The use of eGFR and ACR to predict decline in renal function in people with diabetes

Abstract

Background. There have been few attempts to estimate progression of kidney disease in people with diabetes in a single large population with predictive modelling. The aim of this study was to investigate the rate of progression of chronic kidney disease in people with diabetes according to their estimated glomerular filtration rate (eGFR) and presence of albuminuria.

Methods. Data were collected on all people with diabetes in Salford, UK, where an eGFR could be calculated using the four-variable MDRD formula and urinary albumin–creatinine ratio (uACR) was available. All data between 2001 and 2007 were used in the model. Classification of albuminuria status was based on the average of their first two uACR measurements. A longitudinal mixed effect dynamic regression model was fitted to the data. Parameters were estimated by maximum likelihood.

Results. For the analysis of the population, average progression of eGFR, uACR and drug prescribing were available in 3431 people. The regression model showed that in people with diabetes and macroalbuminuria, eGFR declined at 5.7% per annum, while the eGFR of those with microalbuminuria or without albuminuria declined at 1.5% and 0.3% per annum, respectively, independently of age (P < 0.0001).

Conclusions. The longitudinal effect of time on eGFR showed that people with diabetes and macroalbuminuria have an estimated 19 times more rapid decline in renal function compared with those without albuminuria. This study demonstrates that the progression of kidney disease in diabetic people without albuminuria is relatively benign compared with those with albuminuria.