Shen Zhongxia, Cui Lijun, Mou Shaoqi, Ren Lie, Yuan Yonggui, Shen Xinhua, Li Gang
School of Medicine, Southeast University, Nanjing, China.
Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, China.
Front Psychiatry. 2022 Jun 22;13:881241. doi: 10.3389/fpsyt.2022.881241. eCollection 2022.
S100 calcium-binding protein B (S100B) is a neurotrophic factor that regulates neuronal growth and plasticity by activating astrocytes and microglia through the production of cytokines involved in Generalized Anxiety Disorder (GAD). However, few studies have combined S100B and cytokines to explore their role as neuro-inflammatory biomarkers in GAD.
Serum S100B and cytokines (IL-1β, IL-2, IL-4, and IL-10) of 108 untreated GAD cases and 123 healthy controls (HC) were determined by enzyme-linked immunosorbent assay (ELISA), while Hamilton Anxiety Rating Scale (HAMA) scores and Hamilton Depression Rating Scale (HAMD) scores were measured to evaluate anxiety and depression severity. This was used to help physicians identify persons having GAD. Machine learning techniques were applied for feature ordering of cytokines and S100B and the classification of persons with GAD and HC.
The serum S100B, IL-1β, and IL-2 levels of GAD cases were significantly lower than HC ( < 0.001), and the IL-4 level in persons with GAD was significantly higher than HC ( < 0.001). At the same time, IL-10 had no significant difference between the two groups ( = 0.215). The feature ranking distinguishing GAD from HC using machine learning ranked the features in the following order: IL-2, IL-1β, IL-4, S100B, and IL-10. The accuracy of S100B combined with IL-1β, IL-2, IL-4, and IL-10 in distinguishing persons with GAD from HC was 94.47 ± 2.06% using an integrated back propagation neural network based on a bagging algorithm (BPNN-Bagging).
The serum S-100B, IL-1β, and IL-2 levels in persons with GAD were down-regulated while IL-4 was up-regulated. The combination of S100B and cytokines had a good diagnosis value in determining GAD with an accuracy of 94.47%. Machine learning was a very effective method to study neuro-inflammatory biomarkers interacting with each other and mediated by plenty of factors.
S100钙结合蛋白B（S100B）是一种神经营养因子，通过产生参与广泛性焦虑症（GAD）的细胞因子激活星形胶质细胞和小胶质细胞，从而调节神经元生长和可塑性。然而，很少有研究将S100B和细胞因子结合起来探讨它们作为GAD神经炎症生物标志物的作用。
采用酶联免疫吸附测定（ELISA）法测定108例未经治疗的GAD患者和123例健康对照（HC）的血清S100B和细胞因子（IL-1β、IL-2、IL-4和IL-10），同时测量汉密尔顿焦虑量表（HAMA）评分和汉密尔顿抑郁量表（HAMD）评分以评估焦虑和抑郁严重程度。这有助于医生识别患有GAD的人。应用机器学习技术对细胞因子和S100B进行特征排序，并对GAD患者和HC进行分类。
GAD患者的血清S100B、IL-1β和IL-2水平显著低于HC（<0.001），GAD患者的IL-4水平显著高于HC（<0.001）。同时，两组间IL-10无显著差异（= = 0.215）。使用机器学习区分GAD和HC的特征排名按以下顺序排列这些特征：IL-2、IL-1β、IL-4、S100B和IL-10。使用基于装袋算法的集成反向传播神经网络（BPNN-Bagging），S100B与IL-1β、IL-2、IL-4和IL-10结合区分GAD患者和HC的准确率为94.47±2.06%。
GAD患者血清S-100B、IL-1β和IL-2水平下调，而IL-4上调。S100B与细胞因子的组合在诊断GAD方面具有良好的价值，准确率为94.47%。机器学习是研究由多种因素相互作用和介导的神经炎症生物标志物的一种非常有效的方法。
