Fröhlich Holger, Bontridder Noémi, Petrovska-Delacréta Dijana, Glaab Enrico, Kluge Felix, Yacoubi Mounim El, Marín Valero Mayca, Corvol Jean-Christophe, Eskofier Bjoern, Van Gyseghem Jean-Marc, Lehericy Stepháne, Winkler Jürgen, Klucken Jochen
Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany.
Bonn-Aachen International Center for IT (b-it), University of Bonn, Bonn, Germany.
Front Neurol. 2022 Feb 28;13:788427. doi: 10.3389/fneur.2022.788427. eCollection 2022.
Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.
近年来，医学领域（传感器设备、特定的智能手机应用程序），尤其是神经学领域，对数字技术的兴趣急剧增加。源自数字技术的定量测量方法可以提供数字生物标志物（DMs），从而能够对疾病症状进行定量和持续监测，甚至在诊所之外也能实现。这包括持续且灵敏地监测治疗反应的可能性，从而为快速调整用药方案提供了机会。此外，数字生物标志物未来可能有助于早期诊断、患者亚组分层以及临床结果预测。因此，数字生物标志物可以补充甚至在某些情况下替代基于检查者的传统结局测量方法以及在脑脊液、血液、尿液、唾液或其他体液中测量的分子生物标志物。总体而言，数字生物标志物在新兴的精准医学领域可能发挥重要作用。然而，要实现这一愿景需要专门的研究。首先，需要开发并应用先进的数据分析方法，从原始信号中提取候选数字生物标志物。其次，这些候选数字生物标志物需要通过以下方式进行验证：（a）证明它们与既定的临床结局测量方法的相关性，以及（b）与既定生物标志物相比，证明它们的诊断和/或预后价值。这些方面再次需要使用包括机器学习在内的先进数据分析方法。此外，必须充分考虑与敏感患者数据的收集和处理以及使用机器学习方法分析这些数据以实现更好的疾病个体化治疗相关的伦理、法律和社会问题。以帕金森病（PD）作为复杂多因素疾病的主要例子，本文的目的是批判性地回顾关于使用数字生物标志物的研究现状，讨论尚未解决的挑战，并突出新出现的方向。
