Oxidative Stress Biomarkers in Predictive Multi-Class Modeling of Depression Severity with Diabetes Mellitus, Cardiovascular Disease and Hypertension Comorbidity

In this study, depression severity was defined by the Patient Health Questionnaire (PHQ-9) and five machine learning algorithms were applied to classify depression severity in the presence of diabetes mellitus (DM), cardiovascular disease (CVD), and hypertension (HT) utilizing oxidative stress (OS) biomarkers (8-isoprostane, 8-hydroxydeoxyguanosine, reduced glutathione and oxidized glutathione), demographic details, and medication for eight hundred and thirty participants. The results show that the Random Forest (RF) outperformed other classifiers with the highest accuracy of 92% in a 4-class depression classification when considering all OS biomarkers along with DM, CVD and HT. RF also achieved the highest accuracy of 91% in 3-class classification when studying depression in presence of DM only and an accuracy of 88% and 87% in 5-class classification when investigating depression with CVD and HT, respectively. Moreover, RF performed best in the 3-class depression model with an accuracy of 85% when examining depression severity in the presence of OS biomarkers only. Our findings suggest that depression severity can be accurately identified with RF as a base classifier and that OS is a major contributor to depression severity in the presence of comorbidities. Biomarker analysis can supplement DSM-5-based diagnostics as part of personalized medicine and especially as point of care testing has become available for many of the given OS biomarkers.Clinical Relevance- Depression is the most common form of psychiatric disorder that has an oxidative stress etiology. Current diagnosis relies primarily on the Diagnostic and Statistical Manual for Mental Disorders (DSM-5), which may be too general and not informative for optimal multi-comorbidity diagnostics and treatment. Understanding the role of oxidative stress associated with depression can provide additional information for timely detection, comprehensive assessment, and appropriate intervention of depression illness.