Anthropometric Equations to Predict Visceral Adipose Tissue in European and American Youth
Bibliographic record
Abstract
Objective To investigate whether prediction equations including a limited but selected number of anthropometrics that consider differences in subcutaneous abdominal adipose tissue may improve prediction of the visceral adipose tissue (VAT) in youth. Study design Anthropometrics and abdominal adipose tissue by MRI were available in 7-18 years old youth with overweight or obesity: 181 White Europeans and 186 White and Black Americans. Multivariable regressions were performed to develop and validate the VAT anthropometric predictive equations in a cross-sectional study. Results A model with both waist circumference (WaistC) and hip circumference (HipC) (VAT = [1.594 × WaistC] – [0.681 × HipC] + [1.74 × Age] – 48.95) more strongly predicted VAT in girls of White European ethnicity (R 2 = 50.8%; standard error of the estimate [SEE] = 13.47 cm 2 ), White American ethnicity (R 2 = 41.9%; SEE, 15.63 cm 2 ), and Black American ethnicity (R 2 = 25.1%; SEE, 16.34 cm 2 ) ( P < .001), than WaistC or BMI. In boys, WaistC was the strongest predictor of VAT; HipC did not significantly improve VAT prediction. Conclusions A model including both WaistC and HipC that considers differences in subcutaneous abdominal adipose tissue more accurately predicts VAT in girls and is superior to commonly measured anthropometrics used individually. In boys, other anthropometric measures did not significantly contribute to the prediction of VAT beyond WaistC alone. This demonstrates that selected anthropometric predictive equations for VAT can be an accessible, cost-effective alternative to imaging methods that can be used in both clinics and research.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".