Abstract
Blood pressure (BP) usually increases upon awakening––a physiological mechanism called morning BP surge (MBPS). BP values above the MBPS threshold are associated with target organ damage, including left ventricular hypertrophy and proteinuria. Despite these data, there have been no studies that have investigated the association between elevated MBPS and the development of incident chronic kidney disease (CKD). In this study, patients with essential hypertension were included and underwent ambulatory BP measurements and MBPS. Patients were followed for a median of 3.33 years. In total, 622 patients were enrolled. The mean age of patients was 57.6±12.4 years, 54.0% were men, 16.7% had diabetes, and 10.6% had prevalent cardiovascular disease. During follow‐up, 32 patients developed CKD. Higher MBPS, analyzed both as continuous and categorical variables, was associated with incident CKD in all models. Elevated MBPS is associated with kidney function deterioration and the development of CKD. Studies are needed to further examine underlying mechanisms regarding MBPS and these renal outcomes.
Elevation in blood pressure (BP) upon awakening is a commonly observed physiological mechanism called morning BP surge (MBPS).1 However, BP values above the MBPS threshold are associated with target organ damage, including left ventricular hypertrophy, proteinuria, and stroke.2, 3 Although the exact mechanisms regarding elevated MBPS is unknown,4 various factors are thought to play a role in this elevation including environmental factors,5 age and sex,6, 7 physical activity,8 sympathetic nervous tone,9 renin‐angiotensin system,10 arterial stiffness,11 nondipping status,12 and endothelial dysfunction.13
Chronic kidney disease (CKD) is a worldwide health problem associated with adverse outcomes and high healthcare costs.14 The pathogenesis of CKD involves hemodynamic, inflammatory, or oxidative pathways, which eventually cause glomerulosclerosis, tubulointerstitial fibrosis, and kidney atrophy.15, 16
Traditionally, elevated BP has been accepted as a risk factor for CKD and recent guidelines from the Kidney Disease Improving Global Outcomes (KDIGO) initiative advocate a BP treatment target of <140/90 mm Hg in patients with CKD who have no proteinuria and a stricter target of <130/80 mm Hg in patients with albuminuria.17
As stated above, MBPS is another hemodynamic parameter related to BP and is associated with target organ damage. Specifically regarding the kidney, MBPS is associated with albumin‐creatinine ratio as a marker of renal dysfunction,18 but, to the best of our knowledge, no previous study evaluated the relationship between MBPS and kidney function alteration over time. Therefore, in this prospective study, we sought to investigate whether baseline MBPS is associated with kidney function decline and incident CKD development.
Materials and methods
The participants of this prospective study included patients with hypertension who were attending an outpatient hypertension clinic and underwent 24‐hour ambulatory BP monitoring (ABPM) using validated devices between January 2008 and December 2011. All included patients had essential hypertension, either untreated or taking antihypertensive therapy.
Exclusion criteria were as follows: chronic liver disease, any kind of malignancy, infection, arrhythmias, obstructive sleep apnea, alcohol intake, and being a night worker. There were no black or Hispanic patients in the present study.
Kidney function was estimated using the Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) formula. The study complied with the Declaration of Helsinki, the study protocol was approved by the Yuksek Ihtisas Education and Research Hospital ethics committee, and informed consent was obtained from all participants. Patients’ blood tests and clinical data collection were performed on the day of application of the ABPM device. Diabetes mellitus was defined according to the World Health Organization (WHO) definition.19 Smokers were defined as current smokers. Body mass index was calculated as weight (kg)/height (m2).
Blood samples were obtained from patients in the morning after 12 hours of fasting. Office BP measurements were performed following validated guidelines.20
The noninvasive 24‐hour ABPM device (Mobil‐O‐Graph; IEM GmbH, Stolberg, Germany) used has been described elsewhere.21 Briefly the readings were recorded at 20‐ and 30‐minute intervals in the daytime and at nighttime, respectively. The respective daytime and nighttime hours were defined using certain time intervals, which ranged from 6 am to 10 pm and from 10 pm to 6 am. Sleep and wake periods were determined on the basis of diary activity reports. Interactive software was utilized to assess the recordings. Any patient lacking ≥20% of the measurements was excluded from the study.
