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Jul 18, 2013 - Recent studies have suggested that visit-to-visit variability of blood pressure (BP) is correlated with microalbuminuria in patients with diabetes ...
Journal of Human Hypertension (2014) 28, 37–43 & 2014 Macmillan Publishers Limited All rights reserved 0950-9240/14 www.nature.com/jhh

ORIGINAL ARTICLE

Visit-to-visit blood pressure variability is related to albuminuria variability and progression in patients with type 2 diabetes S Noshad, M Mousavizadeh, M Mozafari, M Nakhjavani and A Esteghamati Recent studies have suggested that visit-to-visit variability of blood pressure (BP) is correlated with microalbuminuria in patients with diabetes, independent of mean pressure. We investigated the contribution of BP variability to albuminuria progression in normoalbuminuric type 2 diabetes patients. BP and urinary albumin excretion of patients were assessed in each visit during a median follow-up of 31 months. Variability was assessed using standard deviation, coefficient of variation, standard deviation independent of mean, peak, average real variability, and average real variability independent of mean. Of 194 patients enrolled, 31 subjects (16.0%) developed microalbuminuria. Systolic blood pressure (SBP) variability indices (except for coefficient of variation and average real variability) were significant predictors of microalbuminuria in multivariate Cox regression models (hazard ratio ranging from 2.02 to 2.76). The same was not observed for diastolic blood pressure. Using linear regression, SBP variability significantly correlated with some but not all indices of albuminuria variability. Peak SBP was the strongest predictor of albuminuria variability in multivariate models (standardized beta ranging from 0.216 to 0.339). In conclusion, visit-to-visit variability of SBP is an independent risk factor for development of microalbuminuria in patients with diabetes, and is associated with an increased variability in albuminuria. Journal of Human Hypertension (2014) 28, 37–43; doi:10.1038/jhh.2013.36; published online 18 July 2013 Keywords: blood pressure visit-to-visit variability; microalbuminuria; type 2 diabetes; diabetic nephropathy

INTRODUCTION Diabetic kidney disease is the leading cause of end-stage renal disease (ESRD) in patients with diabetes.1 When a diabetes patient reaches ESRD, his or her survival rate is comparable to that of a patient with metastasized carcinoma of the gastrointestinal tract.2 In the United States, ESRD is associated with an annual estimated cost of 15 675$ per patient. Even progression to microalbuminuria increases the patient’s medical costs by 65%.3 Microalbuminuria is a sensitive early marker for diabetes kidney disease, often preceding the more detrimental events seen in advanced stages of nephropathy.1,4 Risk factors contributing to the development and worsening of microalbuminuria include genetics, ethnicity, male gender, advancing age, lack of adequate glycemic control, disturbed lipid profile, overweight and obesity, smoking, and elevated blood pressure (BP).2,5,6 Aside from these established risk factors, search for novel yet modifiable contributing factors with the aim of prevention is still ongoing. Recently, two studies in patients with type 1 and 2 diabetes have suggested that visit-tovisit variability in BP is linked to diabetic kidney disease and that this association is independent of the information gained from mean BP.7,8 In a sample of 422 patients with type 2 diabetes followed up for 1 year, variability in SBP significantly correlated with degree of albuminuria.8 Despite these preliminary promising findings, to date, data elucidating how BP fluctuations affect changes in albuminuria over time are lacking. Therefore, the present study was designed to investigate whether visit-to-visit variability in BP is a significant predictor of progression to microalbuminuria independent of mean BP. Additionally, available evidence indicates that increment and

decrement of systolic blood pressure (SBP) are independent predictors of progress and regress of albuminuria, respectively.5,9 Given the considerable fluctuation observed in albuminuria, we tested the hypothesis whether visit-to-visit variability in SBP might in turn be correlated with albuminuria variability.

