American Journal of Epidemiology Advance Access published online on September 5, 2008
American Journal of Epidemiology, doi:10.1093/aje/kwn209
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Practice of Epidemiology |
Albuminuria Assessed From First-Morning-Void Urine Samples Versus 24-Hour Urine Collections as a Predictor of Cardiovascular Morbidity and Mortality
Correspondence to Dr. H. J. Lambers Heerspink, Department of Clinical Pharmacology, University Medical Center Groningen, P.O. Box 30.001, 9700 RB Groningen, The Netherlands (e-mail: H.J.Lambers.Heerspink{at}med.umcg.nl).
Received for publication April 8, 2008. Accepted for publication June 13, 2008.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Screening for albuminuria has been advocated because it is associated with cardiovascular morbidity and all-cause mortality. The "gold standard" to assess albuminuria is 24-hour urinary albumin excretion (UAE). Because 24-hour urine collection is cumbersome, guidelines suggest measuring albuminuria in a first morning void, either as urinary albumin concentration (UAC) or adjusted for creatinine concentration, the albumin:creatinine ratio (ACR). To decide which albuminuria measure to use in clinical practice, it is essential to know which best predicts clinical outcome. In a sample representative of the Groningen (the Netherlands) population (n = 3,414), the authors compared UAC, ACR, and UAE as predictors of cardiovascular events and all-cause mortality. During a median follow-up of 7.5 years, which ended December 31, 2005, they observed 278 events (a major adverse cardiovascular event or mortality). The area under the receiver operating characteristic curve predicting events was 0.65 for UAE, 0.62 for UAC (P = 0.06 vs. UAE), and 0.66 for ACR (P = 0.80 vs. UAE; P = 0.01 vs. UAC). When sex-specific subgroups were considered, UAE was superior to UAC in predicting outcome (P = 0.04) for females, whereas, for males as well as females, no difference was found between ACR and UAE. To predict cardiovascular morbidity and all-cause mortality, measuring ACR in a first-morning-void urine sample is a good alternative to measuring 24-hour UAE.
age groups; albuminuria; cardiovascular diseases; creatinine
Abbreviations: ACR, albumin:creatinine ratio; ICD-9, International Classification of Diseases, Ninth Revision; PREVEND, Prevention of REnal and Vascular End-stage Disease; ROC, receiver operating characteristic
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Various large cohort studies have shown that microalbuminuria is a strong risk predictor for cardiovascular morbidity and all-cause mortality. Recent epidemiologic studies have demonstrated that even a small increase in urinary albumin excretion is associated with increased risk of cardiovascular morbidity and mortality (1–3). This association holds true for patients with diabetes and hypertension, and even in the general population (1–5). Screening for high urinary albumin levels has therefore been advocated.
Because urinary albumin excretion follows a circadian rhythm, the preferred method to collect urine for albumin assessment is to do so for 24 hours. However, 24-hour urine collection is inconvenient for patients. Easier and more practical alternatives have been proposed (6). These alternatives include measurement of urinary albumin concentration or albumin:creatinine ratio (ACR) in a first morning void (Table 1). Guidelines for estimating urinary albumin levels indeed propose using the ACR (7).
