Implication of Small Vessel Disease MRI Markers in Alzheimer’s Disease and Lewy Body Disease 1

Abstract

Background:

Small vessel disease (SVD) magnetic resonance imaging (MRI) markers including deep and periventricular white matter hyperintensities (PWMH), lacunes, and microbleeds are frequently observed in Alzheimer’s disease (AD) and Lewy body disease (LBD), but their implication has not been clearly elucidated.

Objective:

To investigate the implication of SVD MRI markers in cognitively impaired patients with AD and/or LBD.

Methods:

We consecutively recruited 57 patients with pure AD-related cognitive impairment (ADCI), 49 with pure LBD-related cognitive impairment (LBCI), 45 with mixed ADCI/LBCI, and 34 controls. All participants underwent neuropsychological tests, brain MRI, and amyloid positron emission tomography. SVD MRI markers including the severity of deep and PWMH and the number of lacunes and microbleeds were visually rated. The relationships among vascular risk factors, SVD MRI markers, ADCI, LBCI, and cognitive scores were investigated after controlling for appropriate covariates.

Results:

LBCI was associated with more severe PWMH, which was conversely associated with an increased risk of LBCI independently of vascular risk factors and ADCI. PWMH was associated with attention and visuospatial dysfunction independently of vascular risk factors, ADCI, and LBCI. Both ADCI and LBCI were associated with more lobar microbleeds, but not with deep microbleeds.

Conclusion:

Our findings suggest that PWMH could reflect degenerative process related with LBD, and both AD and LBD independently increase lobar microbleeds.

Keywords

Alzheimer’s disease Lewy body disease mixed disease periventricular white matter hyperintensity small vessel disease white matter hyperintensity

INTRODUCTION

Alzheimer’s disease (AD) and Lewy body disease (LBD), including Parkinson’s disease (PD) and dementia with Lewy bodies (DLB), are the two most common causes of neurodegenerative dementia. However, small vessel disease (SVD) frequently coexists with both AD and LBD [1 –3], and patients with AD and LBD have significantly more SVD than normal individuals [4]. White matter hyperintensities (WMHs), lacunes, and microbleeds on magnetic resonance imaging (MRI) are considered to reflect SVD burden in patients with or without dementia [5], and exert detrimental effect to cognition [6].

Several studies have shown the effect of SVD MRI markers on prognosis, course of dementia, and cognitive function in normal elderly and AD patients [7 –10]. According to some studies, WMH reflects SVD pathology that contributes to cognitive impairment independently of AD [6]; however, other studies consider that WMH reflects degenerative brain changes from AD pathology [9, 10]. Most of the previous studies have investigated the independent effects of AD and SVD; however, they have not considered the impact of LBD simultaneously [4, 11]. Considering the frequent co-occurrence of AD and LBD pathologies and possible interaction between the two diseases [12], a comprehensive investigation of AD and LBD is required to determine the true impact of SVD MRI markers on cognitive impairment.

In the present study, we assessed the relationship among vascular risk factors, SVD MRI markers, AD, LBD, and cognitive function in patients with AD-related cognitive impairment (ADCI), LBD-related cognitive impairment (LBCI), and those with both ADCI and LBCI (mixed disease), further including healthy controls. We hypothesized that SVD MRI markers may be related with vascular risk factors and contribute to cognitive dysfunction independently of AD and LBD.

MATERIAL AND METHODS

Participants

In this cross-sectional study, 57 patients with pure ADCI, 45 patients with pure LBCI, 49 patients with mixed disease of ADCI/LBCI, and 34 healthy controls were recruited from a university-based memory and movement clinic from January 1, 2016 to December 31, 2018. Study participants included healthy controls and patients with AD dementia (ADD), mild cognitive impairment (MCI) due to AD (AD-MCI), PD-MCI, PD dementia (PDD), MCI due to DLB (DLB-MCI), and dementia due to DLB. All participants underwent neurological examination, neuropsychological tests, 3-Tesla (3T) MRI, ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET), and ¹⁸F-florbetaben (FBB) PET. All patients with ADD met the criteria for probable ADD with high levels of biomarker evidence [13], and all patients with AD-MCI met the criteria for high likelihood of MCI due to AD based on the National Institute on Aging-Alzheimer’s Association workgroup guidelines for AD [14]. All these patients were identified as having significant cerebral amyloid-β deposition on FBB PET scans and were regarded as having ADCI. Clinical diagnoses of PD and DLB were based on the United Kingdom PD Brain Bank diagnostic criteria [15] and the 2017 revised criteria for DLB [16], respectively. PD-MCI was diagnosed based on the modified criteria of Petersen [17] and the Movement Disorder Society criteria. PDD was diagnosed based on the clinical diagnostic criteria for probable PDD [18, 19]. Patients with MCI meeting the probable DLB criteria except for the presence of dementia were considered to have DLB-MCI. Clinical features of AD and LBD were evaluated using semi-structured questionnaires administered by caregivers. All patients with PD were confirmed to have dopaminergic depletion on ¹⁸F-N-fluoropropyl-2b-carbomethoxy-3b-(4-iodophenyl) nortropane (FP-CIT) PET scan. All patients with DLB exhibited the core features of DLB and were confirmed to have dopaminergic depletion on FP-CIT PET scan and/or DLB-related metabolic patterns on FDG PET scan. If patients with LBCI had significant amyloid-β deposition on FBB PET, they were regarded as having mixed disease. Also, if patients with ADCI had significant parkinsonism (Unified Parkinson’s Disease Rating Scale part III > 16), they all underwent FP-CIT PET scans. These patients were regarded as having mixed disease if dopaminergic depletion was observed on FP-CIT PET. Control participants did not have subjective symptoms of cognitive impairment or a history of neurological or psychiatric illnesses. They had normal findings on neurological examination, neuropsychological test, brain MRI, FDG PET, and FBB PET. Exclusion criteria were as follows: 1) history of traumatic brain injury; 2) pure vascular cognitive impairment (VCI); 3) other degenerative causes of dementia; 4) drug-induced cognitive impairment; and 5) other causes sufficiently explaining cognitive impairment, including epilepsy, psychiatric disorder, normal pressure hydrocephalus, and brain structural lesions (e.g., tumor, large infarct, or hemorrhage).

