Abstract
In the current study, the authors sought to assess whether the time rate of systolic and diastolic blood pressure variation is associated with advanced subclinical stages of carotid atherosclerosis and plaque echogenicity assessed by gray scale median. The authors recruited 237 consecutive patients with normotension and hypertension who underwent 24‐hour ambulatory blood pressure monitoring and carotid artery ultrasonography. There was an independent association between low 24‐hour systolic time rate and increased echogenicity of carotid plaques (adjusted odds ratio for highest vs lower tertiles of gray scale median, 0.470; 95% confidence interval, 0.245–0.902 [P = .023]). Moreover, increased nighttime diastolic time rate independently correlated with the presence (adjusted odds ratio, 1.328; P = .015) and number of carotid plaques (adjusted odds ratio, 1.410; P = .003). These results indicate differential associations of the systolic and diastolic components of time rate of blood pressure variation with the presence, extent, and composition of carotid plaques and suggest that when blood pressure variation is assessed, both components should be considered.
1. INTRODUCTION
It is well established that the detrimental effects of hypertension depend not only on increased average blood pressure (BP) but also on BP variability (BPV).1, 2 Both in patients with hypertension and normotension, short‐term systolic BP (SBP) variability has been associated with common carotid intima‐media thickness,2, 3, 4, 5 a surrogate of cardiovascular risk and target organ damage in hypertension. Some evidence also suggests that non–flow‐limiting carotid plaques are more prevalent in patients with increased short‐term BPV.6 Furthermore, short‐term BPV has been associated with stroke7 and cardiovascular mortality.8 Stroke and acute coronary syndromes are commonly triggered by rupture of vulnerable plaques in the carotid or coronary arteries, respectively.9, 10 Although current data indicate that increased BPV is associated with accelerated atherosclerosis in these arterial networks,2, 3, 4, 5, 6, 11, 12 its possible association with plaque composition is not known. We hypothesized that time rate (TR) of BP variation, a 24‐hour marker of BPV, would be associated with carotid plaque composition. Filling this gap in the literature may improve our understanding of the “missing link” between increased BPV and cardiovascular events and trigger further research to this direction. Carotid plaques can be characterized noninvasively by their echo‐density,13 which reflects characteristics of increased vulnerability to rupture14 and cardiovascular risk.15 We therefore aimed to examine whether systolic TR (STR) and diastolic TR (DTR) of BP variation are associated with subclinical carotid plaque burden and plaque echo‐density in a cohort of untreated patients referred for hypertension evaluation and without clinical cardiovascular diseases or diabetes mellitus.
2. MATERIALS AND METHODS
2.1. Patients
From a consecutive series of 580 patients who were referred for evaluation to the hypertension center of our department between September 2008 and January 2011, 237 individuals fulfilled the following inclusion criteria: (1) no history or clinical evidence of clinically evident hypertension‐related complications (coronary artery disease, heart failure, cerebrovascular disease, renal insufficiency, or peripheral artery disease), (2) no clinical signs or laboratory evidence of secondary causes of arterial hypertension, (3) no previous antihypertensive treatment, (4) no previous statin treatment, (5) no history of diabetes mellitus, and (6) no history or electrocardiographic evidence of atrial fibrillation. All patients underwent office BP measurements, 24‐hour ambulatory BP monitoring (ABPM) and carotid artery ultrasonography. Hypertension was defined according to 24‐hour ABPM criteria (24‐hour BP ≥ 130/80 mm Hg). The study was approved by the local scientific committee and all participants provided informed consent.
2.2. Office and ambulatory BP measurements
Office BP was measured in both arms in order to detect a difference >10 mm Hg between the right and left arms using a clinically validated automated sphygmomanometer (Omron 705IT; Omron Healthcare). When no such difference was recorded, the cuff was applied to the nondominant arm; otherwise, the cuff was applied to the arm with the highest BP. Three measurements were taken at a 1‐minute interval and were averaged to obtain a single systolic and diastolic office BP value. During the measurements, the participants remained seated with the arm supported and placed at the level of the heart.
