A Novel Application of Finite Gaussian Mixture Model (GMM) Using Real and Simulated Biomarkers of Cardiovascular Disease to Distinguish Adolescents with and Without Obesity

• Activity Number: 288 - SLDS CSpeed 5
• Type: Contributed
• Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Jobayer Hossain and Babu Balagopal*
• Companies: Nemours Children's Health System and Nemours Children's Health System
• Keywords: Classification; Gaussian mixture models; Principal component analysis; Cardiovascular disease; Biomarker

Abstract

Obesity-induced derangements in adipose tissue and other organs lead to the development of cardiovascular disease (CVD). The loss of CV-health in children is a continuum, and the manifestation of overt CVD takes several years. Therefore, robust biomarkers are crucial for its early prediction, prevention and management. This study examines if the confluence of biomarkers of CVD can distinguish adolescents with obesity from their normal-weight counterparts to illustrate obesity as a strong risk factor of CVD. The circulating concentrations of five novel biomarkers were measured in a well-controlled study in 21 adolescents. Application of the Gaussian mixture model to these biomarkers identified two distinct groups that matched with the obesity status of participants. Classification of biomarkers from a simulation study of 1000 data points, each comprising a vector of five biomarkers and the classification identifier, resulted in two groups that matched with the classification identifier in the simulated dataset. The precise identification of obesity by the pattern of concurring biomarkers of CVD in real and simulated datasets confirms obesity as a strong risk factor of CVD.