Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK.
Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK.
Brain. 2021 Apr 12;144(3):975-988. doi: 10.1093/brain/awaa461.
Dementia is one of the most debilitating aspects of Parkinson's disease. There are no validated biomarkers that can track Parkinson's disease progression, nor accurately identify patients who will develop dementia and when. Understanding the sequence of observable changes in Parkinson's disease in people at elevated risk for developing dementia could provide an integrated biomarker for identifying and managing individuals who will develop Parkinson's dementia. We aimed to estimate the sequence of clinical and neurodegeneration events, and variability in this sequence, using data-driven statistical modelling in two separate Parkinson's cohorts, focusing on patients at elevated risk for dementia due to their age at symptom onset. We updated a novel version of an event-based model that has only recently been extended to cope naturally with clinical data, enabling its application in Parkinson's disease for the first time. The observational cohorts included healthy control subjects and patients with Parkinson's disease, of whom those diagnosed at age 65 or older were classified as having high risk of dementia. The model estimates that Parkinson's progression in patients at elevated risk for dementia starts with classic prodromal features of Parkinson's disease (olfaction, sleep), followed by early deficits in visual cognition and increased brain iron content, followed later by a less certain ordering of neurodegeneration in the substantia nigra and cortex, neuropsychological cognitive deficits, retinal thinning in dopamine layers, and further deficits in visual cognition. Importantly, we also characterize variation in the sequence. We found consistent, cross-validated results within cohorts, and agreement between cohorts on the subset of features available in both cohorts. Our sequencing results add powerful support to the increasing body of evidence suggesting that visual processing specifically is affected early in patients with Parkinson's disease at elevated risk of dementia. This opens a route to earlier and more precise detection, as well as a more detailed understanding of the pathological mechanisms underpinning Parkinson's dementia.
痴呆是帕金森病最具致残性的方面之一。目前尚无经过验证的生物标志物可用于跟踪帕金森病的进展，也无法准确识别哪些患者会发展为痴呆以及何时发展。了解处于痴呆高风险的帕金森病患者中可观察到的疾病变化序列，可能为识别和管理将发展为帕金森痴呆的个体提供综合的生物标志物。我们旨在使用两个独立的帕金森队列中的数据驱动统计建模来估计临床和神经退行性事件的序列及其序列的可变性，重点关注因发病年龄而处于痴呆高风险的患者。我们更新了一种基于事件的模型的新版本，该模型最近才得到扩展，以自然应对临床数据，从而首次在帕金森病中应用该模型。观察性队列包括健康对照者和帕金森病患者，其中被诊断为 65 岁或以上的患者被归类为具有痴呆高风险。该模型估计，处于痴呆高风险的患者的帕金森病进展始于帕金森病的典型前驱特征（嗅觉、睡眠），随后是早期视觉认知缺陷和大脑铁含量增加，随后是黑质和皮质中神经退行性变的顺序不太确定，认知神经心理学缺陷，多巴胺层的视网膜变薄以及视觉认知的进一步缺陷。重要的是，我们还描述了序列的变异性。我们在队列内发现了一致的、经过交叉验证的结果，并且在两个队列中都存在的特征子集上也达成了共识。我们的排序结果为越来越多的证据提供了有力支持，这些证据表明，在处于痴呆高风险的帕金森病患者中，视觉处理特别容易受到早期影响。这为早期、更精确的检测以及对帕金森痴呆基础病理机制的更详细理解开辟了道路。
