|Year : 2021 | Volume
| Issue : 2 | Page : 92-97
Mild cognitive impairment and its lifestyle-related risk factors in the elderly: A community-based cross-sectional study
Anku Moni Saikia, Vinoth Rajendran
Department of Community Medicine, Gauhati Medical College, Guwahati, Assam, India
|Date of Submission||16-Feb-2021|
|Date of Acceptance||29-Jun-2021|
|Date of Web Publication||10-Feb-2022|
Dr. Vinoth Rajendran
Senior Resident, Department of Community Medicine, Gauhati Medical College and Hospital, Bhangagarh, Guwahati - 781 032, Assam
Source of Support: None, Conflict of Interest: None
Context: Alzheimer's dementia (AD), an irreversible condition is an important cause of disability in old age. Mild cognitive impairment (MCI) is an intermediate state between normal cognition and dementia. Amnestic MCI (aMCI) is the precursor of AD. Identification of modifiable lifestyle risk factors help in the prevention of aMCI, and thereby in the prevention of AD. Aim: The purpose of this study was to determine the prevalence of aMCI and different lifestyle factors associated with types of MCI. Methods: This cross-sectional study was conducted amongst the elderly (≥60 years). A sample of 576 persons was selected using a multistage sampling technique. Vernacular adaptation of Hindi Mini-Mental State Examination tool was used to screen dementia and MCI. Geriatric Depression Scale-15 was used for screening depression. Data were collected using a pre-designed and pretested schedule and SPSS was used for data entry and analysis. Results: The prevalence of MCI was found to be 22.4% among the elderly. Out of all MCI cases, the prevalence of aMCI was 38.8% in this study. The lack of social and leisure engagement was found to be significantly associated with the type of MCI. Conclusion: The comparatively higher prevalence of aMCI is just the tip of the iceberg. Lack of social and leisure engagement is a highly predictive risk factor.
Keywords: Amnestic mild cognitive impairment, elderly, mild cognitive impairment, physical activity, smoking, alcohol use, social and leisure engagement
|How to cite this article:|
Saikia AM, Rajendran V. Mild cognitive impairment and its lifestyle-related risk factors in the elderly: A community-based cross-sectional study. J Mahatma Gandhi Inst Med Sci 2021;26:92-7
|How to cite this URL:|
Saikia AM, Rajendran V. Mild cognitive impairment and its lifestyle-related risk factors in the elderly: A community-based cross-sectional study. J Mahatma Gandhi Inst Med Sci [serial online] 2021 [cited 2023 Jun 4];26:92-7. Available from: https://www.jmgims.co.in/text.asp?2021/26/2/92/337431
| Introduction|| |
Along with the whole world, India is also experiencing a demographic transition at a fast pace. In India, the number of older persons is projected to grow by 64% between 2015 and 2030, which is similar to the projected growth rate in China which is 71% over the same period.
The increasing number of older people has imposed a huge impact on society, as longer survival is also accompanied by greater decline in health and different domains of function. With increasing life expectancy, there is increasing morbidity and disability, thereby making older people more dependent physically and socially. An important cause of disability and dependency in old age is dementia.
Dementia is a neurocognitive disorder with deterioration in memory, thinking, behavior, and the ability to perform everyday activities. Although it commonly affects older people, it is not considered a part of normal aging. There are different forms of dementia such as Alzheimer's disease (AD), vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. AD is the most common form of dementia in old age. Mild cognitive impairment (MCI) is an intermediate stage between dementia and normal cognition. It is a syndrome defined as cognitive decline greater than expected for an individual's age and education level but that does not interfere notably with activities of daily life. It is much more prevalent among older adults and it ranges from 3% to 19% in population-based epidemiological studies. MCI is broadly classified as amnestic and non-amnestic types. It has been termed amnestic type as it mainly emphasis on memory loss. Based on Peterson's Criteria, amnestic MCIs (aMCI) are those having: (1) memory complaints; (2) impaired memory function for age and education; (3) preserved general cognitive function; (4) intact activities of daily living; and (5) not demented.,
Subjects with aMCI usually progress to AD at a higher rate., However, there is heterogeneity in the observation of different studies. Neuroimaging studies support the view that aMCI shares features with AD-like hippocampal atrophy, the presence of which can predict the conversion of AD. Identifying the modifiable risk factors of MCI and adopting appropriate interventions will help in reducing the problem of MCI and slow down the progress towards AD. This study was done to find out the prevalence of MCI along with its subtypes and to assess the association of various lifestyle-related risk factors with aMCI.
