Advertisement

BMC Public Health

, 19:462 | Cite as

Factors associated with motoric cognitive risk syndrome among low-income older adults in Malaysia

  • Huijin Lau
  • Mat Ludin Arimi Fitri Email author
  • Suzana Shahar
  • Manal Badrasawi
  • Brian C. Clark
Open Access
Research

Abstract

Background

Motoric cognitive risk (MCR) syndrome is characterized by slow gait and memory complaints that could be used to predict an increased risk of dementia. This study aims to determine the MCR syndrome and its risk factors among low-income (B40) older adults in Malaysia.

Methods

Data from TUA cohort study involving 1366 older adults (aged 60 years and above) categorized as low-income were analysed, for risk of MCR syndrome based on defined criteria. Chi-square analysis and independent t test were employed to examine differences in socioeconomic, demographic, chronic diseases and lifestyle factors between MCR and non-MCR groups. Risk factors of MCR syndrome were determined using hierarchical logistic regression.

Results

A total of 3.4% of participants fulfilled the criteria of MCR syndrome. Majority of them were female (74.5%, p = 0.001), single/widow/widower/divorced (55.3%, p = 0.002), living in rural area (72.3%, p = 0.011), older age (72.74 ± 7.08 year old, p <  0.001) and had lower years of education (3.26 ± 2.91 years, p = 0.001) than non-MCR group. After adjustment for age, gender and years of education, participants living in rural area (Adjusted OR = 2.19, 95% CI = 1.10–4.35, p = 0.026), with obesity (Adjusted OR = 3.82, 95% CI = 1.70–8.57, p = 0.001), diabetes (Adjusted OR = 2.04, 95% CI = 1.01–4.11, p = 0.046), heart disease (Adjusted OR = 2.50, 95% CI = 1.00–6.20, p = 0.049) and cancer (Adjusted OR = 6.57, 95% CI = 1.18–36.65, p = 0.032) were associated with increased risk of MCR syndrome.

Conclusion

Only 3.4% of older adults from low-income group were identified as having MCR syndrome. Women, those living in rural areas, had obesity, diabetes, heart disease and cancer were more likely to have MCR syndrome. Further investigation on MCR as a predementia syndrome will help in development of preventive strategies and interventions to reduce the growing burden of dementia, especially among individuals with low socioeconomic status.

Keywords

Motoric cognitive risk Low income group Rural Obesity Chronic disease 

Abbreviations

ADL

Activities of Daily Living

B40

Bottom 40%

BMI

Body Mass Index

CHE

Castatrophic Eealth expenditure

CI

Confidence Interval

GDS

Gereiatric Depression Scale

MCI

Mild Cognitive Impairment

MCR

Motoric Cognitive Risk

MMSE

Mini Mental State of Examination

OR

Odd Ratio

SD

Standard Deviation

SPSS

Statistical Package for Social Sciences

TUA

Towards Useful Aging

χ2

Chi-square

Background

Malaysia is fast becoming an aging nation and is expected to reach this status by year 2035 [1]. Aging is accompanied by gradual loss of health and physical strength, especially in the aspects of health and physical strength to the elderly [2]. Other than age, studies showed that health determinants among the elderly include adequate exercise, regular medical check-ups, and the absence of health problems [3]. Older adults’ attitudes towards aging may also affect their physical performance [4]. The prevalence of dementia is expected to rise 3 to 4 times higher in Malaysia as compared to developed countries [1]. Therefore, as a developing country, Malaysia is facing challenges to minimize the healthcare burden and to sustain the medical expenses of the growing number of older population. Abu Bakar et al. [5] found that elderly women were more marginalized and at a disadvantage in socioeconomic aspects of their lives. Therefore, it is essential to increase the accessibility of simple and cost effective dementia risk assessments in order to curtail health care costs.

Gait speed has been accepted as a simple, reliable and valid functional measurement of motor control, strength and gait pattern [6]. Studies suggest that coexistence of cognitive complaints with reduced gait speed may indicate an increased risk of dementia [7, 8, 9, 10]. Motoric cognitive risk (MCR) syndrome is a newly defined pre-dementia syndrome characterized by slow gait speed with preserved physical functioning and cognitive complaints without dementia [11]. It can be detected without complex cognitive assessments and is accessible in various clinical settings [12].

