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

Abstract 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 a...

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Main Authors: Maryam Khoshnejat (Author), Kaveh Kavousi (Author), Ali Mohammad Banaei-Moghaddam (Author), Ali Akbar Moosavi-Movahedi (Author)
Format: Book
Published: BMC, 2020-08-01T00:00:00Z.
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LEADER 00000 am a22000003u 4500
001 doaj_f4d65d1de4e94d7d94d375c6b7e7841b
042 |a dc 
100 1 0 |a Maryam Khoshnejat  |e author 
700 1 0 |a Kaveh Kavousi  |e author 
700 1 0 |a Ali Mohammad Banaei-Moghaddam  |e author 
700 1 0 |a Ali Akbar Moosavi-Movahedi  |e author 
245 0 0 |a Unraveling the molecular heterogeneity in type 2 diabetes: a potential subtype discovery followed by metabolic modeling 
260 |b BMC,   |c 2020-08-01T00:00:00Z. 
500 |a 10.1186/s12920-020-00767-0 
500 |a 1755-8794 
520 |a Abstract 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. 
546 |a EN 
690 |a Type 2 diabetes 
690 |a Subtype 
690 |a Classification 
690 |a Clustering 
690 |a Flux variability analysis 
690 |a Muscle 
690 |a Internal medicine 
690 |a RC31-1245 
690 |a Genetics 
690 |a QH426-470 
655 7 |a article  |2 local 
786 0 |n BMC Medical Genomics, Vol 13, Iss 1, Pp 1-12 (2020) 
787 0 |n http://link.springer.com/article/10.1186/s12920-020-00767-0 
787 0 |n https://doaj.org/toc/1755-8794 
856 4 1 |u https://doaj.org/article/f4d65d1de4e94d7d94d375c6b7e7841b  |z Connect to this object online.