| Methods|| |
This community-based, cross-sectional study was undertaken in Guwahati City, Kamrup Metro District of Assam, India, from January 2019 to December 2019. The city is the only metropolitan city of the entire northeastern states of India and the state capital is located in the city. The elderly (≥60 years) who are living in the city for more than a year and those who gave consent were included in the study. An equal number of males and females were selected for the study. Critically ill elderly, elderly in old-age homes and nursing homes, elderly living alone with severe hearing loss and failing to comprehend the questions, severe depression on Geriatric Depression Scale-15 (GDS-15), known or diagnosed cases of Parkinson's disease, elderly found to have dementia or severe cognitive impairment on Hindi Mini-Mental State Examination (HMMSE) scoring <17, and history of stroke or head injury were excluded.
Taking prevalence of 14.89%, and applying the formula n = 4pq/L2, where prevalence (p) =14.89%, q = (100-p), and l = 20% relative error (i.e. 20% of 14.89%), the sample size was calculated as 572, taking relative error of 20% of p. A multistage sampling technique was adopted. There are 31 wards in Guwahati Municipal Corporation. For this study, 50% of the total wards, i.e. 16 wards, were selected randomly. To get an equal number of elderly from each ward, a minimum of 36 elderly were required per ward to get the minimum sample size of 572. The final sample size calculated was 576 (16 × 36 = 576). The list of households with the house numbers of a selected ward was considered as the sampling frame for that ward. Thirty-six households from each ward were randomly selected using the lottery method from the sampling frame assuming that each household would have one elderly. If an elderly was not found in the selected household, the adjoining household was visited until the sampled elderly was found. Subsequently, all 36 randomly selected households were visited. The process continued until the required sample was met.
Initially, the elderly were subjected to GDS-15, and those who scored <5 in GDS-15 and <17 in HMMSE were excluded from enrolling in the study. MCI was screened by using the vernacular adaptation of HMMSE. All the elderly who were present in the household were taken. The data were collected using a predesigned and pretested schedule. Functional status was assessed by activities of daily living (ADL) and instrumental ADL (IADL). Katz Index and Lawton Index were used to measure ADL and IADL, respectively. The lifestyle variables studied were living arrangement, smoking status, alcohol consumption, physical activity, and social and leisure activities. However, a duration of at least 6 months was considered to define lifestyle-related factors as adequate or vice versa. The information obtained was verified with the close family member or caregiver as and when necessary. Privacy and confidentiality were maintained during the interview.
aMCI and non-aMCI (na-MCI) are classified as per Peterson Criteria., Classification of smoking status was done as per the Centers for Disease Control and Prevention guidelines. Classification of physical activity was done as per the World Health Organization guidelines.
Operational definitions were made regarding alcohol use and social and recreational engagement. Alcohol use has been classified as never user (fewer than 12 drinks in a lifetime), ex-user (at least 12 drinks in any one year in a lifetime but no drinks in the past year), and current user (at least 12 drinks in the past year). Current users were further classified as mild (1–5 drinks/week), moderate (5–10 drinks/week), and high (more than 10 drinks/week) based on the frequency of alcohol consumption per week.
The social and leisure engagement was assessed for the last 6 months and based on the type of activities in leisure times and hours spent per activity per week. The duration of at least 6 months of social and leisure engagement was considered for defining adequate and vice versa. Activities such as television viewing (1 point), hobbies/crafts (1 point), participating in community work (1 point), participating in social gatherings/religious work (1 point), visiting friends/neighbors (1 point), and interacting with family members (1 point) were considered for social and leisure engagement. For each activity, one point was given and a total of 6 points were given. Those who have scored more than 2 scores with a duration of at least 5 hours per week were considered adequate for social and leisure engagement and vice versa.
