The Association of White Matter Hyperintensities with Frailty in Patients with Very Mild to Moderate Alzheimer’s Disease

Abstract

Background:

Frailty, one of serious global health problems in the elderly, is a growing concern in patients with Alzheimer’s disease (AD) because of its high prevalence in AD and its impact on the prognosis.

Objective:

To investigate the quantitative association between white matter hyperintensities (WMH) and frailty in AD.

Methods:

A total of 144 outpatients were included. All subjects were evaluated by using Korean version of the CERAD assessment battery and diagnosed very mild to moderate AD. WMH volume was calculated using automated segmentation analysis from the 3D MRI image and further partitioned according to the distance from the ventricular surface. Using the Korean Frailty Index, prefrailty was defined by the scores of 3 and 4 and frailty by the score of 5 and higher.

Results:

In total, 23.6%were frailty, 32.6%were pre-frailty, and 43.8%were classified as a robust group. The frailty group had higher WMH volume compared to the robust group (p = 0.02), and these trends remained significant after linear regression analyses. According to the subclassification of WMH, using the robust group as a reference, total WMH (OR = 6.297, p = 0.013, 95%CI = 1.463–27.114), juxtaventricular WMH (OR = 12.955, p = 0.014, 95%CI = 1.687–99.509), and periventricular WMH (OR = 3.382, p = 0.025, 95%CI = 1.163–9.8531) volumes were associated with frailty, but deep WMH volume was not.

Conclusions:

A quarter of patients with very mild to moderate AD is suffering from frailty. Our study provides the evidence of a cross-sectional relationship between WMH volume and frailty, and there is a difference in the association between the subclassification of WMH volume and frailty.

Keywords

Alzheimer’s disease frailty magnetic resonance imaging white matter hyperintensities

INTRODUCTION

Frailty is recognized as an age-related clinical condition [1, 2], conceptualized as a syndrome of decreased physiological reserve due to the accumulation of multidimensional deficits [2, 3], increasing the risk of major adverse events including diminished physical performance and ultimately loss of independence [4 –6]. With respect to demographic conditions, the number of older adults is increasing, and with this, the prevalence of frailty. Alzheimer’s disease (AD) is considered part of this trend. Furthermore, there is enough evidence to suggest that both frailty and AD are the main drivers of disability related to aging, as well as increased mortality [7, 8]. Against this backdrop, frailty and AD have emerged as priority areas in both research and clinical settings because of their high prevalence, impact on the individual’s quality of life, and public health [9 –11].

White matter hyperintensities (WMH), a surrogate marker for cerebral small vessel disease [12], is generally accepted to increase with age. In the general population, the prevalence of WMH appearing on magnetic resonance imaging (MRI) ranges from up to 21%at age around 64, and up to 94%at age 82 years [13]. WMH can negatively affect physical performance [14] such as gait speed, balance disturbances, and cognitive function, which might, in turn, increase frailty severity. This means that WMH and frailty are common in their contribution to detrimental outcomes [15]. Considering that WMH is generally much more severe and widespread in patients with AD [16 –18], we hypothesized that both prevalence and severity of WMH is predominant in AD [19], and that it might be one of the major processes accelerating frailty. Some previous studies have identified the prospective and cross-sectional association of WMH with frailty [20, 21]. However, the number of studies is still limited, and the studies have not conducted on patients with AD [22, 23]. Among the few conducted studies, there are limitations in the volumetric analysis of the brain because they mainly used qualitative or semi-quantitative visual rating scales for measuring WMH volumes or used quantitative volumetric methods to examine the regional distribution of WMH volume only. Furthermore, we wondered whether the impact of WMH volume on frailty differed according to the specific anatomical regions of WMH.

Although WMH have been frequently divided into periventricular white matter hyperintensity (PWMH) and deep white matter hyperintensity (DWMH) [24, 25], these dichotomous definitions are somewhat arbitrary and vary across studies, which potentially contributes to inconsistencies in results and makes comparisons difficult [26]. A new subclassification of WMH [24], which may have better etiological and functional relevance than the simple dichotomization, was suggested to reduce possible heterogeneities of PWMH and DWMH and to improve the value of WMH as etiological or prognostic markers in research and clinical settings. Specifically, this subclassification stratifies WMH into juxtaventricular (JVWMH, within 3 mm from the ventricular surface), periventricular (PVWMH, 3–13 mm from ventricular surface), and deep WMH (DWMH, 13 mm or further from the ventricular surface) locations [24]. According to the new subclassification of WMH, we investigated the association between three subclassified WMH volumes and frailty, respectively.

