Endocrine Abstracts

Introduction: Diabetic kidney disease (DKD) is a prevalent microvascular complication of diabetes mellitus (DM), associated with significantly worse prognosis. If addressed in early stages, progression can be delayed or even arrested. In clinical practice, diagnosis is based on eGFR and uACR or albuminuria. However, given the limitations of these markers, especially in early stages of the disease, there is a need to identify novel biomarkers [1]. Omics are a new field of study, involving comprehensive analysis of various types of biological data. In DKD, they are utilized in scientific research, but have not yet been implemented in routine clinical practice [2]. The intent of this research was to give a concise overview of the currently available data on targeted omics in the context of DKD in patients with T1DM and T2DM.

Methods: A structured search in PUBMED and EMBASE was conducted, followed by a manual search in the reference list of the selected articles. Articles describing the use of non-targeted omics, were excluded.

Results: 28 articles were withheld, describing 25 different (panels of) targeted omics, both in urine and in blood. They belonged exclusively to the group of the proteomics and the metabolomics. The most researched urinary proteomic marker is CKD273, a classifier consisting of 273 peptides, originally developed in the context of chronic kidney disease (CKD), with promise in DKD. It is suggested to be especially predictive in the early stages of DKD, up to 1.5 years before the occurrence of albuminuria [3], and that it could play a role in monitoring/predicting therapeutic response [4]. In plasma, the proteomics KIM-1 and TNFR-1 could serve as a marker for both diagnosis and therapeutic response, either alone or in combination with other markers. However, results are less consistent as compared to CKD273 [5–7]. Few clinical conclusions can be drawn regarding urinary and plasma metabolomics, as most studies have short follow up and consist of non-targeted analysis.

Discussion/Conclusion: Omics hold promise as potential markers in DKD, but further research is needed to evaluate their performance in routine clinical practice. Most studies have (self-reported) limited power, lack external validation, and are primarily post hoc analyses. In addition, ethical issues need to be considered. As it concerns high-end technology which is not widely available, the cost is higher than that of the currently used markers [8]. Future research should take the heterogeneity and complexity of DKD into account, but also look at different confounders (genetics, medication, age, diet, diurnal variations, and other environmental and demographic factors).

References: 1. Selby NM, Taal MW. An updated overview of diabetic nephropathy: Diagnosis, prognosis, t reatmentgoals and latest guidelines. Diabetes Obes Metab 2020;22 Suppl 1:3–15. https://doi.org/10.1111/DOM.14007.

2. Tofte N, Persson F, Rossing P. Omics research in diabetic kidney disease: new biomarker dimensionsand new understandings? J Nephrol 2020;33. https://doi.org/10.1007/s40620-020-00759-4.

3. Zürbig P, Jerums G, Hovind P, MacIsaac RJ, Mischak H, Nielsen SE, et al. Urinary proteomics for earlydiagnosis in diabetic nephropathy. Diabetes 2012;61:3304–13. https://doi.org/10.2337/db12-0348.

4. Siwy J, Klein T, Rosler M, von Eynatten M. Urinary Proteomics as a Tool to Identify Kidney Responders to Dipeptidyl Peptidase-4 Inhibition: A Hypothesis-Generating Analysis from the MARLINA-T2D Trial. Proteomics Clin Appl 2019;13. https://doi.org/10.1002/PRCA.201800144.

5. Colombo M, McGurnaghan SJ, Blackbourn LAK, Dalton RN, Dunger D, Bell S, et al. Comparison ofserum and urinary biomarker panels with albumin/creatinine ratio in the prediction of renal function decline in type 1 diabetes. Diabetologia 2020;63:788–98. https://doi.org/10.1007/S00125-019-05081-8

6. Nowak N, Skupien J, Niewczas MA, Yamanouchi M, Major M, Croall S, et al. Increased plasma kidneyinjury molecule-1 suggests early progressive renal decline in non-proteinuric patients with type 1 diabetes. Kidney Int 2016;89:459–67. https://doi.org/10.1038/KI.2015.314.

7. Sen T, Li J, Neuen BL, Neal B, Arnott C, Parikh CR, et al. Effects of the SGLT2 inhibitor canagliflozinon plasma biomarkers TNFR-1, TNFR-2 and KIM-1 in the CANVAS trial. Diabetologia 2021;64:2147–58. https://doi.org/10.1007/S00125-021-05512-5.

8. Critselis E, Vlahou A, Stel VS, Morton RL. Cost-effectiveness of screening type 2 diabetes patientsfor chronic kidney disease progression with the CKD273 urinary peptide classifier as compared to urinary albumin excretion. Nephrology Dialysis Transplantation 2018;33. https://doi.org/10.1093/ndt/gfx068.