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Cognitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review Francesco Panza, MD, PhD1,2,3*; Vincenzo Solfrizzi, MD, PhD4*; Maria Rosaria Barulli, PhD1,2; Andrea Santamato, MD5; Davide Seripa, PhD3; Alberto Pilotto, MD3,6; and Giancarlo Logroscino, MD, PhD1,2

1 Neurodegenerative Disease Unit, Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy 2 Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico“, Tricase, Lecce, Italy 3 Geriatric Unit & Laboratory of Gerontology and Geriatrics, Department of Medical Sciences, IRCCS “Casa Sollievo della Sofferenza”, San Giovanni Rotondo, Foggia, Italy 4 Geriatric Medicine-Memory Unit and Rare Disease Centre, University of Bari Aldo Moro, Bari, Italy 5 Department of Physical Medicine and Rehabilitation,“OORR Hospital”, University of Foggia, Italy 6 Geriatric Unit, Azienda ULSS 16 Padova, Hospital S. Antonio, Padova, Italy #

These two authors contributed equally to the work

Running head: Frailty and Cognitive Decline Tables: 2; Figures: 2 Address correspondence and reprints to: Francesco Panza, MD, PhD Neurodegenerative Disease Unit, Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy and Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico“, Tricase, Lecce, Italy Email: [email protected]

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Abstract Advancing age is the focus of recent studies on familial and sporadic Alzheimer’s disease (AD) suggesting a prolonged preclinical phase several decades before the onset of dementia symptoms. Influencing some age-related conditions, such as frailty, may have an impact on the prevention of late-life cognitive disorders. Frailty reflects a nonspecific state of vulnerability and a multisystem physiological change with increased risk for adverse health outcomes in older age. In this systematic review, frailty indexes based on a deficit accumulation model were associated with late life cognitive impairment and decline, incident dementia and AD. Physical frailty constructs were associated with late-life cognitive impairment and decline, incident AD and mild cognitive impairment, vascular dementia, non-AD dementias, and AD pathology in older persons with and without dementia, so also proposing cognitive frailty as a new clinical condition with coexisting physical frailty and cognitive impairment in nondemented older subjects. Considering both physical frailty and cognitive impairment as a single complex phenotype may be central in the prevention of dementia and its subtypes with secondary preventive trials on cognitive frail older subjects. The mechanisms underlying the cognitive-frailty link are multifactorial and vascular, inflammatory, nutritional, and metabolic influences may be of major relevance. There is a critical need for randomized controlled trials of intervention investigating the role of nutrition and/or physical exercise on cognitive frail subjects with the progression to dementia as primary outcome. These preventive trials and larger longitudinal population-based studies targeting cognitive outcomes could be useful in further understanding the cognitive-frailty interplay in older age.

Keywords: Alzheimer’s disease; mortality; neurodegeneration; sarcopenia and muscle aging; physical frailty; frailty index; dementia; MCI; vascular dementia; cognitive impairment; cognitive decline; cognitive frailty

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CONTENTS Introduction Operational definitions of frailty and proposed models Methods Cognitive Frailty: deficit accumulation and multidimensional approach Population-based studies Hospital-based studies Cognitive Frailty: phenotypic/physical approach Late-life cognitive impairment/decline Cross-sectional studies Longitudinal studies Dementia, AD, and non-AD dementias Cross-sectional studies Longitudinal studies Operational definition of cognitive frailty Neurobiological mechanisms underlying cognitive frailty Deficit accumulation/multidimensional approach of frailty and late-life cognitive disorders: possible underlying mechanisms Phenotypic/physical approach of frailty and late-life cognitive disorders: possible underlying mechanisms Conclusions Methodological issues Studies of intervention on cognitive frailty

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Introduction The impact on dementia and late-life cognitive disorders of increasing age, the single greatest risk of developing Alzheimer's disease (AD),1 appears to be devastating owing to population aging. Advancing age is the focus of recent studies on familial2 and sporadic AD,3 showing a prolonged preclinical phase of more than two decades before the onset of dementia in which -amyloid (A deposition is slow and protracted. In a general rethinking of the field, the 2011 criteria developed by National Institute on Aging and the Alzheimer’s Association proposed three stages of AD, i.e., preclinical AD, mild cognitive impairment (MCI) due to AD, and dementia due to AD.4-6 The 2011 criteria suggested that the disease begins before the development of symptoms, and that new diagnostic procedures may have the potential to identify brain changes that precede the development of symptoms,4 so suggesting prevention trials in asymptomatic genetic forms of AD and in cognitively normal individuals suspected to be at an asymptomatic stage of sporadic AD. However, these amyloid-based prevention trials started only at the end of 2013,7 and drugs currently used for AD treatment produce limited clinical benefits, partially stabilizing patients’ symptoms, without disease-modifying potential. Therefore, at present, the management of potential risk factors is believed to be the most effective means of preventing dementia, AD, and MCI.

Operational definitions of frailty and proposed models If advancing age is the driving risk factor for AD, some age-related conditions may be strictly linked to AD and late-life cognitive disorders. Among these conditions, frailty is a multidimensional geriatric syndrome reflecting a nonspecific state of vulnerability and a multisystem physiological change.8 Frailty has also been associated with an increased risk of adverse health-related outcomes in older persons, including falls, disability, hospitalizations, and mortality.8 Although the operational definition of frailty is widely recognized as being as yet unsettled, and how best to operationalize this syndrome is still controversial,9 in general, two approaches predominate. In fact, some definitions are based on physical diminution in older persons. In particular, the most widely cited is the “phenotypic” or physical definition of frailty or the “biological syndrome model”, proposed by Fried and colleagues working with the

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Cardiovascular Health Survey (CHS). By convention, the CHS definition of physical frailty proposes five items: unintentional weight loss, exhaustion, weakness, slow walking speed, and low levels of physical activity. An older individual is said to be frail when three or more are present, “pre-frail” when they exhibit only one or two of these characteristics, and “robust” when they have none.8 Other definitions, criticizing this concept, suggested that an integral approach is needed for the concept of frailty, an approach in which the focus is not exclusively on physical problems in older people, but which also incorporates psychological and social problems, and is thus based on the integral functioning of the individual.10 A recent integral conceptual working definition of frailty takes into account of the principles formulated earlier and combines essential components of existing conceptual definitions of frailty.10 This definition indicates frailty as a dynamic state affecting an individual who experiences losses in one or more domains of human functioning (physical, psychological, social), which is caused by the influence of a range of variables and which increases the risk of adverse outcomes.10 Therefore, an emerging consensus promotes a definition of frailty on the basis of a multidimensional approach,10-14 so the evaluation of frailty employs a frailty index, which is calculated by considering a number of potential deficits. These deficits can be symptoms, signs, diseases, disabilities or abnormal laboratory values,11 so developing an integral conceptual definition of frailty as a multisystem physiological change occurring in the elderly that determines an increase of risk for accelerated physical and cognitive decline, disability and death even in absence of specific diseases.10,11 Therefore, the second model that emerged in recent years is the frailty index based on accumulation of deficits or cumulative burden index proposed by Rockwood and collegues,15 in which frailty is defined as an accumulation of health conditions and deficits. Genetic, epigenetic, and environmental factors, such as nutrition and physical activity, are strongly related to frailty.16,17 However, not only physical but also psychological, cognitive and social factors contribute to this multidimensional syndrome and need to be taken into account in its definition and treatment. Cognition has already been considered as a component of frailty,18 and it has been demonstrated that it is associated with adverse health outcomes.19,20 Therefore, the prevention of frailty may be important in preventing adverse cognitive-related outcomes, including delirium21 and late-life

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cognitive disorders.

