Abstract
Metabolic syndrome (MetS) in young adults (age 20–39) is often undiagnosed. A simple screening tool using a surrogate measure might be invaluable in the early detection of MetS. Methods. A chi-squared automatic interaction detection (CHAID) decision tree analysis with waist circumference user-specified as the first level was used to detect MetS in young adults using data from the National Health and Nutrition Examination Survey (NHANES) 2009-2010 Cohort as a representative sample of the United States population (n = 745). Results. Twenty percent of the sample met the National Cholesterol Education Program Adult Treatment Panel III (NCEP) classification criteria for MetS. The user-specified CHAID model was compared to both CHAID model with no user-specified first level and logistic regression based model. This analysis identified waist circumference as a strong predictor in the MetS diagnosis. The accuracy of the final model with waist circumference user-specified as the first level was 92.3% with its ability to detect MetS at 71.8% which outperformed comparison models. Conclusions. Preliminary findings suggest that young adults at risk for MetS could be identified for further followup based on their waist circumference. Decision tree methods show promise for the development of a preliminary detection algorithm for MetS.
Highlights
Metabolic Syndrome (MetS) is a collection of cardiometabolic risk factors that includes excessive central adiposity, elevated triglycerides (TG) and fasting plasma glucose (FPG), decreased HDL-cholesterol (HDL), and hypertension [1]
Four major predictor variables reached significance to be included in this model including waist circumference, TG, HDL, and FPG
The subset of subjects categorized by a waist circumference between 94 and 103 cm and the level of TG greater than 162 mg/dl had a MetS prevalence of 57.8% compared to TG less than or equal to 162 mg/dl (5.1% MetS). These results indicate that further testing for MetS might not be warranted for subjects presenting with a waist circumference less than 86 cm but would be recommended for those in either of the subcategories of waist circumference
Summary
Metabolic Syndrome (MetS) is a collection of cardiometabolic risk factors that includes excessive central adiposity, elevated triglycerides (TG) and fasting plasma glucose (FPG), decreased HDL-cholesterol (HDL), and hypertension [1]. When these risk factors are present in tandem, they increase the risk of heart attack, stroke, and cardiovascular morbidity and/or mortality affecting one in three adults in the United States (US) [2]. Creating an early detection model that stratifies the severity of cardiometabolic and anthropometric factors used in the MetS diagnosis based on proxy measures obtained in a clinical setting would be invaluable for clinicians aiming to provide improved patient-centered care [7]
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