Motoric Cognitive Risk Syndrome Among Chinese Older Adults with White Matter Lesions: A Cross-Sectional Observational Study

Abstract

Brain aging is characterized by the declines in motor and cognitive features. The present study is to detect motor cognitive risk syndrome (MCRS) in older adults with white matter lesions (WML). 134 WML aged patients were recruited and diagnosed with the criteria for MCRS. Numerous cognitive function tests and walking tests were performed. The frequency of MCRS is 28.35%. Verbal fluency test, Mini-Mental State Examination, and dual-task walking speed were independent risk factor of MCRS. These findings indicated that MCRS was common in WML seniors. MCRS was associated with the pathologies of WML in older adults.

Keywords

Aging cerebral small vessel disease dual-task walking magnetic resonance imaging motoric cognitive risk syndrome white matter lesions

INTRODUCTION

With the worldwide growth in the number of older adults, geriatric medicine is paying more and more attention on motoric cognitive risk syndrome (MCRS), a common-found disorder in aged individuals. According to the recent data, the prevalence of MCRS is around 8.0% in Europe, 7.0% in the United States, and 6.3% in Japan [1]. And the latest survey in southern Italy showed that about 9.9% aged populations suffer from MCRS [2]. MCRS is considered as a predementia state featured by the existence of both subjective cognitive complaints and objective slow gait in older adults without dementia [3]. Older adults with MCRS are reported to have higher risk of developing cognitive decline and dementia [4]. The Canadian Consortium on Neurodegeneration in Aging recently tried to propose a preliminary battery, including usual pace gait speed, dual task walking speed, Montreal Cognitive Assessment (MoCa) as well as trail making test (TMT), to identify persons at risk of MCRS [1].

For a long period, cognitive impairment and gait abnormalities have been explored in isolation [5], whereas evidence has progressively suggested that impairments in cognitive and physical dimensions are frequently concurrent [6]. The bidirectional relationship between motor and cognitive domains is becoming even pronounced, with the support of technology-based tools used in gait assessment [7]. For example, nearly 20% of cognitively impaired individuals may be suffered from physically frail or vice versa [8, 9]. Thus, MCRS, first stated by Verghese et al. [10] in 2013, reflects the manifestation of the intimate correlation between cortical gait control and cognitive function, and is related to the enhanced risk of major neurocognitive disorders [11].

White matter lesions (WML) are commonly found in the older adults, which occurs in approximately 80% of aged adults over 60 years in the general population [12]. Its two major clinical symptoms are gait abnormalities and cognitive deficits [13]. More specifically, our previous studies found that older adults with WML demonstrated poorer performance during dual task walking tasks (e.g., speed, gait variability, bilateral coordination) and neuropsychological assessments (e.g., Verbal Fluency Test, Mini-Mental State Examination (MMSE)) [14, 15], whereas investigations of MCRS in aged people with WML is still controversial. For example, Mergeche et al. [16] concluded that WML was not obviously related to MCRS in this studied sample of participants; however, the findings from Gomez and colleagues [17] indicated that MCRS might show a predementia syndrome featured by obvious white matter abnormalities. Thus, this study aimed to explore MCRS in Chinese old adults with WML, so as to find out the relationship between MCRS and WML. Besides, the associated factors with MCRS such as cognitive and gait domains in Chinese aged patients with WML were also investigated.

MATERIALS AND METHODS

Participants

From June 2019 to August 2020, a cross-sectional observational clinical research was launched, and the data of 134 seniors aged 65 years or older were acquired from the Neurology Department at the Seventh Medical Center of PLA General Hospital. The patients were recruited consecutively from our outpatient. Most of these participants had strong will of MRI scan, with complaints about slight dizziness, mild headache, mood disturbances, cognitive disorders, etc. The research protocol was approved by the Academic Ethic Committee of the Biological Sciences Division of the Seventh Medical Center of Chinese PLA General Hospital in Beijing, China. The ethical approval for the final version is included as Supplementary Material. All the recruited patients voluntarily signed an informed consent to participate in the current study.

