Metabolic Risk Factors Associated with Visceral and Subcutaneous Adipose Tissue in a Sex‐Specific Manner in Seven‐Year‐Olds
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate how visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) volumes were associated with metabolic risk factors in 7-year-old children. METHODS: A total of 81 children (52% girls) from a Swedish birth cohort were studied. At 6 years of age, anthropometric data, fasting insulin, glucose, cholesterol, and blood pressure were collected on 53 children with normal weight and 28 children with overweight or obesity, and insulin resistance was estimated. At 7 years of age, magnetic resonance imaging quantified VAT and SAT. Sex and regression analyses were conducted. RESULTS: SAT was more strongly associated with metabolic risk factors than VAT. The associations between VAT and metabolic risk factors were stronger in girls (P < 0.05). When VAT was adjusted for birth weight and maternal BMI and education, it accounted for 51% of insulin variance (β = 11.72; P = 0.001) but only in girls. The key finding of this study was that adjusted SAT accounted for 63% of the fasting insulin variance in girls (β = 2.76; P < 0.001). Waist circumference was the best anthropometric marker for insulin resistance. CONCLUSIONS: Insulin resistance was associated with abdominal adipose tissue and its associated metabolic risk factors in children as young as 7 years old.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".