Adiponectin and Leptin Metabolic Biomarkers in Chinese Children and Adolescents
Bibliographic record
Abstract
Objective. To evaluate leptin and adiponectin as biomarkers of metabolic syndrome (MS) risk factors even in nonobese children/adolescents. Methods. Serum leptin, adiponectin, leptin:adiponectin ratio, lipids, glucose, and insulin concentrations as well as body size parameters and pubertal development were evaluated in a large population of Chinese children/adolescents (n = 3505, 6-18 years, 1722 girls and 1783 boys). Results. Leptin concentration increased while adiponectin decreased with obesity, both were influenced by pubertal development. Central obesity had an additive effect on leptin levels (above obesity alone). Leptin/adiponectin increased 8.4-fold and 3.2-fold in overweight/obesity, and 15.8- and 4.5-fold with obesity plus MS, in early and late puberty, respectively. Even in normal weight children/adolescents, higher leptin and lower adiponectin concentrations associated with increased risk profile. Conversely, overweight/obese with lower leptin or higher adiponectin concentrations had a less compromised metabolic profile. Conclusion. Leptin, adiponectin, and leptin:adiponectin ratio are informative biomarkers for obesity, central obesity, MS, and abnormal metabolic profile even in normal weight children/adolescents.
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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 | 0.000 | 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".