Abstract
The authors previously reported that the visit‐to‐visit variability of blood pressure is correlated with renal function decline in nondiabetic chronic kidney disease. Little is known about the association between visit‐to‐visit variability and renal function decline in patients with diabetic chronic kidney disease. The authors retrospectively studied 69 patients with diabetic chronic kidney disease stage 3a, 3b, or 4. The standard deviation and coefficient of variation of blood pressure in 12 consecutive visits were defined as visit‐to‐visit variability of blood pressure. The median observation period was 32 months. In univariate correlation, the standard deviation and coefficient of variation of blood pressure were not significantly associated with the slope of estimated glomerular filtration rate. There was no significant association between the visit‐to‐visit variability of blood pressure and renal function decline in patients with diabetic chronic kidney disease, in contrast with our previous study of nondiabetic patients with chronic kidney disease.
The number of patients living with chronic kidney disease (CKD) or end‐stage renal disease (ESRD) is growing rapidly.1, 2, 3 The increasing financial burden of renal replacement therapy poses a serious threat to healthcare systems around the globe.4 The leading cause of ESRD is diabetes mellitus.5 Although renin‐angiotensin inhibition has recently become available, the number of patients with diabetic CKD continues to rise. The decline in the glomerular filtration rate (GFR) in diabetic CKD is highly variable, ranging from 2 to 20 mL/min/year.6, 7, 8 Mean blood pressure (BP), albuminuria, hemoglobin A1c, and serum cholesterol are known risk factors for the decline in GFR in diabetic CKD.9
Recently, the visit‐to‐visit variability (VVV) of BP has been shown to be a novel risk factor for stroke,10, 11 cardiovascular events,11, 12 and all‐cause mortality in the general population.13 It was reported that VVV of BP also predicts cardiovascular events and all‐cause mortality in patients with CKD.14, 15 We reported that the VVV of BP is associated with renal function decline in nondiabetic CKD.16 Okada and colleagues17 reported that VVV of BP is a risk factor for the development and progression of albuminuria in patients with type 2 diabetes. However, little is known about the association of VVV of BP with renal function decline in patients with diabetic CKD. Therefore, we evaluated the relationship between VVV of BP and renal function decline in patients with diabetic CKD. In addition, we compared this relationship with that found in a previous study of nondiabetic CKD.16
Methods
Study Patients
We retrospectively studied patients with diabetic CKD stage 3a, 3b, or 4 (estimated GFR [eGFR] 15–59 mL/min per 1.73 m2 at the first visit)18 who had visited our nephrology clinic at Iwakuni Medical Center between September 1994 and February 2013 (n=127). Diabetic CKD was defined according to the Kidney Disease Outcomes Quality Initiative guideline for diabetes and chronic kidney disease.19 Exclusion criteria were absence of diabetic retinopathy, low or rapidly decreasing GFR, rapidly increasing proteinuria or nephrotic syndrome, refractory hypertension, presence of active urinary sediment, signs or symptoms of other systemic disease, or >30% reduction in GFR within 2 to 3 months after initiation of an angiotensin‐converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB).19 The clinical courses of patients with diabetic CKD were followed through their medical charts until the start of dialysis, all‐cause death, or February 2013, whichever occurred first. Patients who were alive without the need for dialysis in February 2013 were excluded if their observation periods were less than 2 years or if they had made fewer than 12 visits. We ultimately enrolled 69 patients. This study was approved by the institutional review board of Iwakuni Medical Center.
