Adiposity and aerobic fitness are associated with metabolic disease risk in children
Bibliographic record
Abstract
To examine the relative association of physical activity, cardiorespiratroy fitness (CRF), and adiposity with risk for metabolic disease in prepubescent children. Forty-six prepubescent children (age, 9.4 ± 1.7 years; 24 males) were assessed for adiposity (%fat) via dual-energy X-ray absorptiometry, CRF with a peak graded exercise test, and physical activity using pedometers. Metabolic disease risk was assessed by a composite score of the following factors: waist circumference (WC), mean arterial pressure (MAP), triacylglycerol (TAG), total cholesterol to high-density lipoprotein cholesterol ratio (TC/HDL-C ratio), glucose, and insulin. Adiposity was correlated with metabolic disease risk score, as well as homeostasis model assessment of insulin resistance (HOMA-IR), TAG, TC/HDL-C ratio, WC, insulin, and MAP (r range = 0.33 to 0.95, all p < 0.05). Physical activity was negatively associated with metabolic disease risk score, as well as HOMA-IR, TAG, WC, insulin, and MAP (r range = -0.32 to -0.49, all p < 0.05). CRF was inversely associated with metabolic disease risk score and HOMA-IR, TAG, TC/HDL-C ratio, WC, insulin, and MAP (r range = -0.32 to -0.63, all p < 0.05). Compared across fitness-physical activity and fatness groups, the low-fit-high-fat and the low-activity-high-fat groups had higher metabolic risk scores than both low-fat groups. Regression analyses revealed sexual maturity (β = 0.27, p = 0.044) and %fat (β = 0.49, p = 0.005) were the only independent predictors of metabolic disease risk score, explaining 4.7% and 9.5% of the variance, respectively. Adiposity appears to be an influential factor for metabolic disease risk in prepubescent children, and fitness is protective against metabolic disease risk in the presence of high levels of adiposity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".