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Motoric Cognitive Risk Syndrome: Prevalence and Risk Factors in Japanese Seniors
JAMDA 16 (2015) 1103.e21e1103.e25
JAMDA journal homepage: www.jamda.com
Original Study
Motoric Cognitive Risk Syndrome: Prevalence and Risk Factors in Japanese Seniors Takehiko Doi PhD, PT a, b, c, *, Joe Verghese MBBS c, d, Hiroyuki Shimada PhD, PT e, Hyuma Makizako PhD, PT a, Kota Tsutsumimoto PhD, PT a, Ryo Hotta PhD a, Sho Nakakubo MS, PT a, Takao Suzuki PhD, MD f, g a Section for Health Promotion, Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan b Japan Society for the Promotion of Science, Tokyo, Japan c Department of Neurology, Albert Einstein College of Medicine, Yeshiva University, Bronx, New York d Department of Medicine, Albert Einstein College of Medicine, Yeshiva University, Bronx, New York e Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan f National Center for Geriatrics and Gerontology, Obu, Aichi, Japan g Department of Gerontology, J.F. Oberlin University Graduate School, Tokyo, Japan
a b s t r a c t Keywords: Gait cognition dementia lifestyle
Objectives: Motoric cognitive syndrome (MCR), a newly described predementia syndrome characterized by cognitive complaints and slow gait, is associated with increased risk of developing dementia. Due to the potential differences in health, behavioral, and lifestyle factors between races that can influence dementia risk, it is important to examine risk factors for MCR in different countries. This study aimed to report the prevalence as well as modifiable factors associated with MCR in Japanese communitydwelling older adults. Design: A cross-sectional design. Setting: General community. Participants: A total of 9683 older adults (52% women, mean age: 73.6 years) participating in the National Center for Geriatrics and Gerontology Study of Geriatric Syndromes. Measurements: Participants were screened for presence of MCR at baseline. The association of selected modifiable risk factors (medical illness, depressive symptoms, and falls) and lifestyle variables (obesity, physical inactivity, smoking, and alcohol consumption) with MCR was examined using multivariate logistic regression analysis. Results: At cross-section, 619 participants met criteria for MCR, with an overall prevalence 6.4% (95% CI 5.9 e6.9). A higher prevalence of MCR was seen with advancing age (P < .001), but there were no sex differences. Diabetes (adjusted odds ratio [OR] 1.47, P ¼ .001), depressive symptoms (OR 3.57, P < .001), and falls (OR 1.45, P < .001) were associated with increased risk of MCR. Among the lifestyle factors, obesity (OR 1.26, P ¼ .018) and physical inactivity (OR 1.57, P < .001) were associated with increased risk of MCR. Conclusion: MCR is common in the elderly Japanese population. The potentially modifiable risk and lifestyle factors identified for MCR should be further studied to develop interventions. Ó 2015 AMDA e The Society for Post-Acute and Long-Term Care Medicine.
The Motoric Cognitive Risk syndrome (MCR) is a predementia syndrome characterized by the presence of cognitive complaints and slow gait in older individuals without dementia or mobility
disability.1e3 MCR was associated with increased risk of developing major cognitive decline and dementia, including Alzheimer disease (AD) and vascular dementia in elderly cohorts based in the United
The authors declare no conflicts of interest. This study was funded by Health and Labour Sciences Research grants (Comprehensive Research on Aging and Health); Grant-in-Aid for JSPS Fellows (259435); Grant-in-Aid for Scientific Research (B) (grant number 23300205); Grant-in-Aid for Young Scientists (A) (15H05369), and Research Funding for Longevity Sciences (22e16) from the National Center for Geriatrics and Gerontology, Japan.
* Address correspondence to Takehiko Doi, PhD, PT, Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7e430 Morioka-cho, Obu, Aichi 474e8511, Japan. E-mail address: [email protected] (T. Doi).
http://dx.doi.org/10.1016/j.jamda.2015.09.003 1525-8610/Ó 2015 AMDA e The Society for Post-Acute and Long-Term Care Medicine.
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States and Europe.1,2 An MCR prevalence study based in 17 countries reported a pooled prevalence of 9.7%.1 Although this study included a Japanese cohort of 514 older adults,1 more information is needed to obtain reliable estimates of MCR and underlying etiologies in Japan. Medical illness and other potentially modifiable risks, such as strokes, Parkinson disease, depressive symptoms, obesity, and sedentariness, have been reported to increase risk of developing MCR in Western populations.3 However, due to the potential differences in health, behavioral, and lifestyle factors among races, it is important to examine risk factors for MCR in different countries. On the other hand, if risk factor profiles for MCR are discovered to be similar in different countries, then common global strategies to develop preventive measures for older individuals at risk for dementia could be developed. Given the large elderly population and high rates of dementia reported in Japan,4 there is a pressing need to identify individuals at high risk for dementia to institute preventive measures early and to plan care. Because diagnosing MCR does not require extensive neuropsychological tests or biomarkers, this clinical dementia risk stratification strategy is particularly attractive to implement in community settings, without the need for clinicians or other health professionals. Hence, to further elucidate the epidemiology and pathogenesis of MCR in Japan, we conducted a cross-sectional study to establish the prevalence and associated risk factors for MCR in 9683 older participants in the National Center for Geriatrics and GerontologyeStudy of Geriatric Syndromes (NCGG-SGS). Methods Participants Subjects eligible for this study were participants of a populationbased cohort study (NCGG-SGS). The overall goal of the NCGG-SGS was to establish a screening system for geriatric syndromes and to validate evidence-based interventions for preventing geriatric syndromes. The NCGG-SGS participants were recruited from Nagoya or Obu cities in Japan. The baseline study assessments were done from August 2011 to February 2012 and in June 2013 at Obu city, and from July to December 2013 in Nagoya city. A total of 10,885 individuals were enrolled in the NCGG-SGS.5 Inclusion criteria in this study were residing either in Obu or Nagoya city, age 65 years or older in the Obu site and 70 and older at the Nagoya site, and independent in basic activities of daily living (ADLs). Independence in ADLs was confirmed during interviews conducted by trained research staff about the following items: eating/feeding, dressing, bathing and showering, functional mobility, climbing up and down stairs, personal hygiene and grooming, and toilet hygiene. Exclusion criteria included having a history of cerebrovascular disease, Parkinson disease, depression, dementia, or participants with major cognitive impairment defined as those who scored 17 or lower on the Mini-Mental State Examination (MMSE).6 Medical history was collected in the face-to-face interview by a nurse. Participants with missing data for variables used to define these criteria and MCR were also excluded. After exclusions, 9683 older adults (89%) were eligible for this study. Compared with the eligible participants, those excluded were older (eligible participants: 73.6 5.5 y vs excluded participants: 75.1 5.7 y, P < .001), had lower education (11.7 2.6 y vs 11.4 2.8 y, P < .001), and took more medications (2.6 2.5 vs 4.4 3.1, P < .001), but there were no sex differences. All participants provided written informed consent, and the ethics committee of the National Center for Geriatrics and Gerontology approved this study. MCR Criteria As previously described,1e3 MCR criteria builds on operational criteria for mild cognitive impairment (MCI) syndrome.7 The only
