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

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.
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.
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.
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.