All 237 patients underwent 24‐hour ABPM on a usual working day. They were instructed to act and work as usual and to keep their nondominant arm still and relaxed at their side during measurements. Ambulatory BP was recorded using oscillometric Spacelabs 90207 equipment (SpaceLabs). Readings were obtained automatically at 15‐minute intervals throughout the 24‐hour study period. Daytime and nighttime subperiods were defined by using a fixed‐narrow time interval approach according to the European Society of Hypertension position paper on ABPM.16 The daytime period was defined as 9 am to 9 pm and the nighttime period as 1 am to 6 am. The transition periods (9 pm to 1 am and 6 am to 9 am) were discarded from our analysis in order to maximize accuracy to reliably delineate each period. For each patient we computed mean 24‐hour SBP and diastolic BP (DBP) as well as the SD of SBP and DBP. Pulse pressure (PP) was estimated as the difference between SBP and DBP. The degree of BP dipping (percentage) was computed according to the formula (1‐nighttime BP/daytime BP) × 100. According to the BP dipping, patients were divided into dippers if the nocturnal BP fall was >10% of daytime values and nondippers if the nocturnal BP fall was <10%.17 All patients were instructed to rest and sleep during the nighttime and to maintain their usual activities during the day. None of the study participants were bedridden or hospitalized during ABPM.
2.3. ABPM data analysis for BPV
The TR of BP variation was defined as the first derivative of the BP values against time. This parameter focuses on the subsequent changes between consecutive BP recordings, how fast or how slow and in which direction BP values change, and is more sensitive to the sequential order of BP readings than the SD index, which merely reflects the upward and downward BP excursions around the mean. Because we have discrete values, the derivatives are approximated by differences. We cite an example to explain how this index is calculated. Given two BP readings, Si and Si+1 at time indices ti and ti+1, respectively, the rate of BP change was defined as follows: ri = Si+1 –Si/ti+1 – ti.3 For each patient we computed 24‐hour and daytime and nighttime STR and DTR of BP variation.
2.4. Carotid artery ultrasonography
The left and right common carotid arteries, the carotid bulb, and the internal carotid arteries were examined in the anterolateral, posterolateral, and mediolateral directions with a high‐resolution ultrasound Doppler system (Vivid 7; GE), equipped with an 8‐ to 14‐MHz linear array transducer. Patients were examined in the supine position, with the head turned 45 degrees from the site being scanned. Both carotid arteries were scanned longitudinally to visualize intima‐media thickness and carotid plaques. Plaque was defined as a focal structure encroaching into the arterial lumen of at least 0.5 mm or 50% of the surrounding intima‐media thickness value, or with a thickness >1.5 mm as measured from the media‐adventitia interface to the intima‐lumen interface.18
2.5. Analysis of plaque echogenicity
Ultrasonographic images were digitized and imported into artery measurement system automated software for dedicated analysis of plaque echogenicity.19 We used the semiautomated method to evaluate echogenicity of carotid plaques, as described elsewhere.20 Following gray‐level normalization and manual outlining of the plaque, the program calculated gray scale median (GSM), an index of echogenicity. Echolucent plaques were characterized by low values of this index. Figure 1 depicts two examples of plaque analysis with high and low GSM. These measurements were repeated in 50 random patients, giving a coefficient of variation of 8.1% for GSM in the plaques.
Figure 1.
Evaluation of the echogenicity of atherosclerotic plaques using a dedicated software artery measurement system. (A) An echolucent plaque with a low measured gray scale median (GSM) at 20. (B) An echogenic plaque with a high measured GSM at 90
2.6. Statistical analysis
Continuous variables are expressed as mean ± SD and nominal/ordinal variables as absolute or percentage values. Normal distribution of all continuous variables was tested with the parametric test Shapiro‐Wilk and graphically with P‐P plots. Logarithmic transformation was used in cases of skewed data. Correlations between variables were evaluated using Pearson's or Spearman's correlation coefficient. Subsequently, we implemented multivariable regression models to assess the independent association of indices of BPV with the outcomes of the study. All models were adjusted for traditional cardiovascular risk factors (ie, age, sex, smoking, hypertension, and hyperlipidemia). In a second level, all models were further adjusted for mean 24‐hour SBP, interchangeably with the history of hypertension because of c‐linearity limitations. Collinearity of independent variables was assessed by estimating variable inflation factor. A cutoff value of four or greater indicated an increased variable inflation factor for multicollinearity on the basis of previous literature. Moreover, interaction terms between the presence of hypertension and TR were tested at the P < .01 or P < .05 level. Taking into account the limited sample size (n = 66) of patients with plaques, we implemented resampling techniques by using permutation tests to confirm the regression analysis results on the association between GSM and TR of BP variation.21 Statistical analysis was performed with STATA package version 11.1 (StataCorp). Statistical significance was set at a P value <.05.
