Longitudinal Analysis of Sleep Duration and Cardiometabolic Risk in Young Children
Bibliographic record
Abstract
BACKGROUND: The aim of this study is to determine if sleep duration in early childhood is associated with cardiometabolic risk (CMR) in later childhood as assessed by a CMR cluster score [sum of age- and sex-standardized z-scores of waist circumference (WC), systolic blood pressure, triglycerides, glucose, and (inverse) high-density lipoprotein (HDL)]. Secondary objectives included examining sleep duration and the individual CMR factors and BMI z-score. PATIENTS AND METHODS: A prospective cohort study was conducted using data from the TARGet Kids! practice-based research network in Toronto, Canada. Children (n = 597) with parent-reported 24-hour sleep duration in early childhood (12-36 months) and a follow-up visit (36-96 months) with all five CMR factors were included in the analysis. Multivariable linear regression was used to assess the relationship between early childhood sleep duration and later childhood CMR, adjusting for relevant covariates. RESULTS: Average 24-hour sleep duration in early childhood [mean age: 28.1 (6.6) months] was 11.8 (1.4) hours, with 87% meeting or exceeding total sleep recommendations for their age. Sleep duration in early childhood was not associated with the CMR cluster score in later childhood. Shorter sleep duration was associated with higher HDL concentrations [adjusted β = -0.028 (95% confidence interval: -0.049 to -0.007), p = 0.009]. CONCLUSIONS: Further research is needed to determine if early childhood sleep duration is associated with HDL in later childhood. Future studies, which investigate sleep quality in addition to sleep duration, may be helpful.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".