Association of autosomal dominant polycystic kidney disease with cerebral small vessel disease

Abstract

Cilia dysfunction in autosomal-dominant polycystic kidney disease (ADPKD) may impair the integrity of glymphatic system and be implicated in the progression of cerebral small vessel disease (SVD), although the link between the two diseases has not been investigated. We evaluated the association of ADPKD pathology with SVD pattern and severity. Overall, 304 individuals in an ADPKD (chronic kidney disease stage ≤4 and age ≥50 years) cohort and their age, sex, and estimated glomerular filtration rate (eGFR)-matched controls were retrospectively included. ADPKD severity was classified into 1 A-B, 1 C, and 1 D-E, according to age and height-adjusted total kidney volume. SVD parameters included white-matter hyperintensity (WMH) severity scale, enlarged perivascular space (ePVS) score, and degree of lacunes or cerebral microbleeds (CMBs). After adjustments for age, sex, eGFR, and cerebrovascular risk factor parameters, ADPKD was associated with higher ePVS scores (P < 0.001), but not with the WMH severity or degree of lacunes or CMBs. In the ADPKD subgroup, higher ADPKD severity class was associated with higher ePVS scores (P < 0.001), WMH severity (P = 0.003), and degree of lacunes (P = 0.002). ADPKD associated cilia dysfunction may induce chronic cerebral glymphatic system dysfunction, which may contribute to the specific progression of ePVS compared with other SVD markers.

Keywords

Autosomal dominant polycystic kidney disease cilia dysfunction enlarged perivascular space glymphatic system small vessel disease

Introduction

Autosomal dominant polycystic kidney disease (ADPKD) is a prevalent genetic multisystem disorder caused by mutations in PKD1 and PKD2 genes, which encode polycystin-1/-2 (PC1/PC2) proteins, respectively. The central nervous system (CNS) manifestations of ADPKD include increased frequencies of intracranial aneurysm and dolichoectasia.^1,2 However, several pathophysiological characteristics of ADPKD suggest that this disease may also be associated with the pathogenesis of cerebral small vessel disease (SVD). First, PC1 and PC2 deficiencies in the vascular endothelium are associated with impaired vessel relaxation and secondary activation of the renin-angiotensin-aldosterone system, which are the main mechanisms of aging-related progression of SVD.^3–5 Second, as PC1 and PC2 proteins are strongly expressed in motile cilia along the ependymal cell lines and are a major mediator of cerebrospinal fluid (CSF) circulation, ADPKD pathology may also provoke disturbances in CSF circulation.^6–10 Third, PC1 and PC2 proteins may also be involved in maintaining the blood-brain barrier (BBB) integrity and glymphatic system function.^11–13 PC1 and PC2 are expressed in astrocytes and vascular endothelium,⁹ which constitute BBB and perivascular space, and the PC1/PC2 polycystin complex senses chemical and mechanical stimuli and maintains the structural and functional integrities of endothelial cells and astrocytes.^8,10,14 As cerebral waste clearance via the glymphatic system relies on the convective movement of perivascular CSF into parenchymal interstitial fluid (ISF) space and adequate drainage into the perivenular space,^6,12,15 ADPKD can mediate the development of SVD through mechanisms involving the glymphatic system and microvascular function. However, the link between the two diseases has not been fully investigated.

Each of the major magnetic resonance image (MRI) parameters of SVD, such as white matter hyperintensity (WMH), enlarged perivascular space (ePVS), cerebral microbleeds (CMBs), and lacunar infarction, may reflect different underlying pathomechanisms of SVD.^5,16 Especially, ePVS may be a particularly sensitive marker for the glymphatic system dysfunction, as the dilated perivascular space reflects flow stagnation owing to the chronically impaired exchange between CSF and ISF or more downstream flow of the glymphatic system.^16,17 In our study, we hypothesized that ADPKD pathology may contribute to a distinct pattern of SVD and evaluated the SVD profile in a cohort of patients with ADPKD, and compared it with the age and sex-matched control subjects.

Materials and methods

Study participants

This study was a retrospective cohort cross-sectional study of patients with ADPKD. From all consecutive patients enrolled in a tertiary hospital cohort with ADPKD, patients aged ≥50 years, who underwent brain MRI/MR angiography (MRA) evaluations from January 2007 to August 2020, were initially included. Among the initially included 385 patients, the ADPKD group was defined according to the following criteria: (1) no significant (≥50%) stenosis in the cerebral arteries, (2) without a documented history of chronic disease involving the CNS such as stroke other than an old (≥90 days) lacunar infarction or dementia, (3) with kidney computed tomography (CT) evaluations, (4) with kidney CT findings compatible with the criteria for typical ADPKD,¹⁸ and (5) without a severe kidney dysfunction (chronic kidney disease [CKD] stage 1–3, estimated glomerular filtration rate [eGFR] ≥30 mg/dL). The criteria of 50 years of age was decided based on published reports that observed a progression of SVD initiating from the age of 50 years onward, as well as our previous studies regarding WMH and other SVD parameters.^19–23 According to this criteria, 13 patients with significant stenosis in the internal carotid arteries (ICAs) or middle cerebral arteries (MCAs), 11 with a documented chronic disease involving the CNS, nine without kidney CT evaluation or with an atypical PKD feature in the kidney CT, and 25 with end-stage renal disease were sequentially excluded, and the remaining 327 patients were included in the ADPKD group. The indications for MRI/MRA evaluations were for the surveillance of concomitant intracranial aneurysm or atherosclerotic brain lesion in every patient.

