Association of Gait Speed,Psychomotor Speed,and Dementia

Abstract

Background:

Gait speed (GS) and psychomotor speed (PS) could be considered as two different dimensions of age-related slowness and both measures are associated with higher risk of adverse health-related outcomes among elderly people.

Objective:

To determine the association between GS, PS, and incident dementia among community-dwelling older adults.

Methods:

Twelve-year longitudinal study of 1,265 participants in the Bordeaux Three-City Study, a French prospective cohort designed to determine the risk of dementia and cognitive impairment attributable to cardiovascular risk factors. Participants completed a battery of cognitive tests, including time to complete the Trail Making Test A, and a walking speed test. The incidence of dementia was determined over the 12-year follow-up period. Cox proportional hazards models with delayed entry were used to estimate the cumulative risk of dementia and were adjusted for sex, education, and ApoE4 genotype.

Results:

Mean age of participants was 74.0 years (SD 4.8). Over the 12-year follow-up, 203 participants developed dementia. GS and PS were both independent predictors of incident all-cause dementia after 12 years of follow-up. For a one SD increase of either GS or PS, the hazard ratio (HR) for Alzheimer’s disease was 1.2 (95% CI = 1.02–1.32) and 1.4 (95% CI = 1.2–1.61), respectively; whereas for incident vascular dementia, the HR was 1.3 (95% CI = 1.05–1.71) and 1.5 (95% CI = 1.16–2.08), respectively. No significant interaction between GS and PS was observed.

Conclusions:

In older French people aged 65+, our findings showed that both low GS and PS were independently associated with risk of incident Alzheimer’s disease and vascular dementia.

Keywords

Dementia elderly epidemiology gait speed psychomotor speed vascular process

INTRODUCTION

Slowness is one of the fundamental characteristics of aging [1] and is one of the main components of the decline in cognitive and physical functions. Slowness in physical function is usually measured by gait speed (GS) [2 –4], while slowness in cognitive function can be measured by processing speed (PS) such as time to complete Trail Making Test A (TMT-A) [5, 6], which is less effortful part of the test, than the other two processing test (TMT-B and the Isaac Set Test [7]) used in the Three-city (3C) Study [8]. Previous work conducted in the 3C Study showed that low GS and low PS were quite correlated [6] but not perfectly concordant [9], which could be explained by the fact that the two speed measures have different correlates (cognitive for PS and physical for GS). Moreover, both measures were independent predictors of mortality [9]. The 3C-Dijon and 3C-Bordeaux studies have already shown that GS is specifically associated with the risk of incident vascular dementia (VaD) either as single factor [10] or as a component of frailty [11]. On the other hand, low PS is recognized as one of the main characteristics of vascular cognitive disorders [10, 12]. Thus, vascular processes could be considered as a factor associated with age-related slowness. In turn, this may suggest that a common underlying mechanism explains the concomitant impairment in both GS and PS. In fact, GS and PS could be regarded as two different dimensions of age-related slowness; however, both performances were associated with higher risk of adverse health-related outcomes. If the vascular process is the major component of slowness, low GS and PS should be specifically associated with VaD. Alternatively, if another consequence of aging explained slowness, GS and PS could be related to either Alzheimer’s disease (AD) or other type of dementia, particularly those related to Lewy body/Parkinson’s disease.

The aim of the present study was to determine the association between GS, PS, and incident all-cause dementia (VaD, AD, or other dementia) among French community-dwelling older adults. To achieve these goals, we used the Bordeaux-3C cohort study data, a French longitudinal population-based cohort specifically designed to determine the risk of dementia and cognitive impairment attributable to cardiovascular risk factors.

