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Hypertension. 2006;48:301-308
Published online before print July 3, 2006, doi: 10.1161/01.HYP.0000232644.98208.65
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(Hypertension. 2006;48:301.)
© 2006 American Heart Association, Inc.

Original Articles

Ambulatory Pulse Pressure and Progression of Urinary Albumin Excretion in Older Patients With Type 2 Diabetes Mellitus

Walter Palmas; Andrew Moran; Thomas Pickering; Joseph P. Eimicke; Jeanne Teresi; Joseph E. Schwartz; Lesley Field; Ruth S. Weinstock; Steven Shea

From the Department of Medicine (W.P., L.F., S.S.), Department of Epidemiology, Joseph Mailman School of Public Health (S.S.), and Department of Biomedical Informatics (S.S.), Columbia University, New York, NY; Department of Medicine (A.M.), University of California at San Francisco; General Internal Medicine Section (A.M.), San Francisco Veterans Affairs Medical Center, Calif; Behavioral Cardiovascular Health and Hypertension Program (T.P.), Columbia University, New York, NY; Hebrew Home for the Aged at Riverdale (J.P.E., J.T.), Bronx, NY; Department of Psychiatry and Behavioral Science (J.E.S.), State University of New York at Stony Brook; Joslin Diabetes Center and Division of Endocrinology, Diabetes and Metabolism (R.S.W.), State University of New York, Upstate Medical University, Syracuse; Department of Veterans Affairs (R.S.W.), VA Medical Center, Syracuse, NY.

Correspondence to Walter Palmas, Division of General Medicine, 622 W 168th St, PH 9-East, New York, NY 10032. E-mail wp56{at}columbia.edu


*    Abstract
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 *Abstract
down arrowIntroduction
 down arrowMethods
 down arrowResults
 down arrowDiscussion
 down arrowReferences
 
We studied whether ambulatory blood pressure monitoring added to office blood pressure in predicting progression of urine albumin excretion over 2 years of follow-up in a multiethnic cohort of older people with type-2 diabetes mellitus. Participants in the Informatics for Diabetes Education and Telemedicine study underwent a baseline evaluation that included office and 24-hour ambulatory blood pressure measurement and a spot urine measurement of albumin-to-creatinine ratio (ACR). Measurements of albumin-to-creatinine ratio were repeated 1 and 2 years later. In bivariate analyses, ambulatory 24-hour pulse pressure was the blood pressure variable most strongly associated with follow-up ACR. Repeated-measures mixed linear models (n=1040) were built adjusting for baseline ACR ratio, clustered randomization, time to follow-up, and multiple covariates. When both were entered into the model, ambulatory 24-hour pulse pressure and office pulse pressure were independently associated with follow-up ACR (ß [SE]=0.010 [0.002], P<0.001, and 0.004 [0.001], P=0.002, respectively). Cox proportional hazards models examined associations with progression of albuminuria in 954 participants without macroalbuminuria at baseline, adjusting for all of the covariates independently associated with follow-up ACR in mixed linear models. Ambulatory 24-hour pulse pressure, but not office pulse pressure, was independently associated with progression of albuminuria (P=0.015 and 0.052, respectively). The adjusted hazards ratio (95% CI) per each 10-mm Hg increment in ambulatory pulse pressure was 1.23 (1.04 to 1.42). In conclusion, ambulatory pulse pressure may provide additional information to predict progression of albuminuria in elderly diabetic subjects above and beyond office blood pressure.


Key Words: albuminuria • blood pressure monitoring, ambulatory • diabetes mellitus


*    Introduction
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 *Introduction
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Albuminuria, defined as increased urinary albumin excretion (>30 mg/24 h), is an independent predictor of cardiovascular morbidity and mortality in people with and without diabetes mellitus.1–4 Furthermore, the association of albumin urinary excretion with major cardiac events has been shown to extend to urine albumin levels below the currently accepted cutoff point for microalbuminuria.5,6 Albuminuria is prevalent in elderly and middle-aged people with type 2 diabetes mellitus, and it has often been found at the time of diagnosis.7–9 Systemic blood pressure measured at the office, particularly systolic blood pressure (SBP), is an important determinant of albuminuria. It predicts the progression of albuminuria and deterioration of renal function in diabetic10,11 and nondiabetic nephropathy.11,12

