Introduction
Post hoc analyses and secondary analyses of numerous clinical trials have demonstrated a strong relationship between albuminuria and proteinuria and progression of kidney and cardiovascular diseases (1–10). This is evident whether patients have diabetic or nondiabetic forms of chronic kidney disease (CKD). Less well understood is the role of other clinical and demographic factors that may indicate an increased proclivity to higher baseline proteinuria and proteinuria change over time.
The recognition that brachial BP levels are generally (and variably) higher than those in the aorta has raised concern that the predictive value of brachial BP for a clinical outcome may be matched or superseded by knowledge of central aortic BP values, which can now be measured noninvasively (11–13). The exact relationship between central and peripheral measures of BP and vascular stiffness, and risk for increasing proteinuria, is unknown. Clinical studies have demonstrated that higher measures of peripheral (brachial artery) BP correlate with increasing levels of proteinuria and increased risk for progression of kidney disease (14–16). However, the kidneys, since they are supplied directly from the central aortic circulation, may provide a biomeasure of central aortic pressure load. We hypothesized that proteinuria could be more closely related to central measures of BP when compared with brachial artery cuff measurements (17,18). Moreover, we also hypothesized that measures of aortic vascular stiffness could also be directly related to the level of proteinuria. The purpose of this ancillary study of the Chronic Renal Insufficiency Cohort (CRIC) study was to evaluate both peripheral and central measures of BP and vascular stiffness and their relationship to the level of proteinuria.
Materials and Methods
Study Population
The CRIC study included a racially and ethnically diverse group of adults, 21 to 74 years, with both diabetic and nondiabetic kidney disease. Inclusion in the CRIC study was based on age specific estimated glomerular filtration rate (GFR) levels as follows: 20 to 70 ml/min per 1.73 m2 for patients age 21 to 44 years, 20 to 60 ml/min per 1.73 m2 for patients aged 45 to 64 years, and 20 to 50 ml/min per 1.73 m2 for patients aged 65 to 74 years. A total of 3939 patients were recruited into the CRIC study, of whom 3324 were eligible for central BP measures and pulse wave (PWV). Only those patients with an irregular pulse, known aortic valve disease, or poor quality wave forms were excluded. CRIC participants were recruited between June 2003 and March 2007 from 13 sites and seven centers in the United States (Baltimore, Philadelphia, Cleveland, Detroit, Chicago, New Orleans, and Oakland/San Francisco).
Procedures
At each CRIC study visit, several core measurements were ascertained and have been described elsewhere (19). Most pertinent to this study, three brachial BP measures were obtained in the sitting position after at least 5 minutes of quiet rest by trained staff. An aneroid sphygmomanometer was used with cuff size based on the patient's arm circumference.
Aortic pulse wave measurements were performed the same day as the BP measurements (20). These were performed supine after at least 5 minutes of rest, using the right carotid and right femoral arteries. The SphygmoCor PVx (At Cor Medical, West Ryde, Australia) device was used at each site (21). Three electrocardiographic leads were attached to the patient: one on the left arm, one on the right arm, and one on the left lower abdomen or leg. This provided a standard limb lead II for electrocardiographic tracing. The distance from the sternal notch to the point of the palpable carotid pulse was measured in millimeters and entered into the computer program. The right femoral pulse was palpated and the distance to the umbilicus from the sternal notch, and then from the umbilicus to the point of femoral palpation, was measured in millimeters and also entered into the program. A Millar tonometer, attached to an electronic module interface, was placed perpendicular to the carotid pulse and repositioned in small increments until a stable wave form was observed. After capturing 10 seconds of stable wave form, the same sequence was repeated using the femoral artery. After the second wave form was captured, the computer generated an estimation of aortic PWV with an SD. If the SD was greater than 15% of the PWV, the study was repeated. Enrollment into the PWV ancillary study began in 2005. The PWV measures were obtained on subjects at their second year follow-up visit.
Central aortic pressures were estimated using applanation tonometry of the radial artery as reported previously using the same SphygmoCor device (21). Briefly, in the supine position, the probe was centered over the radial artery with continuous real-time monitoring on a laptop computer at bedside. Once 10 seconds of stable waveform were obtained, the tracing was saved and the data quality for pulse wave analysis analyzed immediately for pre-established quality parameters (20,22). If these parameters were not met, the study was repeated. A 24-hour urine collection for protein was obtained on each participant on the day of the BP and central hemodynamic measures. Protocol-specified laboratory measurements were obtained on participants at each annual visit. The estimated GFR was determined according to the abbreviated Modification of Diet in Renal Disease formula (23), using creatinine values calibrated to the Cleveland Clinic laboratory. Ethnicity was self-described by each participant.