METHODS

Study population

A detailed description of the methodology used for the 3C study can be found in a previous publication [8]. Briefly, 9,294 men and women volunteers aged 65 or older, initially non-institutionalized, without dementias were recruited in three French cities: Bordeaux, Dijon, and Montpellier between 1999 and 2000. At baseline, trained nurses and psychologists collected information including socio-demographic and lifestyle characteristics, educational level, self-reported chronic diseases, depressive symptoms, and functional status during face-to-face interviews at the participants’ home. In addition, participants underwent a comprehensive cognitive evaluation. The Ethical Committee of the University Hospital of Kremlin-Bicêtre (Paris, France) approved the 3C study and all participants provided written informed consent. Although the baseline characteristics of all participants of the 3C study were comparable, in the present study, only data from Bordeaux was analyzed because GS was not assessed in Montpellier and it was measured using a different method at the Dijon center [6].

Gait speed assessment

GS was assessed using the six-meter walk test (12–14) during baseline face-to-face interviews in all participants aged≤85. Participants were asked to perform a three-meter walk and back to the start line at their usual walking speed and then as fast and safely as possible without running. Use of their usual walking aid was allowed for safety reasons. The interviewer measured the time in seconds needed to cover the six-meter walk and it was operationalized as the ratio between distance and time, and expressed in meters by seconds (m/s). Those subjects unable to perform this task or unable to walk independently were excluded from the analysis. Patients who walked with assistance were also excluded from analysis.

Psychomotor speed assessment

PS was assessed using the time needed to complete the TMT-A, which was administered according to current guidelines [13]. Participants were instructed to connect, as quickly and accurately as possible 25 circles, containing numbers in sequential order distributed over a sheet of paper. The time required to complete the test was registered [14 –16]. Subjects with at least one error in the TMT-A were excluded from the analysis.

Correlates

Socio-demographic characteristics included age, sex, and educational level. Educational level was defined as a five-level variable: primary without diploma, primary with diploma, secondary, baccalaureate degree, and postgraduate level. DNA was extracted from white blood cells. Apolipoprotein E (ApoE) genotyping was carried out at Lille Genopole.

Outcome

A standardized three-step procedure was used to diagnose cases of dementia. First, screening was based on a neuropsychological examination performed by trained psychologists that included a battery of cognitive tests assessing memory, attention, language, and visuospatial abilities. Magnetic resonance images or computed tomography scans data was collected, if available. Second, a neurologist examined participants suspected of having dementia on the basis of their neuropsychological performance [particularly, the Mini-Mental State Examination [17], the Benton Visual Retention Test, and the Isaac Set Test [7], which were administered uniformly in all patients]. Finally, a common independent committee of neurologists analyzed all suspected dementia cases according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) [18]. This committee reviewed by teleconference all potential cases of dementia to obtain a consensus on its diagnosis and etiology based on the existing information. The etiology of each dementia case was determined according to the current diagnostic guidelines [19, 20]. Regarding VaD diagnosis, this was based on the National Institute of Neurological Disorders and Stroke and the Association Internationale pour la Recherche et l’Enseignement en Neurosciences (NINDS-AIREN) criteria [20]. For this study, VaD cases also encompassed mixed dementia cases (neurodegenerative and vascular). The diagnosis of AD was made according to the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria [19] and included probable AD and possible AD. Other dementias included dementia with Lewy bodies, frontotemporal dementia, Parkinson’s disease dementia, and dementia of unknown etiology.

Statistical analysis

Chi-square tests and t tests were used to describe the categorical and continuous variables of the sample. Cox proportional hazards models with delayed entry were performed to estimate the relationship between PS, GS, and risk of incident dementia over time. This method allows making a direct adjustment for age. At baseline, education and sex were included as covariates. Additionally, Cox proportional hazards models were used to examine the relationship between PS, GS, and the incidence of dementia according to etiology. The time variable for the individuals who have dementia was censored at the date of dementia diagnosis. The interaction between GS and PS was assessed. To allow direct effect-size comparisons between the prediction that GS and PS have on incident dementia, all hazard ratios (HR) are presented as SD changes. p-values < 0.05 were considered statistically significant and 95% confidence intervals (95% CI) are provided. All analyses were performed using SAS, version 9.2 (SAS Institute, Inc., Cary, NC).