The data collected were analyzed using IBM SPSS Statistics version 25 (Armon, NY: IBM Corp). Proportions were calculated for different study variables. The Chi-square test was used for the analysis of categorical variables. The criteria of significance used in the study were P < 0.05. Binary logistic regression analysis was used for the different study variables. Written informed consent was obtained from the participants before undertaking the study. The study was approved by the Institutional Ethics Committee.
| Results|| |
Out of the total 576 elderly, the majority (62.5%) were in the age group of 60-69 years and 7.8% were above 80 years of age. The prevalence of MCI was 22.4% where aMCI was 8.7% and na-MCI was 13.7% [Figure 1]. In 70–79 age group, aMCI is seen to be almost similar to na-MCI [Figure 2]. Regarding age group, the mean ± standard deviation of aMCI and na-MCI was found to be 69 ± 5.94 years and 67 ± 7.59 years, respectively. Of the total elderly with MCI (22.4%), 10.2% were female and 12.2% were male [Figure 2]. However, a-MCI was comparatively higher among the males than the females [Figure 3].
|Figure 1: Distribution of the elderly according to the prevalence of mild cognitive impairment and its types|
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|Figure 2: Distribution of types of mild cognitive impairment according to the age group|
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|Figure 3: Gender-wise distribution of types of mild cognitive impairment|
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MCI has been classified as per Peterson's criteria as aMCI and na-MCI. Among the elderly with MCI cases (n = 129), the majority (61.2%) were naMCI. Accordingly, 38.8% of the elderly who were found to be having intact IADL and ADL status were classified under aMCI. While assessing the relationship between various lifestyle risk factors and types of MCI, smoking, physical activity, and social leisure engagement were found to be significant [Table 1] . However, while analyzing with binary logistic regression, only lack of social and leisure engagement was found to be a potential risk factor for MCI [Table 2].
|Table 1: Association of lifestyle risk factors with types of mild cognitive impairment|
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|Table 2: Binary logistic regression on lifestyle risk factors of mild cognitive impairment|
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| Discussion|| |
A comparatively higher prevalence of MCI (22.4%) was an important observation that invites more in-depth research in this part of India. Variation has been observed in the prevalence of MCI across the world., In contrast to the present finding, a prevalence rate of 5.3% for MCI in 60–76-year-old subjects was reported in another study which was conducted in the city of Kuopio in eastern Finland. In the study done in Shanghai, the prevalence for MCI was found to be 19.9% using the Chinese version of MMSE. In a study done in Taiwan, a similar prevalence of MCI was found, i.e. 22.2% by using the MMSE scale.
Although India has limited research on this issue, a study done in the South India metropolitan area found a prevalence of 26.06% among community-dwelling older adults, which is similar to the current finding. In contrast, a low prevalence rate of 5.1% for cognitive impairment was reported in the study conducted in Shimla hills located in North India.
Variation in the prevalence of MCI could be due to different screening tools used in different studies along with methodological differences and different criteria for defining and classifying MCI. Furthermore, the sociodemographic characteristic of population in various places could be one reason for these variations. However, there are limited community-based studies from India on the types of MCI. Reasonably, a higher prevalence (38.8%) of aMCI among MCI cases is an eye-opener for this unaddressed issue which compels to undertake further longitudinal study with a bigger sample size.
However, variation of prevalence of aMCI has been observed across the globe. A prevalence of 17.9% of aMCI was reported in a study done in Peru using the memory alteration test. In another study done in China using Montreal Cognitive Assessment and the MMSE found out that the prevalence of MCI, a-MCI, and na-MCI stated was 14.2%, 12.2%, and 2.0% respectively. This is probably due to the heterogeneity in the type of sampling, the test used to assess aMCI, and also the criteria for defining the entity.
While eliciting a relationship between living arrangement and type of MCI, no association was found. However, this could be due to social and cultural practices of joint family and social taboos on allowing the elderly to live alone. Living status is associated with a strong social network and cognitive impairment. However, no study was found to look at living status with aMCI.
Lack of social and leisure activities is associated significantly with mild cognitive impairment. A systematic review and meta-analysis conducted on MCI and cognitive leisure activities revealed that participation in leisure activities reduces the risk of cognitive impairment. Mentally stimulating leisure activities were significantly associated with later-life cognition, better memory, speed of processing, and executive functioning, and less decline in overall cognition, language, and executive functioning. Many studies stated that cognitive activity participation lowers the risk of development of amnestic mild cognitive impairment. Similar findings are reported in the study done in the urban slums, Kerala, South India, where they stated that MCI was negatively associated with household recreational activities. Increased frequency of social activity and adults who are socially active may experience less cognitive decline in old age.
