Potential Role of Serum and Urinary Biomarkers in Diagnosis and Prognosis of Diabetic Nephropathy.

PURPOSE OF REVIEW: Diabetic nephropathy (DN) is a progressive kidney disease caused by alterations in kidney architecture and function, and constitutes one of the leading causes of end-stage renal disease (ESRD). The purpose of this review is to summarize the state of the art of the DN-biomarker field with a focus on the new strategies that enhance the sensitivity of biomarkers to predict patients who will develop DN or are at risk of progressing to ESRD.

OBJECTIVE: In this review, we provide a description of the pathophysiology of DN and propose a panel of novel putative biomarkers associated with DN pathophysiology that have been increasingly investigated for diagnosis, to predict disease progression or to provide efficient personal treatment.

METHODS: We performed a review of the literature with PubMed and Google Scholar to collect baseline data about the pathophysiology of DN and biomarkers associated. We focused our research on new and emerging biomarkers of DN.

KEY FINDINGS: In this review, we summarized the critical signaling pathways and biological processes involved in DN and highlighted the pathogenic mediators of this disease. We next proposed a large review of the major advances that have been made in identifying new biomarkers which are more sensitive and reliable compared with currently used biomarkers. This includes information about emergent biomarkers such as functional noncoding RNAs, microRNAs, long noncoding RNAs, exosomes, and microparticles.

LIMITATIONS: Despite intensive strategies and constant investigation, no current single treatment has been able to reverse or at least mitigate the progression of DN, or reduce the morbidity and mortality associated with this disease. Major difficulties probably come from the renal disease being heterogeneous among the patients.

IMPLICATIONS: Expanding the proteomics screening, including oxidative stress and inflammatory markers, along with metabolomics approaches may further improve the prognostic value and help in identifying the patients with diabetes who are at high risk of developing kidney diseases.