PATIENTS AND METHODS Patients In the present study, data from the registry of the diabetes clinic of Vali-asr hospital (Tehran, Iran) were used. In a retrospective manner, medical records of patients with type 2 diabetes visited between December 2006 and October 2012 were reviewed. Patients were found eligible if they met the following requirements: (1) having been diagnosed with type 2 diabetes mellitus, (2) having been visited in the diabetes clinic regularly for a minimum of 2 years (five visits scheduled B6 months apart), and a maximum of 4 years (nine visits scheduled B6 months apart), (3) being normoalbuminuric at the time of entering the study (baseline), (4) not having been diagnosed with serious comorbid diseases of the heart, lung, liver, brain and kidney, (5) not having or developing end-stage complications associated with diabetes, including limb amputation, myocardial infarction, cerebrovascular events or significant loss of sight during the study period and (6) not previously been taking antihypertensive medications or being started on medication during the study period. It has been shown previously that use of antihypertensive medication (medication class, dose and adherence to therapy) can significantly influence visit-to-visit variability.10–12 Additionally, cases with missing data on multiple key variables were excluded. At our diabetes clinic, 24-h urine collections are routinely requested every 6 months; in-between follow-up visits might be scheduled, nevertheless. In the present study, however, visits with available 24-h urine collections, scheduled B6 months apart,

Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran. Correspondence: Dr A Esteghamati, Professor of Endocrinology and Metabolism, Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, Tehran University of Medical Sciences, P.O. Box 13145-784, Tehran 14197-33147, Iran. E-mail: [email protected] Received 7 November 2012; revised 20 February 2013; accepted 28 March 2013; published online 18 July 2013

Blood pressure variability and microalbuminuria S Noshad et al

38 were used for analysis. In the Vali-asr hospital diabetes clinic, patients are provided with an informed consent form for the use of medical information in research. Cases with no consent form were also not included. A panel (AE, SN and MM) reviewed all patient records. Enrollees were followed up for a minimum and maximum of 24 and 48 months, respectively. The end point of the cohort was defined as progression from normoalbuminuria to microalbuminuria. If progression to microalbuminuria occurred before 24 months (fifth visit), subjects were excluded (n ¼ 8). Follow-up was discontinued if the end point reached, and the follow-up time was recorded as the earliest time of the two consecutive urine collections in the range of microalbuminuria (see definitions below).

Ethics statement Written informed consent was obtained from each subject and was officially recorded by the interviewing physician. All procedures regarding human subjects were conducted in accordance with the guidelines laid down in the Declaration of Helsinki (October 2008). The Tehran University of Medical Sciences board of ethics approved the study protocol.

Assessment History and physical examination. Patients’ demographics and past medical history were recorded using a pre-designed standard questionnaire. A single physician (AE) performed all measurements and examinations. After sitting for at least 10 min, two BP measurements were obtained from each patient with a 5-min interval; average of the two readings was recorded. Inter-arm variability within each individual was not assessed, and all measurements were conducted using the right arm. A standard mercury sphygmomanometer (Riester, Big Ben adults, Jungingen, Germany) was used for measurements. SBP was defined as the appearance of first Korotkoff sound, while disappearance of the fifth sound marked the diastolic pressure (DBP). Employing a digital scale (Beurer, GS49, Ulm, Germany), weight was measured and was recorded with 0.1 kg precision. Height was measured using a measuring tape and the nearest 0.1 cm was measured. Body mass index (BMI) was calculated as weight in kg divided by height in m squared (kg/m2). Laboratory evaluations. At each visit, patients were instructed to go on an overnight fasting of 12 h and return the next day for a blood test. The next morning, 10 ml of venous blood was drawn from each patient. Fasting plasma glucose concentrations were determined using the glucose oxidase method. Concentrations of fasting insulin were determined using radioimmunoassay techniques (Immunotech, Prague, Czech Republic). Glycated hemoglobin (HbA1c) levels were assessed via the highperformance liquid chromatography method. Serum concentrations of lipids, including total cholesterol, high-density lipoprotein cholesterol, lowdensity lipoprotein cholesterol and triglycerides, were determined using enzymatic methods with commercially available kits (Pars Azmun, Karaj, Iran). The Jaffe method was employed to assess serum concentrations of creatinine (Pars Azmun). With baseline creatinine concentrations available, estimated glomerular filtration rate (eGFR) was calculated using the formula of CKD-EPI:13 eGFR ¼ 144ðCr=cÞp ð0:993Þage , where c (constant) ¼ 0.7 in women and 0.9 in men; P ¼  0.329 in women with Cr p0.7,  1.209 in women with Cr 40.7,  0.411 in men with Cr p0.9 and  1.209 in men with Cr 40.9 mg dl  1. Assessment of albuminuria. In baseline and subsequent follow-up visits patients were informed to collect 24-h urine samples within few days before or after the visit. Completeness of the collected sample was checked by measuring urinary creatinine excretion. A repeat measurement was requested if creatinine excretion levels were lower than 20 mg kg  1 per 24 h and 15 mg kg  1 per 24 h for men and women, respectively.14 Urinary albumin excretion (UAE) was determined by calorimetric methods using commercial kits (ZiestChem Diagnostics, Tehran, Iran).