22,23

In fact, among potentially modifiable risk factors, the impact of several

operational definitions of frailty on late-life cognitive decline and dementia has been the subject of recent interest. In particular, a condition in which there is an interacting role of frailty and MCI has been termed the “Frail Brain”,24 and an international consensus group comprised of investigators from the International Academy of Nutrition and Aging (IANA) and the International Association of Gerontology and Geriatrics (IAGG) has recently proposed the concept of “cognitive frailty” for describing the simultaneous presence of both physical frailty and cognitive impairment in older individuals without dementia.25 In 2011, we reviewed frailty models and their possible links with predementia and dementia syndromes,22 and this topic was updated in another narrative review article.23 However, no systematic review has been published on this intriguing topic, also to the light of the recently proposed cognitive frailty model. The present article is a systematic review in which we examined the possible role of different frailty models in modulating the risk of AD, dementia, vascular dementia (VaD), MCI, and latelife cognitive impairment/decline, with a special focus on some underlying mechanisms of the proposed associations between different frailty models and cognition in older age.

Methods In the present systematic review article, we followed the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines, adhering to the PRISMA 27-item checklist.26 We reviewed clinical and epidemiological reports from the international literature published before June 2014, not specifying a lower date limit, and including both cross-sectional and longitudinal populationand hospital-based studies that provided a description of the diagnostic criteria used for the diagnosis of phenotypic/physical/biological frailty or a frailty model based on deficit accumulation/multidimensional approach also identified with frailty instruments/indexes. Furthermore, these studies provided also the diagnostic criteria for the diagnoses of MCI, AD, VaD, unspecified dementia or the neuropsychological tools used for defining late-life cognitive impairment/decline. The studies included had to present original data. The mortality studies were also included, while we excluded papers based on frailty in specific patient populations, such as chronic kidney disease, HIV, and cancer sufferers. This systematic review

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was based upon searches of US National Library of Medicine (PubMed), Ovid MEDLINE, EMBASE, Google Scholar, Web of Science, and Scopus databases the following terms to identify the risk exposure (frailty OR physical frailty OR frailty index) combined with terms to determine the outcomes of interest [cognitive AND (impairment OR decline OR disorders) OR cognition OR Alzheimer’s disease OR dementia OR mild cognitive impairment OR vascular dementia]. A search filter was developed to include only human studies. There were no language restrictions on the search. Figure 1 shows the stages in obtaining studies for inclusion in the present report (PRISMA Four-phase Flow Diagram). From 1344 articled identified with multiple electronic searches, we screened titles and abstracts of the citations downloaded from the searches identifying 377 potential relevant articles chosen for a closer review. Excluding other 298 articles not meeting inclusion criteria, we obtained full copies of the 79 potentially suitable reports for further assessment. After inclusion of 2 articles of interest from the reference lists of the selected articles and exclusion of other 23 articles, 58 studies met study eligibility criteria, and were finally included in the overall systematic review (Tables 1 and 2). We did not use formal methods of assessment of the quality of the studies included in the present systematic review, given also the lack of randomized clinical trials (RCTs), but we described in depth study design and sample together with frailty and cognitive assessment and principal results of the included studies in Tables 1 and 2. Finally, we used a narrative synthesis to summarize the findings of the included studies, subdividing the articles for the two principal frailty models (deficit accumulation/multidimensional approach or phenotypic/physical approach), with further subdivisions in relation to the sample and study design (population- or hospitalbased sample and cross-sectional or longitudinal design) and the cognitive outcomes of the included studies (late-life cognitive impairment/decline or dementia, AD, and non-AD dementias), when possible.

Cognitive Frailty: deficit accumulation and multidimensional approach Both physical or phenotypic frailty or frailty indexes with a multidimensional nature are generally associated with a greater risk for adverse health-related outcomes such as falls, disability, hospitalization, permanent institutionalization, and death,27 and also adverse cognitive-related outcomes such as delirium21 and late-life cognitive disorders.22,23 A recent conceptualization of frailty is based on a holistic

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view of the person

and frailty state cannot be seen in isolation but there is an interplay with

psychological frailty and social frailty. In fact, in recent years, frailty is acknowledged to be not only a biological or physiological state, but also a multidimensional concept. The multidimensional nature of the concept of frailty demands a multidisciplinary approach. In a systematic review that evaluated clinimetric properties, a list of eight frailty risk factors that are mentioned to be of great importance to the concept of frailty were identified,11 including in the physical dimension, nutritional status, physical activity, mobility, strength and energy, in the psychological dimension, cognition and mood, and in the social dimension, lack of social contacts and social support . On this basis, at least twenty frailty instruments have been described,11 and cognition was present in only 40% of them.22 All these frailty instruments are multidimensional in nature, and mostly based on a standardized Comprehensive Geriatric Assessment (CGA).22 However, the overall results of the assessment by using these frailty instruments, suggested that they are mainly developed and validated as risk assessment tools, and not as possible outcome measures.11

Population-based studies Only a few studies made a comparison between frailty instruments, concluding that different tools may identify older people at risk of adverse health outcome,28 but they may capture different sub-populations.29 In particular, in older individuals institutionalized in nursing homes, comparing the CHS physical definition of frailty,8 the Canadian Study of Health and Aging (CSHA) Clinical Frailty Scale,20 and the 70-item CSHA Frailty Index from the Clinical Examination,28 while all these frailty measures were significantly associated with an increased risk of mortality, disability and cognitive decline, measured with the Mini Mental State Examination (MMSE) in its modified form (3MS), when pairs of frailty measures were included in the models, only the Frailty Index was associated with a higher risk of mortality and decline in the 3MS (Table 1).30 Furthermore, a study examined the relationships among seven frailty domains (nutrition, physical activity, mobility, strength, energy, cognition, and mood), using data from three population-based studies. In two of these studies presence of deficits for all domains separated from absence of deficits. In the third population-based study, there was separation in all domains except cognition. All these data may suggest that frailty is a multidimensional concept for which the relationships among domains differ according to the

