Construction of a nitro-oxidative stress-driven, mechanistic model of mood disorders: A nomothetic network approach

Abstract

Major depression is accompanied by increased IgM-mediated autoimmune responses to oxidative specific epitopes (OSEs) and nitric oxide (NO)-adducts. These responses were not examined in bipolar disorder type 1 (BP1) and BP2. IgM responses to malondialdehyde (MDA), phosphatidinylinositol, oleic acid, azelaic acid, and NO-adducts were determined in 35 healthy controls, and 47 major depressed (MDD), 29 BP1, and 25 BP2 patients. We also measured serum peroxides, IgG to oxidized LDL (oxLDL), and IgM/IgA directed to lipopolysaccharides (LPS). IgM responses to OSEs and NO-adducts (OSENO) were significantly higher in MDD and BP1 as compared with controls, and IgM to OSEs higher in MDD than in BP2. Partial Least Squares (PLS) analysis showed that 57.7% of the variance in the clinical phenome of mood disorders was explained by number of episodes, a latent vector extracted from IgM to OSENO, IgG to oxLDL, and peroxides. There were significant specific indirect effects of IgA/IgM to LPS on the clinical phenome, which were mediated by peroxides, IgM OSENO, and IgG oxLDL. Using PLS we have constructed a data-driven nomothetic network which ensembled causome (increased plasma LPS load), adverse outcome pathways (namely neuro-affective toxicity), and clinical phenome features of mood disorders in a data-driven model. Based on those feature sets, cluster analysis discovered a new diagnostic class characterized by increased plasma LPS load, peroxides, autoimmune responses to OSENO, and increased phenome scores. Using the new nomothetic network approach, we constructed a mechanistically transdiagnostic diagnostic class indicating neuro-affective toxicity in 74.3% of the mood disorder patients.