Definitions Type 2 diabetes was defined based on the American Diabetes Association criteria.15 Hypertension was defined according to the guidelines developed by the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC-7) as follows: SBP X140 mm Hg and/or DBP X90 mm Hg.16 Normoalbuminuria was defined as UAE o30 mg per day repeated on two consecutive measurements. Patients met the outcome of progression to microalbuminuria if they had UAE Journal of Human Hypertension (2014) 37 – 43

between 30 and 299 mg per 24 h in at least two consecutive urinary protein measurements.

Statistical analysis Analyses were conducted using SPSS version 19 for Windows (IBM, New York, NY, USA). In all tests, a P value of less than 0.05 was used to reject the null hypothesis. Continuous variables are described as mean±s.d. Categorical variables are presented as proportions or ratios. Correlation of variability indices with mean was investigated using Pearson’s correlation coefficient. The efficacy of each index of visit-to-visit variability of SBP and DBP in predicting progression to microalbuminuria was evaluated using Cox proportional regression model. Hazard ratios (HRs) with 95% confidence intervals (95% CI) were calculated in each case. In each model, the bottom quartile served as the reference category and HR was determined for top vs bottom quartile. In addition to the univariate model, a multivariate model was also constructed to control for the possible confounding effect of the following variables: age, sex, duration of diabetes, number of visits, mean pressure, baseline HbA1c, and baseline eGFR. Previous studies have indicated that mean SBP is a major determinant of macro- and microvascular complications of diabetes.17 Therefore, in all models (except for the one including mean pressure itself as the parameter of interest), a further adjustment for mean SBP was made. To examine whether SBP variability interrelates with UAE variability, stepwise uni- and multivariate linear regression models were employed. For multivariate models, the same confounding variables were entered in the final model. Indices of visit-to-visit variability. The following indices were used: (1) within-individual s.d.; (2) within-individual coefficient of variation (CV); (3) as s.d. still correlates with mean BP, a new parameter, that is, ‘s.d. independent of mean’ (SDIM), was estimated using curve fitting. Employing the method described by Rothwell et al.;18 a regression equation of s.d. ¼ c  meanx, where c is constant, was fitted. SDIM was then defined by transforming s.d. to s.d. divided by meanx. (4) Peak was defined as the difference between within-individual maximum and mathematical average. (5) In addition to these traditional measures, an index that takes into account the order of measurements was determined: Average real variability (ARV) was calculated using the formula provided by Mena P  1 et al.:19 ARV ¼ 1=n  1 nk ¼ 1 jBPk þ 1  BPk j, where n is the number of visits. UAE ARV was defined employing the same equations for SBP. (6) ARV independent of mean (ARVIM) was determined in a similar manner to SDIM. (7) Along with indices of variability, mean BP was also calculated as the mathematical average of within-individual measurements.

