An overview and update on the epidemiology of flavonoid intake and cardiovascular disease risk
Bibliographic record
Abstract
There is an accumulating body of literature reporting on dietary flavonoid intake and the risk of cardiovascular disease (CVD) in prospective cohort studies. This makes apparent the need for an overview and update on the current state of the science. To date, at least 27 prospective cohorts (in 44 publications) have evaluated the association between estimated habitual flavonoid intake and CVD risk. At this time, the totality of evidence suggests long-term consumption of flavonoid-rich foods may be associated with a lower risk of fatal and non-fatal ischemic heart disease (IHD), cerebrovascular disease, and total CVD; disease outcomes which are principally, though not exclusively, composed of cases of atherosclerotic CVD (ASCVD). To date, few studies have investigated outcome specific ASCVD, such as peripheral artery disease (PAD) or ischemic stroke. Of the flavonoid subclasses investigated, evidence more often implicates diets rich in anthocyanins, flavan-3-ols, and flavonols in lowering the risk of CVD. Although inferences are restricted by confounding and other inherent limitations of observational studies, causality appears possible based on biological plausibility, temporality, and the relative consistency of the reported associations. However, whether the associations observed represent a benefit of the isolated bioactives per se, or are a signal of the bioactives acting in concert with the co-occurring nutrient matrix within flavonoid-bearing foods, are issues of consideration. Thus, the simple interpretation, and the one most relevant for dietary advice, is that consumption of flavonoid-rich foods or diets higher in flavonoids, appear nutritionally beneficial in the prevention of CVD.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".