Table 4: Prediction/discrimination of hypertension 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 Casesi Non-casesj AUCo AUC

Hypertensionq, grade 1 (≥140/90 mmHg)
DXABIA InBodyr2691855%0.214−1%6%1.7%0.0170.030.1270.060.000
BMI3352586%0.2202%3%1.9%0.0060.000.9770.040.073
Estimates3352586%0.2082%3%1.5%0.0190.030.3830.070.000
BIA InBodyBMI2691854%0.3601%3%0.5%0.534−0.030.330−0.030.147
Estimate2691853%0.5020%3%0.1%0.8850.000.9790.010.606
BMIEstimate3352580%0.8030%0%−0.4%0.1440.030.0210.040.000

Hypertension, grade 2 (≥160/100 mmHg)
DXABIA InBody93361−1%0.848−4%3%1.4%0.0630.020.3960.050.000
BMI117476−9%0.128−8%−1%−1.2%0.049−0.090.064−0.030.255
Estimate117476−8%0.154−7%−1%−1.2%0.036−0.010.7460.010.626
BIA InBodyBMI93361−9%0.161−8%−1%−2.5%0.003−0.110.006−0.080.000
Estimate93361−10%0.096−9%−1%−2.8%0.001−0.040.309−0.040.044
BMIEstimate1174761%0.6821%0%0.0%0.8700.070.0010.040.000

Dyslipidaemiat
DXABIA InBody111304−2%0.616−5%3%−0.1%0.928−0.030.161−0.010.510
BMI1243456%0.3202%4%3.5%0.015−0.010.8160.020.378
Estimate1243454%0.4960%4%2.7%0.040−0.020.6400.010.766
BIA InBodyBMI1113048%0.1496%2%3.1%0.0220.020.5680.030.162
Estimate1113046%0.2405%2%2.5%0.0440.010.7340.020.390
BMIEstimate124345−2%0.237−2%−1%−0.8%0.111−0.010.598−0.010.148

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 noncases. 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).
qDefinitions of hypertension according to European Societies for Hypertension and Cardiology {Mancia, 2007 #2897}.
rEstimation of FM% with bioimpedance device InBody (720) (Biospace, Korea).
sAnthropometry-based estimate; arithmetic mean of FM% estimations according to prediction methods Deurenberg et al. [12], Gallagher et al. [15], and Larsson et al. [14].
tTriacylglycerols ≥ 1.7 mmol/L or HDL cholesterol ≤ 1.29 mmol/L in men or HDL ≤ 1.03 mmol/L in women.