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population characteristics. These domains, with the possible exception of cognition, appeared to aggregate together and share a common underlying construct.12 Alternatively, it may be that frailty involves specific aspects of cognition not measured in the three studies, such as executive function or psychomotor speed,31,32 rather than overall impairment. More recently, the same authors analyzed data from five population-based studies of aging, and among frailty markers consistently aggregated in the five samples, strength had the highest contribution overall in explaining differences among participants across the samples; mobility and energy followed as the next most discriminating markers; and nutrition and cognition appeared to be least discriminating.13 Furthermore, in predicting 6-year incidence of disability on two population-based cohorts, the “best model” in each cohort was found to be a model including between 5 and 7 frailty markers including cognition, mobility, nutrition, physical activity, and strength. However, adding frailty markers to age, sex, and chronic disease produced only a modest increase of predictive accuracy by up to 3% in both cohorts (Table 1).14 A Canadian study of 23,952 home care recipients found that 40% of participants classified in the frailest category using a frailty index based on an accumulation of deficits approach had a diagnosis of dementia compared to 11% of those in the least frail category.33 In a nondemented population from the Oxford Project To Investigate Memory and Aging (OPTIMA), a significant relationship between visuomotor processing speed and both a frailty index and a modified version of phenotypic frailty was found, while a relationship independent of the MMSE score was only demonstrated in the frailty index. The relationship of visuomotor processing speed with frailty was not evident using a modified version of the Edmonton Frail Scale (Table 1).34 Other three Canadian studies, using the population-based sample of the CSHA of adults in the community and institutionalized care, proposed that different measures of frailty at baseline were associated in a 5-year follow-up with cognitive decline35,36 and dementia and AD over 5and 10-year intervals (Table 1).37 Finally, findings based on a frailty index from the Healthy Aging and Intellectual Disability study (HA-ID) with older adults with all levels of intellectual disabilities suggested that the least frail group was characterized by the absence of mobility and physical fitness limitations, relative independence, less specific medical problems, and less signs of depression/dementia.38

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Hospital-based studies Only very few studies explored the multidimensional impairment as a frailty concept in hospitalized older patients. In particular, the CGA-based Multidimensional Prognostic Index (MPI) was effective in predicting short- and long-term mortality risk in elderly subjects with dementia admitted to a geriatric hospital ward (Table 1),39 and given that in patients with dementia, clinical outcome and mortality result from a combination of psychological, biological, functional and environmental factors, tools that effectively identify patients with different life expectancy should be multidimensional in nature. More recently, a multicentre study on 1,306 hospitalized patients in France showed that that screening for frailty with four different indexes based on multidimensional impairment at the beginning of a hospital stay can strongly predict one-year institutionalization and mortality related to frailty, but not rapid cognitive decline (loss of ≥ 3 points on MMSE) (Table 1).40 Finally, a Japanese population-based study also showed a reciprocal relationship suggesting that cognitive impairment may indicate the development of frailty measured with the CSHA Clinical Frailty Scale (Table 1). 41 In conclusion, for frailty operationalized with the deficit accumulation and multidimensional approach, the findings from population- and hospital-based studies supported the concept that considering multidimensional aggregate information and frailty syndrome could be very important for predicting short- and long-term risk of dementia and cognitive decline or all-cause mortality in older subjects with dementia, and that it may be important for the identification of the more adequate management of these demented patients.

Cognitive Frailty: phenotypic/physical approach Late-life cognitive impairment/decline The physical or biological definition of frailty has been also proposed in several studies linking frailty models and late-life cognitive impairment/decline or dementia.22,23,25 A series of cross-sectional18,42-59 and longitudinal studies8,18,60-69 investigated the relationship between physical/biological frailty and MCI or latelife cognitive impairment/decline (Table 2).

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Cross-sectional studies Among cross-sectional studies, in the transversal component of the Italian Longitudinal Study on Aging (ILSA), both lower cognition and greater depressive symptoms were associated with physical frailty.48 Other findings from the Three-City Study suggested that cognitive impairment improved the predictive validity of the operational definition of physical frailty, increasing the risk to develop disability. On the contrary, risk of death also tended to be higher in cognitively impaired frail participants than in their nonfrail counterparts without cognitive impairment, even if the results were not statistically significant.18 In the Mexican Study of Nutritional and Psychosocial Markers of Frailty, in which physical frailty phenotype was operalizionated slightly modifying the CHS criteria and cognitive impairment was considered as an additional frailty criterion, low physical activity and cognitive impairment appeared to be the more important contributors of functional disability.42 In the Jerusalem Longitudinal Cohort Study, prevalence of cognitive impairment (MMSE≤24) in frail older subjects was 53.3%, with frailty status significantly associated with cognitive impairment.44 Also in 4,000 community-dwelling older Chinese adults, cognitive impairment was independently associated with higher physical frailty with adjustment for age, physical activity level, and appendicular muscle mass.45 In a population-based study from South Korea, frail older subjects showed a higher percentage of cognitive impairment, with some gender differences (55.8% in men, 35.2% in women).58 In different population-based studies from Spain and Brazil, the prevalence of cognitive impairment in physical frail older subjects ranged from 20%50 to 55.4%57 with an intermediate value of 39%,52,53 while in a sample of 5,104 older community dwellers in Japan, the combined prevalence of frailty and MCI was only 2.7%, with a significant relationships between frailty and MCI.55 The coexistence of physical frailty and cognitive impairment was confirmed also in hospital-based samples from India (19.3%)49 and Peru (41.9%)59 and in population from a French tertiary center (33.1%) (Table 2).54 Furthermore, in a sample from an Irish tertiary center, frailty was a distinct entity measurable in AD and MCI that correlates with age and increasing comorbid illness rather than markers of cognitive decline and illness severity.46 In the same sample, frailty and neuropsychiatric symptoms were determinants of health-related quality of life in the earlier stages of cognitive impairment, and functional limitation in the later stages.47 In amnestic MCI, lower performance on dimensions of

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physical frailty (i.e., slow gait speed, low physical activity, and low grip strength) was associated with worse performance on a measure of severity of cognitive impairment (Table 2).56 Finally, some cognitive domains were found significantly associated with frailty in some cross-sectional studies on community dwelling older people, i.e., executive functions and processing speed51 and poorer sustained attention (Table 2).43

Longitudinal studies In longitudinal studies, lower cognition was associated with the frailty physical phenotype in the CHS, despite exclusion of those subjects with MMSE < 18.8 Several other studies have also reported that physical frailty was associated with low cognitive performance at baseline.60-62,68 In particular, in the Hispanic Established Populations Epidemiologic Studies of the Elderly (H-EPESE), the baseline MMSE total score was significantly predictive of frailty at 1-year follow-up in men, but not in women.62 On the other hand, in a Brazilian cohort study conducted with community-dwelling older adults, frailty was associated with a subsequent decline in cognitive function within a 1-year period when measured using the MMS, while no association was found between frailty and cognitive decline measured by the Clinical Dementia rating Scale (CDR) or between frailty and the incidence of cognitive impairment.68 After adjustment, in ThreeCity Study, frail persons with cognitive impairment were significantly more likely to develop disability over a 4-year period.18 Two longitudinal population-based studies indicated frailty syndrome as a predictor of cognitive impairment in a 10-year follow-up,64 and of the rate of cognitive decline in a 3-year period.63 The Rush Memory and Aging Project also found that physical frailty increased the risk for MCI,66 although there is still controversy as to whether cognitive impairment may be a symptom of frailty or whether MCI is a separate syndrome, or indeed, a sign of early dementia.66 In a large population-based study conducted in Hong Kong, underweight, grip strength and chair stand predicted cognitive decline in men, while only grip strength predicted lower MMSE at follow-up in women.67 Furthermore, a study provided preliminary empirical support for the existence of subdimensions of physical frailty within the CHS model.8 In particular, two subdimensions were identified, and cognitive impairment was part of a frailty subdimension including slower gait, weaker grip, and lower physical activity, further increasing evidence that physical