difference in the operational definitions is that the objective cognitive impairment criterion based on cognitive tests in MCI is substituted by slow gait in MCR. All other criteria remain the same, and MCR does not require cognitive tests. Participants with MCR need to have subjective memory complaints and slow gait. The presence of subjective memory complaints was elicited from the standardized memory loss question on the 15-item Geriatric Depression Scale (GDS) administered by well-trained research staff: “Do you feel you have more problems with memory than most?”8 A positive response (“yes”) on the question was used to define subjective memory complaints. The same question was used to define subjective cognitive complaints in 8 of the 22 cohorts included in the worldwide MCR prevalence study.1 Subjective cognitive complaints have been reported to be associated with increased risk of dementia.9 However, MCR was shown to have improved predictive validity for dementia compared with subjective cognitive complaints or slow gait alone in multiple cohorts.1 Gait speed was measured over a 6.4-m level course in a well-lit area and 2 m at either end of the course was not included in the measurement to account for initial acceleration and terminal deceleration. The testers timed participants over the middle 2.4-m section indicated by markers on the floor. Participants were instructed to walk at their normal comfortable pace. The walking time over 2.4 m was converted to speed (m/s) to be consistent with other studies in this area.1,10 Participants were allowed to walk with aids, such as canes, if needed (31 participants walked with aids). Slow gait was defined as gait speed at normal pace that was 1.0 SDs or below age- and sexappropriate mean values established in the NCGG-SGS database. Diagnosing slow gait independent of clinical gait evaluations, which may be prone to variable sensitivity and specificity as well as being examiner dependent, is a strength. Furthermore, although slow gait may result from neurological as well as non-neurological causes,11 previous studies have shown slow gait by itself or as part of the MCR syndrome was associated with increased risk of developing dementia, irrespective of the underlying etiologies.1,2 Modifiable Risk Factors and Covariates Based on previous studies of MCR as well as those of dementia risk factors in Japan,3,12,13 we selected several risk factors. Our approach was also consistent with the overall goal of the parent NCGG-SGS study, which was to develop evidence-based interventions for preventing cognitive decline. We selected specific medical diseases,3,12,14 depressive symptoms,3,12 and falls that have been related to both mobility and cognitive impairments in previous studies.15 Participants were interviewed by nurses about the presence or absence of selected diseases (hypertension, diabetes, heart disease, and osteoporosis) and prescription medications (total number). Research staff obtained information on depressive symptoms (GDS) and occurrence of falls over the previous year (“Did you have any history of a fall within the past year?’’). Depressive symptoms were assessed using the 15-item GDS, and presence of significant depressive symptoms was defined as a score of 6 or more.8,16 A fall was defined as “an unexpected event in which the person comes to rest on the ground, floor, or lower level.”17 We also assessed the following lifestyle factors based on previous studies that have linked them to either MCR or dementia risk3,12: obesity, physical inactivity, smoking, and alcohol consumption. Height and weight were measured and used to calculate body mass index (BMI: kg/m2) using standard formula. Obesity was defined as BMI value of 25 and over, as previously described in the Japanese elderly population.18 Physical inactivity was defined by 2 questions used in our previous study10: (1) “Do you engage in moderate levels of physical exercise or sports aimed at health?” and (2) “Do you engage in low levels of physical exercise aimed at health?” Physical inactivity was defined as not engaging in both low and moderate levels of activity, responding “no” to both
T. Doi et al. / JAMDA 16 (2015) 1103.e21e1103.e25
Statistical Analysis Baseline characteristics were compared between “MCR” and “no MCR” groups using unpaired t test or c2 test. The comparison of MCR prevalence between age and sex groups was also conducted using the c2 test. The independent variables included the selected modifiable risk factor (medical illness, depressive symptom, and fall), lifestyle variables (obesity, physical inactivity, smoking, and alcohol consumption), and MMSE. The associations between MCR (dependent variable) and independent variables were analyzed using multivariate logistic regression analysis and reported as adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs). The multivariate logistic regression analysis was conducted in 2 models. Model 1 tested each independent variable individually adjusted for demographic data (age, sex, education, and medication use). Model 2 included all independent variables and the demographic data. To account for potential of diagnostic misclassification in dementia cases, we repeated multivariate logistic regression adjusting for covariates in model 2 and MMSE among participants with MMSE scores of 24 and above. Analyses were performed using SPSS statistics software, Version 20 (IBM Corp., Chicago, IL). Statistical significance was set at P < .05 in all analyses.
10
8 Percent prevalence rate [%]
these questions. Current habits of smoking and drinking alcohol were also interviewed. Alcohol consumption was assessed in terms of number of drinking days per week, and classified into 3 groups: no drinking, drinks 1 to 4 days, or drinks 5 or more days.
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6
4
2
0
65-69 yrs
70-74 yrs
75-79 yrs
80-84 yrs
85 yrs over
Men
5.8%
4.5%
7.4%
8.6%
8.0%
Total 6.1%
Women
4.9%
6.8%
8.0%
6.1%
9.8%
6.6%
Total
5.3%
5.6%
7.7%
7.4%
8.9%
6.4%
Fig. 1. Prevalence of MCR separated by age and sex group.
(95% CI 6.7%e8.9%); 80e84 y, 7.4% (95% CI 6.0%e9.1%); 85 y and older, 8.9% (95% CI 6.3%e12.3%) (P for trend: P < .001). There were no sex differences in the prevalence of MCR (P ¼ .268). Figure 1 summarizes MCR prevalence by age and sex categories.