Statistical Analyses
Continuous variables are presented as mean ± SD or (95% confidence intervals). Categorical variables are expressed as a percentage. Certain variables were prespecified as covariates in the analyses because of their purported relationship to aortic PWV (age, systolic BP) and their role as determinants of the progression of kidney disease (ethnicity, gender, smoking status, waistline circumference, etc.). The log-transformed 24-hour urine protein was designated as the outcome variable in this cross-sectional analysis, and hemodynamic measures, including brachial and central arterial BP, were considered key predictors. Multiple regressions were performed sequentially. First, several variables known to be associated with proteinuria designated as key confounders—including age, ethnicity, gender, estimated GFR, heart rate, smoking status, waist circumference, clinical site, and use of renin-angiotensin system blocking (RAS) drugs—were entered into a baseline regression model. All analyses were stratified based on diabetic status, given diabetes' crucial role in predicting proteinuria in CKD, and we specifically recruited to have 50% diabetic kidney disease representation in the CRIC cohort. We also stratified by age, given the importance of this variable on pulse wave amplification. We also fit a joint model to include diabetics and nondiabetics to formally evaluate for possible interactions. Next, peripheral hemodynamic measures (brachial systolic and pulse pressure) were added to the basic and joint model to determine their incremental value in predicting proteinuria. Finally, central hemodynamic measures PWV, and central systolic and pulse pressure, were entered into the two models separately to assess their independent role as determinants of proteinuria across the cohort. We made a decision to choose these central hemodynamic measures instead of the augmentation index standardized to a heart rate of 75 beats per minute, or augmentation pressure, because of their lack of statistical significance as univariate measures in the model of log transformed 24-hour urine. All analyses were executed in SAS 9.2 (SAS Institute, Cary, NC).
The CRIC subcohort undergoing aortic pulse wave measurements was not randomly selected from the core study population. To minimize the introduction of selection bias, all regression analyses were adjusted by inverse probability weights to ensure results were applicable to the core CRIC study group, as reported previously (24).
Results
Demographic Characteristics of All Participants
The entire CRIC cohort of 3939 patients included 3324 who had their second-year visit before April 2009. Of 3324 participants, 716 did not have a PWV measurement, 42 had an unacceptably poor quality measurement, 415 lacked a 24-hour urine protein at the time of the pulse wave velocity visit, and seven did not have an estimated GFR from the pulse-wave visit. This left 2144 patients for analysis. Table 1 exhibits differences between CRIC participants who were included in the study versus those who were not. The observed statistical differences were not clinically meaningful but reflect the nonrandom selection of CRIC participants who underwent pulse wave measurements. It is important to consider that the CRIC population that we studied is not necessarily representative of the U.S. CKD population. Note that in the analytic cohort, ethnicity was balanced between minority populations and non-Hispanic Caucasians, and there were slightly more men. A large proportion of patients were on RAS-blocking drugs, and only 11% were current smokers.
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Table 1: Demographics of CRIC participants and the analytic cohort
Note the well controlled mean BP of 126/71 mmHg but increased waistline (104 cm and body mass index 31 kg/m2). Mean estimated GFR was approximately 44.8 ml/min per 1.73 m2, and mean 24-hour urine protein was 790 mg.
Table 2 illustrates the analytic cohort by diabetic status. Diabetics had higher peripheral and central BP, as well as greater vascular stiffness, as indicated by the higher PWV relative to the nondiabetics (10.6 versus 8.6 m/s, respectively, P < 0.001).
Table 2: CRIC analytic cohort by diabetes status
Figure 1 exhibits the correlation between the key hemodynamic measures examined in the study. There was a strong correlation between brachial systolic BP (SBP) and central SBP, but any value for brachial artery SBP can be associated with an almost 30-mmHg variation in central SBP.
Figure 1: This scatterplot matrix figure (n = 2122) illustrates the relationship (Pearson correlation coefficient, P value) between (A) central systolic BP (CSBP) mmHg and peripheral systolic BP (SBP) mmHg (0.97, P < 0.0001); (B) peripheral pulse pressure (PP) mmHg and CSBP mmHg (0.74, P < 0.0001); (C) central pulse pressure (CPP) mmHg and SBP mmHg (0.78, P < 0.0001); and (D) CPP mmHg and PP mmHg (0.95, P < 0.0001).
African-American ethnicity, current smoking, heart rate, and waistline, when examined as univariate factors, were significantly associated with increased proteinuria. Likewise, female gender, RAS blocker therapy (diabetics), and higher estimated GFR were associated with less proteinuria (Table 3). Table 4 presents the multivariable models evaluating diabetic and nondiabetic kidney disease participants, separately examining factors associated with the log transformation of 24-hour urine protein in the study sample.
