Association of Motoric Cognitive Risk Syndrome with Cardiovascular Disease and Risk Factors: Results from an Original Study and Meta-Analysis

Abstract

Background:

Motoric cognitive risk (MCR) syndrome, a recently described pre-dementia syndrome, has been associated with cardiovascular disease and their risk factors (CVDRF).

Objective:

To determine whether MCR syndrome was associated with CVDRF in French community-dwelling older adults, and to quantitatively evaluate, with a systematic review and meta-analysis, the association of MCR syndrome with CVDRF.

Methods:

Based on a cross-sectional design, 238 older adults without dementia were selected from the French GAIT study. An English and French systematic Medline and Embase search (without limiting date of publication) was also conducted in February 2017 using the terms “motoric cognitive risk syndrome” OR “motoric cognitive risk” OR “motoric risk”. The systematic review and meta-analysis included 8 studies. CVDRF were defined as cardiovascular diseases, hypertension, diabetes, stroke, obesity and abnormal waist-hip ratio (WHR).

Results:

The prevalence of MCR syndrome in the current original study was 16.8%. MCR syndrome was associated with abnormalWHR(Odds ratio [OR] >2.8 with p < 0.020) and high blood pressure (OR >2.5 with p < 0.025). Of the 202 originally identified abstracts, 7 (3.5%) were selected for the systematic review. The meta-analysis showed that all pooled OR were significant with a p-value <0.001 (OR = 1.41 for cardiovascular diseases, 1.21 for hypertension, 1.44 for diabetes, 2.05 for stroke, and 1.34 for obesity). When pooling all CVDRF, the overall OR was 1.38 (95% CI, 1.33–1.45) with p-value <0.001.

Conclusion:

MCR syndrome is significantly associated with CVDRF. These findings suggest that a vascular mechanism may underlie the pathophysiology of MCR syndrome.

Keywords

Cognitive disorders gait disorders meta-analysis prediction

INTRODUCTION

The past decade has been characterized by increased interest in identifying and validating biomarkers for early diagnosis of dementia [1]. The emergence of biomarkers has extensively contributed to the diagnosis of pre-dementia stages, but their use has limitations in many settings [1 –3]. For instance, access to comprehensive neuropsychological testing or expansive neuroimaging may be difficult and the compliance to lumbar puncture for an early diagnosis of dementia may be low [1 –4]. The cost of biomarkers also limits their use, and as such many of them are not accessible in primary care or in low-income countries [4, 5]. Hence, there is a need to optimize the accessibility of clinical dementia risk assessments in community-dwelling populations, in order to initiate effective preventive measures.

Over the past 5 years, two clinical characteristics have been reported as predictors of dementia. First, subjective cognitive impairment (SCI) (i.e., perceived changes in cognition in the absence of objective evidence) has been identified as a pre-mild cognitive impairment stage and is, therefore, considered the earliest clinical stage of dementia [2]. Second, poor gait performance, such as slow gait speed, has also been associated with the occurrence of dementia [6]. Recently, a pre-dementia syndrome combining cognitive complaint and slow gait speed, known as motoric cognitive risk (MCR) syndrome, has been associated with the occurrence of Alzheimer’s disease (AD) and vascular dementia (VaD) [7 –9]. MCR syndrome definition is based on two clinical symptoms, which do not rely on complex, cost and time-consuming assessment [7]. Thus, MCR syndrome is, easy to diagnose in clinical routine and may be a solution to screen individuals at risk of dementia in general population and in primary care, before a referral to a memory clinic. MCR syndrome is a pre-dementia stage similar to mild cognitive impairment (MCI) [7 –9] and, thus, should also be considered as a new opportunity to improve characterization of individuals involved in clinical trials for the development of drugs treating dementia. Finally, MCR syndrome is a newly reported syndrome with a high prevalence around 9.7% in the community-dwelling population. Therefore, there is a need to better understand the underlying neuropathological mechanisms leading to dementia [7 –9].

There are mixed results regarding the subtype (i.e., AD or VaD) of dementia predicted by MCR syndrome. A stronger association with VaD compared to AD has been reported in some studies, whereas in other studies AD was the only condition predicted by MCR syndrome [6 –9]. More recently, a systematic review and meta-analysis, which examined the association of poor gait performance with the incidence of dementia, provided evidence that poor gait performance, and in particular slow walking speed, predicts dementia especially VaD [6]. Consequently, even if the subtype of dementia predicted by MCR syndrome remains uncertain, previous results suggest a specific association between MCR syndrome and VaD [6].

Cardiovascular diseases and their risk factors (CVDRF) such as high blood pressure, obesity and diabetes contribute to VaD [10 –12]. The association of MCR syndrome with CVDRF has been reported in both American and Japanese populations and in aggregate multinational populations [7 , 14]. Furthermore, this association still remains uncertain in the European population. Indeed, published data of pooled samples of individuals from different countries [8, 15] make it impossible to determine the association in the European population. Notably, the only study published in Europe (France) did not find any significant differences between MCR and non-MCR individuals for CVDRF [16]. Potential differences in health conditions, as well as differences in behavioral, cultural and lifestyle factors may influence the association of MCR syndrome with CVDRF in different populations. Thus, it is important to determine whether MCR syndrome is effectively associated with CVDRF among different populations, and whether this association changes between them. Demonstrating that MCR syndrome is associated with CVDRF, regardless of the origin of the population, will help to understand the pathophysiological mechanisms underlying MCR syndrome and develop common global strategies for preventive measures for those at risk of dementia.

