Abstract
Background
It is unclear whether short‐term blood pressure variability is associated with renal outcomes in patients with chronic kidney disease.
Methods and Results
This study analyzed data from participants in the C‐STRIDE (Chinese Cohort Study of Chronic Kidney Disease) who had chronic kidney disease stages 1 to 4. Short‐term blood pressure variability was measured by calculating the weighted SD (w‐SD) of systolic blood pressure (SBP). Renal outcomes were defined as dialysis initiation and/or transplantation. Risk factors associated with w‐SD of SBP were evaluated by linear regression. Associations of short‐term SBP variability with renal outcomes were evaluated by Cox regression. In total, 1421 patients with chronic kidney disease were included in this study (mean age, 49.4±13.6 years; 56.2% men; estimated glomerular filtration rate, 50.5±29.3 mL/min per 1.73 m2; proteinuria, 0.9 [0.3–2.0] g/d). Mean w‐SD of SBP was 12.6±4.4 mm Hg. w‐SD of SBP was independently associated with older age, 24‐hour SBP, blood pressure circadian pattern, and angiotensin II receptor blocker treatment. During a median follow‐up of 4.9 years, 237 patients developed renal outcomes (37.01 per 1000 patient‐years). The incidence rate increased across the quartiles of w‐SD (log‐rank P=0.005). w‐SD of SBP was associated with an increased risk of renal outcomes, both as a continuous variable (hazard ratio [HR], 1.47; 95% CI, 1.09–1.99) and as a categorical variable (quartile 4 versus quartile 1: HR, 1.60; 95% CI, 1.08–2.36), independent of 24‐hour SBP, daytime SBP, and nighttime SBP.
Conclusions
Short‐term SBP was independently associated with the risk of dialysis initiation and/or transplantation in patients with chronic kidney disease.
Keywords: ambulatory blood pressure monitoring, chronic kidney disease, renal replacement therapy, short‐term blood pressure variability
Subject Categories: Clinical Studies, Hypertension
Nonstandard Abbreviations and Acronyms
- ABPM
ambulatory blood pressure monitoring
- BP
blood pressure
- BPV
blood pressure variability
- CBP
clinic blood pressure
- CKD
chronic kidney disease
- C‐STRIDE
Chinese Cohort Study of Chronic Kidney Disease
- CVD
cardiovascular disease
- eGFR
estimated glomerular filtration rate
- HR
hazard ratio
- SBP
systolic blood pressure
- w‐SD
weighted SD
Clinical Perspective
What Is New?
Our study demonstrated that short‐term systolic blood pressure variability was associated with higher risk of renal outcomes, irrespective of the 24‐hour, diurnal, and nocturnal systolic blood pressure.
What Are the Clinical Implications?
Our study brings new evidence to the potential role of short‐term blood pressure variability in chronic kidney disease progression, which might affect the evaluation and management of hypertension in patients with chronic kidney disease once verified in future studies.
Chronic kidney disease (CKD) is an important public health burden worldwide.1, 2 Hypertension, both as a common cause and comorbidity of CKD, is highly prevalent in patients with CKD, resulting in the development and progression of kidney disease. There is considerable evidence that hypertension control is important for the management of patients with CKD, although real‐world management of these patients remains unsatisfactory.3, 4, 5 Ambulatory blood pressure (ABP) monitoring (ABPM), an automated monitoring method to detect blood pressure (BP) values during a daily cycle under nonmedical conditions, performs better than traditional clinic BP (CBP) measurement in the assessment of BP control status and prediction of long‐term prognosis.6, 7
The use of ABPM has led to increasing awareness of short‐term BP variability (BPV), which indicates the intraindividual fluctuation in BP levels during a 24‐hour period. This component of BP adds a layer of complexity in the evaluation and management of hypertension. Studies in general populations and patients with primary hypertension have shown that short‐term BPV is associated with organ damage and cardiovascular events, independent of average 24‐hour ABP and CBP, respectively.8, 9, 10, 11, 12 The results of cross‐sectional studies have suggested that short‐term BPV is higher in patients with CKD than in individuals without CKD; in addition, BPV progressively increased with deterioration of renal function.13, 14 Furthermore, short‐term BPV has been associated with organ damage in patients with CKD, suggesting that it has a pathophysiological role in CKD development. However, a recent prospective cohort study from Italy did not demonstrate an association between short‐term BPV and CKD progression.15, 16 Therefore, it remains unclear whether short‐term BPV is useful for risk stratification in patients with CKD.
To better understand associations of short‐term BPV with renal outcomes, we analyzed data from C‐STRIDE (Chinese Cohort Study of Chronic Kidney Disease) to evaluate associations of short‐term BPV with dialysis initiation and/or transplantation and to identify clinical determinants of short‐term BPV in patients with CKD.
Methods
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Study Population
This was a multicenter, prospective cohort study of patients with CKD stages 1 to 4 from C‐STRIDE. The design and methods of C‐STRIDE have been described in detail elsewhere.17, 18 From November 2011 to December 2016, a total of 3700 participants from 39 clinical centers in 22 provinces of China were enrolled in C‐STRIDE. The basic characteristics of the participants in C‐STRIDE are listed in Table S1. Among the enrolled patients, 2114 had undergone ABPM; 693 were excluded because of missing data regarding SD values in ABPM records. Finally, 1421 patients were included in the present analysis (Figure 1). The patients included in the current analysis had a distribution of baseline characteristics comparable to those of patients who were excluded (Table S1). The study protocol was approved by the ethics committee of Peking University First Hospital and was in compliance with the tenets of the Declaration of Helsinki. All participants provided written informed consent before enrollment in the study.
Figure 1. Flowchart of patient enrollment.
ABPM indicates ambulatory blood pressure monitoring; Q, quartile; w‐SD, weighted SD of systolic blood pressure.
BP Measurements
CBP was measured with mercury sphygmomanometers in patients in the sitting position, 3 times at 1‐minute intervals, by an experienced nurse. Participants were advised to avoid ingestion of spicy foods or stimulant drinks (eg, coffee or tea), to avoid smoking and strenuous exercise for at least 90 minutes before the BP examination, and to rest for at least 5 minutes before the BP examination. CBP values were recorded as the mean of the 3 readings.
ABP was measured using equipment that belonged to each participating center. The type and manufacturer of the equipment were not specified before the study, but the equipment was required to be approved for clinical use by the State Food and Drug Administration of China. Diurnal and nocturnal BPs were arbitrarily defined as 7 am to 10 pm and 10 pm to 7 am, respectively. ABP was recorded at 15‐minute intervals during the day and at 30‐minute intervals during the night. Diurnal BP was the mean value of 15 hours (7 am–10 pm), while nocturnal BP was the mean value of 9 hours (10 pm–7 am). Valid measurements were regarded as successful documentation of at least 70% of BP readings taken during a 24‐hour period. Both CBP and ABP measurements were taken from the nondominant arm with an appropriate cuff size based on arm circumference at the time of enrollment.
Short‐Term Systolic BPV Definition
Weighted SD (w‐SD) was used in the present study to assess short‐term systolic BPV. w‐SD was defined as the mean SD of diurnal and nocturnal systolic BP (SBP), weighted for the duration of the daytime and nighttime interval, respectively.19 The w‐SD was calculated as the following formula: w‐SD=(diurnal SD×15 hours+nocturnal SD×9 hours)/24 hours. Diurnal and nocturnal SDs of SBP were derived directly by ABPM within each individual collection period and recorded as mean values.
