Slow Gait Speed Predicts Incident Dementia,Mortality,and Long-Term Functional Outcome in Cerebral Small-Vessel Disease

Abstract

Background:

Gait impairment is observed in patients with small vessel disease (SVD); however, the association between gait function and long-term outcome remains unclear.

Objectives:

This study aimed to clarify the predictive value of gait function on incident dementia, survival and functional outcome.

Methods:

Data were derived from a Japanese cohort of patients with SVD. This study included 522 participants who underwent 3-m timed up and go test (TUG), and gait speed, TUG time, was divided into tertiles. Magnetic resonance imaging was used to evaluate severity of white matter hyperintensities, lacunes, and medial temporal atrophy. Primary outcome was dementia. All-cause death and functional outcome by modified Rankin scale at the last visit was also evaluated.

Results:

The median age was 71 years, and median TUG time was 9.91 s. During follow-up period of 4.8 years, 32 cases of dementia occurred. Cox proportional hazard analysis revealed that slow gait speed (TUG time > 10.88 s) was associated with a significantly higher risk of incident dementia than fast (TUG time < 9.03) and middle (TUG time, 9.04–10.87 s) speeds after adjusting risk factors, Mini-Mental State Examination, SVD severity and brain atrophy (adjusted hazard ratio, 2.73; 95% confidence interval, 1.16–6.42, p = 0.022). Slow speed was also associated with mortality and poor functional outcome compared with other speeds (adjusted odds ratio, 4.19; 95% confidence interval 1.92–9.18, p < 0.001).

Conclusions:

Gait function was associated with incident dementia, mortality and poor functional outcome independently of cognitive function, brain atrophy, and SVD severity.

Keywords

Alzheimer’s disease dementia functional outcome gait speed small vessel disease

INTRODUCTION

In addition to lacunar stroke, intracerebral hemorrhage, and vascular dementia, cerebral small-vessel disease (SVD) shows insidious clinical signs such as motor function decline, cognitive impairment, mood disorder, urinary incontinence, and dysphagia,1,2, 1,2 mainly due to network disruption. 3 Among several aspects of motor function, gait and balance function were found to be associated with SVD severity in a cross-sectional study.4,5, 4,5 Furthermore, a global association was found between gait disturbance and cognitive impairment in older adults,6 –8 middle-aged population, 9 and patients with cerebral SVD. 10 Gait disturbance or slow gait speed is a key element of cognitive frailty 11 and motoric cognitive risk syndrome, 12 which are risk factors for incident dementia or cognitive decline. In the general population, a slow gait speed or its decline was found to increase the risk of incident dementia or cognitive decline.13 –17 Furthermore, in several prospective studies of disability-free survival, gait impairment or slow gait speed could predict survival in older adults.18 –22 However, no studies have examined the involvement of gait speed on incident dementia, mortality, or poor long-term functional outcome considering baseline brain imaging findings and cognitive function, which are closely associated with dementia.

The 3-m timed up and go test (TUG) is a reliable gait and balance test developed for frail older individuals 23 and is widely used for patients with idiopathic normal pressure hydrocephalus. 24

In this study, we aimed to clarify the predictive value of gait function evaluation on incident dementia, survival and functional outcome independent of risk factors and SVD severity, medial temporal atrophy and global cognitive function.