To date, no systematic critical evaluation of studies with the purpose of examining the association of MCR syndrome with CVDRF has been performed. We hypothesized that MCR syndrome would be associated with CVDRF in the French population and that the profile of associated CVDRF would change across different populations. To test this hypothesis, we used the database of an original study performed in France in addition to a systematic review and meta-analysis of studies previously conducted on MCR syndrome and CVDRF. Consequently, the aims of the current study were to: 1) determine whether MCR syndrome was associated with CVDRF in a sample of French community-dwelling older adults, and 2) quantitatively evaluate the association between MCR syndrome and CVDRF by systematically reviewing the literature.

METHODS

Original study

Population and study design

A total of 238 individuals (40 with MCR syndrome and 198 with non-MCR syndrome), who had been recruited in the “Gait and Alzheimer Interactions Tracking” (GAIT) study, were selected for the present study [17]. The GAIT study is a cross-sectional study that was conducted in France between November 2009 and November 2015. The study procedure has been previously described in detail elsewhere [17]. In summary, all eligible participants were referred to the memory clinic of Angers University Hospital, France, for a cognitive complaint evaluation. The GAIT study inclusion criteria were: aged 65 years and over, community-dwelling with an adequate understanding of French. Exclusion criteria included acute medical illness in the past month, extrapyramidal rigidity of the upper limbs, neurological and psychiatric diseases other than cognitive impairment, and severe medical conditions that affected gait with an inability to walk for 15 minutes unassisted. For the present analysis, we excluded participants with dementia and those without cognitive or gait assessments as well as participants without information on CVDRF, which were defined as heart and blood vessel disorders (i.e., coronary heart disease, cerebrovascular disease, peripheral vascular disease, rheumatic and congenital heart disease and deep vein thrombosis and pulmonary embolism) and their related risk factors (i.e., hypertension, diabetes, obesity, abnormal waist hip ratio (WHR)) [18].

Study assessments

A full-standardized examination was performed on all participants. Cognitive complaint was recorded using a standardized questionnaire [19]. Age, sex, the number of drugs taken daily, body mass index (BMI; kg/m²), WHR defined as the ratio of the circumference of the waist to that of the hips), as well as systolic and diastolic blood pressure when lying for 3 minutes, at rest, were recorded. High blood pressure has been defined as a systolic pressure ≥140 mmHg or a diastolic pressure ≥90 mmHg [20, 21], as well as the use of antihypertensive drugs, which is defined by the use of at least one of the following drug therapies: renin-angiotensin inhibitor agents, beta-blocking agents, diuretics, calcium channel blockers and central antihypertensive agents. Diabetes has been defined as using insulin or anti-diabetic pills. Cardiovascular diseases have been defined as the presence of at least one of the following diseases: coronary heart disease, cerebrovascular disease, peripheral vascular disease, rheumatic and congenital heart disease, deep vein thrombosis and pulmonary embolism [18]. Cardiovascular diseases were collapsed into a single “Yes” versus “No” category. Hypertension, diabetes obesity and abnormal WHR were considered as cardiovascular risk factors [18].

Motoric cognitive risk syndrome

The diagnosis of MCR syndrome was made according to criteria described by Verghese et al. [7]: a combination of SCI with the presence of slow gait in the absence of dementia or any mobility disability. As cognitive complaint was the reason for referral to the memory clinic of Angers University Hospital, France, for the GAIT study, all participants met the cognitive complaint criteria. Gait speed was measured using a GAITRite^® system (Gold walkway, 972 cm long) and expressed in m/s [13]. Slow gait speed was defined as gait speed one standard deviation (SD) or more below age-and sex-appropriate mean values established in the present cohort, as done in previous studies [7 –9].

Standard protocol approvals, registrations, and patient consents

This study was conducted in accordance with the ethical standards set forth in the Helsinki Declaration (1983). Participants in the study were included after obtaining written informed consent for research. The Angers Local Ethics Committee approved the study protocol.

45.1.5 Statistics

The participants’ characteristics were summarized using means and SD or frequencies and percentages, as appropriate. Participants were classified into two groups: non-MCR and MCR. Between-group comparisons were performed using an unpaired t-test or chi square test, as appropriate. Multiple logistic regression analyses were performed to examine the association between MCR syndrome (i.e., dependent variable) and CVDRF (i.e., independent variable), adjusted according to the participants’ characteristics. p-values less than 0.05 were considered as statistically significant. All statistics were performed using SPSS (version 23.0; SPSS, Inc., Chicago, IL).

Systematic literature search and meta-analysis

Search strategy and data extraction

An English and French systematic Medline (Pubmed) and EMBASE (Ovid, EMBASE) search, with no limit of publication date, was conducted in February 2017 using the terms “motoric cognitive risk syndrome” OR “motoric risk” OR “motoric cognitive risk”. An iterative process was used to ensure that all relevant articles were obtained. A further hand search of bibliographic references of selected papers and existing reviews was also conducted to identify potential studies not captured in the electronic database searches.

