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Omics research in diabetic kidney disease: new biomarker dimensions and new understandings?

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  1. Is metformin associated with acute kidney injury? A case-control study of patients with type 2 diabetes admitted with acute infection

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  1. Effects of Dapagliflozin in Patients With Kidney Disease, With and Without Heart Failure

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  2. A large remaining potential in lipid-lowering drug treatment in the type 2 diabetes population: A Danish nationwide cohort study

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The use of "omics" is increasing in research areas looking to identify biomarkers or early preclinical signs of disease or to increase understanding of complex pathological processes that determines prognosis of the disease. Diabetic kidney disease is no exception as it is an area in need of further improvement of both understanding and prognosis. In addition, there is a notion that pretreatment investigations using techniques like proteomics, lipidomics and metabolomics can help individualize therapy thus fulfilling the wish for personalized medicine. An increasing number of cohort studies using these techniques are published, but only few have been validated in external cohorts or even replicated by other groups. In essence, to achieve clinical impact and usefulness, prospective validation is needed. So far, only the urinary proteomics based PRIORITY study has tried to do this, as discussed in this review. Other areas are promising, but are currently lacking such efforts. In this review we report and discuss the current status of urinary proteomics as well as plasma metabolomics and lipidomics with an overview of the results so far, and with some comments and perspectives regarding future developments and implementation. As is evident, these techniques are promising, but there is still some way before widespread clinical use can be foreseen.

Original languageEnglish
JournalJournal of Nephrology
Volume33
Issue number5
Pages (from-to)931-948
Number of pages18
ISSN1121-8428
DOIs
Publication statusPublished - Oct 2020

    Research areas

  • Biomarkers, Diabetes, Diabetic kidney disease, Metabolomics, Omics, Proteomics

ID: 60286817