The relationship between auditory brainstem response, nerve conduction studies, and metabolic risk factors in type II diabetes mellitus

Abo-Elfetoh, Noha; Mohamed, Enass Sayed; Tag, Lubna M; Gamal, Rania M.; Gandour, Abeer M; Razek, Mohamed R.A; El-Baz, Mona A.H.; Eldeen, Manal E Ez

doi:10.4103/1110-161x.192253

Cited by 1 publication

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Type 2 diabetes risk is substantially increased by DUSP8 expression in individuals [ 62 ]. Patients with T2D are more likely to experience ABR abnormalities than those without the disease [ 63 ]. In both type 2 diabetes and atherosclerosis, the protein PRRC2A was found to be present [ 64 ].…”

Section: Resultsmentioning

confidence: 99%

Statistical Bioinformatics to Uncover the Underlying Biological Mechanisms That Linked Smoking with Type 2 Diabetes Patients Using Transcritpomic and GWAS Analysis

et al. 2022

View full text Add to dashboard Cite

Type 2 diabetes (T2D) is a chronic metabolic disease defined by insulin insensitivity corresponding to impaired insulin sensitivity, decreased insulin production, and eventually failure of beta cells in the pancreas. There is a 30–40 percent higher risk of developing T2D in active smokers. Moreover, T2D patients with active smoking may gradually develop many complications. However, there is still no significant research conducted to solve the issue. Hence, we have proposed a highthroughput network-based quantitative pipeline employing statistical methods. Transcriptomic and GWAS data were analysed and obtained from type 2 diabetes patients and active smokers. Differentially Expressed Genes (DEGs) resulted by comparing T2D patients’ and smokers’ tissue samples to those of healthy controls of gene expression transcriptomic datasets. We have found 55 dysregulated genes shared in people with type 2 diabetes and those who smoked, 27 of which were upregulated and 28 of which were downregulated. These identified DEGs were functionally annotated to reveal the involvement of cell-associated molecular pathways and GO terms. Moreover, protein–protein interaction analysis was conducted to discover hub proteins in the pathways. We have also identified transcriptional and post-transcriptional regulators associated with T2D and smoking. Moreover, we have analysed GWAS data and found 57 common biomarker genes between T2D and smokers. Then, Transcriptomic and GWAS analyses are compared for more robust outcomes and identified 1 significant common gene, 19 shared significant pathways and 12 shared significant GOs. Finally, we have discovered protein–drug interactions for our identified biomarkers.

show abstract

Section: Resultsmentioning

confidence: 99%