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The rationale for using these alternative albuminuria measures is based on cross-sectional comparison studies showing good correlations of urinary albumin concentration and ACR in urine portions and urinary albumin excretion in 24-hour urine collection (6, 8). However, it is unknown which of these 3 albuminuria measures best identifies subjects at increased risk of cardiovascular morbidity or mortality (9). Therefore, we investigated prospectively which albuminuria measure serves best to predict cardiovascular morbidity and all-cause mortality. An important issue to consider is that creatinine is a waste product of muscle catabolism. Since the ACR depends not only on urinary albumin but also on urinary creatinine excretion, it will be affected by gender and age because muscle mass is lower in females than in males and decreases with age. Therefore, in a secondary analysis, we also studied the impact of gender and age on the performance of urinary albumin excretion, urinary albumin concentration, and ACR in predicting outcome, as well as the relation between gender and age and the 3 albuminuria measures.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Population
This study was conducted among subjects who participate in the Prevention of REnal and Vascular End-stage Disease (PREVEND) study, which began in 1997. This prospective cohort study in the city of Groningen (the Netherlands) investigates the natural course of urinary albumin excretion and its relation to renal and cardiovascular disease. Details of the study protocol have been published elsewhere (10, 11). In summary, all inhabitants of the city of Groningen aged 28–75 years were sent a questionnaire and a vial to collect a first-morning-void urine sample (prescreening). Of these subjects, 40,856 responded (47.8%) and returned a vial to a central laboratory for urinary albumin and urinary creatinine assessment. From these 40,856 subjects, the PREVEND cohort was selected with the aim to create a cohort enriched for the presence of high albuminuria. After exclusion of patients with type 1 diabetes mellitus (defined as requiring the use of insulin) and pregnant females (defined by self-report), all subjects with a urinary albumin concentration of
10 mg/L (n = 7,768) were invited, and 6,000 participated. Furthermore, a randomly selected control group with a urinary albumin concentration of <10 mg/L (n = 3,394) was invited, and 2,592 participated. These 8,592 subjects constitute the actual PREVEND cohort and were asked to collect 2 consecutive 24-hour urine samples (baseline screening).
Enrichment for subjects with higher albuminuria values in the PREVEND cohort may bias comparison of the diagnostic performance of the albuminuria measures for this study. For the current study, we therefore considered a subcohort of the 8,592 subjects representative of the Groningen population. For this purpose, we included all subjects with a urinary albumin concentration of <10 mg/L who completed the first screening (n = 2,592) and added a subset of the "oversampled" subjects whose urinary albumin concentration was 10 mg/L by proportionally taking a computer-generated, random subset (n = 840) (12). After exclusion of 18 participants known to have proteinuria or renal disease, a cohort of 3,414 participants was created. As expected, the characteristics of the population participating in the prescreening survey were similar to those of the population generated for this study (Table 2).
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Measurements
For the prescreening survey, participants were instructed in writing to collect a midstream first-morning-void urine sample (first morning urine collection after awakening from sleep) in a polyethylene tube and to store this sample at 4°C prior to sending it to the central laboratory. For the baseline survey, participants filled out an extended questionnaire regarding demographics, race, cardiovascular and renal history, and smoking status. Furthermore, anthropomorphic measurements (body weight and height) were performed, blood pressure was recorded, and blood samples were taken. After thorough written and oral instructions on how to perform a 24-hour urine collection, participants collected urine during 24 hours. They were instructed to postpone urine collection in case of a urinary tract infection or menstruation and to refrain from heavy exercise during the collection period. Participants were instructed to store urine in polyethylene containers at 4°C for a maximum of 4 days prior to the visit. For this study, urinary albumin concentration and ACR in a first-morning-void sample obtained during prescreening were used, as well as the 24-hour urinary albumin excretion obtained at the baseline screening. The PREVEND study has been approved by the local ethics committee and is performed in accordance with Declaration of Helsinki guidelines.
Analytical methods
Urinary albumin and creatinine concentrations in fresh urine samples were determined within 24 hours after delivery. Urinary albumin concentration was measured with the Behring BNII analyzer (Dade Behring, Marburg, Germany) in a central laboratory by using standard laboratory methods. The intraassay and interassay coefficients of variation for urinary albumin measured by the BNII analyzer were 2.2% and 2.6%, respectively, with a threshold of 2.3 mg/L. Urinary creatinine was determined by Kodak Ektachem dry chemistry (Eastman Kodak, Rochester, New York), with intraassay and interassay coefficients of variation of 0.9% and 2.9%, respectively.