METHODS

Study design and patients

Data were derived from a prospective study, conducted by the Tokyo Women’s Medical University Cerebral Vessel Disease (TWMU CVD) Registry (Registration-URL: https://www.umin.ac.jp/ctr/index.htm; UMIN000026671). Written informed consent was obtained from all participants. This study was approved by the Institutional Review Board of Tokyo Women’s Medical University (approval number 3621). The research protocol and inclusion criteria of the TWMU CVD registry were previously described in detail. 25 Briefly, this study consecutively included patients aged 40 years and older, who presented with cerebral vessel disease on magnetic resonance imaging (MRI) and with one or more cerebrovascular risk factors, such as arterial hypertension, diabetes mellitus, dyslipidemia, coronary artery disease, atrial fibrillation, or smoking. The exclusion criteria for the registry were any type of aphasia, evidence of dementia (Clinical Dementia Rating (CDR)≥1), and dependence on activities of daily living and walking. Each CDR score was based on interviews with the participants and someone familiar with them who served as a collateral source. Patients who experienced vascular events within 1 month of enrollment were excluded. The protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki in line with the Ethical Guidelines for Epidemiological Research by the Japanese government and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Between October 2015 and July 2019, 1011 outpatients were enrolled in TWMU CVD. MRI was generally performed to examine lesions in cases with a history of stroke or suspicious neurological symptoms (e.g., headache, vertigo, dizziness, numbness, syncope, or subjective memory impairment). In all consenting patients, gait function was assessed using the 3-m TUG. 23,24 TUG measures the time required to complete the six components of standing up from an armless chair, walking 3 m, turning 180°, walking back to the chair, turning 180° again, and sitting back on the chair as quickly as possible. They could do one trial to check whether they understood the instructions before the actual time was measured. The Mini-Mental State Examination (MMSE) was used in all patients to screen suspected cases of cognitive decline. To examine incident dementia and functional outcome, patients who refused testing for MMSE (N = 28) and had MMSE score < 24 (n = 77), and mRS≧2 (n = 46) were excluded. Finally, all analyses were based on 522 patients with complete baseline data (Fig. 1).

Fig.1

Flowchart of this study. MMSE, Mini-Mental State Examination; mRS, modified Rankin scale.

MRI protocol and assessment

Each participants underwent brain MRI within 1 year of entry into the registry. MRI assessment included white matter hyperintensities (WMH) consisting of periventricular hyperintensities (PVH), and deep white matter hyperintensities (DWMH), lacunar infarctions (LI) for SVD, and medial temporal atrophy (MTA) for brain atrophy. 26,27 The WMH severity was visually rated using axial fluid-attenuated inversion recovery (FLAIR) images. Based on the Fazekas scale 28 (0, none; 1, mild; 2, moderate; 3, severe), PVH and DWMH were scored as 0–3. Lesions in the basal ganglia, internal capsule, centrum semiovale, or brainstem, with hypointensity on T1-weighted imaging (T1WI), hyperintensity on T2-weighted imaging (T2WI), and a hyperintense rim around the cavity on FLAIR were defined as LI, 29 and the sizes ranged from 3 to 15 mm. MTA was rated using a 4-point scale: 0 = none, 1 = questionable, 2 = apparent, and 3 = severe; range 0–3. 26,27 One point was awarded for WMH if PVH Fazekas 3 (extending into the deep white matter) and /or DWMH Fazekas 2–3 (confluent or early confluent) was present. 30 For LI, 1 point was given if ≧ 1 asymptomatic lesions were present. All SVD-related findings were rated by two board-certified neurologists who were blinded to clinical details. The interrater κ for each SVD feature or MTA score was between 0.80–0.85.

Follow-up and functional outcomes

The patients were followed up until March 2023, and all-cause dementia and death were evaluated. Patients who withdrew consent and those lost during follow-up were censored at the last visit. Physical findings, treatments, clinical events, and mRS scores were recorded during follow-up visits. A relative or caregiver was interviewed via telephone if patients could not be followed up. The mRS score at the last visit was determined, and poor functional outcome was defined as a mRS score of ≧3.

Diagnosis of dementia

A neurologist prospectively assessed cognitive status using MMSE and CDR. Patients visited outpatient clinics to control for risk factors every 3 months to prevent stroke and vascular events. Changes in patients’ general medical conditions were obtained annually through medical records and interviews. Furthermore, to rate patients on the CDR, several aspects of everyday cognitively driven functioning were assessed at each clinical visit. The patients were cognitively screened using MMSE at follow-up visits. The final follow-up data were collected from April 2022 to March 2023. During follow-up period, neurologists periodically examined patients with suspected cognitive decline. Clinically significant cognitive impairment was defined as an MMSE score of <24 or a decline of >1.5 standard deviation (SD) of the change in score, as previously reported. 31 This corresponded to a decline of ≧3 points in this study. In addition, patients were considered to have probable dementia if they had two consecutive semiannual CDR scores ≧1 and did not revert to normal cognition. To avoid missing incident dementia cases, the medical records of all participants were continuously monitored at our clinic and other clinics to obtain information on diagnosed dementia. Furthermore, for patients who could not visit the clinic, a phone interview to collect clinical data was conducted with the patient and caregiver whenever possible. Finally, an independent committee of neurologists reviewed all potential cases of dementia, according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). 32 Dementia subtypes were diagnosed according to standardized criteria. 33,34 The criteria for mixed dementia were met when the investigator considered that the clinical picture presented aspects of both Alzheimer’s disease (AD) dementia and vascular dementia (VaD). 35 Time to dementia was defined as the time between the baseline visit and the date of dementia diagnosis. In addition, patients were followed up until death or refusal to participate further.