The present study was approved by the Institutional Review Board of Yonsei University Severance Hospital. Written informed consent was obtained from all participants.

Definition of vascular risk factors

Data concerning medical history and current medication of each patient were obtained by clinicians based on the patient’s and caregiver’s report at the time of evaluation. We considered the presence of hypertension, diabetes mellitus, dyslipidemia, coronary artery disease (CAD), and stroke as vascular risk factors. Participants were regarded as having hypertension, diabetes mellitus, or dyslipidemia if they were previously diagnosed or were currently taking any antihypertensive, antidiabetic, or lipid-lowering medications, respectively. Previous stroke history was regarded to be positive if the patients or their caregivers reported ischemic and/or hemorrhagic stroke history or if there were transient or permanent history of focal neurologic deficits with abrupt onset and relevant imaging evidence (severe cerebral arterial stenoses explaining the focal neurologic deficit or old cerebral infarctions) on brain MRI. A previous history of CAD was regarded as positive if the participants or their caregivers reported a history of CAD. In addition, if subjects were taking antiplatelet agents prescribed in other hospitals, their medical history including CAD history, was identified by telephone call.

Neuropsychological test

All participants underwent a standardized neuropsychological battery called the Seoul Neuropsychological Screening Battery [20], which contains the following scorable tests: digit span test (forward and backward); Korean version of the Boston Naming Test (K-BNT); Rey-Osterrieth Complex Figure Test (RCFT; copying, immediate recall, 20 min delayed recall, and recognition); Seoul Verbal Learning Test (SVLT; immediate recall, 20 min delayed recall, and recognition), phonemic and semantic Controlled Oral Word Association Test (COWAT); and Stroop Test (word and color reading). General cognitive function was evaluated using the Korean-version of the Mini-Mental State Examination (MMSE) [21], and the Clinical Dementia Rating Sum of Boxes (CDR-SOB). The operational definition for the presence of cognitive dysfunction has been described previously [22]. Scales administered by caregiver for activities of daily living (ADL) including Seoul instrumental ADL [23] and Korean-instrumental ADL [24] were used to define the presence of dementia.

MRI acquisition and measurement of SVD MRI markers

All MRI scans were acquired using a Philips 3T scanner (Philips Intera; Philips Medical System, Best, The Netherlands) with a SENSE head coil (SENSE factor = 2). A high-resolution, T1-weighted MRI volume dataset was obtained from all subjects with a 3-dimensional T1-turbo field echo sequence configured with the following acquisition parameters: axial acquisition with a 224×224 matrix; 256×256 reconstructed matrix with 182 slices; 220 mm field of view; 0.98×0.98×1.2 mm³ voxels; echo time of 4.6 ms; repetition time of 9.6 ms; flip angle of 8°; and slice gap of 0 mm. Conventional 2-dimensional fluid-attenuated inversion recovery images were obtained to evaluate WMHs. Conventional 2-dimensional T2^*-weighted gradient-recalled-echo imaging in the axial plane was also obtained for the detection of cerebral microbleeds with the following parameters: 256×205 matrix; section thickness, 5 mm; echo time, 16 ms; repetition time, 560 ms; and flip angle, 18°.

The severity of WMH was rated visually using the modified Fazekas scale. Periventricular WMH (PWMH) areas were classified as P0 (no cap/band), P1 (cap and band < 5 mm), P2 (5 mm≤cap or band < 10 mm), and P3 (10 mm≤cap or band), and deep WMH (DWMH) areas were classified as D0 (no deep white matter lesion), D1 (maximum diameter of deep white matter lesion < 10 mm), D2 (10 mm≤lesion < 25 mm), and D3 (≥25 mm). Lacunes were defined as round or ovoid, subcortical, fluid-filled cavities (signal similar to CSF) of between 3 mm and about 15 mm in diameter, consistent with a previous acute small subcortical infarct or hemorrhage in the territory of a perforating arteriole. Microbleeds were defined as round-shaped and homogenously low-signal lesions of less than 10 mm in diameter on T2^*-weighted gradient-recalled-echo images. WMHs, lacunes, and microbleeds were manually rated by two blinded neurologists (M.P. and K.B.). Patients fulfilling the modified Boston criteria based upon MRI findings were regarded as having probable cerebral amyloid angiopathy (CAA) [25].