Table 3: The influence of univariate measures in the model on L- transformed 24-hour urine protein
Table 4: Multivariable model to examine the incremental effect of peripheral and central measures of blood pressure and pulse wave velocity on the log-transformed 24-hour urine
With addition of the hemodynamic parameters, SBP measured peripherally accounted for a significant portion of variation in urinary protein in diabetics (R2 of 0.40 compared with R2 of 0.28 in a model without systolic BP, P < 0.001) as well as nondiabetics (R2 of 0.38 compared with R2 of 0.34 in a model without SBP, P < 0.001). The association of differences in BP with differences in proteinuria (as measured by the regression coefficient) was stronger in diabetics than nondiabetics, as evidenced by the greater increment in the R2 obtained by adding these variables. With the addition of peripheral pulse pressure and central pulse pressure, there were limited improvements in the predicted variation in urinary protein, and with little incremental change in the R2 value. When PWV, as a measure of arterial stiffness, was added to the model, there was a significant increment in R2 to 0.42 in diabetics (P < 0.001) but less so in nondiabetics (P < 0.001) although it received significant. Thus, PWV seems to explain a portion of the variation in urinary protein excretion, as measured cross-sectionally in diabetics, beyond that accounted for by SBP. The addition of central systolic and pulse pressure added little to the predictive ability of the model, both for diabetics and nondiabetics. In the joint model, including both diabetics and nondiabetics, the only interaction we were able to identify of statistical significance (P = 0.04) was between diabetics and PWV.
Since pulse wave amplification is known to differ between younger and older subjects, we conducted a separate analysis according to age group over or under 55 years. In the multivariable model, stratified by age, we noted similar patterns where peripheral SBP accounted for a significant portion of the variation in urinary protein excretion.
In the fully adjusted model, with diabetes included as a model term, the interaction term for SBP with diabetes was 0.057/10 mmHg (P = 0.17). The interaction term for pulse pressure with diabetes was 0.038/10 mmHg (P = 0. 45). The interaction term for PWV and diabetes was significant (0.041 m/s, P < 0.05).
Before inclusion of hemodynamic parameters into the diabetes stratified multivariable model, the covariates-only portion of the model explained about 28% of the variance in 24-hour urine protein excretion in diabetics compared with 33% of that in nondiabetics. Incremental addition of the brachial SBP increased the R2 value to a greater degree in the diabetics compared with the nondiabetics. The addition of brachial or central pulse pressure did not increase the R2 value further in either the diabetic or the nondiabetic group. When PWV was added to the model on the background of covariates, SBP and either brachial or central pulse pressure, there was a modest but significant increase of 0.02 in the R2 of 24-hour urine protein excretion observed in the diabetics but no change in the R2 in the nondiabetic group. The fully adjusted model explained 42% of the variance in 24-hour urine protein excretion in diabetics and 38% of that in nondiabetics.
Discussion
Proteinuria signifies not only a membrane barrier defect comprised of podocytes, glomerular-based membrane, and the vascular endothelium but also the presence of generalized vascular disease (9,25). Clinical trials show that sustained elevation of brachial artery BP correlates with the rate of progression of kidney disease, and that reduction of brachial artery BP, particularly in the presence of increased proteinuria, results in a slowing of the progression of kidney disease (14–16,26,27). The benefits of both lower BP and the therapeutic advantage of renin angiotensin system blockade, likely through reduction in glomerular capillary pressure, have been demonstrated in both experimental (28) models and in humans (14–16). Others have shown that proteinuria is the most important correlate of SBP in older men with CKD (29). However, little is known about the degree to which central BP levels and measures of aortic stiffness (PWV) contribute to ambient levels of proteinuria. Herein we describe our efforts to understand more about the relationships between central and peripheral measures of BP, along with measures of vascular stiffness, in more than 2000 participants with CKD. This provided a unique opportunity to create a model incorporating not only demographic and clinical features but also simultaneous measures of both peripheral and central BP and vascular stiffness. We observed that older age, African-American ethnicity, current smoking, increased waist circumference, and male gender were associated with higher levels of proteinuria in this CKD population. The participants had well controlled BP, with a majority below 130/80 mmHg, with many taking drugs that block the RAS.
We created our model using a variety of clinical and demographic variables. Our outcome was the natural log transformation of the 24-hour protein. We sequentially added hemodynamic measures into this linear regression model, at first independently and then in a model adjusted for a variety of different clinical factors evaluated separately. The multivariable model demonstrated both the powerful effect of SBP and the additional independent role of PWV as a measure of vascular stiffness on variations in urinary protein excretion in the diabetic CKD population. Central pressure measurements did not add much to the overall, in either diabetics or nondiabetics, when brachial measures were taken into account.