RESULTS

A total of 2,104 participants from the Bordeaux-3C center were initially included; however, subjects were excluded due to missing data on one of both GS and PS measures (n = 316), prevalent dementia (n = 22), specific conditions causing GS and PS impairment (arthritis, Parkinson syndrome, visual impairment, dependence, etc.) or incomplete measures of GS or PS (n = 423). We also excluded 78 participants, which were lost to follow-up. Analyses are based on 1,265 (60.1%) participants (Supplementary Figure 1, flow chart). Those excluded (n = 761) were older (p < 0.01) but did not differ on sex, level of education or dementia status at the end of follow-up. Mean age of the cohort was 74.0 years (SD 4.8), 60.4% were women, and 19.0% were positive for ApoE4. The descriptive characteristics of the sample (n = 1,265) per incident dementia events are presented in Table 1. A total of 203 incident cases of dementia (16.0%) were recorded during the 12-year follow-up period. Those who developed dementia were women (p < 0.001), older (p < 0.01), had more ApoE4 genotype (p < 0.01), had reduced GS (p < 0.001), and had reduced PS (p < 0.01).

Table 1

Baseline characteristics of all participants and according to dementia events at follow-up (n = 1,265)

Characteristics	Total sample	Dementia event at follow up
	N = 1,265	No (n = 1,062)	Yes (n = 203)	p-value
	(Mean (SD) or n (%))	(Mean (SD) or n (%))	(Mean (SD) or n (%))
Age (y)	74.0 (4.8)	73.5 (4.7)	76.5 (4.6)	<0.01
Gender (women)	764 (60.4)	622 (58.6)	142 (69.9)	<0.01
Education
Primary without diploma	136 (10.8)	104 (9.8)	32 (15.8)	0.06
Primary with diploma	298 (23.6)	245 (23.1)	53 (26.2)
Secondary	356 (28.1)	308 (29.0)	48 (23.8)
Baccalaureate degree	262 (20.7)	223 (21.0)	39 (19.3)
Postgraduate degree	212 (16.8)	182 (17.1)	30 (14.9)
ApoE4	225 (19.0)	178 (17.8)	47 (25.5)	0.01
Diabetes (%)	120 (10.2)	92 (9.2)	28 (15.5)	0.04
Hypertension (%)	977 (77.2)	819 (77.1)	158 (77.8)	0.82
Stoke (%)	38 (3.0)	33 (3.1)	5 (2.5)	0.63
Current smoker (%)	68 (5.4)	61 (5.7)	7 (3.4)	0.01
MMSE Score (SD)	27.5 (1.9)	27.7 (1.8)	26.7 (2.2)	<0.01
TMT-A (seconds)	60.1 (23.6)	57.8 (21.2)	72.0 (31.0)	<0.01
Gait Speed (m/s)	1.4 (0.3)	1.4 (0.3)	1.5 (0.4)	<0.01

TMT-A, Trail Making Test A; ApoE4, Apolipoprotein E epsilon 4.

Table 2 shows the incidence of dementia according to GS and PS. Cox proportional hazards models adjusted for sex, education, and ApoE genotype showed that for each SD decline in GS (Model 1) at baseline, the risk of developing dementia was 1.22 (95% CI = 1.09–1.36; p < 0.01). For each SD decline in PS (Model 2), the risk of developing dementia was 1.45 (95% CI = 1.29–1.63; p < 0.01). When GS and PS were included in the same model (Model 3), both were independent predictors of dementia development and the HRs remained almost unchanged (HR = 1.20, 95% CI = 1.07–1.34 for GS and HR = 1.44, 95% CI = 1.28–1.62 for PS). Considering GS and PS in the model after adjusted for gender, educational level, ApoE4, and Mini-Mental State Exam scores, similar results were obtained: the two variables were associated with increased risk of dementia (HR 1.220, 95% CI 1.089–1.366; p≤001 and HR 1.428, 95% CI 1.262–1.616; p≤0.01, respectively). When GS and PS were included in the same model, both were independent associated with dementia, and the HRs remained almost unchanged (Table 3).