A multi-country study reported that the pooled prevalence of MCR syndrome among older adults aged 60 and above was 9.7% [12]. A recent large-scale population study in Japan established the modifiable risk factors associated with MCR [13]. The findings reported that risk factors such as diabetes, depressive symptoms, falls and obesity were associated with increased risk of MCR syndrome.

As yet little is known about the occurrence of MCR syndrome and its risk factors among low-income populations. In Malaysia, the low income or B40 group is the bottom 40% of households with an income of less than RM3, 900 per month. The median and mean household income for this group is RM3, 000 per month and RM2, 848 per month, respectively [14]. The present study aims to determine the prevalence of MCR syndrome and its risk factors among low-income (B40) community dwelling older adults in Malaysia.

Methods

Study design and participants

The participants eligible for this study were selected from baseline data of a population-based study focusing on neuroprotective model for healthy longevity (TUA) [15]. The TUA study is described elsewhere (cite reference). This study was conducted in four states of Malaysia (Selangor, Perak, Kelantan and Johor) from November 2014 till September 2015. A total of 1366 multiethnic (Malay, Chinese, Indian) participants were identified as low income (i.e., household income of less than RM 3900 per month) together with other inclusion criteria including: 1) community dwelling older adults aged 60 and above, 2) no psychiatric and mental disorders, included dementia 3) no terminal illnesses and 4) preserved functional ability.

MCR criteria

MCR syndrome was first proposed by Verghese et al. [11] which is a high-risk clinical syndrome with strong predictive validity for dementia that builds on mild cognitive impairment (MCI) operational definitions [16]. The objective cognitive impairment criterion in MCI is substituted with slow gait in MCR syndrome. Cognitive tests are not needed in diagnosing MCR syndrome. Participants were defined as having MCR syndrome if they meet the criteria as outlined in Table 1.
Table 1

Criteria of MCR syndrome

 

Criteria of MCR syndrome

1.

Absence of dementia

2.

Subjective memory complaints

3.

Slow gait

4

Preserved activities of daily living (ADL)

Subjects were defined as not having dementia if they scored less than 14 in Mini Mental State Examination (MMSE). A single dichotomous question “Do you feel you have more problems with memory than most?” on Geriatric Depression Scale (GDS) was administered by trained enumerators to elicit the presence of subjective memory complaints. Participants who answered “yes” on this question were defined as having subjective memory complaints. The same question was used to define subjective cognitive complaints in Doi et al.’s [13] study, as well as other cohorts included in the worldwide MCR prevalence study [16]. Preserved activities of daily living including eating/feeding, dressing, bathing and showering, functional mobility, climbing up and down stairs, personal hygiene and grooming, and toilet hygiene, were determined using ADL questionnaire [17]. Gait speed was measured using a 6 m-distance walk on a level floor over time. Participants were instructed to walk back and forth over the marked distance at their usual pace. Slow gait was defined as 1 SD below mean population gait speed [11].

Potential risk and confounding factors

Potential sociodemography risk factors comprising age, gender, educational years, smoking habit, alcohol consumption, marriage status, and strata status (urban and rural) were determined using a sociodemography questionnaire. Obesity was defined as body mass index (BMI) ≥ 30 kg/m2. The presence of chronic diseases (hypertension, diabetes, hypercholesterolemia, arthritis, stroke, cardiovascular disease, chronic obstructive lung disorder, and cancer) was determined using a self-reported medical history questionnaire. Participants were classified as having depressive symptoms if they scored five and above on a 15-items Geriatric Depressive Scale (GDS).

Statistical analysis

All data were analysed using IBM Statistical Package for Social Science (SPSS) version 22 (IBM Corp., Chicago, IL). Significant value was set at p <  0.05. Comparison of characteristics between MCR and non-MCR groups were analysed using chi-squared (χ2) tests for categorical variables and independent t-test for continuous variables. Hierarchical binary logistic regression was employed to determine the risk factors of MCR syndrome, adjusted for age, gender and educational years. Results were reported as adjusted odd ratio and 95% confidence interval (CI).

