Physiosomatic symptoms are an important part of schizophrenia phenomenology. The aim of this study is to examine the biomarker, neurocognitive and symptomatic correlates of physiosomatic symptoms in schizophrenia. We recruited 115 schizophrenia patients and 43 healthy controls and measured the Fibromyalgia and Chronic Fatigue Syndrome Rating (FF) scale, schizophrenia symptom dimensions, and the Brief Assessment of Cognition in Schizophrenia. We measured neuro-immune markers including plasma CCL11 (eotaxin), interleukin-(IL)-6, IL-10, Dickkopf protein 1 (DKK1), high mobility group box 1 protein (HMGB1) and endogenous opioid system (EOS) markers including κ-opioid receptor (KOR), μ-opioid receptor (MOR), endomorphin-2 (EM2) and β-endorphin. Patients with an increased FF score display increased ratings of psychosis, hostility, excitement, formal though disorders, psycho-motor retardation and negative symptoms as compared with patients with lower FF scores. A large part of the variance in the FF score (55.1%) is explained by the regression on digit sequencing task, token motor task, list learning, IL-10, age (all inversely) and IL-6 (positively). Neural network analysis shows that the top-6 predictors of the FF score are (in descending order): IL-6, HMGB1, education, MOR, KOR and IL-10. We found that 45.1% of the variance in a latent vector extracted from cognitive test scores, schizophrenia symptoms and the FF score was explained by HMGB1, MOR, EM2, DKK1, and CCL11. Physiosomatic symptoms are an integral part of the phenome of schizophrenia. Neurotoxic immune pathways and lowered immune regulation coupled with alterations in the EOS appear to drive the physiosomatic symptoms of schizophrenia.

生理症状是精神分裂症现象学的重要组成部分。本研究的目的是检查精神分裂症生理症状的生物标志物、神经认知和症状相关性。我们招募了 115 名精神分裂症患者和 43 名健康对照者，并测量了纤维肌痛和慢性疲劳综合征评分 (FF) 量表、精神分裂症症状维度和精神分裂症认知的简要评估。我们测量了神经免疫标志物，包括血浆 CCL11（eotaxin）、白细胞介素-（IL）-6、IL-10、Dickkopf 蛋白 1（DKK1）、高迁移率族框 1 蛋白（HMGB1）和内源性阿片系统（EOS）标志物，包括κ-阿片受体 (KOR)、μ-阿片受体 (MOR)、内吗啡肽-2 (EM2) 和 β-内啡肽。FF 评分增加的患者表现出精神病、敌意、兴奋、与 FF 评分较低的患者相比，形式上的障碍、精神运动迟缓和阴性症状。FF 分数的很大一部分差异 (55.1%) 可以通过对数字排序任务、标记运动任务、列表学习、IL-10、年龄（都成反比）和 IL-6（正）的回归来解释。神经网络分析显示，FF 得分的前 6 个预测因子是（按降序排列）：IL-6、HMGB1、教育、MOR、KOR 和 IL-10。我们发现从认知测试分数、精神分裂症症状和 FF 分数中提取的潜在向量中 45.1% 的方差是由 HMGB1、MOR、EM2、DKK1 和 CCL11 解释的。生理症状是精神分裂症现象的一个组成部分。