Recognizing and treating the early stages of type 2 diabetes (T2D) is the most cost effective way to decrease prevalence, before heart disease, renal disease, blindness, and limb amputation become inevitable. In this study, we employ high resolution gut microbiome metatranscriptomic analysis of stool samples from 53,970 individuals to identify predictive biomarkers of type 2 diabetes progression and potential for diagnosis and treatment response. The richness of the metatranscriptomic data enabled us to develop a T2D risk model to delineate individuals with glycemic dysregulation from those within normal glucose levels, with ROC-AUC of 0.83+/-0.04. This risk score can predict the probability of having insulin dysregulation before detecting high glycated hemoglobin (HbA1c), the standard-of-care marker for prediabetes and diabetes. Additionally, a machine learning model was able to distinguish novel metatranscriptomic features that segregate patients who receive metformin and are able to control their HbA1c from those who do not. These discoveries set the stage for developing multiple therapeutic avenues for prevention and treatment of T2D.

中文翻译：

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肠道微生物组活动可预测大型人群中 2 型糖尿病和二甲双胍控制的风险

在心脏病、肾病、失明和截肢变得不可避免之前，识别和治疗 2 型糖尿病 (T2D) 的早期阶段是降低患病率的最具成本效益的方法。在这项研究中，我们对来自 53,970 名个体的粪便样本进行高分辨率肠道微生物组宏转录组学分析，以确定 2 型糖尿病进展的预测性生物标志物以及诊断和治疗反应的潜力。宏转录组学数据的丰富性使我们能够开发 T2D 风险模型，以从正常血糖水平范围内描绘血糖失调的个体，其 ROC-AUC 为 0.83+/-0.04。该风险评分可以在检测高糖化血红蛋白 (HbA1c)（糖尿病前期和糖尿病的标准治疗标志物）之前预测出现胰岛素失调的可能性。此外，机器学习模型能够区分新的宏转录组学特征，这些特征将接受二甲双胍治疗并能够控制 HbA1c 的患者与未接受二甲双胍治疗的患者区分开来。这些发现为开发预防和治疗 T2D 的多种治疗途径奠定了基础。