Empiricism or rationalism: how should we measure proteinuria?. (July 2013)

• Record Type: Journal Article
• Authors: Methven, Shona; MacGregor, Mark S
• Is Part Of: Annals of clinical biochemistry. Volume 50:Number 4(2013)
• Page Start: 296
• Page End: 300
• Publication Date: 2013-07
• Subjects: Chronic kidney disease -- albumin -- creatinine ratio -- protein; Clinical chemistry -- Periodicals; Clinical biochemistry -- Periodicals; 616.075
• DOI: 10.1177/0004563212473283
• Languages: English
• ISSNs: 0004-5632

Abstract

Proteinuria is the cardinal sign of renal disease, therefore accurate identification of clinically significant proteinuria is essential to the diagnosis and management of kidney disease. Spot samples are now widely used, namely protein: creatinine ratio (uPCR) and albumin: creatinine ratio (uACR). In this article we review the evidence comparing uPCR and uACR including clinical, laboratory and financial arguments. uPCR has a superior performance to uACR to predict 24-hour total proteinuria, the measurement on which the evidence for interventions in chronic kidney disease is based. Furthermore a retrospective study comparing uPCR and uACR as predictors of renal outcome found comparable performance to predict all-cause mortality, commencement of renal replacement therapy and doubling of serum creatinine. Only uPCR takes account of non-albumin proteinuria which has been shown to have prognostic significance. uACR was been thought to be superior at low levels (where there is less 'noise' from physiological urinary proteins), but uPCR has recently been shown to perform well at levels equivalent to <0.5 g/day (and even within the reference range) as a predictor of outcomes. uACR is measured using an immunoassay that may be technically superior, but is not without shortcomings (such as antigen excess) and is 2–10 times more expensive than uPCR. The theories explaining the superiority of albumin are appealing. However, the available comparative data do not seem to support the theory. We cannot explain the disparity, but in science, if the data do not fit the existing theory, then maybe it's time for a new theory.