Prevalence and Associations of Cognitive Impairment in Adult Patients with Moyamoya Disease: A Systematic Review and Meta-Analysis

Abstract

Background:

Cognitive impairment, and in the long term Alzheimer’s disease, vascular, or mixed dementia, are potential complications of moyamoya disease (MMD), of which the prevalence and associations are not well established.

Objective:

We performed a systematic review and meta-analysis to investigate the prevalence of cognitive impairment in adult patients with MMD as well as its clinical and demographic correlates.

Methods:

We performed a systematic search of four electronic databases: PubMed (MEDLINE), EMBASE, Scopus, and Cochrane Library, profiling studies from inception until 7 May 2023. Clinical data consisting of population characteristics, comorbidities, cognitive assessment tools used, and prevalence of cognitive impairment was extracted.

Results:

Seventeen studies were included in the meta-analysis, with a total study population of 1,190 patients. All studies assessed cognition, and the overall prevalence of cognitive impairment in MMD patients was 54.59%. A subgroup analysis identified that the prevalence of executive dysfunction in MMD patients was 31.55%. We performed a meta-regression analysis which identified that cognitive impairment was not associated with age, education level, or a history of ischemic or hemorrhagic stroke.

Conclusions:

A substantial proportion of MMD patients have cognitive impairment, and cognitive impairment was found to have no association with a history of stroke. Further research is necessary to investigate the longitudinal relationship of MMD and cognitive impairment, and the impact of bypass surgery on cognitive impairment.

Keywords

Alzheimer’s disease cognitive impairment moyamoya disease prevalence

INTRODUCTION

Moyamoya disease (MMD) is a chronic vaso-occlusive disease involving stenoses at the terminal region of the internal carotid artery, middle cerebral artery, and/or proximal anterior cerebral artery, resulting in compensatory formation of collateral blood vessels with a smoke-like appearance on digital subtraction angiography [1]. It is a disease with higher incidence in certain ethnic groups, namely East Asians, including the Japanese, Koreans, and Chinese [2 –4]. The clinical presentation of MMD is varied and may present with ischemia from the stenoses or thromboembolic phenomena, or with hemorrhagic symptoms from rupture of the weak collateral blood vessels [2, 5].

Patients with MMD have a higher risk of both hemorrhagic and ischemic stroke [6], and this may inherently predispose to post-stroke cognitive impairment, and in the long term development of Alzheimer’s disease, vascular, or mixed dementia [7]. Prior studies have suggested that of the various cognitive domains, executive function is most commonly affected in adult patients with MMD [8]. However, cognitive impairment has been observed in the absence of stroke, and therefore the mechanism of cognitive impairment in MMD in such patients is unclear [9]. One theory posits that long-term cerebral hypoperfusion affects white matter microstructure and connectivity, while another theory posits that ischemia happens commonly in arterial border zones for MMD patients, which can lead to cognitive impairment even in the absence of stroke [10].

While prior studies have characterized the prevalence of cognitive impairment in adults and children with MMD, significant clinical associations and the general prevalence of cognitive impairment in MMD remain unknown [11]. As such, we aimed to perform a comprehensive systematic review and meta-analysis to characterize the prevalence, clinical associations and cognitive features of cognitive impairment in adult MMD patients.

METHODS

A systematic review and meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [12]. A clear protocol describing the aims and processes was established prior to the start of the study in consensus with all authors, and registered on PROSPERO (registration number: CRD42023432620).

Search methods and selection criteria

A comprehensive literature search was performed on four electronic databases: PubMed (MEDLINE), EMBASE, Scopus, and Cochrane library, from inception until 7 May 2023. The complete search strategy is available in Supplementary Table 1.

The inclusion and exclusion criteria for the studies are listed in Table 1. As each study had defined cognitive impairment differently, it was not possible to enforce a standardized definition of cognitive impairment across all studies. Due to differences in each study’s exclusion criteria, it was also not possible to ensure that all studies excluded patients with confounding conditions such as neuropsychiatric disease. However, all the included studies had used validated cognitive screening or assessment tools.

