Computed Tomography and Dual-Energy X-Ray Asorptiometry body composition parameter harmonisation to universalise adipose tissue measurements in a population-based cross-sectional study.

To harmonise computed tomography (CT) and dual-energy x-ray absorptiometry (DXA) body composition measurements allowing easy conversion in longitudinal assessments and across cohorts to assess cardiometabolic risk and disease. Retrospective cross-sectional observational study from 1996 to 2008 included participants in the Pennington Center Longitudinal Study (PCLS) (N = 1967; 571 African American/1396 White). Anthropometrics, whole-body DXA and abdominal CT images were obtained. Multi-layer segmentation techniques (Analyze; Rochester, MN) quantified visceral adipose tissue (VAT). Clinical biomarkers were obtained from routine blood samples. Linear models were used to predict CT-VAT from DXA-VAT and examine the effects of traditional biomarkers on cross-sectional-VAT. Predicted CT-VAT was highly associated with measured CT-VAT using ordinary least square linear regression analysis and random forest models (R = 0.84; 0.94, respectively, p < .0001). Model stratification effects showed low variability between races and sexes. Overall, associations between measured CT-VAT and DXA-predicted CT-VAT were good (R > 0.7) or excellent (R > 0.8) and improved for all stratification groups except African American men using random forest models. The clinical effects on measured CT-VAT and DXA-VAT showed no significant clinical difference in the measured adipose tissue areas (mean difference = 0.22 cm). Random forest modelling seamlessly predicts CT-VAT from measured DXA-VAT to a degree of accuracy that falls within the bounds of universally accepted standard error.