Statistical analyses

All analyses were performed using JMP 14 Pro (SAS Institute, Cary, NC, USA). Quantitative variables were expressed as means±standard deviation (SD) or medians with interquartile ranges (IQR) for normally and non-normally distributed data, respectively, whereas categorical variables were expressed as frequencies and percentages. Comparisons among multiple groups were performed using the one-way analysis of variance or the Kruskal–Wallis test for quantitative variables and the χ2 test for qualitative variables, as appropriate. To examine the effect of gait speed on dementia, survival and functional outcome, patients were categorized into three groups based on the TUG time tertiles as follows: slow (gait speed, ≥10.88 s), middle (gait speed, 9.04–10.87 s), and fast (gait speed, ≤9.03 s). Event rates were estimated using the Kaplan–Meier method, and intergroup differences were assessed using the log-rank test. Cox proportional hazards regression models were used to evaluate the association between gait speed and the risk of incident dementia and all-cause death by calculating the hazard ratios with 95% confidence intervals (CIs). Multiple logistic regression analysis was used to evaluate the association of gait speed with the risk of poor functional outcome at the last visit. We also examined the effect of gait speed using a cut-off value of 11 s because 11 s are the cut-off value in TUG for diagnosis of musculoskeletal ambulation disability symptom complex (MADS). 36 For all analyses, statistical significance was set at a p-value of <0.05. All variables with a p-value of <0.10 in the univariate analysis, including age, sex, diabetes mellitus, CKD, baseline MMSE score, WMH, presence of lacunes, and MTA, were entered in the multivariable models.

RESULTS

Between October 2015 and June 2019, 1,011 outpatients were enrolled in the TWMU CVD study (Fig. 1). After the exclusion of 338 patients who did not undergo the 3-m TUG including 13 patients with spine disease, and knee or hip joint arthropathy, the remaining 522 patients were included in this study. During a mean follow-up period of 4.8 years, 58 patients who withdrew consent primarily because of the transfer of care to another provider and 28 patients who were lost to follow-up were censored at the last visit. The baseline characteristics of the study participants are summarized in Table 1. Supplementary Table 1 summarizes the relationship between gait speed and risk factors, cognitive function, and MRI findings. Older age, female sex, diabetes mellitus, atrial fibrillation, CKD, previous cerebrovascular disease, low MMSE score, MTA severity, presence of severe or moderate WMH, and lacunes were significantly associated with slow gait speed.

Table 1

Baseline patient characteristics with respect to incident dementia

	Total	All-cause dementia	No dementia	p
Patient Number	522	32	490
Age, median (IQR), y	71 (63–77)	78 (74–81)	70 (62–76)	<0.001
Sex, male, %	61.3	37.5	62.9	0.004
Hypertension, %	65.9	56.3	66.5	0.235
Body mass index	23.1 (20.9–25.4)	22.9 (20.9–25.8)	23.2 (20.9–25.8)	0.883
Diabetes mellitus, %	26.3	18.8	26.7	0.321
Dyslipidemia, %	49.8	43.8	51.2	0.412
Atrial fibrillation, %	12.2	25	11.3	0.022
Chronic kidney disease, %	46.2	62.5	45.1	0.056
Current smoking, %	8.2	9.4	8.2	0.809
Previous cerebrovascular disease, %	50.4	40.6	51	0.254
Previous cardiovascular disease, %	15.9	12.5	15.1	0.686
Cognitive function, MMSE score	28 (26–30)	27 (26–28)	28 (26–30)	0.002
Education, y	16 (12–16)	16 (12–16)	16 (12–16)	0.666
MRI findings
WMH 0/1, %	24.3	48.4	23.1	0.001
PVH Grade 0/1/2/3, %	9/74/12/4	3/61/22/13	9/75/12/4	0.003
DWMH Grade 0/1/2/3, %	16/60/17/7	3/49/32/16	16/62/16/6	0.003
Lacunes 0/1, %	46.2	53.3	45.7	0.415
Medial temporal lobe atrophy (0–6)	2 (2–4)	4 (3–4)	2 (2–4)	<0.001
Gait speed, TUG time s, median (IQR)	9.91 (8.63–11.53)	11.79 (10.54–14.12)	9.75 (8.6–11.31)	<0.001

MMSE, Mini-Mental State Examination; WMH, white matter hyperintensity; TUG, 3-m timed up and go test.