Study selection

One member of the team (HS) screened the abstracts from the initial search and obtained records deemed potentially relevant. Initial screening criteria for the abstracts were: 1) human studies, 2) articles in English and French, 3) original study with peer-reviewed publications in an indexed scientific journal, 4) MCR syndrome and CVDRF as outcomes and 5) two groups of participants including MCR and non-MCR syndrome individuals. If a study met the initial selection criteria or its eligibility could not be determined from the title and abstract (or the abstract was unavailable), the full text was retrieved. Two reviewers (HS and OB) then independently assessed the full text to determine its inclusion status. Disagreements were resolved by a third reviewer (GA). The full articles were screened using the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) checklist, which describes items that should be included in reports of cohort studies [22]. Furthermore, the quality of each study included in the meta-analysis was assessed using the PRISMA checklist [23]. Final selection of criteria was therefore applied when at baseline assessment participants were free of dementia and when the prediction was about dementia. The study selection procedure is presented in the PRISMA flow diagram [23] (Fig. 1).

Fig. 1.

Flow diagram of study selection process.

Qualitative analysis

Of the 202 originally identified abstracts, 107 (53.0%) studies were dismissed because 10 studies examined animals, 32 studies were written in a language other than English or French, 8 studies were not peer-reviewed publications published in indexed scientific journals, and 57 studies did not use MCR syndrome as an outcome. Following a thorough examination of the 12 studies that met the initial inclusion criteria, 5 studies (41.7%) were excluded because 3 did not have a control group (i.e., non-MCR individuals) and 2 studies did not use CVDRF as outcomes. The remaining 7 studies were included in the systematic review.

Meta-analysis

The association of MCR syndrome with CDVRF was determined using odds ratios (OR) with a 95% CI. When ORs were not provided in the results and when information to calculate it was provided, it was calculated using Dag-stat, a spreadsheet for the calculation of comprehensive statistics for the assessment of diagnostic tests and inter-rater agreement that provides a comprehensive range of statistics for 2 by 2 tables [24]. The analyses used different outcomes: cardiovascular diseases were collapsed into a single category “Yes” versus “No”, in which stroke, hypertension, diabetes, and obesity were the dependent variables with MCR syndrome as the independent variable. Fixed effects meta-analyses were performed on the estimates to generate summary values. Results are presented as forest plots. Heterogeneity between studies was assessed using Cochrane’s Chi-squared test for homogeneity (Chi²), and the amount of variation due to heterogeneity was estimated by calculating the I² [25]. Statistical analyses were performed using the software program WINPEPI Computer Programs for Epidemiologists (version 11.54) [26].

RESULTS

As shown in Table 1, the prevalence of MCR syndrome in the GAIT study was 16.8% (n = 40). Participants with MCR syndrome took more medications (p = 0.010), had a greater mean value of BMI (p < 0.001) and WHR (p = 0.006), which was also more frequently abnormal (p = 0.013) compared to non-MCR syndrome participants. They also had a greater prevalence of high blood pressure (p = 0.018) and diabetes (p = 0.009) compared to non-MCR syndrome participants. Logistic regression showed a significant association between MCR syndrome and abnormal WHR (p = 0.013), high blood pressure (p = 0.016) and diabetes (p = 0.022) when the model was adjusted for age and sex (Table 2). This association was confirmed in the backward regression model for WHR (p = 0.019) and high blood pressure (p = 0.024), but not for diabetes. The fully adjusted regression model reported no significant association. Only a trend was reported for WHR (p = 0.064) and high blood pressure (p = 0.068).

Table 1

Comparisons of participant characteristics according to motoric cognitive risk (MCR) syndrome status (n = 238)

	Total population(n = 238)	MCR individuals(n = 40)	Non-MCR individuals(n = 198)	p *
Age, mean±SD (y)	71.4±3.6	71.2±3.0	71.4±3.7	0.725
Female, n (%)	89 (37.4)	15 (37.5)	74 (37.4)	0.988
Number of therapeutic classes taken daily,	3.0±2.9	4.1±3.8	2.8±2.6	0.010
mean±SD
Body mass index (kg/m²)
Mean±SD	26.1±4.0	28.3±5.9	25.6±3.4	<0.001
≥25 †	133 (55.9)	27 (67.5)	106 (53.5)	0.105
Waist hip ratio
Mean±SD	0.9±0.1	1.0±0.1	0.9±0.1	0.006
Abnormal ‡	156 (65.5)	33 (82.5)	123 (62.1)	0.013
Blood pressure # (mmHg),
Systolic, mean±SD	138.2±15.1	139.0±16.5	138.0±14.8	0.717
Diastolic, mean±SD	80.5±10.4	82.5±10.6	80.0±10.4	0.173
High blood pressure ¶	145 (60.9)	31 (77.5)	114 (57.6)	0.018
Cardiovascular diseases §	118 (49.6)	24 (60.0)	94 (47.5)	0.148
Diabetes **	5 (2.1)	3 (7.5)	2 (1.0)	0.009

^*Comparison based on unpaired t-test or chi square, as appropriate;

^†Definition of obesity;

^‡Value ≥0.96 for male and 0.81 for female;

^#Mean value in rest and lying condition for 3 minutes;

^¶ Defined as a systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥ 90 mmHg or use of antihypertensive drugs;

^§§Presence of at least one of the following diseases: coronary heart disease, cerebrovascular disease, peripheral vascular disease, rheumatic and congenital heart disease, deep vein thrombosis and pulmonary embolism;

^**Use of insulin or anti-diabetic pills. p-value significant (i.e., p < 0.05) indicated in bold.

