DOI: 10.1111/ggi.14040
REVIEW ARTICLE EPIDEMIOLOGY, CLINICAL PRACTICE AND HEALTH
Association between cognitive reserve dimensions and frailty among older adults: A structured narrative review Alberto Sardella,1 Antonino Catalano,2 Vittorio Lenzo,1 Federica Bellone,1 Francesco Corica,2 Maria C Quattropani1 and Giorgio Basile2 1
Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy 2 School and Unit of Geriatrics, Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy Correspondence Alberto Sardella PsyD, Department of Clinical and Experimental Medicine, University of Messina, Messina (Italy), Via Consolare Valeria, 1 – 98124, Messina, Italy. Email: [email protected] Received: 9 June 2020 Revised: 18 August 2020 Accepted: 31 August 2020
Frailty is a broadly investigated geriatric condition, which is characterized by an increased vulnerability to stressors. It represents an extremely relevant public health issue, increasingly conceptualized in a multidimensional perspective. The concept of cognitive reserve (CR), as originally conceptualized by Stern, has been developed in the past decades as a potential factor able to determine individual differences in cognitive vulnerability and trajectories occurring with aging. Our purpose was to provide a comprehensive review of the literature exploring the relationship between CR dimensions, selected according to the Stern model, and frailty status. A review of the literature on the association between potential CR dimensions and frailty was carried out through PubMed, Web of Knowledge and Scopus. CR expressed in terms of education, occupation, premorbid intelligence quotient and leisure time activities was associated with frailty in both cross-sectional and longitudinal observations. The majority of reviewed evidence suggests a potential protective role of CR factors against the onset and the worsening of frailty among older adults. To the best of our knowledge, this is the first attempt to provide a comprehensive overview regarding the association between CR dimensions and frailty. Education, occupation, premorbid intelligence quotient and leisure time activities are able to interact with the general concept of frailty, rather than simply affecting the cognitive trajectory towards dementia. The lack of a unique and operationalized approach to the assessment of CR, as well as the wide heterogeneity of frailty evaluation tools and criteria, denote some methodological critical issues that need to be overcome. Geriatr Gerontol Int 2020; ••: ••–••. Keywords: aging, cognitive reserve, cognitive reserve dimensions, elderly, frailty.
Introduction Frailty: Operational definitions and models Frailty is a broadly investigated geriatric condition, which is characterized by an increased vulnerability to stressors as a result of reduced homeostatic reserves.1,2 Prevalence data of frailty in community populations are heterogeneous, often due to the different criteria used for defining frailty across the studies. Despite these limitations, a higher prevalence of frailty has been accounted in the context of chronic conditions, such as dementia3 or cancer.4 Frailty has been conceptualized as a dynamic condition along aging trajectories, frequently associated with the onset of adverse health outcomes in community populations5 and in clinical settings.6 Within the broad construct of frailty, both physical and psychological components have been acknowledged. In this context, although different approaches have been proposed to measure physical frailty, the two most representative models are the phenotype model described by Fried et al.,7 and the deficit model designed by Rockwood et al.8 According to the first model, frailty is based on an a priori defined set of five criteria investigating specific physical variables (i.e. weight loss, fatigue, reduced gait speed, poor handgrip strength and sedentary habits). Individuals can be clustered as robust, pre-frail or frail depending on the number of presented criteria.
