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- Vasc Health Risk Manag
- v.8; 2012
- PMC3310357
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Vasc Health Risk Manag. 2012; 8: 169–176.
Published online 2012 Mar 14. doi:10.2147/VHRM.S30507
PMCID: PMC3310357
PMID: 22454561
Takeshi Takami,1 Shigeru Yamano,2 Sadanori Okada,3 Mio Sakuma,4 Takeshi Morimoto,4 Hiroshi Hashimoto,5 Satoshi Somekawa,3 and Yoshihiko Saito3
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Abstract
Purpose
Blood pressure (BP), age, and reduced renal function are major risk factors for white-matter lesions (WMLs) in the general population. However, it remains unclear whether or not the BP itself or other parameters related to the BP are associated with WMLs in hypertensive patients with well-controlled BP. We investigated the relationships of the presence of WMLs with the central systolic BP (cSBP) and estimated glomerular filtration rate (eGFR) in treated hypertensive patients.
Method
We studied 185 hypertensive patients with median duration of hypertension, 10.0 years, whose BP is controlled to SBP and diastolic BP (DBP) of 139 ± 17 and 79 ± 10 mmHg, respectively. We measured cSBP and brain magnetic resonance imaging (MRI) was examined within 2 weeks after last BP and biological measurements.
Results
Patients with higher-grade WMLs, as assessed by the presence of Scheltens deep white-matter hyperintensity (SDWMH) in the frontal (grade 0–2 vs 3–6) and parietal areas (grade 0–2 vs 3–6) where small arteries are affected at earlier stage of hypertension, as well as that of Fazekas deep white-matter hyperintensity (FDWMH) (grade 2–3 vs 0–1) and Fazekas periventricular hyperintensity (FPVH) (grade 1–3 vs 0) were older, had higher serum creatinine levels, a longer duration of hypertension, and lower eGFR values. The grade of the WMLs was not associated with either the cSBP or the brachial SBP. In logistic regression analyses after adjustment for age, sex, cSBP, and hypertension duration, showed significant association between eGFR and WMLs. The patients with lower eGFR (<60 mL/minute/1.73 m2) tended to have higher grade WMLs. The odds ratio was 2.87 for FDWMH (P = 0.017), 1.99 for FPVH (P = 0.131), and 2.33 for SDWMH in the parietal area (P = 0.045).
Conclusion
Presence of WMLs was associated with eGFR, but not with either the brachial SBP or cSBP in hypertensive patients with well-controlled BP.
Keywords: white-matter lesions, central systolic blood pressure, estimated glomerular filtration rate
Introduction
White-matter lesions (WMLs) are frequently observed on cerebral magnetic resonance imaging (MRI) of elderly patients without apparent neurological symptoms. In addition to age, hypertension has also been consistently reported to be a common risk factor for the development of cerebral WMLs.1–3 The association between hypertension and the development of WMLs has been established in cross-sectional4 as well as longitudinal studies.1 Several studies have examined the prevalence of WMLs in hypertensive and high-normotensive subjects.5,6 White-matter hyperintensities (WMHs) seen on MRI scans are associated with degenerative changes in the arterioles that are related to atherosclerosis, suggesting that cerebral arteriosclerosis of the penetrating vessels is a major factor in the pathogenesis of ischemic WMLs.4
Recently, several noninvasive parameters have been described for the assessment of vascular stiffness. Central systolic blood pressure (cSBP) and the augmentation index (AI) are parameters that can be estimated noninvasively from the central arterial waveforms through radial arterial pulse wave analysis.7–9 AI and cSBP are reported to be closely related to several risk factors for atherosclerosis and future cardiovascular events.9–11
A recent study showed that the cSBP was more strongly correlated with the presence of WMLs than the brachial systolic blood pressure (brachial SBP).12
Recently, the relationship between chronic kidney disease (CKD) and cardiovascular disease has also been highlighted. Furthermore, recent studies have reported the existence of an association between CKD (lower estimated glomerular filtration rates [eGFR]) and the presence of WMLs.13,14 Renal dysfunction may be related to cerebral small vessel disease, for example WMLs.
Therefore, this study chose cSBP and eGFR as variables of interest. The association of WMLs with cSBP and eGFR in treated hypertensive patients has not yet been studied. Correlations between the presence of subcortical WMLs and clinical parameters have also not been studied yet.
In this study, we investigated the relationships of the presence of WMLs with the cSBP and eGFR in treated hypertensive patients.
