Metabolomics as a tool for the early diagnosis and prognosis of diabetic kidney disease

• Published in: Medicinal research reviews 2022-07, Vol.42 (4), p.1518-1544
• Main Authors: Pereira, Pedro R., Carrageta, David F., Oliveira, Pedro F., Rodrigues, Anabela, Alves, Marco G., Monteiro, Mariana P.
• Format: Article
• Language: English
• Subjects: Albuminuria; Biomarkers - metabolism; Diabetes; diabetes kidney disease; diabetes mellitus; Diabetes Mellitus, Type 2 - therapy; Diabetic Nephropathies - diagnosis; Diabetic Nephropathies - metabolism; Diabetic nephropathy; Early Diagnosis; Humans; Kidney diseases; Kidney Failure, Chronic; mass spectroscopy; Metabolism; Metabolites; metabolomics; Metabolomics - methods; nuclear magnetic resonance; Prognosis
• ISSN: 0198-6325; 1098-1128
• DOI: 10.1002/med.21883

Diabetic kidney disease (DKD) is one of the most prevalent comorbidities of diabetes mellitus and the leading cause of the end‐stage renal disease (ESRD). DKD results from chronic exposure to hyperglycemia, leading to progressive alterations in kidney structure and function. The early development of DKD is clinically silent and when albuminuria is detected the lesions are often at advanced stages, leading to rapid kidney function decline towards ESRD. DKD progression can be arrested or substantially delayed if detected and addressed at early stages. A major limitation of current methods is the absence of albuminuria in non‐albuminuric phenotypes of diabetic nephropathy, which becomes increasingly prevalent and lacks focused therapy. Metabolomics is an ever‐evolving omics technology that enables the study of metabolites, downstream products of every biochemical event that occurs in an organism. Metabolomics disclosures complex metabolic networks and provide knowledge of the very foundation of several physiological or pathophysiological processes, ultimately leading to the identification of diseases' unique metabolic signatures. In this sense, metabolomics is a promising tool not only for the diagnosis but also for the identification of pre‐disease states which would confer a rapid and personalized clinical practice. Herein, the use of metabolomics as a tool to identify the DKD metabolic signature of tubule interstitial lesions to diagnose or predict the time‐course of DKD will be discussed. In addition, the proficiency and limitations of the currently available high‐throughput metabolomic techniques will be discussed.