Risk Factors for MCR Results Of the 9683 subjects (52% women, mean age: 73.6 years), 619 (6.4%) met criteria for MCR. Table 1 summarizes and compares characteristics between groups. The sex and current smoking history were not significantly different between groups. All other selected characteristics showed significant differences between groups (P < .05). Prevalence The overall prevalence of MCR was 6.4% (95% CI 5.9%e6.9%) in this cohort. An increasing prevalence was seen with age: 65e69 y, 5.3% (95% CI 4.5%e6.3%); 70e74 y, 5.6% (95% CI 4.9%e6.5%); 75e79 y, 7.7%
Table 1 Characteristics in Subjects Between Groups Variables
No MCR, n ¼ 9064
MCR, n ¼ 619
P
Age, y Education, y Sex (women), % Medical condition, % Hypertension Diabetes Heart disease Osteoporosis Depressive symptoms Falls Medication numbers Gait speed, m/s MMSE, score Lifestyle, % Obesity Physical inactivity Smoking Drinking alcohol <4 d/wk 5 d/wk
73.5 (5.5) 11.8 (2.6) 51.7
74.7 (5.9) 11.0 (2.6) 54.0
<.001 <.001 .299
45.6 12.4 16.6 12.9 12.8 16.6 2.5 (2.5) 1.17 (0.21) 26.2 (2.5)
49.9 20.4 22.3 18.4 39.7 26.4 3.5 (3.0) 0.80 (0.15) 25.0 (2.9)
.041 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001
25.0 24.1 8.3
30.7 38.6 9.5
.002 <.001 .293 .045
17.3 27.4
14.3 25.6
Values are mean (SD) or proportion.
Table 2 presents the results of the multivariate logistic regression analyses. Model 1, adjusted for age, sex, education, and medication use, shows that diabetes (OR 1.49, 95% CI 1.20e1.85), depressive symptoms (OR 3.90, 95% CI 3.27e4.66), falls (OR 1.69, 95% CI 1.40e2.05), obesity (OR 1.23, 95% CI 1.02e1.47), physical inactivity (OR 1.89, 95% CI 1.59e2.25), and smoking (OR 1.37, 95% CI 1.02e1.83) were associated with MCR. In the fully adjusted model 2, in which all selected risk factors were entered together in model 1, diabetes (OR 1.47, 95% CI 1.18e1.85), depressive symptoms (OR 3.57, 95% CI 2.97e4.29), and falls (OR 1.45, 95% CI 1.19e1.77) remained associated with MCR among the modifiable risk factors examined. Among the lifestyle factors examined, obesity (OR 1.26, 95% CI 1.04e1.53) and physical inactivity (OR 1.57, 95% CI 1.31e1.89) were associated with MCR in the fully adjusted model 2.
Table 2 The Association With MCR Variables
Modifiable risk factors Hypertension Diabetes Heart disease Osteoporosis Depressive symptoms Falls Lifestyle factors Obesity Physical inactivity Smoking Drinking alcohol 4 d/wk 5 d/wk
Model 1
Model 2
ORs
(95% CI)
P
ORs
(95% CI)
P
0.91 1.49 1.13 1.19 3.90 1.69
(0.76e1.08) .276 0.85 (0.71e1.02) .080 (1.20e1.85) <.001 1.47 (1.18e1.85) .001 (0.91e1.39) .273 1.08 (0.87e1.35) .481 (0.93e1.51) .160 1.23 (0.96e1.57) .103 (3.27e4.66) <.001 3.57 (2.97e4.29) <.001 (1.40e2.05) <.001 1.45 (1.19e1.77) <.001
1.23 (1.02e1.47) .028 1.26 (1.04e1.53) .018 1.89 (1.59e2.25) <.001 1.57 (1.31e1.89) <.001 1.37 (1.02e1.83) .035 1.24 (0.91e1.69) .173 0.84 (0.66e1.08) 0.94 (0.75e1.17)
.179 0.94 (0.73e1.21) .557 1.07 (0.86e1.35)
.624 .543
The absence was reference in each categorical data and alcohol is referred to no drinking. Model 1 analyzed each independent variable individually adjusted for age, sex, education, and medication use. Model 2 included all independent variables and adjusted variables as well as model 1.
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The proportion of participants with MMSE scores 24 or higher were 84.7% in the No MCR group and 70.0% in the MCR group. In the analysis restricted to the 8108 participants with MMSE scores of 24 and above, results were similar with significant associations with MCR seen with diabetes (OR 1.42, 95% CI 1.08e1.86), depressive symptoms (OR 3.56, 95% CI 2.86e4.43), falls (OR 1.51, 95% CI 1.20e1.91), obesity (OR 1.42, 95% CI 1.14e1.78), and physical inactivity (OR 1.64, 95% CI 1.32e2.03). Discussion This large population study provides estimates of the prevalence of MCR in Japanese community-dwelling older adults. In this large Japanese cohort, prevalence of MCR was 6.4%, and ranged from 5.3% to 8.9%. An increasing prevalence of MCR was seen with advancing age, which parallels the epidemiology of dementia in older age groups. There were no sex differences in MCR prevalence, which is consistent with the lack of sex differences noted in MCR studies in other populations.1,3 This is in contrast with MCI, in which a higher prevalence in men has been reported19 in some but not all studies.20 Participants with MCR were older, less educated, and were on a higher number of medications. Even after adjusting for these characteristics, presence of diabetes, depressive symptoms, falls, obesity, and physical inactivity were associated with MCR in Japanese seniors. The previous worldwide study reported a global MCR prevalence of 9.7% (95% CI 8.2%e11.2%) in a meta-analysis including 26,802 older adults from 17 countries.1 However, prevalence varied from 1% to 15% in the 22 studies included in this multicountry investigation.1 This variability may result from the sociodemographic characteristics of the population studied, the age distribution, size of the samples, and the methods used to define slow gait criterion. Another source of variability is the source of the population. A higher prevalence of MCR was seen in samples that are recruited from memory clinics. A lower prevalence of MCR was seen in community samples, as in our study.1 The prevalence of MCR was 13% in another Japanese cohort included in the previous multicountry MCR prevalence study.1 This other Japanese cohort was based in a rural area, and included 514 adults who were 75 years and older (54% women), and whose education ranged from 8.2 to 9.3 years.21 In contrast, our study was conducted in urban neighborhoods, and included 9683 adults aged 65 years and older (52% women) whose mean education was 11.7 years. These differences may explain the different MCR prevalence rates in the 2 Japanese cohorts. Our study showed that potentially modifiable medical and lifestyle factors were associated with MCR. In a previous MCR study based in Western populations, physical inactivity, obesity, and depressive symptoms were reported to increase risk of MCR.3 Our findings are in line with these results, suggesting commonalities in risk factors for MCR across the world. Although further studies are needed, these results raise the possibility of developing common preventive strategies worldwide to prevent conversion of MCR to dementia. Numerous epidemiological studies have recognized physical inactivity, obesity, and depressive symptoms as modifiable risk factors of AD and other types of dementia.12,22,23 Furthermore, physical inactivity is related to brain changes, such as atrophy, disrupted white matter integrity, and accumulation of Alzheimer pathology.24e27 In intervention studies, exercise or enhancing physical activity has positive effects on brain volume and white matter integrity.28e30 Obesity and diabetes were associated with MCR in our cohort. Although obesity is commonly defined as BMI of 30 and over, the mean BMI in Japan and East Asian countries is lower.31 Thus, we used BMI of 25 and over to define obesity in our cohort.18 Nonetheless, it is interesting that obesity defined by this lower cut score remained a risk factor for MCR in Japan. Diabetes is a major risk factor for dementia and AD.23,32,33 Diabetes-related brain changes, such as atrophy,
