## The receiver operating characteristics of a nominal logistical regression using DXA-derived body composition as independent variables to estimate the probability of DM2 or either DM2 or MS in the full DXA population (IG + CV) show that a large percentage of the diseased individuals can be identified without a high cost of a false positive diagnosis.

A) The steep slope of the initial portion of the curve to the inflection point (*) indicates that a high proportion of individuals with DM2 can be identifying using DXA-derived FM and TF. Area under the curve is 0.78 with a standard error of 0.05 (24 subjects with DM2 and 327 without; p<0.0001). The 95% confidence interval is 0.68 to 0.88. [Regression equation: risk of DM2 = 1/(1+e^{−z}), where z = 1.94−0.53(TF)+0.28(FM).] B) Similarly, the ROC for the detection of either DM2 or MS also shows inflection points, consistent with sensitive detection of subjects with disease. The AUC is 0.71±0.03 (103 subjects with disease, 238 without; p<0.0001). The 95% confidence interval for the AUC is 0.64 to 0.77. [Regression equation: k = 2.32−0.24(TF)+0.09(FM)].