© 2020 Japan Geriatrics Society
Conversely, the Rockwood et al. model conceives a multidimensional frailty resulting from of the age-related accumulation of deficits. Frailty is here operationalized into the so-called Frailty Index, which is defined as the ratio between the deficits an individual presents and the number of age-related health variables considered in the evaluation. Fried’s frailty phenotype and Rockwood’s Frailty Index have been commonly considered as alternative approaches. Accordingly, the frailty phenotype provides an immediate identification of nondisabled older adults at risk of negative events; conversely, the Frailty Index meets the needs of a comprehensive geriatric assessment, providing a marker of deficits accumulation. As they have different purposes, these two instruments should be considered complementary, rather than alternative, in the evaluation of older adults.9 To complete the theoretical framework of frailty models, a further model has recently been proposed to embrace a more comprehensive biopsychosocial perspective of the individual, combining physical, psychological and social domains.10 The model assumes that lifespan determinants have an effect on the occurrence of diseases, and might affect the physical, psychological and social domains responsible for frailty.11
Cognitive reserve: The Stern model and its evolution toward years Human aging is a complex and individualized process, which represents the product of the interaction between multiple
|
1
A Sardella et al. biopsychosocial factors. In this context, both progressive clinical and functional changes represent outcomes commonly associated with aging, which also allow physicians to trace different trajectories able to differentiate healthy from pathological aging. In the light of this vision, the concept of reserve has long been suggested as a potential protective factor against the onset of negative age-related outcomes, including functional impairments.12 Different models have been proposed for classifying the concept of reserve, mainly passive and active models. A well-known example of a passive model is represented by the construct of brain reserve, according to which the individual’s reserve derives from structural properties of the brain, such as brain size or neural count.13 This approach is closely associated to the concept of brain reserve capacity (BRC), which presumes that individual differences in BRC can lead to different clinical manifestations. Indeed, this model suggests that once BRC is depleted and specific critical thresholds are exceeded, clinical deficits emerge with different degrees of severity.14 However, a widely discussed limitation of passive models of reserve is that they do not consider individual qualitative differences in the use of available resources. To overcome this limitation, active models of reserve conversely suggest the existence of active processes, which are executed to compensate for emerging deficits. Accordingly, the concept of cognitive reserve (CR) has been developed in the past decades as a potential factor able to describe individual differences in vulnerability to cognitive, functional or clinical decline along aging.15 CR has been defined in terms of “adaptability that helps to explain differential susceptibility of cognitive abilities or day-to-day function to brain aging, pathology or insult”.16 According to the original CR model, given the same level of brain reserve, patients with higher CR use more efficient processing mechanisms, and might be able to take advantage of a wider range of alternate networks to better cope with the onset of deficit.17 CR has been conceptualized as a broad construct that encompasses several different hallmarks of lifespan experiences. According to the Stern model, intelligence, education and occupational attainment were hypothesized as relevant dimensions of CR.17 Consistently, this hypothesis has been originally supported by epidemiological evidence suggesting that lower educational and occupational attainment were associated to an increased risk of developing dementia.18 Similarly, a lower premorbid intelligence quotient (IQ) measured during childhood has been considered a significant risk factor for cognitive impairment and dementia in later life.19 In light of the CR model, individual engagement in leisure activities might also functionally improve cognitive networks and, consequently, might delay the onset of cognitive impairment and dementia.20 A recent consensus has further remarked on the conceptual difference between CR and the passive model of BRC.21 Similarly, CR has also been distinguished from the complementary construct of brain maintenance, which has been suggested to compensate for the fact that, under the umbrella of CR, the possible effect of CR dimensions (i.e. education, occupation, leisure activities, intelligence) on structural brain indicators was not explicitly considered. In fact, the term, brain maintenance, refers to a slowing of age-related brain changes, influenced by CR dimensions and possibly by genetics.21,22
Impact of CR on healthy aging and dementia The debate regarding the impact of CR on aging trajectories cannot be disjointed from a life-course approach, which considers together biological, physical and psychological factors acting over the lifespan.23 As it was previously stated, the CR hypothesis has
2
|
primarily been proposed to better explain individual differences in clinical expression of cognitive aging, as well as the different individuals’ adaptation to neurological diseases, such as dementia. Accordingly, individuals with higher CR (commonly expressed by educational level, job complexity, social and mental activity, intelligence) should be able to tolerate more brain pathology before the onset of cognitive symptoms compared with those having lower CR. In this context, education has been broadly conceived as one of the most relevant indexes of CR, and one of the most widely accepted protective factors in the epidemiological studies of dementia.24,25 Furthermore, the potential protective role of premorbid IQ has been confirmed in the past decades, as expression of CR. An association between lower childhood IQ and increased risk of dementia has been previously reported.26 This evidence has been recently updated by Russ et al., who confirmed the association between low childhood IQ and increased risk of dementia, showing in addition that the association appeared more evident in women than men.27 Consistently, evidence derived from population-based studies has recently highlighted the beneficial impact of occupational status on cognitive aging, showing that carrying out complex jobs in adulthood is associated with reduced risk of dementia.28,29 In this context, the potential role of leisure time activities in the prevention of cognitive decline and incident dementia in older persons has also been debated.20 In particular, less social participation and social interactions constitute significant risk factors for the development of cognitive dysfunction.30 Conversely, an active and frequent social participation seems to affect the brain structure, resulting in an enhanced use of brain networks.12,31
Aim of the review The purpose of the present study was to provide a wide revision of literature for the potential relationship between CR dimensions (according to the Stern model) and frailty. Current evidence will be presented. Scientific implications and further recommendations for clinical practice will be eventually discussed.
