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Utility of Body Mass Index, Waist-to-Height-Ratio and cardiorespiratory fitness thresholds for identifying cardiometabolic risk in 10.4-17.6-year-old children.
Obesity Research & Clinical Practice 2017 September
OBJECTIVE: To examine the utility of Body Mass Index (BMI), Waist-to-Height-Ratio (WHtR) and cardiorespiratory fitness thresholds to identify cardiometabolic risk in youth.
METHODS: Cross-sectional cardiometabolic risk factor variables on 534 children aged 10.4-17.6 years of age (52% boys) from the United Kingdom were used. Binary logistic regression and receiver operating characteristic curves were used to examine the utility of established age and gender specific thresholds for BMI, WHtR and cardiorespiratory fitness to identify individuals with increased cardiometabolic risk (increased clustered triglycerides, HDL-cholesterol, systolic blood pressure and glucose).
RESULTS: A WHtR≥0.5 increased the odds by 11.4 (95% confidence interval 4.7, 27.4, P<0.001) of having increased cardiometabolic risk in boys and by 2.5 (1.2, 5.3, P=0.020) for girls. Similar associations were observed for BMI and cardiorespiratory fitness in both boys and girls with increased cardiometabolic risk. BMI-z, WHtR and cardiorespiratory fitness all showed a significant ability in identifying individuals for increased cardiometabolic risk in boys and girls (P<0.05) despite poor area under the curve (AUC) values (<0.70). Combining anthropometrical variables did improve the diagnostic accuracy for identifying cardiometabolic risk in boys, evidenced by an increased AUC of 0.74 (0.64, 0.85, P<0.001), but not in girls.
CONCLUSION: The magnitude of associations was broadly similar for BMI, WHtR and cardiorespiratory fitness in identifying individuals at increased cardiometabolic risk. Yet, combining BMI with WHtR in boys may provide a more accurate method for identifying those at increased cardiometabolic risk.
METHODS: Cross-sectional cardiometabolic risk factor variables on 534 children aged 10.4-17.6 years of age (52% boys) from the United Kingdom were used. Binary logistic regression and receiver operating characteristic curves were used to examine the utility of established age and gender specific thresholds for BMI, WHtR and cardiorespiratory fitness to identify individuals with increased cardiometabolic risk (increased clustered triglycerides, HDL-cholesterol, systolic blood pressure and glucose).
RESULTS: A WHtR≥0.5 increased the odds by 11.4 (95% confidence interval 4.7, 27.4, P<0.001) of having increased cardiometabolic risk in boys and by 2.5 (1.2, 5.3, P=0.020) for girls. Similar associations were observed for BMI and cardiorespiratory fitness in both boys and girls with increased cardiometabolic risk. BMI-z, WHtR and cardiorespiratory fitness all showed a significant ability in identifying individuals for increased cardiometabolic risk in boys and girls (P<0.05) despite poor area under the curve (AUC) values (<0.70). Combining anthropometrical variables did improve the diagnostic accuracy for identifying cardiometabolic risk in boys, evidenced by an increased AUC of 0.74 (0.64, 0.85, P<0.001), but not in girls.
CONCLUSION: The magnitude of associations was broadly similar for BMI, WHtR and cardiorespiratory fitness in identifying individuals at increased cardiometabolic risk. Yet, combining BMI with WHtR in boys may provide a more accurate method for identifying those at increased cardiometabolic risk.
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