The Prevalence of Metabolic Syndrome and Its Relation to Leptin Levels in Obese Children and Adolescents
Bibliographic record
Abstract
Objective: In this study, we aimed to determine the prevalence of metabolic syndrome (MS) and frequency of metabolic risk factors in pubertal obese children, and to evaluate the relation between metabolic syndrome and plasma leptin levels. Material and Methods: In this study, 451 pubertal children and adolescents aged between 8-18 years admitted with complaints of excess weight. The ones with a body mass index standard deviation score (BMI-SDS) ≥1.81 were included in this study. In all cases, medical history, physical examination, anthropometric measurements, results of biochemical and hormonal assays were evaluated. MS was diagnosed according to International Diabetes Federation (IDF) consensus criteria. Result: Fifty five percent of the study group were males and 45% were females. The median ages for girls and boys were 12.3 (8.0-16.4) and 12.6 (8.9-16.2) years, respectively. There were 89 (19.8%) children with MS. It was found that leptin had a positive relationship with BMI, waist and hip circumference, insulin level, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and LDL cholesterol level, and a negative one with age and HDL cholesterol in all obese cases. However no relation was found between leptin and fasting blood glucose and triglyceride levels. On the other hand, leptin was higher in those with hypertension. Conclusion: An increased prevalence of obesity, together with metabolic risk factors such as dyslipidemia and abnormal blood pressure were observed in childhood, contributing to the onset of MS at younger ages. Leptin was markedly elevated in obese patients with MS. At the same time, it was found associated with metabolic risk factors. Therefore a high leptin level may be a risk factor for MS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".