Background: 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. Methods: 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. Results: 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. Conclusions: 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.

BackgroundType 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. MethodsThe 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.ResultsA 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. Conclusions 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.

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.

Background Type 2 diabetes mellitus (T2DM) is a challenging and globally ubiquitous metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a great role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose hemostasis and the development of insulin resistance and T2DM. Here, we attempted to find metabolic dysregulations that are associated with the onset of T2DM. Besides, metabolite markers of T2DM were explored. Methods We reconstructed a human muscle-specific metabolic model and applied it to perform metabolic analysis in newly diagnosed T2DM patients. We investigated the metabolism reprogramming by using two topology-based and constraint-based approach. Moreover, we applied a machine learning method to predict potential metabolite markers of insulin resistance in muscle.Results Our results showed that metabolic alterations have occurred in carbohydrate, fatty acids, lipids, amino acids, and inositol phosphate metabolisms as well as pathways implicated in building extracellular matrix (ECM). Also, dysregulation of coenzyme Q10 metabolism was observed. Moreover, 13 exchange metabolites were predicted as the potential metabolite markers of insulin resistance in skeletal muscle. The efficiency of these markers in detecting insulin-resistant muscle was validated using a separate muscle gene expression data from another diabetes-related study. Conclusion In this study, the most updated muscle-specific metabolic model was generated and successfully was validated. This model was used for the investigation of metabolic disturbances at the onset of T2DM. Our results indicated the significance of ECM metabolites in insulin resistance, and reinforce the role of coenzyme Q10 as a candidate for further research in insulin resistance and T2DM treatment. The model is freely available and can be used for other muscle metabolic studies. We also predicted metabolite markers of insulin resistance in the skeletal muscle, which can be considered for further empirical investigations.

Background Type 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. Methods We have explored the muscle gene expression pattern in healthy and newly diagnosed T2DM individuals using supervised and unsupervised classification along with examining the potential of sub-typing based on the gene expression pattern in patients.Results A machine-learning technique applied to identify a pattern of gene expression that could potentially discriminate between normoglycemic and diabetic groups. A gene set comprises 26 genes identified, which was able to discriminate healthy from diabetic individuals with 94% accuracy after 10-fold stratified cross-validation. In addition, three distinct clusters with different dysregulated genes and metabolic pathways identified in diabetic patients. Conclusion This study implies that it seems the disease has triggered through different cellular/molecular mechanisms and it has the potential to be sub-typing. Possibly, subtyping of T2DM patients in combination with their real clinical profiles will provide a better understanding of abnormalities in each group and lead to the recommendation of the appropriate precision therapy for each subtype in the future.