Table 2

Cox proportional Hazard model of incidence of dementia at 12-year follow-up according to gait speed and PS

	Model 1			Model 2			Model 3
	HR	95% CI	p	HR	95% CI	p	HR	95% CI	p
Gait speed (SD)	1.218	1.094–1.356	<0.01				1.199	1.074–1.338	<0.01
PS (SD)				1.452	1.291–1.633	<0.01	1.441	1.280–1.623	<0.01

No significant interaction was found between GS and PS (β= 0.001, RR = (0.993 to 1.002), p = 0.95). HR, hazard ratio; SD, standard deviation; 95% CI, 95% confidence interval; TMT-A, Trail Making Test A; ApoE4, Apolipoprotein E epsilon 4. Model 1: Adjusted for gender, educational level and ApoE4. Model 2: Adjusted for gender, educational level and ApoE4. Model 3: Walking speed and TMT-A speed were considered in the same model, adjusted for gender, educational level and ApoE4.

Table 3

Cox proportional Hazard model of incidence of dementia at 12-year follow-up according to gait speed and PS. Adjusted for gender, educational level, ApoE4, and MMSE

	Model 1			Model 2			Model 3
	HR	95% CI	p	HR	95% CI	p	HR	95% CI	p
Gait speed (SD)	1.220	1.089–1.366	<0.01				1.202	1.071–1.349	<0.01
PS (SD)				1.428	1.262–1.616	<0.01	1.420	1.253–1.609	<0.01

HR, hazard ratio; SD, standard deviation; 95% CI, 95% confidence interval; ApoE4, Apolipoprotein E epsilon 4; MMSE, Mini-Mental State Exam. Model 1: Adjusted for gender, educational level, MMSE, and ApoE4. Model 2: Adjusted for gender, educational level, MMSE, and ApoE4. Model 3: Walking speed and TMT-A speed were considered in the same model, adjusted for gender, educational level, MMSE and ApoE4.

In the analysis per dementia etiology (Table 4), for each decrease of one SD in either GS or PS, there was an increased risk of developing AD (HR = 1.40, 95% CI = 1.22 to 1.61; p < 0.01; HR = 1.16, 95% CI = 1.02 to 1.32; p = 0.02, respectively), or VaD (HR = 1.56, 95% CI 1.16 to 2.08; p = 0.003; HR = 1.34, 95% CI 1.05 to 1.71; p = 0.02, respectively). For the other dementias, only PS was a significant incidence predictor (HR = 1.55, 95% CI 1.17 to 2.07; p = 0.003) after adjusting for sex, educational level, and ApoE4 status. Only a non-significant trend was observed for the relation between GS and other dementias (HR = 1.29, 95% CI 0.99 to 1.69; p = 0.06). No significant interaction between GS and PS was observed.

Table 4

Cox proportional Hazard model of the incidence of dementia type at 12-year follow-up according to GS and PS

	Alzheimer’s disease			Vascular dementia			*Other dementia
	(n = 139)			(n = 31)			(n = 33)
HR	95% CI	p-value	HR	95% CI	p-value	HR	95% CI	p-value
Gait speed (SD)	1.162	1.019–1.324	0.02	1.342	1.051–1.713	0.02	1.293	0.992–1.686	0.06
PS (SD)	1.398	1.217–1.605	<0.01	1.556	1.163–2.082	<0.01	1.554	1.167–2.070	<0.01

HR, hazard ratio; SD, standard deviation; 95% CI, 95% confidence interval; TMT-A, Trail Making Test A. Gait speed and TMT-A speed were considered in the same model, adjusted for gender, educational level and Apolipoprotein E epsilon 4. *Other dementia: dementia with Lewy bodies, frontotemporal dementia, Parkinson’s disease dementia, and dementia of unknown etiology.

