Abstract
ObjectiveWe aimed to identify a novel panel of biomarkers predicting renal function decline in type 2 diabetes, using biomarkers representing different disease pathways speculated to contribute to the progression of diabetic nephropathy.Research Design and MethodsA systematic data integration approach was used to select biomarkers representing different disease pathways. Twenty-eight biomarkers were measured in 82 patients seen at an outpatient diabetes center in The Netherlands. Median follow-up was 4.0 years. We compared the cross-validated explained variation (R2) of two models to predict eGFR decline, one including only established risk markers, the other adding a novel panel of biomarkers. Least absolute shrinkage and selection operator (LASSO) was used for model estimation. The C-index was calculated to assess improvement in prediction of accelerated eGFR decline defined as <-3.0 mL/min/1.73m2/year.ResultsPatients’ average age was 63.5 years and baseline eGFR was 77.9 mL/min/1.73m2. The average rate of eGFR decline was -2.0 ± 4.7 mL/min/1.73m2/year. When modeled on top of established risk markers, the biomarker panel including matrix metallopeptidases, tyrosine kinase, podocin, CTGF, TNF-receptor-1, sclerostin, CCL2, YKL-40, and NT-proCNP improved the explained variability of eGFR decline (R2 increase from 37.7% to 54.6%; p=0.018) and improved prediction of accelerated eGFR decline (C-index increase from 0.835 to 0.896; p=0.008).ConclusionsA novel panel of biomarkers representing different pathways of renal disease progression including inflammation, fibrosis, angiogenesis, and endothelial function improved prediction of eGFR decline on top of established risk markers in type 2 diabetes. These results need to be confirmed in a large prospective cohort.
Highlights
The growing prevalence of type 2 diabetes is a great global health problem
Given the complexity of the multiple pathophysiological processes involved in progression of type 2 diabetes together with the intra-individual variability of biomarkers, it is questionable if a single biomarker may possess useful diagnostic and prognostic power
We established that a combination of different biomarkers representing different pathways that are speculated to be involved in the progression of renal disease improves prediction of estimated glomerular filtration rate (eGFR) decline
Summary
The growing prevalence of type 2 diabetes is a great global health problem. Type 2 diabetes is the leading cause of chronic kidney disease (CKD) in the United States and is associated with high cardiovascular risk [1, 2]. Identification of patients with type 2 diabetes at risk for progressive renal function loss during the early stages of disease may lead to better patient outcomes. Various studies have assessed the performance of single biomarkers representing a single, disease-associated pathway to predict progression of renal function loss in type 2 diabetes [5, 6]. Given the complexity of the multiple pathophysiological processes involved in progression of type 2 diabetes together with the intra-individual variability of biomarkers, it is questionable if a single biomarker may possess useful diagnostic and prognostic power. A combination of biomarkers that capture different pathways of renal damage may provide a more realistic picture of a patient’s actual pathophysiological status and may yield better assessment of disease prognosis performance
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