Study Design
The present study used a retrospective observational design. At the beginning of the observation period, ACE inhibitors or ARBs were prescribed to all patients for renal protection.20, 21 Target BP was ≤130/85 mm Hg (125/75 mm Hg) in patients with proteinuria in excess of 1 g per 24 hours prior to 2003, according to the Fifth Report of the Joint National Committee on Detection, Evaluation, and Treatment of High Blood Pressure (JNC) 5 and JNC 6.22, 23 From 2003 on, the target BP was 130/80 mm Hg according to JNC 7.24 If a patient did not reach the target BP, then other antihypertensive agents other than ACE inhibitors or ARBs were added. Dietary protein was restricted to 0.8 g/kg per day.20, 25 Dietary sodium intake was restricted to 5 g of sodium chloride per day.20 All patients were also seen by a diabetologist in the same hospital, with target levels of hemoglobin A1c according to the Clinical Practice Recommendations by the American Diabetes Association. All patients underwent a medical interview, anthropometric measurements, and both blood and urine examinations in the morning after having fasted overnight. Habitual drinking was defined as alcohol intake >5 days per week, regardless of the amount.
BP Measurements
Office BP and heart rate were measured simultaneously by attending nurses in the morning with a mercury sphygmomanometer. At each office visit, two consecutive readings were taken on the nondominant arm with a 1‐minute interval after 5 minutes of rest in a sitting position; the average of the two readings was adopted as the office BP.
Definitions of BP Variability and BP Instability
Over a series of 12 consecutive visits from the beginning of the observation period, the mean office BP and the VVV of BP (expressed as within‐individual standard deviation [SD] and coefficient of variation [CV]; CV=SD/mean office BP in the 12 visits×100 [%]) were measured. The BP instability indices, expressed as the maximum office BP11 or the delta in office BP for the 12 visits, were also measured. Delta in BP was defined as the difference between the maximum and minimum BP.26
Measurement of Renal Function
Serum creatinine was measured at each office visit using an enzymatic method (before March 2005 the AutoAnalyzer 7150 [Hitachi, Tokyo, Japan] was used; after April 2005, the AU 640 [Beckman Coulter, Brea, CA] was used). eGFR was calculated using the 4‐variable Modification of Diet in Renal Disease equation with a Japanese coefficient of 0.808 calculated as follows27:
eGFR (mL/min per 1.73 m2)=0.808×175×serum creatinine−1.154×age−0.203×0.742 (if female).
Definition of Renal Outcomes
The primary outcome was the rate of decline in renal function, estimated by fitting a regression line through the eGFR measurements for each individual patient.28 This resulted in slopes expressing the yearly decrease in eGFR. eGFR measurements at the beginning and the end of the observation period were used.
Statistical Analyses
All data are expressed as mean±SD or as a percentage, unless otherwise specified. Univariate correlations between the clinical variables and the slope of the eGFR were assessed using Spearman's rank correlations, since the slope of the eGFR was not normally distributed (Shapiro‐Wilk test, P<.001). After adjustments for possible confounding factors (model 1: age, sex, body mass index, urinary protein creatinine ratio, serum total cholesterol, hemoglobin A1c, and mean systolic BP [SBP]; model 2: age, sex, body mass index, urinary protein creatinine ratio, serum total cholesterol, and hemoglobin A1c), multivariate linear regression analyses on the slope of the eGFR were performed. The null hypothesis was rejected when the two‐tailed P value was <.05. All statistical analyses were performed with SPSS version 19 (SPSS, Chicago, IL).
Results
Characteristics of the Patients
Table 1 shows the characteristics of the study patients. The overall mean age of the patients was 66.9 years. At baseline, the mean serum creatinine was 2.12 mg/dL, the mean eGFR was 27 mL/min per 1.73 m2, and the mean office BP was 151/73 mm Hg. The median observation period was 32 months. The median period of 12 consecutive visits from the beginning of the observation period was 10 months. At the end of the observation period, the mean serum creatinine level was 5.88 mg/dL and the mean eGFR was 11.7 mL/min per 1.73 m2. The mean slope of the eGFR was −7.77 mL/min per 1.73 m2 per year, which was approximately 3.5 times the rate previously described in the study of nondiabetic CKD.16 Two patients received only ACE inhibitors. Three patients received only ARBs. Only one patient was taking nonsteroidal anti‐inflammatory drugs. No patients were taking nondihydropyridine calcium channel blockers (CCBs) or human immunodeficiency virus medications.
Table 1.