Statistical analysis

Statistical analyses were performed using SPSS (IBM, version 26.0, IBM Corporation, Armonk, NY, USA) with a significance level of 0.05. The Chi-square test and analysis of variance were used to compare demographics and baseline characteristics among the control, pure ADCI, pure LBCI and mixed disease groups, and post-hoc tests were performed to reveal significant differences between specific groups. To investigate the effects of vascular risk factors, ADCI, and LBCI on SVD MRI markers, generalized linear model analyses were performed for SVD MRI markers using the history of vascular risk factors and the presence of ADCI and LBCI as predictors after controlling for age, sex, and education. Models for DWMH and PWMH were based on ordinal logistic regression models, while models for lacunes and microbleeds were based on Poisson loglinear models. If the interaction term of ADCI and LBCI was significant, it was further included as a predictor.

To evaluate the risk of ADCI related with vascular risk factors and SVD MRI markers, we performed logistic regression analysis for the presence of ADCI with DWMH, PWMH, the number of lacunes and microbleeds, presence of LBCI, and the history of vascular risk factors as predictors after controlling for age, sex, and education. To evaluate the risk of LBCI related with vascular risk factors and SVD MRI markers, we performed logistic regression analysis for the presence of LBCI with the presence of ADCI and the aforementioned variables, except for the presence of LBCI, as predictors. Due to small number of subjects with no or severe DWMH and PWMH (Table 1), DWMH and PWMH were treated as continuous variables.

Table 1

Demographic characteristics of study participants

	Control	Pure ADCI	Mixed disease	Pure LBCI	p
Number	34	57	49	45
Age, y	71.9±6.3	73.5±7.0	74.1±7.3	73.1±7.7	0.560
Gender, male	14 (41.2)	23 (40.4)	15 (30.6)	15 (33.3)	0.657
Education, y	12.7±4.7	10.5±5.1	10.6±5.1	9.2±4.6	0.023^c
MMSE	28.6±1.4	21.6±5.8	20.0±4.7	22.6±4.0	< 0.001^a,b,c,f
CDR-SOB	0.3±0.6	2.4±2.1	4.2±2.7	3.2±1.8	< 0.001^a,b,c,d
Vascular risk factors
Hypertension	11 (32.4)	29 (50.9)	24 (49.0)	27 (60.0)	0.110
Diabetes mellitus	6 (17.6)	10 (17.5)	11 (22.4)	17 (37.8)	0.077
Dyslipidemia	12 (35.3)	24 (42.1)	17 (34.7)	14 (31.1)	0.700
CAD	2 (5.9)	7 (12.3)	4 (8.2)	2 (4.4)	0.499
Previous stroke	1 (2.9)	7 (12.3)	3 (6.1)	2 (4.4)	0.284
SVD MRI markers
DWMH (continuous variable)	1.18±0.58	1.35±0.67	1.41±0.73	1.29±0.55	0.418
DWMH (categorical variable)					0.217
None	2 (5.9)	1 (1.8)	1 (2.0)	2 (4.4)
Mild	25 (73.5)	40 (31.7)	33 (67.3)	28 (62.2)
Moderate	6 (17.6)	11 (19.3)	9 (18.4)	15 (33.3)
Severe	1 (2.9)	5 (8.8)	6 (12.2)	0
PWMH (continuous variable)	1.24±0.65	1.42±0.60	1.65±0.69	1.49±0.63	0.033^b
PWMH (categorical variable)					0.045^b
None	2 (5.9)	0	0	0
Mild	24 (70.6)	36 (63.2)	23 (46.9)	26 (57.8)
Moderate	6 (17.6)	18 (31.6)	20 (40.8)	16 (35.6)
Severe	2 (5.9)	3 (5.3)	6 (12.2)	3 (6.7)
Lacunes	0.09±0.38	0.19±0.61	0.35±0.95	0.07±0.25	0.133
Total microbleeds	0.18±0.39	0.95±3.40	0.45±0.96	0.76±2.88	0.465
Lobar microbleeds	0.09±0.29	0.79±3.24	0.39±0.93	0.49±1.71	0.454
Deep microbleeds	0.06±0.24	0.14±0.52	0.06±0.24	0.24±1.10	0.474
Probable CAA (%)	0	3 (5.3)	4 (8.2)	2 (4.4)	0.403

ADCI, Alzheimer’s disease related cognitive impairment; CAD, coronary artery disease; CDR, Clinical Dementia Rating; CDR-SOB, Clinical Dementia Rating Sum of Boxes; DWMH, deep white matter hyperintensity; LBCI, Lewy body related cognitive impairment; MMSE, Mini-Mental State Examination; PWMH, periventricular white matter hyperintensity; SVD, small vessel disease; CAA, cerebral amyloid angiopathy. Data shown as mean±standard deviations or number (%). p value are results of chi-square tests and analyses of variance to find group differences. ^a significantly different in comparisons between the HC and pure ADCI groups. ^b significantly different in comparisons between the HC and mixed groups. ^c significantly different in comparisons between the HC and pure LBCI groups. ^d significantly different in comparisons between the pure ADCI and mixed groups. ^e significantly different in comparisons between the pure ADCI and pure LBCI groups. ^f significantly different in comparisons between the mixed and pure LBCI groups.

