Clustering of the Metabolic Syndrome Components in Adolescence: Role of Visceral Fat
Bibliographic record
Abstract
Visceral fat (VF) promotes the development of metabolic syndrome (MetS), which emerges as early as in adolescence. The clustering of MetS components suggests shared etiologies, but these are largely unknown and may vary between males and females. Here, we investigated the latent structure of pre-clinical MetS in a community-based sample of 286 male and 312 female adolescents, assessing their abdominal adiposity (VF) directly with magnetic resonance imaging. Principal component analysis of the five MetS-defining variables (VF, blood pressure [BP], fasting serum triglycerides, HDL-cholesterol and glucose) identified two independent components in both males and females. The first component was sex-similar; it explained >30% of variance and was loaded by all but BP variables. The second component explained >20% of variance; it was loaded by BP similarly in both sexes but additional loading by metabolic variables was sex-specific. This sex-specificity was not detected in analyses that used waist circumference instead of VF. In adolescence, MetS-defining variables cluster into at least two sub-syndromes: (1) sex-similar metabolic abnormalities of obesity-induced insulin resistance and (2) sex-specific metabolic abnormalities associated with BP elevation. These results suggest that the etiology of MetS may involve more than one pathway and that some of the pathways may differ between males and females. Further, the sex-specific metabolic abnormalities associated with BP elevation suggest the need for sex-specific prevention and treatment strategies of MetS.
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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 | 0.002 |
| 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 | 0.001 | 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".