Parkinson's disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end.
帕金森症是老年人常见的神经系统疾病，随着全球老龄化的到来，其发病率和死亡率都在增加。本文在回顾帕金森症转化研究的基础上、从信息学角度提出了帕金森症转化研究的一个新科学研究范式、该范式下的四个研究目标和八个挑战。转化研究的传统范式是：从生物医学实验研究的小数据出发、再到大人群中进行验证；这种范式的失败在于疾病异质性，使得基于小数据的模型往往过度拟合、而不具有泛化能力，无法拓展到大数据应用。新的科学范式是基于大数据来识别关键的、可操作的小数据；我们强调从大数据中寻找可执行的关键小数据并用于疾病管理和预防，如：1）帕金森症的遗传易感性用于危险人群筛查；2）生物标志物的发现用于帕金森症的诊断和分级；3）帕金森症的非遗传危险因素如环境因素等用于减少疾病风险； 4）预防帕金森病的健康生活方式等。最后我们讨论了帕金森症精准医疗和健康管理相关的八个信息学挑战、为帕金森症的转化信息学研究指明了方向。





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