OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction

Abstract

Chronic kidney disease (CKD) patients are characterized by a high residual risk for cardiovascular (CV) events and CKD progression. This has prompted the implementation of new prognostic and predictive biomarkers with the aim of mitigating this risk. The 'omics' techniques, namely genomics, proteomics, metabolomics, and transcriptomics, are excellent candidates to provide a better understanding of pathophysiologic mechanisms of disease in CKD, to improve risk stratification of patients with respect to future cardiovascular events, and to identify CKD patients who are likely to respond to a treatment. Following such a strategy, a reliable risk of future events for a particular patient may be calculated and consequently the patient would also benefit from the best available treatment based on their risk profile. Moreover, a further step forward can be represented by the aggregation of multiple omics information by combining different techniques and/or different biological samples. This has already been shown to yield additional information by revealing with more accuracy the exact individual pathway of disease.

Keywords: SNP; albuminuria; chronic renal failure; genomics; metabolomics; precision medicine; proteomics.

Figure 1

Influence of SNPs in the TCF7L2 gene in response to treatment with SU derivatives, GLP1-RA and DPP-4 inhibitors. In the pancreatic β-cell, GLP-1 phosphorylates β-catenin via cAMP-dependent protein kinase A (PKA). This avoids the degradation of the β-catenin that subsequently accumulates, enters the nucleus and forms the transcription factors β-catenin/TCF. This leads to activation of genes such as Isl-1 and Axin2. Overall, this pathway results in pro-insulin processing, β-cell protection from IL-1β and interferon-γ-mediated apoptosis, stimulation of β-cell proliferation and glucose/GLP-1-stimulated insulin secretion. Patients with TCF7L2 gene variants have impaired TCF7L2 expression in pancreatic β-cells resulting in reduced insulin secretion and impaired response to incretins (GLP1-RA, DDP-4 inhibitors). The abnormal response to GLP1-RA and DPP-4 inhibitors is likely to depend on the direct effect of TCF7L2 on PKA.