ArticleBMC medical genomics2020
Unraveling the molecular heterogeneity in type 2 diabetes: a potential subtype discovery followed by metabolic modeling.
Article in BMC medical genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2025Review
- In-depth urinary and exosome proteome profiling analysis identifies novel biomarkers for diabetic kidney disease.Science China. Life sciences · 2023Article
- Precision subclassification of type 2 diabetes: a systematic review.Communications medicine · 2023Article
- A novel diabetes typology: towards precision diabetology from pathogenesis to treatment.Diabetologia · 2022Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
conclusionsThis 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.
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