Identification of Statin’s Action in a Small Cohort of Patients with Major Depression

Abstract

Statins are widely used as an effective therapy for ischemic vascular disorders and employed for primary and secondary prevention in cardiac and cerebrovascular diseases. Their hemostatic mechanism has also been shown to induce changes in cerebral blood flow that may result in neurocognitive improvement in subjects with Major Depressive Disorder. Behavioral data, various blood tests, and resting-state brain perfusion data were obtained at the start of this study and three months post-therapy from a small cohort of participants diagnosed with Major Depressive Disorder. Subjects received either rosuvastatin (10 mg) or placebo with their standard selective serotonin reuptake inhibitors therapy. At the end of the study, patients using rosuvastatin reported more positive mood changes than placebo users. However, standard statistical tests revealed no significant differences in any non-behavioral variables before and after the study. In contrast, feature selection techniques allowed identifying a small set of variables that may be affected by statin use and contribute to mood improvement. Classification models built to assess the distinguishability between the two groups showed an accuracy higher than 85% using only five selected features: two peripheral platelet activation markers, perfusion abnormality in the left inferior temporal gyrus, Attention Switching Task Reaction latency, and serum phosphorus levels. Thus, using machine learning tools, we could identify factors that may be causing self-reported mood improvement in patients due to statin use, possibly suggesting a regulatory role of statins in the pathogenesis of clinical depression.

Keywords

• depression
• feature selection
• machine learning
• rosuvastatin