Mediterranean diet, cardiovascular disease and mortality in diabetes: A systematic review and meta-analysis of prospective cohort studies and randomized clinical trials
Bibliographic record
Abstract
To update the clinical practice guidelines for nutrition therapy of the European Association for the Study of Diabetes, we conducted a systematic review and meta-analysis of prospective cohort studies and randomized clinical trials (RCTs) to evaluate the effect of the Mediterranean diet (MedDiet) on the prevention of cardiovascular disease (CVD) incidence and mortality. We searched Medline, EMBASE (through April 20, 2018) and Cochrane (through May 7, 2018) databases. Pooled relative risks (RRs) and 95% confidence interval (CI) were calculated by the generic inverse variance method. A total of 41 reports (3 RCTs and 38 cohorts) were included. Meta-analyses of RCTs revealed a beneficial effect of the MedDiet on total CVD incidence (RR: 0.62; 95% CI: 0.50, 0.78) and total myocardial infarction (MI) incidence (RR: 0.65; 95% CI: 0.49, 0.88). Meta-analyses of prospective cohort studies, which compared the highest versus lowest categories of MedDiet adherence, revealed an inverse association with total CVD mortality (RR: 0.79; 95% CI: 0.77, 0.82), coronary heart disease (CHD) incidence (RR: 0.73; 95% CI: 0.62, 0.86), CHD mortality (RR: 0.83; 95% CI: 0.75, 0.92), stroke incidence (RR: 0.80; 95% CI: 0.71, 0.90), stroke mortality (RR: 0.87; 95% CI: 0.80, 0.96) and MI incidence (RR: 0.73; 95% CI: 0.61, 0.88). The present study suggests that MedDiet has a beneficial role on CVD prevention in populations inclusive of individuals with diabetes.
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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 | 0.031 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".