RESULTS Of the total of 1427 cases initially screened, 194 patients were found to be eligible for the final analysis. Compared with excluded participants, enrolled subjects did not significantly differ with respect to age, sex, baseline HbA1c, and eGFR (P ¼ 0.818, 0.124, 0.252, and 0.572, respectively). On the other hand, enrolled patients had a longer duration of diabetes at the time of enrollment (6.83±5.20 vs 3.88±5.09 years, Po0.001). Patients were followed up for a median of 31 months (ranging from 24 to 48 months). During the study period, 31 patients (16%) progressed to microalbuminuria (Supplementary Table 1). Five patients had isolated readings in the range of macroalbuminuria, which did not repeat in the visits that followed and hence were classified as normoalbuminuric. Another patient had a 24-h UAE of 630 mg, followed by another reading in the range of microalbuminuria (114 mg per 24 h). The patient was labeled as having progressed to microalbuminuria. Baseline demographic and clinical characteristics of participants are presented in Table 1. Mean age of participants was 51.71 and ranged from 34 to 80 years. Men comprised a little more than half of the study sample (n ¼ 101, 52.1%). Before study commencement, patients had diabetes for a median of 6 years (ranging from 3 to 23 years). At baseline, all subjects were normoalbuminuric (mean urinary albumin: 11.40±10.42 mg per 24 h). Mean eGFR was 70.42±19.50 ml min  1 per 1.73 m2; 30.6%, 50.0%, and 19.4% & 2014 Macmillan Publishers Limited

Blood pressure variability and microalbuminuria S Noshad et al

39 Table 1.

Baseline characteristics of study participants

N Age (years) Sex (female/male) BMI (kg m  2) Duration of diabetes (years) Family history of diabetes (N, %)

194 51.71±12.28 93/101 28.26±4.56 6.83±5.20 122, 62.9%

Anti-diabetes medication (N, %) Metformin Glibenclamide Metformin þ Glibenclamide Fasting plasma glucose (mmol l  1) Fasting insulin (pmol l  1) HbA1c (mmol mol  1) Total cholesterol (mmol l  1) HDL-c (mmol l  1) LDL-c (mmol l  1) Triglycerides (mmol l  1) Serum creatinine (mmol l  1) eGFR (ml min l  1 per 1.73 m2) Urinary albumin excretion (mg per 24 h) Mean follow-up (months)

26 (13.6%) 68 (34.8%) 100 (51.6%) 9.22±3.30 63.52±45.60 62.99±14.28 4.73±1.06 1.17±0.28 2.66±0.84 1.97±1.00 85.88±17.88 70.42±19.50 11.40±10.42 33.60±7.60

Abbreviations: eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin A1c; HDL-c, high-density lipoprotein cholesterol; LDL-c, lowdensity lipoprotein cholesterol.

of participants had chronic kidney disease stage 1, 2 and 3, respectively. A description of variability measures of SBP, DBP and UAE is provided in Table 2. S.d. values for SBP and DBP were 9.86 and 6.82 mm Hg, respectively. CV values were 8% and 9%, respectively. During follow-up time, mean SBP and DBP were 122.04 and 78.17 mm Hg, respectively. Average peak from mean SBP was 9.46 mm Hg; 1.8% of patients had peaks 430 mm Hg. Similarly, the peak from mean DPB was on average 4.28 mm Hg, while 3.7% of patients had peaks exceeding 15 mm Hg. A high variability in albuminuria values was noted (s.d. 17.25 mg per 24 h and CV 65%). Average UAE during multiple visits was 23.22±21.29 mg per 24 h. Correlation analysis using Pearson’s coefficient revealed that mean SBP is significantly correlated with all indices (r ranging from 0.259 for SBP ARV to 0.441 for SBP s.d.), except for SDIM (r ¼ 0.086, P ¼ 0.151) and ARVIM (r ¼ 0.053, P ¼ 0.502). Mean DBP significantly correlated with DBP s.d., CV and ARV (r ranging from 0.758 to 0.871). For mean UAE, significant correlations were observed for all but SDIM and ARVIM indices of variability (r for significant correlations: 0.308–0.800). Results of the univariate and multivariate Cox regression models and calculated HRs for progression to microalbuminuria are demonstrated in Table 3. Regarding SBP variability, s.d., SDIM, CV, peak, ARVIM and also mean SBP were significant predictors of UAE progression in the range of microalbuminuria (HRs ranging from 1.79 for mean to 3.05 for peak) (Figure 1). In multivariate analysis, after adjustment for age, sex, number of visits, mean SBP and baseline glycemic control, and renal function, CV did not reach the required threshold for statistical significance; mean SBP proved to be the major confounding variable. With respect to DBP, none of the variability indices were significantly associated with disease progression in univariate Cox regression models. Similar findings were replicated when adjustment for possible confounding variables was made (Table 3). Results of univariate and multivariate regression models assessing the correlation between select indices of SBP and UAE variability are demonstrated in Table 4. SBP s.d. was significantly associated with mean UAE, as well as UAE peak and ARVIM. On the other hand, peak SBP significantly correlated with UAE mean and variability indices, with the strongest coefficient belonging to UAE SDIM (b ¼ 0.394, P ¼ 0.007), followed by UAE peak (b ¼ 0.381, & 2014 Macmillan Publishers Limited

