The effect of mixed fruit and vegetable concentrates on biomarkers of cardiovascular disease: a review of the clinical evidence
Bibliographic record
Abstract
Increased intake of fruit and vegetable (FV) has been associated with a reduced risk of chronic diseases, including cardiovascular disease (CVD). However, public health campaigns to increase FV intake have had limited success. A variety of mixed FV concentrates are available in the marketplace which may help certain individuals to achieve FV intake recommendations. However, the possible CVD benefits of FV concentrates have not been systematically reviewed. Our purpose, therefore, was to review the clinical trials that have studied the effects of these concentrates on CVD risk factors. A systematic search of EMBASE and MEDLINE databases identified 12 randomized, controlled clinical trials of 2 weeks duration or longer, which reported on at least one CVD risk factor. These studies assessed markers of oxidative stress (e.g. protein carbonyls, interleukin‐6), endothelial function and homocysteine. Daily consumption of FV concentrates significantly increased serum concentrations of antioxidant vitamins in 5 of 6 studies and significantly improved at least one marker (oxidative stress, endothelial function, homocysteine) of CVD risk in 10 of 12 studies. While longer term studies are required, these data indicate that FV concentrates may be of benefit in terms of improving antioxidant vitamin status, decreasing oxidative stress and reducing certain risk factors for CVD. (The study is not funded)
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".