Figure 1.
a–f. Illustration of different MRI body compartments in the sub-study of the German EPIC cohorts.
Table 1.
Anthropometric variables and body compartments as assessed by MRI by sex and age groups1, all values are presented as mean (min, max).
Table 2.
Pearson correlation coefficients (95% CI) between anthropometric and MRI variables adjusted for age and height with the residual method in men (n = 598).
Table 3.
Pearson correlation coefficients (95% CI) between anthropometric and MRI variables adjusted for age and height with the residual method in women (n = 594).
Figure 2.
Prediction of body compartments by anthropometric indices in multiple linear regression analyses (Men, n = 598).
Total model R2 for each body compartment and partial correlation coefficients (95% CI) for anthropometric indices. All variables were adjusted for age and height. TBV = Total body volume, TAT = total adipose tissue, SAT = subcutaneous adipose tissue, VAT = visceral adipose tissue, CAT = coronary adipose tissue, SMT = skeletal muscle tissue, BMI = body mass index, WC = waist circumference, HC = hip circumference. 1Predictors included: BMI, WC, HC. All variables (predictors and outcome) adjusted by age and height with the residual method. 2Partial correlation coefficients (95% CI) are reported for predictor variables.
Figure 3.
Prediction of body compartments by anthropometric indices in multiple linear regression analyses (Women, n = 594).
Total model R2 for each body compartment and partial correlation coefficients (95% CI) for anthropometric indices. All variables were adjusted for age and height. TBV = Total body volume, TAT = total adipose tissue, SAT = subcutaneous adipose tissue, VAT = visceral adipose tissue, CAT = coronary adipose tissue, SMT = skeletal muscle tissue, BMI = body mass index, WC = waist circumference, HC = hip circumference. 1Predictors included: BMI, WC, HC. All variables (predictors and outcome) adjusted by age and height with the residual method. 2Partial correlation coefficients (95% CI) are reported for predictor variables.