Methods Search strategy Despite not being systematic, this review presents some representative strengths of systematic reviews, to provide a rigorous summary of the available evidence. Consistently, a search of the literature was carried out through PubMed, Web of Knowledge and Scopus, matching the following keywords: cognitive reserve, education, occupation, leisure activities, social activities, premorbid IQ, intelligence and frailty. We preliminarily filtered the online search by language (English) and species (human). We considered articles published from 2002 to date, according to the year in which the accounted model of CR was originally described by Stern. The online databases search and the further independent search were completed on 15 February 2020.
Eligibility screening Eligibility of retrieved articles was then assessed following a threestep procedure: (i) screening of the title; (ii) screening of the abstract contents; and (iii) reading of the full text. Disagreements in the eligibility were resolved by consultation with a senior author. The selected articles had to provide information on educational level, intelligence evaluation, occupational status and/or
© 2020 Japan Geriatrics Society
Cognitive reserve and frailty
Figure 1 PRISMA flow chart. The screening procedure for the selection of articles investigating the association between cognitive reserve dimensions and frailty among older adults.
leisure time activities, together with information on frailty assessment. Those articles in which CR dimensions (i.e. education, intelligence, occupation and leisure activities) were not the main outcome, but were still explored as potentially related to frailty, were also included. Review articles were not considered eligible, but used as additional sources for retrieving potential studies not previously identified. Similarly, references of the included studies were checked to identify potential relevant studies missed through database searching.
Results A summary of the screening procedure is provided in Figure 1. A total of 53 studies were finally included and are discussed in the present review.32–84 A synopsis of the included studies characteristics is provided in Table 1. The majority of the included studies (n = 30) measured frailty according to the frailty phenotype model.7 The deficit
© 2020 Japan Geriatrics Society
accumulation model was used in 14 studies.8 Nine studies measured frailty with other tools: the FRAIL scale,5 the Edmonton frail scale,85 the Tilburg Frailty Indicator,11 the Kihon Checklist 86 and the Groningen Frailty Indicator.87 One of the retrieved studies considered the handgrip strength for defining frailty. Education was the most investigated CR dimension in the included studies (n = 51), and often used as covariate of the analyses. Information about occupational status has been collected in 20 studies. A limited number of studies (n = 14) discussed the association between frailty and frequency/quality of leisure time activities. Evidence on the association between IQ and frailty was reported in one study. Community-dwelling older persons represented the most investigated population, in almost all the included studies (n = 50). The remaining studies involved outpatients (referring to oncology and physical medicine clinics) and inpatients (from geriatric and cardiology units). CR expressed in terms of education, occupation, premorbid IQ and leisure time activities was associated to frailty in both cross-
|
3
A Sardella et al. Table 1 Summary of included studies (listed alphabetically) Study
Design/participants
Frailty quantification
Cognitive reserve dimension
AguillarNavarro et al., 2015
Longitudinal Community population
Frailty phenotype
Education
Alexandre et al., 2014
Cross sectional Community population
Frailty phenotype
Education
Alvarado et al., 2008
Cross-sectional Community population
Frailty phenotype
Education Occupation
Avila-Funes et al., 2008
Longitudinal Community population
Frailty phenotype
Education
Biritwum et al., 2016
Cross-sectional Community population
Deficitaccumulation Frailty Index (FI)
Education
Buch et al., 2018
Cross-sectional Community population
Adapted Morley 5 Frail Scale
Education
Buchman et al., 2013
Longitudinal Community population
Frailty phenotype
Education
Carneiro et al., 2016
Cross-sectional Community population
Edmonton Frail Scale (EFS)
Education
Main findings