disturbed white matter integrity, and vascular lesions, may compromise reserve capacity of the brain, and make the diabetic brain more vulnerable to the consequences of further pathological changes, particularly cerebrovascular disease and AD.32 Depressive symptoms are reported to increase risk of cognitive impairment and dementia.12 Furthermore, cognitive impairment, slow gait speed, and depressive symptoms are known to occur simultaneously in older adults.15 Depressive symptoms have negative impacts on mobility as well cognition in community-dwelling older adults.34 Although further study is required, these associations may explain the relationship of depressive symptoms to MCR. Falls were also associated with MCR even after adjusting for several covariates. Our previous study showed that combined presence of MCI and slow gait was strongly associated with risk of falls.35 The reverse is also true; older adults who fall are at risk of traumatic brain injuries, which may have cognitive and motor sequelae. The interactive association between cognition and mobility is thought to be dependent on changes in the brain. Slow gait speed results from age-related changes in the brain, such as atrophy36 and white matter hyperintensities.36,37 Systematic review has indicated that white matter hyperintensities were associated with lower physical function and increased falls.37 Furthermore, white matter hyperintensities are associated with falls independent from the effect of cognitive and sensorimotor functions.38 Among Japanese older adults, white matter hyperintensities were also related to gait disturbance.39 Also, white matter hyperintensities were recognized as a risk factor for falls in Japanese seniors.40 Further longitudinal studies are required to clarify the association between MCR and falls in Japan. Strengths of our study included the large sample that enabled multivariate analysis of risk factors, including several important covariates. On the contrary, the design of the study was crosssectional. Thus, causal relationship needs to be clarified in future prospective studies. Studies also need to include brain measures and biomarkers to understand the pathological mechanisms of MCR. Despite our exclusion criteria, our sample might have included some individuals with mild undiagnosed dementia, although our secondary analysis restricted to participants with MMSE scores within the normal range supports our main findings. The ORs of the selected risk factors with the exception of depressive symptoms were lower than 2, consistent with a previous multicenter MCR study.3 Many of these risk factors share common biological pathways, and may respond to common interventions. Developing interventions for any single risk factor to reduce the incidence of MCR might be less effective than a multifactorial approach targeting multiple risk factors, as suggested by the success of a recent multidomain intervention study to prevent cognitive decline.41 Conclusion Our large cohort study reported MCR prevalence among Japanese seniors. The risk factors for MCR identified in our study need to be further examined in the context of longitudinal observational studies. Our MCR findings will help improve early detection and develop prevention strategies for dementia in Japan and other countries. Acknowledgments We thank the Obu city and Nagoya city (Midoriku) office for help with participant recruitment. References 1. Verghese J, Annweiler C, Ayers E, et al. Motoric cognitive risk syndrome: Multicountry prevalence and dementia risk. Neurology 2014;83:718e726.
T. Doi et al. / JAMDA 16 (2015) 1103.e21e1103.e25 2. Verghese J, Wang C, Lipton RB, et al. Motoric cognitive risk syndrome and the risk of dementia. J Gerontol A Biol Sci Med Sci 2013;68:412e418. 3. Verghese J, Ayers E, Barzilai N, et al. Motoric cognitive risk syndrome: Multicenter incidence study. Neurology 2014;83:2278e2284. 4. Meguro K, Tanaka N, Kasai M, et al. Prevalence of dementia and dementing diseases in the old-old population in Japan: The Kurihara Project. Implications for Long-Term Care Insurance data. Psychogeriatrics 2012;12:226e234. 5. Shimada H, Tsutsumimoto K, Lee S, et al. Driving continuity in cognitively impaired older drivers. Geriatr Gerontol Int; 2015 May 8. [Epub ahead of print]. 6. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12:189e198. 7. Petersen RC. Clinical practice. Mild cognitive impairment. N Engl J Med 2011; 364:2227e2234. 8. Yesavage JA. Geriatric Depression Scale. Psychopharmacol Bull 1988;24: 709e711. 9. van Oijen M, de Jong FJ, Hofman A, et al. Subjective memory complaints, education, and risk of Alzheimer’s disease. Alzheimers Dement 2007;3: 92e97. 10. Shimada H, Makizako H, Doi T, et al. Combined prevalence of frailty and mild cognitive impairment in a population of elderly Japanese people. J Am Med Dir Assoc 2013;14:518e524. 11. Verghese J, LeValley A, Hall CB, et al. Epidemiology of gait disorders in community-residing older adults. J Am Geriatr Soc 2006;54:255e261. 12. Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol 2011;10:819e828. 13. Fujishima M, Kiyohara Y. Incidence and risk factors of dementia in a defined elderly Japanese population:the Hisayama study. Ann N Y Acad Sci 2002;977: 1e8. 14. Chang KH, Chung CJ, Lin CL, et al. Increased risk of dementia in patients with osteoporosis: A population-based retrospective cohort analysis. Age (Dordr) 2014;36:967e975. 15. Hajjar I, Yang F, Sorond F, et al. A novel aging phenotype of slow gait, impaired executive function, and depressive symptoms:relationship to blood pressure and other cardiovascular risks. J Gerontol A Biol Sci Med Sci 2009;64: 994e1001. 16. Wancata J, Alexandrowicz R, Marquart B, et al. The criterion validity of the Geriatric Depression Scale: A systematic review. Acta Psychiatr Scand 2006; 114:398e410. 17. Lamb SE, Jorstad-Stein EC, Hauer K, et al. Development of a common outcome data set for fall injury prevention trials: The Prevention of Falls Network Europe consensus. J Am Geriatr Soc 2005;53:1618e1622. 18. The Examination Committee for Criteria of Metabolic Syndrome in Japan. Definition and diagnosis criteria of metabolic syndrome. J Jpn Soc Intern Med 2005;94:794e809. 19. Petersen RC, Roberts RO, Knopman DS, et al. Prevalence of mild cognitive impairment is higher in men. The Mayo Clinic Study of Aging. Neurology 2010; 75:889e897. 20. Hanninen T, Hallikainen M, Tuomainen S, et al. Prevalence of mild cognitive impairment: A population-based study in elderly subjects. Acta Neurol Scand 2002;106:148e154. 21. Nakamura K, Kasai M, Ouchi Y, et al. Apathy is more severe in vascular than amnestic mild cognitive impairment in a community: The Kurihara Project. Psychiatry Clin Neurosci 2013;67:517e525. 22. Daviglus ML, Bell CC, Berrettini W, et al. NIH state-of-the-science conference statement: Preventing Alzheimer’s disease and cognitive decline. NIH Consens State Sci Statements 2010;27:1e30.
