Abstract
BackgroundType 2 diabetes mellitus (T2DM) is a complex multifactorial disease with a high prevalence in the world. 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, thus plays a critical role in T2DM. Here, we attempted to provide a better understanding of abnormalities in this tissue. MethodsThe muscle gene expression patterns in healthy and newly diagnosed T2DM individuals were explored using supervised and unsupervised classification approach. Moreover, the potential of sub-typing T2DM patients based on the gene expression patterns was evaluated.ResultsA machine-learning technique was applied to identify a gene expression pattern that could discriminate between normoglycemic and diabetic groups. A gene set comprises of 26 genes was found that was able to discriminate healthy from diabetic individuals with 94% accuracy. In addition, three distinct clusters of diabetic patients with different dysregulated genes and metabolic pathways were identified. Conclusions This study implies that it seems the disease has triggered through different cellular/molecular mechanisms, and it has the potential to be categorized in different sub-types. Possibly, subtyping of T2DM patients in combination with their real clinical profiles will provide a better understanding of abnormalities in each group. Thus, this approach will help to recommend the appropriate treatment for each subtype in the future.
Unraveling the molecular heterogeneity in type 2 diabetes: A potential subtype discovery study followed by metabolic modeling