Education was expressed by the total number of years achieved. Participants classified at baseline as frail showed lower education, compared to those classified as non-frail and pre-frail (P < 0.001). Education was expressed by the total number of years achieved. Education was significantly associated with Exhaustion (OR 0.92, CI 95% 0.86–0.99), Weakness (OR 0.92, CI 95% 0.85–0.99) and Slowness (OR 0.88, CI 95% 0.82–0.95) in men (P ≤ 0.05). Education was significantly associated with Weakness (OR 0.92, CI 95% 0.87–0.98) and Slowness (OR 0.94, CI 95% 0.88–0.99) in women (P ≤ 0.05). Education was expressed by: no schooling, primary, secondary and postsecondary. Occupation recorded according to the International Standard Classification of Occupations (ISCO-88). Less than secondary schooling and low-skilled occupation were associated to greater odds of frailty. Education was expressed as having a high educational level (>12 years). Participants classified at baseline as frail showed lower education compared with those classified as nonfrail and pre-frail (P = 0.029) Education was expressed as: no education, less than primary, primary, secondary and higher. The odds of frailty were lower for participants with any educational level (less than primary, primary, higher) compared with participants with no formal education. Higher education (beyond secondary school) was associated with the greatest reduction in odds of frailty. The adjusted odds ratio (aOR) for frailty showed higher odds of frailty for subjects with secondary school education in Ghana (aOR 1.90) and for those with less than primary school in Mexico (aOR 2.39). Education was expressed by the total number of years achieved. Participants classified as frail showed significantly lower educational level (9.35 5.52) compared with those classified as pre-frail (9.91 5.20) and robust (12.34 4.70). Education was expressed by the total number of years achieved. Frailty was associated with education (r = −0.14; P < 0.001). For each additional year of education at baseline there was a 2.6% slower rate of increasing frailty (time × education: estimate −0.003, SE 0.001, P = 0.009) at follow up. Education expressed as: up to 4 years of study, and >4 years of study. Education level of 0–4 years was associated with frailty (PR 1.209, 95% CI 1.12–1.30; P ≤ 0.001), even after multivariate analysis (PR 1.112, 95% CI 1.03–1.18; P = 0.002) (Continues)
4
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© 2020 Japan Geriatrics Society
Cognitive reserve and frailty Table 1 Continued Study
Design/participants
Frailty quantification
Cognitive reserve dimension
Castell et al., 2013
Cross-sectional Community population
Frailty phenotype
Education Occupation
Chamberlain et al., 2016
Cohort study Community population
Deficitaccumulation Frailty Index (FI)
Education
Chang et al., 2011
Cross-sectional Community population
Frailty phenotype Edmonton Frail Scale (EFS)
Education
Chaudhary et al., 2019
Cross-sectional Community population
Deficitaccumulation Frailty Index (FI)
Education
Main findings
Education was expressed as: low, medium, high educational level achieved. The prevalence of frailty was 14.9% (95% CI 11.7–18.7) in participants with low education level, significantly higher compared with the prevalence in those with completed primary education (P < 0.001). Information about participants’ social status were collected, in terms of low, medium and high quality of job. The prevalence of frailty was 14% (95% CI 11.3–17.5) in participants with low social status, significantly higher compared with the prevalence in those with medium-to-high social status. Education was expressed as: less than high school, high school/college, 4-year college/postgraduate. Less than high school education was significantly associated to increased odds of baseline higher frailty index in subjects aged 60–69 years (OR 4.98, 95% CI 3.72–6.67) compared to participants with college graduate education or higher. Less than high school education was significantly associated with increased odds of baseline higher frailty index in participants aged 70–79 years (OR 2.94, 95% CI 2.32–3.73), compared with participants with college graduate education or higher. Less than high school education was a significant predictor of a worse frailty trajectory in participants aged 60–69 years (OR 1.98, 95% CI 1.32–2.96) compared with participants with college graduate education or higher. Less than high school education was a significant predictor of a worse frailty trajectory in participants aged 70–79 years (OR 1.57, 95% CI 1.12–2.22) compared with participants with college graduate education or higher. Education expressed as: 0 years, 7 years of former education. The majority of participants classified as frail according to the Fried Frailty Index (FFI) showed no former education (41.9%) or