Results

Prevalence of MCR syndrome

A total of 3.4% of the subjects fulfilled the criteria for MCR syndrome. Women had a higher prevalence of MCR syndrome (74.5%) compared to men (25.5%) (p = 0.001). As shown in Table 2, respondents with MCR syndrome were significantly older and had lower educational years than those without MCR syndrome (p <  0.001). Majority of them were also living in rural area (p = 0.011), unmarried, divorced, widow or widower (p = 0.002).
Table 2

Comparison of baseline characteristics

Variables

MCR (n = 47)

NON-MCR (n = 1319)

p value

n (%) / Mean ± SD

n (%) / Mean ± SD

Age (years)a

72.74 ± 7.08

68.52 ± 5.88

<  0.001*

Genderb

 Male

12 (25.5)

660 (50.0)

0.001*

 Female

35 (74.5)

659 (50.0)

 

Education (years)a

3.26 ± 2.91

5.04 ± 3.81

< 0.001*

Marrital statusb

0.002

 Married

21 (44.7)

888 (67.3)

 

 Single/widow/widower/divorced

26 (55.3)

431 (32.7)

 

Smoking Habitb

 Smoker

7 (14.9)

239 (18.1)

0.235

 Non Smoker

40 (72.1)

1080 (81.9)

 

Alcohol Consumptionb

 Yes

0 (0)

58 (4.4)

0.260

 No

47 (3.6)

1261 (95.6)

 

Strata Statusb

0.011*

 Rural

34 (72.3)

699 (53.0)

 

 Urban

13 (27.7)

620 (47.0)

 

Obesityb

0.039

 Yes

11 (23.4)

160 (12.1)

 

 No

36 (76.6)

1159 (87.9)

 

GDSa

3.64 ± 2.88

3.03 ± 2.33

0.160

Chronic diseases b

Hypertension

 Yes

27 (57.4)

675 (51.6)

0.461

 No

20 (42.6)

633 (48.4)

 

Diabetes

 Yes

17 (36.2)

342 (26.1)

0.132

 No

30 (63.8)

966 (73.9)

 

Hypercholesterolemia

 Yes

20 (42.6)

530 (40.5)

0.765

 No

27 (57.4)

778 (59.5)

 

Arthritis

 Yes

12 (25.5)

331 (25.3)

1.000

 No

35 (74.5)

977 (74.7)

 

Stroke

 Yes

0 (0)

20 (1.5)

0.393

 No

47 (100)

1288 (98.5)

 

Cardiovascular disease

 Yes

8 (17.0)

116 (8.9)

0.068

 No

39 (83.0)

1192 (91.1)

 

Chronic obstructive lung disease

 Yes

1 (0.4)

4 (0.3)

0.816

 No

228 (99.6)

1183 (99.7)

 

Cancer

 Yes

2 (4.3)

10 (0.8)

0.063

 No

45 (95.7)

1287 (99.2)

 

GDS Geriatric Depressive Scale

aIndependent t-test

bChi-squared test

*Significant at p < 0.05

Risk factors of MCR syndrome

Table 3 shows the findings of hierarchical binary logistic regression analysis, adjusted for age, gender and education years. Increasing age (Adjusted OR: 1.13, 95% CI: 1.074–1.197, p <  0.001), being female (Adjusted OR: 3.67, 95% CI: 1.485–9.070, p = 0.005) and living in rural area (Adjusted OR: 2.19, 95% CI: 1.098–4.348, p = 0.026) increased risk of having MCR syndrome. Other factors associated with increased risk of MCR syndrome were obesity (OR: 3.82, 95% CI: 1.699–8.570, p = 0.001), diabetes (Adjusted OR: 2.04, 95% CI: 1.013–4.109, p = 0.046), cardiovascular disease (Adjusted OR: 2.50, 95% CI: 1.004–6.203, p = 0.049), and cancer (Adjusted OR: 6.57, 95% CI: 1.177–36.650, p = 0.032).
Table 3

