Red meat intake, insulin resistance, and markers of endothelial function among Iranian women
Bibliographic record
Abstract
SCOPE: Few data, with conflicting findings, are available linking red meat consumption to indicators of insulin resistance and endothelial dysfunction. This study aimed to investigate the association of red meat consumption with insulin resistance and endothelial dysfunction among a sample of female nurses in Isfahan, Iran. METHODS AND RESULTS: This cross-sectional study was carried out among 420 female nurses who were selected by a multistage cluster random sampling method. Usual dietary intakes were assessed using a validated food frequency questionnaire. Red meat intake was calculated by summing up the consumption of all kinds of red meat in foods and processed meat in sausages and fast foods. To measure serum concentrations of adhesion molecules and glycemic indexes, a fasting blood sample was taken. After adjustment for potential confounders, high red meat intake was significantly associated with higher fasting plasma glucose, homeostasis model assessment of insulin resistance, and lower quantitative insulin sensitivity check index. Although high red meat intake was significantly associated with higher serum insulin levels and lower homeostasis model assessment of beta-cell function in the crude model, after controlling for BMI, the association was no longer significant. Red meat consumption was associated with high concentrations of E-selectin, soluble vascular cell adhesion molecule-1 (sVCAM-1), and soluble intercellular adhesion molecule-1 (sICAM-1) after adjustment for different potential confounders. CONCLUSION: We found that increased red meat intake was associated with high concentrations of plasma endothelial dysfunction biomarkers and abnormal glucose homeostasis among Iranian women. Prospective studies are required to confirm these findings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".