The control group was selected from the Seoul National University Hospital (SNUH) healthcare program database, which included subjects whose indications for MRI/MRA and laboratory evaluations were from a regular health check-up program. The same inclusion criteria were applied except for the availability of a kidney CT evaluation. From the initially included 5,248 patients, propensity score matching for age, sex, and eGFR was performed using a logistic regression model. The ADPKD group was matched with controls in a 1:1 ratio using a greedy nearest neighbor method. The quality and balance of the matching were evaluated by comparing the standardized mean difference and ratio of variances between the propensity scores of the two groups. The sample size of the study was not calculated due to the retrospective cohort-based design of the current study. As a result, 23 patients in the ADPKD group were unmatched to the controls, and the remaining 304 patients matched with 304 controls were included in the analysis (Figure 1). The design of this study was approved by the SNUH institutional review board (IRB no. 1804-054-936) and adheres to the STROBE guideline for a retrospective cohort study and Helsinki Declaration of 1975 (and as revised in 2013). Informed consent was waived by the IRB, as this study did not intervene with the evaluation or treatment process of the patients, and the patients were anonymized during the study process.

Figure 1.

A flow chart illustrating the study participants.

Clinical and laboratory data

Clinical profiles, including demographics and presence of hypertension, diabetes mellitus, and hyperlipidemia, were obtained from patients’ medical records. Mean blood pressure (MBP, mmHg), pulse pressure (PP, mmHg), eGFR (mg/dL) measured using the CKD Epidemiology Collaboration (CKD-EPI) equation, total cholesterol level (mg/dL), LDL cholesterol level (mg/dL), and hemoglobin A1c (HbA1c) (percent) measured within three months from the MRI evaluation were obtained.^21,23–26

Kidney CT-based classification of ADPKD

Each kidney volume was measured using the ellipsoid equation and summated to obtain the total kidney volume (TKV, mL).¹⁸ Using TKV and patient’s height and age during CT evaluation, height adjusted TKV (HtTKV, mL/m) was measured using an online calculator provided by the Mayo Foundation and Medical Education and Research (mayo.edu/research/documents/pkd-center-adpkd-classification/doc-20094754), and was classified into five subgroups of severity (1 A–1E) according to age and HtTKV.¹⁸

Magnetic resonance imaging analysis

MRI was performed using 1.5 T or 3.0 T units with the protocols that included T1-weighted, T2-weighted, T2-fluid-attenuated inversion recovery (FLAIR), gradient echo/susceptibility-weighted images (GRE/SWI), and MRA. T1/T2-weighted and FLAIR images were obtained with the parameters as follows: slice number = 24–32, slice thickness/gap = 4.0–5.0/0.0–1.0 mm, field-of-view = 220–256 × 220–256 mm, and matrix = 320–352 × 192–256. Repetition time/echo time was 466–2,822/7.8–26 ms for T1-weighted images and 9,000–9,900/97–163 ms for T2-weighted and FLAIR images.

WMH severity was semi-quantitatively measured according to the Age-Related White Matter Changes (ARWMC) scale, which showed excellent reliability and high correlation with WMH volumes.²⁷ ARWMC scores WMH severities in the frontal, parieto-occipital, temporal, basal ganglia (BG), and infratentorial areas with scores ranging from 0 to 3 for each side of the brain, with a total score range of 0–30.²⁸ Fazekas scale for WMH severity was also measured in periventricular and deep white matter regions (score range 0–3 for each region and 0–6 for the total score).²⁹ ePVS was defined as ovoid or linear-shaped small and sharply bordered lesions with T2-hyperintensity and T1-hypointensity. ePVS score was rated at the BG and centrum semiovale (CS) regions using the Wardlaw scale with a score range of 0–4 (0 for no, 1 for 1–10, 2 for 11–20, 3 for 21–40, and 4 for >40 ePVS) for each region and 0–8 for the total score.³⁰ CMB was defined as 2–10 millimeter punctate hypointensity in GRE/SWI images and evaluated separately in the lobar, deep, and infratentorial areas. Lacune was demonstrated as a hyperintense lesion with central hypo-intensity in FLAIR images.⁵ The degree of CMBs and lacunes were categorized as none, one, and multiple (≥2). Two neurologists (WJL, nine years of experience, and KHJ, 20 years of experience) reviewed the images blinded to the clinical data, and consensus was achieved by joint review for the cases with discrepant scores.

Statistical analyses

SPSS 25.0 (SPSS Inc., Chicago, IL, USA) was used for all statistical analyses and propensity score matching. Spearman’s Rho was used for evaluating correlations between continuous variables and SVD scores. Paired t-test, chi-square test, and analysis of variance were used for comparing the parameters among the groups. Shapiro-Wilk test was performed to test the normal distribution of variables. Mann-whitney test, Fisher’s exact test, and Kruskal-Wallis test were used in cases that a variable is not normally distributed or a group of comparison contains less than 10 subjects. Age, sex, eGFR, MBP, PP, and variables with P values <0.15 in univariate analyses were included in regression analyses. However, total cholesterol level, the presence of hypertension, and the presence of diabetes mellitus were discarded from the multivariate analyses, due to the high co-linearity with the other parameters included, such as LDL cholesterol, MBP, and HbA2c respectively, and the redundancy of variables.

Linear regression analyses were performed to quantitatively evaluate the association of ADPKD and its severity class with the severity of WMH measured using ARWMC scores. WMC scores were log-transformed to obtain a normal distribution, and regression was performed separately for the total, lobar, and deep (BG and infratentorial) areas. Multi-collinearity between variables was estimated using the variance inflation factor (VIF). A scatterplot of standardized predicted values and standardized residuals was used to check for the linearity of the regression model. To examine the assumption for the normal distribution, a histogram of the standardized residuals and a normal probability (P-P) plot of the standardized residuals were drawn. Ordinal regression analyses were performed for the semi-quantitative evaluation of the association between ADPKD and its severity class with ePVS scores, for total, CS, and BG regions separately, and the degrees of CMBs and lacunes. To investigate the association between ADPKD severity and SVD parameters, all regression analyses were repeated for the ADPKD group, including the trichotomized ADPKD severity class (1 A–B, 1 C, and 1 D–E). The standardized coefficient for linear regression analyses and odds ratio for ordinal logistic regression analysis, with their 95-percentile confidence interval, were used to determine a statistically significant association. For all analyses, a P-value of <0.05 was considered statistically significant.