Table 1

PECOS, inclusion criteria and exclusion criteria applied to database search

PECOS	Inclusion criteria	Exclusion criteria
Population	-Patients with moyamoya disease	Patients less than 18 years old
Exposure	-Patients with moyamoya disease who display or later developcognitive impairment
Comparison
Outcome	-Reporting of data on at least one validated measure of cognitiveimpairment including cognitive screening tools, or the diagnosisof a cognitive syndrome according to established criteria
Study design	-Studies published in English	-Meta-analysis
	-Cross-sectional studies	-Systematic reviews
	-Cohort studies	-Literature reviews
	-Case-control studies	-Conference Abstracts
	-Case series with 10 or more patients	-Case reports
	-Databases: PubMed, Embase, Cochrane,	-Case series with < 10 patients
	Scopus	-Posters

The initial search results were then reviewed as follows. Duplicate studies were removed. Two reviewers (KZXT, MYK) then independently screened the abstracts based on the inclusion and exclusion criteria. For studies that had overlapping patient samples, the study with the greater number of patients was included. The full text of the abstracts which passed the screening was then retrieved, and these full texts were independently reviewed by both reviewers to arrive at a final decision about inclusion or exclusion. Any disagreements were resolved bydiscussion.

Data extraction

Data was independently extracted from the included studies by two reviewers (KZXT, MYK) on a standardized data extraction form. The data extracted were study year; study design; size of cohort; specific patient characteristics, namely age, sex, education level, medical comorbidities, MMD clinical characteristics, operative status and history of stroke, in addition to the main outcomes of prevalence and subtype of cognitive impairment, type of cognitive assessment tool used, and the diagnostic criteria for cognitive impairment that had been used. For studies which reported cognition at multiple timepoints, for example pre-operative and post-operative cognition, we extracted the data corresponding to the pre-operative timepoint for statistical analysis.

Statistical analysis

In the primary analysis, we estimated the pooled prevalence of cognitive impairment, and calculated 95% confidence intervals for these estimates. Meta-analysis of proportions was performed with a one-step approach using a generalized linear mixed model (GLMM) with the Logit transformation. No continuity correction was applied. Heterogeneity between studies was accounted for using a random effects model. Heterogeneity was quantified with the I² statistic, with ranges of < 30%, 30–60%, and > 60% indicating low, moderate and high levels of heterogeneity, respectively. To explore sources of heterogeneity, we performed pre-specified subgroup analyses by geographical region, cognitive domain evaluated, and type of cognitive measures used, wherever a minimum of two studies per subgroup was available. In the secondary post-hoc analysis, we investigated the predictors of cognitive impairment among MMD patients in the studies; covariates of interest were mean patient age, education level, and prior history of stroke. Meta-regression analysis was performed, utilizing a mixed effects model. The regression coefficient was used to estimate the effect of the predictor on the prevalence of cognitive impairment, which was visualized in a bubble plot; the corresponding p-value and confidence intervals were used to determine the significance of the relationship. All statistical analyses were performed using the meta package in R statistical software (version 4.2.1; R Core Team 2022). Analyses were performed in accordance with the general approach laid out by the Cochrane Handbook [13].

Risk of bias and quality assessment

Study quality and risk of bias was then independently assessed by two reviewers using the Newcastle-Ottawa Scale (NOS). Studies were graded as having a high (< 5 score), moderate (5–7 score), or low (8–9 score) risk of bias. This assessment was summarized in Supplementary Table 2. Overall, most studies were assessed to have a low or unclear risk of bias.

RESULTS

Systematic search

The search yielded a total of 2,075 records, from which 925 duplicates were removed. Following screening of the title and abstract, 1,088 articles were excluded. Of the 62 full texts assessed for eligibility, another 45 articles were excluded. Finally, 17 studies [10 , 14–29] were included in this meta-analysis. The PRISMA flowchart is presented in Fig. 1.

Fig. 1

PRISMA diagram.

Study characteristics

A total of 1,190 patients were included across 17 studies, of which 14 were prospective studies and 3 were retrospective analyses. The population characteristics of the included studies are summarized in Table 2. The number of years spent in education was reported by 10 studies. The mean number of years of education ranged from 8.1 to 14.2 years. Across the 17 studies, the 4 most commonly used cognitive screening and assessment tools were the Trail Making Test Parts A and B (TMT-A/B) (TMT-A: n = 6; TMT-B: n = 7), Wechsler Adult Intelligence Scale, Third Edition (WAIS-III) (n = 6), Mini-Mental State Examination (MMSE) (n = 5), and Montreal Cognitive Assessment (MoCA) (n = 3) (Supplementary Table 3). TMT-B was reported in absence of TMT-A by one study as the study focused specifically on executive dysfunction [21]. Other cognitive screening tools used included other specific neuropsychological tests, which are described in Supplementary Table 3.