Table 2

Multiple logistic regressions showing the association between motoric cognitive risk syndrome (dependent variable) and cardiovascular risk factors (n = 238)

	Model 1 *			Model 2 *			Model 3 *
	OR	[95% CI]	p	OR	[95% CI]	p	OR	[95% CI]	p
Obesity †	1.84	[0.89;3.81]	0.099	1.20	[0.54;2.66]	0.657		–
Abnormal waist hip ratio ‡	3.07	[1.27;7.46]	0.013	2.47	[0.95;6.45]	0.064	2.85	[1.19;6.81]	0.019
High blood pressure #	2.72	[1.21;6.15]	0.016	2.35	[0.94;5.87]	0.068	2.51	[1.13;5.60]	0.024
Cardiovascular diseases¶	1.70	[0.84;3.43]	0.138	1.01	[0.45;2.27]	0.987		–
Diabetes §	1.47	[1.36;54.22]	0.022	4.75	[0.72;31.49]	0.106		–

OR, odd ratio; CI, confidence interval. Model 1: Adjustment for age and sex; Model 2: Fully adjusted model; Model 3: Backward model.

^*All models adjusted for age and sex;

^†Body mass index ≥25 kg/m²;

^‡Value ≥0.96 for male and 0.81 for female;

^#Defined as a systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg or use of antihypertensive drugs;

^¶Presence of at least one of the following diseases: coronary heart disease, cerebrovascular disease, peripheral vascular disease, rheumatic and congenital heart disease, deep vein thrombosis and pulmonary embolism;

^§Use of insulin or anti-diabetic pills. p-value significant (i.e., p < 0.05) indicated in bold.

Table 3 summarizes the 8 studies that were included in the systematic review [7 , 27]. All studies were published over the last 4 years. Five studies were conducted in a single country: United States [7], France (17 plus the current study), Japan [13], and India [14]. Three studies pooled data from different countries [8 , 27]. The number of participants ranged from 139 [14] to 26,802 [8]. All participants were older adults at baseline, with a minimum of either >60 [8 , 26] or >70 [7] years of age. Data collection was based on observational studies with a longitudinal prospective cohort design for two studies [7, 15] and cross-sectional design for 6 studies [8 , 27]. The prevalence of MCR syndrome ranged from 6.4% [13] to 27.3% [14]. A total of 6 (75%) studies found a significant association between MCR syndrome and cardiovascular risk factors [7 , 16]. The two studies that reported no significant association were conducted in India [14, 27]. The profile of the association with cardiovascular risk factors changed across studies. A significant association with cardiovascular diseases occurred in 3 studies [7 , 13], hypertension in 3 studies [7, 8], diabetes in 4 studies [7 , 13], stroke in one study [8] and obesity in 3 studies [13 , 16]. The meta-analysis was performed on 8 studies (i.e., 7 previously published studies identified by literature search as described in the methods section as well as the current original study) with a total of 50,025 participants including 4,287 (8.6%) individuals with MCR syndrome and 45 738 individuals without MCR syndrome. Figure 2 shows the forest plot of the pooled OR for the association between MCR syndrome and each CVDRF (i.e., cardiovascular diseases, hypertension, diabetes, stroke, and obesity), which were computed with meta-analysis techniques. All pooled ORs were significant. The pooled OR was 1.41 (95% CI, 1.29–1.53) with p-value <0.001 for cardiovascular diseases, 1.21 (95% CI, 1.12–1.30) with p-value <0.001 for hypertension, 1.44 (95% CI, 1.31–1.58) with p-value <0.001 for diabetes, 2.05 (95% CI, 1.78–2.36) with p-value <0.001 for stroke, and 1.34 (95% CI, 1.20–1.50) with p value <0.001 for obesity. When pooling all cardiovascular risk factors together, the overall value was 1.38 (95% CI, 1.33–1.45) with p-value<0.001 (Fig. 3).

Table 3

Summary of the main characteristics of selected studies (n = 8) included in the qualitative systematic review exploring the association between motoric cognitive risk syndrome and cardiovascular risk factors