We defined morning BP surge as the morning systolic BP (SBP; averaged SBP for 2 hours just after waking up) minus the lowest nocturnal SBP by using ABPM after the run‐in period (sleep‐trough morning surge as suggested by Kario and colleagues22). All included patients were followed up for time‐to‐event analysis until occurrence of CKD and were recorded by reviewing outpatient clinic visits in the medical records.
Statistical Analysis
Data were expressed as mean±standard deviation (SD) or as percent frequency as appropriate. We categorized participants by quartiles of MBPS and compared the distribution of different characteristics across quartiles with the chi‐square test for nominal variables and Kruskal‐Wallis or one‐way analysis of variance tests for the rest of variables. Change in kidney function was defined by calculating the rates of change in estimated glomerular filtration rate (eGFR) using two measurements of serum creatinine. Incident CKD was defined as an eGFR <60 mL/min per 1.73 m2 in individuals with baseline GFR >60 mL/min per 1.73 m2. Linear mixed models were used to evaluate the association between MBPS with change in kidney function. Kaplan‐Meier and univariable and multivariable Cox regression models were used to evaluate the association between MBPS with incident CKD. Analyses were conducted with MBPS as a continuous variable, but also categorized into quartiles. In the first model, we adjusted for demographic and clinical variables: age, sex, diabetes, prevalent cardiovascular disease, smoking, antihypertensive medication use, and mean 24‐hour SBP. In the second model, we adjusted for biochemical variables: hemoglobin, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol, triglycerides, and uric acid levels. In the third model, we adjusted for all the variables used in the previous two models. To avoid the problem of overfitting caused by the low number of incident CKD outcomes, we performed bootstrapping validation to determine the confidence intervals (CIs) for estimating the β value in multivariable Cox regression. All analyses were performed using SPSS software version 19.0.1 for Windows (SPSS Inc, Chicago, IL).
Results
Among the 622 patients included in this study, the mean±SD age was 57.6±12.4 years, 54.0% were men, 16.7% had diabetes, and 10.6% had prevalent CVD. The mean baseline eGFR was 87.3±24.9 mL/min/1.73 m2. There was no difference between the MBPS quartiles in any of the evaluated demographic, clinical, or biological parameters, with the exception of triglyceride level (Table 1). We observed a trend towards a higher percentage of men and diabetes and a lower eGFR in the highest MBPS quartile (Table 1).
Table 1.
Baseline Demographic, Clinical, and Biological Characteristics of the Study Population by Quartiles of MBPS
| All (N=622) | First Quartile (n=150) | Second Quartile (n=149) | Third Quartile (n=166) | Fourth Quartile (n=157) | P Valuea | |
|---|---|---|---|---|---|---|
| Age, y | 57.6±12.4 | 56.7±12.2 | 57.7±12.1 | 57.2±13.0 | 58.7±12.2 | .53 |
| Men, No. (%) | 336 (54.0) | 78 (52.0) | 78 (52.3) | 82 (49.4) | 98 (68.4) | .09 |
| BMI, kg/m2 | 27.0±1.5 | 27.0±1.5 | 27.1±1.5 | 26.9±1.6 | 27.0±1.5 | .95 |
| Waist circumference, cm | 95.3±9.8 | 95.5±10.1 | 94.8±10.5 | 95.5±8.8 | 95.4±10.1 | .91 |
| Diabetes, No. (%) | 104 (16.7) | 19 (12.7) | 23 (15.4) | 26 (15.7) | 36 (22.9) | .09 |
| Smoking, No. (%) | 134 (21.5) | 27 (18.0) | 28 (18.8) | 35 (21.1) | 44 (28.0) | .13 |
| Prevalent CVD, No. (%) | 66 (10.6) | 18 (12.0) | 15 (10.1) | 21 (12.7) | 12 (7.6) | .47 |
| Antihypertensive medication, No. (%) | 252 (40.5) | 59 (39.3) | 58 (38.9) | 76 (45.8) | 59 (37.6) | .44 |
| Glucose, mg/dL | 105.7±23.9 | 104.2±18.6 | 104.4±21.6 | 104.3±19.9 | 109.9±32.7 | .51 |
| Baseline eGFR, mL/min/1.73 m2 | 87.3±24.9 | 90.8±22.9 | 89.1±25.5 | 85.1±22.9 | 84.5±27.9 | .06 |
| Hemoglobin, g/dL | 14.5±1.5 | 14.4±1.5 | 14.4±1.4 | 14.6±1.4 | 14.6±1.5 | .45 |
| HDL cholesterol, mg/dL | 45.1±12.3 | 45.5±12.6 | 45.4±12.9 | 45.6±12.8 | 43.9±12.8 | .81 |
| LDL cholesterol, mg/dL | 125.7±30.6 | 126.1±32.7 | 123.3±31.9 | 127.0±29.5 | 126.4±28.6 | .46 |
| Triglycerides, mg/dL | 157.7±71.9 | 155.6±70.0 | 154.5±71.1 | 149.9±68.9 | 171.1±76.1 | .02 |
Abbreviations: BMI, body mass index; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; MBPS, morning blood pressure surge. Data are expressed as mean±standard deviation or number (percentage). Bold values indicate statistical significance. aComparison between groups.