Table 2

Baseline characteristics of the included studies

Study	Female, n (%)	Mean age, y	Education level, y	Ethnicity	Stroke history, n (%)	Total patients with MMD, n	Prevalence of cognitive impairment in MMD, %	Cognitive assessment tools used^∧	Definition of cognitive impairment
Bao 2022 [14]	133 (42.5)	46.0*	11.4*	NR	113 (36.1)	313	39.0	MoCA, MMSE	NR
Calviere 2012 [15]	9 (64.3)	36.6±12.9	NR	Caucasian (85.7%), African-West-Indian (14.3%)	8 (57.1)	13	53.8	Neuropsychological tests	Z-score ≥1.7 SDs below normative mean DCS:≥3 tests of executive function or attention impaired
Chan 2023 [10]	49 (80.33)	32	12.78±2.98	Caucasian (49%), Asian (30%), Mixed (7%), Black African Caribbean (5%), Unknown (10%)	24 (39.0)	61	78.7	Neuropsychological tests	Scoring ≤5th percentile on any one test
Festa 2010 [16]	18 (62.1)	39.9±11.2	13.89±2.74	Caucasian (59%), African-American (10%), Hispanic (10%),Asian (21%)	22 (75.9)	29	69.0	Neuropsychological tests	≥2 of 6 cognitive domains > 1.5 SDs below normative mean or ≥1 of 6 domains > 2SD below normative mean
Haas 2022 [17]	50 (74.6)	38.0±43.9	NR	Caucasian (91.0%),Asian (9.0%)	59 (88.1)	67	41.8	Neuropsychological tests	DCS:≥2 abnormal test results
Karzmark 2012 [18]	NR	NR	NR	NR	0 (0.0)	30	23.3	Neuropsychological tests	At least half of test scores ≥1 SD below the mean
Karzmark 2008 [19]	24 (66.7)	36.6±9.9	14.2±1.8	White (75%),Asian (16.7%), African-American (8.33%)	NR	36	30.6	Neuropsychological tests	At least half of test scores ≥1 SD below the mean
Kronenburg 2023 [20]	14 (78.0)	37±27.4	NR	NR	11 (61.1)	18	72.2	Neuropsychological tests	Mean domain z-score < –1.5 SD of the population mean
Lei 2017 [21]	6 (42.9)	37.43±7.7	8.14±4.67	Chinese (100%)	NR	13	23.1	Neuropsychological tests	Executive dysfunction: Scoring 1.5 SDs away from the normative means of both TMT-B and MES-EX.
Lei 2014 [22]	12 (52.2)	40.2±9.5	8.3±5.0	Chinese (100%)	0 (0.0)	23	47.8	MMSE	VCI: 1.5 SDs below the mean of the matched NC group, with correlated vascular disease on neuroimaging.
Lei 2020 [23]	29 (56.9)	41.2±9.1	9.2±4.3	Chinese (100%)	0 (0.0)	51	52.9	MMSE	VCI:1.5 SDs below the mean score of the matched NC group
Liu 2019 [24]	66 (55.0)	43.32±9.83	86.7% <high school education	NR	NR	120	65.0	MMSE	MMSE score < 27
Nakamizo 2014 [25]	25 (80.6)	32.6±10.6	NR	Japanese (100%)	7 (22.6)	31	45.2	MMSE (descriptive), FAB (outcomes)	Executive dysfunction: FAB score ≤16
Shen 2022 [26]	58 (50.4)	39.29±11.82	12.94±2.80	NR	0 (0.0)	115	67.0	MoCA (outcomes), Neuropsychological tests (descriptive)	MoCA score < 26 or < 25 (latter if ≤12 y education)
Sun 2022 [27]	45 (48.9)	37.65±8.65	13.4±3.0	NR	NR	92	31.5	Neuropsychological tests	Z-score>1.7 SD below normative mean on a test, Cognitive dysfunction: cognitive impairment on ≥3 neuropsychological tests.
Su 2013 [28]	12 (46.2)	43.7±8.6	34.62% ≥ college, 12 46.15% middle school, 5 19.23% ≤ primary school	NR	26 (100.0)	26	100.0	MoCA	MoCA score < 25
Weng 2023 [29]	74 (48.7)	44.7±11.7	9.5±4.4	Chinese (100%)	NR	152	65.8	Neuropsychological tests, including MMSE	> 1.5 SDs below normative mean on the neuropsychological scoring scale or > 1.5 SDs above the normative mean on the neuropsychological timing scale

