Abstract 11770: Egg Consumption and Risk of Cardiovascular Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Current dietary guidelines no longer recommend against egg consumption despite the high cholesterol content. However, considerable controversy remains on the relationship between high egg consumption and cardiovascular disease (CVD) risk. The objective of this systematic review and meta-analysis was to explore the association between egg consumption and CVD events. Methods: We systematically searched PubMed, Scopus, and the Cochrane Database of Systematic Reviews from database inception through April 2015 for observational studies with hazard ratio (HRs) or relative risks (RRs) and 95% confidence intervals (95% CI) that reported the association between egg consumption and CVD events which included coronary heart disease (CHD) and stroke. Data were extracted by one reviewer followed by independent screening and extraction of study characteristics and outcome data by two other reviewers. Conflicts were resolved through consensus. Random-effects meta-analyses were used to pool the hazard ratios or relative risks from the included studies. Subgroup analyses were performed to explore the potential sources of heterogeneity. The quality of the included studies and publication bias were assessed. Results: We identified 5 cross-sectional and 6 prospective studies with median follow-up of 13.1 years. A total of 301,339 individuals and 10,262 total CVD events were included with 7,225 CHD and 3,037 stroke cases. Compared to consumption of less than 1 egg/day, higher egg consumption was associated with an increased risk of CVD events (pooled HR, 1.43; 95% CI, 1.01-2.03; p=0.012; I 2 = 65.7% and pooled RR, 1.09; 95% CI, 0.89-1.34; p=0.145; I 2 = 41.4%). Conclusions: Our analysis suggests that higher consumption of eggs (more than 1 egg/day) may be associated with increased risk of cardiovascular disease. Large and diverse prospective community-based cohorts will be necessary to establish optimal dietary recommendations for public health.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".