Ambulatory blood pressure monitoring (ABPM) has been shown in several studies to be superior than office blood pressure in predicting cardiovascular events.13–17 Regarding albuminuria, longitudinal studies in people with diabetes have reported an independent association between the progression of albuminuria and blood pressure measured by ABPM.18–20 However, those studies were limited predominantly to white populations of European ancestry,18–20 had relatively small sample sizes, potentially limiting the reproducibility of multivariate analyses,21 and only 1 of them included people with type 2 diabetes.18 It remains unclear whether ABPM may add to a clinical assessment, which includes an office blood pressure measurement, in predicting changes in urinary albumin excretion in older multiethnic populations with type 2 diabetes.


*    Methods
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 *Methods
down arrowResults
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We studied participants enrolled in the Informatics for Diabetes Education and Telemedicine (IDEATel) Study.22 IDEATel evaluates telemedicine as a means of managing the care of older Medicare beneficiaries (age ≥55 years) with diabetes who reside in medically underserved areas of New York state. There were 2 clinical centers, 1 at Columbia University in New York, NY (urban region) and another at State University of New York Upstate Medical University in Syracuse, NY (rural region). Inclusion criteria in IDEATel were: age ≥55; being a current Medicare beneficiary; having diabetes as defined by a physician’s diagnosis and being on treatment with diet, an oral hypoglycemic agent, or insulin; residence in a federally designated medically underserved area (either of 2 federal designations, Medically Underserved Area or Health Professional Shortage Area) in New York state; and fluency in either English or Spanish. Exclusion criteria were: moderate or severe cognitive impairment, severe visual, mobility or motor coordination impairment, severe comorbid condition, severe expressive or receptive communication impairment, lack of free electrical outlet for home telemedicine unit (HTU), and spending >3 months a year at a location different from their New York state residence. Participants were randomly assigned to a telemedicine intervention group or a control group, who continue to receive their usual diabetes care. Randomization was clustered by the participants’ primary care provider. Participants in the intervention group receive telemedicine diabetes case management through a HTU.23 Nurse case managers provided comprehensive diabetes care at regular intervals through the HTU.

Data Collection
IDEATel participants underwent a baseline examination between December 2000 and October 2002. They all signed informed consent, and the protocol was approved by the corresponding institutional review boards at all of the participating institutions. Prescription drug use was ascertained by interviewer-administered questionnaire. Height, weight, and seated blood pressure were measured; blood and spot urine samples were collected; and 24-hour ABPM was performed. Participants returned for follow-up examinations 1 and 2 years after their baseline visit, and urine samples were collected again. This study examines the relationship between data obtained at baseline and the urinary albumin content, measured by spot urine ACR ratio, at the follow up visits.

Laboratory Measures
Urine albumin level was measured using the immunoprecipitin method (Diasorin) from a random spot samples. Values <5.7 mg/dL were assigned a value of 4.0 mg/dL. Urine creatinine level was measured using the picric acid colorimetric method. Both analyses were performed using a Roche/Hitachi 717 automated analyzer (Roche Diagnostics). Hemoglobin A1c was analyzed by boronate affinity chromatography with the Primus CLC 385 (Primus). Total cholesterol, triglyceride, and high-density lipoprotein (HDL) cholesterol levels were measured using enzymatic colorimetric methods (Vitros, Johnson & Johnson). Low-density lipoprotein cholesterol level was calculated using the Friedewald equation for subjects with triglyceride levels <400 mg/dL and measured directly using a homogeneous assay (Polymedco) for those with triglyceride levels ≥400 mg/dL. Biochemical analyses were performed at Penn Medical Laboratory (currently MedStar, Inc) in Washington, DC.