DCS, Dysexecutive cognitive syndrome; FAB, Frontal assessment battery; MES-EX, Memory and Executive Screening – Executive function; MMD, moyamoya disease, MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; NC, normal control; NR, not reported; SD, standard deviation; TMT, Trail Making Test; VCI, vascular cognitive impairment. *Calculated value. ^∧A detailed breakdown of the specific neuropsychological tests is available in the Supplementary Material.

Prevalence of cognitive impairment in MMD patients

All studies reported prevalence of cognitive impairment in MMD patients. The pooled proportion of cognitive impairment in all MMD patients was 54.59% [95% CI: 43.23–65.49, I² = 85.10%] (Fig. 2). The high degree of heterogeneity was not unexpected given the variety of cognitive assessment tools used.

Fig. 2

Pooled prevalence rates of cognitive impairment among patients with moyamoya disease.

A subgroup analysis was performed with studies grouped by the specific cognitive assessment tool used (Fig. 3). For studies using domain-specific neuropsychological tests, the pooled proportion of cognitive impairment was 46.85% [95% CI: 33.94–60.19, I² = 82.70%]. Cognitive screening tools were analyzed separately. For MMSE, the pooled proportion of cognitive impairment was 58.88% [95% CI: 48.97–68.12, I² = 45.60%]; lastly, for MoCA, the pooled proportion of cognitive impairment was 95.15% [95% CI: 15.85–99.95, I² = 0.0%].

Fig. 3

Pooled prevalence rates of cognitive impairment, grouped by cognitive assessment tool used.

A subgroup analysis was performed with studies grouped by center location, classified into Asian [n = 10] and Western countries [n = 7] (Fig. 4). The pooled proportion of cognitive impairment of MMD patients in studies performed in Asia was 55.79% [95% CI: 40.20–70.31, I² = 86.80%] and the pooled proportion of cognitive impairment in studies performed in Western countries was 52.96% [95% CI: 36.39–68.90, I² = 84.60%].

Fig. 4

Pooled prevalence rates of cognitive impairment, grouped by center location.

Prevalence of executive dysfunction in MMD patients

Prevalence of executive dysfunction in MMD patients was reported by 7 studies. The pooled proportion of executive dysfunction in MMD patients was 31.55% [95% CI: 17.51–50.04, I² = 74.30%] (Fig. 5). The prevalence of other domains of cognitive impairment were not reported by any studies, and studies labelled under ‘Others’ (Fig. 5) include a mix of cognitive domains aside from executive dysfunction.

Fig. 5

Pooled prevalence rates of executive function. *These studies reported the break-down of executive dysfunction and other cognitive domains and were included separately in both subgroups for this analysis.

Meta-regression analysis for demographic and medical covariates

Meta-regression analyses were performed for the prevalence of cognitive impairment against age, mean number of years of education, and a history of stroke (Table 3). The analyses showed a non-significant correlation between the 3 variables and cognitive impairment in MMD patients (Supplementary Figures 1 to 3).

Table 3

Meta-regression for prevalence of cognitive impairment in MMD patients against covariates

Covariate	No. of studies	Coefficient (95% CI)	p
Age	16	0.040 (–0.073 to 0.154)	0.487
Education level	10	0.047 (–0.173 to 0.267)	0.675
Stroke history	12	1.487 (–0.061 to 3.034)	0.060

CI, Confidence interval.

DISCUSSION

Our meta-analysis of 17 studies with a combined sample of 1,190 patients found that current literature reports a substantial prevalence of cognitive impairment of 54.59% in adult patients with MMD, albeit there is significant heterogeneity in the definition of cognitive impairment used across the included studies. This value is higher than reported by a prior meta-analysis of 153 adult patients with MMD conducted in 2018 [11]. Our meta-analysis updates this prior finding and includes a larger sample of patients, and therefore represents current evidence.