The aim of this cross-sectional study was twofold: 1) To examine the quantitative association between WMH volume and frailty in patients with AD; 2) To investigate whether there is any difference in the association between subclassified WMH volume and frailty in patients with AD.

MATERIALS AND METHODS

Subjects

Subjects were recruited for the study from the dementia clinic of Jeju National University Hospital (Jeju-do, Korea; JNUH), between January 2018 and January 2020. All subjects were evaluated using the protocol of the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease assessment battery (CERAD-K), which is composed of a standardized clinical interview, physical and neurological examinations, and laboratory tests. All available information was reviewed by a panel of two experienced dementia research neuropsychiatrists to diagnose AD, according to the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association criteria (NINCDS-ADRDA) and the Clinical Dementia Rating (CDR) index was determined. Subjects with very mild to moderate probable AD and possible AD were included in our analysis. All subjects were fully informed about the study protocol and written statements of informed consent were signed by either the subjects or their legal guardians. The study protocol was approved by the institutional review board of the JNUH.

MRI acquisition

Magnetic resonance imaging (MRI) scans were obtained at JNH on a 3.0 Tesla Philips Intera scanner. Three-dimensional (3D) T1-weighted anatomical images (acquisition voxel size = 1.0×0.5×0.5 mm; 1.0 mm sagittal slice thickness with no inter-slice gap; repetition time = 4.61 ms; echo time = 8.15 ms, number of excitations = 1; flip angle = 8°; field of view = 240×240 mm; and acquisition matrix size = 175×256×256 mm in the x-, y-, and z-dimensions) and 3D fluid-attenuated inverse recovery (FLAIR) images (voxel dimension = 1×1×3 mm³; repetition time = 9,900 ms; echo time = 125 ms; inversion time = 2,800 ms; number of excitations = 1; flip angle = 90°; field of view = 240 mm; axial plane matrix = 256×256 mm; thickness = 3 mm; and no interslice gap) were acquired.

MRI processing and analysis

3D FLAIR images were coregistered to the T1-weighted images using statistical parametric mapping version 12 (SPM12; Wellcome Institute of Neurology, University College London, UK, http://www.fil.ion.ucl.ac.uk/spm/doc/) for Matlab (The MathWorks, Inc., Natick, MA, USA). WMH segmentations were performed on the coregistered 3D FLAIR images using the lesion prediction algorithm [27] of the lesion segmentation toolbox (http://www.statistical-modeling.de/1st/html) for SPM12. We used an in-house Matlab code to further partition the WMH into three categories, as proposed by Kim et al. [24]. First, the ventricle was segmented from the subjects’ T1-weighted image. Next, a distance map of each WMH voxel from the ventricle was calculated. Then, each WMH voxel was assigned to one of the following three categories depending on their distance from the ventricle: JVWMH were defined as areas that are less than 4 voxels away from the ventricle; PVWMH as areas that are between 4 and 13 voxels away from the ventricle; and DWMH as areas that are more than 13 voxels away from the ventricle.

Korean frailty index (KFI)

The KFI was established to provide a simple scale for frailty screening in older Korean adults. The KFI was developed in 2010 by an expert panel of geriatricians including an internist, an epidemiologist, and four family physicians. The index was initially validated using the Cardiovascular Health Study frailty scale, which is used as the gold standard [28]. The KFI covers various domains of geriatric assessment, with eight items including a history of hospitalization, self-reported health status, polypharmacy, weight loss, depressive symptoms, incontinence, sensory problem, and the TUG test (time up and go test). For hospitalization, a history of one or more admissions to any kind of hospital received one point. For self-reported health status, answering “poor” to the question “How do you think your current health status is?” received one point. For weight loss, answering “yes” to the question “Have you experienced weight loss in the past year to the extent that your clothes fit loosely?” received one point. For polypharmacy, taking four or more medications regularly received one point. For depressive mood, answering “sometimes,” “mostly,” or “always” to the question “Have you experienced sadness or depressed mood during the previous 1 month?” received one point. For incontinence, answering “sometimes,” “mostly,” or “always” to the question “Have you experienced incontinence of urine or feces in the previous 1 month?” received one point. For visual or auditory problems, answering “yes” to the question “Do you have any problems with decreased visual acuity or difficulties with hearing in daily life?” received one point. If the TUG test took more than 10 s, the participants received one point for decreased physical performance. Prefrailty was defined by scores of 3 and 4 and frailty by the score of 5 and higher [29].

