Abstract:
Introduction: Chronic kidney disease of uncertain etiology (CKDu), an emerging chronic kidney disease
(CKD) subtype, contributes to significant morbidity and mortality in certain tropical countries. Although
several indicators of CKDu have been previously suggested, sensitive and specific tests to detect early
disease or predict disease progression are currently unavailable. This study focused on evaluating 8 renal
urinary markers, namely neutrophil gelatinase-associated lipocalin (NGAL), Kidney Injury Molecule-1
(KIM1), cystatin C (CST3), beta 2 microglobulin (B2M), osteopontin (OPN), alpha 1 microglobulin (A1M),
tissue inhibitor of metalloproteinase 1 (TIMP1), and retinol binding protein 4 (RBP4), with the hypothesis
that these have distinct expression patterns in patients with CKDu.
Methods: A cross-sectional study was conducted with 5 study groups comprising subjects from CKDu,
endemic CKD, nonendemic CKD, and endemic healthy and nonendemic healthy controls. The urinary
levels of the 8 selected renal biomarkers were quantified using multiplex biomarker assay, and the data
were subjected to systematic analysis using logistic regression algorithm aiming to extract the best marker
combination that could distinctly identify the disease groups noninvasively from the healthy controls.
Results: A 3-marker signature panel comprising A1M, KIM1, and RBP4 was identified to represent the best
minimum marker combination for differentiating all CKD categories, including CKDu, from healthy con trols with an overall sensitivity of $0.867 and specificity $0.765. The marker combination comprising OPN,
KIM1, and RBP4 showed high predictive performance for distinguishing patients with CKDu from patients
with CKD with both sensitivity and specificity $0.93, which was superior to any existing noninvasive
indicator.
Conclusion: In all, our systematic evaluation of urinary markers previously linked to CKD, in general,
allowed identification of exclusive marker panel combination for early diagnosis and confirmation of
CKDu.
