Variations in the prevalence and predictors of prevalent metabolically healthy obesity in adolescents
Bibliographic record
Abstract
BACKGROUND: Obesity is a heterogeneous condition, which includes a subset of individuals that can be classified as having metabolically healthy obesity (MHO), but there is no consensus on what constitutes MHO. Thus, the objective of the study is to examine the prevalence and predictors of prevalent MHO in adolescents using various definitions of MHO. METHODS: Cross-sectional data from the 1999-2010 National Health and Nutrition Examination Surveys were used. Participants included 316 male and 316 female adolescents aged 12-19 years with a BMI ≥ 95th percentile. Two definitions were used to define MHO. First, MHO was defined as having ≤1 metabolic syndrome criteria (excluding waist) and being free of type 2 diabetes, hypertension and dyslipidemia. Second, MHO was defined as being free of all metabolic syndrome criteria, insulin resistance and inflammation. RESULTS: The prevalence of MHO was 42% (male) and 74% (female) using the first definition and 7% (male) and 12% (female) using the second more conservative definition. Lower abdominal obesity (waist circumference) and lower insulin resistance predicted prevalent MHO in male and female adolescents for both definitions (p < 0.01). Associations between dietary components and MHO were weak and inconsistent, while physical activity and inflammation were not associated with MHO in male and female adolescents for both definitions (p > 0.05). CONCLUSIONS: The prevalence of MHO in adolescents varied across definitions, with lower levels of abdominal obesity and insulin resistance as the most consistent predictors of prevalent MHO status.
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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 | 0.003 |
| 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".