REVIEW ARTICLE EPIDEMIOLOGY, CLINICAL PRACTICE AND HEALTH
Association between cognitive reserve dimensions and frailty among older adults: A structured narrative review Alberto Sardella,1 Antonino Catalano,2 Vittorio Lenzo,1 Federica Bellone,1 Francesco Corica,2 Maria C Quattropani1 and Giorgio Basile2 1
Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy 2 School and Unit of Geriatrics, Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy Correspondence Alberto Sardella PsyD, Department of Clinical and Experimental Medicine, University of Messina, Messina (Italy), Via Consolare Valeria, 1 – 98124, Messina, Italy. Email: [email protected] Received: 9 June 2020 Revised: 18 August 2020 Accepted: 31 August 2020
Frailty is a broadly investigated geriatric condition, which is characterized by an increased vulnerability to stressors. It represents an extremely relevant public health issue, increasingly conceptualized in a multidimensional perspective. The concept of cognitive reserve (CR), as originally conceptualized by Stern, has been developed in the past decades as a potential factor able to determine individual differences in cognitive vulnerability and trajectories occurring with aging. Our purpose was to provide a comprehensive review of the literature exploring the relationship between CR dimensions, selected according to the Stern model, and frailty status. A review of the literature on the association between potential CR dimensions and frailty was carried out through PubMed, Web of Knowledge and Scopus. CR expressed in terms of education, occupation, premorbid intelligence quotient and leisure time activities was associated with frailty in both cross-sectional and longitudinal observations. The majority of reviewed evidence suggests a potential protective role of CR factors against the onset and the worsening of frailty among older adults. To the best of our knowledge, this is the first attempt to provide a comprehensive overview regarding the association between CR dimensions and frailty. Education, occupation, premorbid intelligence quotient and leisure time activities are able to interact with the general concept of frailty, rather than simply affecting the cognitive trajectory towards dementia. The lack of a unique and operationalized approach to the assessment of CR, as well as the wide heterogeneity of frailty evaluation tools and criteria, denote some methodological critical issues that need to be overcome. Geriatr Gerontol Int 2020; ••: ••–••. Keywords: aging, cognitive reserve, cognitive reserve dimensions, elderly, frailty.
Introduction Frailty: Operational definitions and models Frailty is a broadly investigated geriatric condition, which is characterized by an increased vulnerability to stressors as a result of reduced homeostatic reserves.1,2 Prevalence data of frailty in community populations are heterogeneous, often due to the different criteria used for defining frailty across the studies. Despite these limitations, a higher prevalence of frailty has been accounted in the context of chronic conditions, such as dementia3 or cancer.4 Frailty has been conceptualized as a dynamic condition along aging trajectories, frequently associated with the onset of adverse health outcomes in community populations5 and in clinical settings.6 Within the broad construct of frailty, both physical and psychological components have been acknowledged. In this context, although different approaches have been proposed to measure physical frailty, the two most representative models are the phenotype model described by Fried et al.,7 and the deficit model designed by Rockwood et al.8 According to the first model, frailty is based on an a priori defined set of five criteria investigating specific physical variables (i.e. weight loss, fatigue, reduced gait speed, poor handgrip strength and sedentary habits). Individuals can be clustered as robust, pre-frail or frail depending on the number of presented criteria.
