Dietary Sugar and Anthropometrics among Young Children in the Guelph Family Health Study: Longitudinal Associations
Bibliographic record
Abstract
Purpose: Our understanding of the influence of sugar intake on anthropometrics among young children is limited. Most existing research is cross-sectional and has focused on sugar-sweetened beverages. The study objective was to investigate longitudinal associations between young children’s total, free, and added sugar intake from all food sources at baseline with anthropometric measures at baseline and 18 months. Methods: The Guelph Family Health Study (GFHS) is an ongoing randomized controlled trial and a family-based health promotion study. Food records and anthropometric data were collected at baseline (n = 109, 55 males; 3.7 ± 1.1 y, mean ± SD) and 18 months (n = 109, 55 males; 5.1 ± 1.1 y) of the GFHS pilots. Associations between sugar intakes and anthropometrics were estimated using linear regression models with generalized estimating equations adjusted for age, sex, household income, and intervention status. Results: Total sugar intake was inversely associated with body weight at 18 months (P = 0.01). There was no effect of time on any other associations between total, free, and added sugar intakes and anthropometrics. Conclusions: Early life dietary sugar intakes may not relate to anthropometric measures in the short term. Further investigation into potential associations between dietary sugar intakes and anthropometric variables over longer time periods is warranted.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, 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".