Albumin-to-Creatinine Ratio
Albumin-to-creatinine ratio (ACR, milligrams of albumin per grams of creatinine) was calculated from a morning spot urine sample. Urine albumin excretion was categorized into normoalbuminuria (ACR <17 in men and <25 in women), microalbuminuria (ACR 17 to 250 in men and 25 to 355 in women), and macroalbuminuria (ACR >250 in men and >355 in women); these thresholds are believed to identify people with a urinary albumin excretion >30 mg/24 h and >300 mg/24 h, respectively.24 In participants without macroalbuminuria at baseline, progression of albuminuria was defined as follows. In those with normoalbuminuria at baseline, progression was defined as microalbuminuria or macroalbuminuria in ≥1 follow-up measurement. In those with microalbuminuria at baseline, progression was defined as macroalbuminuria in ≥1 follow-up measurement.

Resting Blood Pressure Measurement
Resting blood pressure was measured at the IDEATel baseline examination by trained personnel using the Dinamap Monitor Pro 100 (Critikon) automated oscillometric device. Three measurements were obtained after 5 minutes of rest in a quiet room, using a standardized protocol. The average of the second and third measurements was recorded as the resting blood pressure.

ABPM
ABPM was performed using a Spacelabs 90207 oscillometric monitor (SpaceLabs) after a published protocol.25 Blood pressure was recorded every 20 minutes for a 24-hour period with the machine programmed to deflate in 8-mm Hg bleed steps. Sleep and wake intervals were defined based on diary entries and confirmed by a telephone interview on the morning when monitoring ended. Nocturnal dipping was defined as a ratio of mean sleep-to-mean wake SBP of 0.80 to 0.90, inclusive (a decrease in sleep SBP of 10% to 20% relative to wake SBP). Nondipping was defined as a ratio >0.9, and extreme dipping was defined as a ratio <0.8.26

Statistical Analysis
Variables that did not approximate a normal distribution, including ACR, were log transformed for the analyses. Comparisons across categories of albuminuria were made using {chi}2 or Fisher’s exact tests (when any expected cell frequency was <5) for categorical variables and ANOVA for continuous variables. Pearson correlation coefficients were calculated between 2-year follow-up (log transformed) ACR and continuous independent variables.

The goal of the multivariate analyses was to test the independent association of ambulatory blood pressure with urine albumin excretion after adjustment for other covariates, including office blood pressure. Two types of multivariate analyses were performed: repeated-measures mixed linear models, which considered ACR as a continuous variable, and Cox proportional hazards models, which considered albuminuria as a categorical variable.

Repeated-measures mixed linear model analyses were performed with all of the available ACR measurements from the follow-up visits (1 or 2 measurements, as available) as the dependent variable, using longitudinal random effects methods, and adjusting for baseline ACR values, clustered randomization within the parent IDEATel study, and time from baseline to follow-up visits. The following covariates were included at the first step: age, gender, race, randomization status, years since being diagnosed with diabetes, current smoking, use of angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers, number of antihypertensive medications, body mass index (BMI), hemoglobin A1c, total cholesterol, HDL cholesterol, and triglycerides. Office pulse pressure was also entered at the first step, and 24-hour pulse pressure from ABPM was added at the next step. ABPM 24-hour pulse pressure was selected because it had the strongest bivariate correlation with ACR; other ABPM variables were not considered for inclusion in the multivariate model, because they were highly correlated with 24-hour pulse pressure and would have caused excessive multicollinearity.27 Collinearity between office and 24-hour pulse pressure in the final model was deemed acceptable, given the observed tolerance values of 0.463 and 0.643, respectively. Tolerance values <0.20 are considered to indicate excessive collinearity.28

A Cox proportional hazards model analysis was performed to assess whether office pulse pressure was independently associated with progression of albuminuria in participants without macroalbuminuria at baseline, after adjusting for clinical covariates and office blood pressure. The number of events (ie, number of participants exhibiting progression of albuminuria, n=164) made it advisable to limit the number of covariates included in the model. We selected the following covariates based on biological plausibility and their independent association with follow-up ACR in the mixed linear model analysis: age, race, years since being diagnosed with diabetes, current smoking, number of antihypertensive medications, BMI, baseline ACR (log-transformed), hemoglobin A1c, HDL cholesterol, and triglycerides. Office pulse pressure was entered at the initial step, and 24-hour pulse pressure was added next. To address concerns about possible collinearity between office and 24-hour pulse pressure, an alternative Cox model using office SBP instead of office pulse pressure was also examined. All of the predictor variables were considered fixed at baseline (ie, none of them was treated as time dependent). Correctness of the proportional hazards assumption was tested using the Harrell and Lee modification of the Schoenfeld goodness-of-fit test.29 Statistical analyses were performed using SPSS, version 13.0 (SPSS) and SAS, version 9.0 (SAS Institute Inc).