DISCUSSION

In this population-based cohort study of older adults, we have demonstrated that both GS and PS were associated with an increased risk of developing AD, VaD, and other types (only PS) of dementia. Moreover, the effect size of these relationships was comparable for each subtype of dementia. The effect size for a one SD increase in PS (40% to 56% increase) was higher than for that of GS (16% to 34% increase). Nonetheless, there is overlap of the 95% CIs. Interestingly, when GS and PS were considered in the same statistical model as possible predictors of dementia, both were found to be independently related to incidence. Moreover, when considering GS and PS as covariates in the same model, the HRs of each measure did not change. This result suggests that GS and PS were independently associated with a risk of developing dementia over the 12-year follow-up period and that these associations were independent of age, sex, educational level, and ApoE4 genotype and cardiovascular risk factor (hypertension, diabetes, current smoking, and stroke). Our results confirm our previous findings showing that a decline in cognitive speed precede for many years the occurrence of dementia [21]. However, this paper added an important fact: the decline in cognitive or motor speed is not specific of a given disease, but predicts in a same manner all type of dementia. Thus, slowness in cognition and gait could characterize a vulnerable brain at high risk of dementia, even preceding the prodromal stage of neurodegenerative diseases [22, 23].

In the Swedish National Study of Ageing and Care in Kungsholmen, Welmer et al. [24] investigated the relation of walking speed, processing speed, and their changes over time with dementia incidence in older adults. Our results were only partially consistent with those reported in the Swedish National Study of Ageing and Care in Kungsholmen due to some differences: the characteristics of eligible patients; the PS evaluation, which was defined by a composite measure of standard tests (digit cancellation, trail making test-A, pattern comparison); and the author explored the relation both evolution of processing speed, walking speed, and incident of dementia. Therefore, by showing that the predictive value of each measure is independent, our results agree with the view that age-related slowness could not be explained using a single factor approach. Taken together, these results support the idea that GS and PS represent two different dimensions of aging and that both contribute to define subjects with at risk of developing dementia. To the best of our knowledge, the additive effect of GS and PS is a finding that has not been previously reported. We would like to emphasize that the instruments used to measure GS and PS in the present study are short and inexpensive, making them easily implementable in the clinical setting. Our findings support the use of PS and GS as reliable screening tools for older persons, independently of their characteristics and ApoE status.

The fact that GS and PS were associated with dementia of vascular etiology is not surprising. These findings are consistent with other studies that reported similar results [25 –27]. Our result may be partly explained by: 1) the role played by cardiovascular risk factors (such as hypertension, diabetes mellitus, etc.) with poor motor function and dementia in the elderly and its consequences on brain vessels [28, 29]; and 2) GS is significantly associated with muscle strength [30, 31] and muscle loss is highly correlated with inflammation, oxidative stress, and sex steroids levels which have been linked to cognitive decline such as VaD [32]. Moreover, the association between PS and vascular lesions in the brain has been already documented [33, 34].

By the same token, the association between GS, PS, and other dementias are not surprising since motor functions are early impaired in the majority of these diseases [35, 36].

On the other hand, the association between GS, PS, and AD is unexpected even if previous studies have found that slow GS is associated with AD incidence [37 –39]. Slow gait speed may constitute a risk factor for AD. Low GS possibly reflects an age-related reduction in physiologic reserve, which may render the brain more vulnerable to the accumulation of AD pathology and subsequent damage [40]. This result contributes to further understanding of the relation between cognitive and motor function in older adults [41]. Several studies [3 , 42–44] have shown that PS was associated to the risk of AD and the likelihood ratio test confirmed the incremental effect of informant reports in addition to the neuropsychological test scores. In AD, PS decrements are partly attributable to slower perceptuomotor, visual, and attention disorders.

Welmer et al. [24] also reported that cognitive deficits in the psychomotor speed domain may be expected to occur at very early stages of the dementia process, even before other clinical signs are present.

