,but may also result in an undesirable blood lipid profile and increased cardiovascular diseaserisk. The exchange of protein and fat for those of vegetable origin has not been examined. Weconducted a trial to determine the efficacy (metabolic) and effectiveness (ad libitum) of a diethigh in vegetable protein and oil (“Eco-Atkins”) on body weight and blood lipids. Overweighthyperlipidemic men and women were randomized to consume either a low-carbohydrate vegandiet or a high-carbohydrate lacto-ovo vegetarian diet for a 1-month metabolic phase, followed bya 6-month ad libitum phase. A total of 47 participants started the metabolic phase with 39participants continuing on the ad libitum phase. All study foods were provided at 60% of theirestimated energy requirements during the metabolic phase. Participants were then advised tofollow their respective diet during the ad libitum phase. On the metabolic phase, despite similarweight loss for both diets (~4.0 kg), reductions in LDL-C and the ratios of TC:HDL-C andapoB:apo AI were significantly greater for the low-carbohydrate compared with the high carbohydrate diet (−8.1% [P=.002], −8.7% [P=.004], and −9.6% [P=.001], respectively). On the ad libitum phase, weight loss continued to -6.9 kg on the low-carbohydrate and -5.8 kg on thehigh carbohydrate diet (p=0.047). Significant differences between the two diets persisted inLDL-C, TC:HDL-C, and apoB:ApoA1. In conclusion, a low-carbohydrate plant-based diet haslipid-lowering and weight-reducing advantages over a high-carbohydrate diet for cardiovasculardisease risk reduction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".