Cerebral MRI was performed to accomplish patient screening with a 3.0T scanner, while the Fazekas scale was employed for the assessment and rating of WML severity [18]. We rated the WML severity into the grade 1 (“punctate lesions”), the grade 2 (“early confluent lesions”) and the grade 3 (“confluent lesions”), which is shown in Supplementary Figure 1.

The exclusion criteria were presence of major stroke; other reasons for leukoencephalopathy (including immune, demyelination, and genetic); major psychiatric disorders (diagnosed with DSM-IV); use of psychotropic medications or drugs with the side effects of risk of falling (e.g., tranquillizers/sedatives, diuretics, antiparkinsonian drugs); MRI contraindications; dementia (diagnosed with ICD-10) or MMSE score lower than 23 points [19]; use of walking aids.

The general information was offered by the subjects, which included name, sex, age, years of education, as well as the quantity of comorbidities (hyperlipidemia, hypertension, myocardial infarction, angina, stroke, diabetes mellitus, migraine, arthritis, and tumble).

Magnetic resonance imaging measurements

According to a cerebral MRI micrograph (Discovery MR750; GE Healthcare, Waukesha, USA) at 3.0T, the WMLs present were compatible with the grade 1, 2, or 3 CSVD (cerebral small vessel disease). The parameters adopted in the imaging scheme, which was based on the interslice and slice thicknesses of separately 1.5 and 5 mm, were as shown below: in the case of T1 FLAIR (fluid-attenuated inversion recovery) micrographs, the TR (repetition time) was 1,750 ms, TI was 780 ms, TE (echo time) was 23 ms, and FOV (field of view) was 24 cm; in the case of T2 micrographs, TR, TE, and FOV were separately 7,498 ms, 105 ms, and 24 cm.

Definition of MCRS

MCRS referred to the association of the slow walking speed and subjective cognitive complaints without mobility disability or dementia [20]. Walking speed was considered slow when it was one SD (standard deviation) below that of the normal senior cohort based on a portable gait monitor IDEEA (Intelligent Device for Energy Expenditure and Activity). Subjective cognitive complaints were defined utilizing the self-reported memory or concentration problem variable [21]. For instance, every participant was asked, “Do you feel you have more problems with memory than most”.

Walking paradigm for single- and dual-task walking

In the STW (single-task walking) and DTW (dual-task walking) settings, the subjects were requested to walk 25 strides at their habitual (i.e., self-chosen) speed along a long corridor as per our prior procedure [14]. For every session, the start point was marked, as well as the probable endpoint. According to Radovanović et al. [21], during the cognitive dual-task, each subject performed serial subtraction by 3 in the course of walking with a stochastically selected starting number (either 90, 95, 100, or 105).

During the cognitive task, the subjects were required to do their best to be accurate, who were reminded to walk for the entire 25 strides continuously as well. Prior to their arrival at the start line, the subjects began walking 6 steps, who also finished their walking 6 steps ahead the 25th stride, so that the deceleration/acceleration effects could be evaded. Through the stochastic display of the order of the ST (single-task) and DT gait conditions, the systematic bias was prevented. The computational formula for the DTC (dual-task cost) on DT and ST speeds is as shown below [15]. $DCT = | \frac{dual - task value - single - task value}{single - task value} | * 100$ (1)

Neuropsychological assessment

Considering that the major domains of cognitive dysfunction found in WML patients were executive dysfunction [22], the neuropsychological assessments in the current study consisted of category verbal fluency test (VFT) (reflecting executive function) [23], clock drawing test (CDT) (reflecting visuospatial function and attention) [24], trail-making test–part B (reflecting cognitive flexibility), and MMSE (reflecting global cognitive function) [25].