References	Design	Country / Setting / Population	Main results
			Cardiovascular diseases	Hypertension	Diabetes	Stroke	Obesity
Verghese et al. [7]	- Longitudinal prospective -cohort design	- US	OR * † = 1.94 [1.02;3.70]	OR † = 2.00 [1.05;3.81]	OR † = 2.73 [1.46;5.11]	OR † = 1.85 [0.83;4.10]	–
		- Community-dwelling
		- ≥70 years
		- N = 767
		- Prevalence of MCR: 6.8% (n = 52)
Verghese et al. [8]	- Cross-sectional design	- Multicountry	OR † ‡ = 1.41 [1.26;1.57]	OR † = 1.26 [1.15;1.37]	OR † = 1.40 [1.25;1.56]	OR † = 2.07 [1.79;2.39]	–
	-22 studies pooled	- Community-dwelling
		- ≥60 years
		- N = 26,802
		- Prevalence of MCR: 9.7% (n = 2,600)
Doi et al. [13]	- Cross-sectional design	- Japan	¶ § = 1.13 [0.91;1.39]	¶ = 0.91 [0.76;1.08]	¶ = 1.49 [1.20;1.85]	–	¶ = 1.23 [1.02;1.47]
		- Community-dwelling
		- ≥65 years
		- N = 9683
		- Prevalence of MCR: 6.4% (n = 619)
Beauchet et al. [16]	- Cross-sectional design	- France	OR † # = 0.89 [0.24;3.26]	OR † = 1.43 [0.63;3.23]	–	–	OR † = 6.11 [1.75;21.34]
		- Community-dwelling
- ≥65 years
		- N = 171
		- Prevalence of MCR: 16.4% (n = 28)
Ayers et al. [15]	- Longitudinal prospective cohort design	- Multicountry (US and Europe)	OR † ** = 1.63 [1.35;1.97]	–	–	–	OR † = 1.37 [1.18;1.59]
	- 3 studies pooled	- Community-dwelling
		- >65 years
		- N = 11,867
		- Prevalence of MCR: 7.0% (n = 836)
Mergeche et al. [27]	- Cross-sectional design	- France and India	–	OR † = 1.41 [0.85;2.36]	OR † = 1.39 [0.74;2.63]	OR † = 1.58 [0.59;4.23]	–
	- 2 pooled studies	-Community-dwelling
		- >65 years for French sample and >60 years for Indian sample
		- N = 358
		- Prevalence of MCR: 20.7% (n = 74)
Wang et al., 2016 [14]	Cross-sectional design One study	- India	OR † = 0.36 [0.04;3.05]	OR = 1.74 [0.79; 3.83]	OR = 1.24 [0.54; 2.85]	–	–
		- Community-dwelling
		- >60 years
		- N = 139
		- Prevalence of MCR: 27.3% (n = 38)
Beauchet et al. [19]	Cross-sectional design	- France	OR †† = 1.70 [0.84;3.43]	OR †† = 2.72 [1.21;6.15]	OR †† = 1.47 [1.36;54.22]	–	OR †† = 1.84 [0.89;3.81]
		- Community-dwelling
		- ≥65 years
		- N = 238
		- Prevalence of MCR: 16.8% (n = 40)

[95%, confidence interval]

^*Combination of coronary artery disease, previous myocardial infarction and congestive heart failure;

^†Calculated using Dag-stat, a spreadsheet for the calculation of comprehensive statistics for the assessment of diagnostic tests and inter-rater agreement that provides a comprehensive range of statistics for 2 by 2 tables [24];

^‡Angina; ¶ Adjusted for age, sex, education and medication use;

^§Heart disease;

^#Dyslipidemia;

^**Vascular diseases;

^††Adjusted on age and sex.

Fig. 2.

Forest plot of pooled estimated odds ratios for the association between motoric cognitive risk syndrome and: A) cardiovascular diseases, B) hypertension, C) diabetes, D) stroke, and E) obesity. Square box area proportional to the sample size of each study; horizontal lines corresponding to the 95% confidence interval; diamond representing the summary value; vertical line corresponding to a odd ratio combined with relative risk of 1.00, equivalent to no difference.

Fig. 3.

Forest plot of pooled estimated odds ratios for the association between motoric cognitive risk syndrome and pooled cardiovascular risk factors. Square box area proportional to the sample size of each study; horizontal lines corresponding to the 95% confidence interval; diamond representing the summary value; vertical line corresponding to a odd ratio combined with relative risk of 1.00, equivalent to no difference.

DISCUSSION

The original study as well as the systematic review and meta-analysis demonstrate a significant association of MCR syndrome with CVDRF. The type of CVDRF associated with MCR syndrome changed across the studied populations. However, the meta-analysis indicates the presence of a robust and systematic significant association, regardless of the type of CVDRF.

The main finding is that MCR syndrome is significantly associated with CVDRF. The association may be explained, in part, by adverse consequences of CVDRF like cerebrovascular lesions and brain atrophy, which can both lead to memory complaint and slow gait speed [9 , 28–30]. Indeed, over the last decade, a growing body of literature has emerged describing the role played by cardiovascular risk factors such as hypertension or CVD in the development of cognitive impairment as well as in slow gait speed [29, 30]. It has also been reported that MCR syndrome is associated with white matter hyperintensities (WMH) distributed in several brain regions [30]. WMH is suggested to be a product of cerebrovascular disease and reduced vascular integrity [30, 31]. Risk factors for cerebrovascular disease, such as hypertension and diabetes have been associated with the development of WMH [31, 32]. The association of MCR syndrome with CVDRF is also consistent with the fact that MCR syndrome is a better predictor of VaD compared to AD [7]. For instance, the risk of developing AD reported in this study [7] was lower (hazard ratio [HR]: 3.3 (95% Confident interval (CI): 1.55–6.90) compared to VaD (HR: 12.8 (95% CI: 4.98–32.97) [7]. However, this association between CVDRF and MCR does not preclude the potential predictive value of MCR for AD, as CVDRF, especially midlife hypertension or high cholesterol, have also been identified as risk factors for AD [33, 34].