Gait and incident dementia

During a mean follow-up period of 4.78±1.37 years, 32 cases of dementia occurred and 24 patients died. Annual incidence of dementia was 1.31%, which was in line with the previous findings in patients with vascular risk factors [31]. The follow-up durations were 4.76±1.35, 4.92±1.28, and 4.65±1.47 years in the fast (TUG time ≦9.03, n = 174), middle (TUG time, 9.04–10.87 s, n = 174), and slow (TUG time ≧10.88 s, n = 174) gait speed groups, respectively. The follow-up duration was similar in all three groups (Table 1). In the Kaplan-Meier analysis, the likelihood of incident dementia was significantly higher in the slow gait speed group than in the other two groups when compared individually (Fig. 2A, p < 0.001) and when fast and middle gait speeds were combined (Fig. 2B, p < 0.001). Because age remarkably influenced both gait speed and incident dementia, we examined the association in two age groups; under 71 years old and 71 years of age and older. In both age groups, similar trends, slow gait speed and high incidence of incident dementia, was observed (Supplementary Figure 1). Cox proportional hazards regression analysis for dementia is shown in Table 2. One SD increase of gait speed significantly increased risk of incident dementia after adjustment for confounding factors. With the fast and middle gait speed groups used as references, the risk of incident dementia was significantly higher in the slow gait speed group after adjustment for confounding factors (adjusted hazard ratio, 2.73; 95% CI 1.16–6.42, p = 0.022).

Fig. 2

Incident dementia according to the gait speed evaluated using the 3-m timed up and go test (TUG). A, B) Kaplan-Meier analysis of dementia-free survival in the slow (gait speed, ≥10.88 s), middle (gait speed, 9.04–10.87 s), and fast (gait speed, ≤9.03 s) gait speed groups (A), and in slow (gait speed, ≥10.88 s) and middle/fast (gait speed, ≤10.87 s) gait speed groups (B).

Table 2

A: Cox proportional hazard regression analysis of gait speed for incident dementia

				Model 1	Model 2
	Number	Event	Crude HR	Adjusted HR	Adjusted HR
Gait speed (One SD increase)	–	–	1.97 (1.54–2.48) p < 0.001	1.59 (1.19–2.07) p < 0.001	1.66 (1.17–2.30) p = 0.003
Fast and Middle (TUG time ≦10.87 s)	348	9	1 (Reference)	1 (Reference)	1 (Reference)
Slow (TUG time ≧10.88 s)	174	23	5.77 (2.67–12.49) p < 0.001	3.15 (1.40–7.10) p < 0.001	2.56 (1.07–6.14) p = 0.035

Model 1: Adjusted for age and sex. Model 2: Adjusted for model 1 and BMI, diabetes mellitus, atrial fibrillation, chronic kidney disease, previous cerebrovascular disease, MMSE, medial temporal atrophy, WMH and lacunes. MMSE, Mini-Mental State Examination; WMH, white matter hyperintensity; TUG, 3-m timed up and go test.

Table 3

B: Cox proportional hazard regression analysis of gait speed for all-cause death

				Model 1	Model 2
	Number	Event	Crude HR	Adjusted HR	Adjusted HR
Gait speed (One SD increase))	–	–	1.84 (1.37–2.39) p < 0.001	1.74 (1.26–2.34) p = 0.001	1.59 (1.09–2.28) p = 0.013
Fast and Middle(TUG time ≦10.87 s)	348	5	1 (Reference)	1 (Reference)	1 (Reference)
Slow (TUG time ≧10.88 s)	174	19	7.83 (3.15–23.65) p < 0.001	7.14 (2.69–22.58) p < 0.001	6.72 (2.15–20.98) p = 0.001

Model 1: Adjusted for age and sex. Model 2: Adjusted for model 1 and BMI, diabetes mellitus, atrial fibrillation, chronic kidney disease, previous cerebrovascular disease, previous coronary heart disease, MMSE, medial temporal atrophy, WMH and lacunes. MMSE, Mini-Mental State Examination; WMH, white matter hyperintensity; TUG, 3-m timed up and go test.