Table 2. Indices of variability for systolic blood pressure, diastolic blood pressure and urinary albumin excretion

s.d. CV SDIM Mean Peak ARV ARVIM

Systolic blood pressure (mm Hg)

Diastolic blood pressure (mm Hg)

Urinary albumin excretion (mg per 24-h)

9.86±5.01 0.08±0.04 0.02±0.01 122.04±10.06 9.46±7.37 10.13±7.12 0.01±0.01

6.82±3.85 0.09±0.06 0.44±0.27 78.17±5.12 4.50±4.72 5.27±5.50 0.03±0.02

17.25±25.47 0.65±0.36 0.40±0.21 23.22±21.29 22.94±35.96 18.56±26.89 0.37±0.20

Abbreviations: ARV, average real variability; ARVIM, ARV independent of mean; CV, coefficient of variation; SDIM, s.d. independent of mean; SV, successive variability.

Table 3. Hazard ratios calculated from Cox regression models for risk of progression to microalbuminuria in patients with type 2 diabetes Univariate model HRb (95% CI)

P value

Systolic blood pressure (mm Hg) s.d. 2.37 (1.25, 4.50) SDIM 2.49 (1.17, 5.32) CV 2.53 (1.19, 5.40) Mean 1.79 (1.02, 3.13) Peak 3.05 (1.15, 8.09) ARV 2.89 (0.91, 9.14) ARVIM 2.19 (1.03, 4.67)

Multivariate modela HRa (95% CI)

P value

0.008 0.018 0.016 0.029 0.022 0.071 0.043

2.40 2.26 2.06 1.61 2.76 1.64 2.02

(1.07, (1.09, (0.95, (1.01, (1.10, (0.48, (1.03,

5.38) 4.69) 4.48) 2.57) 6.92) 5.69) 3.96)

0.033 0.025 0.067 0.035 0.023 0.431 0.044

Diastolic blood pressure (mm Hg) s.d. 1.96 (0.59, 4.27) 0.088 CV 1.49 (0.82, 2.69) 0.190 SDIM 1.57 (0.76, 3.24) 0.283 Mean 1.09 (0.64, 1.86) 0.751 Peak 1.55 (0.87, 2.75) 0.131 ARV 1.31 (0.72, 2.41) 0.373 ARVIM 1.22 (0.66, 2.26) 0.530

1.65 1.35 1.09 1.13 1.24 1.09 1.17

(0.71, (0.64, (0.40, (0.41, (0.60, (0.26, (0.54,

3.83) 2.83) 3.02) 3.11) 2.53) 4.63) 2.57)

0.247 0.424 0.862 0.787 0.560 0.903 0.687

Abbreviations: ARV, average real variability; ARVIM, ARV independent of mean; CV, coefficient of variation; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin A1c; HR, hazard ratio; SBP, systolic blood pressure; SDIM, s.d. independent of mean. a Adjusted for age, sex, duration of diabetes, number of visits, mean pressure (SBP for top series, DBP for bottom series), baseline HbA1c and baseline eGFR. bHR calculated for top vs bottom quartile.