Statistical analysis

We employed Mann–Whitney U test and one-way ANOVA for assessing the inter-group differences (for MCRS group and WML severity) in the following demographic information variables: TMT-B, CDT, and VFT performances, as well as DTC. The WML frequency of the MCRS-positive population was compared with that of the MCRS-negative ones by the χ2 test. The correlations of the independent parameters with MCRS (dependent parameter) were investigated through the forward stepwise logistic regression. During the analysis, adjustments were made for age, sex, level of education, and quantity of complications. The data presented are means±SDs and IQRs. Differences were regarded as significant when p values were < 0.05. The SPSS 25.0 package (IBM, Armonk, NY, USA) was utilized to make the entire statistical analyses.

RESULTS

The present research found a 28.35% MCRS frequency among the aged Chinese WML patients according to the MCRS diagnostic criteria. The demographic traits were detailed in Table 1 for the entire patients. The MCRS positive (MCRS+) group patients demonstrated statistically lower educational level [(IQR(6–12) versus IQR(9–12), p = 0.033], poorer performance in CDT [(IQR(10–12) versus IQR(12–13), p < 0.001], VFT (14.369±3.044 versus 18.125±3.600, p < 0.001), TMT-B [(IQR(80.50–112.25) versus IQR(57.25–86.00), p < 0.001], MMSE [(IQR(25.75–27) versus IQR(28,29), p < 0.001], and lower DTW speed (60.970±15.156 versus 88.020±21.685, p < 0.001), as well as higher frequency of severe WML (p < 0.001) in comparison with MCRS negative (MCRS-) group.

Table 1

Clinical and demographic characteristics of the subjects with and without MCRS

		Overall (N = 134)	MCRS- (N = 96)	MCRS+ (N = 38)
p
Sex [N (%)]
Male	87 (64.925%)	61 (45.522%)	26 (19.403%)	0.690
Female	47 (35.075%)	35 (26.119%)	12 (8.955%)
Age (x±s, y)	64.575±9.145	63.802±8.949	66.526±9.463	0.121
Educational level [M (P25, P75), y]	9 (8, 12)	9 (9, 12)	9 (6, 12)	0.033
Comorbidity [N (%)]
0	25 (18.657%)	18 (13.433%)	7 (5.224%)	0.390
1	43 (32.090%)	27 (20.149%)	16 (11.940%)
2	46 (34.328%)	34 (25.373%)	12 (8.955%)
3	17 (12.687%)	15 (11.194%)	2 (1.493%)
4	3 (2.234%)	2 (1.493%)	1 (0.746%)
Fazekas score [N (%)]
1	60 (44.776%)	59 (44.030%)	1 (0.746%)	0.000
2	54 (40.299%)	31 (23.134%)	23 (17.164%)
3	20 (14.925%)	6 (4.478%)	14 (10.448%)
VFT (x±s), words	17.060±3.836	18.125±3.600	14.369±3.044	0.000
MMSE [M (P25, P75), score]	28 (27.00, 29.00)	28 (28.00, 29.00)	26 (25.75, 27.00)	0.000
CDT [M (P25, P75), score]	12 (11, 13)	12 (12, 13)	11 (10, 12)	0.000
TMT-B [M (P25, P75), s]	79 (60.00, 92.25)	73.50 (57.25, 86.00)	89.50 (80.50, 112.25)	0.000
DTC [M (P25, P75), % ]	13.87 (6.48, 20.76)	13.24 (6.08, 19.46)	16.49 (9.20, 22.54)	0.092
DTW speed (x±s, cm/s)	80.350±23.442	88.020±21.685	60.970±15.156	0.000

VFT, verbal fluency test; MMSE, Mini-Mental State Evaluation; CDT, clock drawing test; TMT-B, trail making test-B; DTC, dual task cost; DTW, dual task walking.

Depending on the MRI-based Fazekas score, we categorized the aged patients under either one of the three varying WML severities. For cognitive function, VFT score, CDT score TMT-B seconds, and MMSE scores differed significantly among groups in Chinese patients with WML (p < 0.001). For gait function, STW and DTW speed differed between groups, subjects walked slower, with more severe WML burden (p < 0.001). However, we did not find obvious differences in DTC between each group (p = 0.200). Details are presented in Table 2.