3. RESULTS
3.1. Demographic characteristics
Descriptive characteristics as well as hemodynamic indices and carotid plaque parameters of our study population are depicted in Table 1. Table 2 summarizes the calculated TR parameters of BP variation in our study sample.
Table 1.
Demographic characteristics, blood pressure, and carotid plaque parameters of the study population
| Variables | Mean (SD) or Percentage |
|---|---|
| Baseline characteristics | |
| Age, y | 51.8 (11.0) |
| Men | 46 |
| Hypertension | 30 |
| Body mass index, kg/m2 | 27.7 (4.7) |
| Hyperlipidemia | 44 |
| Smoking | 35 |
| Glucose, mmol/L | 5.4 (0.7) |
| Cholesterol, mmol/L | 12.3 (2.5) |
| Triglycerides, mmol/L | 5.1 (3.7) |
| Blood pressure parameters | |
| Office SBP, mm Hg | 146.2 (20.0) |
| Office DBP, mm Hg | 92.4 (42.2) |
| 24‐h SBP, mm Hg | 128.0 (13.9) |
| 24‐h DBP, mm Hg | 79.4 (9.4) |
| 24‐h pulse pressure, mm Hg | 48.5 (9.2) |
| 24‐h HR, beats per min | 74.8 (8.2) |
| SBP variability, mm Hg | 13.8 (3.2) |
| DBP variability, mm Hg | 11.2 (2.3) |
| HR variability, beats per min | 11.2 (2.3) |
| SBP dipping | 8.5 (5.6) |
| DBP dipping | 12.1 (6.9) |
| Carotid plaque parameters | |
| Presence of carotid plaque | 28 |
| No. of plaques (IQR) | 0 (0–1)a |
| Plaque GSM | 61.0 (24.9) |
Abbreviations: DBP, diastolic blood pressure; GSM, gray scale median; HR, heart rate; IQR, interquartile range; SBP, systolic blood pressure.
Table 2.
Descriptives of time rate of BP variation parameters
| Variables | Mean (SD) |
|---|---|
| 24‐h STR of BP variation, mm Hg/min | 0.56 (0.12) |
| Daytime STR of BP variation, mm Hg/min | 0.59 (0.18) |
| Nighttime STR of BP variation, mm Hg/min | 0.51 (0.15) |
| 24‐h DTR of BP variation, mm Hg/min | 0.45 (0.08) |
| Daytime DTR of BP variation, mm Hg/min | 0.46 (0.10) |
| Nighttime DTR of BP variation, mm Hg/min | 0.41 (0.13) |
Abbreviations: BP, blood pressure; DTR, diastolic time rate; STR, systolic time rate.
3.2. Associations of BP variation with carotid plaque echogenicity
In patients with atherosclerotic plaques, TR markers of BP variation did not correlate linearly with GSM as a continuous variable (Table 3). In contrast, 24‐hour STR significantly differed by tertiles of GSM (tertile 1: 0.586 ± 0.087 vs tertile 2: 0.649 ± 0.125 vs tertile 3: 0.541 ± 0.104 mm Hg/min, P = .007). As shown in Table 3, 24‐hour STR was significantly lower in the highest tertile compared with the combined lower tertiles of GSM (Figure 2A), while daytime and nighttime STR marginally followed the same pattern (Table 3). When the population was compared by the lowest versus the combined higher tertiles of GSM, no significant association was observed with TR (Table 3).
Table 3.