Po0.001). In univariate models, SBP ARVIM was significantly correlated with UAE SDIM, UAE ARVIM and mean UAE, but not UAE peak. After adjustment for confounding variables in the same way as before, SBP SDIM and peak retained their statistical significance for the observed associations. For SPB ARVIM, however, associations with UAE SDIM and mean were rendered nonsignificant in the multivariate linear regression model.

DISCUSSION The notion that variability in BP might bear clinical implications is relatively old. First mentions of the subject can be traced back to the work of Parati et al.,20 25 years ago. In a sample of 108 hospitalized patients, the BP variability was assessed using 24-h ambulatory BP measurement.20 It was shown that irrespective of the BP cutoff used, higher variability is associated with higher rate Journal of Human Hypertension (2014) 37 – 43

Blood pressure variability and microalbuminuria S Noshad et al

40

Figure 1. Crude HRs across quartiles of indices of SBP visit-to-visit variability. P for linear trend: s.d. (P ¼ 0.004), SDIM (P ¼ 0.053), CV (P ¼ 0.001), mean (P ¼ 0.137), peak (Po0.001), ARV (P ¼ 0.039), ARVIM (P ¼ 0.027). Patients were followed up for a median of 31 months. Follow-up was discontinued if the end point was reached.

and severity of hypertension-induced target organ damage.20 These preliminary efforts laid the foundation for future works that continued to investigate prognostic significance of variability, using either 24-h monitoring or visit-wise.21–24 For instance, in 1999 Tozawa et al.,25 in a sample of 144 ESRD patients undergoing dialysis, found that visit-to-visit variability is a significant prognostic factor for incident cardiovascular and noncardiovascular fatal events. However, findings from these studies were largely disregarded, and fluctuations in BP were assumed to be rising from technical errors and random occurrences.26 It was the prominent work of Rothwell et al.18 that once again revived the idea. Their analyses on four large cohorts revealed that SBP variability and peak SBP reached are significant predictors of Journal of Human Hypertension (2014) 37 – 43

incident stroke independent of mean SBP.18 Thereafter, other research groups disclosed significant predictive value of SBP variability for all-cause mortality in general as well as diabetic populations.27–29 In the present study, SBP variability proved to be a significant prognostic factor for incident microalbuminuria in normoalbuminuric patients. Moreover, this association was not dependent on mean SBP in both uni- and multivariate models. Along the same lines, Okada et al.,8 in a sample of 422 patients with type 2 diabetes followed for 1 year, observed that SBP CV is significantly correlated with degree of UAE (measured by early-morning spot urine test) in multiple regression models (b ¼ 0.149, P ¼ 0.0072). In another endeavor, DCCT trial results established the relationship & 2014 Macmillan Publishers Limited

Blood pressure variability and microalbuminuria S Noshad et al

41 Table 4. Uni- and multivariate linear regression models for relationship between select indices of systolic blood pressure variability and urinary albumin excretion variability Urinary albumin excretion variability (mg per 24 h) SDIM ba

Mean P value

Peak

ARVIM

b

P value

b

P value

b

P value

Univariate models—SBP variability (mm Hg) SDIM 0.104 0.093 Peak 0.394 0.007 ARVIM 0.176 0.002

0.322 0.327 0.117

o0.001 o0.001 0.041

0.297 0.381 0.053

o0.001 o0.001 0.354

0.181 0.298 0.244

0.011 o0.001 0.001

Multivariate modelsb—SBP variability (mm Hg) SDIM 0.061 0.567 Peak 0.339 0.003 ARVIM 0.099 0.282