Outcome
We determined the combined incidence of cardiovascular morbidity and all-cause mortality during follow-up after the baseline screening. Mortality data are obtained from the Dutch Central Bureau of Statistics. Information on hospitalization for cardiovascular morbidity was received from PRISMANT, the Dutch national registry of hospital discharge diagnoses. The validity of this database has been shown to be good, with 84% of primary diagnoses and 87% of secondary diagnoses matching the diagnoses recorded in patients’ charts (13, 14). All data were coded according to the International Classification of Diseases, Ninth Revision (ICD-9) and the classification of interventions. For this study, cardiovascular outcomes were defined as the combined incidence of acute myocardial infarction (ICD-9 code 410), acute and subacute ischemic heart disease (ICD-9 code 411), coronary artery bypass grafting (ICD-9 code 414) or percutaneous transluminal coronary angioplasty, subarachnoid hemorrhage (ICD-9 code 430), intracerebral hemorrhage (ICD-9 code 431), other intracranial hemorrhage (ICD-9 code 432), occlusion or stenosis of the precerebral (ICD-9 code 433) or cerebral (ICD-9 code 434) arteries, other vascular interventions such as percutaneous transluminal angioplasty or bypass grafting of aorta and peripheral vessels, and mortality. Survival time for participants was defined as the period from the date of urine collection by the participant to the date of the first event or December 31, 2005 (end of follow-up). Subjects were censored if they moved to an unknown destination.
Statistical analyses
Receiver operating characteristic (ROC) curves were used to compare the discriminative power of the 3 different albuminuria measures to predict cardiovascular morbidity and mortality. The ACR depends on urinary creatinine excretion and thus on muscle mass. Because muscle mass differs between the sexes and declines with age, not only gender-specific but also age-specific cutoff values for the ACR indicating abnormal values (e.g., microalbuminuria) have been proposed (15–17). To investigate the effect of gender and age on the predictive performance of the different albuminuria measures, we analyzed males and females separately, as well as subjects above and below the median value of age. In addition, we repeated the ROC analysis by adding age to the model according to the method proposed by Pepe (18). ROC curves do not take into account censoring for subjects lost to follow-up. To test whether censoring affected our findings, we calculated time-dependent ROC curves (19).
Subsequently, sensitivity and specificity at the clinically used cutoff values indicating microalbuminuria were calculated for the 3 different albuminuria measures (20). Of note, sensitivity and specificity of the ACR were calculated at the guideline-recommended sex-independent cutoff value indicating microalbuminuria (30 mg/g) and at the sex-specific cutoff values for microalbuminuria (17 mg/g for males and 25 mg/g for females) that have been proposed (21). Sensitivity was defined as the proportion of subjects experiencing an event during follow-up who had urinary albumin levels indicating microalbuminuria at the prescreening or baseline survey. Specificity was defined as the proportion of subjects not experiencing an event during follow-up who had urinary albumin levels below the cutoff value for microalbuminuria at the prescreening or baseline survey. A McNemar test was used to test statistical differences between sensitivity and specificity among the 3 different albuminuria measures. Multivariate Cox regression analysis was applied to determine the hazard ratio—adjusted for gender, age, blood pressure, cholesterol, and smoking—for subjects with urinary albumin levels in the microalbuminuric range compared with subjects whose urinary albumin levels were in the normoalbuminuric range.
To examine the relation between the 3 different albuminuria measures and age, we divided the study population into sex-specific quintiles of age. A percentage difference at each quintile of age, with the lowest quintile as the reference, was determined for each albuminuria measure, urinary creatinine concentration, as well as 24-hour urinary creatinine excretion. The Wilcoxon rank test with Bonferroni correction for multiple testing was applied to assess statistical significance between the various albuminuria measures at each quintile of age.
In this paper, areas under the ROC curves are presented as means and 95% confidence intervals. For other variables, means and standard deviation are given or, in case of nonnormal distribution, medians with interquartile ranges.
A P value of 
0.05 (2 sided) was considered to indicate a statistically significant difference. Analyses were performed by using SPSS version 14 (SPSS, Inc., Chicago, Illinois) and Stata version 10.0 (Stata Corporation, College Station, Texas) software.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Baseline characteristics
The baseline characteristics of the 3,414 subjects are shown in Table 3. Median 24-hour urinary albumin excretion was 7.0 (interquartile range: 5.3–10.9) mg/24 hours. Median urinary albumin concentration and ACR in a first morning void were 5.7 (interquartile range: 3.4–9.8) mg/L and 4.9 (interquartile range: 3.6–7.7) mg/g, respectively. The prevalence of microalbuminuria, defined as 24-hour urinary albumin excretion
30 mg/24 hours, was 6.1%.