© 2020 Japan Geriatrics Society
Conversely, the Rockwood et al. model conceives a multidimensional frailty resulting from of the age-related accumulation of deficits. Frailty is here operationalized into the so-called Frailty Index, which is defined as the ratio between the deficits an individual presents and the number of age-related health variables considered in the evaluation. Fried’s frailty phenotype and Rockwood’s Frailty Index have been commonly considered as alternative approaches. Accordingly, the frailty phenotype provides an immediate identification of nondisabled older adults at risk of negative events; conversely, the Frailty Index meets the needs of a comprehensive geriatric assessment, providing a marker of deficits accumulation. As they have different purposes, these two instruments should be considered complementary, rather than alternative, in the evaluation of older adults.9 To complete the theoretical framework of frailty models, a further model has recently been proposed to embrace a more comprehensive biopsychosocial perspective of the individual, combining physical, psychological and social domains.10 The model assumes that lifespan determinants have an effect on the occurrence of diseases, and might affect the physical, psychological and social domains responsible for frailty.11
Cognitive reserve: The Stern model and its evolution toward years Human aging is a complex and individualized process, which represents the product of the interaction between multiple
|
1
A Sardella et al. biopsychosocial factors. In this context, both progressive clinical and functional changes represent outcomes commonly associated with aging, which also allow physicians to trace different trajectories able to differentiate healthy from pathological aging. In the light of this vision, the concept of reserve has long been suggested as a potential protective factor against the onset of negative age-related outcomes, including functional impairments.12 Different models have been proposed for classifying the concept of reserve, mainly passive and active models. A well-known example of a passive model is represented by the construct of brain reserve, according to which the individual’s reserve derives from structural properties of the brain, such as brain size or neural count.13 This approach is closely associated to the concept of brain reserve capacity (BRC), which presumes that individual differences in BRC can lead to different clinical manifestations. Indeed, this model suggests that once BRC is depleted and specific critical thresholds are exceeded, clinical deficits emerge with different degrees of severity.14 However, a widely discussed limitation of passive models of reserve is that they do not consider individual qualitative differences in the use of available resources. To overcome this limitation, active models of reserve conversely suggest the existence of active processes, which are executed to compensate for emerging deficits. Accordingly, the concept of cognitive reserve (CR) has been developed in the past decades as a potential factor able to describe individual differences in vulnerability to cognitive, functional or clinical decline along aging.15 CR has been defined in terms of “adaptability that helps to explain differential susceptibility of cognitive abilities or day-to-day function to brain aging, pathology or insult”.16 According to the original CR model, given the same level of brain reserve, patients with higher CR use more efficient processing mechanisms, and might be able to take advantage of a wider range of alternate networks to better cope with the onset of deficit.17 CR has been conceptualized as a broad construct that encompasses several different hallmarks of lifespan experiences. According to the Stern model, intelligence, education and occupational attainment were hypothesized as relevant dimensions of CR.17 Consistently, this hypothesis has been originally supported by epidemiological evidence suggesting that lower educational and occupational attainment were associated to an increased risk of developing dementia.18 Similarly, a lower premorbid intelligence quotient (IQ) measured during childhood has been considered a significant risk factor for cognitive impairment and dementia in later life.19 In light of the CR model, individual engagement in leisure activities might also functionally improve cognitive networks and, consequently, might delay the onset of cognitive impairment and dementia.20 A recent consensus has further remarked on the conceptual difference between CR and the passive model of BRC.21 Similarly, CR has also been distinguished from the complementary construct of brain maintenance, which has been suggested to compensate for the fact that, under the umbrella of CR, the possible effect of CR dimensions (i.e. education, occupation, leisure activities, intelligence) on structural brain indicators was not explicitly considered. In fact, the term, brain maintenance, refers to a slowing of age-related brain changes, influenced by CR dimensions and possibly by genetics.21,22
Impact of CR on healthy aging and dementia The debate regarding the impact of CR on aging trajectories cannot be disjointed from a life-course approach, which considers together biological, physical and psychological factors acting over the lifespan.23 As it was previously stated, the CR hypothesis has
2
|
primarily been proposed to better explain individual differences in clinical expression of cognitive aging, as well as the different individuals’ adaptation to neurological diseases, such as dementia. Accordingly, individuals with higher CR (commonly expressed by educational level, job complexity, social and mental activity, intelligence) should be able to tolerate more brain pathology before the onset of cognitive symptoms compared with those having lower CR. In this context, education has been broadly conceived as one of the most relevant indexes of CR, and one of the most widely accepted protective factors in the epidemiological studies of dementia.24,25 Furthermore, the potential protective role of premorbid IQ has been confirmed in the past decades, as expression of CR. An association between lower childhood IQ and increased risk of dementia has been previously reported.26 This evidence has been recently updated by Russ et al., who confirmed the association between low childhood IQ and increased risk of dementia, showing in addition that the association appeared more evident in women than men.27 Consistently, evidence derived from population-based studies has recently highlighted the beneficial impact of occupational status on cognitive aging, showing that carrying out complex jobs in adulthood is associated with reduced risk of dementia.28,29 In this context, the potential role of leisure time activities in the prevention of cognitive decline and incident dementia in older persons has also been debated.20 In particular, less social participation and social interactions constitute significant risk factors for the development of cognitive dysfunction.30 Conversely, an active and frequent social participation seems to affect the brain structure, resulting in an enhanced use of brain networks.12,31
Aim of the review The purpose of the present study was to provide a wide revision of literature for the potential relationship between CR dimensions (according to the Stern model) and frailty. Current evidence will be presented. Scientific implications and further recommendations for clinical practice will be eventually discussed.