The meta-analysis underscored that all CVDRF were significantly associated with MCR syndrome. Only the strength of the association changed with the type of CVDRF, with the lowest OR being reported for hypertension and the highest for stroke, the magnitude of variation ranging from 1.21 to 2.05. In contrast, the results were more heterogeneous when each study was considered separately. No significant association was reported in the Indian population, whereas it was present for American, Japanese and French individuals. In addition, there was no specific profile of association, except for obesity, which was consistently associated with MCR syndrome in the three previous studies that included obesity as an outcome [13 , 16]. Together, these results suggest that factors like ethnicity, environment and lifestyle may influence the association among populations.

Similar to previous studies, our original study provides evidence for a significant association between MCR syndrome and CVDRF. However, in contrast to the other studies, it underscores that the heterogeneity of this association could be related to potential confounders, which may influence the association. Indeed, adjustment on all clinical characteristics led to non-signification associations, regardless of the type of CVDRF considered. Furthermore, for the first time, this study identified an association between MCR syndrome and abnormal WHR. Like BMI, WHR is used as a measure of obesity but has been shown to be a better predictor of cardiovascular disease than waist circumference alone or body-mass index [35].

Exploring the association of CVDRF with MCR syndrome may be helpful to clarify which neural substrates are affected in this group of individuals and may provide indicators for underlying pathology. Our results suggest a specific association between MCR syndrome and VaD. Assessment of cognitive function in MCR syndrome is also a complementary solution to understand the MCR pathological process. MCR syndrome has been associated with global cognitive impairment, as well with subdomain impairment such as dysexecutive function [36 –39]. In addition, we recently demonstrated that if individuals with MCR syndrome present any cognitive impairment, the most affected cognitive domain was executive function [39]. Finally, the association between MCR syndrome and the occurrence of VaD was stronger than with AD [7 –9].

MCR, like MCI, is pre-dementia syndrome [1 , 39]. Recently, we reported two results, which may be helpful to better understand the relationship between MCR and MCI. First, we showed a higher prevalence of MCI in individuals with MCR compared to those without MCR (47.2% versus 39.5%) [39]. However, the overlap between MCR and MCI was low: 14.5%. Second, we reported lower cognitive performances in individuals combining MCI and MCR syndrome compared to those without MCR syndrome [39]. These results suggest that in the spectrum of the pre-dementia stages, SCI is the first stage, the second stage is an isolated MCI or MCR, and the last stage, just before dementia, would be a combination of both MCI and MCR syndromes.

Our study had several limitations, which should be considered. First, the original study was based on a cross-sectional design, which does not provide an opportunity to make causal associations. Furthermore, participants were recruited from a single center and all participants presented a cognitive complaint, which prevent the generalization of the study findings to all non-demented community-dwelling older adults. Second, the limited number of studies, in addition to variation in the definition of CVDRF across studies included in the systematic review and meta-analysis, may limit the generalization of the present findings. In relation to this point, it is important to consider that the same group of researchers has performed most of the studies included in the meta-analysis, and that these findings need to be replicated by other independent research groups. Third, smoking, which is a cardiovascular risk factor, has not been examined. Cigarette smoking may result in increased risk of arterial diseases, which are the sequelae of atherothrombosis, and thus may lead to both slow gait speed and cognitive impairment via vascular ischemic brain lesions [40]. In addition, the risk factors of smoking and the various dementia vary. For instance, smoking is not a risk factor for dementia with Lewy bodies [41]. Fourth, another limitation may be related to the standardization of definition of MCR syndrome. Although the definition of slow gait speed is standardized (i.e., one SD or more below age-and sex-appropriate mean values), different choice has been done to define SCI. SCI is usually defined by a subjective memory complaint using the item of Geriatric Depression Scale, which is the most common cognitive complaint [7 –9]. Thus, subjective memory complaint is used as a proxy for SCI.

In conclusion, the findings provide evidence for a significant association of MCR syndrome with CVDRF. These findings may help to elucidate the pathophysiological mechanisms that underlie MCR syndrome. Future studies should investigate whether neurodegenerative processes, such as amyloidopathy or synucleinopathy, contribute to the development of MCR in addition to vascular mechanisms.

Footnotes

ACKNOWLEDGMENTS

The original study was financially supported by the French Ministry of Health (Projet Hospitalier de Recherche Clinique national n°2009-A00533-54).

The authors are grateful to the participants for their cooperation and to the authors of the selected articles for providing additional data required for the meta-analysis.

Authors’ disclosures available online ().

References

Dubois

, Hampel

, Feldman

, Scheltens

, Aisen

, Andrieu

, Bakardjian

, Benali

, Bertram

, Blennow

, Broich

, Cavedo

, Crutch

, Dartigues

, Duyckaerts

, Epelbaum

, Frisoni

, Gauthier

, Genthon

, Gouw

, Habert

, Holtzman

, Kivipelto

, Lista

, Molinuevo

, O’Bryant

, Rabinovici

, Rowe

, SallowayS , Schneider

, Sperling

, Teichmann

, Carrillo

, Cummings

, Jack

Jr (2016) Proceedings of the Meeting of the International Working Group (IWG) and the American Alzheimer’s Association on“The Preclinical State of AD”; July 23, 2015; Washington DC, USA.Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria. Alzheimers Dement 12, 292-323.

