Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.
Bonn-Aachen International Center for Information Technology (b-it), University of Bonn, Bonn, Germany.
Front Immunol. 2024 Apr 23;15:1343900. doi: 10.3389/fimmu.2024.1343900. eCollection 2024.
Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.
阿尔茨海默病在全球人口中的患病率不断增加，但目前基于推荐生物标志物的诊断方法仅在专门的诊所可用。由于这些情况，阿尔茨海默病通常被诊断得较晚，而目前可用的治疗方法仅对早期患者有效。基于血液的生物标志物可以填补易于获得和低成本的疾病早期诊断方法的空白。特别是，鉴于最近发现中枢神经系统的免疫细胞与外周免疫细胞之间的交叉对话，基于免疫的血液生物标志物可能是一种很有前途的选择。在这里，我们介绍了阿尔茨海默病中脑-免疫系统交叉对话的研究的最新进展，并回顾了机器学习方法，这些方法可以将多个生物标志物与进一步的信息（例如年龄、性别、APOE 基因型）结合起来，纳入支持早期诊断的预测模型。此外，基于代理的建模等机制建模方法为建模和分析随时间推移的细胞动态提供了可能性。本综述旨在概述与免疫系统相关的基于血液的生物标志物的现状及其在阿尔茨海默病早期诊断中的潜力。