Other measures

Determining APOE genotype was performed by one stage PCR from the venous blood according to the methods described by Wenham et al. [30]. The APOE genotype was determined for all the subjects. Depressive symptoms were assessed using the Short From Geriatric Depression Scale (sGDS-K; range, 0–15; increasingly worse) [31], and comorbidity status was assessed using Charlson’s comorbidity index (CCI, increasingly worse) [32].

Statistical analysis

The frailty phenotypes were compared with respect to demographic and clinical variables. Continuous variables were analyzed using analysis of variance (ANOVA), and chi-squared tests were used to examine differences in categorical variables. The WMH volume (including JVWMH, PVWMH, and DWMH) was log-transformed before analysis because of non-normal distribution. To evaluate the independent association between frailty and WMH volumes, linear regression analysis was performed, using significant variables such as age, APOE ɛ4 allele status, CCI, total hippocampal volume, CDR-SOB, sGDS-K, and estimated intracranial volume (eICV) as covariables. Furthermore, total WMH volume, as well as subclassified WMH volumes were compared among the frailty phenotypes (frail, pre-frail, and robust groups) by using a one-way ANOVA and logistic regression analysis, and WMH volume were compared between the frail and robust groups and between the pre-frail and robust groups adjusted by covariate variables. Differences were considered significant if the p-value was less than 0.05. All statistical analyses were performed using IBM SPSS version 24.0.

RESULTS

Demographic characteristics of subjects

Of the 144 subjects who completed the MRI scanning protocol and KFI, 34 subjects (23.6%) were classified as frail (five or more frail components), 47 subjects (32.6%) were classified as prefrail (three or four frail components), and 63 subjects (43.8%) were classified as robust (one or two frail components). The mean KFI score was 3.0±1.8. The baseline demographics and clinical characteristics of the subjects are shown in Table 1. The mean±SD age of subjects was 80.7±6.6 years and 77.1%were female. The frail group was older and had higher CCI and sGDS-K scores than the pre-frail and robust groups. These variables were considered as covariates before performing regression analysis.

Table 1

Characteristics of the subjects

	Frail	Prefrail	Robust	Total	p
Number (n, %)	34 (23.6)	47 (32.6)	63 (43.8)	144 (100)
Age (y, SD)	83.4±5.7	81.0±5.4	79.1±7.5	80.7±6.6	0.007^*
Female (n, %)	26 (76.5)	38 (80.9)	47 (74.6)	111 (77.1)	0.74
Education (y)	4.7±5.3	4.2±4.0	5.3±5.0	4.8±4.8	0.45
APOE ɛ4 allele (n, %)	8 (24.2)	15 (34.9)	34 (54.8)	57 (41.3)	0.009^*
MMSE-KC	16.6±6.3	16.0±6.2	17.3±5.2	16.7±5.8	0.49
CDR-SOB	5.4±2.6	5.0±2.6	4.8±1.7	5.0±2.2	0.51
sGDS-K	9.6±3.7	7.5±4.4	4.3±3.8	6.6±4.5	< 0.001^†
Hypertension (n, %)	23 (27.6)	28 (59.6)	32 (50.8)	83 (57.6)	0.262
Diabetes mellitus (n, %)	11 (32.4)	12 (25.5)	7 (11.1)	30 (20.8)	0.031^*
Charlson comorbid index	4.9±1.6	4.6±1.2	4.2±1.1	4.5±1.3	0.026^*
Brain volumes
eICV (ml)	1,482.1±110.1	1,489.3±182.5	1,499.9±190.6	1,492.2±171.2	0.88
Hippocampus volume (ml)	5.2±0.7	5.1±0.9	5.1±0.8	5.1±0.8	0.94
Total WMH volume (ml)	25.3±22.1	20.2±17.0	15.2±14.2	19.2±17.7	0.022^*
1st quartile (n, %)	2 (5.9)	12 (25.5)	22 (34.9)
2nd quartile (n, %)	8 (23.5)	12 (25.5)	16 (25.4)
3rd quartile (n, %)	12 (35.3)	8 (17.0)	16 (25.4)
4th quartile (n, %)	12 (35.3)	15 (31.9)	9 (14.3)
KFI scores	5.5±1.3	3.5±0.5	1.3±0.7	3.0±1.8

MMSE-KC, Korean version of Mini-Mental State Examination; APOE, Apolipoprotein E; CDR-SOB, Clinical Dementia Rating scale sum of boxes score; sGDS-K, Korean version of the short form Geriatric Depression Scale; eICV, estimated intracranial volume; WMH, white matter hyperintensities; KFI, Korean Frailty Index. A chi-square comparison of 3 groups was performed for categorical data. A one-way ANOVA comparison of 3 groups was performed on continuous data. Data represent n (percentage), mean±SD. The p value on WMH volumes were performed after log transformed. ^* p < 0.05; ^† p < 0.001.

