Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
Nature. 2013 Jun 6;498(7452):99-103. doi: 10.1038/nature12198. Epub 2013 May 29.
Type 2 diabetes (T2D) is a result of complex gene-environment interactions, and several risk factors have been identified, including age, family history, diet, sedentary lifestyle and obesity. Statistical models that combine known risk factors for T2D can partly identify individuals at high risk of developing the disease. However, these studies have so far indicated that human genetics contributes little to the models, whereas socio-demographic and environmental factors have greater influence. Recent evidence suggests the importance of the gut microbiota as an environmental factor, and an altered gut microbiota has been linked to metabolic diseases including obesity, diabetes and cardiovascular disease. Here we use shotgun sequencing to characterize the faecal metagenome of 145 European women with normal, impaired or diabetic glucose control. We observe compositional and functional alterations in the metagenomes of women with T2D, and develop a mathematical model based on metagenomic profiles that identified T2D with high accuracy. We applied this model to women with impaired glucose tolerance, and show that it can identify women who have a diabetes-like metabolism. Furthermore, glucose control and medication were unlikely to have major confounding effects. We also applied our model to a recently described Chinese cohort and show that the discriminant metagenomic markers for T2D differ between the European and Chinese cohorts. Therefore, metagenomic predictive tools for T2D should be specific for the age and geographical location of the populations studied.
2 型糖尿病(T2D)是复杂的基因-环境相互作用的结果,已经确定了几个风险因素,包括年龄、家族史、饮食、 sedentary lifestyle 和肥胖。将已知的 T2D 风险因素结合起来的统计模型可以部分识别出患有该疾病风险较高的个体。然而,这些研究表明,人类遗传学对这些模型的贡献很小,而社会人口统计学和环境因素的影响更大。最近的证据表明肠道微生物群作为环境因素的重要性,而肠道微生物群的改变与代谢疾病有关,包括肥胖、糖尿病和心血管疾病。在这里,我们使用 shotgun 测序来描述 145 名欧洲女性的粪便宏基因组,这些女性的血糖控制正常、受损或患有糖尿病。我们观察到 T2D 女性的宏基因组在组成和功能上发生了改变,并基于宏基因组图谱开发了一个能够高度准确识别 T2D 的数学模型。我们将该模型应用于糖耐量受损的女性,表明它可以识别出具有类似糖尿病代谢的女性。此外,血糖控制和药物治疗不太可能产生重大混杂影响。我们还将我们的模型应用于最近描述的中国队列,并表明 T2D 的判别宏基因组标志物在欧洲和中国队列之间存在差异。因此,T2D 的宏基因组预测工具应该针对所研究人群的年龄和地理位置特异性。