In the current study, lack of physical activity is significantly associated with aMCI, this may be due to the fact that physical activity influences independence and thus promotes social and leisure engagement. However, on binary logistic regression, it was not found to be significant. This could be due to many confounders which are not addressed in the study. In a systematic review, it was reported that there is uncertainty about the cognitive benefits of physical activity. It was stated that though physical activity improves cardiovascular fitness, it is not known whether it benefits in improving cognitive function too. There are complex and interrelated relationships among physical activity along with other risk factors and cognitive decline. In a prospective Italian study, physical activity is associated with a lower risk of vascular dementia but not of AD. They also recommended studying the biological mechanisms of physical activity and cognition since it is unclear. Although a significant association between smoking and MCI was found initially, later when subjected to binary logistic regression it became nonsignificant. This could be due to the effect of some other factors which may have influenced it. The duration of smoking may have an effect that has not been considered in the present study. This finding is contrary to the other longitudinal studies, which suggest cigarette smoking to be a risk factor for cognitive impairment., In the meta-analysis of prospective studies, it has been found that smokers had an increased risk for AD and cognitive decline. The study conducted in Chicago stated that the odds of developing AD increased by 50% for every 10 years of smoking cessation. Most of the MCI cases have never used alcohol. However, there was no association found between alcohol consumption and MCI. This may be because the duration of alcohol consumption was not considered in the analysis and it may influence the outcome i.e. MCI.
Similarly, in a review article, it was indicated that light-to-moderate drinking may be associated with the decreased risk of dementia and AD. However, protection offered by light-to-moderate drinking against vascular dementia, cognitive decline, and predementia is not definite. Further, they reported that the variation in findings may be attributed due to drinking patterns and interactions with other lifestyle-related (e.g. smoking) or genetic factors (e.g. apolipoprotein E gene variation). In a longitudinal study done to see the progression of either aMCI or AD dementia in adults ≥60 years, the baseline characteristics between nonconverters (n = 1,139) and converters (n = 386) were compared and found that among abusers of alcohol, the nonconverters and converters were 4.6% and 2.1%, respectively, and significantly associated (P < 0.05).
Recall bias may have been introduced due to self-reporting of lifestyle-related factors, imaging techniques could not be used for classifying MCI for diagnosing accurately, and social and leisure engagement may not reflect the lifelong pattern since the social and leisure engagement was studied only for the last 6 months due to the cross-sectional study design. Considering the paucity of studies on this issue, this study may be an eye-opener for further longitudinal studies.
| Conclusion|| |
A high prevalence of aMCI among the elderly invites further research on this issue, but it is just the tip of an iceberg. The aMCI can be used as a predictor of AD. A coordinated effort is required involving the community, policymakers, and nongovernmental organizations to create awareness and providing opportunities for social and leisure engagement in old age. The component of social and leisure engagement needs to be emphasized under the National Programme for Health Care of the Elderly. Further, provision and facilities can be structured at the community level with available resources to engage the elderly in social and leisure activities.
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Conflicts of interest
There are no conflicts of interest.
| References|| |
Zhu L, Fratiglioni L, Guo Z, Aguero-Torres H, Winblad B, Viitanen M. Association of stroke with dementia, cognitive impairment, and functional disability in the very old: A population-based study. Stroke 1998;29:2094-9.
Ganguli M, Dodge HH, Shen C, DeKosky ST. Mild cognitive impairment, amnestic type: An epidemiologic study. Neurology 2004;63:115-21.
Ganguli M, Dodge HH, Shen C, DeKosky ST. Mild cognitive impairment, amnestic type: An epidemiologic study. Neurology 2004;63:115-21.
Kumar ND, Sudhakar TP. Prevalence of cognitive impairment and depression among elderly patients attending the medicine outpatient of a tertiary care hospital in South India. Int J Res Med Sci 2013;1:359-64.
Das SK, Bose P, Biswas A, Dutt A, Banerjee TK, Hazra A, et al
. An epidemiologic study of mild cognitive impairment in Kolkata, India. Neurology 2007;68:2019-26.
de Craen AJ, Heeren TJ, Gussekloo J. Accuracy of the 15-item geriatric depression scale (GDS-15) in a community sample of the oldest old. Int J Geriatr Psychiatry 2003;18:63-6.