Only office BP (both systolic and diastolic) were increased in patients from the highest MBPS quartile. However, there were no differences between the MBPS quartiles in any of the ABPM‐derived measurements (Table 2).
Table 2.
Baseline Office BP and Ambulatory BP Monitoring Data of the Study Population by Quartiles of MBPS
| All (N=622) | First Quartile (n=150) | Second Quartile (n=149) | Third Quartile (n=166) | Fourth Quartile (n=157) | P Valuea | |
|---|---|---|---|---|---|---|
| Systolic BP | ||||||
| Office systolic BP, mm Hg | 163.0±16.9 | 159.9±15.5 | 161.9±15.8 | 163.3±16.7 | 166.8±18.6 | .01 |
| 24‐H systolic BP, mm Hg | 138.2±10.8 | 137.9±9.7 | 136.9±10.9 | 138.3±10.9 | 139.5±11.4 | .22 |
| Daytime systolic BP, mm Hg | 141.3±10.9 | 141.2±9.9 | 139.6±10.7 | 141.6±10.9 | 142.5±11.8 | .18 |
| Nighttime systolic BP, mm Hg | 128.5±15.0 | 128.1±14.6 | 127.4±14.6 | 128.1±15.7 | 130.5±15.1 | .21 |
| Diastolic BP | ||||||
| Office diastolic BP, mm Hg | 104.8±17.3 | 101.6±15.8 | 103.5±14.9 | 104.7±17.3 | 109.2±19.8 | .01 |
| 24‐H diastolic BP, mm Hg | 89.3±9.0 | 89.2±8.2 | 88.5±8.9 | 89.5±9.8 | 89.9±9.0 | .51 |
| Daytime diastolic BP, mm Hg | 91.4±8.4 | 91.5±7.9 | 90.4±8.1 | 91.8±8.7 | 92.1±8.7 | .36 |
| Nighttime diastolic BP, mm Hg | 80.6±10.7 | 80.5±10.8 | 79.8±10.2 | 80.6±11.9 | 81.4±9.9 | .54 |
| Nondippers, No. (%) | 419 (67.4) | 103 (68.7) | 102 (68.5) | 104 (62.7) | 110 (70.1) | .49 |
Abbreviations: BP, blood pressure; MBPS, morning blood pressure surge. Data are expressed as mean±standard deviation or number (percentage). Bold values indicate statistical significance. aComparison between groups.
Change in Kidney Function
Median length of follow‐up was 3.33 years. The mean±SD eGFR at the second evaluation was 83.4±25.7, and the mean yearly change for eGFR in the entire population was 1.172 mL/min/1.73 m2 (95% CI, 0.988–1.135).
Higher MBPS was associated with a significantly faster decline in all (unadjusted and adjusted) models (Table 3). Similar results were also obtained in analyses that evaluated MBPS quartiles, with the highest quartiles being associated with kidney function decline in all models (Table 3). Patients with a nondipping status had a nonsignificant decline in eGFR (0.195 mL/min/1.73 m2 per year; 95% CI, −0.198 to 0.587).
Table 3.
