Regular-fat and low-fat dairy foods and cardiovascular diseases: perspectives for future dietary recommendations
Bibliographic record
Abstract
Most current dietary guidelines for the prevention of cardiovascular diseases (CVD) recommend the consumption of low-fat dairy in place of regular-fat dairy foods, one of the main sources of dietary saturated fatty acids (SFAs). Here, we summarize the data presented and discussions held-relating to the validity of such recommendations-between a panel of international nutrition research experts at a high-level closed workshop on "Saturated Fat in Dairy and Cardiovascular Diseases," which took place in Amsterdam on 15-16 April, 2024. The most recent evidence indicates that overall, consumption of milk, yogurt and cheese, irrespective of fat content, is neutrally associated with CVD risk. There is also no evidence yet from randomized controlled trials that consumption of regular-fat milk, yogurt, and cheese has different effects on a broad array of cardiometabolic risk factors when compared with consumption of low-fat milk, yogurt, and cheese. Thus, the body of evidence does not support differentiation between regular-fat and low-fat dairy foods in dietary guidelines for both adults and children. Strategies focusing primarily on reduction of energy-dense, nutrient-poor foods, the main source of SFAs in Western diets, rather than on the fat content of dairy foods, are more likely to benefit the population's cardiovascular health. Future research is needed to understand better the place of regular-fat and low-fat dairy foods within healthy eating patterns.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".