Data availability

The datasets generated and/or analyzed during the study are available from the corresponding author upon request.

Results

Overall, 608 patients (304 [50.0%] patients in the ADPKD group and 304 [50.0%] in the control group, 292 [48.0%] men, mean age of 63.3 ± 8.4 years [range, 50–88 years]) were included in this study. Measurements of balance of the propensity score matching indicated that the ADPKD and control groups were adequately matched, with standardized mean differences of the propensity scores between groups of 0.00 (good balance <0.25) and a ratio of variances of propensity scores of 0.97 (good balance between 0.5–2, Supplemental Table 1 and Supplemental Figure 1). In the ADPKD group, the mean HtTKV was 1662.0 ± 1,343.5 mL. According to the HtTKV and age, 44 (14.5%) patients were classified as group 1 A, 63 (20.7%) as group 1B, 100 (32.9%) as group 1 C, 78 (25.7%) as group 1 D, and 19 (6.3%) as group 1E. The median interval of the obtained laboratory data was 0.3 days (range, −16 to 14 days) from the MRI evaluation.

Compared with the control, the ADPKD group exhibited a higher frequency of hypertension, lower frequency of diabetes mellitus, and lower levels of MBP, total cholesterol, LDL cholesterol, and HbA1c. For the SVD parameters, the ADPKD group was associated with higher ePVS scores for the total, CS, and BG areas (all, P < 0.001), and higher frequency of lacunes (P = 0.019, Table 1). However, WMH severity and the degrees of CMBs were comparable between the two groups.

Table 1.

Clinical, laboratory, and white matter hyperintensity profiles of the study population.

	Non-ADPKD(N = 304)	ADPKD(N = 304)	P
Age (years)	64.1 ± 7.0	63.9 ± 9.7	0.763
Male sex (%)	147 (48.4%)	145 (47.7%)	0.935
Hypertension (%)	158 (52.0%)	212 (69.7%)	<0.001**
Diabetes mellitus (%)	65 (21.4%)	41 (13.5%)	0.014*
Hyperlipidemia (%)	176 (57.9%)	166 (54.6%)	0.465
Mean blood pressure (mmHg)	114.9 ± 13.8	112.4 ± 14.0	0.023*
Pulse pressure (mmHg)	50.4 ± 10.3	50.5 ± 10.1	0.950
eGFR (mL/min)	77.2 ± 12.2	77.2 ± 17.5	0.992
Total cholesterol (mg/dL)	190.6 ± 40.9	167.0 ± 37.4	<0.001**
LDL cholesterol (mg/dL)	114.2 ± 37.1	92.0 ± 26.0	<0.001**
HbA1c (%)	6.0 ± 0.7	5.7 ± 0.8	<0.001**
Small vessel disease parameters
Total Fazekas score	1 [1–2]	1 [1–2]	0.103
White matter Fazekas score	1 [0–1]	1 [0–1]	0.063
Periventricular Fazekas score	1 [0–1]	1 [0–1]	0.325
Total ARWMC score	4 [3–7]	4 [3–7]	0.773
Lobar ARWMC score	4 [2–6]	4 [2–6]	0.881
Deep ARWMC score	0 [0–2]	0 [0–2]	0.592
Total enlarged perivascular space score	3 [2–3]	4 [3–5]	<0.001**
CS enlarged perivascular space score	2 [1–2]	3 [2–3]	<0.001**
BG enlarged perivascular space score	1 [1–1]	2 [1–2]	<0.001**
Degree of cerebral microbleeds in total brain			0.071
None	234 (77.0%)	242 (79.6%)
One	49 (16.1%)	32 (10.5%)
Multiple (≥2)	21 (6.9%)	30 (9.9%)
Degree of cerebral microbleeds in lobar area			0.128
None	267 (87.8%)	269 (88.5%)
One	31 (10.2%)	22 (7.2%)
Multiple (≥2)	6 (2.0%)	13 (4.3%)
Degree of cerebral microbleeds in deep and infratentorial area			0.144
None	259 (85.2%)	256 (84.2%)
One	38 (12.5%)	26 (8.6%)
Multiple (≥2)	7 (2.3%)	22 (7.2%)
Degree of lacunes			0.019*
None	271 (89.1%)	247 (81.2%)
One	22 (7.2%)	42 (13.8%)
Multiple (≥2)	11 (3.6%)	15 (4.9%)

Note: Data are reported as number (percentage), as mean± standard deviation, or as median [interquartile range, IQR].

ADPKD: autosomal dominant polycystic kidney disease; eGFR: estimated glomerular filtration rate; LDL: low-density lipoprotein; HbA1c: hemoglobin A1c; ARWMC: age-related white matter changes; BG: basal ganglia; CS: centrum semiovale. *P < 0.05, **P < 0.01.

Among the ADPKD subgroups, the higher severity classes were associated with lower eGFR (P < 0.001), higher ARWMC scores for the total and lobar areas (P = 0.049 and P = 0.036, respectively), higher ePVS scores for the total, CS, and BG areas (P < 0.001, P = 0.002, and P = 0.025, respectively), and higher frequency of lacunes (P = 0.003, Supplemental Table 2).

Association of ADPKD with cerebral WMH severity

In the regression analysis for the WMH severity, the log-transformed ARWMC score was associated with age (B coefficient 0.029; 95% confidence interval [CI] 0.024–0.035; P < 0.001), eGFR (B − 0.004; 95% CI −0.007–−0.002; P = 0.001), and MBP (B 0.006; 95% CI 0.003–0.010; P = 0.001), but not with ADPKD (P = 0.143, Table 2 and Figure 2(a), Supplemental Tables 3 and 4 for univariate analyses). When ARWMC was stratified by its location, ADPKD still did not exhibit a significant association with log-transformed lobar or deep ARWMC scores (P = 0.215 and 0.368, respectively, Supplemental Table 5, Supplemental Tables 3 and 4 for univariate analyses).