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performance tests are sensitive indicators of cognitive impairment, and further supporting the hypothesis that cognitive impairment may be intrinsic to frailty.65 In fact, although some have referred to the CHS model of frailty as the “biological” model of frailty (in contrast to other models that include social and psychological criteria), these findings call this into question, because several variables in the CHS phenotype of frailty appear to be integrally related to cognitive impairment.65 Finally, also transitions between frailty states (i.e., transition from pre-frail state to frail or robust states) appeared to be influenced by cognitive impairment, as suggested by a study on 3,018 Chinese community-dwellers with a 2-year follow-up showing that among prefrail participants, lower cognition, hospitalizations, older age, previous stroke, and osteoarthritis were risk factors associated with worsening to frail state or less improvement to robust state.69

Dementia, AD, and non-AD dementias Cross-sectional studies A series of cross-sectional70-73 and longitudinal studies74-79 investigated also the associations of physical/biological frailty with cognitive outcomes as dementia, AD, or VaD (Table 2). In particular, two small Italian studies investigated the prevalence of AD and dementia in patients with frailty identified with Study of Osteoporotic Fractures (SOF) criteria, that operationalized physical frailty with a simpler adaptation of the more complex CHS criteria.70,71 In the first study on 109 AD patients attending an outpatient geriatric clinic, 50% were frail, 28% were pre-frail, and 22% were robust.70 In the second study on 265 outpatients, dementia was identified in 45% of frail participants compared to 32% in pre-frail, and 33% robust, although differences were not statistically significant.71 Cross-sectional findings from the HAID with older adults with different levels of intellectual disabilities suggested significant associations between (pre)frailty and Down syndrome, dementia, care setting, motor disability, and severe intellectual disability.72 In a cross-sectional population-based study in Finland, frail persons were almost 8 times more likely to have cognitive impairment, 8 times more likely to have some kind of dementia, almost 6 times more likely to have VaD, and over 4 times more likely to have AD than persons who were robust.73

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Longitudinal studies Different studies have reported that physical frailty may be associated with incidence of AD,63 VaD,77,78 non-AD dementias,79 and AD pathology in older persons with and without dementia (Table 2).75 In the unadjusted model of the Three-City Study, being frail at baseline led to twice the cumulative risk of dementia at 4 years, although after adjusting for socio-demographic and health covariates frailty status did not remain a statistically significant predictor of dementia.77 Findings from the Rush Memory and Aging Project raised the possibility that AD pathology may contribute to frailty or that frailty and AD pathology share a common pathogenesis.75 In fact, physical frailty proximate to death was related to level of AD pathology on postmortem examination but was not related to the presence of cerebral infarcts or Lewy body disease. This association was similar in persons with and without dementia and was unchanged even after considering level of physical activity, various physical performance measures, and chronic diseases.75 One longitudinal population-based study has examined the association of frailty or change in frailty with incident AD.63 In fact, other findings from the Rush Memory and Aging Project on 820 subjects during a 3year follow-up showed that the risk of developing AD was 2.5 times higher when physical frailty was present at baseline. 63 More recently, physical frailty, parkinsonian signs score, and global motor score were all associated with mortality, incident disability, and incident AD in the Rush Memory and Aging Project.76 Furthermore, combinations of these three motor constructs substantially improved the prediction of adverse health outcomes relative to the constructs considered alone, suggesting that assessments using more than one motor measure might more accurately identify older people at risk for adverse health consequences.76 Some studies, however, have found associations with frailty and specific dementia subtypes.77-79 Over a 3.5year follow-up, in the ILSA, frailty syndrome was associated with a significantly increased risk of overall dementia and, in particular, VaD, while the risk of AD or other types of dementia did not significantly change in frail individuals in comparison with subjects without frailty syndrome.78 A later analysis of the Three City Study confirmed the effect of frailty on incident VaD and overall dementia, but not on AD.77 Data from the population-based Adult Changes in Thought (ACT) study also suggested an association between frailty and incident non-AD dementia ( all dementias not classified as possible or probable AD), but not with AD.79 Finally, some studies showed a reciprocal relationship indicating that cognitive

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impairment may indicate also the development of frailty (Table 2).

80,81

In fact, a low MMSE score was

independently associated with increased risk of physical frailty over a 10-year period in older Mexican Americans80 and over a 2-year period in the same sample in older adults over 75 years,81 suggesting that cognitive status may also be an early marker for future risk of physical frailty (Table 2). In conclusion, both cross-sectional and longitudinal findings from population- and hospital-based studies strongly suggested that also physical frailty models may be associated with late-life cognitive impairment and decline, incident AD and MCI, VaD, non-AD dementias, and AD pathology in older persons with and without dementia.

Operational definition of cognitive frailty On the basis of this growing and extensive body of epidemiological evidence, an international consensus group comprised of investigators from the IANA and the IAGG recently convened in Toulouse, France to establish a definition for “cognitive frailty” in older adults.25 Cognitive frailty is an heterogeneous clinical manifestation characterized by the simultaneous presence of both physical frailty and cognitive impairment. 25 In particular, the proposed criteria defining this novel age-related condition included presence of physical frailty and cognitive impairment, operationalized with the CHS phenotypic/biological model of frailty and with a CDR of 0.5 (questionable dementia, a stage of the dementia continuum similar to MCI) and exclusion of concurrent AD dementia or other dementias.25 The IANA/IAGG consensus group proposed also a series of screening and diagnostic tools exploring and identifying multiple domains/causes of frailty including cognitive and psychological status in order to design effective interventions for cognitive frailty,25 a field needing further development. In 2001, the term “cognitive frailty” was incidentally used by Paganini-Hill and colleagues in a study on clock-drawing test performance and its association with potential protective and risk factors for AD in an older cohort.82 In 2006, the same term was firstly used to indicate a particular state of cognitive vulnerability in MCI and other similar clinical entities exposed to vascular risk with a subsequent increased progression to overt dementia, particularly VaD.83 Very recently, a Motoric Cognitive Risk (MCR) syndrome in nondemented older individuals with cognitive complaints but without significant functional impairment was proposed.84 Operationally, MCR syndrome was defined as having MCI and

