Genomics, Proteomics and Metabolomics Approaches for Predicting Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients

Abstract

Background: There is a continuous rise in the prevalence of Diabetes Mellitus Type 2 (T2DM) worldwide and most patients are unaware of the presence of this chronic disease at the early stages. T2DM is associated with complications related to long-term damage and failure of multiple organ systems caused by vascular changes associated with glycated end products, oxidative stress, mild inflammation, and neovascularization. Among the most frequent complications of T2DM observed in about 20-40% of T2DM patients is Diabetes Nephropathy (DN). Method: Literature search was done in view of highlighting the novel application of genomics, proteomics and metabolomics, as the new prospective strategy for predicting DN in T2DM patients. Results: The complexity of DN requires a comprehensive and unbiased approach to investigate the main causes of disease and identify the most important mechanisms underlying its development. With the help of evolving throughput technology, rapidly evolving information can now be applied to clinical practice. Discussion: DN is also the leading cause of end-stage renal disease, and comorbidity independent of T2DM. In terms of the comorbidity level, DN has many phenotypes; therefore, timely diagnosis is required to prevent these complications. Currently, urine albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) are gold standards for assessing glomerular damage and changes in renal function. However, GFR estimation based on creatinine is limited to hyperfiltration status; therefore, this makes albuminuria and eGFR indicators less reliable for early-stage diagnosis of DN. Conclusion: The combination of genomics, proteomics, and metabolomics assays as suitable biological systems that can provide new and deeper insights into the pathogenesis of diabetes, as well as to discover prospects for developing suitable and targeted interventions.

Identification of PANoptosis genes in w based on bioinformatics analysis and machine learning

“…With the advancement of molecular biology techniques, the role of genetic differences in the progression of DN has become an important research topic [17]. Studying the intricate regulatory associations between genes has the potential to identify better targets for the treatment of DN.…”

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

“…118,119 A comprehensive understanding necessitates merging data from diverse omics approaches. Metabolomics complements other omics data, forming a foundational understanding via gene-transcript-protein-metabolite profiles in human tissues, [120][121][122][123] elucidating the mechanism of TCM treatment for T2DM and identifying therapeutic targets.…”