Background

Functional outcomes are important measures in the overall clinical course of psychosis and individuals at clinical high-risk (CHR), however, prediction of functional outcome remains difficult based on clinical information alone. In the first part of this study, we evaluated whether a combination of biological and clinical variables could predict future functional outcome in CHR individuals. The complement and coagulation pathways have previously been identified as being of relevance to the pathophysiology of psychosis and have been found to contribute to the prediction of clinical outcome in CHR participants. Hence, in the second part we extended the analysis to evaluate specifically the relationship of complement and coagulation proteins with psychotic symptoms and functional outcome in CHR.

Materials and methods

We carried out plasma proteomics and measured plasma cytokine levels, and erythrocyte membrane fatty acid levels in a sub-sample (n = 158) from the NEURAPRO clinical trial at baseline and 6 months follow up. Functional outcome was measured using Social and Occupational Functional assessment Score (SOFAS) scale. Firstly, we used support vector machine learning techniques to develop predictive models for functional outcome at 12 months. Secondly, we developed linear regression models to understand the association between 6-month follow-up levels of complement and coagulation proteins with 6-month follow-up measures of positive symptoms summary (PSS) scores and functional outcome.

Results and conclusion

A prediction model based on clinical and biological data including the plasma proteome, erythrocyte fatty acids and cytokines, poorly predicted functional outcome at 12 months follow-up in CHR participants. In linear regression models, four complement and coagulation proteins (coagulation protein X, Complement C1r subcomponent like protein, Complement C4A & Complement C5) indicated a significant association with functional outcome; and two proteins (coagulation factor IX and complement C5) positively associated with the PSS score. Our study does not provide support for the utility of cytokines, proteomic or fatty acid data for prediction of functional outcomes in individuals at high-risk for psychosis. However, the association of complement protein levels with clinical outcome suggests a role for the complement system and the activity of its related pathway in the functional impairment and positive symptom severity of CHR patients.

中文翻译：

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基于机器学习的预测和补体的影响——凝血途径蛋白对临床结果的影响：来自 NEURAPRO 试验的结果

背景

功能结果是精神病和临床高危 (CHR) 个体整个临床过程中的重要衡量标准，然而，仅基于临床信息预测功能结果仍然很困难。在本研究的第一部分，我们评估了生物学和临床变量的组合是否可以预测 CHR 个体的未来功能结果。补体和凝血途径先前已被确定为与精神病的病理生理学相关，并且已被发现有助于预测 CHR 参与者的临床结果。因此，在第二部分中，我们扩展了分析，以专门评估补体和凝血蛋白与 CHR 中精神病症状和功能结果的关系。

材料和方法

我们在基线和 6 个月随访时对来自 NEURAPRO 临床试验的子样本 (n = 158) 进行了血浆蛋白质组学并测量了血浆细胞因子水平和红细胞膜脂肪酸水平。使用社会和职业功能评估评分（SOFAS）量表测量功能结果。首先，我们使用支持向量机学习技术来开发 12 个月时功能结果的预测模型。其次，我们开发了线性回归模型来了解补体和凝血蛋白的 6 个月随访水平与阳性症状总结 (PSS) 评分和功能结果的 6 个月随访测量之间的关联。

结果和结论

基于包括血浆蛋白质组、红细胞脂肪酸和细胞因子在内的临床和生物学数据的预测模型，在 CHR 参与者的 12 个月随访中预测功能结果不佳。在线性回归模型中，四种补体和凝血蛋白（凝血蛋白 X、补体 C1r 亚组分样蛋白、补体 C4A 和补体 C5）表明与功能结果显着相关；和两种蛋白质（凝血因子 IX 和补体 C5）与 PSS 评分呈正相关。我们的研究不支持使用细胞因子、蛋白质组学或脂肪酸数据来预测精神病高风险个体的功能结果。然而，