Egg Consumption and Cardiometabolic Health
Bibliographic record
Abstract
Despite the fact that the Dietary Guidelines for Americans 2015–2020 no longer emphasize limiting dietary cholesterol intake, confusion remains regarding egg consumption, a rich source of dietary cholesterol, which has historically been linked to increased risk of cardiovascular disease (CVD). In addition, eggs are a rich source of phosphatidylcholine, a form of choline and a precursor of TMAO (trimethylamine N-oxide), an emerging risk factor for CVD. The purpose of this book chapter is to review the existing literature regarding egg consumption and its relationship with CVD risk factors in both healthy and individuals at risk of CVD, and to determine whether eggs should be considered as part of a healthy dietary pattern. The available evidence so far suggests that egg consumption (between 1–3 eggs per day) has little effect on most traditional and non-traditional CVD risk factors, including inflammation, endothelial function, and plasma TMAO and low-density lipoprotein-cholesterol (LDL-C) concentrations. However, egg consumption seems to improve LDL particle phenotype by increasing the number of large LDL particles. Moreover, increases in HDL-C concentrations were consistently observed with egg consumption in both healthy individuals and those at risk of CVD. Despite the lack of evidence that relates egg consumption with CVD, the variability of study designs and populations included makes further investigations necessary.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
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".