Outcomes
Renal outcomes were defined as dialysis initiation and/or transplantation. Patients were followed up at 3‐month intervals, either by phone calls or routine clinical visits. Follow‐up was terminated at the occurrence of death, loss to follow‐up, or a predefined end date (December 31, 2017).
Covariate Definition
Smoking was defined as currently smoking or any history of smoking. Diabetes mellitus was defined as fasting plasma glucose ≥7.0 mmol/L, a self‐reported history of diabetes mellitus, or current use of antidiabetes mellitus medication. Body mass index was calculated by the following formula: body mass index=weight (kg)/height2 (m2). Anemia was defined as hemoglobin level <100 g/L. Dyslipidemia was defined as the presence of at least 1 of following observations: serum total cholesterol level ≥200 mg/dL (5.2 mmol/L per L), triglycerides level ≥150 mg/dL (1.7 mmol/L per L), low‐density lipoprotein cholesterol level ≥130 mg/dL (3.4 mmol/L per L), high‐density lipoprotein cholesterol level <40 mg/dL (1.0 mmol/L per L), or current use of lipid‐lowering drugs. Dipper status was defined as the ratio of nighttime SBP/daytime SBP ≤0.9. Cardiovascular disease (CVD) history was defined as the past occurrence of myocardial infarction, hospital admission for congestive heart failure, or severe cardiac arrhythmia incidents (eg, resuscitated cardiac arrest, ventricular fibrillation, sustained ventricular tachycardia, paroxysmal ventricular tachycardia, atrial fibrillation or flutter, severe bradycardia, or heart block). The glomerular filtration rate was estimated from serum creatinine measurements and demographic characteristics, in accordance with the Chronic Kidney Disease Epidemiology Collaboration equation.20 Patients were classified into 4 stages according to the estimated glomerular filtration rate (eGFR): CKD stage 1 (≥90 mL/min per 1.73 m2), CKD stage 2 (60–89 mL/min per 1.73 m2), CKD stage 3 (30–59 mL/min per 1.73 m2), and CKD stage 4 (15–29 mL/min per 1.73 m2).21
Statistical Analysis
Continuous variables with normal Gaussian distribution are expressed as means±SDs, while variables with non‐normal distributions are expressed as medians and interquartile ranges. Categorical variables are expressed as frequencies and proportions. According to their distributions, 1‐way ANOVA or Kruskal–Wallis test were used to compare differences among groups for continuous variables, while chi‐square test and Fisher exact test were used to compare differences among groups for categorical variables. Univariate and multivariate linear regression analyses were performed to analyze the potential determinant(s) of w‐SD. Variables with significance in univariate analysis were included in multivariate analysis.
The incidence rates of renal outcomes were calculated as numbers of outcomes per 1000 patient‐years. Survival curves of individual quartiles of w‐SD were calculated by Kaplan–Meier methods. Log‐rank tests were used to compare outcome rates among each quartile.
A multivariable Cox proportional hazards regression model was used to investigate associations between w‐SD and renal outcomes. Four models were constructed. Model 1 was adjusted for age (continuous) and sex (male versus female), body mass index (continuous), smoking (yes versus no), history of CVD (yes versus no), antihypertensive therapy (yes versus no), diabetes mellitus (yes versus no), albumin level (continuous), anemia (yes versus no), dyslipidemia (yes versus no), log‐transformed proteinuria level (continuous), dipper status (yes versus no), and eGFR (continuous). To further assess whether the associations were independent of BP value, w‐SD was additionally adjusted for 24‐hour SBP in model 2, daytime SBP in model 3, and nighttime SBP in model 4. Hazard ratios (HRs) and 95% CIs were reported. For Cox regression analysis, in order to reduce the loss of sample, missing values were filled with means for continuous variables with normal distributions and with medians for continuous variables with non‐normal distribution, while categorical variables were filled with a separate category. The proportional hazards assumption was tested by assessing the log‐log plot of survival and using Schoenfeld residuals. No violations were found for any of the covariates. Sensitivity analyses were performed in patients with complete data. Data were analyzed using SPSS Statistics version 22.0 (IBM). A 2‐sided P<0.05 was considered statistically significant.
Results
Baseline Characteristics
Table 1 shows the main demographic and clinical features of the 1421 enrolled patients, stratified by quartiles of w‐SD. The mean age of the cohort was 49.4±13.6 years, with 56.2% men. Notably, 33.8% of patients were smokers, while 24.8% of patients exhibited diabetes mellitus and 12.7% had a history of CVD. Of the patients, 13.5% were classified as having stage 1 CKD, 17% were classified as having stage 2 CKD, 40% were classified as having stage 3 CKD, and 29.5% were classified as having stage 4 CKD. Patients in the highest quartile of w‐SD were older, with a higher prevalence of diabetes mellitus and history of CVD and lowest eGFR. No difference in terms of proteinuria was detected across w‐SD quartiles (P=0.21).
Table 1.
Baseline Demographic and Clinical Characteristics of Patients Stratified by Quartiles of w‐SD
| Total | Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | P Value | |
|---|---|---|---|---|---|---|
| (N=1421) | (n=355) | (n=348) | (n=362) | (n=356) | ||
| Age, y | 49.4±13.6 | 43.5±13.3 | 48.5±13.6 | 51.7±12.8 | 53.7±12.6 | <0.001 |
| Men, No. (%) | 798 (56) | 186 (52) | 203 (58) | 215 (59) | 194 (55) | 0.20 |
