Genetic and Epigenetic Aspects of Type 1 Diabetes Mellitus: Modern View on the Problem.
Ildar MinniakhmetovBulat YalaevRita KhusainovaEkaterina BondarenkoGalina MelnichenkoIvan DedovNatalia MokryshevaPublished in: Biomedicines (2024)
Omics technologies accumulated an enormous amount of data that advanced knowledge about the molecular pathogenesis of type 1 diabetes mellitus and identified a number of fundamental problems focused on the transition to personalized diabetology in the future. Among them, the most significant are the following: (1) clinical and genetic heterogeneity of type 1 diabetes mellitus; (2) the prognostic significance of DNA markers beyond the HLA genes; (3) assessment of the contribution of a large number of DNA markers to the polygenic risk of disease progress; (4) the existence of ethnic population differences in the distribution of frequencies of risk alleles and genotypes; (5) the infancy of epigenetic research into type 1 diabetes mellitus. Disclosure of these issues is one of the priorities of fundamental diabetology and practical healthcare. The purpose of this review is the systemization of the results of modern molecular genetic, transcriptomic, and epigenetic investigations of type 1 diabetes mellitus in general, as well as its individual forms. The paper summarizes data on the role of risk HLA haplotypes and a number of other candidate genes and loci, identified through genome-wide association studies, in the development of this disease and in alterations in T cell signaling. In addition, this review assesses the contribution of differential DNA methylation and the role of microRNAs in the formation of the molecular pathogenesis of type 1 diabetes mellitus, as well as discusses the most currently central trends in the context of early diagnosis of type 1 diabetes mellitus.
Keyphrases
- dna methylation
- genome wide
- healthcare
- single molecule
- gene expression
- genome wide association
- copy number
- single cell
- circulating tumor
- electronic health record
- mental health
- cell free
- big data
- cardiovascular disease
- rna seq
- metabolic syndrome
- social media
- machine learning
- transcription factor
- body mass index
- artificial intelligence
- insulin resistance
- health information
- health insurance
- cardiovascular risk factors
- circulating tumor cells
- genome wide association study
- weight loss
- clinical evaluation