Urinary Tubular Markers’ for Early Detection on Onset and Progression of Diabetic Nephropathy: Systematic Review
-
Published:2018-08-01
Issue:8
Volume:24
Page:6268-6273
-
ISSN:1936-6612
-
Container-title:Advanced Science Letters
-
language:en
-
Short-container-title:adv sci lett
Author:
Marcevianto Kevin Varian1,
Aristya Lara1,
Asadinia Koe Stella1
Affiliation:
1. Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
Abstract
Diabetic nephropathy, a Diabetes Mellitus’ complication, leads to chronic renal failure. Previous studies found that proximal tubule of nephron, as the initial pathogenesis of DN, precedes state of albuminuria as the current biomarker of DN. This study aimed to propose the earliest
and the most accurate tubular biomarkers in predicting onset of DN and assess progression of DN. Systematic review was performed to evaluate the validity and accuracy of tubular biomarkers based on available studies in EBSCO, PubMed, and Google Scholar. Validity was measured by scoring system
including studies’ methodological quality assessment based on Standards for Reporting of Diagnostic Accuracy and adjustment score to DN conventional factors. Accuracy evaluation was reviewed based on accuracy value provided. From the total of 560 studies, 10 included studies were assessed
and 5 biomarkers were compared. All studies were considered valid, except one study on NGAL marker. Based on their likelihood ratios for clinical application, N-acetyl-β-D-glucosaminidase (NAG) has the best accuracy in predicting onset of DN and Cystatin C in assessing progression
of DN. Detection of these biomarkers could be applied clinically through quantitative and semi-quantitative method for potential use in all healthcare levels and locations. This review proposed the usage of NAG and Cystatin C as early and accurate diabetic nephropathy tubular biomarkers in
all healthcare level.
Publisher
American Scientific Publishers
Subject
General Energy,General Engineering,General Environmental Science,Education,General Mathematics,Health(social science),General Computer Science