| Smokers, No. (%) | 473 (34) | 104 (30) | 121 (35) | 124 (35) | 124 (35) | 0.32 |
| DM, No. (%) | 285 (25) | 50 (18) | 65 (25) | 72 (24) | 98 (31) | 0.005 |
| History of CVD, No. (%) | 144 (10) | 21 (6) | 35 (10) | 37 (10) | 51 (14) | 0.003 |
| Causes of CKD, No. (%) | <0.001 | |||||
| DKD | 180 (12.9) | 27 (7.7) | 36 (10.6) | 46 (13.0) | 71 (20.1) | |
| Glomerulonephritis | 835 (59.6) | 251 (71.5) | 209 (61.3) | 194 (54.6) | 181 (51.2) | |
| Other | 385 (27.5) | 73 (20.8) | 96 (28.2) | 115 (32.4) | 101 (28.6) | |
| BMI, kg/m2 | 24.7±3.9 | 23.8±3.9 | 24. 8±3.9 | 25.2±3.8 | 24.8±3.8 | <0.001 |
| Serum albumin, g/L | 39.3±7.0 | 38.9±7.4 | 39.3±6.8 | 38.9±7.3 | 39.9±6.4 | 0.26 |
| FBG, mmol/L | 5.03 (4.53–5.65) | 4.95 (4.51–5.65) | 4.96 (4.47–5.61) | 4.96 (4.46–5.53) | 5.13 (4.71–5.81) | 0.035 |
| Hemoglobin, g/L | 126.0±22.2 | 126.6±21.8 | 127.4±23.0 | 126.0±22.5 | 123.9±21.5 | 0.19 |
| Triglycerides, mmol/L | 1.73 (1.20–2.38) | 1.66 (1.09–2.18) | 1.76 (1.21–2.46) | 1.73 (1.17–2.43) | 1.75 (1.24–2.31) | 0.09 |
| TC, mmol/L | 4.68 (3.81–5.53) | 4.64 (3.78–5.50) | 4.49 (3.76–5.34) | 4.71 (3.94–5.58) | 4.74 (4.00–5.64) | 0.21 |
| HDL‐C, mmol/L | 1.09 (0.91–1.31) | 1.05 (0.93–1.26) | 1.05 (0.87–1.25) | 1.09 (0.89–1.33) | 1.12 (0.94–1.36) | 0.01 |
| LDL‐C, mmol/L | 2.60 (2.07–3.23) | 2.62 (1.99–3.23) | 2.52 (2.02–3.12) | 2.68 (2.07–3.26) | 2.59 (2.20–3.28) | 0.29 |
| 24‐h Proteinuria, g/d | 0.87 (0.33–1.98) | 0.73 (0.31–1.74) | 0.86 (0.33–2.06) | 0.87 (0.29–1.95) | 0.77 (0.28–1.88) | 0.21 |
| Creatinine, μmol/L | 144.7 (101.0–202.0) | 126.0 (84.0–182.8) | 140.9 (97.3–203.8) | 148.0 (108.0–205.5) | 157.5 (119.3–206.5) | <0.001 |
| eGFR, mL/min per 1.73 m2 | 50.5±29.3 | 59.6±32.9 | 51.1±29.4 | 48.6±27.7 | 42.8±24.2 | <0.001 |
| CKD stage, No. % | <0.001 | |||||
| 1 | 192 (13) | 81 (22) | 44 (13) | 44 (12) | 23 (7) | |
| 2 | 241 (17) | 75 (21) | 64 (18) | 54 (15) | 48 (14) | |
| 3 | 569 (40) | 112 (32) | 138 (40) | 156 (43) | 163 (46) | |
| 4 | 419 (30) | 87 (25) | 102 (29) | 108 (30) | 122 (34) |
Values are expressed as mean±SD or 95% CI unless otherwise indicated. Missing data: smokers 22, diabetes mellitus (DM) 270, history of cardiovascular disease (CVD) 8, body mass index (BMI) 178, serum albumin 285, fasting blood glucose (FBG) 315, hemoglobin 60, triglycerides 341, total cholesterol (TC) 342, high‐density lipoprotein cholesterol (HDL‐C) 372, low‐density lipoprotein cholesterol (LDL‐C) 372, and 24‐hour proteinuria 65. CKD indicates chronic kidney disease; DKD, diabetic kidney disease; eGFR, estimated glomerular filtration rate; and w‐SD, weighted SD of systolic blood pressure.
The overall w‐SD of SBP was 12.6±4.4 mm Hg: diurnal SD of SBP was 13.4±5.4 mm Hg and nocturnal SD of SBP was 11.4±4.6 mm Hg. Clinic and ambulatory 24‐hour, diurnal, and nocturnal BP, as well as diurnal and nocturnal SD of SBP, and the proportion of dippers increased across quartiles of w‐SD (Table 2). Consistent with these findings, the proportions of patients with 24‐hour, diurnal, and nocturnal BP at target levels progressively decreased from the lowest quartile to the highest quartile (Table 2). When compared with nondipper patients, dipper patients had higher w‐SD (13.9±4.9 versus 12.2±4.1 mm Hg, P<0.001) and higher diurnal SD of SBP (15.3±6.3 versus 12.7±4.8 mm Hg, P<0.001), whereas nocturnal SD of SBP did not significantly differ (11.6±4.7 versus 11.3±4.6 mm Hg, P=0.23).
Table 2.
Clinic and Ambulatory BP Parameters of Patients Stratified by Quartiles of w‐SD
| Total | Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | P Value | |
|---|---|---|---|---|---|---|
| (N=1421) | (n=355) | (n=348) | (n=362) | (n=356) | ||
| Clinic SBP, mm Hg | 129.2±17.3 | 123.9±15.0 | 128.5±15.6 | 131.8±17.1 | 133.1±20.1 | <0.001 |
| Clinic DBP, mm Hg | 80.5±10.6 | 78.2±10.4 | 80.5±9.5 | 81.3±11.1 | 82.1±11.1 | <0.001 |
| CBP <140/90 mm Hg, No. (%) | 884 (76) | 263 (87) | 227 (77) | 210 (71) | 184 (70) | <0.001 |
| 24‐h SBP, mm Hg | 128.7±17.3 | 120.7±15.4 | 125.5±15.1 | 131.0±16.0 | 137.2±18.3 | <0.001 |
| 24‐h DBP, mm Hg | 78.9±10.8 | 76.3±10.6 | 78.3±10.2 | 80.0±11.4 | 81.1±10.4 | <0.001 |
| 24‐h BP <130/80 mm Hg, No. (%) | 616 (44) | 210 (60) | 159 (46) | 136 (38) | 111 (31) | <0.001 |
| Daytime SBP, mm Hg | 130.6±17.3 | 122.0±15.4 | 127.4±14.8 | 132.8±15.7 | 140.0±18.0 | <0.001 |
| Daytime DBP, mm Hg | 80.5±10.9 | 77.7±10.8 | 79.7±10.1 | 81.5±11.3 | 82.7±10.3 | <0.001 |
| Daytime BP <135/85 mm Hg, No. (%) | 773 (55) | 251 (71) | 209 (60) | 178 (49) | 135 (38) | <0.001 |
| Nighttime SBP, mm Hg | 123.3±18.9 | 115.6±15.8 | 120.6±16.1 | 126.6±18.8 | 130.1±21.1 | <0.001 |
| Nighttime DBP, mm Hg | 74.9±11.8 | 72.4±11.5 | 73.7±10.8 | 76.4±12.8 | 76.9±11.5 | <0.001 |
| Nighttime BP <120/70 mm Hg, No. (%) | 400 (28) | 133 (38) | 101 (29) | 94 (26) | 72 (20) | <0.001 |
| Dipper, No. (%) | 372 (26) | 70 (20) | 76 (22) | 89 (25) | 137 (39) | <0.001 |
| w‐SD, mm Hg | 12.6±4.4 | 8.0±1.2 | 10.5±0.6 | 13.3±1.0 | 18.7±3.3 | <0.001 |
| Diurnal SD, mm Hg | 13.4±5.4 | 8.3±1.8 | 10.9±1.5 | 13.9±2.2 | 20.4±4.9 | <0.001 |
| Nocturnal SD, mm Hg | 11.4±4.6 | 7.5±2.3 | 10.0±2.6 | 12.2±3.3 | 15.9±4.8 | <0.001 |
| Antihypertensive treatment, No. (%) | 918 (65) | 213 (61) | 227 (65) | 253 (70) | 225 (63) | <0.001 |
Values are expressed as mean±SD unless otherwise indicated. Missing counts: clinic systolic blood pressure (SBP) 262, clinic diastolic blood pressure (DBP) 261, 24‐hour SBP 4, 24‐hour average DPB 4, daytime SBP 3, daytime DBP 2, nighttime DBP 5, dipper 3, and antihypertensive treatment 244. BP indicates blood pressure; CBP, clinic blood pressure; and w‐SD, weighted SD of systolic blood pressure.
