Metabolic Classification of Adolescent Depression

Abstract

Major Depressive Disorder (MDD) is the leading cause of disability across multiple developmental stages. Efforts have intensified to identify “biomarkers” of MDD based on emerging links between mood and metabolic function, particularly from inflammatory markers and mitochondrial health. However, little research to date has examined how these findings extend to other metabolic domains, such as cardiovascular health and fat storage. In the present research, we used machine-learning classification to assess whether cardiovascular and body composition variables can accurately distinguish depressed subjects from controls at rates above chance. An age range from 16-20 years old was used based on established age ranges of onset for adolescent MDD.