Heterogeneity in host and gut microbiota hampers microbial precision intervention of type 2 diabetes mellitus (T2DM). Here, we investigate novel features for patient-stratification and bacterial modulators for intervention, using cross-sectional patient cohorts and animal experiments. We collected stool/blood/urine samples from 103 recent-onset T2DM patients and 25 healthy controls (HCs), performed gut microbial composition/metabolite profiling, and combined it with host-transcriptome/metabolome/cytokines and clinical data. Stool-type (dry/loose-stool), a feature of stool-microenvironment recently explored in microbiome studies, was used for T2DM patientstratification as it explained most of the variation in multi-omics dataset among all clinical parameters in our covariate analysis. T2DM with dry-stool (DM-DS) and loose-stool (DM-LS) were clearly differentiated from HC and each other by LightGBM-models, optimal among multiple machine-learning models. Compared to DM-DS, DM-LS exhibited discordant gut microbial taxonomic and functional profiles, severe host metabolic disorder, and excessive insulin secretion. Further cross-measurement-association-analysis linked the differential microbial profiles, in particular Blautia abundances, to T2DM phenotypes in our stratified multi-omics dataset. Notably, oral supplementation of Blautia to T2DM mice induced inhibitory effects on lipid accumulation, weight gain, and blood-glucose elevation with simultaneous modulation of gut bacterial composition, revealing the therapeutic potential of Blautia. Our study highlights the clinical implications of stool-microenvironment stratification and Blautia supplementation in T2DM, offering promising prospects for microbial precision treatment of metabolic diseases.

中文翻译：

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对患者队列进行粪便类型分层的多组学分析以及将 Blautia 鉴定为 T2DM 潜在细菌调节剂


宿主和肠道微生物群的异质性阻碍了 2 型糖尿病 (T2DM) 的微生物精准干预。在这里，我们利用横断面患者队列和动物实验研究患者分层和细菌调节剂干预的新特征。我们收集了 103 名新发 T2DM 患者和 25 名健康对照 (HC) 的粪便/血液/尿液样本，进行肠道微生物组成/代谢物分析，并将其与宿主转录组/代谢组/细胞因子和临床数据相结合。粪便类型（干燥/松散粪便）是最近在微生物组研究中探索的粪便微环境的一个特征，被用于 T2DM 患者分层，因为它解释了我们协变量分析中所有临床参数中多组学数据集的大部分变化。 LightGBM 模型将干便 (DM-DS) 和稀便 (DM-LS) 的 T2DM 与 HC 以及彼此之间明显区分开来，该模型在多个机器学习模型中是最佳的。与 DM-DS 相比，DM-LS 表现出不一致的肠道微生物分类和功能特征、严重的宿主代谢紊乱和胰岛素分泌过多。进一步的交叉测量关联分析将差异微生物特征（特别是 Blautia 丰度）与我们的分层多组学数据集中的 T2DM 表型联系起来。值得注意的是，对 T2DM 小鼠口服补充 Blautia 可诱导对脂质积累、体重增加和血糖升高的抑制作用，同时调节肠道细菌组成，揭示了 Blautia 的治疗潜力。我们的研究强调了粪便微环境分层和 Blautia 补充剂在 T2DM 中的临床意义，为代谢性疾病的微生物精准治疗提供了广阔的前景。