VAT=TAAT-SAAT: innovative anthropometric model to predict visceral adipose tissue without resort to CT-Scan or DXA.

ABSTRACT:

Objective

To investigate whether a combination of a selected but limited number of anthropometric measurements predicts visceral adipose tissue (VAT) better than other anthropometric measurements, without resort to medical imaging.

Hypothesis

Abdominal anthropometric measurements are total abdominal adipose tissue indicators and global measures of VAT and SAAT (subcutaneous abdominal adipose tissue). Therefore, subtracting the anthropometric measurement the more correlated possible with SAAT while being the least correlated possible with VAT, from the most correlated abdominal anthropometric measurement with VAT while being highly correlated with TAAT, may better predict VAT.

Design and methods

BMI participants' range was from 16.3 to 52.9 kg m(-2) . Anthropometric and abdominal adipose tissues data by computed tomography (CT-Scan) were available in 253 patients (18-78 years) (CHU Nord, Marseille) and used to develop the anthropometric VAT prediction models.

Results

Subtraction of proximal thigh circumference from waist circumference, adjusted to age and/or BMI, predicts better VAT (Women: VAT = 2.15 × Waist C - 3.63 × Proximal Thigh C + 1.46 × Age + 6.22 × BMI - 92.713; R(2) = 0.836. Men: VAT = 6 × Waist C - 4.41 × proximal thigh C + 1.19 × Age - 213.65; R(2) = 0.803) than the best single anthropometric measurement or the association of two anthropometric measurements highly correlated with VAT. Both multivariate models showed no collinearity problem. Selected models demonstrate high sensitivity (97.7% in women, 100% in men). Similar predictive abilities were observed in the validation sample (Women: R(2) = 76%; Men: R(2) = 70%). Bland and Altman method showed no systematic estimation error of VAT.

Conclusion

Validated in a large range of age and BMI, our results suggest the usefulness of the anthropometric selected models to predict VAT in Europides (South of France).

SUBMITTER: Samouda H 

PROVIDER: S-EPMC3618381 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature