Scientific Reports (Mar 2024)

From periphery immunity to central domain through clinical interview as a new insight on schizophrenia

  • Wirginia Krzyściak,
  • Marta Szwajca,
  • Natalia Śmierciak,
  • Robert Chrzan,
  • Aleksander Turek,
  • Paulina Karcz,
  • Amira Bryll,
  • Maciej Pilecki,
  • Eva Morava,
  • Anna Ligęzka,
  • Tamas Kozicz,
  • Paulina Mazur,
  • Bogna Batko,
  • Anna Skalniak,
  • Tadeusz Popiela

DOI
https://doi.org/10.1038/s41598-024-56344-3
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 13

Abstract

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Abstract Identifying disease predictors through advanced statistical models enables the discovery of treatment targets for schizophrenia. In this study, a multifaceted clinical and laboratory analysis was conducted, incorporating magnetic resonance spectroscopy with immunology markers, psychiatric scores, and biochemical data, on a cohort of 45 patients diagnosed with schizophrenia and 51 healthy controls. The aim was to delineate predictive markers for diagnosing schizophrenia. A logistic regression model was used, as utilized to analyze the impact of multivariate variables on the prevalence of schizophrenia. Utilization of a stepwise algorithm yielded a final model, optimized using Akaike’s information criterion and a logit link function, which incorporated eight predictors (White Blood Cells, Reactive Lymphocytes, Red Blood Cells, Glucose, Insulin, Beck Depression score, Brain Taurine, Creatine and Phosphocreatine concentration). No single factor can reliably differentiate between healthy patients and those with schizophrenia. Therefore, it is valuable to simultaneously consider the values of multiple factors and classify patients using a multivariate model.