Subgroup meta-analysis of our included studies also found that MMD is frequently associated with executive dysfunction, with a prevalence of 31.55%, demonstrated by studies suggesting a relation between diminished perfusion in the anterior circulation and executive dysfunction [15 , 30–32]. This prevalence is lower than reported by individual studies [10 , 25]. One possible reason for this is that several studies included in this meta-analysis did not utilize cognitive tools specific for screening executive function, resulting in under-reporting of executive dysfunction. Therefore, the true prevalence of executive dysfunction is likely to be higher, and we were unable to investigate this fully given the heterogeneity in cognitive screening tools applied to the limited cohort of patients, given the rarity of MMD. This may be addressed in further studies via the use of cognitive screening tools that are more sensitive to executive dysfunction, such as the MoCA [33].

There was no significant association demonstrated between cognitive impairment and age or education level in our included studies, two well-established risk factors for cognitive impairment [34 –39]. Therefore, the pathogenesis of cognitive impairment in MMD may be unlike that of cognitive impairment in normal aging, or in Alzheimer’s disease [40, 41]. Several studies have indicated that age of onset of MMD and symptom duration were correlated with the prevalence of cognitive impairment [42, 43]. However, we were unable to investigate this correlation due to a lack of data on age of onset and symptom duration in the included studies. Cognitive impairment in MMD was also not associated with a history of ischemic or hemorrhagic stroke, suggesting a different mechanism from other vascular etiologies of cognitive impairment such as vascular dementia. This is in line with prior imaging studies which have suggested that cognitive impairment in MMD may be secondary to long-term cerebral hypoperfusion preferentially affecting white matter microstructure and connectivity, or due to ischemia in arterial border zones [10 , 44]. Prior longitudinal studies have also shown that cognitive impairment in MMD was largely stable over a relatively long period of time in the absence of new vascular events or revascularization surgery in adulthood, after a decline in the initial pediatric years of the disease [10, 45]. In view of this pattern of disease progression, future analyses of cognitive impairment in MMD should aim to analyze pediatric and adult patients separately, and longitudinal analyses should consider the age of first presentation to better delineate disease progression and development of cognitive impairment.

Limitations

We acknowledge several limitations to this study. Firstly, there was significant heterogeneity in the cognitive assessment tests used and the definition of cognitive impairment in each study. Several studies used cognitive screening tools such as the MMSE or MoCA, whereas other studies used comprehensive neuropsychological assessment tools. We ensured that despite this heterogeneity, all included studies used validated assessments of cognition. In addition, subgroup analysis was performed, separating studies by assessment modality, namely MMSE only, MoCA only and neuropsychological assessment only, to better define cognitive impairment for the purposes of this meta-analysis. Secondly, no studies presented the prevalence of dementia, and only presented the prevalence of cognitive impairment. Based on the differing definitions of cognitive impairment adopted by the included studies, the patients included in our study comprise a heterogeneous subgroup. Therefore, we were unable to stratify and investigate patients by severity of their cognitive impairment. Thirdly, studies included were mostly cross-sectional in nature and therefore did not account for the longitudinal aspect of cognitive decline in MMD patients. Lastly, there was limited reporting of treatment modalities used, both medical and/or surgical, which would have been useful to assess contributory and preventive factors for managing cognitive impairment in MMD patients.

Conclusion

We found that a substantial proportion of MMD patients had cognitive impairment. We also demonstrated that cognitive impairment in MMD patients was not associated with age, education level, or a history of stroke. As this conclusion is based on the reports of the included studies, further research is necessary to investigate the longitudinal relationship of MMD and cognitive impairment, as well as the impact of treatment such as extracranial-intracranial bypass surgery on cognitive impairment.

Footnotes

ACKNOWLEDGMENTS

The authors have no acknowledgments to report.

FUNDING

C.H.S. was supported by the National University of Singapore Yong Loo Lin School of Medicine’s Junior Academic Fellowship Scheme, the Singapore Ministry of Health’s National Medical Research Council under its Clinician Scientist Individual Research Grant New Investigator Grant (NMRC/MOH-001080) and the Transition Award (NMRC/TA23jan-0009).

Y.L.L. was supported by the Transition Award (NMRC/TA/MOH-00048) and (NUHS-NHIC MT 2020-03) and NMRC RTF seed fund (MOH-001281).

CONFLICT OF INTEREST

The authors have no conflicts of interest to report.

DATA AVAILABILITY

The data supporting the findings of this study are available within the article and/or its supplementary material.

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

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