Methods
Study population
Study participants were recruited from hypertensive patients under treatment with antihypertensive medications. They were outpatients of the Jingumae Clinic from June 2010 to February 2011. All study participants were aged over 60 years. The exclusion criteria included inability to apply the tonometric system for evaluation, such as in patients with atrial fibrillation, and inability to perform MRI, as in patients with pacemakers and other implants. Informed consent was obtained from all participants prior to study enrollment. The present study was performed under approval of the clinical research protocol by the institutional ethics committee of Nara medical university, and in compliance with the Helsinki Declaration of 1975. Among the 200 participants who agreed with the study objectives and protocols of the study and provided written consent to undergo all the procedures, 15 were excluded due to the aforementioned MRI contraindications or because they had atrial fibrillation, which made use of the tonometric system impossible.
MRI examinations
MRI was performed at one of two centers with a 1.5-T clinical MR unit (Magnetom Avanto; Siemens AG, Erlangen, Germany) equipped with an 8-channel phased array coil, or with a 1.5-T clinical MR unit (GE Signa HDx; General Electric, Harvey, USA) equipped with a 12-channel phased array coil. All examinations included axial sections of conventional spin-echo T1-weighted (TR = 560 to 2100 ms, TE = 8.8 to 12 ms), spin-echo T2-weighted (TR = 3934 to 4000 ms, TE = 94 to 110 ms), and fluid-attenuated inversion recovery (FLAIR; TR = 8400 to 10,000 ms, TI = 2100 to 2600 ms, TE = 110 to 125 ms) sequences. The slice orientation was the axial plane perpendicular to the posterior margin of the pons, and the slice thickness was 5 mm with a 1 or 2 mm gap. Images were obtained using a 256 × 256 matrix or 352 × 352 matrix and a 230 mm field of view.
Assessment of deep white-matter hyperintensities and periventricular hyperintensities
WMLs consist of deep white-matter hyperintensities (DWMH) and periventricular hyperintensities (PVH). DWMH assessment was performed centrally by a single rater blinded to the clinical data of the study participants . The severity of the WMHs was rated visually on axial FLAIR images using the Fazekas scale15 (absent, grade 0; punctuate, grade 1; early-confluent, grade 2; confluent, grade 3) and Scheltens rating scale16 (range, 0 to 30), in which scores 0 to 6 are given for four subcortical white-matter regions (frontal, parietal, temporal, occipital), and scores from 0 to 2 for 3 periventricular regions (frontal caps, occipital caps, bands). Basal ganglia and infratentorial hyperintensities were not rated for this study.
PVHs were graded as 0 = absence, 1 = ‘caps’ or pencilthin lining, 2 = smooth ‘halo’, 3 = irregular PVH extending into the deep white matter using the Fazekas scale15, and scored from 0 to 2 for three periventricular lesions using the Scheltens rating scale.16
Grade 2 or 3 DWMHs classified according to the Fazekas scale are progressive and likely malignant, whereas grades 0 to 1 are not progressive.17,18 Grade 0 to 1 DWMHs as classified by the Fazekas scale are equivalent in severity to grades 0, 1, and 2 as classified by Scheltens rating scale.16 Therefore, we divided patients with DWMHs into two clinical groups according the lesion grade.
Grade 0 PVH represents the normal condition, whereas grades 1, 2, 3 PVHs are abnormal. Therefore, we also divided patients with PVHs into two groups according to the severity grade of the lesions.
Measurement of the CBP and AIx@75
Measurements of the CBP and AI corrected to a heart rate of 75 bpm (AIx@75) were performed as described previously. 19 The pulse pressure waveform of the radial artery was recorded using an automated tonometry system (HEM-9000AI; Omron Healthcare, Kyoto, Japan) with the patient in the sitting position after having rested for at least 5 minutes. The waveform was automatically calibrated using the built-in oscillometric brachial sphygmomanometer, and the peak and trough of the radial pressure wave were adjusted for the brachial SBP and diastolic blood pressure (DBP), respectively. The second peak (late systolic inflection) was automatically detected by an algorithm programmed into the HEM-9000AI system using the second maxima of the fourth derivative of the radial pressure waveform to determine the radial AI, as well as the late or second SBP (SBP2). This algorithm is described in greater detail elsewhere.20 The height of the second peak corresponds to the SBP2 value obtained using HEM-9000AI. The value of SBP2 is very similar to that of aortic CBP recorded using invasive techniques;21 and thus, SBP2 was used as an estimate of CBP. CBP determined using HEM9000-AI is comparable to that determined using a generalized aorta–radial transfer function.22,23 The AI was calculated using the following formula: (SBP2 – DBP)/(the first peak SBP – DBP) × 100. Because the AI is influenced by the heart rate, AI was normalized for a heart rate of 75 bpm (AIx@75), as proposed by Wilkinson et al.24 In this study, these measurements were performed by a single highly experienced investigator.
