Systemic Inflammation Precedes Microalbuminuria in Diabetes

Aim: The aim of the case-control study was to investigate if serum biomarkers indicative of vascular inflammation and endothelial dysfunction can predict the development of microalbuminuria in patients with diabetes mellitus type 2. Methods: Among participants enrolled in the ROADMAP (Randomized Olmesartan And Diabetes MicroAlbuminuria Prevention) and observational follow-up (OFU) studies, a panel of 15 serum biomarkers was quantified from samples obtained at initiation of the study and tested for associations with the development of new-onset microalbuminuria during follow-up. A case-control study was conducted with inclusion of 172 patients with microalbuminuria and 188 matched controls. Nonparametric inferential, nonlinear regression, mediation, and bootstrapping statistical methods were used for the analysis. Results: The median follow-up time was 37 months. At baseline, mean concentrations of C-X-C motif chemokine ligand 16 (CXCL-16), transforming growth factor (TGF)–β1 and angiopoietin-2 were higher in patients with subsequent microalbuminuria. In the multivariate analysis, after adjustment for age, sex, body mass index, glycated hemoglobin, duration of diabetes, low-density lipoprotein (LDL), smoking status, blood pressure, baseline urine albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate (eGFR), time of follow-up and cardiovascular disease, CXCL-16 (odds ratio [OR] 2.60, 95% confidence interval [CI] 1.71–3.96), angiopoietin-2 (OR 1.50, 95% CI 1.14–1.98) and TGF-β1 (OR 1.03, 95% CI 1.01–1.04) remained significant predictors of new-onset microalbuminuria (P < 0.001). Inclusion of these biomarkers in conventional clinical risk models for prediction of microalbuminuria increased the area under the curve (AUC) from 0.638 to 0.760 (P < 0.001). Conclusion: In patients with type 2 diabetes, elevated plasma levels of CXCL-16, angiopoietin-2, and TGF-β1 are independently predictive of microalbuminuria. Thus, these serum markers improve renal risk models beyond established clinical risk factors.