The strengths of the present study are the large number of participants selected in a community-based setting and the follow-up quality of the cohort. GS, PS, and global cognition were performed by trained psychologists. A selection bias could have overestimated our result. The number of missing values for PS was high, representing more than 10% of the sample. This illustrates the technical difficulties in obtaining this measure. Subjects with at least one error in the TMT-A were also excluded from the analysis [14]. But the number of missing values for GS and the number of exclusions for TMT-A was less frequent (less than 5%). The exclusion of subjects due to missing data and/or being lost to follow-up may have influenced our results, deriving in underestimating the relationship between both GS and PS and risk of dementia. Another limitation of our study is that we have not analyzed all the possible determinants of the two measures particularly coronary and renal disease, muscle weakness, previous stroke, and metabolic disease. In fact, as classically observed in epidemiologic studies, excluded participants had more co-morbidities and greater disability.

Conclusion

In older French people aged 65+, our findings showed that both low GS and PS were independently associated with risk of AD and VaD. Slowness is a major component of brain aging, whatever its mechanism.

Footnotes

ACKNOWLEDGMENTS

The Three-City Study is conducted under a partnership agreement between the Institut National de la Santé et de la Recherche Médicale (Inserm), the Institut de Santé Publique et Développement of the Victor Segalen-Bordeaux 2 University, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The Three-City study is also supported by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, Mutuelle Générale de l’Éducation Nationale, Institut de la Longévité, Regional Councils of Aquitaine and Bourgogne, Fondation de France, and the Ministry of Research-Inserm Program “Cohortes et collections de données biologiques”. Biological assays regarding hemostatic parameters were supported by a grant from the Agence Nationale de la Recherche (ANR 2007-LVIE-005-01). Agence Nationale de la Recherche ANR PNRA 2006, LongVie 2007 and the “Fondation Plan Alzheimer” (FCS 2009-2012) also contributed.

Authors’ disclosures available online ().

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

References

Montero-Odasso

, Verghese

, Beauchet

, Hausdorff

(2012) Gait and cognition: A complementary approach to understanding brain function and the risk of falling. J Am Geriatr Soc 60, 2127–2136.

Krall

, Carlson

, Fried

, Xue

(2014) Examining the dynamic, bidirectional associations between cognitive and physical functioning in older adults. Am J Epidemiol 180, 838–846.

Fitzpatrick

, Buchanan

, Nahin

, Dekosky

, Atkinson

, Carlson

, Williamson

; Ginkgo Evaluation of Memory (GEM) Study Investigators (2007) Associations of gait speed and other measures of physical function with cognition in a healthy cohort of elderly persons. J Gerontol A Biol Sci Med Sci 62, 1244–1251.

Rosano

, Simonsick

, Harris

, Kritchevsky

, Brach

, Visser

, Yaffe

, Newman

(2005) Association between physical and cognitive function in healthy elderly: The health, aging and body composition study. Neuroepidemiology 24, 8–14.

Dobbs

, Shergill

(2013) How effective is the Trail Making Test (Parts A and B) in identifying cognitively impaired drivers? Age Ageing 42, 577–581.

Soumaré

, Tavernier

, Alpérovitch

, Tzourio

, Elbaz

(2009) A cross-sectional and longitudinal study of the relationship between walking speed and cognitive function in community-dwelling elderly people. J Gerontol A Biol Sci Med Sci 64, 1058–1065.

Isaacs

, Kennie

(1973) The Set test as an aid to the detection of dementia in old people. Br J Psychiatry J Ment Sci 123, 467–470.

3C Study Group (2003) Vascular factors and risk of dementia: Design of the Three-City Study and baseline characteristics of the study population. Neuroepidemiology 22, 316–325.

Tabue-Teguo

, Le Goff

, Avila-Funes

, Frison

, Helmer

, Féart

, Amieva

, Dartigues

(2015) Walking and psychomotor speed in the elderly: Concordance, correlates and prediction of death. J Nutr Health Aging 19, 468–473.

10.

Elbaz

, Shipley

, Nabi

, Brunner

, Kivimaki

, Singh-Manoux

(2014) Trajectories of the Framingham general cardiovascular risk profile in midlife and poor motor function later in life: The Whitehall II study. Int J Cardiol 172, 96–102.

11.

Avila-Funes

, Carcaillon

, Helmer

, Carrière

, Ritchie

, Rouaud

, Tzourio

, Dartigues

, Amieva

(2012) Is frailty a prodromal stage of vascular dementia? Results from the Three-City Study. J Am Geriatr Soc 60, 1708–1712.

