Association between serum cholesterol and eating behaviours during early childhood: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Modifiable behaviours during early childhood may provide opportunities to prevent disease processes before adverse outcomes occur. Our objective was to determine whether young children's eating behaviours were associated with increased risk of cardiovascular disease in later life. METHODS: In this cross-sectional study involving children aged 3-5 years recruited from 7 primary care practices in Toronto, Ontario, we assessed the relation between eating behaviours as assessed by the NutriSTEP (Nutritional Screening Tool for Every Preschooler) questionnaire (completed by parents) and serum levels of non-high-density lipoprotein (HDL) cholesterol, a surrogate marker of cardiovascular risk. We also assessed the relation between dietary intake and serum non-HDL cholesterol, and between eating behaviours and other laboratory indices of cardiovascular risk (low-density lipoprotein [LDL] cholesterol, apolipoprotein B, HDL cholesterol and apoliprotein A1). RESULTS: A total of 1856 children were recruited from primary care practices in Toronto. Of these children, we included 1076 in our study for whom complete data and blood samples were available for analysis. The eating behaviours subscore of the NutriSTEP tool was significantly associated with serum non-HDL cholesterol (p = 0.03); for each unit increase in the eating behaviours subscore suggesting greater nutritional risk, we saw an increase of 0.02 mmol/L (95% confidence interval [CI] 0.002 to 0.05) in serum non-HDL cholesterol. The eating behaviours subscore was also associated with LDL cholesterol and apolipoprotein B, but not with HDL cholesterol or apolipoprotein A1. The dietary intake subscore was not associated with non-HDL cholesterol. INTERPRETATION: Eating behaviours in preschool-aged children are important potentially modifiable determinants of cardiovascular risk and should be a focus for future studies of screening and behavioural interventions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".