Cheese Intake is Inversely Associated with LDL Cholesterol in Young Children
Bibliographic record
Abstract
Purpose: To determine if intake (servings/day) of total dairy and/or dairy subtypes (milk, cheese, and yogurt) were associated with biomarkers related to dyslipidemia, insulin sensitivity and inflammation in a sample of cardio-metabolically healthy young children from the Guelph Family Health Study at the University of Guelph, Guelph, Ontario, Canada. Methods: Baseline data from 42 children (aged 2.0–6.2 years) from 33 families who provided a dietary assessment and a fasted blood sample were included in this cross-sectional analysis. Linear and logistic regressions using generalized estimating equations were used for analysis and models were adjusted for age, gender, and household income. Results: In total, 42 children (3.74 ± 1.23 years old; mean (± SD)) consumed median (25th percentile, 75th percentile) servings/day of 1.70 (1.16, 2.81) for total dairy, 0.74 (0.50, 1.70) for milk, 0.63 (0.00, 1.16) for cheese, and 0.00 (0.00, 0.38) for yogurt. Cheese intake was significantly inversely associated with LDL cholesterol (−0.16 (95% CI: −0.29, −0.03) mmol/L per serving; P = 0.02)). No other associations between dairy intake and biomarkers were significant. Conclusions: Cheese intake was inversely associated with LDL cholesterol in this preliminary study of cardio-metabolically healthy young children, thereby warranting further research on dairy intake and cardiometabolic risk factors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".