Department of Chemistry, College of Science, University of Kufa, Kufa, Iraq.
Medical Laboratory Technology Department, College of Medical Technology, The Islamic University, Najaf, Iraq.
Curr Top Med Chem. 2020;20(9):747-758. doi: 10.2174/1568026620666200128143948.
In schizophrenia, pathway-genotypes may be constructed by combining interrelated immune biomarkers with changes in specific neurocognitive functions that represent aberrations in brain neuronal circuits. These constructs provide an insight on the phenome of schizophrenia and show how pathway-phenotypes mediate the effects of genome X environmentome interactions on the symptomatology/phenomenology of schizophrenia. Nevertheless, there is a lack of knowledge how to construct pathway-phenotypes using Partial Least Squares (PLS) path modeling and Soft Independent Modeling of Class Analogy (SIMCA).
This paper aims to provide a step-by-step utilization guide for the construction of pathwayphenotypes that reflect aberrations in the neuroimmune - brain circuit axis (NIBCA) in deficit schizophrenia.
This NIBCA index is constructed using immune biomarkers (CCL-2, CCL-11, IL-1β, sIL-1RA, TNF-α, sTNFR1, sTNFR2) and neurocognitive tests (Brief Assessment of Cognition in Schizophrenia) predicting overall severity of schizophrenia (OSOS) in 120 deficit SCZ and 54 healthy participants. Using SmartPLS path analysis, a latent vector is extracted from those biomarkers and cognitive tests, which shows good construct reliability (Cronbach alpha and composite reliability) and replicability and which is reflectively measured through its NIBCA manifestations. This NIBCA pathwayphenotype explains 75.0% of the variance in PHEMN (psychotic, hostility, excitation, mannerism and negative) symptoms. Using SIMCA, we constructed a NIBCA pathway-class that defines deficit schizophrenia as a qualitatively distinct nosological entity, which allows patients with deficit schizophrenia to be authenticated as belonging to the deficit schizophrenia class.
In conclusion, our nomothetic approach to develop a nomological network combining neuro-immune and neurocognitive phenome markers to predict OSOS and cross-validate a diagnostic class generated replicable models reflecting the key phenome of the illness, which may mediate the effects of genome X environmentome interactions on the final outcome phenome features, namely symptomatology and phenomenology.
在精神分裂症中，途径-基因型可以通过将相关的免疫生物标志物与特定神经认知功能的变化相结合来构建，这些变化代表了大脑神经元回路的异常。这些构建提供了对精神分裂症表型的深入了解，并展示了途径表型如何介导基因组 X 环境组相互作用对精神分裂症症状/表型的影响。然而，目前还缺乏使用偏最小二乘法（PLS）路径建模和软独立建模分类分析（SIMCA）构建途径表型的知识。
本文旨在为构建反映神经免疫-大脑回路轴（NIBCA）异常的缺陷型精神分裂症途径表型提供一步一步的实用指南。
本研究使用免疫生物标志物（CCL-2、CCL-11、IL-1β、sIL-1RA、TNF-α、sTNFR1、sTNFR2）和神经认知测试（Brief Assessment of Cognition in Schizophrenia）来构建这个 NIBCA 指数，这些测试预测了 120 名缺陷型精神分裂症患者和 54 名健康参与者的整体严重程度（OSOS）。使用 SmartPLS 路径分析，从这些生物标志物和认知测试中提取出一个潜在向量，该向量显示出良好的结构可靠性（Cronbach alpha 和综合可靠性）和可重复性，并通过其 NIBCA 表现进行反射性测量。这个 NIBCA 途径表型解释了 PHEMN（精神病性、敌意、兴奋、刻板和阴性）症状的 75.0%方差。使用 SIMCA，我们构建了一个 NIBCA 途径分类，将缺陷型精神分裂症定义为一种具有定性区别的分类实体，这使得缺陷型精神分裂症患者能够被认证为属于缺陷型精神分裂症类别。
总之，我们采用了一种唯象的方法，结合神经免疫和神经认知表型标志物来开发一个预测 OSOS 的同名网络，并对一个产生可复制模型的诊断类别进行交叉验证，这些模型反映了疾病的关键表型，这可能会调节基因组 X 环境组相互作用对最终结果表型特征的影响，即症状和表型。
