Effect of the protein:carbohydrate ratio in hypoenergetic diets on metabolic syndrome risk factors in exercising overweight and obese women
Bibliographic record
Abstract
Overweight and obesity are growing problems both in Canada and around the world. Obesity is associated with a number of chronic diseases including type 2 diabetes and CVD, which puts a tremendous burden on the health care systems in place. The present study sought to investigate whether there were differences in the effectiveness of three low-fat, hypo- and isoenergetic diets differing in protein:carbohydrate ratio, low protein (LP, 1 g protein:4 g carbohydrate), normal protein (NP, 1 g protein:2 g carbohydrate) or high protein (HP, 1 g protein:1 g carbohydrate), on weight loss and markers of the metabolic syndrome (MetS) in overweight women. Subjects were randomly assigned to receive one of three intervention diets, all of which included a 60 min exercise programme three times/week for 12 weeks. Of the total subjects, fifty-four overweight and obese local women with MetS risk factors completed the study. All groups had similar improvements in body weight, insulin sensitivity, lipid profile, blood pressure and fitness. Subjects reported that the NP diet was easier to comply with and achieved better improvements in body fat, waist circumference and waist:hip ratio, and preservation of lean mass compared with the other two diets. In conclusion, energy restriction and exercise both facilitate weight loss in overweight and obese subjects and reduce symptoms of the MetS. A diet with a 1:2 protein:carbohydrate ratio promoted better improvements than either the LP or HP diets, and may be superior in reducing long-term chronic disease risk in this population.
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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) | 0.001 | 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 | 0.000 | 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".