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23. Forti P, Pisacane N, Rietti E, et al. Metabolic syndrome and risk of dementia in older adults. J Am Geriatr Soc 2010;58:487e492. 24. Liang KY, Mintun MA, Fagan AM, et al. Exercise and Alzheimer’s disease biomarkers in cognitively normal older adults. Ann Neurol 2010;68:311e318. 25. Tian Q, Erickson KI, Simonsick EM, et al. Physical activity predicts microstructural integrity in memory-related networks in very old adults. J Gerontol A Biol Sci Med Sci 2014;69:1284e1290. 26. Doi T, Makizako H, Shimada H, et al. Objectively measured physical activity, brain atrophy, and white matter lesions in older adults with mild cognitive impairment. Exp Gerontol 2015;62:1e6. 27. Makizako H, Liu-Ambrose T, Shimada H, et al. Moderate-intensity physical activity, hippocampal volume, and memory in older adults with mild cognitive impairment. J Gerontol A Biol Sci Med Sci 2015;70:480e486. 28. Voss MW, Heo S, Prakash RS, et al. The influence of aerobic fitness on cerebral white matter integrity and cognitive function in older adults: Results of a one-year exercise intervention. Hum Brain Mapp 2013;34: 2972e2985. 29. Erickson KI, Voss MW, Prakash RS, et al. Exercise training increases size of hippocampus and improves memory. Proc Natl Acad Sci U S A 2011;108: 3017e3022. 30. Suzuki T, Shimada H, Makizako H, et al. A randomized controlled trial of multicomponent exercise in older adults with mild cognitive impairment. PLoS One 2013;8:e61483. 31. Finucane MM, Stevens GA, Cowan MJ, et al. National, regional, and global trends in body-mass index since 1980: Systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet 2011;377:557e567. 32. Biessels GJ, Strachan MW, Visseren FL, et al. Dementia and cognitive decline in type 2 diabetes and prediabetic stages:towards targeted interventions. Lancet Diabetes Endocrinol 2014;2:246e255. 33. Pasquier F, Boulogne A, Leys D, et al. Diabetes mellitus and dementia. Diabetes Metab 2006;32:403e414. 34. Chang T, Lung F, Yen Y. Depressive symptoms, cognitive impairment, and metabolic syndrome in community-dwelling elderly in Southern Taiwan. Psychogeriatrics; 2014 Dec 17. [Epub ahead of print]. 35. Doi T, Shimada H, Park H, et al. Cognitive function and falling among older adults with mild cognitive impairment and slow gait. Geriatr Gerontol Int 2015;15:1073e1078. 36. Seidler RD, Bernard JA, Burutolu TB, et al. Motor control and aging: Links to age-related brain structural, functional, and biochemical effects. Neurosci Biobehav Rev 2010;34:721e733. 37. Zheng JJ, Delbaere K, Close JC, et al. Impact of white matter lesions on physical functioning and fall risk in older people: A systematic review. Stroke 2011;42: 2086e2090. 38. Zheng JJ, Lord SR, Close JC, et al. Brain white matter hyperintensities, executive dysfunction, instability, and falls in older people: A prospective cohort study. J Gerontol A Biol Sci Med Sci 2012;67:1085e1091. 39. Iseki K, Hanakawa T, Hashikawa K, et al. Gait disturbance associated with white matter changes: A gait analysis and blood flow study. Neuroimage 2010;49: 1659e1666. 40. Ogama N, Sakurai T, Shimizu A, et al. Regional white matter lesions predict falls in patients with amnestic mild cognitive impairment and Alzheimer’s disease. J Am Med Dir Assoc 2014;15:36e41. 41. Ngandu T, Lehtisalo J, Solomon A, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): A randomised controlled trial. Lancet 2015;385:2255e2263.
JAMDA journal homepage: www.jamda.com
Original Study
Motoric Cognitive Risk Syndrome: Prevalence and Risk Factors in Japanese Seniors Takehiko Doi PhD, PT a, b, c, *, Joe Verghese MBBS c, d, Hiroyuki Shimada PhD, PT e, Hyuma Makizako PhD, PT a, Kota Tsutsumimoto PhD, PT a, Ryo Hotta PhD a, Sho Nakakubo MS, PT a, Takao Suzuki PhD, MD f, g a Section for Health Promotion, Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan b Japan Society for the Promotion of Science, Tokyo, Japan c Department of Neurology, Albert Einstein College of Medicine, Yeshiva University, Bronx, New York d Department of Medicine, Albert Einstein College of Medicine, Yeshiva University, Bronx, New York e Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan f National Center for Geriatrics and Gerontology, Obu, Aichi, Japan g Department of Gerontology, J.F. Oberlin University Graduate School, Tokyo, Japan
a b s t r a c t Keywords: Gait cognition dementia lifestyle
Objectives: Motoric cognitive syndrome (MCR), a newly described predementia syndrome characterized by cognitive complaints and slow gait, is associated with increased risk of developing dementia. Due to the potential differences in health, behavioral, and lifestyle factors between races that can influence dementia risk, it is important to examine risk factors for MCR in different countries. This study aimed to report the prevalence as well as modifiable factors associated with MCR in Japanese communitydwelling older adults. Design: A cross-sectional design. Setting: General community. Participants: A total of 9683 older adults (52% women, mean age: 73.6 years) participating in the National Center for Geriatrics and Gerontology Study of Geriatric Syndromes. Measurements: Participants were screened for presence of MCR at baseline. The association of selected modifiable risk factors (medical illness, depressive symptoms, and falls) and lifestyle variables (obesity, physical inactivity, smoking, and alcohol consumption) with MCR was examined using multivariate logistic regression analysis. Results: At cross-section, 619 participants met criteria for MCR, with an overall prevalence 6.4% (95% CI 5.9 e6.9). A higher prevalence of MCR was seen with advancing age (P < .001), but there were no sex differences. Diabetes (adjusted odds ratio [OR] 1.47, P ¼ .001), depressive symptoms (OR 3.57, P < .001), and falls (OR 1.45, P < .001) were associated with increased risk of MCR. Among the lifestyle factors, obesity (OR 1.26, P ¼ .018) and physical inactivity (OR 1.57, P < .001) were associated with increased risk of MCR. Conclusion: MCR is common in the elderly Japanese population. The potentially modifiable risk and lifestyle factors identified for MCR should be further studied to develop interventions. Ó 2015 AMDA e The Society for Post-Acute and Long-Term Care Medicine.