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slow gait (1.0 standard deviation or more below age and sex-based norms). Longitudinal data from the Einstein Aging Study suggested that participants with the MCR syndrome were more likely to develop dementia, especially VaD. 84 In a very recent multicountry study of 26,802 older adults, pooled prevalence of MCR was 9.7% with this predementia syndrome associated with a 2-fold increased risk of developing incident cognitive impairment in 4,812 participants without dementia, even after accounting for vascular disease and baseline cognitive status.85 The cognitive frailty model included a full measure of frailty in comparison with the MRC syndrome and it may also represent a precursor of neurodegenerative processes and AD.25 Therefore, this new clinical entity should be validated as a possible determinant of the principal cognitive-related outcomes, i.e., dementia and its different subtypes. At present, no epidemiological evidence of a progression of cognitive frailty towards dementia is available. However, several studies investigated this new complex clinical phenotype as a possible determinant of the principal health-related outcomes of the different frailty models, i.e., falls, disability, hospitalizations, and mortality. In fact, some longitudinal population-base studies investigated some cognitive frailty models linked to increased falls86 or disability and all-cause mortality (Table 2 and Figure 2).18,48,70,87-90 In a cohort study on 125 older frail patients, after adjustment for parkinsonism and walking aids, patients with dementia walked 0.44 m/s faster than patients without dementia, suggesting that the high risk of falls in dementia may be partially explained by the loss of control of gait velocity.86 Probable explanations could be frontal lobe disinhibition and lack of insight, causing patients with dementia to walk relatively too fast in the context of their frailty, operationalized in this study with a high number of drugs, high Cumulative Illness Rating Scale - Geriatrics score, slow mean gait velocity, and low handgrip strength.86 In the ILSA, frail demented patients were at higher risk of all-cause mortality over 3- and 7year follow-up periods, but not of disability.48 In a sample of 616 people over 70 years who had been admitted to the general hospital with acute medical illness, people with dementia had half the survival time of those without dementia over a 1-year of follow-up. The effect of dementia on mortality was reduced after adjustment, particularly by the Waterlow score, a scale for evaluating the risk of pressure sores and a marker of frailty, so confirming the role of a cognitive frailty model in survival also in hospitalized older adults.90 In another population-based Italian study, the Conselice Study of Brain Aging

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(CSBA), over a 7-year follow-up, the authors identified an increased mortality risk of the physical phenotype of frailty plus an impairment on the clock drawing test (CDT) in comparison with not frail with a normal CDT, but they did not find any significant effect modifier combining the two measurements which did not improve their individual prognostic abilities.89 Other two recent studies investigated the survival of patients with frailty and cognitive impairment. Findings from the Three-City Study suggested that cognitive impairment improved the predictive validity of the operational definition of physical frailty, increasing the risk to develop disability. On the contrary, risk of death also tended to be higher in cognitively impaired frail participants than in their non-frail counterparts without cognitive impairment, even if the results were not statistically significant.18 In a small Italian study on 109 AD patients attending an outpatient geriatric clinic, one year after enrolment, frailty was an independent predictor of death after correction for age, sex, dependence in the basic activities of daily living (ADL), severity of cognitive impairment, and comorbidity.70 Moreover, more recently, in the H-EPESE, the cognitive frailty construct was operalizionated with the CHS frailty phenotype plus MMSE < 21 and followed as a possible determinant of all-cause mortality in a 10-year follow-up.87 As MMSE score declined over time, the percent of frail individuals increased and frailty and cognitive impairment were independent risk factors for mortality after controlling for all covariates. However, when both cognitive impairment and frailty (“cognitive frailty”) were added to the model, hazard ratio for individuals with cognitive impairment was no longer statistically significant,87 suggesting that frailty may be a stronger predictor of mortality than cognitive impairment at least in this population of older Mexican Americans. Finally, also institutionalized older individuals with severe frailty categorized with a multidimensional model as the CSHA Clinical Frailty Scale and severe cognitive impairment as compared to all other patients were significantly less probable to survive one year (Table 1).88 In this particular population of severely disabled older patients, the mortality risk assessment appeared to be best performed with measures of frailty and cognitive function considered jointly, as suggested in the complex clinical phenotype of cognitive frailty. In conclusion, the recently proposed construct of cognitive frailty should be validated demonstrating a real improvement in the specificity of prognosis, pathophysiology, or treatment.91 Some hospital- and population-based studies showed that cognitive impairment represented an added value for the prediction of adverse health-related

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outcomes in physical frail older subjects, i.e., increased risk of falls, disability, and all-cause mortality. However, cognitive frailty models should be validated for the risk of developing cognitive-related outcomes, i.e., dementia and its subtypes.

Neurobiological mechanisms underlying cognitive frailty Current epidemiological evidence supported the links among components of various models of frailty and cognitive decline, although limited efforts have been directed for investigating possible underlying mechanisms explaining these suggested associations. Several mediators or possible pathways have been suggested, but experimental evidence is lacking. The concept of frailty in older adults is univocal, although several operational definitions could underlie different mechanism linking this clinical syndrome with cognitive impairment in older age. The different mechanisms are not mutually exclusive, without an unidirectional flow, and with reciprocal interactions, underscoring the need for other studies to further explicate the biological basis of these associations.

Deficit accumulation/multidimensional approach of frailty and late-life cognitive disorders: possible underlying mechanisms Frailty can be measured in relation to the accumulation of deficits using a frailty index. These frailty instruments can be developed from most ageing databases counting deficits in health, defined as symptoms, signs, disabilities, and diseases.15 The variables included in a frailty index must meet five criteria (related to health status, age, and adverse outcome, not saturated too early, and covering a range of systems),92 including also factors that are known to be related to dementia and late-life cognitive disorders.36 For example, the CSHA Clinical Frailty Scale20 includes impairment in Instrumental ADL (IADL), which are recognized as part of a dementia prodrome, if not actually a risk factor.93 Further, the Frailty Index-CGA94 includes items such as hypertension and other vascular risk factors or depression, which are well recognized to increase the risk of dementia (Figure 2).83 Although one of the critiques of the deficit accumulation/multidimensional approach was that pathophysiology was not incorporated into this model, the link between frailty and cardiovascular risk was confirmed by a study in which using a