To evaluate the effects of vascular risk factors and SVD MRI markers on cognitive function, we performed general linear models (GLMs) for cognitive z-scores with vascular risk factors and SVD MRI markers as predictors (Model 1). Since cognitive z-scores were standardized based on age- and education-matched norms, analyses were controlled for only sex. To evaluate the effects of SVD MRI markers independent of ADCI and/or LBCI, we performed additional analyses (Model 2), further including the ADCI and/or LBCI as predictors. If the interaction term of ADCI and LBCI was significant, it was further included as a predictor. As a sensitivity analysis to find the association between the severity group of WMHs and cognitive function, additional GLMs were performed treating DWMH and PWMH as categorical variables. Multiple statistical tests across eight neuropsychological tests were corrected using the Bonferroni method and a corrected p value of < 0.05 was considered significant.

As previous studies showed that CAA is related with AD pathology [26], LB pathology [27], and SVD markers including PWMH [28], we performed additional analyses using the presence of probable CAA as a predictor or a covariate instead of the number of microbleeds. To evaluate the risk of probable CAA related with vascular risk factors, SVD MRI markers, ADCI, and LBCI, logistic regression analysis for the presence of probable CAA was performed with DWMH, PWMH, the number of lacunes, presence of LBCI, presence of ADCI, and the history of vascular risk factors as predictors after controlling for age, sex, and education.

RESULTS

Demographics and clinical characteristics

The demographic and clinical characteristics of the study participants are summarized in Table 1. The duration of education was shorter in the pure LBCI group compared to that in the control group. The control group had a higher mean MMSE score and a lower mean CDR-SOB than the three disease groups. Compared with the mixed group, the pure LBCI group had a higher mean MMSE score and the pure ADCI group had a lower mean CDR-SOB. No significant group difference was noted in terms of vascular risk factors. Regarding SVD MRI markers, the mixed group had more severe PWMH than did the control group. There was no significant group difference in regard of DWMH, number of lacunes and microbleeds, and probable CAA.

Effects of vascular risk factors, ADCI, and LBCI on SVD MRI markers

The severity of DWMH was not significantly associated with any vascular risk factor or presence of ADCI or LBCI (Table 2). The severity of PWMH was positively associated with the presence of LBCI but was not significantly associated with any vascular risk factor or the presence of ADCI. Greater number of lacunes were associated with the presence of ADCI and marginally associated with the history of previous stroke. No significant ADCI^*LBCI effects on DWMH, PWMH, or lacunes were noted. However, ADCI and LBCI had significant negative interaction effects on the number of total microbleeds, lobar microbleeds, and deep microbleeds. After considering the interaction effects, the presence of ADCI and that of LBCI were independently associated with a higher number of total and lobar microbleeds, whereas they were not associated with the number of deep microbleeds. The negative interaction effects could be interpreted in that the numbers of total and lobar microbleeds were comparable in all disease groups, while the number of deep microbleeds in the mixed group was smaller than those in the pure ADCI and pure LBCI groups. The history of diabetes mellitus, dyslipidemia, and CAD were associated with fewer total and lobar microbleeds, while hypertension was associated with increased number of deep microbleeds.

Table 2

Effects of vascular risk factors, ADCI, and LBCI on SVD MRI markers

	DWMH		PWMH		Lacunes		Microbleeds		Lobar microbleeds		Deep microbleeds
	Beta (SE)	p	Beta (SE)	p	Beta (SE)	p	Beta (SE)	p	Beta (SE)	p	Beta (SE)	p
Hypertension	0.55 (0.37)	0.132	0.45 (0.34)	0.184	0.35 (0.39)	0.363	0.23 (0.21)	0.278	–0.05 (0.24)	0.844	1.15 (0.49)	0.019
Diabetes mellitus	–0.09 (0.41)	0.826	0.14 (0.38)	0.714	0.15 (0.43)	0.737	–0.94 (0.32)	0.003	–0.90 (0.37)	0.015	–1.06 (0.67)	0.112
Dyslipidemia	–0.46 (0.36)	0.214	–0.20 (0.34)	0.557	0.10 (0.39)	0.806	–0.83 (0.25)	0.001	–0.91 (0.31)	0.003	–0.93 (0.50)	0.064
CAD	–0.16 (0.59)	0.790	0.09 (0.55)	0.866	–1.61 (1.04)	0.121	–2.58 (1.01)	0.011	–2.31 (1.02)	0.023	NA
Stroke	0.77 (0.63)	0.225	0.92 (0.57)	0.109	0.93 (0.48)	0.055	–0.83 (0.61)	0.173	–0.82 (0.75)	0.274	–0.26 (1.07)	0.809
ADCI	0.20 (0.34)	0.566	0.52 (0.33)	0.112	1.27 (0.46)	0.006	1.33 (0.44)	0.002	1.80 (0.60)	0.003	1.15 (0.82)	0.161
LBCI	0.05 (0.34)	0.886	0.75 (0.32)	0.021	0.23 (0.37)	0.532	0.96 (0.46)	0.035	1.28 (0.63)	0.042	1.30 (0.82)	0.114
ADCI^*LBCI	NS		NS		NS		–1.79 (0.53)	0.001	–1.86 (0.69)	0.007	–2.96 (1.09)	0.007