Factors that significantly associated with MCR syndrome

Independent variables

Adjusted OR

95% CI

p value*

Lower

Upper

Age

1.13

1.074

1.197

< 0.001

Gender

 Male

    

 Female

3.67

1.485

9.070

0.005

Strata Status

 Urban

    

 Rural

2.19

1.098

4.348

0.026

Obesity

 No

    

 Yes

3.82

1.699

8.570

0.001

Diabetes

 No

    

 Yes

2.04

1.013

4.109

0.046

Cardiovascular disease

 No

2.50

1.004

6.203

0.049

 Yes

    

Cancer

 No

    

 Yes

6.57

1.177

36.650

0.032

Hierarchical binary logistic regression, Adjusted for age, Gender and educational years, *Significiant at p < 0.05

Discussion

This study showed that the prevalence of MCR syndrome among low- income community dwelling older adults in an Asian country (Malaysia) was 3.4%. This figure is lower than findings from studies conducted in other Asian countries. A meta-analysis showed that the MCR syndrome prevalence among adults from Korea, China, Japan (Kurihara Project) and India (Kerala-Einstein Study), ranged from 10 to 15% [12]. The differences could be due to several factors including age range, sample size and target group [12, 13]. For instance, the highest MCR syndrome prevalence (15%) was reported in an Indian cohort, which enrolled participants with memory complaints only. In addition, the age range of subjects from the present study was 60 to 92 years old, different from that reported in Japan (74 to 95 years old) and Korea (65 to 102 years old). Sample sizes of cohorts in India (n = 271), Japan (n = 514) and Korea (n = 549) were also smaller as compared to the present study [12].

Demographic characteristics of the elderly varied in rural and urban settings in terms of loneliness, lack of financial stability, and emotional strain [18]. Single elderly with poor general health status living in the rural areas were at higher risk of depression [19]. According to Koris et al. [20], majority of the elderly from low- income groups experience castatrophic health expenditure (CHE), with the total direct expenses exceeding 10% of household income. Malaysian elderly in rural areas expressed greater need for health services and experienced more financial hardship than those in urban areas [21]. They still have to be formally employed to maintain their livelihood [19]. Complex neuropsychological testing or neuroimaging services are often limited in rural areas. Therefore, determination of MCR syndrome can be used to predict the risk of developing cognitive impairment and dementia, especially for elderly in rural areas and belonging to the low-income category.

Previous studies showed no significant gender disparities in MCR prevalence [12, 13]. However, in the present study, women had a significantly higher prevalence of MCR compared to men. A study conducted among Malaysian older adults found that women had significantly higher prevalence of frailty (11.8%) than men (5.2%) (p <  0.001) [22]. This could be due to the fact that women have lower muscle mass [23] and lose their lean body mass faster than men during the aging process [24] putting them at a higher risk of becoming physically frail.

Similar to the previous studies, participants with MCR were older, less educated, had obesity and diabetes [11, 13]. A meta-analysis on MCR demonstrated that MCR syndrome is significantly associated with cardiovascular disease and its risk factors such as hypertension, diabetes, stroke and obesity [25]. These findings suggest that a vascular mechanism may underlie the pathophysiology of MCR syndrome. Cardiovascular risk factors increased the risk of cerebral ischemia affecting the periventricular white matter [26, 27]. Brain white matter plays an important role in executive function and cognitive processing, as well as control of gait [26, 28]. The effects of diabetes on cognitive decline may relate to macrovascular and microvascular complications. Macrovascular complications such as hyperglycaemia, hyperlidipedimia, hypertension and inflammation may lead to brain structural changes and loss of brain volume [29, 30]. Additionally, microvascular change such as diabetic retinopathy was also associated with lower verbal fluency, mental flexibility and processing speed [31]. Previous studies that have examined the association between arthritis and cognition suggested that arthritis might increase the risk for cognitive impairment [32, 33, 34]. Arthritis and cognitive impairment are both associated with factors such as fatigue, pain, depression and increased risk of physical inactivity. However, arthritis was not significantly associated with risk of MCR in the present study.