Quantitative association between the WMH and frailty

After performing log transformation of WMH volume, the frail group had a higher WMH volume than the robust group (p = 0.002, after post-hoc analysis of ANOVA). Furthermore, the distribution of frail subjects tended to increase as the WMH volume increased. In the third and fourth quartiles of WMH volume, the number of frail subjects was higher than that in the first and second quartiles of WMH volume (Table 1). These trends between frailty and WMH volume remained significant after the multivariate linear regression analysis model was performed. WMH volume was significantly associated with KFI after controlling for age, gender, education, APOE ɛ4 allele status, CCI, total hippocampal volume, CDR-SOB, sGDS-K, and (eICV) (beta = 0.274, p = 0.001), while hippocampal volume was not related to KFI (beta = 0.016, p = 0.843) (Table 2, Model 1). Additionally, considering that hypertension is not included as an individual factor in CCI, we performed an analysis including hypertension and diabetes mellitus as variables that were strongly related to WMH volume instead of CCI. At the results, a significant association between WMH volume and frailty was consistently found (beta = 0.263, p = 0.001) (Table 2, Model 2).

Table 2

Multivariate linear regression analysis of factors associated with Korean Frailty Index

	Factors	B	SE	beta	p
Model 1	Age	–0.017	0.025	–0.063	0.491
	Female	0.331	0.421	0.075	0.434
	Education	0.047	0.032	0.120	0.146
	CDR-SOB	0.060	0.060	0.070	0.321
	sGDS-K	0.220	0.031	0.530	<0.001^†
	APOE ɛ4 allele	–0.626	0.278	–0.166	0.026^*
	Charlson comorbid index	0.042	0.112	0.030	0.706
	Hippocampal volume	0.037	0.184	0.016	0.843
	eICV	0.000	0.001	–0.017	0.867
	WMH volume	1.123	0.333	0.274	0.001^*
Model 2	Age	–0.015	0.024	–0.054	0.542
	Female	0.308	0.420	0.070	0.464
	Education	0.048	0.031	0.123	0.133
	CDR-SOB	0.078	0.060	0.092	0.190
	sGDS-K	0.210	0.031	0.507	<0.001^†
	APOE ɛ4 allele	–0.590	0.276	–0.157	0.035^*
	Hypertension	0.310	0.273	0.083	0.259
	Diabetes mellitus	0.468	0.317	0.103	0.142
	Hippocampal volume	0.007	0.187	0.003	0.971
	eICV	0.000	0.001	–0.017	0.864
	WMH volume	1.007	0.325	0.263	0.001^*

CDR-SOB, Clinical Dementia Rating scale sum of boxes score; sGDS-K, Korean version of the short form Geriatric Depression Scale; APOE, Apolipoprotein E; eICV, estimated intracranial volume; WMH, white matter hyperintensities. WMH volumes were log transformed before performing linear regression. B, regression coefficient; SE, standard error of B; beta, standardized regression coefficient. ^** p < 0.05, ^† p < 0.001.

Association between the subclassified WMH and frailty

According to the new subclassification of WMH, there was a group difference in the volume of WMH. The frail group showed a higher total WMH, JVWMH, and PVWMH volume than the robust groups (p = 0.003, p = 0.004, and p = 0.005 respectively, after post-hoc ANOVA) (Table 3). There was no group difference in WMH volumes between the pre-frail and robust groups. In the multinomial logistic regression analysis model, using the robust group as a reference, total WMH volume (OR = 6.297, p = 0.013, 95%CI = 1.463–27.114), JVWMH volume (OR = 12.955, p = 0.014, 95%CI = 1.687–99.509), and PVWMH (OR = 3.382, p = 0.025, 95%CI = 1.163–9.8531) were associated with frailty (Table 4). JVWMH volume was only related to pre-frailty (OR = 5.174, p = 0.049, 95%CI = 1.008–26.562).