0.198 0.216 0.171

0.040 0.028 0.087

0.245 0.297 0.193

0.011 0.002 0.028

0.174 0.318 0.218

0.033 o0.001 0.013

Abbreviations: ARVIM, average real variability independent of mean; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; SBP, systolic blood pressure; SDIM, s.d. independent of mean. aStandardized b-coefficient. bMultivariate models are adjusted for age, sex, duration of diabetes, number of visits, mean systolic pressure, baseline HbA1c and baseline eGFR. A stepwise approach was employed and the final b-coefficient was derived from a model including both significant and nonsignificant variables.

between BP variability and diabetic nephropathy.7 Based on their findings, SBP and DBP variability (measured by s.d.) along with mean SBP were all significant predictors of development/ progression to proteinuria in patients with type 1 diabetes.7 In contrast to their findings, in our data neither mean DBP nor any of its variability indices were predictive of progression to microalbuminuria. Available evidence indicates that while in patients with type 1 diabetes DBP provides comparable prognostic ability in prediction of renal outcomes, as for SBP, its predictive utility declines in populations with type 2 diabetes that are generally older and exert higher levels of arterial stiffness.30,31 This might well be the case for DBP variability too. Similar to our findings, Munter et al.28 demonstrated that visit-to-visit variability of DBP does not predict all-cause mortality after 5.5 years of follow-up; on the other hand, SBP does. Also, another possibility should be kept in mind. As shown in our results (Table 2), the degree of variability in DBP is considerably lower than what is observed in SBP. It might be the case that our current techniques for measurement of DBP lack the adequate sensitivity to precisely measure a difference, for example, as little as 4 mm Hg. Hence, it is possible that much of the true variability in DBP, unlike SBP, would go undetected in this setting due to lack of measurement precision. More recently, other research groups have also examined the association between visit-to-visit variability and chronic kidney disease. In a recent study by Kawai et al.,32 143 patients hospitalized with diagnoses like diabetes, chronic kidney disease and hypertension underwent renal Doppler ultrasonography. Their findings disclosed that SBP s.d. measured during six or more visits is linked to a higher arterial resistive index, a surrogate marker for renal dysfunction.32 Here, for the first time, the inter-relationship between variability in SBP and albuminuria was investigated. We learned that multiple variability indices of SBP are significantly correlated with variability indices derived from albuminuria in univariate models. Additionally, when put in multivariate models, much of the observed association retained its significance. Herein, multiple indices were used to represent variability. s.d. and CV were regarded as traditional indicators of variability; these indices measure dispersion of the variable of interest around its mean. ARV, on the other hand, takes into account absolute differences between consecutive measurements and might in fact be a better indicator of the ‘real’ variability. Nonetheless, we observed that irrespective of the & 2014 Macmillan Publishers Limited

method used, SBP variability significantly correlates with UAE progression and variability, and choice of index of interest per se is not decisive. UAE variability has long been recognized.33 Previous endeavors on the subject have shown that UAE CV between multiple measurements could be as high as 25–50%.33,34 However, this variability was often attributed to external factors (such as exercise, urinary tract infection, and protein intake), or was accounted for by variations in the methodology used.33,35 Chau et al.,36 in a sample of patients with and without diabetes, showed that UAE variability significantly increases at the point where UAE is X 30.8 mg per 24 h. A sudden and significant change in UAE variability at the cutoff used for definition of microalbuminuria confirms the presence of a direct association between UAE variability and albuminuria progression. More recently, Perkins et al.37 delved into the clinical importance of eGFR variability. Surprisingly, Cox regression models revealed an increased risk of mortality in patients with the highest eGFR variability and the observed associations were independent of proteinuria.37 It is perceivable that variability in renal function (and equivalently in UAE) is not only an indicator of progressive kidney disease, but also might indeed bear clinical significance with respect to prediction of long-term cardiovascular outcomes. The exact molecular mechanisms connecting BP variability to kidney damage are poorly understood and much remains to be unraveled. Nonetheless, a number of studies have suggested that the sympathetic nervous system might have a role in this regard. Sympathetic nervous system override is concomitantly present in both renal failure and hypertension.38,39 This hyperadrenergic state is not confined to advanced renal disease, but is well detected in the earlier forms of disease, confirming that sympathetic dysregulation is involved in both development and advancement of kidney injury.40 Increased arterial stiffness is associated with diminished baroreflex sensitivity, and this may explain the augmented BP variability in the elderly and hypertensive patients.41 Simply put, in this view, insulin resistance and atherosclerosis result in a disturbed sympathetic nervous system function via baroreflex-dependent and independent mechanisms. Subsequently, variability in BP, especially SBP, is increased. Fluctuations in BP have been shown to cause impaired endothelial function of the microvasculature, thereby contributing to vessel damage in kidneys and other target organs.42,43 Journal of Human Hypertension (2014) 37 – 43

