Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden.
Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K.
Diabetes. 2018 Oct;67(10):1911-1922. doi: 10.2337/dbi17-0045.
The detailed characterization of human biology and behaviors is now possible at scale owing to innovations in biomarkers, bioimaging, and wearable technologies; "big data" from electronic medical records, health insurance databases, and other platforms becoming increasingly accessible; and rapidly evolving computational power and bioinformatics methods. Collectively, these advances are creating unprecedented opportunities to better understand diabetes and many other complex traits. Identifying hidden structures within these complex data sets and linking these structures to outcome data may yield unique insights into the risk factors and natural history of diabetes, which in turn may help optimize the prevention and management of the disease. This emerging area is broadly termed "precision medicine." In this Perspective, we give an overview of the evidence and barriers to the development and implementation of precision medicine in type 2 diabetes. We also discuss recently presented paradigms through which complex data might enhance our understanding of diabetes and ultimately our ability to tackle the disease more effectively than ever before.
由于生物标志物、生物成像和可穿戴技术的创新，人类生物学和行为的详细特征现在可以大规模进行描述；电子病历、健康保险数据库和其他平台的“大数据”越来越容易获得；计算能力和生物信息学方法也在迅速发展。这些进展共同为更好地了解糖尿病和许多其他复杂特征创造了前所未有的机会。在这些复杂的数据集内识别隐藏结构，并将这些结构与结果数据联系起来，可能会深入了解糖尿病的危险因素和自然病史，从而有助于优化疾病的预防和管理。这一新兴领域通常被称为“精准医学”。在本观点中，我们概述了在 2 型糖尿病中开发和实施精准医学的证据和障碍。我们还讨论了最近提出的通过复杂数据增强我们对糖尿病的理解并最终提高我们有效应对该疾病的能力的范例。