ADCI, Alzheimer’s disease related cognitive impairment; CAD, coronary artery disease; DWMH, deep white matter hyperintensity; LBCI, Lewy body related cognitive impairment; MRI, magnetic resonance imaging; NA, not applicable; NS, not significant; PWMH, periventricular white matter hyperintensity; SE, standard error; SVD, small vessel disease. Data are results of generalized linear models for SVD MRI markers using ADCI and LBCI as predictors after controlling for age, sex, education, hypertension, diabetes mellitus, dyslipidemia, CAD and stroke. If the effect of ADCI^*LBCI was significant, the interaction term was further included as a predictor. Models for DWMH and PWMH were based on ordinal logistic regression models, while models for lacunes and microbleeds were based on Poisson loglinear models.

Risk of ADCI and LBCI related with vascular risk factors and SVD MRI markers

When independent effects of vascular risk factors and SVD MRI markers on the risks of ADCI and LBCI were evaluated, more severe PWMH was associated with an increased risk of LBCI (Table 3). No vascular risk factors or SVD MRI markers were significantly associated with the risk of ADCI.

Table 3

Independent effects of vascular risk factors and SVD MRI markers on the risk of ADCI and LBCI

	ADCI		LBCI
	OR (95%CI)	p	ORs (95%CI)	p
Hypertension	1.03 (0.51–2.04)	0.943	1.42 (0.71–2.84)	0.316
Diabetes mellitus	0.46 (0.21–1.02)	0.055	1.99 (0.87–4.56)	0.102
Dyslipidemia	1.22 (0.61–2.44)	0.574	0.63 (0.31–1.26)	0.191
CAD	2.41 (0.65–8.87)	0.187	0.47 (0.14–1.63)	0.236
Stroke	2.17 (0.51–9.21)	0.293	0.43 (0.11–1.63)	0.216
DWMH (continuous variable)	0.98 (0.52–1.87)	0.963	0.75 (0.40–1.41)	0.377
PWMH (continuous variable)	1.36 (0.74–2.49)	0.323	2.14 (1.16–3.92)	0.014
Lacunes	1.69 (0.85–3.36)	0.134	1.02 (0.60–1.75)	0.933
Microbleeds	1.02 (0.89–1.17)	0.778	0.96 (0.84–1.09)	0.518
ADCI	NA		0.65 (0.34–1.24)	0.191
LBCI	0.64 (0.34–1.22)	0.178	NA

ADCI, Alzheimer’s disease related cognitive impairment; CAD, coronary artery disease; CI, confidence interval; DWMH, deep white matter hyperintensity; LBCI, Lewy body related cognitive impairment; NA, not applicable; OR, odds ratio; PWMH, periventricular white matter hyperintensity; SVD, small vessel disease. Data are results of logistic regression for the presence of ADCI and that of LBCI adjusted for age, sex, and education. Predictors included hypertension, diabetes mellitus, dyslipidemia, CAD, stroke, DWMH, PWMH, lacunes, and microbleeds. DWMH and PWMH were included as continuous variables rather than categorical variables.

Effects of vascular risk factors, SVD MRI markers, ADCI, and LBCI on cognitive function

GLMs for cognitive z-scores using vascular risk factors and SVD MRI markers as predictors revealed that more severe PWMH was associated with lower digit span backward, K-BNT, RCFT copy, COWAT semantic, COWAT phonemic, and Stroop color reading scores (Supplementary Table 1). More severe DWMH was associated with a higher RCFT copy score. After further controlling for the presence of ADCI, the statistical significance between PWMH and digit span backward score disappeared. After further controlling for the presence of LBCI, more severe DWMH was associated with a higher RCFT copy score, while more severe PWMH was associated with lower RCFT copy and Stroop color reading scores.

When ADCI and LBCI were simultaneously included as predictors, ADCI and LBCI had negative interaction effects on digit span backward, SVLT delayed recall, RCFT delayed recall, COWAT semantic, and COWAT phonemic scores in that the pure ADCI, pure LBCI, and mixed groups had comparable scores (Table 4). The presence of ADCI was associated with lower scores in all neuropsychological tests except for RCFT copy, while the presence of LBCI was associated with lower scores in all neuropsychological tests. After controlling for ADCI and LBCI, more severe DWMH was associated with a higher RCFT copy score, while more severe PWMH was associated with lower RCFT copy and Stroop color reading scores.

