The Relationship Between Different Subcutaneous Adipose Tissue Layers, Fat Mass and Leptin in Obese Children and Adolescents
Bibliographic record
Abstract
We studied the relationships of subcutaneous adipose tissue layers (SAT-layers), body fat mass (FM) and waist-to-hip ratio (WHR) with leptin in obese children and adolescents. Twenty-nine obese children and adolescents (12 boys: age: 11.3 +/- 3.7 yr; body mass index [BMI]: 28.5 +/- 4) and 17 girls (age: 12.2 +/- 2.2 yr; BMI: 29.8 +/- 4.7) (mean +/- SD) were studied. FM was estimated by bioelectrical impedance. SAT-layers were determined at 15 different body sites from 1-neck to 15-calf by the Lipometer optical device. Leptin and insulin were determined by RIA. Maturity was associated with a greater thickness of certain SAT-layers from the upper body and with a lower thickness of SAT-layers from the abdominal region and lower extremities. Significant correlations were found for all estimates of adiposity and leptin (all p<0.001). Waist and hip circumferences were not correlated to leptin after adjustment for FM. SAT-layers from the upper body were significantly and positively correlated to leptin. Multiple regression analysis revealed FM as a main contributor to the variation in leptin (R2=0.53, p<0.0001). FM together with SAT-layers 5-front chest and 13-rear thigh explained 72% of the variation in leptin (p<0.0001). In a body fat distribution model, hip circumference together with SAT-layers 4-upper back and 2-triceps explained 75% of the variation in leptin (p< 0.0001). The results suggest that SAT-layers and their topography are main determinants for leptin in obese children and adolescents. Maturity in obese children is associated with higher values of upper body SAT-layers and lower values of abdominal and lower extremities SAT-layers. Whether leptin is under the control of certain subcutaneous adipose tissue depots from the upper body remains to be elucidated by longitudinal studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".