Factors Associated With w‐SD
w‐SD was positively associated with age, body mass index, diabetes mellitus, history of CVD, 24‐hour SBP, dipper status, and antihypertensive therapy (ie, with angiotensin II receptor blocker, calcium antagonist, or β‐blocker medication), whereas it was negatively associated with cause of CKD (glomerulonephritis versus diabetic kidney disease) and eGFR in univariate analysis. After multivariable adjustment, w‐SD remained significantly associated with age, 24‐hour SBP, dipper status, and angiotensin II receptor blocker therapy; the association with eGFR was lost (Table 3). Proteinuria was not associated with w‐SD in unadjusted or adjusted analysis.
Table 3.
Univariate and Multivariate Liner Regression Analysis of w‐SD
| Univariate | Multivariatea | |||
|---|---|---|---|---|
| B | P Value | B | P Value | |
| Age | 0.085 | <0.001 | 0.06 | <0.001 |
| Sex (male vs female) | 0.044 | 0.851 | ||
| BMI | 0.089 | 0.005 | 0.003 | 0.93 |
| Smoking (yes vs no) | 0.29 | 0.239 | ||
| DM (yes vs no) | 0.985 | 0.001 | −0.48 | 0.21 |
| CVD history (yes vs no) | 1.582 | <0.001 | 0.31 | 0.35 |
| Causes of CKD (glomerulonephritis vs DKD) | −1.967 | <0.001 | −0.078 | 0.87 |
| Anemia (yes vs no) | 0.052 | 0.888 | ||
| Dyslipidemia (yes vs no) | 0.255 | 0.549 | ||
| eGFR | −0.027 | <0.001 | −0.005 | 0.25 |
| Log‐transformed 24‐h proteinuria | −0.29 | 0.168 | ||
| 24‐h SBP | 0.089 | <0.001 | 0.076 | <0.001 |
| Dipper | 1.724 | <0.001 | 1.92 | <0.001 |
| Antihypertensive therapy | ||||
| ACEI (yes vs no) | −0.432 | 0.12 | ||
| ARB (yes vs no) | 0.869 | <0.001 | 0.658 | 0.002 |
| CCB (yes vs no) | 1.72 | <0.001 | 0.129 | 0.599 |
| α‐Blocker (yes vs no) | 0.723 | 0.254 | ||
| β‐blocker (yes vs no) | 1.309 | <0.001 | 0.312 | 0.233 |
| Diuretic (yes vs no) | 1.029 | 0.023 | −0.218 | 0.599 |
ACEI indicates angiotensin‐converting enzyme inhibitor; ARB, angiotensin II receptor blocker; BMI, body mass index; CCB, calcium channel blocker; CKD, chronic kidney disease; CVD, cardiovascular disease; DKD, diabetic kidney disease; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure; and w‐SD, weighted SD of systolic blood pressure.
Variables included in the multivariate analysis were those with significance in univariate analysis.
Outcome Analysis
During a median follow‐up of 4.9 years (interquartile range, 4.0–5.6 years), 237 patients initiated dialysis and/or received transplantation, corresponding to an outcome rate of 37.01 per 1000 patient‐years. Cox regression analysis showed that w‐SD, when expressed as a continuous variable, was associated with 47% greater risk of renal outcomes (HR, 1.47; 95% CI, 1.09–1.99) for each 10‐mm Hg increase after adjustment for demographic and traditional risk factors. The HR remained statistically significant after further adjustments for 24‐hour SBP, daytime SBP, and nighttime SBP (Table 4). In addition, diurnal SD of SBP was independently associated with renal outcomes (HR, 1.36; 95% CI, 1.08–1.72), whereas nocturnal SD of SBP was not (HR, 1.18; 95% CI, 0.89–1.55) (Table S2).
Table 4.
Multivariate Cox Regression Analysis of the Association of Continuous w‐SD With Renal Outcome
| Unadjusted | Model 1 | Model 2 | Model 3 | Model 4 | |
|---|---|---|---|---|---|
| HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | |
| w‐SD (per 10 mm Hg) | 1.45 (1.11–1.88) | 1.47 (1.09–1.99) | 1.45 (1.04–2.02) | 1.45 (1.04–2.02) | 1.46 (1.05–2.03) |
| 24‐h SBP (per 10 mm Hg) | 1.29 (1.22–1.38) | … | 0.97 (0.89–1.06) | … | … |
| Daytime SBP (per 10 mm Hg) | 1.29 (1.21–1.37) | … | … | 1.01 (0.93–1.09) | … |
| Nighttime SBP (per 10 mm Hg) | 1.27 (1.20–1.35) | … | … | … | 1.01 (0.93–1.09) |
| Age | 1.00 (0.99–1.01) | 0.97 (0.96–0.98) | 0.97 (0.96–0.98) | 0.97 (0.96–0.98) | 0.97 (0.96–0.98) |
| Sex (male vs female) | 1.32 (1.01–1.71) | 1.74 (1.21–2.51) | 1.74 (1.20–2.50) | 1.74 (1.20–2.50) | 1.74 (1.20–2.51) |
| BMI | 0.96 (0.93–1.00) | 0.99 (0.95–1.02) | 0.98 (0.95–1.02) | 0.98 (0.95–1.02) | 0.99 (0.95–1.02) |
| Smoker | 1.45 (1.12–1.88) | 1.08 (0.76–1.52 | 1.08 (0.76–1.52) | 1.08 (0.76–1.52) | 1.08 (0.76–1.52) |
| DM | 1.98 (1.48–2.64) | 1.28 (0.91–1.79) | 1.27 (0.91–1.79) | 1.27 (0.91–1.79) | 1.27 (0.91–1.79) |
| History of CVD | 1.17 (0.82–1.68) | 0.98 (0.66–1.45) | 0.98 (0.66–1.45) | 0.98 (0.66–1.45) | 0.98 (0.66–1.45) |
| Antihypertensive treatment | 2.30 (1.50–3.51) | 1.31 (0.84–2.05) | 1.31 (0.84–2.04) | 1.31 (0.84–2.04) | 1.31 (0.84–2.04) |
| Dyslipidemia | 1.02 (0.63–1.64) | 0.95 (0.57–1.57) | 0.94 (0.57–1.57) | 0.94 (0.57–1.57) | 0.94 (0.57–1.57) |
| Serum albumin | 0.95 (0.94–0.97) | 0.96 (0.94–0.99) | 0.96 (0.94–0.99) | 0.96 (0.94–0.99) | 0.96 (0.94–0.99) |
| Anemia | 3.84 (2.89–5.11) | 1.92 (1.39–2.64) | 1.92 (1.39–2.64) | 1.92 (1.39–2.64) | 1.92 (1.39–2.64) |
| Log‐transformed 24‐h proteinuria | 4.43 (3.42–5.75) | 2.11 (1.50–2.95) | 2.10 (1.49–2.94) | 2.10 (1.49–2.94) | 2.10 (1.49–2.95) |
| eGFR | 0.93 (0.92–0.94) | 0.93 (0.92–0.94) | 0.93 (0.92–0.94) | 0.93 (0.92–0.94) | 0.93 (0.92–0.94) |
| Dipper | 0.71 (0.52–0.97) | 0.91 (0.67–1.28) | 0.91 (0.65–1.27) | 0.91 (0.65–1.27) | 0.92 (0.65–1.31) |
Model 1: adjusted for age and sex, smoking, body mass index (BMI), diabetes mellitus (DM), history of cardiovascular disease (CVD), antihypertensive treatment, serum albumin, anemia, dyslipidemia, dipper, log‐transformed 24‐hour proteinuria, and estimated glomerular filtration rate (eGFR). Model 2: model 1+24‐hour systolic blood pressure (SBP). Model 3: model 1+daytime SBP. Model 4: model 1+nighttime SBP. HR indicates hazard ratio; and w‐SD, weighted SD of systolic blood pressure.
