Thomsen Soren K, Gloyn Anna L
Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford, OX3 7LE, UK.
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
Diabetologia. 2017 Jun;60(6):960-970. doi: 10.1007/s00125-017-4270-y. Epub 2017 Apr 26.
Type 2 diabetes is a global epidemic with major effects on healthcare expenditure and quality of life. Currently available treatments are inadequate for the prevention of comorbidities, yet progress towards new therapies remains slow. A major barrier is the insufficiency of traditional preclinical models for predicting drug efficacy and safety. Human genetics offers a complementary model to assess causal mechanisms for target validation. Genetic perturbations are 'experiments of nature' that provide a uniquely relevant window into the long-term effects of modulating specific targets. Here, we show that genetic discoveries over the past decades have accurately predicted (now known) therapeutic mechanisms for type 2 diabetes. These findings highlight the potential for use of human genetic variation for prospective target validation, and establish a framework for future applications. Studies into rare, monogenic forms of diabetes have also provided proof-of-principle for precision medicine, and the applicability of this paradigm to complex disease is discussed. Finally, we highlight some of the limitations that are relevant to the use of genome-wide association studies (GWAS) in the search for new therapies for diabetes. A key outstanding challenge is the translation of GWAS signals into disease biology and we outline possible solutions for tackling this experimental bottleneck.
2型糖尿病是一种全球性流行病,对医疗保健支出和生活质量有重大影响。目前可用的治疗方法不足以预防合并症,但新疗法的进展仍然缓慢。一个主要障碍是传统临床前模型在预测药物疗效和安全性方面存在不足。人类遗传学提供了一个补充模型,用于评估靶点验证的因果机制。基因扰动是“自然实验”,为了解调节特定靶点的长期影响提供了一个独特的相关窗口。在这里,我们表明,过去几十年的基因发现准确地预测了(现在已知的)2型糖尿病的治疗机制。这些发现突出了利用人类基因变异进行前瞻性靶点验证的潜力,并建立了未来应用的框架。对罕见的单基因糖尿病形式的研究也为精准医学提供了原理证明,并讨论了该范式在复杂疾病中的适用性。最后,我们强调了与在寻找糖尿病新疗法中使用全基因组关联研究(GWAS)相关的一些局限性。一个关键的突出挑战是将GWAS信号转化为疾病生物学,我们概述了应对这一实验瓶颈的可能解决方案。