The Motoric Cognitive Risk syndrome (MCR) is a predementia syndrome characterized by the presence of cognitive complaints and slow gait in older individuals without dementia or mobility
disability.1e3 MCR was associated with increased risk of developing major cognitive decline and dementia, including Alzheimer disease (AD) and vascular dementia in elderly cohorts based in the United
The authors declare no conflicts of interest. This study was funded by Health and Labour Sciences Research grants (Comprehensive Research on Aging and Health); Grant-in-Aid for JSPS Fellows (259435); Grant-in-Aid for Scientific Research (B) (grant number 23300205); Grant-in-Aid for Young Scientists (A) (15H05369), and Research Funding for Longevity Sciences (22e16) from the National Center for Geriatrics and Gerontology, Japan.
* Address correspondence to Takehiko Doi, PhD, PT, Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7e430 Morioka-cho, Obu, Aichi 474e8511, Japan. E-mail address: [email protected] (T. Doi).
http://dx.doi.org/10.1016/j.jamda.2015.09.003 1525-8610/Ó 2015 AMDA e The Society for Post-Acute and Long-Term Care Medicine.
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States and Europe.1,2 An MCR prevalence study based in 17 countries reported a pooled prevalence of 9.7%.1 Although this study included a Japanese cohort of 514 older adults,1 more information is needed to obtain reliable estimates of MCR and underlying etiologies in Japan. Medical illness and other potentially modifiable risks, such as strokes, Parkinson disease, depressive symptoms, obesity, and sedentariness, have been reported to increase risk of developing MCR in Western populations.3 However, due to the potential differences in health, behavioral, and lifestyle factors among races, it is important to examine risk factors for MCR in different countries. On the other hand, if risk factor profiles for MCR are discovered to be similar in different countries, then common global strategies to develop preventive measures for older individuals at risk for dementia could be developed. Given the large elderly population and high rates of dementia reported in Japan,4 there is a pressing need to identify individuals at high risk for dementia to institute preventive measures early and to plan care. Because diagnosing MCR does not require extensive neuropsychological tests or biomarkers, this clinical dementia risk stratification strategy is particularly attractive to implement in community settings, without the need for clinicians or other health professionals. Hence, to further elucidate the epidemiology and pathogenesis of MCR in Japan, we conducted a cross-sectional study to establish the prevalence and associated risk factors for MCR in 9683 older participants in the National Center for Geriatrics and GerontologyeStudy of Geriatric Syndromes (NCGG-SGS). Methods Participants Subjects eligible for this study were participants of a populationbased cohort study (NCGG-SGS). The overall goal of the NCGG-SGS was to establish a screening system for geriatric syndromes and to validate evidence-based interventions for preventing geriatric syndromes. The NCGG-SGS participants were recruited from Nagoya or Obu cities in Japan. The baseline study assessments were done from August 2011 to February 2012 and in June 2013 at Obu city, and from July to December 2013 in Nagoya city. A total of 10,885 individuals were enrolled in the NCGG-SGS.5 Inclusion criteria in this study were residing either in Obu or Nagoya city, age 65 years or older in the Obu site and 70 and older at the Nagoya site, and independent in basic activities of daily living (ADLs). Independence in ADLs was confirmed during interviews conducted by trained research staff about the following items: eating/feeding, dressing, bathing and showering, functional mobility, climbing up and down stairs, personal hygiene and grooming, and toilet hygiene. Exclusion criteria included having a history of cerebrovascular disease, Parkinson disease, depression, dementia, or participants with major cognitive impairment defined as those who scored 17 or lower on the Mini-Mental State Examination (MMSE).6 Medical history was collected in the face-to-face interview by a nurse. Participants with missing data for variables used to define these criteria and MCR were also excluded. After exclusions, 9683 older adults (89%) were eligible for this study. Compared with the eligible participants, those excluded were older (eligible participants: 73.6 5.5 y vs excluded participants: 75.1 5.7 y, P < .001), had lower education (11.7 2.6 y vs 11.4 2.8 y, P < .001), and took more medications (2.6 2.5 vs 4.4 3.1, P < .001), but there were no sex differences. All participants provided written informed consent, and the ethics committee of the National Center for Geriatrics and Gerontology approved this study. MCR Criteria As previously described,1e3 MCR criteria builds on operational criteria for mild cognitive impairment (MCI) syndrome.7 The only
difference in the operational definitions is that the objective cognitive impairment criterion based on cognitive tests in MCI is substituted by slow gait in MCR. All other criteria remain the same, and MCR does not require cognitive tests. Participants with MCR need to have subjective memory complaints and slow gait. The presence of subjective memory complaints was elicited from the standardized memory loss question on the 15-item Geriatric Depression Scale (GDS) administered by well-trained research staff: “Do you feel you have more problems with memory than most?”8 A positive response (“yes”) on the question was used to define subjective memory complaints. The same question was used to define subjective cognitive complaints in 8 of the 22 cohorts included in the worldwide MCR prevalence study.1 Subjective cognitive complaints have been reported to be associated with increased risk of dementia.9 However, MCR was shown to have improved predictive validity for dementia compared with subjective cognitive complaints or slow gait alone in multiple cohorts.1 Gait speed was measured over a 6.4-m level course in a well-lit area and 2 m at either end of the course was not included in the measurement to account for initial acceleration and terminal deceleration. The testers timed participants over the middle 2.4-m section indicated by markers on the floor. Participants were instructed to walk at their normal comfortable pace. The walking time over 2.4 m was converted to speed (m/s) to be consistent with other studies in this area.1,10 Participants were allowed to walk with aids, such as canes, if needed (31 participants walked with aids). Slow gait was defined as gait speed at normal pace that was 1.0 SDs or below age- and sexappropriate mean values established in the NCGG-SGS database. Diagnosing slow gait independent of clinical gait evaluations, which may be prone to variable sensitivity and specificity as well as being examiner dependent, is a strength. Furthermore, although slow gait may result from neurological as well as non-neurological causes,11 previous studies have shown slow gait by itself or as part of the MCR syndrome was associated with increased risk of developing dementia, irrespective of the underlying etiologies.1,2 Modifiable Risk Factors and Covariates Based on previous studies of MCR as well as those of dementia risk factors in Japan,3,12,13 we selected several risk factors. Our approach was also consistent with the overall goal of the parent NCGG-SGS study, which was to develop evidence-based interventions for preventing cognitive decline. We selected specific medical diseases,3,12,14 depressive symptoms,3,12 and falls that have been related to both mobility and cognitive impairments in previous studies.15 Participants were interviewed by nurses about the presence or absence of selected diseases (hypertension, diabetes, heart disease, and osteoporosis) and prescription medications (total number). Research staff obtained information on depressive symptoms (GDS) and occurrence of falls over the previous year (“Did you have any history of a fall within the past year?’’). Depressive symptoms were assessed using the 15-item GDS, and presence of significant depressive symptoms was defined as a score of 6 or more.8,16 A fall was defined as “an unexpected event in which the person comes to rest on the ground, floor, or lower level.”17 We also assessed the following lifestyle factors based on previous studies that have linked them to either MCR or dementia risk3,12: obesity, physical inactivity, smoking, and alcohol consumption. Height and weight were measured and used to calculate body mass index (BMI: kg/m2) using standard formula. Obesity was defined as BMI value of 25 and over, as previously described in the Japanese elderly population.18 Physical inactivity was defined by 2 questions used in our previous study10: (1) “Do you engage in moderate levels of physical exercise or sports aimed at health?” and (2) “Do you engage in low levels of physical exercise aimed at health?” Physical inactivity was defined as not engaging in both low and moderate levels of activity, responding “no” to both
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Statistical Analysis Baseline characteristics were compared between “MCR” and “no MCR” groups using unpaired t test or c2 test. The comparison of MCR prevalence between age and sex groups was also conducted using the c2 test. The independent variables included the selected modifiable risk factor (medical illness, depressive symptom, and fall), lifestyle variables (obesity, physical inactivity, smoking, and alcohol consumption), and MMSE. The associations between MCR (dependent variable) and independent variables were analyzed using multivariate logistic regression analysis and reported as adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs). The multivariate logistic regression analysis was conducted in 2 models. Model 1 tested each independent variable individually adjusted for demographic data (age, sex, education, and medication use). Model 2 included all independent variables and the demographic data. To account for potential of diagnostic misclassification in dementia cases, we repeated multivariate logistic regression adjusting for covariates in model 2 and MMSE among participants with MMSE scores of 24 and above. Analyses were performed using SPSS statistics software, Version 20 (IBM Corp., Chicago, IL). Statistical significance was set at P < .05 in all analyses.