*    Results
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Sampling for this study is summarized in the Figure. There were 1040 IDEATel participants with complete baseline data and ≥1 ACR measurement at follow-up. Of them, 991 returned for a 1-year follow-up visit, and 918 returned for a 2-year follow-up visit. As a result, 885 participants had 2 ACR measurements during follow-up, whereas 155 had 1 follow-up measurement.


Figure 1
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  		Sampling for this study.

Participant characteristics at baseline are summarized in Table 1, categorized by their albuminuria status at the 2-year follow-up visit. Greater albuminuria was associated with higher office and ambulatory blood pressure, less nocturnal blood pressure dipping, higher serum levels of low-density lipoprotein cholesterol and triglycerides, lower levels of HDL cholesterol, and higher prevalence of smoking. Overall, there was an association between greater severity of albuminuria and clinical characteristics compatible with higher cardiovascular risk.


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  		TABLE 1. Selected Baseline Characteristics of 918 Patients, Categorized by Albuminuria Status at Their 2-Year Follow-Up Visit: IDEATel Study, New York, 2000–2004

Table 2 summarizes the bivariate associations between 2-year follow-up ACR and baseline blood pressure variables. ABPM 24-hour pulse pressure showed the strongest association with the 2-year follow-up ACR (correlation coefficient=0.33; P<0.001). Mean awake and sleep pulse pressure exhibited an association that was almost as strong (correlation coefficient=0.32 in both cases; P<0.001). The sleep-to-awake SBP ratio was not significantly associated with ACR.


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  		TABLE 2. Results of Bivariate Correlations of Baseline Office and Ambulatory Blood Pressure With 2-Year Follow-Up Urine ACR in 918 Patients: IDEATel Study, New York, 2000–2004

Follow-up ACR measurements were available in 1040 participants. The mean follow-up was 22.5±5 months. Table 3 summarizes the findings of the repeated-measures mixed model analysis examining the association of baseline characteristics with ACR at follow-up. In the first model, when baseline clinical and laboratory characteristics were evaluated with office blood pressure measurements, office pulse pressure was an independent predictor of follow-up ACR (ß [SE]=0.008 [0.001]; P<0.001). When ABPM 24-hour pulse pressure was added, it was independently associated with follow-up ACR (ß [SE]=0.010 [0.002]; P<0.001), whereas office pulse pressure continued to exhibit an independent association with follow-up ACR (P=0.002). Other variables independently associated with follow-up ACR in this model were Hispanic race (negative association), BMI, hemoglobin A1c, duration of diabetes, and the number of antihypertensive medications.


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  		TABLE 3. Results of Repeated-Measures Mixed Linear Models for 1040 Participants With 1-Year (n=991) and 2-Year (n=918) Follow-Up Urine ACR as the Outcome

There were 373 participants with microalbuminuria at baseline. Of them, 55 showed improvement to normoalbuminuria during follow-up. In addition, 69 participants had macroalbuminuria at baseline, and 12 of them showed improvement to microalbuminuria or normoalbuminuria during follow-up. Those 67 participants with improvement in albuminuria during follow-up had a higher HDL cholesterol level at baseline as compared with those without improvement (49.6±13 versus 45.2±14 mg/dL; P=0.018). No other baseline characteristic, including ambulatory and office pulse pressure, was significantly different between those 2 groups.