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Discriminative power of the 3 albuminuria measures
A total of 278 events were observed during a median follow-up of 7.5 years, equivalent to an event rate of 10.9 per 1,000 person-years. The numbers of (first) cardiovascular events that occurred during follow-up are presented in Table 4, with respect to both the composite endpoint and its individual components. The area under the ROC curve of the 3 different albuminuria measures to predict cardiovascular morbidity and mortality in the overall population was 0.65 (95% confidence interval: 0.62, 0.69) for 24-hour urinary albumin excretion, 0.62 (95% confidence interval: 0.59, 0.66) for first-morning-void urinary albumin concentration (P = 0.06 vs. urinary albumin excretion), and 0.66 (95% confidence interval: 0.62, 0.70) for first-morning-void ACR (P = 0.80 vs. urinary albumin excretion; P = 0.01 vs. urinary albumin concentration, Table 5).
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In a secondary analysis, the possible influence of muscle mass on the predictive performance of especially the ACR was investigated. To this end, gender- and age-specific subgroup analyses were conducted. In gender-specific subgroups, the area under the ROC curve of urinary albumin excretion was higher compared with that of urinary albumin concentration in males (0.64 vs. 0.62, respectively; P = 0.29) and females (0.66 vs. 0.59, respectively; P = 0.04). The area under the ROC curves for urinary albumin excretion versus ACR was not statistically different for males (0.64 vs. 0.68, respectively; P = 0.12) or females (0.66 vs. 0.66 respectively; P = 0.74). The area under the ROC curve of the ACR compared with urinary albumin concentration was statistically significantly higher for both males (0.68 vs. 0.62, respectively; P < 0.01) and females (0.66 vs. 0.59, respectively; P < 0.01).
With respect to age-specific subgroups, in subjects aged 47.0 years or younger (median), the area under the ROC curve of urinary albumin excretion (0.58) appeared to be numerically higher compared with that of urinary albumin concentration (0.52) and the ACR (0.52), although differences did not reach statistical significance (P = 0.29 and P = 0.74, respectively). This finding may be due to a lack of power, given the low event rate at younger ages. In subjects older than age 47.0 years, differences in area under the ROC curves of urinary albumin excretion, urinary albumin concentration, and ACR were minimal (0.65, 0.64, and 0.64, respectively). With age included in the ROC analyses, the area under the ROC curves for the 3 expressions of albuminuria were similar (area under the ROC curve = 0.80 for the 3 albuminuria measures, Table 5). Of note, when cardiovascular outcomes were analyzed separately, essentially similar results were obtained. Finally, time-dependent ROC analyses revealed no change in the area under the ROC curves for each albuminuria measure during follow-up, indicating that censoring did not affect our findings.
Sensitivity and specificity of the 3 albuminuria measures at clinically used cutoff values
The sensitivity of urinary albumin excretion did not differ significantly from that of urinary albumin concentration and ACR when sex-specific cutoff values were taken into account (this finding held true in the overall population and in gender- and age-specific subgroups (Table 6)). However, sensitivity of the ACR, calculated at the sex-independent cutoff value of 30 mg/g, was significantly lower compared with urinary albumin excretion as well as urinary albumin concentration. Again, this finding was true in the overall population and in gender- and age-specific subgroups.
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The specificity of urinary albumin excretion was significantly higher when compared with urinary albumin concentration in the overall population, as well as in gender- and age-specific subgroups. In contrast, specificity of urinary albumin excretion was similar to that of the ACR when sex-specific cutoff values were taken into account but was lower when adopting the sex-independent cutoff value for the ACR.