10
8 Percent prevalence rate [%]
these questions. Current habits of smoking and drinking alcohol were also interviewed. Alcohol consumption was assessed in terms of number of drinking days per week, and classified into 3 groups: no drinking, drinks 1 to 4 days, or drinks 5 or more days.
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6
4
2
0
65-69 yrs
70-74 yrs
75-79 yrs
80-84 yrs
85 yrs over
Men
5.8%
4.5%
7.4%
8.6%
8.0%
Total 6.1%
Women
4.9%
6.8%
8.0%
6.1%
9.8%
6.6%
Total
5.3%
5.6%
7.7%
7.4%
8.9%
6.4%
Fig. 1. Prevalence of MCR separated by age and sex group.
(95% CI 6.7%e8.9%); 80e84 y, 7.4% (95% CI 6.0%e9.1%); 85 y and older, 8.9% (95% CI 6.3%e12.3%) (P for trend: P < .001). There were no sex differences in the prevalence of MCR (P ¼ .268). Figure 1 summarizes MCR prevalence by age and sex categories.
Risk Factors for MCR Results Of the 9683 subjects (52% women, mean age: 73.6 years), 619 (6.4%) met criteria for MCR. Table 1 summarizes and compares characteristics between groups. The sex and current smoking history were not significantly different between groups. All other selected characteristics showed significant differences between groups (P < .05). Prevalence The overall prevalence of MCR was 6.4% (95% CI 5.9%e6.9%) in this cohort. An increasing prevalence was seen with age: 65e69 y, 5.3% (95% CI 4.5%e6.3%); 70e74 y, 5.6% (95% CI 4.9%e6.5%); 75e79 y, 7.7%
Table 1 Characteristics in Subjects Between Groups Variables
No MCR, n ¼ 9064
MCR, n ¼ 619
P
Age, y Education, y Sex (women), % Medical condition, % Hypertension Diabetes Heart disease Osteoporosis Depressive symptoms Falls Medication numbers Gait speed, m/s MMSE, score Lifestyle, % Obesity Physical inactivity Smoking Drinking alcohol <4 d/wk 5 d/wk
73.5 (5.5) 11.8 (2.6) 51.7
74.7 (5.9) 11.0 (2.6) 54.0
<.001 <.001 .299
45.6 12.4 16.6 12.9 12.8 16.6 2.5 (2.5) 1.17 (0.21) 26.2 (2.5)
49.9 20.4 22.3 18.4 39.7 26.4 3.5 (3.0) 0.80 (0.15) 25.0 (2.9)
.041 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001
25.0 24.1 8.3
30.7 38.6 9.5
.002 <.001 .293 .045
17.3 27.4
14.3 25.6
Values are mean (SD) or proportion.
Table 2 presents the results of the multivariate logistic regression analyses. Model 1, adjusted for age, sex, education, and medication use, shows that diabetes (OR 1.49, 95% CI 1.20e1.85), depressive symptoms (OR 3.90, 95% CI 3.27e4.66), falls (OR 1.69, 95% CI 1.40e2.05), obesity (OR 1.23, 95% CI 1.02e1.47), physical inactivity (OR 1.89, 95% CI 1.59e2.25), and smoking (OR 1.37, 95% CI 1.02e1.83) were associated with MCR. In the fully adjusted model 2, in which all selected risk factors were entered together in model 1, diabetes (OR 1.47, 95% CI 1.18e1.85), depressive symptoms (OR 3.57, 95% CI 2.97e4.29), and falls (OR 1.45, 95% CI 1.19e1.77) remained associated with MCR among the modifiable risk factors examined. Among the lifestyle factors examined, obesity (OR 1.26, 95% CI 1.04e1.53) and physical inactivity (OR 1.57, 95% CI 1.31e1.89) were associated with MCR in the fully adjusted model 2.
Table 2 The Association With MCR Variables
Modifiable risk factors Hypertension Diabetes Heart disease Osteoporosis Depressive symptoms Falls Lifestyle factors Obesity Physical inactivity Smoking Drinking alcohol 4 d/wk 5 d/wk
Model 1
Model 2
ORs
(95% CI)
P
ORs
(95% CI)
P
0.91 1.49 1.13 1.19 3.90 1.69
(0.76e1.08) .276 0.85 (0.71e1.02) .080 (1.20e1.85) <.001 1.47 (1.18e1.85) .001 (0.91e1.39) .273 1.08 (0.87e1.35) .481 (0.93e1.51) .160 1.23 (0.96e1.57) .103 (3.27e4.66) <.001 3.57 (2.97e4.29) <.001 (1.40e2.05) <.001 1.45 (1.19e1.77) <.001
1.23 (1.02e1.47) .028 1.26 (1.04e1.53) .018 1.89 (1.59e2.25) <.001 1.57 (1.31e1.89) <.001 1.37 (1.02e1.83) .035 1.24 (0.91e1.69) .173 0.84 (0.66e1.08) 0.94 (0.75e1.17)
.179 0.94 (0.73e1.21) .557 1.07 (0.86e1.35)
.624 .543
The absence was reference in each categorical data and alcohol is referred to no drinking. Model 1 analyzed each independent variable individually adjusted for age, sex, education, and medication use. Model 2 included all independent variables and adjusted variables as well as model 1.