Statistical analyses
We presented categorical variables as numbers and percentages and assessed the differences in the categorical variables according to the MRI scores using the chi-square test, while describing continuous variables as mean values ± standard deviations, unless otherwise indicated. Based on their distribution, we estimated the differences in the continuous variables according to the MRI scores by Student’s t-test or the Wilcoxon’s rank sum test. We used logistic regression analyses to assess the association between high MRI scores and low eGFR (<60 mL/minute/1.73 m2) adjusted for age (≥70 years), sex, cSBP (≥140 mmHg) and the quartile of hypertension duration. We performed all statistical analyses using JMP software (v. 9.0; SAS Institute Inc, Cary, NC) and STATA (v. 10; Stata Corporation, College Station, TX). Differences at P values of less than 0.05 were considered to be statistically significant.
Results
The patient characteristics are shown in Table 1. In total, 185 patients were included in this study, of which 84 (45%) were male. The mean (± standard deviation) age of the patients was 70 (±7) years. 3 (2%) had a history of myocardial infarction, 16 (9%) had a history of angina pectoris, 1 (0.5%) had a history of aortic disease, 1 (0.5%) had a history of peripheral arterial disease, and 8 (4%) had a history of heart failure. As for risk factors other than hypertension, 56 patients (30%) smoked habitually, 38 (21%) had diabetes mellitus, and 101 (55%) were dyslipidemic. Calcium-channel blockers, angiotensin-receptor blockers, angiotensin-converting enzyme, and β-blockers were used by 58%, 69%, 24%, and 2% of the study participants, respectively.
Table 1
Patient characteristics
| Number of patients | 185 |
| Age year [Mean (SD)] | 70 (7) |
| Male n (%) | 84 (45) |
| Height cm [Mean (SD)] | 157 (9) |
| Weight kg [Mean (SD)] | 60 (11) |
| BMI kg/m2 [Mean (SD)] | 24.3 (3.7) |
| Waist circumference cm [Mean (SD)] | 88 (10) |
| Duration of hypertension [Median (Q1–Q4)] | 10.0 (5.0–20.0) |
| Current smoking or past smoking n (%) | 56 (30) |
| Current smoking n (%) | 21 (11) |
| Regular alcohol drinkers n (%) | 50 (27) |
| Hyperlipidemia n (%) | 101 (55) |
| Diabetes mellitus n (%) | 38 (21) |
| Hyperuricemia n (%) | 18 (10) |
| Protenuria n (%) | 6 (3) |
| Laboratory data | |
| T-cho mg/dL [Mean (SD)] | 202 (30) |
| TG mg/dL [Mean (SD)] | 154 (89) |
| HDL-cho mg/dL [Mean (SD)] | 55 (15) |
| LDL-cho (direct method) mg/dL [Mean (SD)] | 117 (25) |
| LDL-cho (indirect method) mg/dL [Mean (SD)] | 116 (28) |
| Serum creatinine mg/dL [Mean (SD)] | 0.8 (0.2) |
| eGFR mL/min/1.73 m2 [Mean (SD)] | 69.7 (14.7) |
| Past medical history | |
| Cardiac infarction n (%) | 3 (2) |
| Angina pectoris n (%) | 16 (9) |
| Aortic disease n (%) | 1 (0.5) |
| Peripheral artery disease n (%) | 1 (0.5) |
| Heart failure n (%) | 8 (4) |
| Blood pressure, heart rate, rAIx | |
| SBP mmHg [Mean (SD)] | 139 (17) |
| DBP mmHg [Mean (SD)] | 79 (10) |
| PP mmHg [Mean (SD)] | 60 (13) |
| HR/min [Mean (SD)] | 72 (11) |
| SBP2 mmHg [Mean (SD)] | 139 (18) |
| CSBP mmHg [Mean (SD)] | 143 (20) |
| CPP mmHg [Mean (SD)] | 64 (16) |
| rAIx% [Mean (SD)] | 84 (15) |
| rAIx@75% [Mean (SD)] | 83 (13) |
| Current medication | |
| CCBs n (%) | 107 (58) |
| β-blockers n (%) | 4 (2) |
| ACE-Is n (%) | 44 (24) |
| ARBs n (%) | 128 (69) |
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Abbreviations: BMI, body mass index; Q1, ≤5 years; Q2, 5–10 years; Q3, 10–20 years; Q4, >20 years; T-cho, total lipoprotein cholesterol; TG, triglyceride; HDL-cho, high-density lipoprotein cholesterol; LDL-cho, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; SBP, brachial systolic blood pressure; DBP, brachial diastolic blood pressure; PP, brachial pulse pressure; HR, heart rate; SBP2, secondary systolic pressure; CSBP, central systolic blood pressure; CPP, central pulse pressure; rAIx, radial augmentation index; rAI×@75, radial augmentation index normalized for a heart rate of 75/minute; CCB, calcium channel blockers; ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin II receptor blockers.
The mean (± standard deviation) SBP was 139 (±17 mmHg, that of the cSBP was 143 (±20) mmHg, and that of AIx@75 was 83% (±13)%.