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murine-based frailty index and comparing middle-aged to old rodents showed that this frailty index corresponded to the degree of shortening of cardiac myocytes.95 Recently, a frailty index was developed using a series of 19 deficits not known to predict dementia [the nontraditional risk factors index (Frailty Index-NTRF)].37 The Frailty Index-NTRF excluded items such as motor slowing and weight loss which are known as dementia risk factors.22 Similarly, the Frailty Index-NTRF did not include disability items such as IADL, often counted as deficits in frailty indexes or used in other frailty definitions.15 However, among the nontraditional risk factors for dementia and AD of the Frailty Index-NTRF, there were hearing and vision impairment, and in recent years, there has been growing attention on the possible correlations between sensorial abnormalities and the development of late-life cognitive disorders (Figure 2). In particular, for age-related hearing impairment, commonly known as presbycusis, in longitudinal population-based studies both peripheral and central auditory processing dysfunctions appeared to be associated with accelerated cognitive decline and incident cognitive impairment and AD.96,97 For agerelated vision impairment, change in visual acuity in older adults predicted change in cognitive functioning.98 Furthermore, visual disturbance is often an early complaint of AD patients,99 with studies reporting reduced visual performance on tests of visual field, color vision, and contrast sensitivity.99 Furthermore, similar retinal changes such as glaucoma and age-related macular degeneration together with A deposition have been implicated in AD, with the hope that these retinal diseases and AD could be targeted simultaneously for treatment and monitoring.99 Hearing and vision impairments have been also included in well-known frailty measures such as the Frailty Index-CGA,94 the 70-item CSHA Frailty Index from the Clinical Examination,28 the Groningen Frailty Indicator,100 or among the suggested components of Puts model.101 Finally, genetic determinants associated to increased risk of AD and agerelated cognitive decline, such as apolipoprotein E (APOE) and angiotensin converting enzyme (ACE) polymorphisms, have been also associated with the CGA-based MPI102 and disability,103 suggesting a potential role of the APOE, also described as a “frailty gene”, and ACE polymorphisms on frailty in older patients, especially if frailty is considered in relation to the accumulation of deficits (Figure 2). However, findings from the population-based CSHA did not confirm the association of APOE with both physical frailty and frailty index,104 requiring further studies before any conclusion can be drawn.

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20

Phenotypic/physical approach of frailty and late-life cognitive disorders: possible underlying mechanisms The increasing body of epidemiological evidence systematically reviewed in the present article suggested that factors associated with the development of physical frailty and its components were also associated with the development of dementia and AD (Figure 2). Among these factors, cardiovascular risk and common vascular diseases have been related to both frailty105 and AD.83 In fact, several studies showed that comorbidities like congestive heart failure (CHF), myocardial infarction, peripheral arterial diseases (PAD), diabetes mellitus (DM), and hypertension increased the risk for frailty.45,105 The association between physical frailty and increased risk of incident AD may be linked to an underlying increased risk of stroke and cerebrovascular disease (CVD). In fact, findings from the CHS suggested that physical frailty was clearly related to subclinical vascular biomarkers and higher degree of infarct-like lesions in the brain.106 Moreover, in older patients after myocardial infarction within six months of discharge, physical-based frailty status was an independent predictors of ischemic stroke.107 Longitudinal population-based studies also suggested physical frailty as a prodromal stage of VaD,77,78 while recent findings from the Health and Retirement Survey showed that participants with vascular depression at baseline were significantly more likely to develop frailty,108 suggesting that the interplay between depression and vascular burden may underlie the frailty-cognition link. Finally, also sarcopenia, an agerelated decline in skeletal muscle mass as well as muscle function (defined by muscle strength or physical performance) and a reliable marker of frailty, may be accelerated by comorbid conditions including vascular diseases such as CHF and PAD.109 Sarcopenia could worsen prognosis of many diseases including AD, and a link also exists between sarcopenia-related declines and cognitive functioning (Figure 2).110 There was an accelerated lean mass loss in AD, associated with brain atrophy and cognitive performance,111 suggesting a relationship between sarcopenia and dementia, with also evidence that the improved physical performance connected with sarcopenia treatment can slow progression of dementia syndrome.112

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Beyond the possible role of vascular risk factors and vascular-related diseases, there are several other potential pathways by which physical frailty could contribute to cognitive decline, although, at present, the mechanisms underlying this suggested association remained unclear. DM, hyperglycaemia, and peripheral insulin resistance constituted a link among cardiovascular risk, metabolic disturbances, and physical frailty (Figure 2).113 In fact, epidemiological and basic research have proposed a model of cognitive impairment linked to metabolic syndrome (MetS) and metabolic disorders, suggesting for research purposes a “metabolic-cognitive syndrome” in patients with MetS plus cognitive impairment of degenerative or vascular origin.114 On the other hand, MetS and its components (impaired glucose tolerance, abdominal or central obesity, hypertension, hypertriglyceridemia, and reduced high-density lipoprotein cholesterol) have been associated in several population-base studies with late-life cognitive decline, MCI, overall dementia, AD, and VaD.114 Sarcopenia may have also a central role in these links given that its metabolic effects included a decrease in resting metabolic rate secondary to decreased fatfree mass and decreased physical activity, leading to a higher prevalence of insulin resistance, type 2 DM, dyslipidemia, and hypertension. However, a large population-based Chinese study suggested that metabolic conditions themselves are associated with physical frailty independently of sarcopenia and cognitive impairment in older age.45 The impact of several nutritional factors on physical frailty and its components has been the subject of recent interest,17,115 and current epidemiological evidence supported also the hypothesis that diet-related factors may be associated with cognitive decline in later life (Figure 2).116 A more sedentary lifestyle, a reduction in metabolic cell mass and, consequently, lower energy expenditure and dietary intake are important contributors to the progression of frailty. Undernutrition may be a major cause of frailty, and older persons with protein energy undernutrition, a treatable condition, have poorer cognitive performance.117 The transition from independence to disability in older adults is characterized by detectable changes in body composition and physical function. Epidemiological studies have shown that weight loss, reduced caloric intake, and the reduced intake of specific nutrients are associated with such changes,115 with weight loss proposed as a dementia risk factor.22 Cognitive impairment and female gender have been also suggested as risk factors for unintentional loss of fat mass, most likely due to

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behavioural factors and inability to plan for and maintain an healthy diet.

118

These suggestions also

proposed to examine dietary patterns in relation to physical frailty. In community-dwelling older adults, after a 6-year follow-up, higher adherence to a Mediterranean-style diet was associated with lower odds of developing frailty compared with those with lower adherence.119 More recently, higher adherence to MeDi score was also cross-sectionally associated with lower risk of being frail.120 The interplay among dietary patterns, frailty, and cognition was confirmed by cumulative evidence showing that adherence to a Mediterranean-type diet could be associated with slower cognitive decline, reduced risk of progression from MCI to AD, reduced risk of AD, and decreased mortality in AD patients.116 Some very recent systematic reviews and meta-analyses found that a higher adherence to the MeDi was associated with a reduced risk of cognitive impairment, MCI, and AD, as well as the transition from MCI to AD.121 Once again, sarcopenia may explain the cognitive-frailty link given its association with the development of frailty and cognitive impairment, through also oxidative stress.122 Sarcopenia is also associated with low serum levels of testosterone in men,123 and reduced testosterone and other androgen hormones may be involved in the development of frailty and cognitive decline. In fact, testosterone may promote synaptic plasticity in the hippocampus and regulate A deposition,124 and age-related depletion of testosterone is thought to be associated with frailty by reducing muscle mass and strength.125 Furthermore, during aging, a reduction in sex steroids, growth hormone, and vitamin D levels are associated with increases in the baseline levels of inflammatory proteins.126 In fact, another of the underlying pathogenetic factors linking physical frailty to cognition may be inflammation. Increased markers of inflammation such as C-reactive protein (CRP) or proinflammatory interleukins (IL) are common and chronic inflammation has been implicated in frailty,127 cognitive impairment,128 and dementia.129 A few studies directly confirmed the possible link among chronic inflammation, physical frailty, and cognition.65,130 In fact, in the study on the possible subdimensions of the CHS model of physical frailty, in the expanded model, one of the subdimensions identified with elevated predictive validity for mortality included higher IL-6 and CRP.65 Furthermore, levels of circulating CRP mediated the relationship between muscle strength and poor cognitive function, although the association was verified only in women.130 Chronic inflammation is also implicated in a series of

