Key result
Waist circumference, BMI, and body fat show equivalent accuracy for predicting insulin resistance.
Cross-Sectional (n=4,008)
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Background The aim of the present study was to determine the cut-off points for obesity-related measures based on insulin resistance, which contributes to the clustering of borderline risk factors in the early stage of metabolic syndrome (MetS). Methods We determined the cut-off points of waist circumference (WC), BMI, and percentage of body fat (%fat) in 2129 men and 1879 women by receiver operating characteristics (ROC) analysis to predict homeostasis model assessment of insulin resistance (HOMA-IR) >2.5, and compared them with cut-offs in current use and those determined by linear regression corresponding to HOMA-IR=2.5. We then compared diagnostic efficiency in predicting insulin resistance among WC, BMI, and %fat. Results ROC analysis yielded cut-off points of WC 88 cm and 82 cm, BMI 25 kg/m 2 and 23 kg/m 2 ,a nd %fat 24% and 29% for men and women, respectively. The cut-off points derived from ROC had better diagnostic efficiency than those from linear regression or currently used values. WC, BMI, and %fat appeared to be equivalent in the diagnosis of obesity in early-stage MetS, as shown by comparable values for area under the curve and 95% CI. Conclusion The optimal cut-off points for predicting insulin resistance were determined as WC 88 cm and 82 cm, BMI 25 kg/m 2 and 23 kg/m 2 , and %fat 24% and 29% for men and women, respectively. (Ningen Dock 2011 ; 25 :5 3-59)
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Yamada et al. (2011) conducted a cross-sectional in Early-stage metabolic syndrome and insulin resistance (n=4,008). ROC-derived cut-off points for waist circumference, BMI, and body fat percentage vs. Cut-off points in current use and those determined by linear regression corresponding to HOMA-IR=2.5 was evaluated on Prediction of insulin resistance (HOMA-IR >2.5). ROC-derived cut-offs for predicting HOMA-IR >2.5 were WC 88/82 cm, BMI 25/23 kg/m2, and body fat 24%/29% in men/women, showing equivalent diagnostic efficiency across measures.
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