Table 2

Cognitive and gait function of the subjects with different levels of WML

		Mild WML (N = 54)	Moderate WML (N = 60)	Severe WML (N = 20)
p
Gender [N (%)]	Male 33 (24.627%)	Male 39 (29.104%)	Male 15 (11.194%)	0.539
	Female 21 (15.672%)	Female 21 (15.672%)	Female 5 (3.731%)
Age (x±s, y)	65.44±8.20	62.43±9.29	68.65±9.79	0.020
Educational level [M (P25, P75), y]	9 (6, 12)	9 (8.25, 12)	9 (9, 14.25)	0.725
Comorbidity (x±s)	1.44±0.90	1.48±1.08	1.55±1.10	0.923
VFT (x±s, words)	19.00±3.58	16.24±2.97	13.05±2.95	0.000
CDT [M (P₂₅, P₇₅), score]	19.00 (17.00, 22.00)	16.00 (14.00, 18.00)	12.50 (11.00, 16.00)	0.000
TMT-B [M (P₂₅, P₇₅), s]	60.50 (52.25, 80.00)	84.00 (72.00, 91.25)	106.00 (84.00, 106.75)	0.000
MMSE [M (P₂₅, P₇₅), score]	29.00 (28.00, 29.00)	27.00 (26.00, 28.00)	26.50 (25.00, 27.00)	0.000
STW speed [M (P₂₅, P₇₅), cm/s]	107.50 (91.00, 118.75)	87.50 (72.75, 101.50)	40.25 (58.50, 81.50)	0.000
DTW speed [M (P₂₅, P₇₅), cm/s]	91.50 (77.00, 104)	76.00 (64.00, 91,00)	76.00 (58.50, 66.75)	0.000
DTC [M (P₂₅, P₇₅), % ]	17.34 (6.40, 17.82)	13.77 (6.12, 19.81)	17.35 (10.83, 22.61)	0.200

VFT, verbal fluency test; MMSE, Mini-Mental State Evaluation; CDT, clock drawing test; TMT-B, trail making test-B; DTC, dual task cost; DTW, dual task walking; STW, single task walking.

A further logistic regression was conducted to investigate the linkages of MCRS to the motor and cognitive capacities. The MCRS was correlated with VFT (OR = 0.555, p = 0.033) and MMSE score (OR = 0.031, p < 0.001), DTW speed (OR = 0.024, p < 0.001) negatively, and with TMT-B time (OR = 1.098, p = 0.043) positively among Chinese patients with WML. MCRS was still correlated with VFT (OR = 0.525, p = 0.040), MMSE score (OR = 0.027, p < 0.001), and DTW speed (OR = 0.023, p < 0.001) significantly, after adjusting for age, sex, educational status, and comorbidities. Details are presented in Fig. 1.

Fig. 1

Forest plot of the logistic regression for associated factors with MCRS. (a) Model 1: Logistic regression for risk factors associated with MCRS. (b) Model 2: Logistic regression for risk factors associated with MCRS. Adjusted for age, sex, educational level, and comorbidities. MCRS, motoric cognitive risk syndrome; VFT, verbal fluency test; CDT, clock drawing test; TMT-B, trail making test-B; DTW, dual task walking; DTC, dual task cost; OR, odd ratio; CI, confidence interval.

DISCUSSION

Several recent studies [26 –28] reports that the prevalence of MCRS is within the range from 4.0% to 9.6% among community-dwelling older adults in mainland China. In the current work, we discovered that approximately 28.35% older adults with WML had MCRS. Although our findings should not be considered the representative of the general population. The prevalence of MCRS in general population of older adults in China is 7.92% [29]. This frequency (28.35%) of MCRS in neurology clinic-based WML seniors is firstly reported in China, which is higher than the data (27.30%) found in southwest India [30].

