The metabolic load‐capacity model and cardiometabolic health in children and youth with obesity
Bibliographic record
Abstract
BACKGROUND: The metabolic load-capacity index (LCI), which represents the ratio of adipose to skeletal muscle tissue-containing compartments, is potentially associated with cardiometabolic diseases. OBJECTIVES: To examine the associations between the LCI and cardiometabolic risk factors in children and youth with obesity. METHODS: . LCI by air-displacement plethysmography (ADP) was calculated as fat mass divided by fat-free mass, and LCI by ultrasound (US) as subcutaneous adipose tissue divided by skeletal muscle thickness. Sex-specific medians stratified participants into high versus low LCI. Single (inflammation, insulin resistance, dyslipidemia and hypertension) and clustered cardiometabolic risk factors were evaluated. Linear and logistic regression models tested the associations between these variables, adjusted for sexual maturation. RESULTS: Thirty-nine participants (43.6% males; 59% mid-late puberty) aged 12.5 (IQR: 11.1-13.5) years were included. LCI by ADP was positively associated with markers of inflammation and dyslipidemia; having a higher LCI predicted dyslipidemia in logistic regression. Similarly, LCI by US was positively associated with markers of dyslipidemia and blood pressure. In mid-late pubertal participants, LCI by US was positively associated with markers of insulin resistance and inflammation. CONCLUSIONS: Participants with unfavourable cardiometabolic profile had higher LCI, suggesting its potential use for predicting and monitoring cardiometabolic health in clinical settings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".