TY - JOUR
T1 - From periphery immunity to central domain through clinical interview as a new insight on schizophrenia
AU - Krzyściak, Wirginia
AU - Szwajca, Marta
AU - Śmierciak, Natalia
AU - Chrzan, Robert
AU - Turek, Aleksander
AU - Karcz, Paulina
AU - Bryll, Amira
AU - Pilecki, Maciej
AU - Morava, Eva
AU - Ligęzka, Anna
AU - Kozicz, Tamas
AU - Mazur, Paulina
AU - Batko, Bogna
AU - Skalniak, Anna
AU - Popiela, Tadeusz
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
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U2 - 10.1038/s41598-024-56344-3
DO - 10.1038/s41598-024-56344-3
M3 - Article
C2 - 38459093
AN - SCOPUS:85185949007
SN - 2045-2322
VL - 14
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 5755
ER -