Methods Search strategy Despite not being systematic, this review presents some representative strengths of systematic reviews, to provide a rigorous summary of the available evidence. Consistently, a search of the literature was carried out through PubMed, Web of Knowledge and Scopus, matching the following keywords: cognitive reserve, education, occupation, leisure activities, social activities, premorbid IQ, intelligence and frailty. We preliminarily filtered the online search by language (English) and species (human). We considered articles published from 2002 to date, according to the year in which the accounted model of CR was originally described by Stern. The online databases search and the further independent search were completed on 15 February 2020.
Eligibility screening Eligibility of retrieved articles was then assessed following a threestep procedure: (i) screening of the title; (ii) screening of the abstract contents; and (iii) reading of the full text. Disagreements in the eligibility were resolved by consultation with a senior author. The selected articles had to provide information on educational level, intelligence evaluation, occupational status and/or
© 2020 Japan Geriatrics Society
Cognitive reserve and frailty
Figure 1 PRISMA flow chart. The screening procedure for the selection of articles investigating the association between cognitive reserve dimensions and frailty among older adults.
leisure time activities, together with information on frailty assessment. Those articles in which CR dimensions (i.e. education, intelligence, occupation and leisure activities) were not the main outcome, but were still explored as potentially related to frailty, were also included. Review articles were not considered eligible, but used as additional sources for retrieving potential studies not previously identified. Similarly, references of the included studies were checked to identify potential relevant studies missed through database searching.
Results A summary of the screening procedure is provided in Figure 1. A total of 53 studies were finally included and are discussed in the present review.32–84 A synopsis of the included studies characteristics is provided in Table 1. The majority of the included studies (n = 30) measured frailty according to the frailty phenotype model.7 The deficit
© 2020 Japan Geriatrics Society
accumulation model was used in 14 studies.8 Nine studies measured frailty with other tools: the FRAIL scale,5 the Edmonton frail scale,85 the Tilburg Frailty Indicator,11 the Kihon Checklist 86 and the Groningen Frailty Indicator.87 One of the retrieved studies considered the handgrip strength for defining frailty. Education was the most investigated CR dimension in the included studies (n = 51), and often used as covariate of the analyses. Information about occupational status has been collected in 20 studies. A limited number of studies (n = 14) discussed the association between frailty and frequency/quality of leisure time activities. Evidence on the association between IQ and frailty was reported in one study. Community-dwelling older persons represented the most investigated population, in almost all the included studies (n = 50). The remaining studies involved outpatients (referring to oncology and physical medicine clinics) and inpatients (from geriatric and cardiology units). CR expressed in terms of education, occupation, premorbid IQ and leisure time activities was associated to frailty in both cross-
|
3
A Sardella et al. Table 1 Summary of included studies (listed alphabetically) Study
Design/participants
Frailty quantification
Cognitive reserve dimension
AguillarNavarro et al., 2015
Longitudinal Community population
Frailty phenotype
Education
Alexandre et al., 2014
Cross sectional Community population
Frailty phenotype
Education
Alvarado et al., 2008
Cross-sectional Community population
Frailty phenotype
Education Occupation
Avila-Funes et al., 2008
Longitudinal Community population
Frailty phenotype
Education
Biritwum et al., 2016
Cross-sectional Community population
Deficitaccumulation Frailty Index (FI)
Education
Buch et al., 2018
Cross-sectional Community population
Adapted Morley 5 Frail Scale
Education
Buchman et al., 2013
Longitudinal Community population
Frailty phenotype
Education
Carneiro et al., 2016
Cross-sectional Community population
Edmonton Frail Scale (EFS)
Education
Main findings
