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Should Waist Circumference Cutoffs in the Context of Cardiometabolic Risk Factor Assessment be Specific to Sex, Age, and BMI?
Metabolic Syndrome and related Disorders 2018 September
OBJECTIVE: A sex-specific standard waist circumference (WC) is widely used to determine cardiometabolic risk across ages even though aging impacts the link between fat distribution and cardiometabolic risk. The objective was to propose WC thresholds that better predict metabolic abnormalities according to sex, age, and body mass index (BMI) categories.
METHODS: First, receiver operating characteristic analyses were performed to identify optimal age (20-49, 50-64, and 65-80 years) and BMI (normal weight, overweight, obese I, and obese II+) specific WC thresholds to correctly identify at-risk individuals, that is, presenting ≥2 cardiometabolic risk factors of metabolic syndrome (n = 23,482; NHANES 2007-2014). Second, cross-validation analyses (n = 18,686; NHANES 1999-2006) were used to validate these WC optimal thresholds. Univariate logistic regression models with WC as an independent predictor were performed to quantify odds of being at-risk for each age and BMI subgroups.
RESULTS: When age and BMI categories were considered in the identification of optimal WC thresholds, sensitivity to correctly identify at-risk individuals significantly improved.
CONCLUSIONS: Our results indicate that the use of WC thresholds that are specific to age and BMI subcategories significantly increases the capacity to accurately identify at-risk individuals. They would thus be highly appropriate for clinicians in the context of efficient cardiometabolic risk assessment and intervention recommendations.
METHODS: First, receiver operating characteristic analyses were performed to identify optimal age (20-49, 50-64, and 65-80 years) and BMI (normal weight, overweight, obese I, and obese II+) specific WC thresholds to correctly identify at-risk individuals, that is, presenting ≥2 cardiometabolic risk factors of metabolic syndrome (n = 23,482; NHANES 2007-2014). Second, cross-validation analyses (n = 18,686; NHANES 1999-2006) were used to validate these WC optimal thresholds. Univariate logistic regression models with WC as an independent predictor were performed to quantify odds of being at-risk for each age and BMI subgroups.
RESULTS: When age and BMI categories were considered in the identification of optimal WC thresholds, sensitivity to correctly identify at-risk individuals significantly improved.
CONCLUSIONS: Our results indicate that the use of WC thresholds that are specific to age and BMI subcategories significantly increases the capacity to accurately identify at-risk individuals. They would thus be highly appropriate for clinicians in the context of efficient cardiometabolic risk assessment and intervention recommendations.
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