Table 2.

Regression analyses for factors associated with white-matter hyperintensity severity, enlarged perivascular space scores, the presence of cerebral microbleeds, and the presence of lacunes.

Total white matter hyperintensity score^a	B (95% CI)	β	P
Intercept	–0.508 (–1.145–0.130)		0.119
Age (year)	0.029 (0.024–0.035)	0.413	<0.001**
Male sex	–0.052 (–0.136–0.033)	–0.043	0.228
ADPKD	–0.068 (–0.158–0.023)	–0.056	0.143
eGFR (mL/min)	–0.004 (–20.007––0.002)	–0.112	0.002**
LDL cholesterol (mg/dL)	–0.001 (–0.002–0.001)	–0.047	0.225
HbA1c (%)	–0.025 (–0.081–0.032)	–0.031	0.393
Mean blood pressure (mmHg)	0.006 (0.003–0.010)	0.147	0.001**
Pulse pressure (mmHg)	0.004 (–0.001–0.009)	0.069	0.123
Total enlarged perivascular space score^b	B (95% CI)	P
Age (year)	0.083 (0.065–0.102)	<0.001**
Male sex	0.031 (–0.258–0.321)	0.832
ADPKD	2.337 (1.981–2.692)	<0.001**
eGFR (mL/min)	–0.005 (–0.015–0.004)	0.285
LDL cholesterol (mg/dL)	0.000 (–0.005–0.005)	0.976
HbA1c (%)	–0.071 (–0.265–0.123)	0.474
Mean blood pressure (mmHg)	0.012 (–0.001–0.025)	0.080
Pulse pressure (mmHg)	0.009 (–0.009–0.027)	0.313
Degree of cerebral microbleeds in total brain^c	B (95% CI)	P
Age (year)	0.050 (0.025–0.074)	<0.001**
Male sex	0.170 (–0.226–0.565)	0.401
ADPKD	–0.120 (–0.543–0.303)	0.578
eGFR (mL/min)	–0.006 (–0.020–0.007)	0.360
LDL cholesterol (mg/dL)	0.000 (–0.007–0.006)	0.927
HbA1c (%)	0.221 (–0.011–0.453)	0.062
Mean blood pressure (mmHg)	–0.010 (–0.028–0.008)	0.286
Pulse pressure (mmHg)	0.022 (–0.003–0.047)	0.083
Degree of lacunes^d	B (95% CI)	P
Age (year)	0.066 (0.037–0.094)	<0.001**
Male sex	0.060 (–0.417–0.536)	0.806
ADPKD	0.512 (–0.004–1.027)	0.052
eGFR (mL/min)	–0.016 (–0.031–0.000)	0.055
LDL cholesterol (mg/dL)	–0.002 (–0.010–0.006)	0.598
HbA1c (%)	0.068 (–0.216–0.351)	0.641
Mean blood pressure (mmHg)	0.001 (–0.020–0.022)	0.918
Pulse pressure (mmHg)	0.033 (0.003–0.063)	0.029*

Note: White matter hyperintensity scores were measured using age-related white matter changes (ARWMC) scores. ARWMC scores were log-transformed to obtain normal distributions.

B: unstandardized coefficient; β: standardized coefficient; CI: confidence interval; ADPKD: autosomal dominant polycystic kidney disease; eGFR: estimated glomerular filtration rate; LDL: low-density lipoprotein; HbA1c: hemoglobin A1c. *P < 0.05, **P < 0.01.

^aR²=0.261 and P < 0.001 for the linear regression equation.

^bR²=0.379 and P < 0.001 for the ordinal regression equation. Thresholds assigned to ePVS score 0 was 2.149 (standard error [SE] 1.193), to ePVS score 1 was 4.416 (1.136), to ePVS score 2 was 6.322 (1.140), to ePVS score 3 was 7.732 (1.150), to ePVS score 4 was 8.983 (1.165), to ePVS score 5 was 10.155 (1.183), to ePVS score 6 was 11.356 (1.205), and to ePVS score 7 was 13.230 (1.281).

^cR²=0.086 and P < 0.001 for the ordinal regression equation. Thresholds assigned to none was 5.323 (1.481), to one CMB was 6.473 (1.490), and to multiple CMBs was 7.620 (1.512).

^dR²=0.139 and P = 0.001 for the ordinal regression equation. Thresholds assigned to none was 7.271 (1.705), to one lacune was 8.731 (1.724), and to multiple lacunes was 10.125 (1.810).

Figure 2.

Plots for the age and small vessel disease parameters in the whole study population and in the autosomal dominant polycystic kidney disease (ADPKD) group. In panel A, dot plots with linear trend lines with 95% confidence for ADPKD and control groups are depicted for ARWMC scores for the whole study population. ADPKD was not associated with a higher total ARWMC score (P=0.143). In panel B, plots with linear trend lines for each severity subgroups are depicted for ARWMC scores within the ADPKD group. A higher ADPKD severity class was associated with a higher total ARWMC score (P=0.003). In panel C, plots for ePVS scores are depicted for ADPKD and control groups. ADPKD was associated with a higher total ePVS score (P < 0.001). In panel D, plots for ePVS scores are depicted for each severity subgroups within the ADPKD group. A higher ADPKD severity class was associated with a higher total ePVS score (P < 0.001). ARWMC: age-related white matter change, PVS: enlarged perivascular space.

Figure 3.

Representative cases.