The MRI study results are shown in Table 2. The WMLs were rated in severity visually on axial FLAIR images using the Fazekas scale;18 84% had grade 1 or 2 FDWMHs, and 91% had grade 0 or 1 FPVHs. Using the Scheltens rating scale,19 85% had grade 0 lesions in the temporal region, whereas 89% had grade 1–6 lesions in the parietal region.
Table 2
Distribution of MRI findings in white-matter lesions
| MRI findings | Number of cases (%) | ||||||
|---|---|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | ||||
| Fazekas scale | |||||||
| DWMH | 14 (8) | 96 (52) | 60 (32) | 15 (8) | |||
| PVH | 83 (45) | 86 (46) | 15 (8) | 1 (1) | |||
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | |
| Scheltens scale | |||||||
| DWMH | |||||||
| Frontal | 81 (44) | 76 (41) | 7 (4) | 18 (10) | 2 (1) | 1 (1) | 0 (0) |
| Parietal | 21 (11) | 64 (35) | 27 (15) | 31 (47) | 23 (11) | 8 (4) | 13 (7) |
| Occipital | 103 (56) | 54 (29) | 8 (4) | 16 (9) | 1 (1) | 2 (1) | 1 (1) |
| Temporal | 157 (85) | 23 (12) | 1 (1) | 4 (3) | 0 (0) | 0 (0) | 0 (0) |
| PVH caps | |||||||
| Frontal | 83 (45) | 92 (50) | 10 (5) | – | – | – | – |
| Occipital | 90 (49) | 67 (36) | 28 (15) | – | – | – | – |
| Lat ventricular bands | 170 (94) | 10 (5) | 2 (1) | – | – | – | – |
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Abbreviations: DWMH, deep white-matter hyperintensities; PVH, periventricular hyperintensities.
Comparison of the clinical parameters between WMLs with low and high Fazekas scores are shown in Table 3. The patients with higher-grade WMLs, (for both FDWMH [grade 2–3 versus 0–1] and FPVH [grade 1–3 versus 0]) were older, and had a higher serum creatinine level, a longer duration of hypertension, and a lower eGFR. The WML grade was not associated with either the cSBP or brachial SBP. Comparison of the clinical parameters between patients with WMLs assigned low and high Scheltens scores are shown in Table 4. The patients with higher-grade WMLs (both frontal SDWMH [grade 0–2 versus 3–6] and parietal SDWMH [grade 0–2 versus 3–6]) were older, and had a higher serum creatinine level, a longer duration of hypertension, and a lower eGFR. The WML grade was not associated with either the cSBP or the brachial SBP.
Table 3
Comparison of clinical parameters between low and high Fazekas scores
| Score grade | FDWMH | FPVH | |||||
|---|---|---|---|---|---|---|---|
| 0–1 | 2–3 | P value | 0 | 1–3 | P value | ||
| Number of patients | 110 | 75 | 83 | 102 | |||
| Age (years) | Mean (SD) | 67 (5) | 74 (6) | <0.0001 | 67 (5) | 72 (7) | <0.0001 |
| BMI (kg/m2) | Mean (SD) | 24.1 (3.6) | 24.6 (3.9) | 0.38 | 23.7 (3.3) | 24.9 (4.0) | 0.03 |
| Male | n (%) | 49 (45) | 35 (47) | 0.78 | 39 (47) | 45 (44) | 0.7 |
| Current or past smoking | n (%) | 34 (31) | 22 (29) | 0.82 | 29 (35) | 27 (26) | 0.21 |
| Hyperlipidemia | n (%) | 60 (55) | 41 (55) | 0.99 | 49 (59) | 52 (51) | 0.27 |
| Diabetes mellitus | n (%) | 23 (21) | 15 (20) | 0.88 | 18 (22) | 20 (20) | 0.73 |
| SBP (mmHg) | Mean (SD) | 139 (17) | 139 (16) | 0.78 | 138 (16) | 140 (17) | 0.28 |
| DBP (mmHg) | Mean (SD) | 79 (10) | 78 (11) | 0.58 | 79 (9) | 79 (11) | 0.67 |
| PP (mmHg) | Mean (SD) | 60 (13) | 61 (13) | 0.77 | 58 (12) | 62 (13) | 0.059 |
| CPP (mmHg) | Mean (SD) | 64 (16) | 64 (15) | 0.85 | 62 (16) | 66 (16) | 0.17 |
| SBP2 (mmHg) | Mean (SD) | 129 (19) | 131 (18) | 0.6 | 128 (18) | 132 (19) | 0.17 |
| CSBP (mmHg) | Mean (SD) | 144 (20) | 142 (20) | 0.66 | 142 (20) | 144 (21) | 0.4 |
| rAIx (%) | Mean (SD) | 85 (16) | 84 (12) | 0.63 | 84 (15) | 84 (14) | 0.88 |
| rAIx@75 (%) | Mean (SD) | 83 (13) | 83 (12) | 0.95 | 82 (13) | 84 (13) | 0.45 |
| HR/min | Mean (SD) | 71 (12) | 73 (10) | 0.14 | 71 (12) | 73 (10) | 0.14 |
| Serum creatinine (mg/dL) | Mean (SD) | 0.71 (0.16) | 0.83 (0.24) | <0.0001 | 0.71 (0.16) | 0.80 (0.23) | 0.007 |
| eGFR (mL/min/1.73 m2) | Mean (SD) | 74.1 (13.2) | 63.2 (14.5) | <0.0001 | 74.3 (12.4) | 65.9 (15.4) | <0.0001 |
| Duration of hypertension (years) | Median (interquartile range) | 10 (5–15) | 15 (8–20) | 0.0002 | 10 (5–15) | 12 (5.75–20) | 0.0059 |
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Abbreviation: FDWMH, deep white-matter hypertintensities of Fazekas scale; FPVH, periventricular hyperintensites of Fazekas scale; BMI, body mass index; SBP, brachial systolic blood pressure; DBP, brachial diastolic blood pressure; PP, brachial pulse pressure; CPP, central pulse pressure; SBP2, secondary systolic blood pressure; CSBP, central systolic blood pressure; rAIx, radial augmentation index; rAIx@75, radial augmentation index normalized for a heart rate of 75/min; HR, heart rate; eGFR, estimated glomerular filtration rate.