Furthermore, MCRS+ individuals had a more severe WML severity than did MCRS- patients. These results were not in consistence with those of research on Indian seniors with CSVD [28], but in accordance with the Gomez’s group [17]. According to our previous studies [14, 25], WML patients demonstrated tiny but typical cognitive and walking dysfunctions. These empirical studies have revealed that the older adults with WML often exhibited a clinically apparent neurological symptoms, and were categorized as “silent or covert”. However, along with the emergence new concepts, evidence indicated that WML is not “silent” [31]. Thus, we assumed that aged individuals with WML were potential candidates of MCRS, which could be a transition to future dementia and disabilities. In addition, what cannot be ignored is that the risk of dementia is stronger for MCRS than for either slow gait speed or subjective memory complaint alone [32]. And all these findings implied that more research is still needed to detect the MCRS in WML patients.

We also tried to explore the associated factors with MCRS in Chinese aged patients with WML. Firstly, logistic regression analysis demonstrated that VFT negatively correlated with MCRS. It has been confirmed that VFT was a predictor for mild cognitive impairment in Parkinson’s disease previously by Galtier and colleagues [33]. Together with the conclusion of Galtier et al. [33], our findings implied that VFT, a well know executive function assessment, might be able to reflect both motor abnormalities and cognitive dysfunction. Secondly, we also discovered that the DTW speed showed significant and negative relationship to MCRS. This phenomenon could be explained that DTW is a simultaneous paradigm, requiring a high load of simultaneous cognitive and motor function [34], whereas we did not find the similar trends between DTC and MCRS. There existed a discrepant association between MCRS and DWT speed/DTC. The reason might be the fact that most aged WML patients had gait disorder, and there is a floor impact of gait velocity during single task walking in MCRS+ individuals [35]. Accompanied with the significant association between MCRS and VFT time as well as MMSE score, DTW speed seems to be a proper tool that indicates the cognitive and motor deficits in WML patients.

Several shortcomings need to be considered. At first, the sample size was not large. Second, some aspects of gait analysis parameters, including stride length and swing time were not included in our study. We will overcome this limitation in future, because a recent study revealed that subtypes of MCRS are common and associated with accelerated motor and cognitive functional decline [36]. 3T-WMH volume should be used to quantify WML in future study. Also, derived TMT B-A, instead of TMT-B should be used to measure cognitive flexibility. All these points would be potential points to improve the quality of our work. In summary, MCRS was frequently encountered among aged Chinese patients suffering from WML in neurology clinic. More moderate and severe WML were found in MCRS positive individuals. MCRS showed negative correlation with the DTW speed as well as MMSE in older adults with CSVD. These findings illustrated that WML was associated with MCRS pathology in older adults.

Footnotes

ACKNOWLEDGMENTS

We appreciate Dr. Shushi Tian for supporting MRI reading.

FUNDING

The current work was funded by WU Jieping Foundation (Grant No.: 320.6750.18456) and Shanghai Natural Science Foundation (#19ZR1457000).

CONFLICT OF INTEREST

The authors have no conflict 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: .

References

Maggio

, Lauretani

(2019) Prevalence, incidence, and clinical impact of cognitive-motoric risk syndrome in Europe, USA, and Japan: Facts and numbers update 2019. J Cachexia Sarcopenia Muscle 10, 953–955.

Bortone

, Griseta

, Battista

, Castellana

, Lampignano

, Zupo

, Sborgia

, Lozupone

, Moretti

, Giannelli

, Sardone

, Panza

(2021) Physical and cognitive profiles in motoric cognitive risk syndrome in an older population from Southern Italy. Eur J Neurol 28, 2565–2573.

Sekhon

, Allali

, Launay

, Chabot

, Beauchet

(2017) The of pre-dementia stages: Cognitive profile spectrum of motoric cognitive risk syndrome and relationship with mild cognitive impairment. Eur J Neurol 24, 1047–1054.

Xiang

, Liu

, Sun

(2022) Motoric cognitive risk syndrome: Symptoms, pathology, diagnosis, and recovery. Front Aging Neurosci 13, 728799.

