Anthropometric Equations to Predict Visceral Adipose Tissue in European and American Youth
Notice bibliographique
Résumé
ObjectiveTo 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 designAnthropometrics 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.ResultsA 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 (R2 = 50.8%; standard error of the estimate [SEE] = 13.47 cm2), White American ethnicity (R2 = 41.9%; SEE, 15.63 cm2), and Black American ethnicity (R2 = 25.1%; SEE, 16.34 cm2) (P < .001), than WaistC or BMI. In boys, WaistC was the strongest predictor of VAT; HipC did not significantly improve VAT prediction.ConclusionsA 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. 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. 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. 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 (R2 = 50.8%; standard error of the estimate [SEE] = 13.47 cm2), White American ethnicity (R2 = 41.9%; SEE, 15.63 cm2), and Black American ethnicity (R2 = 25.1%; SEE, 16.34 cm2) (P < .001), than WaistC or BMI. In boys, WaistC was the strongest predictor of VAT; HipC did not significantly improve VAT prediction. 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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».