Characteristics of the Patients
| Diabetic CKD (n=69) | |
|---|---|
| Age, y | 66.9±8.6 |
| Male sex, % | 67 |
| Body mass index, kg/m2 | 24.1±3.8 |
| Duration of diabetes, y | 14.0±9.6 |
| Current smoking, % | 21 |
| Habitual drinking, % | 38 |
| Hypertension, % | 96 |
| Dyslipidemia, % | 64 |
| Coronary artery disease, % | 7 |
| Stroke, % | 6 |
| Antihypertensive agents other than ACE inhibitor and ARB | |
| Dihydropyridine calcium channel blocker, % | 80 |
| Diuretic, % | 43 |
| β‐Blocker, % | 14 |
| α‐Blocker, % | 16 |
| Statin, % | 23 |
| Insulin, % | 35 |
| Serum creatinine, mg/dL | 2.12±0.70 |
| eGFR, mL/min/1.73 m2 | 27±12 |
| Blood urea nitrogen, mmol/L | 11.5±4.2 |
| Serum albumin, g/L | 36±7 |
| Serum uric acid, μmol/L | 406±81 |
| Hemoglobin concentration, g/L | 112±20 |
| Fasting glucose, mmol/L | 8.1±3.4 |
| Hemoglobin A1c, % | 6.8±1.4 |
| Serum total cholesterol, mmol/L | 5.3±1.3 |
| Urinary protein creatinine ratio, g/gCr | 4.9±4.4 |
| Mean office SBP, mm Hg | 151±15 |
| Mean office DBP, mm Hg | 73±10 |
| SD of office SBP, mm Hg | 18.3±5.2 |
| SD of office DBP, mm Hg | 9.8±2.7 |
| CV of office SBP, % | 12.2±3.5 |
| CV of office DBP, % | 13.5±3.7 |
| Maximum office SBP, mm Hg | 183±20 |
| Maximum office DBP, mm Hg | 90±11 |
| Delta in office SBP, mm Hg | 60±18 |
| Delta in office DBP, mm Hg | 33±10 |
Abbreviations: ACE, angiotensin‐converting enzyme; ARB, angiotensin receptor blocker; CKD, chronic kidney disease; Cr, creatinine; CV, coefficient of variation; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure; SD, standard deviation. Data are shown as mean±standard deviation or percentage.
Correlations Between BP Parameters and the Slope of the eGFR
Table 2 shows the univariate correlations of clinical variables with the slope of the eGFR. SD and CV of office SBP were not significantly associated with the slope of eGFR (Figure). This was in contrast with the results of our previous study of nondiabetic CKD, which showed a significant association between VVV of BP and the slope of eGFR in univariate correlation analysis.16 Table 3 shows the results of the multivariate linear regression analysis that determined the association of clinical variables with the slope of eGFR, after adjustments for confounders. In model 1, SD and CV of office SBP were not significantly associated with the slope of the eGFR. This was in contrast with the results of our previous study of nondiabetic CKD,16 which showed a significant association between VVV of BP and the slope of eGFR in multivariate regression analysis.16
Table 2.
Univariate Correlations on the Slope of the eGFR in Patients With Diabetic CKD (n=69)
| ρ | P Value | |
|---|---|---|
| Age, y | 0.33 | .005 |
| Body mass index, kg/m2 | 0.15 | .22 |
| Duration of diabetes, y | 0.06 | .64 |
| eGFR, mL/min/1.73 m2 | −0.16 | .19 |
| Blood urea nitrogen, mmol/L | 0.17 | .12 |
| Serum albumin, g/L | 0.54 | <.001 |
| Serum uric acid, μmol/L | 0.10 | .44 |
| Hemoglobin concentration, g/L | 0.31 | .010 |
| Fasting glucose, mmol/L | −0.06 | .63 |
| Hemoglobin A1c, % | 0.22 | .08 |
| Serum total cholesterol, mmol/L | −0.11 | .37 |
| Urinary protein creatinine ratio, g/gCr | −0.53 | <.001 |
| Mean office SBP, mm Hg | −0.32 | .007 |
| Mean office DBP, mm Hg | −0.32 | .007 |
| SD of office SBP, mm Hg | −0.04 | .74 |
| SD of office DBP, mm Hg | −0.001 | .99 |
| CV of office SBP, % | 0.05 | .68 |
| CV of office DBP, % | 0.17 | .17 |
| Maximum office SBP, mm Hg | −0.25 | .041 |
| Maximum office DBP, mm Hg | −0.19 | .11 |
| Delta in office SBP, mm Hg | −0.09 | .45 |
| Delta in office DBP, mm Hg | 0.03 | .79 |
Abbreviations: CKD, chronic kidney disease; Cr, creatinine; CV, coefficient of variation; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure; SD, standard deviation.
