Estimating Urine Albumin-to-Creatinine Ratio from Protein-to-Creatinine Ratio: Development of Equations using Same-Day Measurements

Abstract

Background: Urine albumin-to-creatinine ratio (ACR) and protein-to-creatinine ratio (PCR) are used to measure urine protein. Recent guidelines endorse ACR use, and equations have been developed incorporating ACR to predict risk of kidney failure. For situations in which PCR only is available, having a method to estimate ACR from PCR as accurately as possible would be useful.

Methods: We used data from a population-based cohort of 47,714 adults in Alberta, Canada, who had simultaneous assessments of urine ACR and PCR. After log-transforming ACR and PCR, we used cubic splines and quantile regression to estimate the median ACR from a PCR, allowing for modification by specified covariates. On the basis of the cubic splines, we created models using linear splines to develop equations to estimate ACR from PCR. In a subcohort with eGFR<60 ml/min per 1.73 m², we then used the kidney failure risk equation to compare kidney failure risk using measured ACR as well as estimated ACR that had been derived from PCR.

Results: We found a nonlinear association between log(ACR) and log(PCR), with the implied albumin-to-protein ratio increasing from <30% in normal to mild proteinuria to about 70% in severe proteinuria, and with wider prediction intervals at lower levels. Sex was the most important modifier of the relationship between ACR and PCR, with men generally having a higher albumin-to-protein ratio. Estimates of kidney failure risk were similar using measured ACR and ACR estimated from PCR.

Conclusions: We developed equations to estimate the median ACR from a PCR, optionally including specified covariates. These equations may prove useful in certain retrospective clinical or research applications where only PCR is available.

Keywords: albuminuria; chronic kidney disease; gender difference; proteinuria.

Graphical abstract

Figure 1.

This scatterplot (on log/log scale) of measured ACR versus measured PCR shows the non-linearity of the log(ACR) versus log(PCR) relationship, and the weaker correlation between ACR and PCR at lower proteinuria levels. Cohort size is 47,714 pairs; the graph includes a 20% random sample of 9466 pairs. The green, red, and black lines show the thresholds for A1 versus A2, A2 versus A3, and A3 versus nephrotic range, respectively. The blue dots represent men and the red dots represent women. The blue and red lines show the values predicted by one of the linear spline models for median ACR (model L2), for men (blue) and women (red). To convert ACR or PCR from milligrams per gram to milligrams per millimole, multiply by 0.113.

Figure 2.

This scatterplot of the ratio of measured ACR/measured PCR versus measured PCR for a 20% random sample of ACR/PCR pairs shows the variation in the albumin-to-protein with proteinuria level. Measured PCR is shown on a log scale, whereas measured ACR/measured PCR is shown as a percent. The green, red, and black vertical lines show the thresholds for A1 versus A2, A2 versus A3, and A3 versus nephrotic range of PCR, respectively. The blue dots represent men and the red dots represent women, whereas the curved blue and red lines show the values predicted by one of the cubic spline models for median ACR (model C2), for men (blue) and women (red).

Figure 3.

This scatterplot of measured ACR versus predicted median ACR from the full cubic spline model (C4), for a 20% random sample shows a relatively symmetrical distribution around the line of identity (the diagonal line) indicating unbiased prediction. The blue dots represent men and the red dots represent women. To convert ACR or PCR from milligrams per gram to milligrams per millimole, multiply by 0.113.

Figure 4.

The estimated median and 95% CIs of ACR at the KDIGO A1/A2 and A2/A3 PCR thresholds of 150 and 500 mg/g, vary by covariate. To convert ACR or PCR from milligrams per gram to milligrams per millimole, multiply by 0.113. Age is in years and eGFR is in ml/min per 1.73 m², The estimates are on the basis of quantile regression models for the 50th percentile of log (ACR), with log(PCR) transformed with a restricted cubic spline, and with each model containing only the specified covariate, the spline terms, and the interactions between the specified covariate and the spline terms.