JPND: Early Detection of Alzheimer's Disease Subtypes

阿尔茨海默病（AD）是一项全球性的健康和经济负担，目前全世界约有4700万患者。目前尚不存在可证明的改善疾病的治疗方法。将痴呆症患者的发病时间推迟5年，可以使整个欧盟每年的护理费用减少36%，约为880亿欧元。迄今为止，在大多数治疗试验中，阻碍成功结果的一个关键混淆因素是阿尔茨海默病在发病、机制和临床表现上的高度变异。E-DADS旨在通过基于脑成像、认知标记物和流体生物标记物定义AD临床表现的数据驱动亚型来解决这种异质性，这些生物标记物在疾病发病前数年就可以从预测性风险因素（遗传、合共病、生理和生活方式因素）中可靠地识别出来。为了实现这一目标，我们开发了一种新的多视角学习策略，将临床队列中观察到的终末期疾病表现与临床前队列中早期或高危个体的特征以及来自人口或老龄化研究的一般预感染人群联系起来。由于大量人口数据的可用性，丰富表型的AD队列和机器学习的进步，这种方法现在才成为可能。E-DADS独特组装必要的数据和专业知识。识别阿尔茨海默病亚型并在发病前数年预测它们的能力将通过精准医学显著推进阿尔茨海默病的研究和临床管理。首先，它确定了不同的同质群体，揭示了疾病机制的性质和可变性，最终确定了有效的药物靶点。其次，它可以丰富未来的临床试验，针对可能从特定干预措施中受益的特定患者群体。第三，它强调了潜在的生活方式干预，可能会影响或延迟疾病的早期发作。E-DADS提供了基础技术，通过机器学习和大数据分析以及原型软件工具来实现这一目标，从而实现未来的翻译和吸收。

Alzheimer's disease (AD) is a global health and economic burden with currently about 47 million affected individuals worldwide. No provably disease-modifying treatments exist. Delaying disease onset in dementia patients by five years can reduce care costs by 36% about 88B euro per year across the EU. A key confound preventing successful outcomes in most treatment trials to date has been AD's high variation in onset, mechanism, and clinical expression. E-DADS aims to untangle this heterogeneity by defining data-driven subtypes of the clinical manifestation of AD based on brain imaging, cognitive markers, and fluid biomarkers that are robustly identifiable from predictive risk factors (genetics, co-morbidities, physiological and lifestyle factors) years before disease onset. To achieve this we develop a novel multi-view learning strategies that relates end-stage disease manifestations observable in clinical cohorts to features of early-stage or at-risk individuals in preclinical cohorts and the general pre-affected population from population or aging studies. This approach is only possible now due to the availability of large population data, richly phenotyped AD cohorts and advances in machine learning. E-DADS uniquely assembles the necessary data and expertise. The ability to identify AD subtypes and predict them years before onset will significantly advance AD research and clinical management via precision medicine. First, it identifies distinct homogeneous groups, shedding new light on that nature and variability of disease mechanisms ultimately pinpointing effective drug targets. Second, it enables enrichment of future clinical trials for specific groups of patients likely to benefit from a particular intervention. Third, it highlights potential lifestyle interventions that may affect or delay disease onset at very early stages. E-DADS delivers the underpinning technology to achieve this through machine learning and big-data analytics together with a prototype software tool enabling future translation and uptake.