Previous studies also reported that participants with MCR were more depressed compared to non-MCR group [11, 13]. Our colleagues from the same large-scale population study showed that functional status is one of the predictors that significantly associated with geriatric depressive disorders among Malaysian older adults [35]. Depressive symptoms were also reported highest in Mild Cognitive Impairment (MCI) group [36]. Nevertheless, depressive symptom was not associated with the risk of MCR in the present study. Both MCR and non-MCR groups reported not having any depressive symptom as measured using GDS. This might explain the lack of association of depressive syndrome with MCR.

The strength of this study is that it is one of very few studies investigating MCR among low-income populations in Asia. The limitation of the present study is that the true causal relationships could not be derived as this was a cross sectional study. Nevertheless, the multiple factors associated with MCR syndrome in the present study were in agreement with the risk factors of cognitive impairment and dementia [37]. Future validation studies are needed so that this simple clinical approach can be used to improve dementia risk assessments, develop interventions and preventive measures to optimize cognitive performance of Malaysian elderly.

In conclusion, Malaysian older adults from the low-income (B40) group, especially women living in rural areas, with obesity, diabetes, heart disease and cancer were at a higher risk of MCR syndrome. The cost effective MCR concept can be easily applied in various settings, particularly in rural areas that lack of healthcare facilities, to identify high-risk individuals. Further investigation on MCR as a predementia syndrome will help in development of preventive strategies and interventions to reduce the growing burden of dementia, especially among individuals with low socioeconomic status.

Notes

Acknowledgments

We acknowledge the contributions of the LRGS TUA study group including the co-researchers, research assistants, enumerators, phlebotomies, research and science officers. We thank the participants, their family members, community leaders and the local authorities for their cooperation throughout recruitment and data collection processes.

Funding

This study was funded by Ministry of Higher Education Malaysia under the Longterm Research Grant Scheme (LRGS) LRGS/BU/2012/UKM-UKM/K/01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors also acknowledged the financial assistance for publication received from the Research University Grant awarded by the Ministry of Health to the National University of Malaysia specifically for the Consortium of B40 Research (CB40R) under the auspice of B40 Grand Challenges (IDE 2018–01).

Availability of data and materials

All relevant data can be found within the paper.

About this supplement

This article has been published as part of BMC Public Health Volume 19 Supplement 4, 2019: Health and Nutritional Issues Among Low Income Population in Malaysia. The full contents of the supplement are available online at https://bmcpublichealth.biomedcentral.com/articles/supplements/volume-19-supplement-4.

Authors’ contributions

HL was responsible for responsible for conceptualisation, acquisition of data, analysis of data, initial and final draft. AFML and SS were responsible for conceptualisation, initial draft and revising draft for content. MB was responsible for acquisition of data and analysis of data. BCC was responsible for conceptualisation and revising draft for content. All the authors have read and approved the final manuscript.

Ethical approval and consent to participate

This study was approved by Medical Research and Ethics Committee of the Universiti Kebangsaan Malaysia (UKM). Informed consent was also obtained from all participants prior the data collection.

Consent for publication

Not applicable.