Univariate associations between blood pressure variation indices and presence and number of plaques and plaque echogenicity
| Variables | Presence of plaque | No. of plaques | GSM | GSM (lowest vs higher tertiles) | GSM (highest vs lower tertiles) |
|---|---|---|---|---|---|
| 24‐h STR | 1.156 | 1.170 | −0.435 | 0.952 | 0.487 |
| (0.920‐1.451) | (0.936‐1.463) | (−1.492 to 0.622) | (0.605‐1.500) | (0.216‐0.872) | |
| [.213] | [.167] | [.141] | [.835] | [.016] | |
| Daytime STR | 0.980 | 0.993 | −0.298 | 1.105 | 0.659 |
| (0.823‐1.167) | (0.841‐1.172) | (−1.277 to 0.630) | (0.745‐1.637) | (0.419‐1.037) | |
| [.825] | [.936] | [.523] | [.618] | [.072] | |
| Nighttime STR | 1.246 | 1.238 | −0.185 | 0.868 | 0.706 |
| (1.031‐1.505) | (1.035‐1.482) | (−0.900 to 0.528) | (0.625‐1.205) | (0.479‐1.042) | |
| [.022] | [.019] | [.605] | [.399] | [.080] | |
| 24‐h DTR | 1.547 | 1.551 | −0.908 | 1.415 | 0.574 |
| (1.075‐2.225) | (1.087‐2.213) | (−2.402 to 0.586) | (0.739‐2.709) | (0.230‐1.138) | |
| [.019] | [.015] | [.229] | [.294] | [.112] | |
| Daytime DTR | 1.035 | 1.024 | −0.553 | 1.177 | 0.729 |
| (0.775‐1.383) | (0.771‐1.360) | (−1.835 to 0.727) | (0.679‐2.040) | (0.412‐1.285) | |
| [.812] | [.867] | [.391] | [.561] | [.276] | |
| Nighttime DTR | 1.359 | 1.410 | −0.210 | 0.999 | 0.776 |
| (1.094‐1.688) | (1.138‐1.747) | (−1.035 to 0.613) | (0.704‐1.418) | (0.521‐1.156) | |
| [.005] | [.002] | [.612] | [.998] | [.214] |
The odds ratio reflects the risk for each 0.1 change in time rate of blood pressure variation. Bold values indicate significant univariate associations.
Abbreviations: DTR, diastolic time rate; STR, systolic time rate.
Values indicate unadjusted odds ratios (95% confidence intervals), except gray scale median (GSM; unadjusted regression coefficients) and the P values in brackets.
Figure 2.
(A) The systolic time rate of blood pressure (BP) variation during 24 hours (str24) was lower in the highest tertile of carotid gray scale median (GSM). Lower tertiles of GSM included the combined lowest and middle tertiles. (B) The nighttime diastolic rate of BP variation was higher in patients with at least one carotid plaque. P values were derived from multivariable regression models adjusting for traditional risk factors for cardiovascular disease and 24‐hour mean systolic BP. Vertical lines represent 95% confidence intervals around the mean value
By multivariable regression analysis, 24‐hour and nighttime STR were independent determinants of the highest tertile of GSM after adjustment for traditional risk factors (age, sex, smoking, hypertension, and dyslipidemia) for cardiovascular disease and 24‐hour mean SBP (Table 4). Nighttime DTR was also inversely associated with the highest tertile of GSM after adjustment for traditional risk factors, but significance was lost after additional adjustment for 24‐hour SBP. Interaction terms between presence of hypertension and TR indices of BPV were tested and none were significant (P > .01 for all).
Table 4.
Adjusted associations of carotid atherosclerosis and plaque echogenicity with parameters of blood pressure variation
| Outcome | Odds ratio (95% CIs) | P valuea |
|---|---|---|
| 24‐h STR | ||
| Adjusted for TRFs | ||
| Presence of plaque | 1.442 (0.961‐2.163) | .077 |
| No. of plaques | 1.461 (1.013‐2.105)b | .042 |
| GSM in the highest tertile | 0.358 (0.147‐0.874) |
.024 .048 (.023‐.091)c |
| GSM in the lowest tertile | 0.907 (0.496‐1.659) | .753 |
| Adjusted for TRFs+ABPMd | ||
| Presence of plaque | 1.132 (0.888‐1.442) | .315 |
| No. of plaques | 1.167 (0.919‐1.480)a | .204 |
| GSM in the highest tertile | 0.470 (0.245‐0.902) |
.023 .022 (.011‐.039)b |
| GSM in the lowest tertile | 0.961 (0.591‐1.561) | .873 |
| Nighttime STR | ||
| Adjusted for TRFs | ||
| Presence of plaque | 1.257 (0.951‐1.662) | .107 |
| No. of plaques | 0.218 (0.944‐1.571)a | .128 |
| GSM in the highest tertile | 0.442 (0.200‐0.974) |
.043 .043 (.019‐.083)b |
| GSM in the lowest tertile | 0.882 (0.549‐1.417) | .606 |
| Adjusted for TRFs+ABPM | ||
| Presence of plaque | 1.197 (0.978‐1.465) | .081 |
| No. of plaques | 1.199 (0.990‐1.452)a | .063 |
| GSM in the highest tertile | 0.610 (0.387‐0.960) |
.033 .032 (.019‐.052)b |
| GSM in the lowest tertile | 0.923 (0.654‐1.304) | .653 |
| Nighttime DTR | ||
| Adjusted for TRFs | ||
| Presence of plaque | 1.386 (1.032‐1.859) | .030 |
| No. of plaques | 1.481 (1.112‐1.973)a | .007 |
| GSM in the highest tertile | 0.440 (0.199‐0.972) |
.043 .043 (.019‐.083)b |
| GSM in the lowest tertile | 1.198 (0.748‐1.917) | .451 |
| Adjusted for TRFs+ABPM | ||
| Presence of plaque | 1.328 (1.056‐1.670) | .015 |
| No. of plaques | 1.410 (1.124‐1.768)a | .003 |
| 0.662 (0.417‐1.053) |
.082 .100 (.063‐.152)b |
|
| GSM in the lowest tertile | 1.073 (0.734‐1.570) | .714 |
Adjusted traditional risk factors (TRFs) include age, sex, hyperlipidemia, smoking, and hypertension.
