Title of Proposal
Exercise Science - PrePhysical Therapy
Bachelor of Science in Education
Date of Graduation
INTRODUCTION: Metabolic syndrome (MetS) is a constellation of cardiometabolic risk factors that, when presented in tandem, increases the risk of heart disease and insulin resistance. Finding a simple and validated screening method is critical to proactively intervene and attenuate the development of cardiometabolic diseases and improving healthcare outcomes. PURPOSE: This study defined and validated a risk criterion for MetS using MUAC as an alternative criterion for MetS classification risk. METHODS: The sample was derived from National Health & Nutrition Examination Survey 2015-2016 data of adults over 18 years (N = 9,971). MetS was defined using the NCEP ATP III 2005 MetS diagnosis criteria. A recursive partitioning methodology (RPM), using Classification & Regression Tree Algorithm, was employed to create binary MUAC criterion by sex, using 75% of the total sample. Validation of the criteria was performed with the remaining 25% of the total sample. RESULTS: Seventeen percent of the total sample presented with the MetS. The RPM resulted in sex specific MetS criteria with the MUAC criterion being >32cm (p = 0.024) and >29cm (p = 0.024) for males and females, respectively. Those presenting with the risk criteria were 9.84, for males, and 9.23, for females, times more likely to present with MetS than without the MUAC criterion. The overall classification accuracy for both the training and validation models were 83% with no statistical difference between models (p = 0.983). CONCLUSION: MUAC shows promise as an effective screening method for MetS in guiding further diagnostic tests to prevent associated cardiometabolic diseases.
Dr. Judith Juvancic-Heltzel
Dr. Laura Richardson
Boucher, Hayley, "Exploring the Utility of MUAC in Classifying Adult Metabolic Syndrome Using NHANES 2015-16" (2019). Williams Honors College, Honors Research Projects. 854.