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The proportion of participants with MMSE scores 24 or higher were 84.7% in the No MCR group and 70.0% in the MCR group. In the analysis restricted to the 8108 participants with MMSE scores of 24 and above, results were similar with significant associations with MCR seen with diabetes (OR 1.42, 95% CI 1.08e1.86), depressive symptoms (OR 3.56, 95% CI 2.86e4.43), falls (OR 1.51, 95% CI 1.20e1.91), obesity (OR 1.42, 95% CI 1.14e1.78), and physical inactivity (OR 1.64, 95% CI 1.32e2.03). Discussion This large population study provides estimates of the prevalence of MCR in Japanese community-dwelling older adults. In this large Japanese cohort, prevalence of MCR was 6.4%, and ranged from 5.3% to 8.9%. An increasing prevalence of MCR was seen with advancing age, which parallels the epidemiology of dementia in older age groups. There were no sex differences in MCR prevalence, which is consistent with the lack of sex differences noted in MCR studies in other populations.1,3 This is in contrast with MCI, in which a higher prevalence in men has been reported19 in some but not all studies.20 Participants with MCR were older, less educated, and were on a higher number of medications. Even after adjusting for these characteristics, presence of diabetes, depressive symptoms, falls, obesity, and physical inactivity were associated with MCR in Japanese seniors. The previous worldwide study reported a global MCR prevalence of 9.7% (95% CI 8.2%e11.2%) in a meta-analysis including 26,802 older adults from 17 countries.1 However, prevalence varied from 1% to 15% in the 22 studies included in this multicountry investigation.1 This variability may result from the sociodemographic characteristics of the population studied, the age distribution, size of the samples, and the methods used to define slow gait criterion. Another source of variability is the source of the population. A higher prevalence of MCR was seen in samples that are recruited from memory clinics. A lower prevalence of MCR was seen in community samples, as in our study.1 The prevalence of MCR was 13% in another Japanese cohort included in the previous multicountry MCR prevalence study.1 This other Japanese cohort was based in a rural area, and included 514 adults who were 75 years and older (54% women), and whose education ranged from 8.2 to 9.3 years.21 In contrast, our study was conducted in urban neighborhoods, and included 9683 adults aged 65 years and older (52% women) whose mean education was 11.7 years. These differences may explain the different MCR prevalence rates in the 2 Japanese cohorts. Our study showed that potentially modifiable medical and lifestyle factors were associated with MCR. In a previous MCR study based in Western populations, physical inactivity, obesity, and depressive symptoms were reported to increase risk of MCR.3 Our findings are in line with these results, suggesting commonalities in risk factors for MCR across the world. Although further studies are needed, these results raise the possibility of developing common preventive strategies worldwide to prevent conversion of MCR to dementia. Numerous epidemiological studies have recognized physical inactivity, obesity, and depressive symptoms as modifiable risk factors of AD and other types of dementia.12,22,23 Furthermore, physical inactivity is related to brain changes, such as atrophy, disrupted white matter integrity, and accumulation of Alzheimer pathology.24e27 In intervention studies, exercise or enhancing physical activity has positive effects on brain volume and white matter integrity.28e30 Obesity and diabetes were associated with MCR in our cohort. Although obesity is commonly defined as BMI of 30 and over, the mean BMI in Japan and East Asian countries is lower.31 Thus, we used BMI of 25 and over to define obesity in our cohort.18 Nonetheless, it is interesting that obesity defined by this lower cut score remained a risk factor for MCR in Japan. Diabetes is a major risk factor for dementia and AD.23,32,33 Diabetes-related brain changes, such as atrophy,
disturbed white matter integrity, and vascular lesions, may compromise reserve capacity of the brain, and make the diabetic brain more vulnerable to the consequences of further pathological changes, particularly cerebrovascular disease and AD.32 Depressive symptoms are reported to increase risk of cognitive impairment and dementia.12 Furthermore, cognitive impairment, slow gait speed, and depressive symptoms are known to occur simultaneously in older adults.15 Depressive symptoms have negative impacts on mobility as well cognition in community-dwelling older adults.34 Although further study is required, these associations may explain the relationship of depressive symptoms to MCR. Falls were also associated with MCR even after adjusting for several covariates. Our previous study showed that combined presence of MCI and slow gait was strongly associated with risk of falls.35 The reverse is also true; older adults who fall are at risk of traumatic brain injuries, which may have cognitive and motor sequelae. The interactive association between cognition and mobility is thought to be dependent on changes in the brain. Slow gait speed results from age-related changes in the brain, such as atrophy36 and white matter hyperintensities.36,37 Systematic review has indicated that white matter hyperintensities were associated with lower physical function and increased falls.37 Furthermore, white matter hyperintensities are associated with falls independent from the effect of cognitive and sensorimotor functions.38 Among Japanese older adults, white matter hyperintensities were also related to gait disturbance.39 Also, white matter hyperintensities were recognized as a risk factor for falls in Japanese seniors.40 Further longitudinal studies are required to clarify the association between MCR and falls in Japan. Strengths of our study included the large sample that enabled multivariate analysis of risk factors, including several important covariates. On the contrary, the design of the study was crosssectional. Thus, causal relationship needs to be clarified in future prospective studies. Studies also need to include brain measures and biomarkers to understand the pathological mechanisms of MCR. Despite our exclusion criteria, our sample might have included some individuals with mild undiagnosed dementia, although our secondary analysis restricted to participants with MMSE scores within the normal range supports our main findings. The ORs of the selected risk factors with the exception of depressive symptoms were lower than 2, consistent with a previous multicenter MCR study.3 Many of these risk factors share common biological pathways, and may respond to common interventions. Developing interventions for any single risk factor to reduce the incidence of MCR might be less effective than a multifactorial approach targeting multiple risk factors, as suggested by the success of a recent multidomain intervention study to prevent cognitive decline.41 Conclusion Our large cohort study reported MCR prevalence among Japanese seniors. The risk factors for MCR identified in our study need to be further examined in the context of longitudinal observational studies. Our MCR findings will help improve early detection and develop prevention strategies for dementia in Japan and other countries. Acknowledgments We thank the Obu city and Nagoya city (Midoriku) office for help with participant recruitment. References 1. Verghese J, Annweiler C, Ayers E, et al. Motoric cognitive risk syndrome: Multicountry prevalence and dementia risk. Neurology 2014;83:718e726.
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