12.

Qiu

(2012) Preventing Alzheimer’s disease by targeting vascular risk factors: Hope and gap. J Alzheimers Dis 32, 721–731.

13.

Reitan

(1958) Trail making manual for administration, scoring, and interpretation. Department of Neurology, Section of Neuropsychology, Indiana University Medical Center, Indianapolis.

14.

Amieva

, Goff

, Stoykova

, Dartigues

(2009) Trail Making Test A et B (version sans correction des erreurs): Normes en population chez des sujets âgés, issues de l’étude des trois Cités. Rev Neuropsychol 1, 210–220.

15.

Tombaugh

(2004) Trail Making Test A and B: Normative data stratified by age and education. Arch Clin Neuropsychol 19, 203–214.

16.

Armitage

(1946) An analysis of certain psychological tests used for the evaluation of brain injury. Psychol Monogr 60, i–48.

17.

Folstein

, Folstein

, McHugh

(1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12, 189–198.

18.

American Psychiatric Association (2000) Task force on DSM-IV. Diagnostic and Statistical Manual of Mental Disorders, DSM-IV-TR, 4th Ed, American Psychiatric Association, Washington, DC.

19.

McKhann

, Knopman

, Chertkow

, Hyman

, Jack

Jr , Kawas

, Klunk

, Koroshetz

, Manly

, Mayeux

, Mohs

, Morris

, Rossor

, Scheltens

, Carrillo

, Thies

, Weintraub

, Phelps

(2011) 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 7, 263–269.

20.

Román

, Tatemichi

, Erkinjuntti

, Cummings

, Masdeu

, Garcia

, Amaducci

, Orgogozo

, Brun

, Hofman

(1993) Vascular dementia: Diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 43, 250–260.

21.

Amieva

, Mokri

, Le Goff

, Meillon

, Jacqmin-Gadda

, Foubert-Samier

, Orgogozo

, Stern

, Dartigues

(2014) Compensatory mechanisms in higher-educated subjects with Alzheimer’s disease: A study of 20 years of cognitive decline. Brain J Neurol 137, 1167–1175.

22.

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, 857–886.

23.

Kueper

, Speechley

, Lingum

, Montero-Odasso

(2017) Motor function and incident dementia: A systematic review and meta-analysis. Age Ageing, doi: 10.1093/ageing/afx084

24.

Welmer

, Rizzuto

, Qiu

, Caracciolo

, Laukka

et al. (2014) Walking speed, processing speed, and dementia: A population-based longitudinal study. J Gerontol A Biol Sci Med Sci 69, 1503–1510.

25.

Ben Assayag

, Shenhar-Tsarfaty

, Korczyn

, Kliper

, Hallevi

, Shopin

, Auriel

, Giladi

, Mike

, Halevy

, Weiss

, Mirelman

, Bornstein

, Hausdorff

(2015) Gait measures as predictors of poststroke cognitive function: Evidence from the TABASCO study. Stroke 46, 1077–1083.

26.

Verghese

, Wang

, Lipton

, Holtzer

, Xue

(2007) Quantitative gait dysfunction and risk of cognitive decline and dementia. J Neurol Neurosurg Psychiatry 78, 929–935.

27.

Wang

, Larson

, Bowen

, Van Belle

(2006) Performance-based physical function and future dementia in older people. Arch Intern Med 166, 1115–1120.

28.

Jefferson

, Hohman

, Liu

, Haj-Hassan

, Gifford

, Benson

, Skinner

, Lu

, Sparling

, Sumner

, Bell

, Ruberg

(2015) Adverse vascular risk is related to cognitive decline in older adults. J Alzheimers Dis 44, 1361–1373.

29.

Dumurgier

, Elbaz

, Dufouil

, Tavernier

, Tzourio

(2010) Hypertension and lower walking speed in the elderly: The Three-City study. J Hypertens 28, 1506–1514.

30.