Table 4
Comparison of clinical parameters between low and high Scheltens scores
| Score grade | SDWMH frontal | SDWMH parietal | SDWMH occipital | SDWMH temporal | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0–2 | 3–6 | P value | 0–2 | 3–6 | P value | 0–2 | 3–6 | P value | 0–2 | 3–6 | P value | ||
| Number of patients | 164 | 21 | 112 | 73 | 165 | 20 | 181 | 4 | |||||
| Age (years) | Mean (SD) | 69 (6) | 74 (7) | 0.005 | 68 (5) | 73 (7) | <0.0001 | 70 (7) | 73 (5) | 0.03 | 70 (7) | 66 (3) | 0.2 |
| BMI (kg/m2) | Mean (SD) | 24.4 (3.8) | 24.0 (3.2) | 0.7 | 24.1 (3.5) | 24.7 (4.1) | 0.28 | 24.4 (3.7) | 23.6 (4.1) | 0.33 | 24.3 (3.8) | 24.2 (2.5) | 0.95 |
| Male | n (%) | 71 (43) | 13 (62) | 0.11 | 53 (47) | 31 (42) | 0.52 | 72 (43) | 12 (60) | 0.7 | 81 (45) | 3 (75) | 0.23 |
| Current or past smoking | n (%) | 48 (29) | 8 (38) | 0.41 | 35 (31) | 21 (29) | 0.72 | 50 (30) | 6 (30) | 0.21 | 54 (30) | 2 (50) | 0.39 |
| Hyperlipidemia | n (%) | 92 (56) | 9 (43) | 0.25 | 61 (54) | 40 (55) | 0.96 | 91 (55) | 10 (50) | 0.27 | 97 (54) | 4 (100) | 0.07 |
| Diabetes mellitus | n (%) | 34 (21) | 4 (19) | 0.86 | 26 (23) | 12 (16) | 0.26 | 34 (21) | 4 (20) | 0.73 | 38 (21) | 0 (0) | 0.3 |
| SBP (mmHg) | Mean (SD) | 139 (17) | 136 (15) | 0.34 | 139 (17) | 140 (17) | 0.75 | 139 (17) | 138 (14) | 0.28 | 139 (17) | 141 (15) | 0.81 |
| DBP (mmHg) | Mean (SD) | 79 (10) | 79 (11) | 0.96 | 79 (10) | 79 (11) | 0.99 | 79 (10) | 81 (11) | 0.67 | 79 (10) | 89 (9) | 0.06 |
| PP (mmHg) | Mean (SD) | 61 (13) | 58 (13) | 0.43 | 60 (12) | 61 (14) | 0.54 | 61 (13) | 57 (11) | 0.059 | 60 (13) | 53 (9) | 0.23 |
| CPP (mmHg) | Mean (SD) | 65 (16) | 61 (14) | 0.3 | 64 (16) | 65 (16) | 0.63 | 64 (16) | 63 (14) | 0.17 | 64 (16) | 55 (14) | 0.25 |
| SBP2 (mmHg) | Mean (SD) | 130 (19) | 129 (17) | 0.79 | 129 (18) | 132 (19) | 0.31 | 129 (19) | 134 (16) | 0.17 | 130 (18) | 132 (19) | 0.75 |
| CSBP (mmHg) | Mean (SD) | 144 (20) | 140 (17) | 0.43 | 143 (20) | 144 (20) | 0.71 | 143 (20) | 144 (18) | 0.4 | 143 (20) | 144 (20) | 0.95 |
| rAIx (%) | Mean (SD) | 85 (15) | 81 (14) | 0.28 | 84 (16) | 84 (13) | 0.96 | 84 (15) | 87 (10) | 0.88 | 84 (15) | 78 (16) | 0.36 |
| rAIx@75 (%) | Mean (SD) | 83 (13) | 81 (13) | 0.54 | 83 (13) | 83 (12) | 0.66 | 82 (13) | 86 (11) | 0.45 | 83 (13) | 79 (12) | 0.54 |
| HR (/min) | Mean (SD) | 71 (11) | 75 (11) | 0.12 | 71 (11) | 73 (10) | 0.18 | 72 (11) | 73 (8) | 0.14 | 72 (11) | 78 (12) | 0.29 |
| Serum creatinine (mg/dL) | Mean (SD) | 0.75 (0.20) | 0.84 (0.16) | 0.051 | 0.73 (0.17) | 0.81 (0.24) | 0.006 | 0.76 (0.21) | 0.79 (0.18) | 0.48 | 0.76 (0.20) | 0.89 (0.12) | 0.21 |
| eGFR (mL/minute/1.73 m2) | Mean (SD) | 70.5 (0.20) | 63.2 (12.2) | 0.03 | 73.3 (13.7) | 64.2 (14.5) | <0.001 | 69.9 (14.8) | 68.1 (13.7) | 0.6 | 69.9 (14.8) | 62.5 (4.4) | 0.32 |