Table 4

C: Logistic multivariate regression analysis of gait speed for poor functional outcome, mRS > 3

				Model 1	Model 2
	Number	Event	Crude OR	Adjusted OR	Adjusted OR
Gait speed (One SD increase)	–	–	1.90 (1.48–2.44) p < 0.001	1.65 (1.25–2.18) p < 0.001	1.37 (1.00–1.91) p = 0.047
Fast and Middle(TUG time ≦10.87 s)	348	12	1 (Reference)	1 (Reference)	1 (Reference)
Slow (TUG time ≧10.88 s)	174	36	7.30 (3.69–14.46) p < 0.001	5.66 (2.72–11.78) p < 0.001	4.15 (1.83–9.40) p < 0.001

Gait and mortality

Association with risk factors and all-cause death was shown in Supplementary Table 2. In the Kaplan-Meier analysis, the likelihood of death was significantly higher in the slow gait speed group than in the other two groups when compared individually (Fig. 3A, p < 0.001) and when in combination with fast and middle gait speeds (Fig. 3B, p < 0.001). Because age remarkably influenced both gait speed and survival, we examined the association in two age groups; under 71 years old and 71 years of age and older. In both age groups, similar trends, slow gait speed and high incidence of all-cause death, was observed (Supplementary Figure 2). In the Cox proportional hazards regression analysis, one SD increase of gait speed significantly increased risk of all-caused death after adjustment for confounding factors. With the fast and middle gait speed groups used as references, the risk of all-cause death was significantly higher in the slow gait speed group after adjustment for confounding factors (adjusted hazard ratio, 4.96; 95% CI 1.78–16.39, p = 0.002) (Table 2B).

Fig. 3

Mortality and functional outcome according to the gait speed evaluated using the 3-m timed up and go test (TUG). A, B) Kaplan-Meier analysis of all-cause death-free survival in the slow (gait speed, ≥10.88 s), middle (gait speed, 9.04–10.87 s), and fast (gait speed, ≤9.03 s) gait speed groups (A), and in the slow (gait speed, ≥10.88 s) and middle/fast (gait speed, ≤10.87 s) gait speed groups (B). C, D) Modified Rankin scale (mRS) score distribution at last visit in the slow (gait speed, ≥10.88 s), middle (gait speed, 9.04–10.87 s), and fast (gait speed, ≤9.03 s) gait speed groups (C), and in the slow (gait speed, ≥10.88 s) and middle/fast (gait speed, ≤10.87 s) gait speed groups (D).

Gait and poor functional outcome

The comparison of the mRS distribution at the last visit revealed significant differences among the three groups (Fig. 3C) and between the slow gait speed group and the combination of the fast and middle gait speed groups (Fig. 3D). One SD increase of gait speed significantly increased risk of poor functional outcome after adjustment for risk factors, MMSE score and MRI findings. In the univariate logistic regression analysis, the slow gait speed group included significantly more patients with poor outcomes (mRS score ≧3) compared with the fast and middle gait speed groups (20.7% versus 2.9% and 4.0%, respectively, p < 0.001). In the multivariate logistic regression analysis (Table 2C), the slow gait speed group remained at higher risk for poor functional outcome than the combination of the fast and middle gait speed groups, which was used as reference (adjusted odds ratio, 4.19; 95% CI, 1.92–9.18; p < 0.001).

When we used 11 s in TUG test as the cutoff value, similar results were obtained. Slow gait speed (≧11 s) was associated with incident dementia, all-cause death and poor functional outcome independent of risk factors, MMSE score and MRI findings (Supplementary Figure 3 and Supplementary Table 3).

DISCUSSION

This study clarified that slow gait speed could predict incident dementia, mortality, or poor functional outcome after adjusting for confounding factors such as baseline cognitive function, brain atrophy and cerebral SVD severity.