Association of MBPS With Change in Kidney Function (Estimated Using the CKD‐EPI Equation)
| Variable | Patients, No. | β Value (95% Confidence Interval)a | |||
|---|---|---|---|---|---|
| Unadjusted | Demographic and Clinical Variable‐Adjustedb | Biochemical Variable‐Adjustedc | Fully Adjustedd | ||
| Continuous | |||||
| MBPS (per 10 mm Hg) | 622 | −0.549 (−0.750 to −0.349) | −0.554 (−0.754 to −0.353) | −0.570 (−0.770 to −0.370) | −0.556 (−0.756 to −0.355) |
| Quartiles | |||||
| <29 mm Hg | 150 | Reference | Reference | Reference | Reference |
| 29–34 mm Hg | 149 | −0.977 (−1.488 to −0.467) | −0.978 (−1.488 to −0.468) | −0.977 (−1.488 to −0.467) | −0.977 (−1.488 to −0.466) |
| 35–41 mm Hg | 166 | −0.977 (−1.476 to −0.478) | −0.972 (−1.470 to −0.472) | −0.978 (−1.478 to −0.479) | −0.978 (−1.478 to −0.478) |
| >41 mm Hg | 157 | −1.320 (−1.835 to −0.806) | −1.316 (−1.831 to −0.801) | −1.320 (−1.836 to −0.806) | −1.321 (−1.834 to −0.805) |
Abbreviations: CKD‐EPI, Chronic Kidney Disease Epidemiology Collaboration equation; MBPS, morning blood pressure surge. aβ values represent a decline in mL/min/1.73 m2/y in estimated glomerular filtration rate. bAdjusted for age, sex, diabetes, prevalent cardiovascular disease, smoking, and antihypertensive medications. cAdjusted for hemoglobin, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol, triglycerides, and uric acid levels. dAdjusted for age, sex, diabetes, prevalent cardiovascular disease, smoking, antihypertensive medications, hemoglobin, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol, triglycerides, and uric acid levels.
Angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker use and dipping status rather than antihypertensive medication use and mean 24‐hour SBP, respectively, did not significantly influence the final results (Tables S1 and S2).
Incident CKD
For the analysis of the association of MBPS and incident CKD, we evaluated 537 patients from our initial cohort. During the follow‐up, 32 patients developed CKD. Higher MBPS, analyzed both as a continuous and categorical variable, was associated with incident CKD in all models (Figure and Table 4). Nondipping status was not associated with incident CKD in our analysis (hazard ratio, 1.424; 95% CI, 0.639–3.170).
Figure 1.
Kaplan‐Meier analysis for incident chronic kidney disease according to morning blood pressure surge quartiles.
Table 4.
Association of MBPS With Incident CKD
| Variable | Patients, No. | Odds Ratio (95% Confidence Interval) | |||
|---|---|---|---|---|---|
| Unadjusted | Demographic and Clinical Variable‐Adjusteda | Biochemical Variable‐Adjustedb | Fully Adjustedc | ||
| Continuous | |||||
| MBPS (per 10 mm Hg) | 537 | 1.718 (1.121–2.635) | 1.803 (1.207–2.694) | 1.684 (1.163–2.438) | 1.799 (1.169–2.768) |
| Quartiles | |||||
| <29 mm Hg | 132 | Reference | Reference | Reference | Reference |
| 29–34 mm Hg | 134 | 8.317 (1.025–67.461) | 8.121 (0.971–67.932) | 9.352 (1.136–76.984) | 8.099 (0.964–68.009) |
| 35–41 mm Hg | 147 | 12.709 (1.639–98.540) | 12.096 (1.509–96.938) | 12.607 (1.606–98.993) | 11.102 (1.387–88.845) |
| >41 mm Hg | 127 | 11.491 (1.449–91.148) | 14.038 (1.704–115.640) | 10.658 (1.310–86.709) | 12.858 (1.534–107.772) |
Abbreviations: CKD, chronic kidney disease; MBPS, morning blood pressure surge. aAdjusted for age, sex, diabetes, prevalent cardiovascular disease, smoking, and antihypertensive medications. bAdjusted for hemoglobin, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol, triglycerides, and uric acid levels. cAdjusted for age, sex, diabetes, prevalent cardiovascular disease, smoking, antihypertensive medications, hemoglobin, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol, triglycerides, and uric acid levels.
