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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.

Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to a) construct a model which ensembles risk/resilience (R/R), adverse outcome pathways (AOPs), staging, and the phenome of mood disorders, and b) disclose new classes based on these feature sets. This study was conducted using data of 67 healthy controls and 105 mood disordered patients. The R/R ratio, assessed as a combination of the paraoxonase 1 (PON1) gene, PON1 enzymatic activity, and early life time trauma (ELT), predicted the high-density lipoprotein cholesterol – paraoxonase 1 complex (HDL-PON1), reactive oxygen and nitrogen species (RONS), nitro-oxidative stress toxicity (NOSTOX), staging (number of depression and hypomanic episodes and suicidal attempts), and phenome (the Hamilton Depression and Anxiety scores and the Clinical Global Impression; current suicidal ideation; quality of life and disability measurements) scores. Partial Least Squares pathway analysis showed that 44.2% of the variance in the phenome was explained by ELT, RONS/NOSTOX, and staging scores. Cluster analysis conducted on all those feature sets discovered two distinct patient clusters, namely 69.5% of the patients were allocated to a class with high R/R, RONS/NOSTOX, staging, and phenome scores, and 30.5% to a class with increased staging and phenome scores. This classification cut across the bipolar (BP1/BP2) and major depression disorder classification and was more distinctive than the latter classifications. We constructed a nomothetic network model which reunited all features of mood disorders into a mechanistically transdiagnostic model.

There is evidence that immune-inflammatory, stress of reactive oxygen and nitrogen species (IO&NS) processes play a role in the neurodegenerative processes observed in Parkinson's disease (PD). The aim of the present study was to investigate peripheral IO&NS biomarkers in PD. We included 56 healthy individuals and 56 PD patients divided in two groups: early PD stage and late PD stage. Plasma lipid hydroperoxides (LOOH), malondialdehyde (MDA), nitric oxide metabolites (NOx), sulfhydryl (SH) groups, catalase (CAT) activity, superoxide dismutase (SOD) activity, paraoxonase (PON)1 activity, total radical trapping antioxidant parameter (TRAP) and C-reactive protein (CRP) were measured. PD is characterized by increased LOOH, MDA and SOD activity and lowered CAT activity. A combination of five O&NS biomarkers highly significantly predicts PD with a sensitivity of 94.5% and a specificity of 86.8% (i.e., MDA, SOD activity, TRAP, SH-groups and CAT activity). The single best biomarker of PD is MDA, while LOOH and SOD activity are significantly associated with late PD stage, but not early PD stage. Antiparkinson drugs did not affect O&NS biomarkers, but levodopa+carbidopa significantly increased CRP. It is suggested that MDA may serve as a disease biomarker, while LOOH and SOD activity are associated with late PD stage characteristic. New treatments for PD should not only target dopamine but also lipid peroxidation.