Association between age and albuminuria measures
The association between age and the 3 albuminuria measures, urinary creatinine concentration, and 24-hour urinary creatinine excretion are presented in Figure 1. As shown, 24-hour urinary creatinine excretion decreased slightly with age in both males and females. The pattern for urinary creatinine concentration in a 24-hour collection was similar to that for 24-hour urinary creatinine excretion (data not shown). In contrast, urinary creatinine concentration in a first morning void decreased more steeply. Urinary albumin concentration in a first morning void tended to decrease with aging, showing a relative increase at only the highest age quintiles. A different pattern was observed for 24-hour urinary albumin excretion, as well as for the ACR derived from a first morning void. Both albuminuria measures increased steadily with aging, with a more pronounced increase for the ACR.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Albuminuria has been established as a valuable risk marker for cardiovascular morbidity as well as all-cause mortality. Measurement of 24-hour albumin excretion is considered the "gold standard" to assess albuminuria. For practical reasons, the ACR for the first morning void has been advocated as the standard method to estimate albuminuria. To our knowledge, the present study demonstrates for the first time that performance of the ACR in a first morning void to predict outcome is similar to that of 24-hour urinary albumin excretion. In contrast, the predictive performance of 24-hour urinary albumin excretion is superior to that of urinary albumin concentration in a first morning void, with borderline statistical significance. Furthermore, we found that the predictive performance of the ACR was statistically significantly higher than that for urinary albumin concentration. Essentially similar results were obtained in sex-specific subgroups and in the overall population. The differences in area under the ROC curves were eliminated when age-adjusted ROC curves were taken into account. Lastly, we showed that, at the clinically used cutoff value indicating microalbuminuria, sensitivity and specificity of 24-hour urinary albumin excretion did not differ significantly from the ACR in a first morning void when gender-specific cutoff values for the ACR were applied.
Urinary albumin concentration in urine samples is not only dependent on the amount of albumin lost but is also affected by variations in hydration status. If a subject is well hydrated and consequently his or her urine volume is high, urinary albumin concentration will be low, and vice versa. These hydration-status-dependent variations in urinary albumin concentration are likely to influence the predictive performance. Of note, creatinine per unit of time is assumed to be fairly stable over 24 hours (22). If urinary albumin concentration is divided by urinary creatinine concentration, it will mathematically result in a "correction" for intraindividual variations in hydration status. Thus, the obtained ACR is expected to be "better" in predicting cardiovascular morbidity and mortality. Indeed, we found that the predictive performance of the ACR is significantly higher than that of albumin concentration.
In addition, our data show that the ACR-predicted outcome was even slightly better than 24-hour urinary albumin excretion, although not statistically significant. This finding is unexpected for 2 reasons. First, because of the circadian rhythm of albuminuria, which furthermore differs between subjects, we anticipated that albuminuria measures in first-morning-void urine samples would predict outcome less well than 24-hour urinary albumin excretion. Second, although the ACR adjusts for hydration status, creatinine excretion depends on muscle mass. Differences in muscle mass between individuals will also affect the predictive performance of the ACR.
Which factors may explain why the ACR predicted outcome even slightly better than 24-hour urinary albumin excretion did? First, we cannot exclude the possibility that subjects made errors during 24-hour urine collection, which will negatively affect the predictive performance of 24-hour urinary albumin excretion. Similarly, the predictive performance of the ACR in the 24-hour urine samples was found to be exactly similar to that of the ACR in the first morning void. Second, the ACR depends on not only urinary albumin concentration but also urinary creatinine concentration. We demonstrated in a secondary analysis that, in both males and females, urinary creatinine concentration in a first morning void decreases at higher age. This finding is explained not only by a decrease in muscle mass with aging, since 24-hour urinary creatinine excretion, a surrogate for total muscle mass, decreases less with aging than urinary creatinine concentration in a first morning void. These data indicate that at a higher age, nighttime urinary albumin concentration decreases because of dilution. Several factors have been described in the literature that explain the nighttime dilution that occurs with aging. These factors include a change in diurnal rhythm of vasopressin and atrial natriuretic peptide and an inability of the kidney to retain sodium at higher ages (23–29). As mentioned above, it has been advocated to use the ACR to correct for intraindividual variations in urine volume. However, our data show that, because of the loss of muscle mass, the ACR in first-morning-void urine samples increases with aging more steeply than 24-hour urinary albumin excretion. These considerations may seem theoretical but are of clinical importance because they may explain that the ACR performs the best in predicting cardiovascular events and mortality. Since the ACR in a first morning void increases more steeply than 24-hour urinary albumin excretion with aging, it incorporates to a certain extent the predictive value of higher age for cardiovascular morbidity and mortality. The opposite will be true for albumin concentration in a first morning void, since, because of dilution, it increases less with aging when compared with 24-hour urinary albumin excretion. These hypotheses are corroborated by our observation that, after adding age to the predictive model, the area under the ROC curves was exactly similar for all albuminuria measures.
