Dietary composition and its associations with insulin sensitivity and insulin secretion in youth
Bibliographic record
Abstract
The objectives of the present study were to examine the associations between macronutrient intake and insulin sensitivity (IS) and insulin secretion (ISct), taking into consideration moderate-to-vigorous physical activity (MVPA), fitness and sedentary behaviour. Caucasian youth (n 630) aged 8-10 years at recruitment, with at least one obese biological parent, were studied (QUebec Adipose and Lifestyle InvesTigation in Youth cohort). IS was measured using the homeostasis model assessment (HOMA) of insulin resistance and Matsuda IS index. ISct was measured using HOMA2%-β, the ratio of the AUC of insulin:glucose over the first 30 min (AUC I/G(t= 30 min)) of the oral glucose tolerance test and AUC I/G(t= 120 min) over 2 h. Fitness was measured using VO₂(peak), percentage of fat mass by dual-energy X-ray absorptiometry, and 7 d MVPA using accelerometry; screen time (ST) by average daily hours of self-reported television, video game or computer use. Dietary composition was measured using three non-consecutive dietary recalls. Non-parametric smoothing splines were used to model non-linear associations; all models were adjusted for age, sex, season, pubertal stage, MVPA, fitness, ST and adiposity. The percentage of total daily energy from dietary protein, fat, saturated fat and carbohydrate and the consumption of dietary vitamin D, sugar-sweetened beverages, fibre and portions of fruits and vegetables were taken into consideration. No dietary component was associated with any measure of IS after adjusting for MVPA, fitness, ST and adiposity. For every 1% increase in daily protein intake (%), AUC I/G(t= 30 min) decreased by 1·1% (P= 0·033). Otherwise, dietary composition was not associated with ISct. While long-term excess of energy intake has been shown to lead to overweight and obesity, dietary macronutrient composition is not independently correlated with IS or ISct in youth.
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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 | 0.001 |
| 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 | 0.000 |
| 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".