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Abstract: FR-PO444

DUPDN - Diagnostic Urinary Panel for Diabetic Nephropathy: A Model to Predict Progressive CKD

Session Information

Category: Diabetic Kidney Disease

  • 602 Diabetic Kidney Disease: Clinical


  • Phanish, Mysore K., Epsom and St helier University Hospitals NHS Trust, Carshalton, London, United Kingdom
  • Dockrell, Mark E., South West Thames Institute for Renal Research, Surrey, United Kingdom

CKD in patients with diabetes is detected and monitored using eGFR and albuminuria. Both have limitations and a significant proportion of patients with type2 diabetes have CKD without albuminuria. We tested a panel of urinary biomarkers that reflect areas of injury and pathogenic mechanisms to develop a model to predict progressive CKD.


400 patients with diabetes and CKD were recruited and 388 included in analysis. 50% of patients had ACR <3. A random urine sample was collected at baseline and analysed for a panel of biomarkers - ACR, inflammatory cytokines-IL1β, IL6, MCP1, Markers of proximal tubular injury/damage- NAG and RBP, NGAL, Fibronectin (Fn). Patients were followed for 5y. Logistic regression analysis was done to test the association of individual and combination of biomarkers with presence of CKD stage 3 or 4 aand progression to CKD st 3 or 4. ROC AUC results were obtained for each combination of biomarkers to measure their sensitivity and specificity to CKD stages and progression. A mixed model was used for the association of biomarkers with the trajectory of eGFR over 5y. Models with the same numbers of observations were compared using the AIC goodness-of-fit measure.


Comparisons between CKD stages demonstrated significant differences in urinary RBP, NAG, MCP1, IL6. In multivariate analysis, at all levels of ACR, urinary RBP demonstrated better correlation with CKD stage 3 and 4 compared to ACR but this effect was not seen after adjusting for age, sex and race. Adjusted ACR predicted CKD stage 3 as well as any other combination of markers with AUC of 83%. For progression to stage 3 or 4 CKD, unadjusted and adjusted ACR was a poor predictor (With an AUC of 62-67%). Addition of RBP and MCP1 as covariates with adjustment for age, sex and race in patients with ACR of <3 improves the ROC AUC to 75%. Urinary Fn was raised in diabetic patients with early CKD compared to healthy controls. Using 1,243 observations of eGFR over 5y we arrived at 2 models with best AIC values: 1. where the eGFR gradient against time in yrs is associated with baseline ACR and RBP/Cre: 2. where the eGFR gradient against time in yrs is associated with baseline MCP1/Cre and RBP/Cre. This final model showed best AIC value.


Our study suggests that models that include RBP and MCP1 in addition to or instead of ACR improve prediction of future eGFR.


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