| Duration of hypertension (years) | Median (interquartile range) | 10 (5–19.5) | 15 (10–25) | 0.009 | 10 (5–15) | 15 (9–20) | 0.0014 | 10 (5–20) | 13.5 (6.5–19.25) | 0.48 | 10 (5–20) | 12.5 (2.75–34.25) | 0.91 |
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Abbreviations: SDWMH, deep white-matter hypertintensities of Scheltens scale; BMI, body mass index; SBP, brachial systolic blood pressure; DBP, brachial diastolic blood pressure; PP, brachial pulse pressure; CPP, central blood pressure; SBP2, secondary systolic blood pressure; CSBP, central systolic blood pressure; rAIx, radial augmentation index; rAIx@75, radial augmentation index normalized for a heart rate of 75/minute; HR, heart rate; eGFR, estimated glomerular filtration rate.
Logistic regression analyses were conducted to assess the association of the MRI scores with the eGFR after adjustment for age, sex, cSBP, and history of hypertension. The patients with lower eGFR values (<60 mL/minute/1.73 m2) tended to have higher-grade WMLs. The odds ratio for FDWMH (grade 2–3 versus 0–1) was 2.87 (P = 0.017), that for FPVH (grade1–3 versus 0) was 1.99 (P = 0.131), and that for SDWMH in the parietal (grade 3–6 versus 0–2) area was 2.38 (P = 0.045).
Discussion
Among the major factors associated with the development of WMLs is high blood pressure. It was recently reported that the cSBP is correlated more strongly with WMLs than the brachial SBP, and that the cSBP is a better predictor of WMLs than the brachial SBP in relatively early stages of WMLs.12 Another study found that the cSBP was significantly associated with the presence of intracerebral small vessel disease in an apparently healthy general population.25 Both were cross-sectional observational studies of the general population.
However, more than 50% of the subjects in these studies had normal blood pressure, and most were not receiving treatment with antihypertensive drugs. Our study involved only hypertensive patients, and all were receiving antihypertensive drugs. To the best of our knowledge, there have been no prior studies of the relationship between the cSBP and WMLs in hypertensive patients under treatment with antihypertensive agents. Under such conditions, WMLs are not affected by either the cSBP or brachial SBP. The lesions were associated with age, history of hypertension, and eGFR.
In addition, examination of the lesions by site demonstrated that only WMLs in the temporal region26 with less prominent sclerotic changes of the medullary arteries showed no correlation with the eGFR, while correlations were evident for WMLs in regions with prominent sclerotic changes of the medullary arteries.
Despite being influenced by age, the eGFR showed a correlation with the presence of WMLs, even after adjustment for age and history of hypertension (by multivariate analysis).