Previous cohort studies have shown that subjective or objective gait impairment increases the risk of incident dementia13 –17 and mortality18 –21 in the general population; however, most studies did not consider baseline brain MRI findings or baseline cognitive function. Given that brain atrophy or SVD severity are closely associated with both gait function and cognitive function,4,5,37 , 4,5,37 the independent association between gait impairment and incident dementia must be clarified. Our results agree with recent findings that frailty could predict risk of cognitive decline independent of MRI findings in 244 participants. 38

In the general population, both motoric cognitive risk syndrome and cognitive frailty have attracted attention because of their high risk of incident dementia. 39 In these two conditions gait impairment is the key element of physical frailty and is associated with incident dementia independent of cognitive function. 40 In contrast, gait function is not usually evaluated as opposed to MMSE examination for risk stratification of dementia in managing patients with vascular risk factors. Although the fundamental link between slow gait speed and dementia remains unclear, our findings might support the notion that gait function is important for the risk of incident in patients with SVD.

The association between slow gait speed and poor functional outcome is also important because the maintenance of good activities of daily living (ADL) is important in the management of patients with cerebral SVD. In addition to physical frailty, falls are involved in this association because of the association between falls and gait speed or cerebral SVD severity.4,5,41,42 , 4,5,41,42

This study has several limitations. First, the influence of slow walking on AD, vascular dementia, and other types of dementia was not analyzed because of the small number of incidents for each type of dementia. Second, other findings of physical frailty, such as muscle weakness, exhaustion, low activity, and weight loss, were not evaluated in this study. However, in routine clinical settings, gait function could represent physical frailty in most individuals. 43 Third, nearly 40% of the participant candidates in our registry did not undergo gait function evaluation; thus, selection bias could not be excluded. Fourth, patients without T2* imaging were included; thus the influence of cerebral microbleeds on incident dementia could not be eliminated; however, among cerebral SVD categories, WMH and lacunes, not cerebral microbleeds, were shown to increase the risk of incident dementia in a meta-analysis. 44 In the present study, we included WMH and lacunae as SVD severity. Fourth, functional outcome was evaluated using mRS score alone. The addition of other functional outcome index such as the Amsterdam Instrumental Activities of Daily Living (A-IADL) questionnaire 45 is preferable. Fifth, although patients with mRS ≧2 were excluded, we could not exclude patients with potential arthritis or arthropathy in the knee or hip joint. Sixth, integrity of neural network and brain function were not examined in this study, although these could be involved on the association between gait function and cognitive function independent of brain atrophy and SVD severity. 46 Finally, approximately 5% of participants were lost during the follow-up period; thus, the follow-up was not complete.

Conclusions

This study clarified the independent association between slow gait speed and dementia, mortality, and poor functional outcome. Gait function evaluation appears to need to gather attention for the prevention of dementia and poor outcome in patients with vascular risk factors and SVD.

AUTHOR CONTRIBUTIONS

Megumi Hosoya (Data curation; Investigation); Sono Toi (Data curation); Hiroshi Yoshizawa (Supervision; Validation; Writing – review & editing); Kazuo Kitagawa (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Writing – original draft; Writing – review & editing).

Footnotes

ACKNOWLEDGMENTS

The authors have no acknowledgments to report.

FUNDING

This study was supported in part by Research Funding of Longevity Sciences (28-15, 30-1) from the National Center for Geriatrics and Gerontology, Japan.

CONFLICT OF INTEREST

Dr. Kitagawa received personal fee from Kyowa Kirin and Kowa, grants and personal fees from Daiichi Sankyo, and grants from Bayer and Dainihon Sumitomo outside the submitted work. Other authors have no conflicts of interest to disclose.

DATA AVAILABILITY

The data that support the findings of this study are available from the corresponding author on reasonable request.

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

References

Román

, Erkinjuntti

, Wallin

, et al. Subcortical ischaemic vascular dementia. Lancet Neurol 2002; 1: 426–436.

Chung

, Ihara

, Hilal

, et al. Targeting cerebral small vessel disease to promote healthy aging: Preserving physical and cognitive functions in the elderly. Arch Gerontol Geriatr 2023; 110: 104982.

