Serum Adiponectin and Leptin Concentrations in Relation to Body Fat Distribution, Hematological Indices and Lipid Profile in Humans
Bibliographic record
Abstract
The purpose of the study was to evaluate the relationship between serum adiponectin and leptin concentrations and body composition, hematological indices and lipid profile parameters in adults. The study involved 95 volunteers (BMI from 23.3 to 53 kg/m²). Anthropometric parameters were measured: body weight and height, waist and hip circumference, waist-to-hip ratio, body fat mass (BMF), subcutaneous and visceral fat mass (SFM, VFM), lean body mass (LBM), skeletal muscle mass (SMM). In serum we determined adiponectin and leptin concentrations, extracellular hemoglobin, total bilirubin, as well as lipid metabolism (TCh, HDL-Ch, LDL-Ch, TG). Mean adipokine levels were significantly higher in women (p ≤ 0.01), adiponectin significantly negatively correlated with body height and weight, systolic blood pressure and absolute LBM and SMM values. The same relation was observed for erythroid system indicators and lipid indicators. A positive correlation was exceptionally found between adiponectin and HDL-Ch. LEP negatively correlated with some percentage rates (%LBM, %SMM). Only in women, we observed a positive correlation between LEP and body weight, BMI and WHR. Studies on ADPN and the ADPN/LEP ratio as a valuable complementary diagnostic element in the prediction and prevention of cardiovascular diseases need to be continued.
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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 | 0.001 |
| 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 | 0.000 | 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".