The Journal of pediatrics · 2022
Anthropometric Equations to Predict Visceral Adipose Tissue in European and American Youth.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Samouda Hanen, Lee SoJung, Arslanian Silva, Han Minsub, Kuk Jennifer L
Аннотация
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 (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.
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.
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 (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.
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.