Markus

and de Leeuw

. Cerebral small vessel disease: Recent advances and future directions. Int J Stroke 2023; 18: 4–14.

Sharma

, Wang

, McCreary

, et al. Gait and falls in cerebral small vessel disease: A systematic review and meta-analysis. Age Ageing 2023; 52: afad011.

Blahak

, Baezner

, Pantoni

, et al. Deep frontal and periventricular age related white matter changes but not basal ganglia and infratentorial hyperintensities are associated with falls: Cross sectional results from the LADIS study. J Neurol Neurosurg Psychiatry 2009; 80: 608–613.

Rosso

, Verghese

, Metti

, et al. Slowing gait and risk for cognitive impairment: The hippocampus as a shared neural substrate. Neurology 2017; 89: 336–342.

Peel

, Alapatt

, Jones

, et al. The association between gait speed and cognitive status in community-dwelling older people: A systematic review and meta-analysis. J Gerontol A Biol Sci Med Sci 2019; 74: 943–948.

Demnitz

, Hogan

, Dawes

, et al. Cognition and mobility show a global association in middle- and late-adulthood: Analyses from the Canadian Longitudinal Study on Aging. Gait Posture 2018; 64: 238–243.

Rasmussen

LJH

, Caspi

, Ambler

, et al. Association of neurocognitive and physical function with gait speed in midlife. JAMA Netw Open 2019; 2: e1913123.

10.

Jokinen

, Laakso

, Ahlström

, et al. Synergistic associations of cognitive and motor impairments with functional outcome in covert cerebral small vessel disease. Eur J Neurol 2022; 29: 158–167.

11.

Kelaiditi

, Cesari

, Canevelli

, et al. Cognitive frailty: Rational and definition from an (I.A.N.A./I.A.G.G.) international consensus group. J Nutr Health Aging 2013; 17: 726–734.

12.

Verghese

, Annweiler

, Ayers

, et al. Motoric cognitive risk syndrome: Multicountry prevalence and dementia risk. Neurology 2014; 83: 718–726.

13.

Orchard

, Polekhina

, Ryan

, et al. Combination of gait speed and grip strength to predict cognitive decline and dementia. Alzheimers Dement (Amst) 2022; 14: e12353.

14.

Tajimi

, Furuta

, Hirabayashi

, et al. Association of gait speed with regional brain volumes and risk of dementia in older Japanese: The Hisayama study. Arch Gerontol Geriatr 2023; 106: 104883.

15.

, Zhou

, Ye

, et al. Walking pace, handgrip strength, age, APOE genotypes, and new-onset dementia: The UK Biobank prospective cohort study. Alzheimers Res Ther 2023; 15: 9.

16.

Villani

, Ferraro

, Poloni

, et al. Dementia and risk factors: Results from a prospective, population-based cohort study. Life (Basel) 2022; 12: 1055.

17.

Collyer

, Murray

, Woods

, et al. Association of dual decline in cognition and gait speed with risk of dementia in older adults. JAMA Netw Open 2022; 5: e2214647.

18.

Wilson

, Schneider

, Beckett

, et al. Progression of gait disorder and rigidity and risk of death in older persons. Neurology 2002; 58: 1815–1819.

19.

Dumurgier

, Elbaz

, Ducimetière

, et al. Slow walking speed and cardiovascular death in well functioning older adults: Prospective cohort study. BMJ 2009; 339: b4460.

20.

Studenski

, Perera

, Patel

, et al. Gait speed and survival in older adults. JAMA 2011; 305: 50–58.

21.

Nofuji

, Shinkai

, Taniguchi

, et al. Associations of walking speed, grip strength, and standing balance with total and cause-specific mortality in a general population of Japanese elders. J Am Med Dir Assoc 2016; 17: 184.e1–e7.

22.

Neumann

, Thao

LTP

, Murray

, et al. Prediction of disability-free survival in healthy older people. Geroscience 2022; 44: 1641–1655.

23.

Podsiadlo

and Richardson

. The timed “Up & Go”: A test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 1991; 39: 142–148.

24.

Yamada

, Ishikawa

, Miyajima

, et al. Timed up and go test at tap test and shunt surgery in idiopathic normal pressure hydrocephalus. Neurol Clin Pract 2017; 7: 98–108.

25.

