Enrico Paolo, Delvecchio Giuseppe, Turtulici Nunzio, Aronica Rosario, Pigoni Alessandro, Squarcina Letizia, Villa Filippo M, Perlini Cinzia, Rossetti Maria G, Bellani Marcella, Lasalvia Antonio, Bonetto Chiara, Scocco Paolo, D'Agostino Armando, Torresani Stefano, Imbesi Massimiliano, Bellini Francesca, Veronese Angelo, Bocchio-Chiavetto Luisella, Gennarelli Massimo, Balestrieri Matteo, Colombo Gualtiero I, Finardi Annamaria, Ruggeri Mirella, Furlan Roberto, Brambilla Paolo
Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.
Mol Psychiatry. 2023 Mar;28(3):1190-1200. doi: 10.1038/s41380-022-01911-1. Epub 2023 Jan 6.
Psychosis onset is a transdiagnostic event that leads to a range of psychiatric disorders, which are currently diagnosed through clinical observation. The integration of multimodal biological data could reveal different subtypes of psychosis onset to target for the personalization of care. In this study, we tested the existence of subgroups of patients affected by first-episode psychosis (FEP) with a possible immunopathogenic basis. To do this, we designed a data-driven unsupervised machine learning model to cluster a sample of 127 FEP patients and 117 healthy controls (HC), based on the peripheral blood expression levels of 12 psychosis-related immune gene transcripts. To validate the model, we applied a resampling strategy based on the half-splitting of the total sample with random allocation of the cases. Further, we performed a post-hoc univariate analysis to verify the clinical, cognitive, and structural brain correlates of the subgroups identified. The model identified and validated two distinct clusters: 1) a FEP cluster characterized by the high expression of inflammatory and immune-activating genes (IL1B, CCR7, IL12A and CXCR3); 2) a cluster consisting of an equal number of FEP and HC subjects, which did not show a relative over or under expression of any immune marker (balanced subgroup). None of the subgroups was related to specific symptoms dimensions or longitudinal diagnosis of affective vs non-affective psychosis. FEP patients included in the balanced immune subgroup showed a thinning of the left supramarginal and superiorfrontal cortex (FDR-adjusted p-values < 0.05). Our results demonstrated the existence of a FEP patients' subgroup identified by a multivariate pattern of immunomarkers involved in inflammatory activation. This evidence may pave the way to sample stratification in clinical studies aiming to develop diagnostic tools and therapies targeting specific immunopathogenic pathways of psychosis.
精神病发作是一种跨诊断事件，会导致一系列精神疾病，目前这些疾病是通过临床观察来诊断的。多模态生物数据的整合可以揭示精神病发作的不同亚型，从而为个性化治疗提供目标。在本研究中，我们测试了可能存在免疫致病基础的首发精神病（FEP）患者亚组。为此，我们设计了一种数据驱动的无监督机器学习模型，根据12种与精神病相关的免疫基因转录本在外周血中的表达水平，对127例FEP患者和117例健康对照（HC）进行聚类。为了验证该模型，我们采用了一种基于总样本二分法并随机分配病例的重采样策略。此外，我们进行了事后单变量分析，以验证所识别亚组的临床、认知和脑结构相关性。该模型识别并验证了两个不同的聚类：1）一个以炎症和免疫激活基因（IL1B、CCR7、IL12A和CXCR3）高表达为特征的FEP聚类；2）一个由数量相等的FEP患者和HC受试者组成的聚类，该聚类未显示任何免疫标志物的相对过表达或低表达（平衡亚组）。没有一个亚组与特定的症状维度或情感性与非情感性精神病的纵向诊断相关。纳入平衡免疫亚组的FEP患者左侧缘上回和额上回皮质变薄（FDR校正p值<0.05）。我们的结果表明，存在一个由参与炎症激活的免疫标志物多变量模式识别出的FEP患者亚组。这一证据可能为临床研究中的样本分层铺平道路，这些研究旨在开发针对精神病特定免疫致病途径的诊断工具和治疗方法。