Since the predictive performance of the ACR in a first morning void is at least similar to that of 24-hour urinary albumin excretion, the ACR provides a feasible alternative to the more cumbersome collection of a 24-hour urine sample. The next question to be addressed is what cutoff values for the ACR indicating microalbuminuria should be used. According to treatment guidelines from diabetic and nephrology associations, the cutoff value for ACR indicating microalbuminuria is 30 mg/g (20, 30). However, because of differences between the sexes in muscle mass and hence in urinary creatinine excretion, some authors advocate the use of gender-specific cutoff values: 17 mg/g for males and 25 mg/g for females (21). In our study, application of the guideline-recommended albumin:creatinine cutoff value of 30 mg/g resulted in a low sensitivity to identify subjects at risk of cardiovascular morbidity and all-cause mortality compared with the sensitivity at a gender-specific cutoff value. As a consequence, the number of false-negative test results is high, which indicates that a considerable proportion of subjects at risk of adverse outcomes will not be identified if a cutoff value of 30 mg/g is used. Therefore, we advocate applying gender-specific cutoff values for the ACR indicating microalbuminuria: 17 mg/g for males and 25 mg/g for females.
A few issues need to be considered when interpreting our findings. First, this study was designed to assess which albuminuria measure can be used for initial population screening for albuminuria from the perspective of early detection of cardiovascular disease. In this sense, it is not a prerequisite that microalbuminuria be an independent risk factor. Determination of whether the ACR kept its prognostic power after adjustment for established cardiovascular risk markers was therefore not the aim of this study. Second, the absolute values of the ROC curves may seem low in this study. However, when we performed ROC curve analyses for systolic blood pressure and cholesterol to predict cardiovascular outcomes and all-cause mortality, the areas under the ROC curves were 0.69 and 0.64, respectively. These examples illustrate that accepted cardiovascular risk factors, such as systolic blood pressure and cholesterol, by themselves have a modest impact on the area under the ROC curve (31). Third, one might argue that spot urine collections would have been more appropriate, since they can be collected during consultation and are more practical than first-morning-void collections. The rationale to use first morning voids in this study was based on the KDOQI guidelines, advocating the use of first-morning-void collections over spot urine collections for testing of microalbuminuria (20). Finally, keep in mind that these results were obtained in a predominantly nondiabetic, Caucasian cohort. Further research in other populations, including diabetic and hypertensive populations, should corroborate our findings.
In conclusion, measuring the ACR in a first morning void is at least as reliable as measuring the gold standard, 24-hour urinary albumin excretion for prediction of cardiovascular morbidity and mortality. These results are clinically important because they imply that albuminuria measured in first-morning-void collection, which is clinically more feasible than collecting a 24-hour urine sample, is valid for estimating patients' risk.
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ACKNOWLEDGMENTS |
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Author affiliations: Department of Clinical Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands (Hiddo J. Lambers Heerspink, Dick de Zeeuw); and Division of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands (Auke H. Brantsma, Stephan J. L. Bakker, Paul E. de Jong, Ron T. Gansevoort).
The authors thank Dade Behring (Marlburg, Germany) for supplying equipment (Behring Nephelometer II) and reagents for nephelometric measurement of urinary albumin concentration.
Conflict of interest: none declared.
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