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possible mediators of the link between physical frailty and cognition such as hormonal dysregulation, oxidative stress, cardiovascular disease, and social isolation (Figure 2).23,122 Among these suggested mediators, epidemiological131,132 and neuropathological133 evidence suggested that social isolation and loneliness, known contributors to physical frailty,61 may lead to cognitive decline and AD, with limited physical activity further reduced by social isolation.23 Finally, in the Rush Memory and Ageing study, physical frailty proximate to death was related to level of AD pathology on postmortem examination but was not related to the presence of cerebral infarcts or Lewy body disease.75 Moreover, other postmortem findings from the Religious Orders Study have shown that neurofibrillary tangles in the substantia nigra, but not cerebral infarcts, were associated with gait impairment in older persons with and without dementia134 and that level of AD pathology was associated with body mass index proximate to death.74 Given the suggested role of physical frailty as a prodromal stage of VaD,77,78 these neuropathological findings more linked to AD pathology than to CVD pathology appeared to be at odds with a cognitive decline of vascular origin in frail older subjects, although the postmortem assessment of these studies did not directly assess motor brain regions and thus might underestimate the association of frailty with cerebral infarcts or Lewy body pathology. Furthermore, other findings from the Religious Orders Study suggested that participants with MCI and previous stroke were significantly impaired on gait speed compared to participants with no cognitive impairment, while those with MCI but no history of stroke were not impaired on motor measures.135 In fact, strong links have been described between cognition, gait, and vascular disease in older age,136 with the recently proposed motor-based MCR syndrome that may identify older individuals at high risk for transitioning to dementia, especially VaD.84 Very recent findings from the Rush Memory and Ageing study also reported that the presence of microvascular disease (microinfarcts, cerebral amyloid angiopathy, and arteriolosclerosis) in brain autopsies done in 850 community-dwelling older adults was associated with motor impairments measured prior to death,137 suggesting a role of this class of vascular lesions in the development of physical frailty. Therefore, as suggested by brain imaging and postmortem studies, AD pathology and CVD pathology may largely co-exist in cognitive and noncognitive brain

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regions in older persons without dementia or stroke,

associated with a wide range of clinical deficits

including some components of physical frailty as well as cognitive impairment.

Conclusions Modifiable age-related condition linked to late-life cognitive decline and dementia may be important in the prevention of these devastating disorders. Among these conditions, frailty in older adults is a clinical syndrome corresponding to a vulnerability to stressors and it tends to be considered as a major risk for adverse outcomes in older age, including cognitive-related outcomes. A recent and growing body of epidemiological evidence suggested that frailty may increase the risk of future cognitive decline and that cognitive impairment may increase the risk of frailty suggesting that cognition and frailty may interact in advancing aging. Therefore, frailty may represent a novel modifiable target in early dementia. In 2011, we reviewed frailty models and their possible links with predementia and dementia syndromes,22 and this topic was updated in another narrative review article.23 However, the present is the first systematic review on this intriguing topic, also to the light of the recently proposed cognitive frailty model. In particular, the reviewed evidence suggested that frailty indexes based on a deficit accumulation model were associate in hospital- and population-based studies with late life cognitive impairment and decline, incident dementia and AD. Epidemiological evidence strongly suggested that also physical frailty models may be associated with late-life cognitive impairment and decline, incident AD and MCI, VaD, non-AD dementias, and AD pathology in older persons with and without dementia, so also proposing cognitive frailty as a new clinical condition. In fact, a recent consensus conference defined cognitive frailty as a clinical entity with cognitive impairment related to physical causes with a potential reversibility,25 representing a useful target for secondary prevention of cognitive outcomes in older people.25

Methodological issues At present, there is no consensus regarding the definition of frailty for clinical uses. A harmonization of the proposed operational constructs of frailty has been discussed through a Delphi consensus building process in the Frailty Operative Definition-Consensus Conference Project.27 However, although experts

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25 agreed on the importance of a more comprehensive definition of frailty including assessment of physical performance, nutritional status, mental health , and cognition, a consensus was not reached on the proposed diagnostic paths and procedures needed to achieve an operational definition of frailty.27 However, notwithstanding this low consensus on frailty definitions, the evidence summarized in the present systematic review suggested that all principal frailty models were associated with late-life cognitive impairment and decline, incident dementia and AD, also taking in account the heterogeneity of the diagnoses and tools used for the identification of cognitive disorders in older age. On the other hand, a circularity arises from the fact that items included in many frailty measures are either known risk factors for cognitive impairment (e.g., hypertension, social isolation) or early signs of dementia (e.g., motor slowing, weight loss, reduced physical activity, exhaustion). Therefore, if these items individually may increase the risk of dementia, composite and multidimensional frailty measures should have an added value in predicting cognitive risk in older age. Therefore, it could be important also to investigate cognitive frailty constructs different from that physical frailty-based proposed by the IANA/IAGG consensus group, i.e., cognitive frailty models with frailty operationalized using the multidimensional approach with frailty indexes based on deficit accumulation. Furthermore, although the growing body of epidemiological evidence suggested in the present systematic review, the recently proposed cognitive frailty model is, at present, a construct that should be validated demonstrating a real improvement in the specificity of prognosis, pathophysiology, or treatment.91 Some hospital- and population-based studies showed that cognitive impairment represented an added value for the prediction of adverse health-related outcomes in physical frail older subjects, i.e., increased risk of falls, disability, and all-cause mortality. However, in the next future, cognitive frailty models should be validated for the risk of developing cognitive-related outcomes, i.e., dementia and its subtypes. Finally, among limitations of the present systematic review, we must acknowledge that we used only a narrative synthesis to summarize the findings of the included studies, with a lack of a formal assessment of their quality. Although almost all reviewed studies showed associations among different frailty models and late-cognitive disorders, including dementia subtypes, using studies of varying quality may have an impact on the results.

