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Accumulating evidence indicates that oxidative and nitrosative stress (O&NS) pathways play a key role in the pathophysiology of bipolar disorder (BD) and major depressive disorder (MDD). However, only a handful of studies have directly compared alterations in O&NS pathways among patients with MDD and BD types I (BPI) and BPII. Thus, the current study compared superoxide dismutase (SOD1), lipid hydroperoxides (LOOH), catalase, nitric oxide metabolites (NOx), malondialdehyde (MDA), and advanced oxidation protein products (AOPP) between mood disorder patients in a clinically remitted state. To this end 45, 23, and 37 participants with BPI, BPII, and MDD, respectively, as well as 54 healthy controls (HCs) were recruited. Z-unit weighted composite scores were computed as indices of reactive oxygen species (ROS) production and nitro-oxidative stress driving lipid or protein oxidation. SOD1, NOx, and MDA were significantly higher in MDD than in the other three groups. AOPP was significantly higher in BPI than in HCs and BPII patients. BPII patients showed lower SOD1 compared to all other groups. Furthermore, MDD was characterized by increased indices of ROS and lipid hydroperoxide production compared to BPI and BPII groups. Indices of nitro-oxidative stress coupled with aldehyde production or protein oxidation were significantly different among the three patient groups (BDII > BDI > MDD). Finally, depressive symptom scores were significantly associated with higher LOOH and AOPP levels. In conclusion, depression is accompanied by increased ROS production, which is insufficiently dampened by catalase activity, thereby increasing nitro-oxidative damage to lipids and aldehyde production. Increased protein oxidation with formation of AOPP appeared to be hallmark of MDD and BPI. In addition, patients with BPII may have protection against the damaging effects of ROS including lipid peroxidation and aldehyde formation. This study suggests that biomarkers related to O&NS could aid in the differentiation of MDD, BPI, and BPII.

The top-down DSM/ICD categories of mood disorders are inaccurate, and their dogmatic nature precludes both deductive (as indisputable) and inductive (as top-down) remodeling of case definitions. In trials, psychiatric rating scale scores employed as outcome variables are invalid and rely on folk psychology-like narratives. Using machine learning techniques we developed a new precision nomothetic model of mood disorders with a recurrence of illness (ROI) index, a new endophenotype class, namely Major Dysmood Disorder (MDMD), characterized by increased ROI, a more severe phenome, and more disabilities Nonetheless, our previous studies did not compute Research and Diagnostic Algorithmic Rules (RADAR) to diagnose MDMD and score ROI, lifetime (LT), and current suicidal behaviors, as well as the phenome of mood disorders. Here we provide rules to compute bottom-up RADAR scores for MDMD, ROI, lifetime (LT) and current suicidal SI and SA, the phenome of mood disorders, and the lifetime trajectory of mood disorder patients from a family history of mood disorders and substance abuse to adverse childhood experiences, ROI, and the phenome. We also demonstrate how to plot the 12 major scores in a single RADAR graph, which displays all features in a two-dimensional plot. These graphs allow the characteristics of a patient to be displayed as an idiomatic fingerprint, allowing one to estimate the key traits and severity of the illness at a glance. Consequently, biomarker research into mood disorders should use our RADAR scores to examine pan-omics data, which should be used to enlarge our precision models and RADAR graph.