Sugar-containing beverage consumption and cardiometabolic risk in preschool children
Bibliographic record
Abstract
OBJECTIVE: Sugar-containing beverages (SCBs) including 100% fruit juice, fruit drinks and soda substantially contribute to total caloric intake in young children. The objective of this study was to examine whether consumption of SCB is associated with cardiometabolic risk (CMR) in preschool children, along with whether 100% fruit juice and sugar sweetened beverage (SSB) is associated with CMR. STUDY DESIGN: We used a repeated measures study design examining SCB consumption and CMR outcomes measured concurrently in children 3-6 years of age participating in TARGet Kids!, a primary-care, practice-based research network in Canada (2008-2017). To account for within-person variability, multivariable linear regression models using generalized estimating equation was used to examine the association between SCB consumption and CMR score and the individual CMR score components including systolic blood pressure, waist circumference, high-density lipoprotein cholesterol (HDL-c), triglycerides, and glucose. RESULTS: After adjusting for sociodemographic, familial and child-related covariates, higher SCB consumption was associated with elevated CMR score [0.05 (95% CI -0.0001 to 0.09), p = 0.05], including lower HDL-c [-0.02 mmol/L (95% CI -0.03 to -0.01), p = 0.01] and higher triglycerides [0.02 mmol/L (95% CI 0.004 to 0.04), p = 0.02]. When examined separately, higher 100% fruit juice [-0.02 mmol/L (95% CI -0.03 to -0.003), p = 0.02] and SSB[-0.03 mmol/L (95% CI -0.06 to -0.001), p = 0.04] consumption were each associated with lower HDL-c. CONCLUSION: Higher SCB consumption was associated with small elevations of CMR in preschool children. Our findings support recommendations to limit overall intake of SCBs in early childhood, in effort to reduce the potential long-term burden of CMR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".