Lowered Antioxidant Defenses and Increased Oxidative Toxicity Are Hallmarks of Deficit Schizophrenia: a Nomothetic Network Psychiatry Approach

Abstract

There is now evidence that schizophrenia and deficit schizophrenia are neuro-immune conditions and that oxidative stress toxicity (OSTOX) may play a pathophysiological role. Aims of the study: to compare OSTOX biomarkers and antioxidant (ANTIOX) defenses in deficit versus non-deficit schizophrenia. We examined lipid hydroperoxides (LOOH), malondialdehyde (MDA), advanced oxidation protein products (AOPP), sulfhydryl (–SH) groups, paraoxonase 1 (PON1) activity and PON1 Q192R genotypes, and total radical-trapping antioxidant parameter (TRAP) as well as immune biomarkers in patients with deficit (n = 40) and non-deficit (n = 40) schizophrenia and healthy controls (n = 40). Deficit schizophrenia is characterized by significantly increased levels of AOPP and lowered –SH, and PON1 activity, while no changes in the OSTOX/ANTIOX biomarkers were found in non-deficit schizophrenia. An increased OSTOX/ANTIOX ratio was significantly associated with deficit versus non-deficit schizophrenia (odds ratio = 3.15, p ' 0.001). Partial least squares analysis showed that 47.6% of the variance in a latent vector extracted from psychosis, excitation, hostility, mannerism, negative symptoms, psychomotor retardation, formal thought disorders, and neurocognitive test scores was explained by LOOH+AOPP, PON1 genotype + activity, CCL11, tumor necrosis factor (TNF)-α, and IgA responses to neurotoxic tryptophan catabolites (TRYCATs), whereas –SH groups and IgM responses to MDA showed indirect effects mediated by OSTOX and neuro-immune biomarkers. When overall severity of schizophrenia increases, multiple immune and oxidative (especially protein oxidation indicating chlorinative stress) neurotoxicities and impairments in immune-protective resilience become more prominent and shape a distinct nosological entity, namely deficit schizophrenia. The nomothetic network psychiatry approach allows building causal-pathway-phenotype models using machine learning techniques.