Department of Biomedical Sciences, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Machine Learning Lab, Data Science Center in Health, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Nat Med. 2024 Apr;30(4):1143-1153. doi: 10.1038/s41591-024-02843-9. Epub 2024 Mar 12.
Neurodegenerative disorders exhibit considerable clinical heterogeneity and are frequently misdiagnosed. This heterogeneity is often neglected and difficult to study. Therefore, innovative data-driven approaches utilizing substantial autopsy cohorts are needed to address this complexity and improve diagnosis, prognosis and fundamental research. We present clinical disease trajectories from 3,042 Netherlands Brain Bank donors, encompassing 84 neuropsychiatric signs and symptoms identified through natural language processing. This unique resource provides valuable new insights into neurodegenerative disorder symptomatology. To illustrate, we identified signs and symptoms that differed between frequently misdiagnosed disorders. In addition, we performed predictive modeling and identified clinical subtypes of various brain disorders, indicative of neural substructures being differently affected. Finally, integrating clinical diagnosis information revealed a substantial proportion of inaccurately diagnosed donors that masquerade as another disorder. The unique datasets allow researchers to study the clinical manifestation of signs and symptoms across neurodegenerative disorders, and identify associated molecular and cellular features.
神经退行性疾病表现出相当大的临床异质性，并且经常被误诊。这种异质性常常被忽视，难以研究。因此，需要创新的数据驱动方法，利用大量的尸检队列来解决这种复杂性，并改善诊断、预后和基础研究。我们展示了来自 3042 名荷兰脑库捐赠者的临床疾病轨迹，这些捐赠者通过自然语言处理确定了 84 种神经精神体征和症状。这一独特的资源为神经退行性疾病的症状学提供了有价值的新见解。例如，我们确定了经常被误诊的疾病之间存在差异的体征和症状。此外，我们进行了预测建模，并确定了各种脑疾病的临床亚型，表明不同的神经亚结构受到不同的影响。最后，整合临床诊断信息揭示了相当一部分被误诊为另一种疾病的捐赠者。这些独特的数据集使研究人员能够研究神经退行性疾病中各种体征和症状的临床表现，并确定相关的分子和细胞特征。