Because of colinearity, models that adjusted for both TRFs and ambulatory blood pressure monitoring (ABPM) parameters did not include hypertension.
Abbreviations: DTR, diastolic time rate; GSM, gray scale median; STR, systolic time rate.
aObserved statistical significance for the adjusted association of standard deviation (SD) of 24‐h systolic blood pressure instead of 24‐h STR, SD of nighttime systolic blood pressure instead of nighttime STR and SD of nighttime diastolic blood pressure instead of nighttime DTR.
Odds ratio (95% confidence interval) derived from binary (or ordinalb) logistic regression analysis. The odds ratio reflects the risk for each 0.1 change in time rate of blood pressure variability.
cAdjusted P values and corresponding 95% confidence intervals in parentheses as derived from permutation tests.
dABPM parameter: 24‐hour mean systolic blood pressure.
The independent association of 24‐hour and nighttime STR with GSM was confirmed by permutation tests (adjusted P value = .022 [95% CIs for P value: .011–.039] and adjusted P value = .0324 [95% CIs for P value: .019–.052], respectively) (Table 4). Finally, the SD of SBP and DBP at any time period was not associated with GSM as a continuous or dichotomous variable in univariate or multivariate regression models (P > .1 for all).
3.3. Associations of BP variation with the presence and extent of carotid atherosclerosis
As shown in Table 3, 24‐hour nighttime STR and nighttime DTR significantly correlated with the presence of carotid plaques (Figure 2B). Moreover, nighttime STR and DTR and 24‐hour DTR significantly correlated with the number of carotid plaques (Table 3).
By multivariable regression analysis, only nighttime DTR was an independent determinant of both the presence and number of plaques in the carotid arteries after adjustment for traditional risk factors (age, sex, smoking, hypertension, and dyslipidemia) and 24‐hour ABPM parameters (Table 4). In addition, SD of 24‐h SBP was independently associated with both the presence (P = .010) and number of plaques (P = .002) after adjustment for traditional risk factors and 24‐hour mean SBP.
4. DISCUSSION
The novel finding of this study is the observed independent nongraded association between low STR of BP variation and high echogenicity of carotid plaques, which is indicative of higher plaque stability. Another point of interest is that the systolic and diastolic components of BPV differentially correlated with the presence of carotid plaques and plaque composition, which suggests that both systolic and diastolic components should be measured when BPV is assessed.
Accumulating evidence supports the clinical value of BPV in addition to the traditionally measured BP levels. Different markers of short‐term BPV have been associated with flow‐limiting atherosclerosis in the coronary arteries, carotid intima‐media thickening, and increased plaque prevalence of the carotid arteries.3, 4, 5, 6 However, these findings only suggest that BPV correlates with the presence of atherosclerosis but provides no further information for its association with plaque composition. Plaque echogenicity reflects its composition and is known to closely correlate with vulnerability and susceptibility to rupture.22 Plaque rupture, in turn, is the terminal step before the adverse sequelae of atherosclerosis.23 Thus, a flow‐limiting echogenic atherosclerotic stenosis may pose lower risk for development of unstable cardiovascular disease as compared with a non–flow‐limiting echolucent plaque in the carotid or coronary arteries.9, 10 To this end, we found that it is not higher STR of BP variation that was associated with lower plaque echogenicity, but rather lower STR that was associated with higher plaque echogenicity suggestive of more stable plaques. These results may trigger further investigations to improve our understanding on mechanisms linking BPV with cardiovascular risk. Thus, they may be considered hypothesis generating, and a possible atheroprotective effect of low BPV to stabilize atheromatous plaques should be assessed in animal experimental studies. Interestingly, we found a nongraded association rather than a continuum in the correlation between TR and GSM. This is not surprising since, in general, such a nongraded association between GSM and outcome has been observed in the literature.24 Nevertheless, we cannot exclude the possibility that a larger cohort would reveal a weak continuous correlation between TR and GSM. Of interest, we found no significant association between SD and GSM, suggesting that a possible effect of BPV on plaque composition would be more accurately reflected by measuring TR in future research designs.