Among the 954 participants without macroalbuminuria at baseline, 164 experienced progression of albuminuria during follow-up. Uncontrolled blood pressure at baseline was defined for office and ambulatory measurements as SBP >130 mm Hg or diastolic blood pressure >80 mm Hg.30 The proportion of participants with uncontrolled blood pressure was high and almost identical among those with and without progression of albuminuria (Table 4). Cox proportional hazards regression was used to test whether office and 24-hour pulse pressure were independent predictors of the risk of progression of albuminuria (Table 5). Office pulse pressure was not independently associated with progression of albuminuria after adjustment for clinical covariates in the first model (P=0.57). When ambulatory pulse pressure was added in the second model, it was independently associated with progression of albuminuria (P=0.015). The multivariate adjusted hazard ratio ([HR] 95% CI) for increased albuminuria at follow-up per each 10-mm Hg increase in ABPM 24-hour pulse pressure was 1.23 (95% CI, 1.04 to 1.42). Other variables independently associated with progression of albuminuria were age, hemoglobin A1c, number of antihypertensive medications, and current smoking. The prespecified Cox model adjusting for office SBP instead of office pulse pressure rendered similar results (data not shown). In that model, the multivariate adjusted HR (95% CI) per each 10-mm Hg increase in 24-hour pulse pressure was 1.19 (95% CI, 1.03 to 1.36), and the same covariates were identified as independent predictors of progression of albuminuria.


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  		TABLE 4. Blood Pressure Control at Baseline and Progression of Albuminuria in 954 Subjects Without Macroalbuminuria at Baseline: IDEATel Study, New York, 2000–2004


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  		TABLE 5. Results of Proportional Hazards Model for Progression of Albuminuria in 954 Participants Without Macroalbuminuria at Baseline


*    Discussion
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 up arrowAbstract
 up arrowIntroduction
 up arrowMethods
 up arrowResults
 *Discussion
down arrowReferences
 
In this population of elderly people with diabetes, ambulatory pulse pressure, after regression adjustment for office blood pressure measurements and other clinical variables, was independently associated with increased urinary albumin excretion within a relatively short follow-up period. This expands on our previous cross-sectional report of an independent association between ambulatory blood pressure and albuminuria in this population,30 adding longitudinal data and an analysis of pulse pressure.

Our findings add to the body of evidence highlighting the relevance of pulse pressure in the elderly.31,32 Pulse pressure depends on ventricular ejection, arterial stiffness, and the timing of wave reflection back from the arterial tree.33 The ageing process increases arterial stiffness and induces an earlier wave reflection, resulting in higher pulse pressure, whereas diastolic blood pressure may remain within normal limits.33 In the Framingham study, office pulse pressure predicted coronary artery disease in those >60 years, whereas diastolic blood pressure was the strongest predictor in those <50 years.34 Glynn et al reported in a large population-based study that office pulse pressure was a stronger predictor of both cardiovascular and total mortality than SBP.31 In the Cardiovascular Study in the Elderly (CASTEL) study, office pulse pressure was a good predictor of coronary and stroke mortality in elderly women, even when their blood pressure was within the normotensive range.32 In regard to associations of blood pressure and renal function, other studies have reported the value of measuring pulse pressure.35,36 In the Systolic Hypertension in the Elderly Program (SHEP), office pulse pressure was an independent predictor of decline in renal function, although SBP appeared to be a stronger predictor.36 Verhave et al reported an inverse relationship between glomerular filtration rate and office pulse pressure in patients ≥60 years of age with untreated isolated systolic hypertension.35