The incidence rate increased across the quartiles of w‐SD (quartile 1, 27.74; quartile 2, 32.74; quartile 3, 37.30; quartile 4, 50.47 per 1000 patient‐years, log‐rank P=0.005) (Figure 2). Multivariable Cox regression analysis showed that w‐SD in categorical form was associated with an increased risk of renal outcomes (quartile 4 versus quartile 1: HR, 1.85; 95% CI, 1.28–2.66 in unadjusted model and HR, 1.60; 95% CI, 1.08–2.36 in model 1). The findings remained largely unchanged after further adjustments for 24‐hour SBP, daytime SBP, and nighttime SBP (Table 5). The sensitivity analyses showed consistent results (Tables S3 and S4).
Figure 2. Kaplan–Meier hazard curve for renal outcomes by patients with quartiles of weighted SD of systolic blood pressure (w‐SD).
Q1 indicates patients with w‐SD <9.6 mm Hg; Q2, patients with w‐SD ≥9.6 mm Hg and w‐SD <11.9 mm Hg; Q3, patients with w‐SD ≥11.9 mm Hg and w‐SD <15.1 mm Hg; and Q4, patients with w‐SD ≥15.1 mm Hg.
Table 5.
Multivariate Cox Regression Analysis of the Association of Categorized w‐SD With Renal Outcome
| Unadjusted | Model 1 | Model 2 | Model 3 | Model 4 | |
|---|---|---|---|---|---|
| HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | |
| w‐SD | |||||
| Quartile 1 | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) |
| Quartile 2 | 1.19 (0.80–1.78) | 1.26 (0.84–1.89) | 1.26 (0.84–1.90) | 1.25 (0.83–1.88) | 1.25 (0.83–1.88) |
| Quartile 3 | 1.33 (0.90–1.96) | 1.16 (0.78–1.73) | 1.18 (0.79–1.77) | 1.13 (0.75–1.70) | 1.13 (0.75–1.71) |
| Quartile 4 | 1.85 (1.28–2.66) | 1.60 (1.08–2.36) | 1.64 (1.10–2.45) | 1.55 (1.03–2.33) | 1.56 (1.04–2.33) |
| 24‐h SBP (per 10 mm Hg) | 1.29 (1.22–1.38) | … | 0.98 (0.90–1.07) | … | … |
| Daytime SBP (per 10 mm Hg) | 1.29 (1.21–1.37) | … | … | 1.02 (0.94–1.10) | … |
| Nighttime SBP (per 10 mm Hg) | 1.27 (1.20–1.35) | … | … | … | 1.02 (0.94–1.10) |
| Age | 1.00 (0.99–1.01) | 0.97 (0.96–0.99) | 0.97 (0.96–0.98) | 0.97 (0.96–0.99) | 0.97 (0.96–0.99) |
| Sex (male vs female) | 1.32 (1.01–1.71) | 1.74 (1.21–2.50) | 1.75 (1.21–2.53) | 1.73 (1.20–2.49) | 1.73 (1.20–2.50) |
| BMI | 0.96 (0.93–1.00) | 0.99 (0.95–1.02) | 0.97 (0.95–1.02) | 0.98 (0.95–1.02) | 0.98 (0.95–1.02) |
| Smoker | 1.45 (1.12–1.88) | 1.07 (0.76–1.51) | 1.07 (0.77–1.52) | 1.07 (0.75–1.51) | 1.07 (0.75–1.51) |
| DM | 1.98 (1.48–2.64) | 1.32 (0.94–1.85) | 1.33 (0.95–1.87) | 1.31 (0.94–1.84) | 1.30 (0.93–1.83) |
| History of CVD | 1.17 (0.82–1.68) | 0.97 (0.66–1.44) | 0.97 (0.66–1.44) | 0.97 (0.65–1.44) | 0.97 (0.65–1.43) |
| Antihypertensive treatment | 2.30 (1.50–3.51) | 1.32 (0.85–2.06) | 1.33 (0.85–2.07) | 1.32 (0.85–2.06) | 1.32 (0.84–2.05) |
| Dyslipidemia | 1.02 (0.63–1.64) | 0.92 (0.56–1.54) | 0.93 (0.56–1.55) | 0.92 (0.55–1.53) | 0.92 (0.55–1.53) |
| Serum albumin | 0.95 (0.94–0.97) | 0.96 (0.94–0.99) | 0.96 (0.94–0.99) | 0.97 (0.94–0.99) | 0.97 (0.94–0.99) |
| Anemia | 3.84 (2.89–5.11) | 1.94 (1.41–2.68) | 1.94 (1.41–2.67) | 1.94 (1.41–2.67) | 1.94 (1.41–2.67) |
| Log‐transformed 24‐h proteinuria | 4.43 (3.42–5.75) | 2.10 (1.49–2.94) | 2.11 (1.50–2.96) | 2.08 (1.48–2.92) | 2.08 (1.48–2.92) |
| eGFR | 0.93 (0.92–0.94) | 0.93 (0.92–0.94) | 0.93 (0.92–0.94) | 0.93 (0.92–0.94) | 0.93 (0.92–0.94) |
| Dipper | 0.71 (0.52–0.97) | 0.92 (0.67–1.28) | 0.91 (0.66–1.27) | 0.91 (0.65–1.27) | 0.94 (0.66–1.33) |
Model 1: adjusted for age and sex, smoking, body mass index (BMI), diabetes mellitus (DM), history of cardiovascular disease (CVD), antihypertensive treatment, serum albumin, anemia, dyslipidemia, dipper, log‐transformed 24‐hour proteinuria, and estimated glomerular filtration rate (eGFR). Model 2: model 1+24‐hour systolic blood pressure (SBP). Model 3: model 1+daytime SBP. Model 4: model 1+nighttime SBP. HR indicates hazard ratio; quartile 1, patients with weighted SD of SBP (w‐SD) <9.6 mm Hg; quartile 2, patients with w‐SD ≥9.6 mm Hg and w‐SD<11.6 mm Hg; quartile 3, patients with w‐SD ≥11.6 mm Hg and w‐SD <15.1 mm Hg; and quartile 4, patients with w‐SD≥15.1 mm Hg.
Discussion
In the present study, we investigated associations between short‐term systolic BPV and renal outcomes, as well as clinical factors associated with BPV, in a large prospective cohort of patients with CKD. Older age, 24‐hour SBP, dipper status, and angiotensin II receptor blocker therapy were significantly associated with short‐term systolic BPV. Short‐term systolic BPV was independently associated with the risk of renal outcomes, irrespective of 24‐hour, diurnal, and nocturnal SBP. This finding indicates a potential role for short‐term SBP fluctuation in the risk of end‐stage renal disease in patients with CKD.