Chhetri

, Chan

, Vellas

, Cesari

(2017) Motoric cognitive risk syndrome: Predictor of dementia and age-related negative outcomes. Front Med (Lausanne) 4, 166.

Won

, Lee

, Kim

, Yoo

, Kim

, Ng

, Kim

, Son

(2018) Modified criteria for diagnosing “Cognitive Frailty". Psychiatry Investig 15, 839–842.

Bortone

, Sardone

, Lampignano

, Castellana

, Zupo

, Lozupone

, Moretti

, Giannelli

, Panza

(2021) How gait influences frailty models and health-related outcomes in clinical-based and population-based studies: A systematic review. J Cachexia Sarcopenia Muscle 12, 274–297.

Raji

, Al Snih

, Ostir

, Markides

, Ottenbacher

(2010) Cognitive status and future risk of frailty in older Mexican Americans. J Gerontol A Biol Sci Med Sci 65, 1228–1234.

Avila-Funes

, Amieva

, Barberger-Gateau

, Le Goff

, Raoux

, Ritchie

, Carrière

, Tavernier

, Tzourio

, Gutiérrez-Robledo

, Dartigues

(2009) Cognitive impairmentimproves the predictive validity of the phenotype of frailty foradverse health outcomes: The three-city study. J Am GeriatrSoc 57, 453–461.

10.

Verghese

, Wang

, Lipton

, Holtzer

(2013) Motoric cognitive risk syndrome and the risk of dementia. J Gerontol A Biol Sci Med Sci 68, 412–418.

11.

Beauchet

, Cooper-Brown

, Allali

(2021) Motoric cognitive risksyndrome: What’s new? Aging (Albany NY) 13, 7711–7712.

12.

Moran

, Phan

, Srikanth

(2012) Cerebral small vessel disease: A review of clinical, radiological, and histopathological phenotypes. Int J Stroke 7, 36–46.

13.

Siejka

, Srikanth

, Hubbard

, Moran

, Beare

, Wood

, Phan

, Callisaya

(2018) Frailty and cerebral small vessel disease: A cross-sectional analysis of the Tasmanian Study of Cognition and Gait (TASCOG). J Gerontol A Biol Sci Med Sci 73, 255–260.

14.

, Zhào

, Wei

, Liu

, Huang

(2022) Gait characteristicsunder single-/dual-task walking conditions in elderly patients withcerebral small vessel disease: Analysis of gait variability, gaitasymmetry and bilateral coordination of gait. Gait Posture 92, 65–70.

15.

Zhào

, Wei

, Liu

, Gao

, Huang

(2020) Cognitive frailtyamong elderly Chinese patients with cerebral small vessel disease: Astructural MRI study. Front Med (Lausanne) 7, 397.

16.

Mergeche

, Verghese

, Allali

, Wang

, Beauchet

, Kumar

VGP

, Mathuranath

, Yuan

, Blumen

(2016) White matter hyperintensities in older adults and motoric cognitive risk syndrome. J Neuroimaging Psychiatry Neurol 1, 73–78.

17.

Gomez

, Gottesman

, Gabriel

, Palta

, Gross

, Soldan

, Albert

, Sullivan

, Jack

Jr , Knopman

, Windham

, Walker

(2022) The association of motoric cognitive risk with incident dementia and neuroimaging characteristics: The Atherosclerosis Risk in Communities Study. Alzheimers Dement 18, 434–444.

18.

Fazekas

, Chawluk

, Alavi

, Hurtig

, Zimmerman

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

19.

Blumen

, Allali

, Beauchet

, Lipton

, Verghese

(2019) A gray matter volume covariance network associated with the motoric cognitive risk syndrome: A multicohort MRI study. J Gerontol A Biol Sci Med Sci 74, 884–889.

20.

Sekhon

, Allali

, Beauchet

(2019) The association of anxio-depressive disorders and depression with motoric cognitive risk syndrome: Results from the baseline assessment of the Canadian longitudinal study on aging. Geroscience 41, 409–418.

21.

