Genes predisposing to type 1 diabetes mellitus and pathophysiology: a narrative review

Investigation of missense mutation-related type 1 diabetes mellitus through integrating genomic databases and bioinformatic approach

Though genes are already known to be responsible for type 1 diabetes mellitus (T1DM), the knowledge of missense mutation of that disease gene has still to be under covered. A genomic database and a bioinformatics-based approach are integrated in the present study in order to address this issue. Initially, nine variants associated with T1DM were retrieved from the GWAS catalogue. Different genomic algorithms such as PolyPhen2.0, SNPs and GTEx analyser programs were used to study the structural and functional effects of these mutations. Subsequently, SNPnexus was also employed to understand the effect of these mutations on the function of the expressed protein. Nine missense variants of T1DM were identified using the GWAS catalogue database. Among these nine SNPs, three were predicted to be related to the progression of T1DM disease by affecting the protein level. TYK2 gene variants with SNP rs34536443 were thought to have a probably damaging effect. Meanwhile, both COL4A3 and IFIH1 genes with SNPs rs55703767 and rs35667974, respectively, might alter protein function through a possibly damaging prediction. Among the variants of the three genes, the TYK2 gene with SNP rs34536443 had the strongest contribution in affecting the development of T1DM, with a score of 0.999. We sincerely hope that the results could be of immense importance in understanding the genetic basis of T1DM.

Gamma-aminobutyric acid for delaying type 1 diabetes mellitus: an update

The current gold-standard management of hyperglycemia in individuals with type 1 diabetes mellitus (T1DM) is insulin therapy. However, this therapy is associated with a high incidence of complications, and delaying the onset of this disease produces a substantially positive impact on quality of life for individuals with a predisposition to T1DM, especially children. This review aimed to assess the use of gamma-aminobutyric acid (GABA) to delay the onset of T1DM in children. GABA produces protective and proliferative effects in 2 ways, β cell and immune cell modulation. Various in vitro and in vivo studies have shown that GABA induces proliferation of β cells, increases insulin levels, inhibits β-cell apoptosis, and suppresses T helper 1 cell activity against islet antigens. Oral GABA is safe as no serious adverse effects were reported in any of the studies included in this review. These findings demonstrate promising results for the use of GABA treatment to delay T1DM, specifically in genetically predisposed children, through immunoregulatory effects and the ability to induce β-cell proliferation.

Detection of Biomarker Using Aptasensors to Determine the Type of Diabetes

Diabetes mellitus (DM) is a metabolic disorder characterized by elevated blood glucose levels. This disease is so serious that many experts refer to it as the "silent killer". The early detection of diabetes mellitus, whether type 1, type 2 or mitochondrial, is crucial because it can improve the success of treatment and the quality of life for patients. Aptamer-based biosensor diagnosis methods have been widely developed because they have high sensitivity and selectivity in detecting biomarkers of various diseases. Aptamers are short sequences of oligonucleotides or proteins that recognize specific ligands and bind to various target molecules, ranging from small ions to large proteins. They are promising diagnostic molecules due to their high sensitivity and selectivity, ease of modification, low toxicity, and high stability. This article aims to summarize the progress of detection methods, including detection principles, sensitivity, selectivity, and the performance of detection devices, to distinguish between types of diabetes mellitus using electrochemical aptasensors with biomarkers such as glucose, insulin, HbA1c, GHSA, and ATP.