Impact of Diets Rich in Whole Grains and Fruits and Vegetables on Cardiovascular Risk Factors in Overweight and Obese Women: A Randomized Clinical Feeding Trial
Bibliographic record
Abstract
OBJECTIVE: Previous interventions have reported desirable effects of diets rich in whole grains or rich in fruits and vegetables on cardiovascular disease (CVD) risk factors and weight management. However, data are lacking regarding the effect of these fiber sources separately. The aim of this randomized clinical feeding trial was to investigate the effects of fiber-rich diets with different sources of fiber (fruits, vegetables, and whole grains) on weight loss and CVD risk factors in overweight and obese women. METHODS: Overweight and obese women (N = 75) were randomized to one of three weight loss diets that were rich in whole grains, fruits and vegetables, or both for 10 weeks. Body weight, waist circumference, and risk factors of CVD were examined at baseline and 10 weeks. RESULTS: During the 10-week dietary intervention phase, the reductions in weight (p = 0.03), waist circumference (p = 0.001), systolic blood pressure (p = 0.04), fasting blood sugar (p = 0.03), and triglycerides (p = 0.001) were higher in the whole grains group compared with the fruits and vegetables group or the combination diet group. Also, the whole grain group had a greater increase (p = 0.01) in high-density lipoprotein cholesterol compared to the other groups. The change in other risk factors, including diastolic blood pressure and low-density lipoprotein cholesterol, was not different among the three diet groups. Within-group comparisons revealed significant reductions in weight, waist circumference, and fasting blood sugar in all groups. Only the fruits and vegetables group and the whole grains group had significant decreases in low-density lipoprotein cholesterol over 10 weeks (p ≤ 0.03). CONCLUSIONS: This trial suggests that in overweight and obese women, a weight loss diet rich in whole grains may have a more beneficial effect on CVD risk factors than diets rich in fruits and vegetables or a combination of whole grains and fruits and vegetables.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".