The presence of WMLs is significantly related to the risk of stroke, cognitive decline and dementia.27–29 The Northern Manhattan Study demonstrated that the WML volume is associated with moderate-to-severe CKD, which was estimated using the serum creatinine and the co*ckcroft–Gault formula, with eGFR values between 15 and 59 mL/min.13 Wada et al demonstrated that subjects with lower eGFR tended to have a larger number of lacunar infarcts and higher-grade WMLs; moreover, the mean grade of WMLs and the mean number of lacunar infarcts in subjects with albuminuria were greater than those in subjects without albuminuria.14 Our study also showed higher grades of WMLs in patients with lower eGFR values than in those with a higher eGFR. Furthermore, Wada et al also reported the existence of an association between urinary albumin levels and cerebral small vessel disease, independently of traditional cerebrovascular risk factors, in community-based elderly persons.30 In our study, the urinary protein excretion rate of our patients was very low, and urinary albumin was not determined. Similarly, Ikram et al investigated the relationship between kidney function as evaluated by the eGFR, and the occurrence of cerebral small-vessel disease by MRI analysis. They clearly showed that decreased eGFR was related to subclinical markers of cerebral small-vessel disease, such as deep white-matter volume and WMLs independently of cardiovascular risk factors such as age, sex, blood pressure, and diabetes.31 In our study as well, patients with lower eGFR values had higher-grade WMLs, even after adjustments for these risk factors, than those with a higher eGFR. Takahashi et al32 reported that mild renal dysfunction may be associated with an increase in the occurrence of cerebral small-vessel disease, independent of the presence of hypertension.
In our study also, lower eGFR was associated with higher grades of WMLs, independent of hypertension.
This study had limitations. First, hypertensive patients under treatment with various antihypertensive medications were recruited. Calcium channel blockers were used by 58%, angiotensin-receptor blockers by 69%, and angiotensin-converting enzyme inhibitors by 24% of the study participants. The effects of medications on untreated hypertensive patients were not evaluated. Second, in our study, the methodology of WML assessment was performed using visual rating scale which was poorly substantiated and poorly undertaken. To evaluate of WML with precision, we should have used volumetric analysis that has been shown to be a superior method.
In conclusion, WMLs in patients with well-controlled BP were not found to be associated with either the central aortic blood pressure or the brachial blood pressure. Patients with lower eGFR values showed more marked progression of the WMLs than those with a higher eGFR. As to the sites of the WMLs, lesions in the frontal and parietal areas with prominent sclerotic changes of the medullary arteries were more advanced in the patients with lower eGFR values than in those with higher eGFR.
Footnotes
Disclosure
The authors have no conflicts of interest to declare in association with this work.
References
1. Dufouil C, de Kersaint-Gilly A, Basancon V, et al. Longitudinal study of blood pressure and white matter hypertensities: the EVA-MRI cohort. Neurology. 2001;56:921–926. [PubMed] [Google Scholar]
2. Lee S, Park SJ, Ki HK, et al. Prevalence and risk factors of silent cerebral infarction in apparently normal adults. Hypertension. 2000;36:73–77. [PubMed] [Google Scholar]
3. Longstreth WT, Jr, Bernick C, Manolio TA, Bryan N, Jungreis CA, Price TR. Lacunar infarcts defined by magnetic resonance imaging of 3660 elderly people; the cardiovascular health study. Arch Neurol. 1988;55:1217–1225. [PubMed] [Google Scholar]
4. Pantoni L, Garcia JH. The significance of cerebral white matter abnormalities 100 years after Binswanger’s report. A review. Stroke. 1995;26:1293–1301. [PubMed] [Google Scholar]
5. Liao D, Cooper L, Cai J, et al. Presence and severity of cerebral white matter lesions and hypertension, its treatment, and its control. The ARIC study. Atherosclerosis Risk in Communities Study. Stroke. 1996;27:1274–1282. [PubMed] [Google Scholar]
6. Longstreth W, Manolio TA, Arnold A, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke. 1996;27:1274–1282. [PubMed] [Google Scholar]
7. Kelly R, Hayward C, Avolio A, O’Rourke M. Noninvasive determination of age-related changes in the human arterial pulse. Circulation. 1989;80:1652–1659. [PubMed] [Google Scholar]
8. Agabiti-Rosei E, Mancia G, O’Rourke M, et al. Central blood pressure measurements and antihypertensive therapy: a consensus document. Hypertension. 2007;50:154–160. [PubMed] [Google Scholar]
9. Tomita H, Kawamoto R, Tabara Y, Miki T, Kohara K. Blood pressure is the main determinant of the reflection wave in patients with type 2 diabetes. Hypertens Res. 2008;31:493–499. [PubMed] [Google Scholar]