Radovanović

, Perić

, Savić-Pavićević

, Dobricić

, Pesović

, Kostić

, Rakocević-Stojanović

(2016) Comparison of temporal andstride characteristics in myotonic dystrophies type 1 and 2 duringdual-task walking. Gait Posture 44, 194–199.

22.

Zhào

, Chi

, Zhang

, Huang

, Tian

(2021) Spatialnavigation is impaired in elderly patients with cerebral smallvessel disease. Front Neurol 12, 608797.

23.

Amunts

, Camilleri

, Eickhoff

, Heim

, Weis

(2020) Executive functions predict verbal fluency scores in healthy participants. Sci Rep 10, 11141.

24.

Buckley

, Atkins

, Fortunato

, Silbert

, Scott

, Evered

(2021) A novel digital clock drawing test as a screening tool for perioperative neurocognitive disorders: A feasibility study. Acta Anaesthesiol Scand 65, 473–480.

25.

Zhào

, Wei

, Do

, Huang

(2019) Assessing performance ondigital clock drawing test in aged patients with cerebral smallvessel disease. Front Neurol 10, 1259.

26.

Shen

, Zeng

, Xu

, Chen

, Liu

, Chu

, Yang

, Wu

, Chen

(2020) Association between motoric cognitive risk syndrome and frailty among older Chinese adults. BMC Geriatr 20, 110.

27.

Chhetri

, Han

, Dan

, Ma

, Chan

(2020) Motoric cognitive risk syndrome in a Chinese older adult population: Prevalence and associated factors. J Am Med Dir Assoc 21, 136–137.

28.

Yuan

, Zhao

, Ma

, Li

, Zhou

, Wang

, Jiang

, Li

(2021) Prevalence/potential risk factors for motoric cognitive risk and its relationship to falls in elderly Chinese people: A cross-sectional study. Eur J Neurol 28, 2680–2687.

29.

Bai

, Xu

, Lin

(2022) Prevalence and correlates of motoric cognitive risk syndrome in Chinese community-dwelling older adults. Front Aging 3, 895138.

30.

Wang

, Allali

, Kesavadas

, Noone

, Pradeep

, Blumen

, Verghese

(2016) Cerebral small vessel disease and motoric cognitive risk syndrome: Results from the Kerala-Einstein Study. J Alzheimers Dis 50, 699–707.

31.

Mancuso

, Arnold

, Bersano

, Burlina

, Chabriat

, Debette

, Enzinger

, Federico

, Filla

, Finsterer

, Hunt

, Lesnik Oberstein

, Tournier-Lasserve

, Markus

(2020) Monogenic cerebral small-vessel diseases: Diagnosis and therapy. Consensus recommendations of the European Academy of Neurology. Eur J Neurol 27, 909–927.

32.

Semba

, Tian

, Carlson

, Xue

, Ferrucci

(2020) Motoric cognitive risk syndrome: Integration of two early harbingers of dementia in older adults. Ageing Res Rev 58, 101022.

33.

Galtier

, Nieto

, Lorenzo

, Barroso

(2017) Mild cognitive impairment in Parkinson’s disease: Clustering and switching analyses in verbal fluency test. J Int Neuropsychol Soc 23, 511–520.

34.

Montero-Odasso

, Sarquis-Adamson

, Speechley

, Borrie

, Hachinski

, Wells

, Riccio

, Schapira

, Sejdic

, Camicioli

, Bartha

, McIlroy

, Muir-Hunter

(2017) Association of dual-task gait with incident dementia in mild cognitive impairment: Results from the gait and brain study. JAMA Neurol 74, 857–865.

35.

Udina

, Ayers

, Inzitari

, Verghese

(2021) Walking while talking and prefrontal oxygenation in motoric cognitive risk syndrome: Clinical and pathophysiological aspects. J Alzheimers Dis 84, 1585–1596.

36.

Ayers

, Verghese

(2019) Gait dysfunction in motoric cognitive risk syndrome. J Alzheimers Dis 71, S95–S103.