Predictors of Metabolically Healthy Obesity in Children
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of metabolically healthy obesity (MHO) in children and examine the demographic, adiposity, and lifestyle predictors of MHO status. RESEARCH DESIGN AND METHODS: This cross-sectional study included 8-17 year olds with a BMI ≥85th percentile who were enrolled in a multidisciplinary pediatric weight management clinic from 2005-2010. Demographic, anthropometric, lifestyle, and cardiometabolic data were retrieved by retrospective medical record review. Participants were dichotomized as either MHO or metabolically unhealthy obese (MUO) according to two separate classification systems based on: 1) insulin resistance (IR) and 2) cardiometabolic risk (CR) factors (blood pressure, serum lipids, and glucose). Multivariable logistic regression was used to determine predictors of MHO using odds ratios (ORs) with 95% CIs. RESULTS: The prevalence of MHO-IR was 31.5% (n = 57 of 181) and MHO-CR was 21.5% (n = 39 of 181). Waist circumference (OR 0.33 [95% CI 0.18-0.59]; P = 0.0002) and dietary fat intake (OR 0.56 [95% CI 0.31-0.95]; P = 0.04) were independent predictors of MHO-IR; moderate-to-vigorous physical activity (OR 1.80 [95% CI 1.24-2.62]; P = 0.002) was the strongest independent predictor of MHO-CR. CONCLUSIONS: Up to one in three children with obesity can be classified as MHO. Depending on the definition, adiposity and lifestyle behaviors both play important roles in predicting MHO status. These findings can inform for whom health services for managing pediatric obesity should be prioritized, especially in circumstances when boys and girls present with CR factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".