Table 3.
Multivariate Regression Analyses on the Slope of the eGFR in Patients With Diabetic CKD (n=69)
| Independent Variable | β | P Value | Model R 2 |
|---|---|---|---|
| Duration of diabetes, y a | 0.11 | .27 | 0.49 |
| Current smoking (presence=1) a | −0.07 | .48 | 0.49 |
| Habitual drinking (presence=1) a | 0.06 | .56 | 0.45 |
| Serum creatinine, mmol/L a | 0.15 | .12 | 0.50 |
| Blood urea nitrogen, mmol/L a | 0.08 | .39 | 0.49 |
| Serum albumin, g/L a | 0.22 | .05 | 0.51 |
| Serum uric acid, μmol/L a | −0.07 | .46 | 0.49 |
| Hemoglobin concentration, g/L a | 0.11 | .30 | 0.49 |
| Mean office SBP, mm Hg a | −0.32 | .002 | 0.48 |
| Mean office DBP, mm Hg a | −0.19 | .14 | 0.50 |
| SD of office SBP, mm Hg a | 0.10 | .34 | 0.49 |
| SD of office DBP, mm Hg a | 0.10 | .32 | 0.49 |
| CV of office SBP, % a | 0.11 | .29 | 0.49 |
| CV of office DBP, % a | 0.17 | .10 | 0.50 |
| Maximum office SBP, mm Hg b | −0.23 | .027 | 0.44 |
| Maximum office DBP, mm Hg a | −0.07 | .58 | 0.48 |
| Delta in office SBP, mm Hg a | 0.04 | .67 | 0.48 |
| Delta in office DBP, mm Hg a | 0.08 | .43 | 0.49 |
Abbreviations: CKD, chronic kidney disease; CV, coefficient of variation; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure; SD, standard deviation; β, standardized regression coefficient; R 2, multiple coefficient of determination. Model 1: aThese models were adjusted by age, sex, body mass index, urinary protein creatinine ratio, serum total cholesterol, hemoglobin A1c, and mean SBP. Model 2: bThis model was adjusted by age, sex, body mass index, urinary protein creatinine ratio, serum total cholesterol, and hemoglobin A1c.
Figure 1.
Simple correlations between the slope of the estimated glomerular filtration rate (eGFR) and coefficient of variation (CV) of office systolic blood pressure (SBP) (A), and mean office SBP (B) in patients with diabetic chronic kidney disease (CKD) (n=69).
On the other hand, mean office SBP/diastolic BP (DBP) and maximum office SBP were significantly correlated with the slope of eGFR in univariate correlation analysis (ρ=−0.32, P=.007/ρ=−0.32, P=.007; ρ=−0.25, P=.041; Figure). In multivariate regression analysis (model 2), mean office SBP and maximum office SBP were independently significantly associated with the slope of the eGFR (β=−0.32, P=.002; β=−0.23, P=.027).
Correlations Between Other Clinical Variables With the Slope of the eGFR
In univariate correlation analysis, age, serum albumin, hemoglobin concentration, and urinary protein creatinine ratio were significantly correlated with the slope of eGFR (ρ=0.33, P=.005; ρ=0.54, P<.001; ρ=0.31, P=.010; ρ=−0.53, ρ<.001). Hemoglobin A1c was marginally significantly associated with the slope of the eGFR (ρ=0.22, P=.08).