In following regression analyses to evaluate the association of the ADPKD severity with the ADPKD subgroup, higher ADPKD severity class was associated with higher log-transformed total ARWMC score (B 0.115 [for one level change in the classes]; 95% CI 0.041–0.189; P = 0.003, Table 3 and Figure 2(b)). In the analyses for the brain subregions, ADPKD severity class was associated with higher log-transformed lobar ARWMC scores (B 0.109; 95% CI 0.041–0.178; P = 0.002, Supplemental Table 6) but not with deep ARWMC score (P = 0.069). In every analysis, the standardized residuals were normally distributed in a histogram, and the P-P plot distribution was near the comparison line. VIF values for each variable were <1.7.

Table 3.

Regression analyses for the association of ADPKD severity class with white-matter hyperintensity severity, the presence of cerebral microbleeds, and the presence of lacunes in the subgroup with ADPKD.

Total white matter hyperintensity scorea	B (95% CI)	β	P
Intercept	–1.041 (–1.863 to –0.220)		0.013*
Age (year)	0.034 (0.027–0.040)	0.513	<0.001**
Male sex	–0.114 (–0.234–0.006)	–0.090	0.063
ADPKD class	0.115 (0.041–0.189)	0.149	0.003**
eGFR (mL/min)	–0.003 (–0.007–0.001)	–0.084	0.107
LDL cholesterol (mg/dL)	–0.001 (–0.004–0.001)	–0.051	0.308
HbA1c (%)	–0.026 (–0.105–0.053)	–0.031	0.521
Mean blood pressure (mmHg)	0.007 (0.002–0.012)	0.154	0.010*
Pulse pressure (mmHg)	0.001 (–0.006–0.009)	0.024	0.697
Total enlarged perivascular space score^b	B (95% CI)	P
Age (year)	0.117 (0.092–0.142)	<0.001**
Male sex	–0.021 (–0.433–0.392)	0.922
ADPKD class	0.817 (0.549–1.085)	<0.001**
eGFR (mL/min)	0.007 (–0.006–0.02)	0.274
LDL cholesterol (mg/dL)	–0.004 (–0.012–0.004)	0.373
HbA1c (%)	–0.218 (–0.491–0.054)	0.116
Mean blood pressure (mmHg)	0.005 (–0.013–0.024)	0.575
Pulse pressure (mmHg)	0.012 (–0.014–0.038)	0.364
Degree of cerebral microbleeds in total brain^c	B (95% CI)	P
Age (year)	0.077 (0.043–0.111)	<0.001**
Male sex	0.016 (–0.597–0.628)	0.960
ADPKD class	0.215 (–0.170–0.600)	0.274
eGFR (mL/min)	–0.020 (–0.040–0.000)	0.052
LDL cholesterol (mg/dL)	–0.008 (–0.021–0.005)	0.205
HbA1c (%)	0.205 (–0.132–0.542)	0.233
Mean blood pressure (mmHg)	–0.022 (–0.050–0.006)	0.120
Pulse pressure (mmHg)	0.040 (0.000–0.080)	0.051
Degree of lacunes^d	B (95% CI)	P
Age (year)	0.088 (0.052–0.124)	<0.001**
Male sex	–0.228 (–0.874–0.418)	0.489
ADPKD class	0.655 (0.237–1.072)	0.002**
eGFR (mL/min)	–0.016 (–0.036–0.005)	0.137
LDL cholesterol (mg/dL)	–0.006 (–0.019–0.008)	0.412
HbA1c (%)	–0.016 (–0.408–0.375)	0.935
Mean blood pressure (mmHg)	0.015 (–0.013–0.043)	0.299
Pulse pressure (mmHg)	0.015 (–0.025–0.055)	0.451

Note: White matter hyperintensity scores were measured using age-related white matter changes (ARWMC) scores. ARWMC scores were log-transformed to obtain normal distributions.

^aR²=0.351 and P < 0.001 for the linear regression equation.

^bR²=0.364 and P < 0.001 for the ordinal regression equation. Thresholds assigned to ePVS score 0 was 3.549 (standard error [SE] 1.616), to ePVS score 1 was 4.847 (1.509), to ePVS score 2 was 7.04 (1.485), to ePVS score 3 was 8.361 (1.499), to ePVS score 4 was 9.538 (1.52), to ePVS score 5 was 10.822 (1.547), to ePVS score 6 was 12.125 (1.576), and to ePVS score 7 was 14.066 (1.644).

^cR²=0.205 and P < 0.001 for the ordinal regression equation. Thresholds assigned to none was 5.462 (2.019), to one CMB was 6.434 (2.031), and to multiple CMBs was 7.440 (2.110).

^dR²=0.224 and P < 0.001 for the ordinal regression equation. Thresholds assigned to none was 9.346 (2.226), to one lacune was 11.036 (2.263), and to multiple lacunes was 12.965 (2.420).

Association of ADPKD with ePVS severity

In the whole study population, ADPKD was significantly associated with a higher ePVS score (B 2.337; 95% CI 1.981–2.692; P < 0.001), along with age (B 0.083; 95% CI 0.065–0.102; P < 0.001, Table 2 and Figure 2(c), Supplemental Tables 3 and 4 for univariate analyses). When ePVS was stratified by its location, ADPKD was also significantly associated with a higher ePVS score in both CS and BG regions (B 2.015; 95% CI 1.661–2.370; P < 0.001 and B 2.179; 95% CI 1.768–2.591; P < 0.001, respectively, Supplemental Table 7, Supplemental Tables 3 and 4 for univariate analyses).

In the following analyses for the ADPKD subgroup, a higher ADPKD severity class was associated with a higher total ePVS score (B 0.817; 95% CI 0.549–1.085; P < 0.001, Table 3, Figures 2(d), and 3 for representative cases). For the brain subregions, ADPKD severity class was also associated with a higher ePVS score for the CS and BG areas (B 0.692; 95% CI 0.415–0.968; P < 0.001 and B 0.790; 95% CI 0.492–1.087; P < 0.001, respectively, Supplemental Table 8).

