Table 5: Prediction/discrimination of impaired fasting glucose and the metabolic syndrome with degree of obesity as defined by dual-energy X-ray absorptiometry (DXA) bioimpedance analysis (BIA), an anthropometry-based estimate of fat mass percentage (FM%-equation) and BMI.

ROC analysesn
Reference method/modelaNew method/modelb Reclassification index, %fIDI, %kMenWomen
Casesd Non-caseseNetg CasesiNon-casesj AUCo AUC

Impaired fasting glucose (≥5.6 mmol/L = 100 mg/dL)
DXABIA InBodyq164249−6%0.181−7%1%−0.5%0.506−0.030.102−0.010.394
BMI191276−2%0.727−4%2%0.3%0.723−0.040.286−0.020.394
Estimater191276−1%0.771−4%2%0.2%0.809−0.030.404−0.010.597
BIA InBodyBMI1642492%0.7521%0%0.2%0.796−0.010.888−0.010.744
Estimate1642491%0.7691%1%−0.1%0.8820.000.8900.000.981
BMIEstimate1912760%1.0000%0%−0.1%0.7330.010.5140.010.386

Impaired fasting glucose (≥6.1 mmol/L = 110 mg/dL)
DXABIA InBody70343−1%0.901−4%3%3.5%0.009−0.010.5840.010.462
BMI803876%0.3943%4%3.2%0.0090.000.9000.000.918
Estimate803873%0.6160%3%2.6%0.0230.020.4380.010.796
BIA InBodyBMI703437%0.3416%1%−0.7%0.6090.020.648−0.020.504
Estimate703432%0.7541%1%−1.5%0.2050.040.253−0.010.799
BMIEstimate80387−3%0.251−3%-1%−0.6%0.1760.020.3150.010.248

Metabolic syndrome (AHA/NHBLI)s
DXABIA InBody144268−4%0.400−6%2%−0.7%0.691−0.030.1200.010.625
BMI1653014%0.4610%4%2.5%0.257−0.020.6100.020.309
Estimate1653013%0.519−1%4%1.7%0.407−0.020.4660.020.329
BIA InBodyBMI1442683%0.5952%1%0.9%0.6620.010.6970.010.429
Estimate1442684%0.4802%1%0.8%0.6810.010.8120.010.409
BMIEstimate165301−1%0.577−1%0%−0.7%0.252−0.010.6220.000.958

Method of measurement, based on which participants are classified in categories of obesity.
bDifferent method of estimating obesity, the predictive power of which is compared to reference model/reference method.
cNumber of participants.
dNumber of participants that are positive with regard to respective outcome.
eNumber of participants that are negative with regard to respective outcome.
fPercentage improvement (+) or deterioration (−) in predictive power of new model compared to reference model. Categories of obesity/FM% as independent variable.
gNet reclassification of cases + net reclassification of non-cases. A positive number denotes increased predictive power for the new model.
hLikelihood of net reclassification index to be 0, that is, the new model showing no improvement/deterioration over reference model.
iNet reclassification of cases = percentage of cases reclassified by the new model into a higher risk category − percentage of cases reclassified by the new model into a lower risk category.
jNet reclassification of non-cases = percentage of non-cases reclassified by the new model into a lower risk category − percentage of non-cases reclassified by the new model into a higher risk category.
kIntegrated discrimination improvement (+) or deterioration (−) of new model compared to reference model. Categories of obesity/FM% as independent variable in an age-adjusted model.
lMean difference in predicted individual probabilities between cases and non-cases for two models. A positive number denotes increased predictive power for the new model.
mLikelihood of net reclassification index to be 0, that is, the new model showing no improvement/deterioration over reference model.
nMeasures of obesity (BMI/FM%) as continuous variable in a logistic regression model predicting respective outcomes.
oDifference in area under curve of receiver operating characteristic compared to reference method.
pProbability of 0-hypothesis (no difference).
qEstimation of FM% with bioimpedance device InBody (720) (Biospace, Korea).
rAnthropometry-based estimate; arithmetic mean of FM% estimations according to prediction methods Deurenberg et al. [12], Gallagher et al. [15], and Larsson et al. [14].
sDefinition of metabolic syndrome suggested by the common task force from the IDF and the American Heart Association/National Heart, Lung and Blood Institute (AHA/NHBLI) [17].