BP fluctuation during a 24‐hour cycle is a complex physiologic phenomenon, which is regarded as short‐term BPV. Many mechanisms have been proposed to explain an increase in short‐term BPV (eg, emotional, environmental, behavioral, or neurohumoral factors, as well as increased arterial stiffness); however, the specific mechanism remains unknown.22 Notably, interactions of these mechanisms with BPV suggest a potential pathophysiologic association between short‐term BPV and target organ damage. For instance, Ozkayar et al23 identified an association between local activation of the renal renin–angiotensin system and an increase in BPV in patients with hypertension. Aoki et al24 found that wide BPV aggravates preglomerular arteriolosclerosis through a local angiotensin‐mediated mechanism in spontaneously hypertensive rats. In addition, sympathetic activation is a major contributor to BPV. Overactivation of the sympathetic nervous system is known to be involved in the development and progression of CVD and CKD, either directly or through interactions with the angiotensin system.25, 26 Therefore, short‐term BPV has emerged as a potential clinical index with pathophysiological relevance.
There is growing evidence that short‐term BPV is associated with an increased risk of target organ damage and cardiovascular events in the general population and patients with hypertension in a manner independent of mean BP values, which supports its role as a potential cardiovascular risk factor, rather than a limited physiologic response.8, 9, 10, 11, 12 With respect to kidney function, the Jackson Heart Study showed that short‐term BPV was significantly higher in patients with CKD than in patients without CKD.13 A larger study of 16 546 participants from the Spanish ABPM Registry database14 confirmed that short‐term BPV was higher in patients with hypertension with CKD than in patients with hypertension without CKD. In addition, that study revealed a tendency for higher short‐term BPV with progression of CKD. In the present study, we observed that eGFR decreased as w‐SD increased from quartile 1 to quartile 4. Furthermore, short‐term BPV alone has been associated with left ventricular hypertrophy and renal arteriolar hyalinosis in patients with CKD.27 The results from the present study and the prior cross‐sectional studies suggest that short‐term BPV might have potential pathophysiological relevance with respect to CKD progression. However, to the best of our knowledge, there have been minimal longitudinal cohort data regarding the relationship between short‐term BPV and renal outcome. Recently, one study from Italy, which enrolled 465 nondialysis patients with CKD, showed that short‐term BPV did not predict the risk of rapid CKD progression, defined as dialysis or transplantation or at least 50% decline in eGFR.15 Considering these findings, we analyzed data from our CKD cohort study with a follow‐up design to determine whether short‐term BPV is implicated in the progression of CKD. w‐SD was selected as an indicator for short‐term BPV in the present study, based on its ability to reduce the confounding effects of day‐night BP fluctuations.19 Renal outcomes were defined as dialysis initiation and/or transplantation. We found that w‐SD, both as a continuous variable and as a categorical variable, was positively associated with renal outcomes in the present study. The discrepant results between the Italian study and the present study might be related to different patient characteristics in each cohort, such as participant ethnicity and baseline characteristics (eg, age, eGFR, proteinuria, and history of CVD), as well as causes of CKD. For instance, most participants in the Italian cohort exhibited hypertensive nephropathy, while most of our patients exhibited chronic glomerulonephritis. However, because of its large sample size and long follow‐up period, we consider our results to be strong support for the notion that short‐term BPV can serve as an independent predictor of CKD progression. Meanwhile, the current study is an observational cohort study. The causal relationship still could not be fully derived based on the nature of the current study, although important risk factors for end‐stage renal disease such as age and eGFR have been adjusted. Further studies are needed to validate the association and identify the underlying mechanisms.
In this study, we also analyzed clinical factors that were associated with short‐term BPV. The observed associations between dipper status and w‐SD, as well as between angiotensin II receptor blocker treatment and w‐SD, were novel and potentially useful. The proportion of dipper patients progressively increased with the quartiles of w‐SD in the present study. Increased salt excretion during the night, overactivation of the sympathetic and renin–angiotensin systems during the night, and sleeping disturbance are common symptoms in patients with CKD.28, 29 These factors, either alone or in combination, reduce the occurrence of nocturnal BP dipping, which might lead to reduction of short‐term BPV. In the present study, nondippers had lower w‐SD compared with dippers. This appears to be paradoxical, because nondipper status was presumed to be associated with renal progression in patients with CKD. However, BP rhythm is not necessarily associated with BP values. We noticed that the hypertension burden, in addition to the proportion of dipper patients, also increased with the quartiles of w‐SD in the present study. Several recent studies have shown that the hypertension burden, rather than the BP rhythm alone, was implicated in renal outcomes in patients with CKD.30, 31With respect to antihypertensive drug therapy, the unique pharmacokinetics and pharmacodynamics of each drug class, as well as the timing of administration, may also contribute to BP fluctuation.32
Our study had a few notable strengths. First, the source of the data, C‐STRIDE, was a large, multicenter, prospective cohort study of participants with various CKD causes and comorbidities, which was generally representative of Chinese patients with CKD. Second, individuals were followed up regularly to ascertain the occurrence of end point events (ie, outcomes). However, our analysis also had several limitations. First, some of the participants were excluded from this analysis because of incomplete ABPM data necessary to calculate w‐SD, which may have contributed to selection bias. However, the enrolled patients were younger, included a low proportion of smokers, and had a high level of albumin, a low level of serum lipids, and low clinic SBP, compared with patients who were excluded, which meant lower risk of CKD progression. Second, although our multivariable analyses included careful adjustment for covariates, we could not exclude the possibility of residual confounding by other unrecorded covariates that were not identified. Third, a single instance of ABPM was performed for each enrolled patient without longitudinal measurement. The differing sampling rates between diurnal and nocturnal with overall 70% successful recordings may presumably allow for more unsuccessful measurement during sleep. All of these might have introduced bias into the calculation of w‐SD. Finally, only Chinese patients with CKD were included in the analysis, which reduces the generalizability of our findings. Because of these limitations, our findings should be confirmed in additional independent studies.
Conclusions
Our findings suggest that short‐term systolic BPV was significantly associated with renal outcomes, independent of mean SBP level, in patients with CKD. If these results are confirmed in future studies, evaluations and interventions regarding short‐term BPV should be included in the management of patients with CKD. Such changes may slow the progression of renal disease in these patients.
