The good and the bad: what researchers have learned about dietary cholesterol, lipid management and cardiovascular disease risk since the Harvard Egg Study
Bibliographic record
Abstract
BACKGROUND: The prevalence of cardiovascular diseases, while lower than it once was, remains a significant health consideration. AIMS AND METHODS: To review the evolving evidence with respect to what role various factors play in the aetiology of coronary heart disease (CHD). RESULTS: While total cholesterol and low-density lipoprotein cholesterol (LDL-C) were previously believed to play central roles, it has now become clear that neither in isolation is highly significant. For example, some people with very high LDL-C levels do not develop CHD, while others with very low LDL-C levels do. Furthermore, there is a difference between dietary cholesterol and serum cholesterol. Dietary cholesterol, which is found in animal-based foods, raises blood cholesterol in only approximately one-third of people. Conversely, intake of saturated fatty acids and trans fatty acids can result in dyslipidaemia. Furthermore, obesity--particularly abdominal obesity--and metabolic syndrome both are strong independent risk factors for development of cardiovascular disease. CONCLUSIONS: Statin therapy and a diet comprising a portfolio of plant sterols and viscous fibres can both significantly reduce LDL-C levels and C-reactive protein. The latter is a key marker of inflammation and of elevated risk for cardiovascular disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".