Table 4

Effects of vascular risk factors, SVD MRI markers, ADCI, and LBCI on cognitive scores

	Digit span backward		K-BNT		RCFT copy		SVLT delayed recall		RCFT delayed recall		COWAT semantic		COWAT phonemic		Stroop color reading
	Beta (SE)	p	Beta (SE)	p	Beta (SE)	p	Beta (SE)	p	Beta (SE)	p	Beta (SE)	p	Beta (SE)	p	Beta (SE)	p
Hypertension	–0.17 (0.20)	0.381	0.34 (0.26)	0.189	0.02 (0.49)	0.966	0.19 (0.17)	0.273	0.19 (0.14)	0.188	0.03 (0.13)	0.831	–0.06 (0.16)	0.743	–0.02 (0.24)	0.944
Diabetes mellitus	–0.05 (0.23)	0.814	0.37 (0.30)	0.221	0.45 (0.57)	0.429	0.19 (0.20)	0.325	0.14 (0.17)	0.384	0.21 (0.15)	0.173	0.04 (0.19)	0.828	–0.14 (0.28)	0.622
Dyslipidemia	–0.03 (0.19)	0.885	0.07 (0.26)	0.789	0.90 (0.49)	0.070	–0.17 (0.17)	0.315	0.13 ().14)	0.375	0.30 (0.13)	0.021	0.12 (0.16)	0.467	0.48 (0.24)	0.044
CAD	–0.11 (0.34)	0.753	0.03 (0.45)	0.951	–0.33 (0.86)	0.705	0.30 (0.29)	0.313	0.15 (0.25)	0.535	0.03 (0.23)	0.901	–0.04 (0.28)	0.898	0.27 (0.42)	0.523
Stroke	–0.27 (0.36)	0.454	–0.79 (0.48)	0.101	–0.88 (0.91)	0.338	–0.34 (0.31)	0.269	–0.12 (0.26)	0.652	–0.10 (0.24)	0.697	–0.19 (0.30)	0.536	–0.44 (0.44)	0.326
DWMH	0.26 (0.17)	0.127	0.28 (0.23)	0.216	1.24 (0.43)	0.004^*	0.34 (0.15)	0.021	0.08 (0.12)	0.535	0.10 (0.11)	0.394	0.27 (0.14)	0.055	0.45 (0.21)	0.032
PWMH	–0.40 (0.17)	0.019	–0.48 (0.22)	0.030	–1.27 (0.42)	0.003^*	–0.36 (0.14)	0.013	–0.11 (0.12)	0.362	–0.26 (0.11)	0.020	–0.34 (0.14)	0.015	–0.67 (0.21)	0.001^*
Lacunes	0.20 (0.15)	0.173	0.16 (0.20)	0.420	–0.08 (0.37)	0.825	–0.01 (0.13)	0.914	0.19 (0.11)	0.085	0.06 (0.10)	0.538	–0.03 (0.12)	0.801	0.09 (0.18)	0.641
Microbleeds	–0.08 (0.04)	0.045	–0.02 (0.05)	0.714	–0.01 (0.09)	0.877	0.03 (0.03)	0.315	0.01 (0.03)	0.821	0.01 (0.03)	0.712	–0.05 (0.03)	0.140	–0.03 (0.05)	0.567
ADCI	–1.30 (0.26)	<0.001^*	–0.98 (0.24)	<0.001^*	–1.17 (0.46)	0.012	–2.57 (0.22)	<0.001^*	–1.67 (0.19)	<0.001^*	–0.87 (0.17)	<0.001^*	–0.87 (0.21)	<0.001^*	–1.21 (0.32)	<0.001^*
LBCI	–1.40 (0.27)	<0.001^*	–1.02 (0.24)	<0.001^*	–1.89 (0.46)	<0.001^*	–2.07 (0.24)	<0.001^*	–1.49 (0.20)	<0.001^*	–1.19 (0.19)	<0.001^*	–1.37 (0.23)	<0.001^*	–1.85 (0.34)	<0.001^*
ADCI^*LBCI	1.40 (0.36)	<0.001^*	NS		NS		2.16 (0.31)	<0.001^*	1.07 (0.26)	<0.001^*	0.76 (0.24)	0.002^*	0.94 (0.30)	0.002^*	0.93 (0.45)	0.038

ADCI, Alzheimer’s disease related cognitive impairment; CAD, coronary artery disease; COWAT, Controlled Oral Word Association Test; DWMH, deep white matter hyperintensity; K-BNT, Korean version of the Boston Naming Test; LBCI, Lewy body related cognitive impairment; NS, not significant; PWMH, periventricular white matter hyperintensity; RCFT, Rey-Osterrieth Complex Figure Test; SE, standard error; SVD, small vessel disease; SVLT, Seoul Verbal Learning Test. Data are results of general linear models for cognitive z-scores after controlling for sex. Predictors included DWMH, PWMH, lacunes, microbleeds, ADCI, and LBCI. If the effect of ADCI^*LBCI was significant, the interaction term was further included as a predictor. ^*Significant after statistical correction for multiple comparisons across eight neuropsychological tests using the Bonferroni method.

