Type 2 diabetes mellitus (T2DM) is a complex multifactorial disease with a high prevalence worldwide. Insulin resistance and impaired insulin secretion are the two major abnormalities in the pathogenesis of T2DM. Skeletal muscle is responsible for over 75% of the glucose uptake and plays a critical role in T2DM. Here, we sought to provide a better understanding of the abnormalities in this tissue. The muscle gene expression patterns were explored in healthy and newly diagnosed T2DM individuals using supervised and unsupervised classification approaches. Moreover, the potential of subtyping T2DM patients was evaluated based on the gene expression patterns. A machine-learning technique was applied to identify a set of genes whose expression patterns could discriminate diabetic subjects from healthy ones. A gene set comprising of 26 genes was found that was able to distinguish healthy from diabetic individuals with 94% accuracy. In addition, three distinct clusters of diabetic patients with different dysregulated genes and metabolic pathways were identified. This study indicates that T2DM is triggered by different cellular/molecular mechanisms, and it can be categorized into different subtypes. Subtyping of T2DM patients in combination with their real clinical profiles will provide a better understanding of the abnormalities in each group and more effective therapeutic approaches in the future.

中文翻译：

---

揭示2型糖尿病的分子异质性：潜在的亚型发现，然后进行代谢建模。

2型糖尿病（T2DM）是一种复杂的多因素疾病，在世界范围内普遍流行。胰岛素抵抗和胰岛素分泌受损是T2DM发病机制中的两个主要异常。骨骼肌占葡萄糖摄取的75％以上，并且在T2DM中起关键作用。在这里，我们试图提供对该组织异常的更好的理解。使用有监督和无监督的分类方法，在健康和新诊断的T2DM患者中探索了肌肉基因的表达模式。此外，根据基因表达模式评估了亚型T2DM患者的潜力。应用了一种机器学习技术来鉴定一组基因，这些基因的表达方式可以将糖尿病受试者与健康受试者区分开。发现包含26个基因的基因集，能够以94％的准确度区分健康人和糖尿病人。另外，鉴定了具有不同失调基因和代谢途径的三个不同的糖尿病患者群。这项研究表明，T2DM是由不同的细胞/分子机制触发的，可以分为不同的亚型。T2DM患者的亚型及其真实的临床资料将为每个组的异常情况提供更好的了解，并在将来提供更有效的治疗方法。这项研究表明，T2DM是由不同的细胞/分子机制触发的，可以分为不同的亚型。T2DM患者的亚型及其真实的临床资料将为每个组的异常情况提供更好的了解，并在将来提供更有效的治疗方法。这项研究表明，T2DM是由不同的细胞/分子机制触发的，可以分为不同的亚型。将T2DM患者的亚型及其真实的临床资料结合起来，将有助于更好地了解各组的异常情况，并在将来提供更有效的治疗方法。