The Mediterranean Diet and Cardiovascular Disease
Bibliographic record
Abstract
In this article, we critically evaluate the evidence relating to the effects of the Mediterranean diet (MD) on the risk of cardiovascular disease (CVD). Strong evidence indicating that the MD prevents CVD has come from prospective cohort studies. However, there is only weak supporting evidence from randomized controlled trials (RCTs) as none have compared subjects who follow an MD and those who do not. Instead, RCTs have tested the effect of 1 or 2 features of the MD. This was the case in the Prevenciόn con Dieta Mediterránea (PREDIMED) study: the major dietary change in the intervention groups was the addition of either extravirgin olive oil or nuts. Meta-analyses generally suggest that the MD causes small favorable changes in risk factors for CVD, including blood pressure, blood glucose, and waist circumference. However, the effect on blood lipids is generally weak. The MD may also decrease several biomarkers of inflammation, including C-reactive protein. The 7 key features of the MD can be divided into 2 groups. Some are clearly protective against CVD (olive oil as the main fat; high in legumes; high in fruits/vegetables/nuts; and low in meat/meat products and increased in fish). However, other features of the MD have a less clear relationship with CVD (low/moderate alcohol use, especially red wine; high in grains/cereals; and low/moderate in milk/dairy). In conclusion, the evidence indicates that the MD prevents CVD. There is a need for RCTs that test the effectiveness of the MD for preventing CVD. Key design features for such a study are proposed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".