The marked variation in central measures of SBP and pulse pressure, for the given level of brachial artery SBP, demonstrates the potential for the independent contribution these central measures might make to hemodynamics and indicators of end-organ damage such as urinary protein excretion, as shown in Figure 1. We hypothesized that the impact of this variability on the renal circulation might be substantial, especially if there were autoregulatory compromises of preglomerular vascular beds (30). This would allow greater transmission of systemic pressure to the glomerular capillary network, which might result in increasing amounts of proteinuria. However, our data did not support this hypothesis. This could indicate adequate autoregulatory capability or that other nonhemodynamic factors are more determinative of the level of proteinuria.
The importance of PWV and/or vascular stiffness as a determinant of proteinuria, especially in diabetics, is noteworthy. Previously we have reported, in these same CRIC cohort study participants, the independent positive associations of age, blood glucose concentration, African-American ethnicity, waist circumference, mean arterial BP, male gender, and the presence of diabetes with PWV, and a significant negative association with the level of kidney function (20). These same observations were strikingly similar with our dependent variable, which was the log transformed 24-hour urine protein level. Thus, the combination of brachial artery SBP and increased vascular stiffness explains the higher level of 24-hour urine protein. Perhaps this may be associated with increased risk for progression of kidney and cardiovascular disease. Others have reported that PWV is an important predictor of survival in patients with end-stage renal disease (31,32). Likewise, PWV determinations have contributed to the epidemiology of cardiovascular disease adverse outcomes in other populations, including otherwise healthy older people, community-based cohorts, patients with diabetes, and those with hypertension (33–37).
Most prior studies have focused on the association between baseline microalbuminuria and vascular compliance with kidney disease progression. Most of these studies have demonstrated higher values of PWV in those patients with microalbuminuria (34,37–40). Few studies have examined this relationship in patients with higher levels of proteinuria, especially in the presence of CKD.
Our observations provide some clinical perspective. First, SBP remains an important therapeutic target. Although our analysis is only cross-sectional, these current observations, coupled with the evidence from large-scale trials in the treatment of patients with overt proteinuria and chronic kidney disease, indicates the importance of lower SBP treatment goal (14–16). How low is not known.
In patients with diabetic CKD, treatment strategies directed toward aortic compliance could be relevant. Perhaps therapies that target the accumulation of advanced glycation end products would be relevant. Or may be therapies that may influence the formation of extracellular matrix, or vascular calcification as a result of disordered calcium, phosphorus, and vitamin D metabolism. Alterations in the concentration of circulating of pro-inflammatory cytokines and pro-fibrogenic factors such as fibroblast growth factor (FGF)-23 and TGF-β may be important to evaluate as possible treatment targets if it results in improved vascular compliance. These treatments may compliment the benefits of SBP reduction, particularly with RAS-blocking drugs.
Our study had several limitations. The study was cross-sectional, which limits causal inferences. The majority of participants in the CRIC study were on multiple antihypertensive medications. Many of them were on RAS-blocking drugs and had a legacy of good BP control. Thus, these observations may not be generalizable to patients with CKD who have less well controlled BP. There may also be variations in the influence of the antihypertensive medications on large artery stiffness, which could have influenced the results. This is quite plausible given that a substantial portion of variation in urinary protein excretion in the study cohort remains unexplained by measured factors in the analysis. Reduction in SBP and improvement in vascular compliance may result in less proteinuria. The longitudinal nature of our study will allow us an opportunity to tie together these relationships and make directional observations.
In conclusion, in a large and diverse cohort of patients with CKD, we describe the relationship of SBP and PWV, especially in diabetics, in correlating with higher levels of 24-hour urine protein excretion. These observations, coupled with the observations of clinical and demographic features associated with increased proteinuria, may provide clinicians a better opportunity for treatment to reduce proteinuria in patients with CKD.
This is a clinically relevant consideration, especially in patients with CKD, given the incremental risk of reduced GFR and proteinuria with all-cause and cardiovascular mortality in the general population (41).
Disclosures
None.
Acknowledgments
This report work was supported by the following institutions: University of Maryland GCRC (M01 RR-16500), University of Pennsylvania CTRC CTSA (UL1 RR-024134), The Johns Hopkins University (UL1 RR-025005), Case Western Reserve University Clinical and Translational Science Collaborative (University Hospitals of Cleveland, Cleveland Clinical Foundation, and MetroHealth UL1 RR-024989), University of Michigan (GCRC grant M01 RR-000042, CTSA grant UL1 RR-024986), University of Illinois at Chicago CTSA (UL1RR029879), Tulane/LSU/Charity Hospital General Clinical Research Center (RR-05096), Kaiser NIH/NCRR UCSF-CTSI (UL1 RR-024131). We thank Tia A. Paul, University of Maryland School of Medicine, Baltimore, for expert secretarial support.
Published online ahead of print. Publication date available at www.cjasn.org.
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