Sensitivity GLM analyses treating DWMH and PWMH as categorical variables

When DWMH and PWMH were treated as categorical variables, GLMs for cognitive z-scores using vascular risk factors and SVD MRI markers as predictors revealed that DWMH was associated with higher RCFT copy, COWAT phonemic, and Stroop color reading scores, while more severe PWMH was associated with lower digit span backward, RCFT copy, COWAT semantic, and Stroop color reading scores (Supplementary Table 2). The significance of the association between DWMH and neuropsychological test scores remained even after controlling for ADCI or LBCI. The significance of the association between PWMH and neuropsychological test scores remained after controlling for the presence of ADCI, but disappeared after further controlling for the presence of LBCI except for digit span backward.

Sensitivity analyses using the presence of probable CAA as an additional covariate

When we evaluated the effects of vascular risk factors, ADCI, and LBCI on SVD MRI markers after controlling for probable CAA, the severity of PWMH was still positively associated with the presence of LBCI (Supplementary Table 3). The presence of ADCI and probable CAA were independently associated with greater number of lacunes. There were no significant interaction effects of ADCI and LBCI on SVD MRI markers.

When we evaluated the independent effects of vascular risk factors and SVD MRI markers on the risk of ADCI and LBCI after controlling for probable CAA, we did not include the number of microbleeds as a covariate (Supplementary Table 4). More severe PWMH was still associated with an increased risk of LBCI, but no vascular risk factors, SVD MRI markers, and probable CAA were significantly associated with the risk of ADCI.

When we evaluated the independent effects of vascular risk factors, SVD MRI markers, ADCI, and LBCI on the risk of probable CAA, there was no predictor significantly associated with CAA.

DISCUSSION

This study reports the implication of SVD MRI markers by evaluating their relationship with vascular risk factors, ADCI, LBCI, and cognitive dysfunction in subjects encompassing normal elderly and patients with cognitive impairment due to AD and/or LBD. Our major findings are as follows: first, the presence of LBCI was associated with more severe PWMH, and more severe PWMH was associated with an increased risk of LBCI. In addition, after controlling for the ADCI and LBCI effects, more severe PWMH was associated with lower RCFT copy and Stroop color reading scores. Second, the presence of ADCI was associated with greater number of lacunes. Third, both ADCI and LBCI were independently associated with greater number of total and lobar microbleeds, while the presence of mixed disease was associated with fewer deep microbleeds. Taken together, our results suggest that PWMH is closely related with LBD; lobar microbleeds may be an MRI marker related with AD and/or LBD; and deep microbleeds may be an MRI marker specific to SVD in patients with cognitive impairment due to AD and/or LBD.

The presence of LBCI was associated with more severe PWMH, and more severe PWMH was associated with an increased risk of LBCI. These results can be interpreted to indicate that LBCI could have detrimental effects on cerebrovascular pathology reflected on PWMH. However, contrary to our hypothesis, PWMH was not associated with any vascular risk factor and had detrimental effects on attention and visuospatial function, which are cognitive characteristics of DLB [29, 30]. These results suggest that PWMH might reflect underlying degenerative changes related to LBD rather than pure vascular phenomenon. This point of view is consistent with previous studies showing more severe WMH in AD [31, 32] and LBD [32, 33] patients, and those suggesting that WMH could be a manifestation of AD pathology rather than SVD pathology [9 , 34]. McAleese et al. reported a pathological evidence that parietal WMHs observed in AD are a manifestation of axonal degeneration caused by cortical AD pathology [34]. Considering predominant axonal accumulation of α-synuclein [35, 36] and prominent white matter degeneration in LBD [37], white matter or axonal degeneration caused by LB-related pathology could be manifested as PWMH. As a previous study showed an occipital WMH predominance in young carriers of autosomal dominant AD [34], and the occipital cortex could be a site of interaction between AD and LB pathologies [38], future studies focusing on the lobar distribution of WMH are warranted.

Our sensitivity analyses using probable CAA as a predictor, or a covariate showed that the association between PWMH and LBCI did not change even after considering the effects of probable CAA. Although CAA is regarded as one of hallmark of AD pathology [26], previous studies showed that CAA is also associated with DLB [27, 39] and more severe WMH [40, 41] as well. A previous study tested the confounding or mediating effect of CAA on the association between degenerative changes and WMH among mutation carriers and non-carriers in Dominantly Inherited Alzheimer Network [31]. Like our study, they found that the increase in WMH is not fully explained by CAA. Although the definition of CAA in our study was not pathologically confirmed, these results further support the notion that PWMH could be related with degenerative disease rather than SVD independently of CAA.