Association of ADPKD with CMBs or lacunes

In the ordinal regression analyses for the whole study population, ADPKD was not significantly associated with the degree of CMBs (P = 0.578) or degree of lacunes (P = 0.052, Table 2, Supplemental Table 9 for univariate analyses). In the analyses for the ADPKD subgroup, a higher ADPKD severity class was associated with a higher degree of lacunes (B 0.655; 95% CI 0.237–1.072; P = 0.002) but not with the degree of CMB (P = 0.274, Table 3).

Association of ADPKD with SVD parameters in males and females

We performed regression analyses to evaluate the association of ADPKD with SVD parameters separately for male and female patients. For male patients, ADPKD was significantly associated with a higher ePVS score (B 2.353; 95% CI 1.843–2.863; P < 0.001), but not with the log-transformed ARWMC scores (P = 0.179), degree of CMBs (P = 0.994), or degree of lacunes (P = 0.453, Supplemental Table 10). For female patients, ADPKD was also associated with a higher ePVS score (B 2.522; 95% CI 2.010–3.034; P < 0.001), but not with the log-transformed ARWMC scores (P = 0.921), degree of CMBs (P = 0.428), or degree of lacunes (P = 0.053, Supplemental Table 11).

Discussion

The current study observed that ADPKD is more strongly associated with ePVS than other SVD markers, and its severity also correlated with the severity of SVD markers. ADPKD was particularly associated with a higher degree of ePVS scores for the total, CS, and BG areas after adjustments for age, sex, eGFR, and blood pressure markers, although no significant association was found with other SVD markers, such as WMH severity, presence of CMB, or presence of lacunes. Additionally, in the ADPKD subgroup, the severity class of ADPKD, measured using HtTKV, was associated with higher ePVS scores for all areas, total and lobar ARWMC scores, and higher frequency of lacunes. Although a previous study has reported a higher frequency of CMBs in patients with ADPKD,³¹ this study is the first to evaluate the association of ADPKD pathology and its severity with various SVD markers using age and sex-matched controls and the kidney volume measurements, respectively.

Although complex mechanisms contribute to the progression of SVD, each parameter of SVD may represent different underlying disease mechanisms.^{5,11,13,16,32} Arteriolosclerosis-related reduction of microvascular pulsation and endothelial dysfunction at an early stage of the disease induces diffuse parenchymal hypoperfusion, BBB dysfunction, oxidative stress and inflammation, and glymphatic system dysfunction, which may present as WMH and ePVS. Meanwhile, lipohyalinosis and fibroid necrosis at later disease stages provoke occlusion or rupture of microvessels, presenting as lacunes or CMBs.^{21–26,32–34}

However, given that the glymphatic system feeds and drains the brain via CSF transmission, factors involved in the regulation of the CSF circulation might provide a non-microvascular pathomechanistic contribution to the SVD progression.^6,12,13,15 Additionally, given that ePVS represents the fluid accumulation within the perivascular space, ePVS may be a particularly sensitive marker for the glymphatic system dysfunction.³⁵ In this regard, disproportionally enhanced ePVS in ADPKD provides evidence of a novel pathomechanism of glymphatic dysfunction resulting from ADPKD pathology that may contribute to SVD progression.^16,17 Additionally, the association of ADPKD severity with the degrees of WMH and lacunes suggests that the ADPKD related glymphatic dysfunction in higher severity may also affect the development of those markers that share common pathomechanisms with SVD.^{21–26,32–34} However, ADPKD or its severity degree was not associated with the degree of CMBs in this study, which is discordant to a previous report.³¹ The reason for this negative association may be that CMBs mainly represent a late stage with progressed vascular remodeling resulting in the rupture of microvessels.³⁴

Although not directly demonstrated by the present study, several pathophysiological characteristics of ADPKD might offer hypotheses to explain its particular association with ePVS (Figure 4). First, PC1 and PC2 proteins are strongly expressed in motile cilia along the ependymal cell lines, and the finely regulated movement of the ependymal motile cilia is a major mediator of CSF circulation, which is crucial for supplying CSF into the periarteriolar space and draining waste-containing CSF from the perivenular space.^6–10 Second, choroid plexus epithelial cells express primary cilia that inhibit CSF production via neuropeptide FF receptor 2.³⁶ Defects in the primary cilia disinhibit the fluid transcytosis through the choroid plexus epithelial cells resulting in the overproduction of CSF, and possibly, the subsequent development of ePVS.^10,36 Third, PC1 and PC2 proteins might affect the structural and functional integrity of the perivascular space. PC1 and PC2 are expressed in astrocytes and vascular endothelium,⁹ and the PC1-PC2 complex responds to chemical and mechanical stimuli by regulating intracellular calcium concentration and mediating cell-to-cell adhesion,^14,37 which are fundamental for maintaining the BBB integrity and glymphatic system function.^11–13 Fourth, astrocytes are abundant within the primary cilium, which contains lysophosphatidic acid receptor 1 (LPAR1).³⁸ Defects in the primary cilium may result in a dysregulated proliferation of astrocytes via the translocalization of LPAR1 from the primary cilium to the plasma membrane and its subsequent overactivation.³⁸ This may lead to structural deformation of the astrocyte lining and enlargement of the perivascular space. Fifth, PC1 or PC2 deficiency in the microvascular endothelium results in decreased nitric oxide production and impaired endothelium-dependent relaxation of small-sized resistance vessels.³ This mechanism is similar to the primary mechanism of SVD progression related to hypertension or diabetes mellitus and may explain the correlation of ADPKD severity with other SVD markers, such as WMH score and frequency of lacunes.^5,21,32

Figure 4.

Schematic explanation of the pathophysiologic link between ADPKD and progression of enlarged perivascular space.

