In Silico Metabolic Modeling Reveals Potential Muscle Metabolite Markers of Insulin Resistance in Newly Diagnosed Diabetic Patients

Abstract 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.