More severe PWMH was associated with lower RCFT copy and Stroop color reading scores even after controlling for the ADCI and LBCI effects. As quantitative in vivo biomarkers for LBD pathology do not exist currently, we used a binarized approach to define the presence or absence of LBD in this study. This approach has successfully demonstrated independent and interaction effects of AD and LBD on cognitive dysfunction, cortical atrophy [42], and subcortical atrophy [43] in our previous studies. Our results suggest that more severe PWMH could contribute to more severe clinical presentation in LBD patients that could not be explained by a binarized approach. This hypothesis is consistent with recent studies in patients with PD showing that PWMH burden is associated with cognitive dysfunction [44], bradykinesia, and axial symptoms [45]. Future studies using various imaging biomarkers such as dopamine transporter, metabolic, and structural imaging are required to determine the neural correlates of increased PWMH in patients with LBD.

The presence of ADCI was associated with increased number of lacunes; however, the number of lacunes was not significantly associated with the risk of ADCI. Previous studies showed that the number of lacunes is related to vascular risk factors [46] and is an important MRI marker specific to cognitive impairment related with SVD in patients with cognitive impairment due to AD and/or subcortical VCI [6]; however, we could not find any relationship between vascular risk factors and cognitive dysfunction in our study. As we defined the presence of ADCI based on the FBB PET positivity and summed cortical and subcortical lacunes to calculate the total number of lacunes, our result might be consistent with a previous study showing that cortical microinfarcts on 3 T MRI are increased in patients with CAA [47], which is related to β-amyloid deposition [48]. Moreover, this study did not include patients with pure VCI; hence, future studies including patients with pure VCI and differentiating cortical and subcortical lacunes are required to elucidate the implication of lacunes in vascular and degenerative brain changes.

To the best of our knowledge, our study is the first to evaluate the independent effects of ADCI and LBCI on cerebral microbleeds. The presence of ADCI and LBCI were independently associated with increased number of total and lobar microbleeds. The significant negative ADCI^*LBCI effects on total and lobar microbleeds suggest that the pure ADCI and pure LBCI groups had greater number of total and lobar microbleeds than the control group, but comparable ones to the mixed disease group. Our previous study including patients with LBD demonstrated that striatal dopaminergic depletion, an imaging hallmark of LBD, was associated with increased amyloid-β deposition in the occipital cortex [43]. As lobar microbleeds are closely related with CAA and increased cerebral amyloid-β deposition, especially in the occipital cortex [49, 50], our results suggest that both AD and LBD might contribute to CAA.

Regarding the deep microbleeds, the independent effects of ADCI and LBCI were not significant, while ADCI^*LBCI effect was significant. The significant negative ADCI^*LBCI effect on deep microbleeds suggests that the mixed disease group had fewer deep microbleeds than the simple addition of ADCI and LBCI effects. Considering that deep microbleeds are related with VCI, our results could be interpreted in that less amount of vascular contribution might be required to cause cognitive impairment with simultaneous presence of AD and LBD [51]. Therefore, lobar microbleeds could be more closely related with AD and/or LBD, while deep microbleeds may be related to VCI. This is further supported by our finding that lobar microbleeds had a negative association with diabetes mellitus, dyslipidemia, and CAD, while deep microbleeds had a positive association with hypertension.

This study has several strengths. All participants underwent strict neurological examination for parkinsonism and FBB PET in this study. In addition, all the controls and patients with clinical features of LBD such as visual hallucination, cognitive fluctuation, and parkinsonism underwent dopamine transporter imaging. These imaging biomarker-supported clinical diagnoses of AD and LBD could enhance the validity of our study. However, our study has several limitations. First, since this study was performed primarily in a referral-based clinic, some degree of selection bias may exist. In addition, we did not include patients with pure VCI without AD and LBD. This selection bias could explain our finding that more severe DWMH was associated with higher cognitive scores, especially RCFT copy score. Therefore, our results should be cautiously interpreted in patients with cognitive impairment due to AD and/or LBD. Second, the severity of WMH was measured based on visual rating and regional lobar WMHs were not evaluated. Future studies using automatic volume measurements for total and regional WMHs are warranted. Third, we could not perform pathological confirmation of AD, LBD, and CAA. Although we performed FBB PET to confirm AD and FP-CIT PET to confirm LBD, PET scans are not identical to post-mortem confirmation. Moreover, there could be subclinical LBD whose clinical features were not evident at study recruitment. Fourth, we did not differentiate DLB and PDD in LBCI group, although recent autopsy studies showed that DLB patients have more severe CAA and other AD-related changes than PDD patients [27, 52], we thought that if we strictly differentiate PDD from DLB, we could not identify the full spectrum of degenerative processes caused by α-synuclein and evaluate the independent and interactive effects of concomitant AD. Finally, as this was a cross-sectional study, it provides limited grounds for drawing definite causal effects. Longitudinal studies are required to elucidate the actual relationship among PWMH, LB pathology and cognitive function.

However, despite these limitations, our results suggest that PWMH, which has been regarded to represent vascular pathology, could reflect degenerative processes related with LBD, and both AD and LBD could increase lobar microbleeds. We emphasize the clinical relevance of consideration for LBD and mixed AD with LBD in patients with cognitive impairment.

Footnotes

ACKNOWLEDGMENTS

The authors are grateful to all the participants who have taken part in this study.

This study was supported by a Severance Hospital Research fund for Clinical excellence (SHRC) (C-2020-0013).

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

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