Jessen

, Amariglio

, van Boxtel

, Breteler

, Ceccaldi

, Chételat

, Dubois

, Dufouil

, Ellis

, van der Flier

, Glodzik

, van Harten

, de Leon

, McHugh

, Mielke

, Molinuevo

, Mosconi

, Osorio

, Perrotin

, Petersen

, Rabin

, Rami

, Reisberg

, Rentz

, Sachdev

, de la Sayette

, Saykin

, Scheltens

, Shulman

, Slavin

, Sperling

, Stewart

, Uspenskaya

, Vellas

, Visser

, Wagner

; Subjective CognitiveDecline Initiative (SCD-I) Working Group (2014) A conceptualframework for research on subjective cognitive decline inpreclinical Alzheimer’s disease. Subjective Cognitive DeclineInitiative (SCD-I) Working Group. Alzheimers Dement 10, 844-852.

Albert

, DeKosky

, Dickson

, Dubois

, Feldman

, Fox

, Gamst

, Holtzman

, Jagust

, Petersen

, Snyder

, Carrillo

, Thies

, Phelps

(2011) The diagnosis of mild cognitiveimpairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups ondiagnostic guidelines for Alzheimer’s disease. AlzheimersDement 7, 270-279.

Martin

, Kelly

, Khan

, Cullum

, Dening

, Rait

, Fox

, Katona

, Cosco

, Brayne

, Lafortune

(2015) Attitudes and preferencestowards screening for dementia: A systematic review of the literature. BMC Geriatr 15, 66.

Tang

, Harrison

, Errington

, Gordon

, Visser

, Novak

, Dufouil

, Brayne

, Robinson

, Launer

, Stephan

(2015) Current developments in dementia risk prediction modelling: Anupdated systematic review. PLoS One 10, e0136181.

Beauchet

, Annweiler

, Callisaya

, De Cock

, Helbostad

, Kressig

, Srikanth

, Steinmetz

, Blumen

, Verghese

, AllaliG (2016) Poor gait performance and prediction of dementia: Resultsfrom a meta-analysis. J Am Med Dir Assoc 17, 482-490.

Verghese

, Wang

, Lipton

, Holtzer

(2013) Motoric cognitiverisk syndrome and the risk of dementia. J Gerontol A BiolSciMed Sci 68, 412-418.

Verghese

, Annweiler

, Ayers

, Barzilai

, Beauchet

, Bennett

, Bridenbaugh

, Buchman

, Callisaya

, Camicioli

, Capistrant

, Chatterji

, De Cock

, Ferrucci

, Giladi

, Guralnik

, Hausdorff

, Holtzer

, Kim

, Kowal

, Kressig

, Lim

, Lord

, Meguro

, Montero-Odasso

, Muir-Hunter

, Noone

, Rochester

, Srikanth

, Wang

(2014) Motoric cognitive risksyndrome: Multicountry prevalence and dementia risk. Neurology 83, 718-726.

Verghese

, Ayers

, Barzilai

, Bennett

, Buchman

, Holtzer

, Katz

, Lipton

, Wang

(2014) Motoric cognitive risk syndrome: Multicenter incidence study. Neurology 83, 2278-2284.

10.

van de Vorst

, Koek

, de Vries

, Bots

, Reitsma

, Vaartjes

(2016) Effect of vascular risk factors and diseases on mortality in individuals with dementia: A systematic review and meta-analysis. J Am Geriatr Soc 64, 37-46.

11.

Pedditizi

, Peters

, Beckett

(2016) The risk of overweight/obesity in mid-life and late life for the development of dementia: A systematic review and meta-analysis of longitudinal studies. Age Ageing 45, 14-21.

12.

O’Brien

, Thomas

(2015) Vascular dementia. Lancet 386, 1698-1706.

13.

Doi

, Verghese

, Shimada

, Makizako

, Tsutsumimoto

, Hotta

, Nakakubo

, Suzuki

(2015) Motoric cognitive risk syndrome:Prevalence and risk factors in Japanese seniors. J Am Med DirAssoc 16, 1103.e21-5.

14.

Wang

, Allali

, Kesavadas

, Noone

, Pradeep

, Blumen

, Verghese

(2015) Cerebral small vessel disease and motoriccognitive risk syndrome: Results from the Kerala-Einstein Study. J Alzheimers Dis 50, 699-707.

15.

Ayers

, Verghese

(2016) Motoric cognitive risk syndrome and risk of mortality in older adults. Alzheimers Dement 12, 556-564.

16.

Beauchet

, Allali

, Annweiler

, Verghese

(2016) Association of motoric cognitive risk syndrome with brain volumes: Results from the GAIT study. J Gerontol A Biol Sci Med Sci 71, 1081-1088.

17.

Beauchet

, Allali

, Launay

, Herrmann

, Annweiler

(2013) Gaitvariability at fast-pace gait speed: A biomarker of mild cognitiveimpairment?. J Nutr Health Aging 17, 235-239.

18.

