Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts.
Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
Ann N Y Acad Sci. 2018 Jan;1411(1):140-152. doi: 10.1111/nyas.13588.
Metabolic disorders present a public health challenge of staggering proportions. In diabetes, there is an urgent need to better understand disease heterogeneity, clinical trajectories, and related comorbidities. A pressing and timely question is whether we are ready for precision medicine in diabetes. Some biological insights that have emerged during the last decade have already been used to direct clinical decision making, especially in monogenic forms of diabetes. However, much work is necessary to integrate high-dimensional explorations into complex disease architectures, less penetrant biological alterations, and broader phenotypes, such as type 2 diabetes. In addition, for precision medicine to take hold in diabetes, reproducibility, interpretability, and actionability remain key guiding objectives. In this review, we examine how mounting data sets generated during the last decade to understand biological variability are now inspiring new venues to clarify diabetes nosology and ultimately translate findings into more effective prevention and treatment strategies.
代谢紊乱是一个极其严重的公共健康挑战。在糖尿病中,我们急需更好地理解疾病异质性、临床轨迹和相关合并症。一个紧迫而及时的问题是,我们是否已经为糖尿病的精准医学做好了准备。在过去十年中出现的一些生物学见解已经被用于指导临床决策,特别是在单基因形式的糖尿病中。然而,要将高维探索整合到复杂的疾病结构、低穿透性的生物学改变和更广泛的表型(如 2 型糖尿病)中,还有很多工作要做。此外,为了使精准医学在糖尿病中得以实施,可重复性、可解释性和可操作性仍然是关键的指导目标。在这篇综述中,我们探讨了过去十年中为了理解生物学变异性而产生的大量数据集,这些数据集现在如何激发了新的途径来阐明糖尿病的分类学,并最终将研究结果转化为更有效的预防和治疗策略。