Competing interests

The authors report no conflict of interest related to the work.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.
    Olivia TSL, Khan S, Vergara RG, Khan N. Policies and protections for ageing society in Malaysia. Journal of Southeast Asian Research. 2016.  https://doi.org/10.5171/2016.974366.
  2. 2.
    Alavi K, Sail RM, Mohamad MS, Omar M, Subhi N, Chong ST, et al. Exploring the meaning of ageing and quality of life for tge sub-urban older people. Pertanika J. Soc. Sci. & Hum. 2011; 19(S): 41–8. Available from: https://core.ac.uk/download/pdf/153820349.pdf. Accessed 25 Apr 2019.
  3. 3.
    Selvaratnam DP, Abu Bakar N, Haji Idris NA. The health determinants of elderly Malaysiam population. Prosiding Perkem VII 2012; 2: 1195–1199. Available from: https://docplayer.net/32134290-Kesejahteraan-ekonomi-warga-emas-di-malaysia-perbezaan-gender-economic-well-being-of-elderly-in-malaysia-gender-difference.html. Accessed 25 Apr 2019.
  4. 4.
    Singh DKA, Ibrahim A, Chong PK, Subramaniam P. Attitude towards ageing and physical performance among adults 55 years old and above. Malaysian Journal of Public Health Medicine 2018; 1: 10–17. Available from: https://www.mjphm.org.my/mjphm/journals/2018%20-%20Special%20Volume%20(1)/ATTITUDE%20TOWARDS%20AGEING%20AND%20PHYSICAL%20PERFORMANCE%20AMONG%20ADULTS%2055%20YEARS%20OLD%20AND%20ABOVE. Accessed 25 Apr 2019.
  5. 5.
    Abu Bakar N, Haji Idris NA, Selvaratnam DP. Economic well-being of elderly in Malaysia: gender difference. Prosiding Perkem IV. 2009;1:316–23.Google Scholar
  6. 6.
    Dobkin BH. Short-distance walking speed and timed walking distance: redundant measures for clinical trials? Neurology. 2006;66(4):584–6.  https://doi.org/10.1212/01.wnl.0000198502.88147.dd.CrossRefPubMedGoogle Scholar
  7. 7.
    Buracchio T, Dodge HH, Howieson D, Wasserman D, Kaye J. The trajectory of gait speed preceding mild cognitive impairment. Arch Neurol. 2010;67(8):980–6.  https://doi.org/10.1001/archneurol.2010.159.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Mielke MM, Roberts RO, Savica R, Cha R, Drubach DI, Christianson T, et al. Assessing the temporal relationship between cognition and gait: slow gait predicts cognitive decline in the Mayo Clinic study of aging. J Gerontol A Biol Sci. 2013;68(8):929–37.  https://doi.org/10.1093/gerona/gls256.CrossRefGoogle Scholar
  9. 9.
    Verghese J, Lipton RB, Hall CB, Kuslansky G, Katz MJ, Buschke H. Abnormality of gait as a predictor of non-Alzheimer’s dementia. N Engl J Med. 2002;347(22):1761–8.CrossRefGoogle Scholar
  10. 10.
    Verghese J, Wang C, Lipton RB, Holtzer R, Xue X. Quantitative gait dysfunction and risk of cognitive decline and dementia. J Neurol Neurosurg Pyschiatry. 2007;78(9):929–35.  https://doi.org/10.1136/jnnp.2006.106914.CrossRefGoogle Scholar
  11. 11.
    Verghese J, Wang C, Lipton RB, Holtzer R. Motoric cognitive risk syndrome and the risk of dementia. J Gerontol A Biol Sci. 2013;68(4):412–8.  https://doi.org/10.1093/gerona/gls191.CrossRefGoogle Scholar
  12. 12.
    Verghese J, Annweiler C, Ayers E, Barzilai N, Beauchet O, Bennett DA, et al. Motoric cognitive risk syndrome: multicountry prevalence and dementia risk. Neurology. 2014;83(8):718–26.  https://doi.org/10.1212/WNL.0000000000000717.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Doi T, Verghese J, Shimada H, Makizako H, Tsutsumimoto K, Hotta R, et al. Motoric cognitive risk syndrome: prevalence and risk factors in Japanese seniors. J Am Med Dir Assoc. 2015;16(12):1103e21–5.  https://doi.org/10.1016/j.jamda.2015.09.003.CrossRefGoogle Scholar
  14. 14.
