Body Mass Index, Waist Circumference, and the Clustering of Cardiometabolic Risk Factors in Early Childhood
Bibliographic record
Abstract
BACKGROUND: Obesity has its origins in early childhood; however, there is limited evidence of the association between anthropometric indicators and cardiometabolic risk factors in young children. Our aim was to evaluate the associations between body mass index (BMI) and waist circumference (WC) in relation to cardiometabolic risk factors and to explore the clustering of these factors. METHODS: A cross-sectional study was conducted in children aged 1-5 years through TARGet Kids! (n = 2917). Logistic regression was used to evaluate associations between BMI and WC z-scores and individual traditional and possible non-traditional cardiometabolic risk factors. The underlying clustering of these measures was evaluated using principal components analysis (PCA). RESULTS: Child obesity (BMI z-score >2) was associated with high (>90th percentile) leptin [odds ratio (OR) 8.15, 95% confidence interval (CI) 4.56, 14.58] and insulin (OR = 1.76; 95% CI 1.05, 2.94). WC z-score >1 was associated with high insulin (OR 1.59, 95% CI 1.11, 2.28), leptin (OR 5.48, 95% CI 3.48, 8.63) and 25-hydroxyvitamin D < 75 nmol/L (OR 1.39, 95% CI 1.08, 1.79). BMI and WC were not associated with other traditional cardiometabolic risk factors, including non-High Density Lipoprotein (HDL) cholesterol, and glucose. Among children 3-5 years (n = 1035) the PCA of traditional risk factors identified three components: adiposity/blood pressure, metabolic, and lipids. The inclusion of non-traditional risk factors identified four additional components but contributed minimally to the total variation explained. CONCLUSIONS: Anthropometric indicators are associated with selected cardiometabolic risk factors in early childhood, although the clustering of risk factors suggests that adiposity is only one distinct component of cardiometabolic risk. The measurement of other risk factors beyond BMI and WC may be important in defining cardiometabolic risk in early childhood.
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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 | high |
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 | 0.003 |
| 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 | 0.001 | 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, 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".