Consumption of energy-dense diets in relation to metabolic syndrome and inflammatory markers in Iranian female nurses
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between dietary energy density (DED) and risk of metabolic syndrome (MetS), its components and inflammatory markers. DESIGN: Cross-sectional study. Dietary intakes were assessed using a validated dish-based semi-quantitative FFQ. DED was calculated by dividing energy intake (kcal/d) by the total weight of foods only (g/d). MetS was defined based on the National Cholesterol Education Program Adult Treatment Panel III criteria. All associations were examined in the quartiles of DED, with higher quartiles indicating more energy-dense diets. SETTING: Isfahan, Iran. SUBJECTS: Female nurses (n 1036) aged >30 years. RESULTS: After controlling for potential confounders, individuals in the top quartile of DED had 78 % greater chance of MetS compared with those in the first (OR=1·78; 95 % CI 1·36, 2·98; P<0·001). Individuals in the highest quartile of DED were more likely to be abdominally obese (OR=1·51; 95 % CI 1·00, 2·63) and have hypertriacylglycerolaemia (OR=2·95; 95 % CI 1·58, 3·91) and low HDL cholesterol levels (OR=1·36; 95 % CI 1·17, 2·54) compared with those in the lowest quartile. Mean concentration of plasma high-sensitivity C-reactive protein (hs-CRP) across increasing quartiles of DED was 1·7, 1·7, 2·0, 2·4 mg/l (P for trend=0·04). Such increasing concentrations across increasing quartiles of DED were also seen for TNF-α (4·1, 4·5, 4·5, 4·8 ng/l; P for trend=0·03) and IL-6 (1·6, 1·6, 1·5, 2·5 ng/l; P for trend <0·01). CONCLUSIONS: Consumption of high-energy-dense foods was associated with increased chance of MetS, most of its features and inflammatory markers including hs-CRP, TNF-α and IL-6.
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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 | 0.001 |
| 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) | 0.001 | 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".