Remodeling adipose tissue through in silico modulation of fat storage for the prevention of type 2 diabetes

et al. 2020

Preprint

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.

Section: Cluster-based Genome-scale Metabolic Modelingmentioning

confidence: 99%

Unraveling the molecular heterogeneity in type 2 diabetes: A potential subtype discovery followed by metabolic modeling

et al. 2020

Preprint

“…The objective function was set to maximize flux through the production of mitochondrial ATP. In addition, to ensure the viability of the cell, the lower bound of biomass reaction was set to 0.8 of the maximum amount of biomass production in the healthy model [46]. Flux variability analysis (FVA) for each model was applied to get the minimum and maximum possible flux of each reaction using the Cobra Toolbox [47].…”

Section: Cluster-based Genome-scale Metabolic Modelingmentioning

confidence: 99%

Unraveling the Molecular Heterogeneity in type 2 Diabetes: A Potential Subtype Discovery Study Followed by Metabolic Modeling

et al. 2020

Preprint

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 attempt to provide a better understanding of abnormalities in this tissue. We have explored the muscle gene expression pattern in healthy and newly-diagnosed T2DM individuals using supervised and unsupervised classification along with the discovery of potential subtypes of type 2 diabetes.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 of 26 genes identified which was able to discriminate healthy from diabetic individuals with the 94% accuracy after 10-fold stratified cross-validation. In addition, three distinct potential subtypes 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. Therefore, subtyping of T2DM patients 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.

“…Besides, to ensure the viability of the cell, the lower bound of biomass reaction was set to 0.8 of the maximum amount of biomass production in the healthy model [21]. FVA for each model was applied to get the minimum and maximum possible uxes of each reaction using the Cobra Toolbox version 3.0 [22].…”

Section: Cluster-based Genome-scale Metabolic Modelingmentioning

confidence: 99%

Unraveling the molecular heterogeneity in type 2 diabetes: A potential subtype discovery study followed by metabolic modeling

et al. 2020

Preprint

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.