Appendix
C‐STRIDE Collaborators
Peking University First Hospital: Ming‐Hui Zhao and Luxia Zhang; the Affiliated Hospital of Hubei Traditional Chinese Medical College: Xiaoqin Wang and Jun Yuan; the Xiangya Hospital of Central South University: Qiaoling Zhou and Qiongjing Yuan; General Hospital of Ningxia Medical University: Menghua Chen and Xiaoling Zhou; the Second Hospital of Hebei Medical University: Shuxia Fu and Shaomei Li; Guizhou Provincial People's Hospital: Yan Zha and Rongsai Huang; the First Affiliated Hospital of Zhengzhou University: Zhangsuo Liu and Jun Zhang; Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital: Li Wang and Lei Pu; the First Affiliated Hospital of Xinjiang University of Medicine: Jian Liu and Suhua Li; Peking University Shenzhen Hospital: Zuying Xiong and Wei Liang; Xinqiao Hospital: Jinghong Zhao and Jiao Mu; the Second Affiliated Hospital of Kunming Medical College: Xiyan Lian and Yunjuan Liao; the First Affiliated Hospital of Chongqing University of Medicine: Hua Gan and Liping Liao; Shandong Provincial Hospital: Rong Wang and Zhimei Lv; the First Affiliated Hospital of Guangxi University of Medicine: Yunhua Liao and Ling Pan; the First Affiliated Hospital of the Medical College, Shihezi University: Xiaoping Yang and Zhifeng Lin; Yuxi City People's Hospital: Zongwu Tong and Yun Zhu; Beilun People's Hospital in Ningbo: Qiang He and Fuquan Wu; the Second Affiliated Hospital of Tianjin University of Medicine: Rong Li and Kai Rong; the First Affiliated Hospital of Baotou Medical College: Caili Wang and Yanhui Zhang; Peking University Third Hospital: Yue Wang and Wen Tang; Beijing Hospital of Ministry of Health: Hua Wu and Ban Zhao; the Second Hospital of Shanxi University of Medicine: Rongshan Li and Lihua Wang; Shengjing Hospital of China Medical University: Detian Li and Feng Du; the First Affiliated Hospital of Anhui University of Medicine: Yonggui Wu and Wei Zhang; Tianjin Medical University General Hospital: Shan Lin and Pengcheng Xu; the First Affiliated Hospital of Dalian University of Medicine: Hongli Lin; Shandong University Qilu Hospital: Zhao Hu and Fei Pei; the Affiliated Hospital of Hebei University: Haisong Zhang and Yan Gao; Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine: Luying Sun and Xia Li; Chifeng Second Hospital: Wenke Wang and Fengling Lv; the Second Affiliated Hospital of Anhui University of Medicine: Deguang Wang and Xuerong Wang; Qianfoshan Hospital: Dongmei Xu and Lijun Tang; China Rehabilitation Research Center, Beijing Boai Hospital: Yingchun Ma and Tingting Wang; West China Hospital of Sichuan University: Ping Fu and Tingli Wang; the First Affiliated Hospital with Nanjing Medical University: Changying Xing and Chengning Zhang; Minhang Central Hospital: Xudong Xu and Haidong He; the Second Affiliated Hospital of Chongqing University of Medicine: Xiaohui Liao and Shuqin Xie; and the Affiliated Hospital of Chengde Medical College: Guicai Hu and Lan Huang.
Sources of Funding
The study is supported by grants from the Research Special Fund for Public Welfare Industry of Health from the National Health and Family Planning Commission of the People's Republic of China (No. 201002010); the National Key Technology R&D Program of the Ministry of Science and Technology (No. 2011BAI10B01); and the Establishment of Early Diagnosis Pathway and Model for Evaluating Progression of Chronic Kidney Disease (No. D131100004713007) from the Beijing Science and Technology Committee.
Disclosures
None.
Supporting information
Tables S1–S4
Acknowledgments
The authors would like to express gratitude to every participant and member of the C‐STRIDE group for their collaboration. We thank Ryan Chastain‐Gross, PhD, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this article.
(J Am Heart Assoc. 2020;9:e015359 DOI: 10.1161/JAHA.120.015359)
For Sources of Funding and Disclosures, see page 10.
Contributor Information
Yu Wang, Email: ddwangyu@sina.com.
C‐STRIDE (Chinese Cohort Study of Chronic Kidney Disease)*:
Xiaoqin Wang, Jun Yuan, Qiaoling Zhou, Qiongjing Yuan, Menghua Chen, Xiaoling Zhou, Shuxia Fu, Shaomei Li, Yan Zha, Rongsai Huang, Zhangsuo Liu, Jun Zhang, Li Wang, Lei Pu, Jian Liu, Suhua Li, Zuying Xiong, Wei Liang, Jinghong Zhao, Jiao Mu, Xiyan Lian, Yunjuan Liao, Hua Gan, Liping Liao, Rong Wang, Zhimei Lv, Yunhua Liao, Ling Pan, Xiaoping Yang, Zhifeng Lin, Zongwu Tong, Yun Zhu, Qiang He, Fuquan Wu, Rong Li, Kai Rong, Caili Wang, Yanhui Zhang, Yue Wang, Wen Tang, Hua Wu, Ban Zhao, Rongshan Li, Lihua Wang, Detian Li, Feng Du, Yonggui Wu, Wei Zhang, Shan Lin, Pengcheng Xu, Hongli Lin, Zhao Hu, Fei Pei, Haisong Zhang, Yan Gao, Luying Sun, Xia Li, Wenke Wang, Fengling Lv, Deguang Wang, Xuerong Wang, Dongmei Xu, Lijun Tang, Yingchun Ma, Tingting Wang, Ping Fu, Tingli Wang, Changying Xing, Chengning Zhang, Xudong Xu, Haidong He, Xiaohui Liao, Shuqin Xie, Guicai Hu, and Lan Huang
References
- 1. Mills KT, Xu Y, Zhang W, Bundy JD, Chen CS, Kelly TN, Chen J, He J. A systematic analysis of worldwide population‐based data on the global burden of chronic kidney disease in 2010. Kidney Int. 2015;88:950–957. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Hill NR, Fatoba ST, Oke JL, Hirst JA, O'Callaghan CA, Lasserson DS, Hobbs FD. Global prevalence of chronic kidney disease—a systematic review and meta‐analysis. PLoS One. 2016;11:e0158765. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Zhang W, Shi W, Liu Z, Gu Y, Chen Q, Yuan W, Zhang Y, Gong L, Zhou R, Li M, et al. A nationwide cross‐sectional survey on prevalence, management and pharmacoepidemiology patterns on hypertension in Chinese patients with chronic kidney disease. Sci Rep. 2016;6:38768. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. ZhengY CG, Chen XM, Fu P, Chen JH, Ding XQ, Yu XQ, Lin HL, Liu J, Xie RJ, Wang LN, et al. Prevalence, Awareness, and Treatment Rates in Chronic Kidney Disease Patients with Hypertension in China (PATRIOTIC) Collaborative Group. Prevalence, awareness, treatment, and control of hypertension in the non‐dialysis chronic kidney disease patients. Chinese Med J. 2013;126:2276–2280. [PubMed] [Google Scholar]
- 5. Yan Z, Wang Y, Li S, Wang J, Zhang L, Tan H, Li S, Yang L, Pei H, Zhang L, et al. Hypertension control in adults with CKD in China: baseline results from the Chinese Cohort Study of Chronic Kidney Disease (C‐STRIDE). Am J Hypertens. 2018;31:486–494. [DOI] [PubMed] [Google Scholar]
- 6. Minutolo R, Agarwal R, Silvio B, Chiodini P, Bellizzi V, Nappi F, Cianciaruso B, Zamboli P, Conte G, Gabbai FB, et al. Prognostic role of ambulatory blood pressure measurement in patients with nondialysis chronic kidney disease. Arch Intern Med. 2011;171:1090–1098. [DOI] [PubMed] [Google Scholar]
