Immune System Alterations in Treatment-Resistant Schizophrenia: A Systematic Review of the Current Evidence and...

Treatment-resistant schizophrenia (TRS) remains a significant clinical challenge due to limited therapeutic options and a poor understanding of its underlying biology. Recent findings suggest that immune system dysregulation may play a critical role in the pathophysiology of TRS. This systematic review aimed to synthesize current evidence on immunological abnormalities associated with TRS, with a focus on inflammatory markers, immune cell profiles, and the role of autoantibodies, and to explore their potential utility in diagnosis and treatment. A systematic review of the literature was conducted in accordance with PRISMA guidelines, incorporating clinical, molecular, and translational studies on immunological markers in patients with TRS. Included studies assessed cytokine levels, immune cell phenotypes, autoantibodies, genetic factors, and the effects of immunomodulatory therapies. Emphasis was placed on findings differentiating TRS from treatment-responsive schizophrenia. TRS is associated with distinct immune profiles, including elevated IL-6, IL-8, TNF-α, and sCD25 levels, overexpression of CD33 on monocytes and expansion of CD123+ plasmacytoid dendritic cells. Autoantibodies, particularly those targeting glutamatergic receptors, are more prevalent in TRS and decrease with clozapine treatment. Predictive models using IgM autoantibodies and genetic variants show promise for early identification of at-risk individuals. Emerging immunomodulatory treatments such as rituximab, levamisole, and senolytics are under investigation, offering potential for personalized strategies. Immunological dysfunction represents a reproducible and biologically relevant feature of TRS. Integration of immune biomarkers into clinical practice may enhance diagnostic precision and inform personalized therapeutic approaches. Future research should prioritize standardized biomarker protocols and longitudinal studies to validate causal associations and optimize treatment algorithms.