OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction

• Published in: International journal of molecular sciences Vol. 23; no. 1; p. 336
• Main Authors: Provenzano, Michele, Serra, Raffaele, Garofalo, Carlo, Michael, Ashour, Crugliano, Giuseppina, Battaglia, Yuri, Ielapi, Nicola, Bracale, Umberto Marcello, Faga, Teresa, Capitoli, Giulia, Galimberti, Stefania, Andreucci, Michele
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.12.2021; MDPI
• Subjects: albuminuria; Animals; Biological properties; Biomarkers; Biopsy; Cardiovascular diseases; Chronic illnesses; Clinical trials; Diabetes; Drugs; Genomics; Humans; Kidney diseases; Metabolism; Metabolomics; Models, Biological; Mortality; Nephrology; Patients; Precision Medicine; Prognosis; Proteomics; Renal Insufficiency, Chronic - drug therapy; Renal Insufficiency, Chronic - genetics; Review; Risk assessment; Risk factors; SNP; Transcriptomics; Urine; albuminuria; SNP; proteomics; chronic renal failure; metabolomics; genomics; precision medicine
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms23010336

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.