World Health Organization (2017) Cardiovascular diseases (CVDs). http://www.who.int/mediacentre/factsheets/fs317/en/

19.

Beauchet

, Launay

, Chabot

, Levinoff

, Allali

(2017) Subjective memory impairment and gait variability in cognitivelyhealthy individuals: Results from a cross-sectional pilot study. J Alzheimers Dis 55, 965-571.

20.

Pickering

, Hall

, Appel

, Falkner

, Graves

, Hill

, Jones

, Kurtz

, Sheps

, Roccella

(2005) Recommendations for blood pressure measurement in humans and experimental animals: Part1: Blood pressure measurement in humans: A statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation 111, 697-716.

21.

Pickering

, Hall

, Appel

, Falkner

, Graves

, Hill

, Jones

, Kurtz

, Sheps

, Roccella

; Council on High BloodPressure Research Professional and Public Education Subcommittee, American Heart Association (2005) Recommendations for blood pressuremeasurement in humans: An AHA scientific statement from the Councilon High Blood Pressure Research Professional and Public EducationSubcommittee. J Clin Hypertens 7, 102-109.

22.

von Elm

, Altman

, Egger

, Pocock

, Gotzsche

, Vandenbroucke

(2008) The Strengthening the Reporting of Observational Studiesin Epidemiology (STROBE) statement: Guidelines for reportingobservational studies. J Clin Epidemiol 61, 344-349.

23.

Liberati

, Altman

, Tetzlaff

, Mulrow

, Gøtzsche

, Ioannidis

, Clarke

, Devereaux

, Kleijnen

, Moher

(2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. J Clin Epidemiol 62, e1-34.

24.

Mackinnon

(2000) A spreadsheet for the calculation of comprehensive statistics for the assessment of diagnostic tests and inter-rater agreement. Comput Biol Med 30, 127-134. https://www.biostats.com.au/DAG_Stat/index.html

25.

Higgins

, Thompson

(2002) Quantifying heterogeneity in ameta-analysis. Stat Med 21, 1539-1558.

26.

Abramson

(2011) WINPEPI updated: Computer programs forepidemiologists, and their teaching potential. Epidemiol Perspect Inno 8, 1.

27.

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.

28.

Pase

, Herbert

, Grima

, Pipingas

, Rourke

(2012) Arterial stiffness as a cause of cognitive decline and dementia: A system aticreview and meta-analysis. Intern Med J 42, 808-815.

29.

Pantoni

, Garcia

(1997). Pathogenesis of leukoaraiosis: Areview. Stroke 28, 652-659.

30.

Young

, Halliday

, Kril

(2008) Neuropathologic correlates of white matter hyperintensities. Neurology 71, 804-811.

31.

Longstreth

Jr , Manolio

, Arnold

, Burke

, Bryan

, Jungreis

, Enright

, O’Leary

, Fried

(1996) Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke 27, 1274-1282.

32.

Yoshita

, Fletcher

, Harvey

, Ortega

, Martinez

(2006) Extent and distribution of white matter hyperintensities in normal aging, MCI, and AD. Neurology 67, 2192-2198.

33.

Kivipelto

, Helkala

, Laakso

, Hänninen

, Hallikainen

, Alhainen

, Soininen

, Tuomilehto

, Nissinen

(2001) Midlife vascular risk factors and Alzheimer’s disease in later life:Longitudinal, population based study. BMJ 322, 1447-1451.

34.

Norton

, Matthews

, Barnes

, Yaffe

, Brayne

(2014) Potentialfor primary prevention of Alzheimer’s disease: An analysis of population-based data. Lancet Neurol 13, 788-794.

35.

Mørkedal

, Romundstad

, Vatten

(2016) Mortality fromischaemic heart disease: Age-specific effects of blood pressurestratified by body-mass index: The HUNT cohort study in Norway. J Epidemiol Community Health 65, 814-819.

36.

Kumai

, Meguro

, Kasai

, Nakamura

, Nakatsuka

(2016) Neuroepidemiologic and neurobehavioral characteristics of motoriccognitive risk syndrome in an old-old population: The Kurihara Project. Dement Geriatr Cogn Dis Extra 6, 176-182.

37.

Allali

, Ayers

, Verghese

(2016) Motoric cognitive risksyndrome subtypes and cognitive profiles. J Gerontol A Biol SciMed Sci 71, 378-384.

38.

Maguire

, Killane

, Creagh

, Donoghue

, Kenny

, Reilly

(2018) Baseline association of motoric cognitive risk syndrome withsustained attention, memory, and global cognition. J Am Med Dir Assoc 19, 53-58.

39.

Sekhon

, Allali

, Launay

, Chabot

, Beauchet

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

40.

Price

, Mowbray

, Lee

, Rumley

, Lowe

, Fowkes

(1999) Relationship between smoking and cardiovascular risk factors in the development of peripheral arterial disease and coronary artery disease: Edinburgh Artery Study. Eur Heart J 20, 344-353.

41.

Boot

, Orr

, Ahlskog

, Ferman

, Roberts

, Pankratz

, Dickson

, Parisi

, Aakre

, Geda

, Knopman

, Petersen

, Boeve

(2013) Risk factors for dementia with Lewy bodies: Acase-control study. Neurology 81, 833-840.