Blood pressure variability and microalbuminuria S Noshad et al

42 In our study, of the various variability indices studied, peak SBP consistently showed robust associations with UAE variability and produced the largest b-coefficients, supporting the notion that the effects of SBP variability on albuminuria are largely explained by intermittent surges in BP. Recently, Shantsila et al.44 reported that patients with malignant hypertension sustain micro- and macrovascular dysfunction long after return of BP to its normal levels. Intermittent subacute peaks in SBP are believed to place kidneys in a situation of temporary stress (marked by transient rise in albuminuria) that is not entirely compensated after the insult is removed. Accumulation of these repetitive injuries consequently contributes to glomerulosclerosis and other pathologic features that are typical of diabetic kidney disease. A number of limitations in our study deserve to be discussed. First, our outcome event was only limited to microalbuminuria. Whether these findings can be extrapolated to more advanced stages of the disease (incident macroalbuminuria, serum creatinine doubling, progression to ESRD) remains to be determined. Second, Levitan et al.45 have suggested that both the number and spacing of visits have an impact on intra-individual variability of BP. To account for these confounding effects, we only included subjects within a narrow range of visits (5–9) with a relatively fixed interval between them. Also, in all multivariate regression models adjustment for number of visits were made. Nonetheless, residual confounding effect cannot be ruled out. Third, although the reproducibility of BP variability measures has been evaluated and confirmed, similar assessment for UAE variability measures has not been performed.46 Given the relapse and remitting nature of albuminuria, current available variability measures might not adequately represent the true point-to-point variance that exists in UAE. CONCLUSION Our study complements the available literature by presenting intriguing data on the influence of BP variability on microalbuminuria development in a cohort of patients with type 2 diabetes. Our findings indicate that variability in SBP positively correlates with variability in UAE. Future experimental studies are paramount to clarify the exact molecular underpinnings of this association, and to determine clinical implications that are likely to emerge.

What is known about this topic  It is known that irrespective of the blood pressure cutoff used, higher variability is associated with higher rate and severity of hypertensioninduced target organ damage.  Visit-to-visit variability of blood pressure is correlated with microalbuminuria in patients with type 2 diabetes, independent of mean pressure.  Systolic and diastolic blood pressure variability (measured by s.d.), along with mean SBP, are both significant predictors of proteinuria progression in patients with type 1 diabetes. What this study adds  Visit-wise variability in systolic blood pressure significantly predicts progression to microalbuminuria.  Visit-to-visit variability in systolic blood pressure positively correlates with variability in urinary albumin excretion.  Peak systolic pressure reached during the follow-up period shows the strongest association with variability in urinary albumin excretion.

CONFLICT OF INTEREST The authors declare no conflict of interest.

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AUTHOR CONTRIBUTIONS AE conceived the study, participated in its design, coordination and acquisition of data. MG was involved in recruiting patients and collecting data. SN performed statistical analyses. SN and MM contributed to patient recruitment and also prepared an early draft of the manuscript. MN participated in interpretation of the results and editing the manuscript. All authors read and approved the final manuscript.

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Supplementary Information accompanies this paper on the Journal of Human Hypertension website (http://www.nature.com/jhh)

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Journal of Human Hypertension (2014) 37 – 43