10. Qureshi G, Brown R, Salciccioli L, et al. Relationship between aortic atherosclerosis and non-invasive measures of arterial stiffness. Atherosclerosis. 2007;195:e190–e194. [PubMed] [Google Scholar]
11. Soga J, Nakamura S, Nishioka K, et al. Relationship between augmentation index and flow-mediated vasodilation in the brachial artery. Hypertens Res. 2008;31:1293–1298. [PubMed] [Google Scholar]
12. Shrestha I, Takahashi T, Nomura E, et al. Association between central systolic blood pressure, white matter lesions in cerebral MRI and carotid atherosclerosis. Hypertens Res. 2009;32:869–874. [PubMed] [Google Scholar]
13. Khatri M, Wright CB, Nickolas TL. Chronic kidney disease is associated with white matter hypertensity volume: the Northern Manhattan Study (NOMAS) Stroke. 2007;38:3121–3126. [PMC free article] [PubMed] [Google Scholar]
14. Wada M, Nagasawa H, Iseki C. Cerebral small vessel disease and chronic kidney disease (CKD): results of a cross sectional study in community-based Japanese elderly. J Neurol Sci. 2008;272:36–42. [PubMed] [Google Scholar]
15. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol. 1987;149:351–356. [PubMed] [Google Scholar]
16. Scheltens P, Barkhof F, Leys D, et al. A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. J Neurol Sci. 1993;114:7–12. [PubMed] [Google Scholar]
17. Schmidt R, Enzinger C, Ropele S, Schmidt H, Fazekas F. Progression of cerebral white matter lesions; 6-year results of the Austrian stroke prevention study. Lancet. 2003;361:2046–2048. [PubMed] [Google Scholar]
18. Dufouil C, Chalmers J, Coskun O, et al. Effects of blood pressure lowering on cerebral white matter hyperintensites in patients with stroke: the PROGRESS (Perindopril Protection Against Recurrent Stroke Study) Magnetic Resonance Imaging Substudy. Circulation. 2005;112:1644–1650. [PubMed] [Google Scholar]
19. Takami T, Saito Y. Effects of azelnidipine plus olmesartan versus amlodipine plus olmesartan on central blood pressure and left ventricular mass index: the AORTA study. Vasc Health Risk Manag. 2011;7:383–390. [PMC free article] [PubMed] [Google Scholar]
20. Melenovsky V, Borlang BA, Fetics B, Kesler K, Shively L, Kass DA. Estimation of central pressure augmentation using automated radial artery tonometry. J Hypertens. 2007;25:1403–1409. [PubMed] [Google Scholar]
21. Takazawa K, Kobayashi H, Shindo N, Tanaka N, Yamash*ta A. Relationship between radial and central artery pulse wave and evaluation of central aortic pressure using the radial artery pulse wave. Hypertens Res. 2007;30:219–228. [PubMed] [Google Scholar]
22. Richardson CJ, Maki-Petaja KM, McDonnell BJ. Comparison of estimates of central systolic blood pressure and peripheral augmentation index obtained from the Omron HEM-9000AI and SphygmorCor system. Artery Res. 2009;3:24–31. [Google Scholar]
23. Hickson SS, Butlin M, Mir FA, et al. Anglo-Cardiff Collaboration Trial Investigators. The accuracy of central SBP determined from the second systolic peak of the peripheral pressure waveform. J Hypertens. 2009;27:1784–1788. [PubMed] [Google Scholar]
24. Wilkinson IB, Mohammad NH, Tyrrell S, et al. Heart rate dependency of pulse pressure amplification and arterial stiffness. Am J Hypertens. 2002;15:24–30. [PubMed] [Google Scholar]
25. Ochi N, Kohara K, Tabara Y, et al. Association of central systolic blood pressure with intracerebral small vessel disease in Japanese. Am J Hypertens. 2010;23:889–894. [PubMed] [Google Scholar]
26. Furuta A, Ishii N, Nishihara Y, Horie A. Medullary arteries in aging and dementia. Stroke. 1991;22:442–446. [PubMed] [Google Scholar]
27. Vermeer SE, Hollander M, Van Dijk EJ, Hofman A, Koudstaal PJ, Breteler MMB. Silent brain infarcts and white matter lesions increase stroke risk in the general population: the Rotterdam Scan Study. Stroke. 2003;34:1126–1129. [PubMed] [Google Scholar]
28. Prins ND, Van Dijk EJ, Den Heijer T. Central white matter lesion and the risk of dementia. Arch Neurol. 2004;61:1531–1534. [PubMed] [Google Scholar]
29. Au R, Massaro JM, Wolf PA. Association of white matter hyperintensity volume with decreased cognitive functioning: the Framingham Heart Study. Arch Neurol. 2006;63:246–250. [PubMed] [Google Scholar]
30. Wada M, Nagasawa H, Kurita K, et al. Microalbuminuria is a risk factor for cerebral small vessel disease in community-based elderly subjects. J Neurol Sci. 2007;255:27–34. [PubMed] [Google Scholar]
31. Ikram MA, Vemooij MW, Hofman A, Nissen WJ, Van Der Lugt, Breterler MMB. Kidney function is related to cerebral small vessel disease. Stroke. 2008;39:55–61. [PubMed] [Google Scholar]
32. Takahashi W, Tsukamoto Y, Takizawa S, Kawada S, Takagi S. Relationship between chronic kidney disease and white matter hypertensities on magnetic resonance imaging. J Stroke Cerebrovasc Dis. 2012;21:18–23. [PubMed] [Google Scholar]
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