Our results are also consistent with previous studies suggesting that ambulatory pulse pressure may provide additional prognostic information, above and beyond office pulse pressure. In an analysis in older patients with isolated systolic hypertension, nested within the Systolic Hypertension in Europe Trial, 24-hour pulse pressure was an independent predictor of major cardiovascular events, including stroke, myocardial infarction, and cardiovascular death, and consistently outperformed office pulse pressure as a predictor.37 In a study of middle age patients with essential hypertension, ambulatory pulse pressure performed better than office blood pressure to predict cardiovascular morbidity, although it was only marginally better than office pulse pressure to predict cardiovascular mortality.38 To the best of our knowledge, we are the first to study the incremental value of ambulatory pulse pressure in elderly people with diabetes in regard to progression of urine albumin excretion. Knudsen et al39 found a cross-sectional association between ambulatory pulse pressure and albuminuria in middle-aged Danish patients with type-2 diabetes. The same group in Denmark reported the value of ABPM to predict changes in albuminuria in younger subjects with type 1,19 and type 2 diabetes.18 They identified changes in ambulatory SBP over time as the most important blood pressure variable to predict a rise in albuminuria. As compared with the office measurement of blood pressure, ABPM offers several theoretical advantages, including more numerous measurements that improve reproducibility, and a better correlation with other end-organ damage, such as left ventricular hypertrophy.40,41 ABPM also allows the assessment of circadian variations in blood pressure, such as the physiological fall during sleep (known as "dipping"), and it is not subject to the artifactual increase in the physician-measured blood pressure, described as the "white-coat effect."40 However, the cost-effectiveness of ABPM in guiding medical management has not yet been established in the literature. In addition, the acceptability of ABPM may pose a challenge to clinicians. Little et al42 have reported that ambulatory monitoring has less acceptability than office measurements and home self-monitoring, probably because of greater discomfort and disturbance of life and sleep. In the IDEATel study, 11% of the 1665 participants refused the ABPM at the baseline visit.

Some limitations of our study should be noted. First, we measured albumin urinary excretion using a single-spot urine sample. A 24-hour urine collection, or 3 measurements instead of 1, probably provide a more accurate measurement of renal albumin excretion, but assessment of albuminuria in a spot urine sample has been accepted as an accurate estimate43–46 and a feasible alternative in large studies.47 Furthermore, there is no reason to expect that misclassification of albuminuria caused by our sampling procedure would be nondifferential with respect to the exposures. Second, our sample was composed of older subjects, with long-standing diabetes mellitus and prevalent end-organ damage at the time of enrollment, with a high prevalence of hypertension and nondipping. Therefore, our findings may not be applicable to younger patients with a more recent onset of type 2 diabetes, who have less end-organ damage and better-preserved autonomic function. For example, sleep blood pressure patterns have exhibited a strong association with progression of albuminuria in adolescents with type 1 diabetes,20 whereas our sample had a much higher prevalence of nondipping. Third, the IDEATel baseline examination did not include an assessment of renal function, such as a serum creatinine measurement. Patients with advanced renal failure were excluded from enrollment, but we do not know whether the addition of a serum creatinine measurement would have significantly altered the results of our multivariate analyses. Finally, renal biopsies were not performed, and participants were not evaluated for retinopathy. The possibility of nondiabetic nephropathy cannot be excluded, especially when diabetic retinopathy is not present.48,49

The strengths of this study include a longitudinal design that examines the temporal relationship between exposures and the outcome. In addition, our sample was large, well characterized, elderly, and multiethnic, with an adequate representation of women; we had abundant information with regard to pertinent covariates, including a detailed medication inventory and comprehensive laboratory and anthropometric measurements. Moreover, ABPM was performed using a well-validated methodology. Finally, we performed 2 types of multivariate analysis, repeated-measures mixed linear models and Cox proportional hazards regression, and obtained similar results.

Perspectives
Our main finding is that ambulatory pulse pressure improves the prediction of increased urine albumin excretion in older people with type 2 diabetes mellitus, when added to clinical characteristics and office blood pressure measurements. We believe this finding is important, because it contributes additional evidence about the relevance of pulse pressure in the elderly; current guidelines for the management of hypertension still rely heavily on systolic and diastolic blood pressure.50 In addition, the evaluation of pulse pressure by ambulatory monitoring appears to provide information above and beyond that provided by office blood pressure measurements. Further studies of the use and cost-effectiveness of ABPM in providing care for people with diabetes appear warranted.


*    Acknowledgments
 
Sources of Funding

This research was supported by Cooperative Agreement 95-C-90998 from the Centers for Medicare and Medicaid Services.

Disclosures

None.

Received April 14, 2006; first decision April 27, 2006; accepted June 7, 2006.


*    References
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 up arrowMethods
 up arrowResults
 up arrowDiscussion
 *References
 

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