Estimation of renal function in patients with diabetes

Diabetes Metab. 2011 Nov;37(5):359-66. doi: 10.1016/j.diabet.2011.05.002. Epub 2011 Jun 15.

Abstract

Diabetes is the leading cause of chronic kidney disease (CKD), which makes estimation of renal function crucial. Serum creatinine is not an ideal marker of glomerular filtration rate (GFR), which also depends on digestive absorption, and the production of creatinine in muscle and its tubular secretion. Formulas have been devised to estimate GFR from serum creatinine but, given the wide range of GFR, proteinuria, body mass index and specific influence of glycaemia on GFR, the uncertainty of these estimations is a particular concern for patients with diabetes. The most popular recommended formulas are the simple Cockcroft-Gault equation, which is inaccurate and biased, as it calculates clearance of creatinine in proportion to body weight, and the MDRD equation, which is more accurate, but systematically underestimates normal and high GFR, being established by a statistical analysis of results from renal-insufficient patients. This underestimation explains why the MDRD equation is repeatedly found to give a poor estimation of GFR in patients with recently diagnosed diabetes and is a poor tool for reflecting GFR decline when started from normal, as well as the source of unexpected results when applied to epidemiological studies with a 60mL/min/1.73m(2) threshold as the definition of CKD. The more recent creatinine-based formula, the Mayo Clinic Quadratic (MCQ) equation, and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) improve such underestimation, as both were derived from populations that included subjects with normal renal function. Determination of cystatin C is also promising, but needs standardisation.

Publication types

  • Review

MeSH terms

  • Biomarkers / metabolism
  • Diabetic Nephropathies / diagnosis*
  • Diabetic Nephropathies / metabolism
  • Diabetic Nephropathies / physiopathology*
  • Humans
  • Kidney Function Tests / methods*
  • Models, Biological*

Substances

  • Biomarkers