Background Diabetes mellitus (DM) affects hundreds of millions of people worldwide. Genetic research plays a cru-cial role in managing diabetes by providing valuable insights into genetic predispositions, facilitating early diagno-sis, and enabling personalized treatment strategies. Identification of important genetic markers has paved the way for the creation of targeted therapies, enhancing treatment outcomes and promoting preventive care for both type 1 diabetes mellitus ( T1DM) and type 2 diabetes mellitus ( T2DM). The aim of this study is to explore the role of different genes in the development of DM and its related complications. Methodology A comprehensive literature search was conducted from October 27 to November 14, 2024, to enlist articles related to genes involved in development of DM and its complications in search engines including PubMed, Medline, Google Scholar, and Scopus. We included original articles, case–control studies, cohort studies, review arti-cles, systematic review, and meta-analysis published between January 1, 2014, and November 14, 2024 in our study. Results In T1DM; research has historically concentrated on the role of HLA class II genes. However, recent studies have brought attention to the role of HLA class I genes in the disease’s development, suggesting a broader role of genetics than previously understood. CTLA4, IL2RA, and PTPN22, genes were also significantly linked to T1DM. In T2DM; TCF7L2 was found to be the most potent gene for its development among others genes such as LCAT, APOE, FTO. For gesta-tional diabetes mellitus (GDM), MTNR1B, CDKAL1, and IRS1 genes played an important role. Conclusion Genetics played an important role in the understanding of DM. Researchers have identified new genetic loci that can serve as diagnostic markers for DM and its associated compilations such as diabetic kidney disease (DKD), diabetic neuropathy (DN), diabetic retinopathy (DR) and cardiovascular diseases (CVDs). TCF7L2 and HLA class II are the strongest risk factors for T2DM and T1DM, respectively. Understanding the genetics of DM and its complications is essential for improving early detection, enhancing treatment outcomes, and developing targeted therapies for DM patients.

Insulin-producing β-cells are destroyed in type 1 diabetes mellitus (T1DM), a chronic autoimmune disease that results in complete insulin insufficiency and metabolic dysfunction. According to a survival study that used p values, some hub genes are important for predicting and diagnosing illness. Scientists have inferred medicines to identify possible therapies that interact with the identified hub genes. The GSE10586 gene expression dataset from the Gene Expression Omnibus (GEO) was used for this investigation, which included 27 samples from 15 healthy controls and 12 diabetic patients. Normalization methods such as variance stabilization normalization (VSN) were used as part of the data pretreatment. A protein‒protein interaction (PPI) network was constructed, principal component analysis (PCA) was performed, heatmaps were created, and the Limma algorithm was used to analyze differential gene expression. Using DAVID v6.8 and KEGG pathway annotations, the functional enrichment of differentially expressed genes (DEGs) was evaluated. Furthermore, a computational study revealed CERS6 to be one of the potential hub genes. Four drugs, methotrexate, eliglustat, myriocin and statin, were the focus of further studies on the basis of predictions made via ChemSpider and PubChem database analysis. To determine the optimal binding positions of these drugs with CERS6, we used molecular docking techniques. The binding affinity of methotrexate was 8.48 kcal/mol, that of myriocin was 7.85 kcal/mol, that of eliglustat was − 6.62 kcal/mol, and that of serine was − 4.90 kcal/mol against the binding pocket’s active residues. To determine how consistently each drug interacted with the CERS6 protein over time, molecular dynamics (MD) simulations were run. Throughout the simulation intervals, both medications were confirmed to be stable, with minor alterations in the CERS6 protein loop region. Therefore, the investigation of structure-based drug design has potential for identifying specific therapeutic targets. Ten hub genes were identified via network analysis of differentially expressed genes. These hub genes could serve as novel targets for T1DM detection, prognosis, and targeting. CERS6 exhibited the highest degree of interaction. Methotrexate, eliglustat, myriocin and statins were identified as potential drugs for CERS6. Overall, these findings provide valuable insights that could pave the way for new experimental strategies in T1DM therapy.