Egg consumption and insulin metabolism in the Insulin Resistance Atherosclerosis Study (IRAS)
Bibliographic record
Abstract
OBJECTIVE: To examine the association between egg consumption and measures of insulin sensitivity (SI), acute insulin response (AIR) and metabolic clearance rate of insulin (MCRI). DESIGN: Cross-sectional analysis. SETTINGS: Egg consumption, categorized as <1/week, 1 to <3/week, 3 to <5/week and ≥5/week, was measured using a validated FFQ. SI, AIR and MCRI were determined from frequently sampled intravenous glucose tolerance tests. SUBJECTS: Non-diabetic participants (n 949) in the Insulin Resistance Atherosclerosis Study (IRAS). RESULTS: Egg consumption was inversely associated with SI and MCRI, and positively associated with fasting insulin in regression models adjusted for demographic, socio-economic, lifestyle and dietary factors (β = -0·22, 95 % CI -0·38, -0·045, P = 0·05 for SI; β = -0·20, 95 % CI -0·34, -0·055, P = 0·005 for MCRI; β = 0·35, 95 % CI 0·15, 0·54, P = 0·002 for fasting insulin; all P values for linear trend). These associations remained significant after additionally adjusting for energy intake or dietary saturated fat, although dietary cholesterol and BMI attenuated these associations to non-significance. Egg consumption was not associated with AIR. CONCLUSIONS: Dietary cholesterol and BMI appear to mediate the inverse association of egg consumption with insulin sensitivity and clearance. Alternatively, egg consumption may be clustered with other dietary behaviours which increase BMI, hence negatively impacting on insulin sensitivity and clearance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".