The possible mechanisms mediating alterations in plaque composition in patients with high short‐term BPV are not clear. Increased BPV may promote vascular inflammation by increasing endothelial expression of cytokines, which leads to accumulation of macrophages and formation of atherosclerotic plaques.25, 26, 27 The presence of activated inflammatory cells through various pathways leads to instability of atherosclerotic plaque and its possible rupture.14 In an experimental study, increased BPV prevented the production of nitric oxide,28 the depletion of which plays a pivotal role in plaque instability.29 These changes affect the structure and composition of the atherosclerotic plaque, which will eventually either become rich in lipids or fibrous tissue and calcium and will produce either echolucent or echogenic characteristics, respectively.
In a previous study, both systolic and diastolic within‐visit BPV correlated with the presence of plaque in the internal carotid artery in patients with normotension and hypertension under treatment.6 However, within‐visit BPV does not exclude white‐coat hypertension and cannot assess BP during sleep as compared with TR, which is derived from ABPM and provides this information. Accordingly, we found nighttime TR of BP variation to be the strongest independent determinant of the presence of subclinical carotid atherosclerosis. These findings are in agreement with previous data in 78 patients, reporting nighttime DBP variability assessed by average real variability to be associated with the number of carotid plaques in the left common carotid artery.5 Finally, it has been recently suggested that nighttime BP values may largely affect cardiovascular risk.30 In agreement with this concept, we found that nighttime DTR and STR of BP variation were respectively associated with carotid atherosclerosis and plaque echogenicity, suggesting that both components should be taken into consideration when assessing BPV.
STUDY LIMITATIONS
Certain limitations of the present study should be acknowledged. First, a cause and effect relationship between TR of BP variation and carotid atherosclerosis cannot be extrapolated from the cross‐sectional design of this study. Second, as expected, only a fraction of the total population had atheromatous plaques and therefore the GSM analysis was limited to these patients. Because of the limited sample size for this analysis, we cannot exclude the possibility that some weak associations may have been missed. However, as also reported in the Results section, we additionally performed permutation tests, which confirmed the observed statistically significant differences derived from regression analyses. Furthermore, given that patients with atheromatous carotid plaques are stratified as high cardiovascular risk,31 our GSM findings were derived from a high cardiovascular risk subgroup of our population by definition and therefore they cannot be generalized to lower risk groups of individuals with hypertension and normotension. Nevertheless, these findings build on the rest of our results that show associations between TR and the severity of carotid atherosclerosis in the total population.
CONCLUSIONS
In a population of untreated patients with hypertension or normotension without diabetes mellitus or clinically overt cardiovascular disease, we found differential associations between the STR and DTR of BP variation and the presence, extent, and composition of carotid plaques. These findings suggest that when BP variation is assessed, both components should be calculated and interpreted. Furthermore, our observation that patients with more stable plaques as assessed by GSM had lower STR of BP variation is hypothesis generating and should trigger further research to improve our understanding of the link between BPV and cardiovascular risk.
CONFLICT OF INTEREST
The authors declare no conflicts of interest with respect to this article.
AUTHOR CONTRIBUTION
Athanasios Kolyviras: Acquisition of data, analysis of data, drafting of the article; Efstathios Manios: Conception and design, interpretation of data; Georgios Georgiopoulos and Thomas Gustavsson: Analysis and interpretation of data; Fotios Michas, Efthimia Papadopoulou, and Ageliki Laina: Acquisition of data; John Kanakakis, Christos Papamichael, and Georgios Stergiou: Critical revision for important intellectual content; Nikolaos Zakopoulos: Conception and design; Kimon Stamatelopoulos: Conception and design, analysis and interpretation of data, critical revision for important intellectual content, and final approval of the version to be published.
Kolyviras A, Manios E, Georgiopoulos G, et al. Differential associations of systolic and diastolic time rate of blood pressure variation with carotid atherosclerosis and plaque echogenicity. J Clin Hypertens. 2017;19:1070–1077. 10.1111/jch.13070
Athanasios Kolyviras and Efstathios Manios contributed equally to this work.
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