Discussion
This study showed that VVV of BP was not significantly associated with the slope of eGFR in patients with diabetic CKD stage 3A, 3B, or 4, whereas mean office SBP/DBP, maximum office SBP, age, serum albumin, hemoglobin concentration, and urinary protein creatinine ratio were significantly correlated with the slope of eGFR. To the best of our knowledge, this is the first study to explore the association of the VVV of BP and renal function decline in patients with diabetic CKD.
The present results are in contrast with our previous study findings, which showed a significant association between VVV of BP and progression of nondiabetic CKD.16 The discrepancy might be attributable to the effect of proteinuria on renal function decline in diabetic CKD, as shown in the present study. Proteinuria is known to be a predominant risk factor for renal function decline in diabetic CKD. This effect may obscure the effect of VVV of BP in diabetic CKD at a later stage. Another possible explanation is the effect of CCBs ameliorating the VVV of BP.29 CCBs were prescribed for 80% of the patients in the present study and for 50% of the patients with nondiabetic CKD in the previous study.16 The frequent usage of CCBs may have blunted the association between the VVV of BP and renal function decline in the present study.
In the present study, VVV of BP was not significantly associated with the slope of eGFR. This result seems in contrast with the study by Okada and colleagues,30 which showed that VVV of SBP was significantly associated with ankle‐brachial index and pulse wave velocity in patients with diabetes. The impact of VVV of BP in diabetes may differ between renal function decline and atherosclerosis.
The results of the present study seem inconsistent with those of a study by Okada and colleagues,17 which showed that VVV of BP was significantly associated with the development and progression of albuminuria in diabetic patients. A possible reason for this discrepancy is that the pathophysiology of diabetic CKD differs from stage to stage. When a patient with diabetic CKD has preserved renal function and no or low‐grade albuminuria, the main mechanisms of diabetic CKD are considered to be glomerular basement membrane thickening,31, 32 afferent and efferent glomerular arteriolar hyalinosis,33 podocyte loss,34, 35, 36 and mesangial proliferation. However, as renal function declines in diabetic CKD, focal and global glomerulosclerosis, tubular atrophy, interstitial expansion and fibrosis, and glomerulotubular junction abnormalities play major roles.37 These different mechanisms of diabetic CKD may explain the different effects of VVV of BP, since the present study included patients with lower eGFR compared with the patients studied by Okada and colleagues.
In the present study, mean office SBP/DBP, maximum office SBP, age, serum albumin, hemoglobin concentration, and urinary protein creatinine ratio were significantly correlated with the slope of eGFR, consistent with previous studies.9, 38
Study Limitations
One of the limitations of the present study is the small sample size. There is a possibility of type 2 error. Since we showed a significant association between VVV of BP and renal function decline in nondiabetic CKD in a small sample size, renal function decline in diabetic CKD seems to be less affected by VVV of BP, at the least. Further study with a larger sample size is needed. The retrospective nature of the present study is another limitation. A prospective study is needed to examine the presence of the true association. One other limitation of the present study is the absence of data regarding patient adherence to medications or diet. However, Muntner and colleagues39 reported that low antihypertensive medication adherence explains only a small proportion of VVV of BP, indicating that the absence of data does not have a major impact on the result of the present study. Another limitation is the absence of data regarding training or assessment of BP measuring skill. However, Muntner and colleagues40 showed that VVV of SBP in a real‐world setting has modest reproducibility, indicating that strict training or assessment of BP measuring skill is not mandatory for evaluating the VVV of BP.
Conclusions
The present study showed no significant association between VVV of BP and renal function decline in patients with diabetic CKD stage 3a, 3b, or 4.
Acknowledgments
None.
Conflicts of Interest
None declared.
Source of Funding
None declared.
J Clin Hypertens (Greenwich). 2014;16:362–366. DOI: 10.1111/jch.12293. ©2014 Wiley Periodicals, Inc.
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