Chronic kidney disease (CKD) has been increasing over the last years, with a rate between 0.49% to 0.87% new cases per year. Currently, the number of affected people is around 850 million worldwide. CKD is a slowly progressive disease that leads to irreversible loss of kidney function, end-stage kidney disease, and premature death. Therefore, CKD is considered a global health problem, and this sets the alarm for necessary efficient prediction, management, and disease prevention. At present, modern computer analysis, such as in silico medicine (ISM), denotes an emergent data science that offers interesting promise in the nephrology field. ISM offers reliable computer predictions to suggest optimal treatments in a case-specific manner. In addition, ISM offers the potential to gain a better understanding of the kidney physiology and/or pathophysiology of many complex diseases, together with a multiscale disease modeling. Similarly, -omics platforms (including genomics, transcriptomics, metabolomics, and proteomics), can generate biological data to obtain information on gene expression and regulation, protein turnover, and biological pathway connections in renal diseases. In this sense, the novel patient-centered approach in CKD research is built upon the combination of ISM analysis of human data, the use of in vitro models, and in vivo validation. Thus, one of the main objectives of CKD research is to manage the disease by the identification of new disease drivers, which could be prevented and monitored. This review explores the wide-ranging application of computational medicine and the application of -omics strategies in evaluating and managing kidney diseases.

BACKGROUND: Adipose tissue accumulation in specific body compartments has been associated with diabetes, hypertension and dyslipidemia. Perirenal fat (PRF) may lead to have direct lipotoxic effects on renal function and intrarenal hydrostatic pressure. This study was undertaken to explore the association of PRF with cardiovascular risk factors and different stages of chronic kidney disease (CKD). METHODS: We studied 103 patients with CKD of different stages (1 to 5). PRF was measured by B-mode renal ultrasonography in the distal third between the cortex and the hepatic border and/or spleen. RESULTS: The PRF thickness was greater in CKD patients with impaired fasting glucose than in those with normal glucose levels (1.10 ± 0.40 cm vs. 0.85 ± 0.39 cm, P < 0.01). Patients in CKD stages 4 and 5 (glomerular filtration rate [GFR] < 30 mL/min/1.73 m²) had the highest PRF thickness. Serum triglyceride levels correlated positively with the PRF thickness; the PRF thickness was greater in patients with triglyceride levels ≥ 150 mg/dL (1.09 ± 0.40 cm vs. 0.86 ± 0.36 cm, P < 0.01). In patients with a GFR < 60 mL/min/1.73 m², uric acid levels correlated positively with the PRF thickness (P < 0.05). CONCLUSION: In CKD patients, the PRF thickness correlated significantly with metabolic risk factors that could affect kidney function.
Adipose Tissue ; Dyslipidemias ; Fasting ; Filtration ; Glucose ; Humans ; Hydrostatic Pressure ; Hypertension ; Kidney ; Renal Insufficiency ; Renal Insufficiency, Chronic ; Risk Factors ; Spleen ; Triglycerides ; Ultrasonography ; Uric Acid