Routine Clinical Measures of Adiposity as Predictors of Visceral Fat in Adolescence: A Population-Based Magnetic Resonance Imaging Study
Bibliographic record
Abstract
OBJECTIVE: Visceral fat (VF) increases cardiometabolic risk more than fat stored subcutaneously. Here, we investigated how well routine clinical measures of adiposity, namely body mass index (BMI) and waist circumference (waist), predict VF and subcutaneous fat (SF) in a large population-based sample of adolescents. As body-fat distribution differs between males and females, we performed these analyses separately in each sex. DESIGN AND METHODS: VF and SF were measured by magnetic resonance imaging in 1,002 adolescents (482 males, age 12-18 years). Relationships of BMI and waist with VF and SF were tested in multivariable analyses, which adjusted for potentially confounding effects of age and height. RESULTS: In both males and females, BMI and waist were highly correlated with VF and SF, and explained 55-76% of their total variance. When VF was adjusted for SF, however, BMI and waist explained, respectively, only 0% and 4% of VF variance in males, and 4% and 11% of VF variance in females. In contrast, when SF was adjusted for VF, BMI and waist explained, respectively, 36% and 21% of SF variance in males, and 48% and 23% of SF variance in females. These relationships were similar during early and late puberty. CONCLUSIONS AND RELEVANCE: During adolescence, routine clinical measures of adiposity predict well SF but not VF. This holds for both sexes and throughout puberty. Further longitudinal studies are required to assess how well these measures predict changes of VF and SF over time. Given the clinical importance of VF, development of cost-effective imaging techniques and/or robust biomarkers of VF accumulation that would be suitable in everyday clinical practice is warranted.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".