Table 3

The difference of WMH volumes among frail, prefrail and robust group

	Frail ^a	Prefrail ^b	Robust ^c	p ^†	Post hoc ^‡
WMH volume (ml)
Total WMH	25.3±22.1	20.2±17.0	15.2±14.2	0.003^*	a > c
JVWMH	10.2±5.6	9.2±5.6	7.0±4.5	0.004^*	a > c
PVWMH	14.1±14.4	10.5±11.1	7.8±9.3	0.005^*	a > c
DWMH	1.8±3.7	1.2±1.9	0.9±1.5	0.13	a = b = c

WMH, white matter hyperintensities; JVWMH, juxtaventricular white matter hyperintensities; PVWMH, periventricular white matter hyperintensities; DWMH, deep white matter hyperintensities. ^†ANOVA, ^‡ Bonferroni post hoc comparison. Brain volumes are in ml and are represented as mean±SD. WMH volumes were log transformed before performing ANOVA. ^* p < 0.05.

Table 4

Multinominal logistic regression analysis of total and subclassified volumes associated with prefrailty and frailty

	Frail versus Robust					Prefrail versus Robust
	B	SE	OR	p	95%CI	B	SE	OR	p	95%CI
Total WMH	1.840	0.745	6.297	0.013	1.463–27.114	0.868	0.577	2.382	0.13	0.768–7.387
JVWMH	2.561	1.040	12.955	0.014	1.687–99.509	1.644	0.835	5.174	0.049	1.008–26.562
PVWMH	1.218	0.544	3.382	0.025	1.163–9.831	0.417	0.424	1.517	0.33	0.661–3.481
DWMH	0.658	0.411	1.932	0.11	0.846–4.320	0.272	0.338	1.313	0.42	0.677–2.544

WMH, white matter hyperintensities; JVWMH, juxtaventricular white matter hyperintensities; PVWMH, periventricular white matter hyperintensities; DWMH, deep white matter hyperintensities. Multinominal logistic regression analysis with the dependent nominal variable of 3 levels (frail, prevail, and robust) and adjustment for age, gender, years of education, CDR-SOB, Korean version of the short from geriatric depression scale, Charlson comorbid index, apolipoprotein E ɛ4 allele status, hippocampal volume and estimated intracranial volume. WMH volumes were log transformed before performing regression analyses. B, regression coefficient; SE, standard error of B; OR, odds ratio; 95%CI, 95%confidential interval.

DISCUSSION

Frailty will be one of the most serious global public health problems in the coming century. The rapid expansion of the aging population has brought a concomitant rise in the number of older adults with frailty [33], which in turn places an increased burden on the healthcare system worldwide [34]. Therefore, effective strategies that target the prevention and management of frailty in an aging population could reduce the burden on both the individual and the health system. According to the definition of frailty based on the KFI, the frequency of frailty was 23.6%among patients with AD in this study. It means that, about a quarter of very mild to moderate AD were suffering from frailty. This frequency was approximately three times higher than the 8.1%result from 2,886 community-dwelling residents in the Korean Frailty Aging Cohort study [28]. Although it is difficult to compare the prevalence of frailty in AD due to different definitions of frailty across studies, a recent meta-analysis reported that the prevalence of frailty ranged 11.1%to 50.0%and the pooled prevalence was 31.9%in AD [35]. Based on these studies, we expected that the frequency of frailty would be more prevalent in patients with AD than in community-dwelling residents. These findings suggest that frailty could potentially be targeted by interventions to reduce related adverse outcomes in frail individuals not only in the general population but also especially in AD.

There have been a few studies on the relationship between WMH and frailty in patients with AD. Furthermore, these studies had methodological limitations in using visual analogue scales for the evaluation of WMH. To the best of our knowledge, this is the first study to investigate the association between WMH volume and frailty in AD via an accurate methodology including quantitative volumetric measurement of WMH volume with adjustment for intracranial volume.

As a result, we established WMH volume as a potential risk factor for frailty. WMH volume was independently associated with greater frailty as measured by the KFI. As the WMH volume was log-transformed because of its non-normal distribution, the observed ORs were high and had wide 95% CIs. The findings of OR should be interpreted after a reverse log transformation of WMH volumes. The OR of frailty associated with total log WMH volume was 6.297, which indicated that the risk of frailty increased 6.297 times for every 1 unit increase in log WMH volume. Considering log-transformed WMH volumes, the OR of 6.297 can be interpreted as a 6.297-times increased risk of frailty for every 10 times increase in WMH volume (e.g., 0.1 ml ⟶ 1 ml, 1 ml ⟶ 10 ml, 10 ml ⟶ 100 ml).