    Department of Statistics Malaysia. Report of household income and basic amenities survey 2016. [Press release] (9 October 2017). https://www.dosm.gov.my/v1/index.php?r=column/pdfPrev&id=RUZ5REwveU1ra1hGL21JWVlPRmU2Zz09. Accessed 25 Apr 2019.
  15. 15.
    Shahar S, Omar A, Vanoh D, Hamid TA, Mukari SZM-S, Din NC, et al. Approaches in methodology for population-based longitudinal study on neuroprotective model for healthy longevity (TUA) among Malaysian older adults. Aging Clin Exp Res. 2016;28(6):1089–104.  https://doi.org/10.1007/s40520-015-0511-4.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Petersen RC. Clinical practice. Mild cognitive impairment. N Engl J Med. 2011;364(23):2227–34.  https://doi.org/10.1056/NEJMcp0910237.CrossRefPubMedGoogle Scholar
  17. 17.
    Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffee MW. Studies of illness in the aged: the index of ADL: a standardized measure of biological and psychosocial function. JAMA. 1963;185:914–9.CrossRefGoogle Scholar
  18. 18.
    Selvaratnam D, Poo BT. Lifestyle of the elderly in rural and urban Malaysia. Ann N Y Acad Sci. 2007;1114:317–25.  https://doi.org/10.1196/annals.1396.025. CrossRefGoogle Scholar
  19. 19.
    Manaf MRA, Mustafa M, Abdul Rahman MR, Yusof KH, Abd Aziz NA. Factors influencing the prevalence of mental health problems among Malay elderly residing in a rural community: a cross-sectional study. PLoS One. 2016.  https://doi.org/10.1371/journal.pone.0156937. CrossRefGoogle Scholar
  20. 20.
    Koris R, Nor NM, Haron SA, Ismail NW, Junid SMAS, Nur AM, et al. Sociodemographic, cognitive status and comorbidity determinants of catastrophic health expenditure among elderly in Malaysia. Int J Econs & Mgmt 2017; 11(S3):673–690. http://www.ijem.upm.edu.my/vol11noS3/(7)%20IJEM%20(S3)%202017%20R2%20Socio-demographic,%20Cognitive%20Status%20and%20Comorbidity%20Determinants%20of%20Catastrophic%20Health%20Expenditure%20among%20Elderly%20in%20Malaysia.pdf. Accessed 25 Apr 2019.
  21. 21.
    Shahar S, Earland J, Abd Rahman S. Social and health profiles of rural elderly Malays. Singap Med J. 2001;42(5):208–13.Google Scholar
  22. 22.
    Badrasawi M, Shahar S, Kaur Ajit Singh D. Risk factors of frailty among multi-ethnic Malaysian older adults. Int J Gerontol. 2017;11(3):154–60.  https://doi.org/10.1016/j.ijge.2016.07.006. CrossRefGoogle Scholar
  23. 23.
    Janssen I, Heymsfield SB, Wang Z, Ross R. Skeletal muscle mass and distribution in 468 men and women aged 18–88 yr. J Appl Physiol (1985). 2000; 89(1): 81–8. Available from: doi:  https://doi.org/10.1152/jappl.2000.89.1.81.CrossRefGoogle Scholar
  24. 24.
    Visser M, Kritchevsky S, Goodpaster B, Newman AB, Nevitt MC, Stamm E, et al. Leg muscle mass and composition in relation to lower extremity performance in men and women aged 70 to 79: the health, aging and body composition study. J Am Geriatr Soc. 2002;50(5):897–904.CrossRefGoogle Scholar
  25. 25.
    Beauchet O, Sekhon H, Barden J, Liu-Ambrose T, Chester VL, Szturm T, et al. Association of motoric cognitive risk syndrome with cardiovascular disease and risk factors: results from an original study and meta-analysis. J Alzheimers Dis. 2018;64(3):875–87.  https://doi.org/10.3233/JAD-180203.CrossRefPubMedGoogle Scholar
  26. 26.
    Santos CY, Snyder PJ, Wu W-C, Zhang M, Echeverria A, Alber J. Pathophysiologic relationship between Alzheimer’s disease, cerebrovascular disease, and cardiovascular risk: a review and synthesis. Alzheimers Dement. 2017;7:69–87.  https://doi.org/10.1016/j.dadm.2017.01.005.CrossRefGoogle Scholar
  27. 27.