ADPKD severity was associated with ARWMC scores for the total and lobar areas, although the association was not significant for the BG and infratentorial areas, which can be explained by the different primary pathology underlying WMH in lobar and deep brain structures. The lobar gray matter produces larger amounts of cerebral wastes when compared with the deep brain structures, which means that SVD in this area may be more dependent on the glymphatic waste clearance system dysfunction. In contrast, deep brain structures are supplied by the penetrating arterioles ramifying directly and perpendicularly from the basal large feeding arteries, making SVD in this area dependent on conventional cerebrovascular risk factors, thus contributing to the chronic reduction of microvascular compliance.^{5,15,22,39,40}

Our study has some limitations. First, owing to the retrospective cross-sectional study design, a causal relationship between ADPKD pathology and the long-term changes in the ePVS and other SVD markers was not demonstrated. Second, this study did not directly evaluate the alteration in the glymphatic system function, which may be the key to determining ADPKD-associated mechanisms. Prospective studies with longitudinal follow-up of patients to more precisely investigate the association between ADPKD and the cerebral SVD progression are warranted.^21,23,41 Additionally, further studies may evaluate the impact of ADPKD on the glymphatic system function and other SVD pathomechanisms, using more advanced investigation modalities such as glymphatic MRI, dual-echo arterial spin labeling MRI, diffusion-weighted image-based brain connectivity maps, and cortical thickness measurement.^42–46

In conclusion, ADPKD is more strongly associated with ePVS than other SVD markers, and its severity also correlated with the severity of SVD. Multiple pathomechanisms of ADPKD-associated cilia dysfunction may induce chronic dysfunction of the cerebral glymphatic system, resulting in the ePVS augmented pattern of SVD.

Supplemental Material

sj-pdf-1-jcb-10.1177_0271678X211037869 - Supplemental material for Association of autosomal dominant polycystic kidney disease with cerebral small vessel disease

Supplemental material, sj-pdf-1-jcb-10.1177_0271678X211037869 for Association of autosomal dominant polycystic kidney disease with cerebral small vessel disease by Woo-Jin Lee, Keun-Hwa Jung, Hyunjin Ryu, Kook-Hwan Oh, Jeong-Min Kim, Soon-Tae Lee, Kyung-Il Park, Kon Chu, Ki-Young Jung, Manho Kim and Sang Kun Lee in Journal of Cerebral Blood Flow & Metabolism

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (Information and Communications Technology) (2018M3C7A1056889).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Authors’ contributions

WJL contributed in study data acquisition, data analysis and interpretation, and drafting manuscript. KHJ contributed in initial conceptualization and design of the study, manuscript revision, and supervision of the entire procedures in this study. HJR participated in patient management, study design, manuscript revision. KHO provided study resource and participated in patient management and manuscript revision. JMK contributed in MR image acquisition and analysis. STL contributed in conception and design of the study. KIP contributed in patient management, and manuscript revision. KC participated in MR image management and analysis. KYJ contributed in initial conceptualization and design of the study. MHK participated in manuscript review and editing. SKL participated in data analysis and management of data.

Supplemental material

Supplemental material for this article is available online.

References

Yoshida

Higashihara

Maruyama

, et al. Relationship between intracranial aneurysms and the severity of autosomal dominant polycystic kidney disease. Acta Neurochir (Wien) 2017; 159: 2325–2330.

Schievink

Torres

Wiebers

, et al. Intracranial arterial dolichoectasia in autosomal dominant polycystic kidney disease. J Am Soc Nephrol 1997; 8: 1298–1303.

Wang

Iversen

Wilcox

, et al. Endothelial dysfunction and reduced nitric oxide in resistance arteries in autosomal-dominant polycystic kidney disease. Kidney Int 2003; 64: 1381–1388.

Chapman

Johnson

Gabow

, et al. The renin–angiotensin–aldosterone system and autosomal dominant polycystic kidney disease. N Engl J Med 1990; 323: 1091–1096.

Wardlaw

Smith

Dichgans

Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol 2013; 12: 483–497.

Wang

Olbricht

WL.

Fluid mechanics in the perivascular space. J Theor Biol 2011; 274: 52–57.

Driscoll

Bhalla

Liapis

, et al. Autosomal dominant polycystic kidney disease is associated with an increased prevalence of radiographic bronchiectasis. Chest 2008; 133: 1181–1188.

Faubel

Westendorf

Bodenschatz

, et al. Cilia-based flow network in the brain ventricles. Science 2016; 353: 176–178.

Worthington

Cathcart

RS.

Ependymal cilia: distribution and activity in the adult human brain. Science 1963; 139: 221–222.

10.

Wodarczyk

Rowe

Chiaravalli

, et al. A novel mouse model reveals that polycystin-1 deficiency in ependyma and choroid plexus results in dysfunctional cilia and hydrocephalus. PLoS One 2009; 4: e7137.

11.

Wardlaw

Doubal

Valdes-Hernandez

, et al. Blood–brain barrier permeability and long-term clinical and imaging outcomes in cerebral small vessel disease. Stroke 2013; 44: 525–527.

12.

Iliff

Wang

Liao

, et al. A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid β. Sci Transl Med 2012; 4: 147ra111.

13.

Kress

Iliff

Xia

, et al. Impairment of paravascular clearance pathways in the aging brain. Ann Neurol 2014; 76: 845–861.

14.

Ibraghimov-Beskrovnaya

Dackowski

Foggensteiner

, et al. Polycystin: in vitro synthesis, in vivo tissue expression, and subcellular localization identifies a large membrane-associated protein. Proc Natl Acad Sci USA 1997; 94: 6397–6402.

15.

Iliff

Wang

Zeppenfeld

, et al. Cerebral arterial pulsation drives paravascular CSF–interstitial fluid exchange in the murine brain. J Neurosci 2013; 33: 18190–18199.

16.

Brown

Benveniste

Black

, et al. Understanding the role of the perivascular space in cerebral small vessel disease. Cardiovasc Res 2018; 114: 1462–1473.

17.

Ramirez

Berezuk

McNeely

, et al. Imaging the perivascular space as a potential biomarker of neurovascular and neurodegenerative diseases. Cell Mol Neurobiol 2016; 36: 289–299.