Education was expressed by the total number of years achieved. Participants classified at baseline as frail showed lower education, compared to those classified as non-frail and pre-frail (P < 0.001). Education was expressed by the total number of years achieved. Education was significantly associated with Exhaustion (OR 0.92, CI 95% 0.86–0.99), Weakness (OR 0.92, CI 95% 0.85–0.99) and Slowness (OR 0.88, CI 95% 0.82–0.95) in men (P ≤ 0.05). Education was significantly associated with Weakness (OR 0.92, CI 95% 0.87–0.98) and Slowness (OR 0.94, CI 95% 0.88–0.99) in women (P ≤ 0.05). Education was expressed by: no schooling, primary, secondary and postsecondary. Occupation recorded according to the International Standard Classification of Occupations (ISCO-88). Less than secondary schooling and low-skilled occupation were associated to greater odds of frailty. Education was expressed as having a high educational level (>12 years). Participants classified at baseline as frail showed lower education compared with those classified as nonfrail and pre-frail (P = 0.029) Education was expressed as: no education, less than primary, primary, secondary and higher. The odds of frailty were lower for participants with any educational level (less than primary, primary, higher) compared with participants with no formal education. Higher education (beyond secondary school) was associated with the greatest reduction in odds of frailty. The adjusted odds ratio (aOR) for frailty showed higher odds of frailty for subjects with secondary school education in Ghana (aOR 1.90) and for those with less than primary school in Mexico (aOR 2.39). Education was expressed by the total number of years achieved. Participants classified as frail showed significantly lower educational level (9.35 5.52) compared with those classified as pre-frail (9.91 5.20) and robust (12.34 4.70). Education was expressed by the total number of years achieved. Frailty was associated with education (r = −0.14; P < 0.001). For each additional year of education at baseline there was a 2.6% slower rate of increasing frailty (time × education: estimate −0.003, SE 0.001, P = 0.009) at follow up. Education expressed as: up to 4 years of study, and >4 years of study. Education level of 0–4 years was associated with frailty (PR 1.209, 95% CI 1.12–1.30; P ≤ 0.001), even after multivariate analysis (PR 1.112, 95% CI 1.03–1.18; P = 0.002) (Continues)
4
|
© 2020 Japan Geriatrics Society
Cognitive reserve and frailty Table 1 Continued Study
Design/participants
Frailty quantification
Cognitive reserve dimension
Castell et al., 2013
Cross-sectional Community population
Frailty phenotype
Education Occupation
Chamberlain et al., 2016
Cohort study Community population
Deficitaccumulation Frailty Index (FI)
Education
Chang et al., 2011
Cross-sectional Community population
Frailty phenotype Edmonton Frail Scale (EFS)
Education
Chaudhary et al., 2019
Cross-sectional Community population
Deficitaccumulation Frailty Index (FI)
Education
Main findings
Education was expressed as: low, medium, high educational level achieved. The prevalence of frailty was 14.9% (95% CI 11.7–18.7) in participants with low education level, significantly higher compared with the prevalence in those with completed primary education (P < 0.001). Information about participants’ social status were collected, in terms of low, medium and high quality of job. The prevalence of frailty was 14% (95% CI 11.3–17.5) in participants with low social status, significantly higher compared with the prevalence in those with medium-to-high social status. Education was expressed as: less than high school, high school/college, 4-year college/postgraduate. Less than high school education was significantly associated to increased odds of baseline higher frailty index in subjects aged 60–69 years (OR 4.98, 95% CI 3.72–6.67) compared to participants with college graduate education or higher. Less than high school education was significantly associated with increased odds of baseline higher frailty index in participants aged 70–79 years (OR 2.94, 95% CI 2.32–3.73), compared with participants with college graduate education or higher. Less than high school education was a significant predictor of a worse frailty trajectory in participants aged 60–69 years (OR 1.98, 95% CI 1.32–2.96) compared with participants with college graduate education or higher. Less than high school education was a significant predictor of a worse frailty trajectory in participants aged 70–79 years (OR 1.57, 95% CI 1.12–2.22) compared with participants with college graduate education or higher. Education expressed as: 0 years, 7 years of former education. The majority of participants classified as frail according to the Fried Frailty Index (FFI) showed no former education (41.9%) or
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