Previous cross-sectional studies showed inconsistent associations between frailty and WMH volume, with some showing a negative association [22, 23], while others showed a positive association [36, 37]. These discrepancies may have been due to different samples, different definitions of frailty, and different measurement methods of WMH. Using a similar methodology despite different samples, previous studies reported that frail individuals had significantly higher WMH volume than robust and pre-frail older adults [38 –43]. These findings, combined with our results, suggest that WMH volume could in fact increase frailty severity over time in patients with AD as well as in older adults generally.

Furthermore, we observed associations between subclassified WMH volumes defined by the distance from the ventricle and frailty. This study follows Kim et al.’s new classification of WMH [24] defined by the difference in etiology, histopathology, functional correlates, and imaging methodologies. They suggested the term JVWMH, which is a part of the non-ischemic WMH lesions, located within 3 mm from the ventricular surface and directly contiguous to the ventricular surface, since they likely result from CSF leakage. In this study, DWMH volume was not associated with frailty and prefrailty. JVWMH volume, PVWMH volume, and total WMH volume were significantly associated with frailty and only JVWMH volume was linked to pre-frailty. It is postulated that the different clinical values of subclassified WMH volume may result from the order of development and the relative percentage of subclassified WMH. When WMH volume was divided into quartiles, the relative percentage of JVWMH volume to total WMH volume decreased gradually with the increasing relative percentage of PVWMH volume from the first quartile to the fourth quartile (first quartile: JVWMH 80.2%, PVWMH 27.2%, DWMH 5.3%, second quartile: JVWMH 60.7%, PVWMH 39.2%, DWMH 6.3%, third quartile: JVWMH 50.5%, PVWMH 48.3%, DWMH 4.6%, fourth quartile: JVWMH 36.1%, PVWMH 59.9%, DWMH 6.3%), suggesting that PVWMH developed subsequently to the development of JVWMH but DWMH developed early and kept a constant relative percentage. Putting together the above results, PVWMH volume was not sufficient to have an effect in the prefrail group and DWMH volume was too small to influence overall frailty.

When we performed additional analysis to examine whether brain atrophy or hippocampal atrophy was associated with frailty, there were no associations with frailty. Frailty in patients with AD was associated with increased WMH volume, not brain atrophy and hippocampal atrophy, which are well-known core pathologies of AD. One study showed a significant relationship between physical frailty and gray matter volumes in the cerebellum, hippocampus, and several other cerebral regions. However, this study was methodologically different from our study in that the subjects of the study were recruited from community-dwelling adults and the large age differences between the frail, pre-frail, and robust groups were not adjusted for anatomical brain region volumes. Another similar study reported that reduced gray matter volume in the hippocampus was associated only with slowness, which is one component of frailty, and not with physical frailty per se, in community-dwelling adults.

The present study had some limitations. Our samples were obtained from a clinical population diagnosed with AD, which may have resulted in selection bias, and it cannot represent the general population. This is a cross-sectional study; hence, it does not allow for the conclusion of directionality, with the potential that WMH may be a marker or a result of an accumulation of deficits. Our path analyses did not reveal their direct causative sequence, although it might be helpful to understand their complex relationships. In addition, we were unable to consider other brain pathologies, such as arteriolosclerosis, atherosclerosis or amyloid burden, which that have been examined in prior autopsy studies. Further prospective longitudinal studies are needed to determine the causal relationship between WMH and frailty.

CONCLUSION AND IMPLICATIONS

About a quarter of very mild to moderate AD were suffering from frailty. Our study provides evidence of a cross-sectional association between WMH volume and frailty in patients with AD. Furthermore, we suggest the different clinical values in the relationship between subclassified WMH volume and frailty. Further research with a longitudinal design and on a pathological basis would strengthen the evidence for a relationship between WMH volume, subclassified WMH volume and frailty, and assist in ascertaining the direction of these associations.

Footnotes

ACKNOWLEDGMENTS

This study was supported by a research grant from Jeju National University Hospital in 2018 (Grant No. JNUH 2018-11-013).

Author’s disclosures available online ().

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

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