    Solfrizzi V, Panza F, Colacicco AM, D’Introno A, Capurso C, Torres F, et al. Vascular risk factors, incidence of MCI, and rates of progression to dementia. Neurology. 2004;63(10):188291.  https://doi.org/10.1212/01.WNL.0000144281.38555.E3.CrossRefGoogle Scholar
  28. 28.
    Smith EE, O’Donnell M, Dagenais G, Lear SA, Wielgosz A, Sharma M, et al. Early cerebral small vessel disease and brain volume, cognition, and gait. Ann Neurol. 2015;77(2):251–61.  https://doi.org/10.1002/ana.24320.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Reijmer YD, van den Berg E, Ruis C, Kappelle LJ, Biessels GJ. Cognitive dysfunction in patients with type 2 diabetes. Diabetes Metab Res Rev 2010; 26(7): 507–519. doi:  https://doi.org/10.1002/dmrr.1112.CrossRefGoogle Scholar
  30. 30.
    Strachan MW, Reynolds RM, Marioni RE, Price JF. Cognitive function, dementia and type 2 diabetes mellitus in the elderly. Nat Rev Endocrinol. 2011;7(2):108–14.  https://doi.org/10.1038/nrendo.2010.228.CrossRefPubMedGoogle Scholar
  31. 31.
    Ding J, Strachan MW, Reynolds RM, Frier BM, Deary IJ, Fowkes FGR, et al. Diabetic retinopathy and cognitive decline in older people with type 2 diabetes: the Edinburgh type 2 diabetes study. Diabetes. 2010;59(11):2883–9.  https://doi.org/10.2337/2Fdb10-0752.
  32. 32.
    Huang SW, Wang WT, Chou LC, Liao CD, Liou TH, Lin HW. Osteoarthritis increases the risk of dementia: a nationwide cohort study in Taiwan. Sci Rep 2015; 5: 10145. Available from: https://www.nature.com/articles/srep10145. Accessed 25 Apr 2019.
  33. 33.
    Lu K, Wang HK, Yeh CC, Huang C-Y, Sung P-S, Wang L-C, et al. Association between autoimmune rheumatic diseases and the risk of dementia. Biomed Res Int. 2014;2014:861812.  https://doi.org/10.1155/2014/861812.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Wallin K, Solomon A, Kareholt I, Tuomilehto J, Soininen H, Kivipelto M. Midlife rheumatoid arthritis increases the risk of cognitive impairment two decades later: a population-based study. J Alzheimers Dis. 2012;31(3):669–76.  https://doi.org/10.3233/JAD-2012-111736.CrossRefPubMedGoogle Scholar
  35. 35.
    Vanoh D, Shahar S, Yahya HM, Hamid TA. Prevalence and determinants of depressive disorders among community-dwelling older adults: findings from the towards useful aging study. Int J Gerontol. 2016;10(2):81–5.  https://doi.org/10.1016/j.ijge.2016.02.001. CrossRefGoogle Scholar
  36. 36.
    Vanoh D, Shahar S, Din NC, Omar A, Vyrn CA, Razali R, et al. Predictors of poor cognitive status among older Malaysian adults: baseline findings from the LRGS TUA cohort study. Aging Clin Exp Res. 2017;29(2):173–82.  https://doi.org/10.1007/s40520-016-0553-2.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Baumgart M, Synder HM, Carrillo MC, Fazio S, Kim H, Johns H. Summary of the evidence on modifiable risk factors for cogntiive decline and dementia: a population-based perspective. Alzheimers Dement. 2015;11(6):718–26.  https://doi.org/10.1016/j.jalz.2015.05.016. CrossRefGoogle Scholar

Copyright information

© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Huijin Lau
    • 1
  • Mat Ludin Arimi Fitri 
    • 1
    • 4
    Email author
  • Suzana Shahar
    • 1
  • Manal Badrasawi
    • 2
  • Brian C. Clark
    • 3
  1. 1.Centre for Healthy Aging and Wellness, Faculty of Health SciencesUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia
  2. 2.Department of Applied Chemistry and Applied Biology, College of Applied SciencesPalestine Polytechnic UniversityHebronPalestine
  3. 3.Ohio Musculoskeletal and Neurological Institute (OMNI) and Department of Biomedical SciencesOhio UniversityAthensUSA
  4. 4.Program of Biomedical Science, Faculty of Health SciencesUKM KL Jalan Raja Muda Abd AzizKuala LumpurMalaysia

Personalised recommendations