Shim YS, Yang DW, Kim HJ, Park YH, Kim S. Characteristic differences in the mini-mental state examination used in Asian countries. BMC Neurol 2017;17:141.
Hartigan I. A comparative review of the Katz ADL and the Barthel Index in assessing the activities of daily living of older people. Int J Older People Nurs 2007;2:204-12.
Cvecka J, Tirpakova V, Sedliak M, Kern H, Mayr W, Hamar D. Physical activity in elderly. Eur J Transl Myol 2015;25:249-52.
Dupuis SL, Smale BJ. An examination of relationship between psychological well-being and depression and leisure activity participation among older adults. Soc Leisure 1995;18:67-92.
Hänninen T, Hallikainen M, Tuomainen S, Vanhanen M, Soininen H. Prevalence of mild cognitive impairment: A population-based study in elderly subjects. Acta Neurol Scand 2002;106:148-54.
Yu ES, Liu WT, Levy P, Zhang MY, Katzman R, Lung CT, et al
. Cognitive impairment among elderly adults in Shanghai, China. J Gerontol 1989;44:S97-106.
Wu MS, Lan TH, Chen CM, Chiu HC, Lan TY. Socio-demographic and health-related factors associated with cognitive impairment in the elderly in Taiwan. BMC Public Health 2011;11:22.
Mohan D, Iype T, Varghese S, Usha A, Mohan M. A cross-sectional study to assess prevalence and factors associated with mild cognitive impairment among older adults in an urban area of Kerala, South India. BMJ Open 2019;9:e025473.
Sharma D, Mazta SR, Parashar A. Prevalence of cognitive impairment and related factors among elderly: A population-based study. J Dr NTR Univ Health Sci 2013;2:171.
Sánchez SS, Abanto J, Sanchez-Boluarte A, Boluarte-Carbajal A, Sanchez-Coronel D, Custodio-Capuñay N, et al.
Frequency and associated factors of amnestic mild cognitive impairment at four senior citizen clubs in Lima, Peru. Dement Neuropsychol 2019;13:321-8.
Rao D, Luo X, Tang M, Shen Y, Huang R, Yu J, et al.
Prevalence of mild cognitive impairment and its subtypes in community-dwelling residents aged 65 years or older in Guangzhou, China. Arch Gerontol Geriatr 2018;75:70-5.
Muangpaisan W, Assantachai P, Intalapaporn S, Pisansalakij D. Quality of life of the community-based patients with mild cognitive impairment. Geriatr Gerontol Int 2008;8:80-5.
Yates LA, Ziser S, Spector A, Orrell M. Cognitive leisure activities and future risk of cognitive impairment and dementia: Systematic review and meta-analysis. Int Psychogeriatr 2016;28:1791-806.
Angevaren M, Aufdemkampe G, Verhaar HJ, Aleman A, Vanhees L. Physical activity and enhanced fitness to improve cognitive function in older people without known cognitive impairment. Cochrane Database Syst Rev 2008;16:CD005381.
Ravaglia G, Forti P, Lucicesare A, Pisacane N, Rietti E, Bianchin M, et al.
Physical activity and dementia risk in the elderly: Findings from a prospective Italian study. Neurology 2008;70:1786-94.
Launer LJ, Feskens EJ, Kalmijn S, Kromhout D. Smoking, drinking, and thinking. The Zutphen Elderly Study. Am J Epidemiol 1996;143:219-27.
Anstey KJ, von Sanden C, Salim A, O'Kearney R. Smoking as a risk factor for dementia and cognitive decline: A meta-analysis of prospective studies. Am J Epidemiol 2007;166:367-78.
Aggarwal NT, Bienias JL, Bennett DA, Wilson RS, Morris MC, Schneider JA, et al.
The relation of cigarette smoking to incident Alzheimer's disease in a biracial urban community population. Neuroepidemiology 2006;26:140-6.
Panza F, Frisardi V, Seripa D, Logroscino G, Santamato A, Imbimbo BP, et al.
Alcohol consumption in mild cognitive impairment and dementia: Harmful or neuroprotective? Int J Geriatr Psychiatry 2012;27:1218-38.
Ahn S, Mathiason MA, Salisbury D, Yu F. Factors predicting the onset of amnestic mild cognitive impairment or Alzheimer's dementia in persons with subjective cognitive decline. J Gerontol Nurs 2020;46:28-36.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2]