Saturated fats, dairy foods and cardiovascular health: No longer a curious paradox?
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are a major cause of death and morbidity in many parts of the world, and many dietary guidelines limit the intake of saturated fatty acids (SFA) as they are regarded as an important risk factor for CVDs due to their association with increased blood cholesterol. Dairy foods are often a major contributor to dietary intake of SFA, and since many dietary guidelines contain restrictions on SFA intake, this can lead to a moderation of dairy food intake despite meta-analyses generally showing dairy to have a neutral or negative association with CVDs. Many prospective studies and randomised controlled trials do not support a simple positive association between SFA intake and the risk of atherosclerotic CVD and its components although some early studies had a number of methodological weakness. Studies that included blood cholesterol data do broadly support the positive relationship between SFA and blood low-density lipoprotein cholesterol (LDL-C) but without increased CVD risk resulting, despite LDL being a causal factor in atherosclerotic CVD. These data suggest that LDL-C alone is not a consistently good predictor or cause of CVD risk, perhaps particularly in relation to dairy food consumption although some non-dairy food studies have also shown LDL-C reduction was not reflected in reduced CVD risk. This narrative review examines some reasons for these findings. Overall, restrictions on dairy food intake do not seem warranted, although there remains a need to further understand the association of different dairy food types with chronic diseases, perhaps particularly for type 2 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".