Anthropometric and dietary predictors of insulin sensitivity in 10- to 14-year-old boys and girls
Bibliographic record
Abstract
The high prevalence of pediatric obesity has made preventing chronic diseases through healthy lifestyle behaviours a priority within pediatrics. Examining the association between diet and insulin sensitivity (IS) in youth may provide important insights for tailoring preventative dietary interventions. The objective of this study was to explore the associations among anthropometry, diet, and IS in 10- to 14-year-olds. In this cross-sectional study, the primary outcome measure was IS, measured noninvasively using a (13)C glucose breath test. Exposure variables included body mass index (BMI) z score and several dietary variables, including glycemic index (GI), glycemic load, and fiber, magnesium, vegetable and fruit, and fat intakes, all of which were derived from a validated, Web-based 24-h recall tool. Multiple regression analyses were performed for boys and girls separately. In total, 378 students (227 girls) aged 12.1 ± 1.2 years were studied. In this sample ∼24% of youth were considered overweight or obese (BMI z score = 0.41 ± 0.93). Multiple regression analyses showed that BMI z score was negatively and independently associated with (13)C insulin sensitivity score ((13)CISS) in both boys and girls (boys: β = -0.501; girls: β = -0.446; both p < 0.001). GI was negatively and independently related to (13)CISS in boys (β = -0.195, p < 0.05) but not in girls. Other dietary variables were not associated with IS. In addition to BMI z score, a low GI diet predicted (13)CISS in boys but not in girls. This finding suggests that interventions that reduce BMI (in both sexes) and include a low GI diet among boys may improve IS.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".