Performance of novel biomarkers for prediction of diabetic kidney disease in patients with diabetes mellitus.

Abstract

Introduction: Diabetic kidney disease (DKD) is a common and serious complication in patients with diabetes mellitus (DM). This study was aimed to reveal the validity of seven emerging novel biomarkers of angiopoietin-like-4 (ANGPTL4), neutrophil gelatinase-associated lipocalin (NGAL), monocyte chemoattractant protein-1 (MCP-1), growth differentiation factor-15 (GDF15), fibroblast growth factor-23 (FGF23), n-terminal osteopontin (ntOPN) and pyruvate kinase muscle isozyme M2 (PKM2) in detecting DM patients at high risk of DKD and establish prediction models for DKD onset in DM patients.

Methods: This was a cross-sectional study of 348 adult patients with Type 1 DM for at least 5 years, or Type 2 DM, followed by a prospective observational cohort of 141 adult DM patients without renal involvement at baseline and follow-up for at least 2 years. We performed logistic regression analysis to analyze the relationship between the variables and the risk of DKD occurrence, and receiver operator characteristic (ROC) analysis to assess the predictive ability of multi-biomarker panels for DKD onset.

Results: In the cross-sectional cohort, the seven urinary biomarkers were all elevated in DKD patients, of which the high levels of urinary ntOPN, GDF15, NGAL, MCP-1 and FGF23 significantly increased the risk of DKD diagnosis; the urinary MCP-1 alone performed best in DKD detection with the largest area under the ROC curve (AUC). In the prospective cohort, the high levels of urinary GDF15, MCP-1, ANGPTL4 and FGF23 significantly increased the risk of DKD development, and the model constructed based on the above four biomarkers had the largest AUC (0.873) for predicting the 2-year risk of DKD occurrence.

Conclusion: Our study demonstrated that the four-biomarker model performed the best in predicting DKD, which could provide more accurate tools for DKD risk prediction, thereby improving the prognosis in DM patients.