- 7. Gabbai FB, Rahman M, Hu B, Appel LJ, Charleston J, Contreras G, Faulkner ML, Hiremath L, Jamerson KA, Lea JP, et al; African American Study of Kidney D, Hypertension Study G . Relationship between ambulatory BP and clinical outcomes in patients with hypertensive CKD. Clin J Am Soc Nephrol. 2012;7:1770–1776. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Kikuya M, Hozawa A, Ohokubo T, Tsuji I, Michimata M, Matsubara M, Ota M, Nagai K, Araki T, Satoh H, et al. Prognostic significance of blood pressure and heart rate variabilities: the Ohasama study. Hypertension. 2000;36:901–906. [DOI] [PubMed] [Google Scholar]
- 9. Mancia G, Bombelli M, Facchetti R, Madotto F, Corrao G, Trevano FQ, Grassi G, Sega R. Long‐term prognostic value of blood pressure variability in the general population: results of the Pressioni Arteriose Monitorate e Loro Associazioni Study. Hypertension. 2007;49:1265–1270. [DOI] [PubMed] [Google Scholar]
- 10. Hansen TW, Thijs L, Li Y, Boggia J, Kikuya M, Bjorklund‐Bodegard K, Richart T, Ohkubo T, Jeppesen J, Torp‐Pedersen C, et al; International Database on Ambulatory Blood Pressure in Relation to Cardiovascular Outcomes I . Prognostic value of reading‐to‐reading blood pressure variability over 24 hours in 8938 subjects from 11 populations. Hypertension. 2010;55:1049–1057. [DOI] [PubMed] [Google Scholar]
- 11. Palatini P, Reboldi G, Beilin LJ, Casiglia E, Eguchi K, Imai Y, Kario K, Ohkubo T, Pierdomenico SD, Schwartz JE, et al. Added predictive value of night‐time blood pressure variability for cardiovascular events and mortality: the Ambulatory Blood Pressure‐International Study. Hypertension. 2014;64:487–493. [DOI] [PubMed] [Google Scholar]
- 12. Verdecchia P, Angeli F, Gattobigio R, Rapicetta C, Reboldi G. Impact of blood pressure variability on cardiac and cerebrovascular complications in hypertension. Am J Hypertens. 2007;20:154–161. [DOI] [PubMed] [Google Scholar]
- 13. Tanner RM, Shimbo D, Dreisbach AW, Carson AP, Fox ER, Muntner P. Association between 24‐hour blood pressure variability and chronic kidney disease: a cross‐sectional analysis of African Americans participating in the Jackson Heart Study. BMC Nephrol. 2015;16:84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Sarafidis PA, Ruilope LM, Loutradis C, Gorostidi M, de la Sierra A, de la Cruz JJ, Vinyoles E, Divison‐Garrote JA, Segura J, Banegas JR. Blood pressure variability increases with advancing chronic kidney disease stage: a cross‐sectional analysis of 16 546 hypertensive patients. J Hypertens. 2018;36:1076–1085. [DOI] [PubMed] [Google Scholar]
- 15. Borrelli S, Garofalo C, Mallamaci F, Tripepi G, Stanzione G, Provenzano M, Conte G, De Nicola L, Zoccali C, Minutolo R. Short‐term blood pressure variability in nondialysis chronic kidney disease patients. J Hypertens. 2018;36:2398–2405. [DOI] [PubMed] [Google Scholar]
- 16. Mallamaci F, Tripepi G, D'Arrigo G, Borrelli S, Garofalo C, Stanzione G, Provenzano M, De Nicola L, Conte G, Minutolo R, et al. Blood pressure variability, mortality, and cardiovascular outcomes in CKD patients. Clin J Am Soc Nephrol. 2019;14:233–240. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Gao B, Zhang L, Wang H, Zhao M. Chinese cohort study of chronic kidney disease: design and methods. Chinese Med J. 2014;127:2180–2185. [PubMed] [Google Scholar]
- 18. Yuan J, Zou XR, Han SP, Cheng H, Wang L, Wang JW, Zhang LX, Zhao MH, Wang XQ. Prevalence and risk factors for cardiovascular disease among chronic kidney disease patients: results from the Chinese Cohort Study of Chronic Kidney Disease (C‐STRIDE). BMC Nephrol. 2017;18:23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Bilo G, Giglio A, Styczkiewicz K, Caldara G, Maronati A, Kawecka‐Jaszcz K, Mancia G, Parati G. A new method for assessing 24‐h blood pressure variability after excluding the contribution of nocturnal blood pressure fall. J Hypertens. 2007;25:2058–2066. [DOI] [PubMed] [Google Scholar]
- 20. Stevens LA, Claybon MA, Schmid CH, Chen J, Horio M, Imai E, Nelson RG, Van Deventer M, Wang HY, Zuo L, et al. Evaluation of the chronic kidney disease epidemiology collaboration equation for estimating the glomerular filtration rate in multiple ethnicities. Kidney Int. 2011;79:555–562. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Andrassy KM. Comments on ‘KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease’. Kidney Int. 2013;84:622–623. [DOI] [PubMed] [Google Scholar]
- 22. Parati G, Ochoa JE, Lombardi C, Bilo G. Assessment and management of blood‐pressure variability. Nat Rev Cardiol. 2013;10:143–155. [DOI] [PubMed] [Google Scholar]
- 23. Ozkayar N, Dede F, Akyel F, Yildirim T, Ateş İ, Turhan T, Altun B. Relationship between blood pressure variability and renal activity of the renin–angiotensin system. J Human Hypertens. 2015;30:297–302. [DOI] [PubMed] [Google Scholar]
- 24. Aoki Y, Kai H, Kajimoto H, Kudo H, Takayama N, Yasuoka S, Anegawa T, Iwamoto Y, Uchiwa H, Fukuda K, et al. Large blood pressure variability aggravates arteriolosclerosis and cortical sclerotic changes in the kidney in hypertensive rats. Circ J. 2014;78:2284–2291. [DOI] [PubMed] [Google Scholar]
- 25. Barrett CJ, Ramchandra R, Guild SJ, Lala A, Budgett DM, Malpas SC. What sets the long‐term level of renal sympathetic nerve activity. Circ Res. 2003;92:1330–1336. [DOI] [PubMed] [Google Scholar]
- 26. DiBona GF. Neural control of the kidney. Hypertension. 2003;41:621–624. [DOI] [PubMed] [Google Scholar]
- 27. Isobe S, Ohashi N, Ishigaki S, Tsuji N, Tsuji T, Kato A, Yasuda H. Increased nocturnal blood pressure variability is associated with renal arteriolar hyalinosis in normotensive patients with IgA nephropathy. Hypertens Res. 2017;40:921–926. [DOI] [PubMed] [Google Scholar]
- 28. Conway J, Boon N, Davies C, Jones JV, Sleight P. Neural and humoral mechanisms involved in blood pressure variability. J Hypertens. 1984;2:203–208. [DOI] [PubMed] [Google Scholar]
- 29. Fukuda M, Munemura M, Usami T, Nakao N, Takeuchi O, Kamiya Y, Yoshida A, Kimura G. Nocturnal blood pressure is elevated with natriuresis and pro‐ teinuria as renal function deteriorates in nephropathy. Kidney Int. 2004;65:621‐625. [DOI] [PubMed] [Google Scholar]
- 30. Kado H, Kusaba T, Matoba S, Hatta T, Tamagaki K. Normotensive non‐dipping blood pressure profile does not predict the risk of chronic kidney disease progression. Hypertens Res. 2019;42:354–361. [DOI] [PubMed] [Google Scholar]
- 31. Ida T, Kusaba T, Kado H, Taniguchi T, Hatta T, Matoba S, Tamagaki K. Ambulatory blood pressure monitoring‐based analysis of long‐term outcomes for kidney disease progression. Sci Rep. 2019;9:19296. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Mancia G, Ferrari A, Pomidossi G, Parati G, Bertinieri G, Grassi G, Gregorini L, di Rienzo M, Zanchetti A. Twenty‐four‐hour blood pressure profile and blood pressure variability in untreated hypertension and during antihypertensive treatment by once‐a‐day nadolol. Am Heart J. 1984;108:1078–1083. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Tables S1–S4