18.

Irazabal

Rangel

Bergstralh

, et al.; CRISP Investigators. Imaging classification of autosomal dominant polycystic kidney disease: a simple model for selecting patients for clinical trials. J Am Soc Nephrol 2015; 26: 160–172.

19.

Lin

Huang

W-Q

Q-L

, et al. Incidence and risk factors of leukoaraiosis from 4683 hospitalized patients: a cross-sectional study. Medicine (Baltimore) 2017; 96: e7682.

20.

Garnier-Crussard

Bougacha

Wirth

, et al. White matter hyperintensities across the adult lifespan: relation to age, Aβ load, and cognition. Alzheimers Res Ther 2020; 12: 127–111.

21.

Lee

W-J

Jung

K-H

Ryu

, et al. Progression of cerebral white matter hyperintensities and the associated sonographic index. Radiology 2017; 284: 824–833.

22.

Lee

W-J

Jung

K-H

Ryu

, et al. Association of cardiac hemodynamic factors with severity of white matter hyperintensities in chronic valvular heart disease. JAMA Neurol 2018; 75: 80–87.

23.

Lee

W-J

Jung

K-H

Ryu

, et al. Cystatin C, a potential marker for cerebral microvascular compliance, is associated with white-matter hyperintensities progression. PLoS One 2017; 12: e0184999.

24.

den Heijer

Skoog

Oudkerk

, et al. Association between blood pressure levels over time and brain atrophy in the elderly. Neurobiol Aging 2003; 24: 307–313.

25.

Jeerakathil

Wolf

Beiser

, et al. Stroke risk profile predicts white matter hyperintensity volume the framingham study. Stroke 2004; 35: 1857–1861.

26.

Ikram

Vernooij

Hofman

, et al. Kidney function is related to cerebral small vessel disease. Stroke 2008; 39: 55–61.

27.

Van Straaten

Fazekas

Rostrup

, et al.; LADIS Group. Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke 2006; 37: 836–840.

28.

Wahlund

L-O

Barkhof

Fazekas

, et al.; European Task Force on Age-Related White Matter Changes. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke 2001; 32: 1318–1322.

29.

Kapeller

Barber

Vermeulen

, et al. Visual rating of age-related white matter changes on magnetic resonance imaging: scale comparison, interrater agreement, and correlations with quantitative measurements. Stroke 2003; 34: 441–445.

30.

Potter

Chappell

Morris

, et al. Cerebral perivascular spaces visible on magnetic resonance imaging: development of a qualitative rating scale and its observer reliability. Cerebrovasc Dis 2015; 39: 224–231.

31.

Tsai

L-K

Chen

P-L

Tsai

H-H

, et al. Cerebral microbleeds in autosomal dominant polycystic kidney disease. J Stroke 2020; 22: 153–156.

32.

Webb

Simoni

Mazzucco

, et al. Increased cerebral arterial pulsatility in patients with leukoaraiosis arterial stiffness enhances transmission of aortic pulsatility. Stroke 2012; 43: 2631–2636.

33.

Jefferson

Beiser

Himali

, et al. Low cardiac index is associated with incident dementia and Alzheimer disease: the Framingham heart study. Circulation 2015; 131: 1333–1339.

34.

Ter Telgte

van Leijsen

Wiegertjes

, et al. Cerebral small vessel disease: from a focal to a global perspective. Nat Rev Neurol 2018; 14: 387–398.

35.

Jiaerken

Lian

Huang

, et al. Dilated perivascular space is related to reduced free-water in surrounding white matter among healthy adults and elderlies but not in patients with severe cerebral small vessel disease. J Cereb Blood Flow Metab. Epub Ahead of print 4 April 2021. DOI: 10.1177/0271678X211005875.

36.

Narita

Kawate

Kakinuma

, et al. Multiple primary cilia modulate the fluid transcytosis in choroid plexus epithelium. Traffic 2010; 11: 287–301.

37.

, et al. Structure of the human PKD1-PKD2 complex. Science 2018; 361: eaat9819. DOI: 10.1126/science.aat9819.

38.

Sterpka

Chen

Neuronal and astrocytic primary cilia in the mature brain. Pharmacol Res 2018; 137: 114–121.

39.

Banerjee

Kim

Fox

, et al. MRI-visible perivascular space location is associated with alzheimer's disease independently of amyloid burden. Brain 2017; 140: 1107–1116.

40.

Lee

W-J

Jung

K-H

Ryu

, et al. Echocardiographic index E/e’in association with cerebral white matter hyperintensity progression. PloS One 2020; 15: e0236473.

41.

Benjamin

Zeestraten

Lambert

, et al. Progression of MRI markers in cerebral small vessel disease: sample size considerations for clinical trials. J Cereb Blood Flow Metab 2016; 36: 228–240.

42.

Ringstad

Vatnehol

SAS

Eide

PK.

Glymphatic MRI in idiopathic normal pressure hydrocephalus. Brain 2017; 140: 2691–2705.

43.

Chen

Huang

, et al. Microstructural disruption of the right inferior fronto‐occipital and inferior longitudinal fasciculus contributes to WMH‐related cognitive impairment. CNS Neurosci Ther 2020; 26: 576–588.

44.

Hoogeveen

Pelzer

Ghariq

, et al. Cerebrovascular reactivity in retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations. J Cereb Blood Flow Metab 2021; 41: 831–840.

45.

Mayer

Frey

Schlemm

, et al. Linking cortical atrophy to white matter hyperintensities of presumed vascular origin. J Cereb Blood Flow Metab 2021; 41: 1682–1691. 0271678X20974170.

46.

De Guio

Duering

Fazekas

, et al. Brain atrophy in cerebral small vessel diseases: extent, consequences, technical limitations and perspectives: the HARNESS initiative. J Cereb Blood Flow Metab 2020; 40: 231–245.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.58 MB