Variations in the Prevalence of Metabolic Syndrome in Adolescents According to Different Criteria Used for Diagnosis: Which Definition Should Be Chosen for This Age Group?
Bibliographic record
Abstract
BACKGROUND: Despite the increasing prevalence of metabolic syndrome in adolescents, there is no consensus for its diagnosis. METHODS: A cross-sectional study was conducted to compare the prevalence of metabolic syndrome in adolescents by different definitions, evaluate their concordance, and suggest which definition to apply in this population. A total of 851 adolescents between 10 and 18 years of age were evaluated. Anthropometric (weight, height, waist circumference), biochemical (glucose, lipid profile), and blood pressure data were taken. The prevalence of metabolic syndrome was determined by the definitions of the International Diabetes Federation (IDF) and four published studies by Cook et al., de Ferranti et al., Agudelo et al., and Ford et al. Concordance was determined according to the kappa index. RESULTS: The prevalence of metabolic syndrome was 0.9%, 3.8%, 4.1%, 10.5%, and 11.4%, according to the IDF, Cook et al., Ford et al., Agudelo et al., and de Ferranti et al. definitions, respectively. The most prevalent components were hypertriglyceridemia and low high-density lipoprotein cholesterol, whereas the least prevalent components were abdominal obesity and hyperglycemia. The highest concordance was found between the definitions by Cook et al. and Ford et al. (kappa=0.92), whereas the greatest discordance was between the de Ferranti et al. and IDF definitions (kappa=0.14). CONCLUSIONS: Metabolic syndrome and its components were conditions present in the adolescents of this study. In this population, with a high prevalence of dyslipidemia and a lower prevalence of abdominal obesity and hyperglycemia, the recommendation to diagnose metabolic syndrome would be that used by Ford et al.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".