Discussion
In the present study, we showed that MBPS was associated with decline in kidney function and development of CKD in a relatively large patient population, independently of demographic, clinical, and biological risk factors. To the best of our knowledge, our findings are novel and have not been demonstrated before.
Previously, increased MBPS has been related to end organ damage including cerebrovascular disease,22 cardiovascular remodeling,23 and urinary albumin excretion.18 Traditionally, cardiovascular events such as myocardial infarction and stroke peak in the morning hours.24, 25 Although the mechanism by which the incidence of cardiovascular events is higher in the morning is unclear, increase in sympathetic nervous system and renin‐angiotensin system activity, and cortisol levels, could contribute to the exaggerated morning BP levels.26
As stated earlier, increased MBPS has been associated with albumin/creatinine ratio.18 Albuminuria is a component of the metabolic syndrome and may represent a marker of the increased risk of renal and cardiovascular disease associated with insulin resistance and endothelial dysfunction. Proteinuria is a sign of established kidney damage and plays a direct pathogenic role in the progression of renal and cardiovascular disease.27 Glomerular ultrafiltration of excessive amounts of plasma‐derived proteins and associated factors incites tubulointerstitial damage and might amplify an inherent susceptibility of the kidney to become dysfunctional in several disease conditions. In addition, noxious substances in the proteinuric ultrafiltrate promote apoptotic responses and multiple changes in the phenotype of tubule cells with generation of inflammatory and fibrogenic mediators. The severity of tubular interstitial damage is highly correlated to the degree of deterioration of renal failure even more than glomerular lesions.28 Thus, one can hypothesize that as increased urinary albumin excretion is related to increased MBPS and increased urinary albumin excretion is related to deterioration of kidney function, the relationship between elevated MBPS and kidney dysfunction is not unusual.
Another explanation may be related to arterial structure itself. In a previous study, Rizzoni and colleagues29 showed that morning rise of BP was associated with structural alterations of small resistance arteries, as evaluated by the tunica media to internal lumen ratio of subcutaneous small resistance arteries. The authors concluded that subcutaneous small artery structure is related to morning rise of BP, possibly because an altered vascular structure may amplify BP changes. In line with that finding, MBPS has been associated with renal resistive index as a measure of atherosclerosis.30 Thus, increased arterial function and structure may be other potential explanations for MBPS and development of CKD.
Study Limitations
We are aware that our results may be interpreted with caution and there are some limitations that deserve mention. Firstly, we did not evaluate sleep period by validated instruments such as actigraphy. Secondly, the analysis was performed with baseline MBPS only and not with serial measurements, which could be relevant knowing the reported poor reproducibility of MBPS. Thirdly, some patients were treated for hypertension. However, it is not possible to compare each drug effect on MBPS since combinations of antihypertensive agents were used by patients, and patient number did not provide sufficient power for such calculations. Indeed, this limitation is always present in studies of essential hypertension unless the patients are newly diagnosed and have not used any antihypertensive medications. Nevertheless, the analysis was adjusted for antihypertensive medication. We performed our analysis based on actual patient data since we aimed to describe these relationships in routine daily practice situations that clinicians deal with in their everyday clinical practice. Lastly, we did not evaluate urinary protein/albumin excretion, which is related to CKD.
Conclusions
We have demonstrated for the first time that elevated MBPS is associated with kidney function deterioration and the development of CKD. Studies are needed to examine underlying mechanisms regarding MBPS and these renal outcomes.
Conflict of interest
The authors declare no conflict of interest.
Financial disclosure
None.
Supporting information
Table S1. Association of morning blood pressure surge with change in kidney function (estimated using the Chronic Kidney Disease Epidemiology Collaboration equation).
Table S2. Association of morning blood pressure surge with change in kidney function (estimated using the Chronic Kidney Disease Epidemiology Collaboration equation).
J Clin Hypertens (Greenwich). 2016;18:444–448. DOI: 10.1111/jch.12707. © 2015 Wiley Periodicals, Inc.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1. Association of morning blood pressure surge with change in kidney function (estimated using the Chronic Kidney Disease Epidemiology Collaboration equation).
Table S2. Association of morning blood pressure surge with change in kidney function (estimated using the Chronic Kidney Disease Epidemiology Collaboration equation).