Department of Medical Diagnostic, Faculty of Pharmacy, Jagiellonian University Medical College, 30-688, Krakow, Poland.
Department of Child and Adolescent Psychiatry, Faculty of Medicine, Jagiellonian University Medical College, 31-501, Krakow, Poland.
Sci Rep. 2024 Mar 8;14(1):5755. doi: 10.1038/s41598-024-56344-3.
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.
通过先进的统计模型来识别疾病预测因子，可以发现精神分裂症的治疗靶点。在这项研究中，对 45 名被诊断患有精神分裂症的患者和 51 名健康对照者进行了多方面的临床和实验室分析，包括磁共振波谱与免疫学标志物、精神病学评分和生化数据。目的是描绘出用于诊断精神分裂症的预测标志物。使用了逻辑回归模型，用于分析多变量变量对精神分裂症患病率的影响。使用逐步算法得出了最终模型，该模型使用赤池信息量准则和对数联系函数进行了优化，其中包含了 8 个预测因子（白细胞、反应性淋巴细胞、红细胞、血糖、胰岛素、贝克抑郁评分、脑牛磺酸、肌酸和磷酸肌酸浓度）。没有单一因素可以可靠地区分健康患者和精神分裂症患者。因此，同时考虑多个因素的值并使用多变量模型对患者进行分类是很有价值的。