Marsh

, Miller

, Saikin

, Rejeski

, Hu

, Lauretani

, Bandinelli

, Guralnik

, Ferrucci

(2006) Lower extremity strength and power are associated with 400-meter walk time in older adults: The InCHIANTI study. J Gerontol A Biol Sci Med Sci 61, 1186–1193.

31.

Rantanen

, Guralnik

, Izmirlian

, Williamson

, Simonsick

, Ferrucci

, Fried

(1998) Association of muscle strength with maximum walking speed in disabled older women. Am J Phys Med Rehabil 77, 299–305.

32.

Quan

, Xun

, Chen

, Wen

, Wang

, Chen

, He

(2017) Walking pace and the risk of cognitive decline and dementia in elderly populations: A meta-analysis of prospective cohort studies. J Gerontol A Biol Sci Med Sci 72, 266–270.

33.

Duering

, Gonik

, Malik

, Zieren

, Reyes

, Jouvent

, Hervé

, Gschwendtner

, Opherk

, Chabriat

, Dichgans

(2013) Identification of a strategic brain network underlying processing speed deficits in vascular cognitive impairment. Neuroimage 66, 177–183.

34.

Vasquez

, Zakzanis

(2015) The neuropsychological profile of vascular cognitive impairment not demented: A meta-analysis. J Neuropsychol 9, 109–136.

35.

Dahdal

, Meyer

, Chaturvedi

, Nowak

, Roesch

, Fuhr

, Gschwandtner

(2016) Fine motor function skills in patients with Parkinson disease with and without mild cognitive impairment. Dement Geriatr Cogn Disord 42, 127–134.

36.

Fling

, Dale

, Curtze

, Smulders

, Nutt

, Horak

(2016) Associations between mobility, cognition and callosal integrity in people with parkinsonism. Neuroimage Clin 11, 415–422.

37.

Gillain

, Dramé

, Lekeu

, Wojtasik

, Ricour

, Croisier

, Salmon

, Petermans

(2016) Gait speed or gait variability, which one to use as a marker of risk to develop Alzheimer disease? A pilot study. Aging Clin Exp Res 28, 249–255.

38.

Del Campo

, Payoux

, Djilali

, Delrieu

, Hoogendijk

, Rolland

, Cesari

, Weiner

, Andrieu

, Vellas

; MAPT/DSA Study Group (2016) Relationship of regional brain β-amyloid to gait speed. Neurology 86, 36–43.

39.

Camargo

, Weinstein

, Beiser

, Tan

, DeCarli

, Kelly-Hayes

, Kase

, Murabito

, Seshadri

(2016) Association of physical function with clinical and subclinical brain disease: The Framingham Offspring Study. J Alzheimers Dis 53, 1597–1608.

40.

Nadkarni

, Perera

, Snitz

, Mathis

, Price

, Williamson

, DeKosky

, Klunk

, Lopez

(2017) Association of brain amyloid-β with slow gait in elderly individuals without dementia: Influence of cognition and apolipoprotein E ɛ4 genotype. JAMA Neurol 74, 82–90.

41.

Kikkert

, Vuillerme

, van Campen

, Hortobágyi

, Lamoth

(2016) Walking ability to predict future cognitive decline in old adults: A scoping review. Ageing Res Rev 27, 1–14.

42.

B Ble

, Volpato

, Zuliani

, Guralnik

, Bandinelli

, Lauretani

, Bartali

, Maraldi

, Fellin

, Ferrucci

(2005) Executive function correlates with walking speed in older persons: The InCHIANTI study. J Am Geriatr Soc 53, 410–415.

43.

Holtzer

, Verghese

, Xue

, Lipton

(2006) Cognitive processes related to gait velocity: Results from the Einstein Aging Study. Neuropsychology 20, 215–223.

44.

Rabin

, Wang

, Katz

, Derby

, Buschke

, Lipton

(2012) Predicting Alzheimer’s disease: Neuropsychological tests, self-reports, and informant reports of cognitive difficulties. J Am Geriatr Soc 60, 1128–1134.