Kubota

, Iijima

, Shirai

, et al. Association between cerebral small vessel disease and central motor conduction time in patients with vascular risk. J Stroke Cerebrovasc Dis 2019; 28: 2343–2350.

26.

Kim

, Kim

, Choi

, et al. T1-weighted axial visual rating scale for an assessment of medial temporal atrophy in Alzheimer’s disease. J Alzheimers Dis 2014; 41: 169–178.

27.

Ishii

, Meguro

, Yamaguchi

, et al. Different MRI findings for normal elderly and very mild Alzheimer’s disease in a community: Implications for clinical practice the Tajiri Project. Arch Gerontol Geriatr 2006; 42: 59–71.

28.

Fazekas

, Chawluk

, Alavi

, et al. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol 1987; 149: 351–356.

29.

Duering

, Biessels

, Brodtmann

, et al. Neuroimaging standards for research into small vessel disease-advances since 2013. Lancet Neurol 2023; 22: 602–618.

30.

Staals

, Makin

, Doubal

, et al. Stroke subtype, vascular risk factors, and total MRI brain small-vessel disease burden. Neurology 2014; 83: 1228–1234.

31.

Miwa

, Tanaka

, Okazaki

, et al. Chronic kidney disease is associated with dementia independent of cerebral small-vessel disease. Neurology 2014; 82: 1051–1057.

32.

Regier

, Kuhl

and Kupfer

. The DSM-5 Classification and criteria changes. World Psychiatry 2013; 12: 92–98.

33.

McKhann

, Knopman

, Chertkow

, et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 263–269.

34.

Gorelick

, Scuteri

, Black

, et al. Vascular contributions to cognitive impairment and dementia: A statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2011; 42: 2672–2713.

35.

Bombois

, Debette

, Bruandet

, et al. Vascular subcortical hyperintensities predict conversion to vascular and mixed dementia in MCI patients. Stroke 2008; 39: 2046–2051.

36.

Ito

. Diagnosis of musculoskeletal ambulation disability symptom complex (MADS)]. Clin Calcium 2008; 18: 1560–1565 [in Japanese].

37.

Hosoya

, Toi

, Seki

, et al. Association between total cerebral small vessel disease score and cognitive function in patients with vascular risk factors. Hypertens Res 2023; 46: 1326–1334.

38.

Siejka

, Srikanth

, Hubbard

, et al. Frailty is associated with cognitive decline independent of cerebral small vessel disease and brain atrophy. J Gerontol A Biol Sci Med Sci 2022; 77: 1819–1826.

39.

Sugimoto

, Sakurai

, Ono

, et al. Epidemiological and clinical significance of cognitive frailty: A mini review. Ageing Res Rev 2018; 44: 1–7.

40.

Yaqub

, Darweesh

SKL

, Dommershuijsen

, et al. Risk factors, neuroimaging correlates and prognosis of the motoric cognitive risk syndrome: A population-based comparison with mild cognitive impairment. Eur J Neurol 2022; 29: 1587–1599.

41.

Adam

, Fitzpatrick

, Leary

, et al. The association between gait speed and falls in community dwelling older adults with and without mild cognitive impairment. Int J Environ Res Public Health 2021; 18: 3712.

42.

, Yang

, Wei

, et al. Periventricular white matter hyperintensities are associated with gait and balance in patients with minor stroke. Front Neurol 2022; 13: 941668.

43.

Binotto

, Lenardt

and Rodríguez-Martínez

MDC

. Physical frailty and gait speed in community elderly: A systematic review. Rev Esc Enferm USP 2018; 52: e03392.

44.

Debette

, Schilling

, Duperron

, et al. Clinical significance of magnetic resonance imaging markers of vascular brain injury: A systematic review and meta-analysis. JAMA Neurol 2019; 76: 81–94.

45.

Jutten

, Peeters

CFW

, Leijdesdorff

SMJ

, et al. Detecting functional decline from normal aging to dementia: Development and validation of a short version of the Amsterdam IADL Questionnaire. Alzheimers Dement (Amst) 2017; 8: 2–35.

46.

Markus

, van Der Flier

, Smith

, et al. Framework for Clinical Trials in Cerebral Small Vessel Disease (FINESSE): A review. JAMA Neurol 2022; 79: 1187–1198.