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Studies of intervention on cognitive frailty Cognitive impairment and dementia are causally linked to frailty. Considering both physical frailty and cognition together as a single complex phenotype may have important clinical and research implications. Interventions in older age are increasingly multi-modal, targeting cognitive, motor, and psychosocial components of aging.54,139,140 Among modifiable age-related conditions, cognitive frailty could have therefore an important role in the prevention of dementia and its subtypes, and nutrition and physical exercise may be factors potentially affecting the frailty status in older age.16,17 For secondary preventive trials of subjects presenting cognitive frailty, there are not yet clear evidences for the efficacy of a specific treatment. However, there have been a limited number of randomized clinical trials (RCTs) investigating the role of nutrition and/or physical exercise on frailty with also improvement of cognitive outcomes. Some systematic reviews of home-based and group-based exercise interventions for frail older people showed that exercise have the potential to improve outcomes of mobility and functional ability.141,142 Furthermore, also nutritional interventions might be able to address impaired nutrition and weight loss of frailty. A recent meta-analysis of 12 studies on over1800 older persons suggested that complete caloric nutritional supplements not only produced weight gain but also improved cognition.143 In frail nursing home residents, a nutrient and energy dense oral nutritional supplement not only improved nutritional status, but also tended to improve quality of life.144 The role of both nutrition and exercise was examined in only one pilot RCT conducted among Chinese men and women aged 65–79 years.145 The intervention groups received either nutritional consultation or problem-solving therapy (psychotherapy model) and the control groups had neither nutritional consultation nor problem-solving therapy. Primary outcome was improvement of the CHS phenotype of frailty by at least one category (from pre-frail to robust, or from frail to pre-frail or robust). The group that received nutritional consultation showed significantly higher improvement of the frailty phenotype compared to the control group after 3 months of intervention, but not at 6 and 12 months.145 Finally and more importantly, findings from very recent RCTs suggested that physical exercise training in combination with protein supplementation146 or alone147 improved also cognitive outcomes in frail and pre-frail states, opening new viable routes for the prevention of cognitive decline in cognitive frail state. In particular, 3 months of a combination of aerobic and strength exercise on

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frail older adults significantly improved increased scores in measures of working memory, processing speed and executive functions.147 Furthermore, in frail and pre-frail older subjects, 24 weeks of resistancetype exercise training in combination with protein supplementation improved the cognitive domain information processing speed and the same training without protein supplementation was beneficial for attention and working memory.146 There are difficulties in the design and conduction of RCTs in older persons, especially targeting complex conditions as cognitive frailty. For example, if a change of dietary habits may play a major part on the prevention of frailty and late-life cognitive disorders, what is the role of genetic risk factors, or if these associations may be valid in populations with different dietary patterns. From a pathophysiological point of view, the etiology of the cognitive-frailty link appeared to be multifactorial and besides hormonal and inflammatory processes, nutritional, vascular, neuropathological, and metabolic influences may be of major relevance. However, at present, a very few studies directly confirmed these possible underlying mechanisms between frailty and cognitive impairment. Therefore, other questions may address the underlying mechanisms and which is the most relevant component among the suggested mediators between frailty and cognition. However, it is also likely that some individuals manifesting the proposed criteria for cognitive frailty may be particularly vulnerable due to combined risks and may be less responsive to interventions.140 Answers to these questions will help us to better define the target populations for future preventive and therapeutic strategies also in older age. In this complex relationship, further investigation of the influence of vascular, metabolic, hormonal, and nutritional determinants on the cognitive-frailty relationship is warranted to better understand causal mechanisms. RCTs of intervention on individuals identified as cognitive frail with the progression to dementia as primary outcome and larger longitudinal population-based studies targeting cognitive outcomes could be useful in further understanding the interplay between cognitive impairment and frailty in older adults.

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Acknowledgements

This research was supported by Programmi di Ricerca Scientifica di Rilevante Interesse Nazionale

(PRIN) 2009 Grant 2009E4RM4Z.

Conflicts of interest

There are no conflicts of interest.

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Legends to the figures

Figure 1 PRISMA Four-phase Flow Diagram of retrieved and selected articles showing associations among different frailty models and Alzheimer’s disease, dementia, vascular dementia, mild cognitive impairment, and late-life cognitive impairment/decline.

Figure 2 Overview of the principal underlying mechanisms linking different frailty models to mild cognitive impairment (MCI), Alzheimer’s disease (AD), vascular dementia (VaD), and late-life cognitive impairment/decline. The recently proposed construct of cognitive frailty was linked with red dashed lines to both cognitive-related outcomes (i.e., possible progression to AD pathology and VaD) and adverse health-related outcomes (i.e., increased risk of disability and all-cause mortality).

ARHI: age-related hearing impairment; ARVI: age-related hearing impairment; CDR: Clinical Dementia rating Scale; IADL: instrumental activities of daily living

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2004;52:1996-2002. 99. Frost S, Martins RN, Kanagasingam Y. Ocular biomarkers for early detection of Alzheimer's disease. J Alzheimers Dis 2010;22:1-16. 100. Steverink N, Slaets JP, Schuurmans H, Frieswijk N, Slaets JP. Measuring frailty. Development and testing of the Groningen Frailty Indicator (GFI). Gerontologist 2001;41:236–237. 101. Puts MT, Lips P, Deeg DJ. Static and dynamic measures of frailty predicted decline in performancebased and self-reported physical functioning. J Clin Epidemiol 2005;58:1188–1198. 102. Pilotto A, Matera MG, Ferrucci L, Sancarlo D, Leandro G, D'Onofrio G, Seripa D, Addante F, Franceschi M, Dallapiccola B. Association of apolipoprotein E and angiotensin converting enzyme gene polymorphisms with the multidimensional impairment in older patients. Rejuvenation Res 2009;12:239247. 103. Kulminski A, Ukraintseva SV, Arbeev KG, Manton KG, Oshima J, Martin GM, Yashin AI. Association between APOE epsilon 2/epsilon 3/epsilon 4 polymorphism and disability severity in a national long-term care survey sample. Age Ageing 2008;37:288-293. 104. Rockwood K, Nassar B, Mitnitski A. Apolipoprotein E-polymorphism, frailty and mortality in older adults. J Cell Mol Med 2008;12:2754-2761. 105. Afilalo J, Karunananthan S, Eisenberg MJ, Alexander KP, Bergman H. Role of frailty in patients with cardiovascular disease. Am J Cardiol 2009;103:1616-1621. 106. Newman AB, Gottdiener JS, Mcburnie MA, Hirsch CH, Kop WJ, Tracy R, Walston JD, Fried LP; Cardiovascular Health Study Research Group. Associations of subclinical cardiovascular disease with frailty. J Gerontol A Biol Sci Med Sci 2001;56:M158-166. 107. Lichtman JH, Krumholz HM, Wang Y, Radford MJ, Brass LM. Risk and predictors of stroke after myocardial infarction among the elderly: results from the Cooperative Cardiovascular Project. Circulation 2002;105:1082-1087. 108. Paulson D, Lichtenberg PA. Vascular depression: an early warning sign of frailty. Aging Ment Health 2012;17:85-93. 109. Buford TW, Anton SD, Judge AR, Marzetti E, Wohlgemuth SE, Carter CS, Leeuwenburgh C, Pahor

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M, Manini TM. Models of accelerated sarcopenia: critical pieces for solving the puzzle of age-related muscle atrophy. Ageing Res Rev 2010;9:369-383. 110. Nourhashémi F, Andrieu S, Gillette-Guyonnet S, Reynish E, Albarède JL, Grandjean H, Vellas B. Is there a relationship between fat-free soft tissue mass and low cognitive function? results from a study of 7,105 women. J Am Geriatr Soc 2002;50:1796-1801. 111. Burns JM, Johnson DK, Watts A, Swerdlow RH, Brooks WM. Reduced lean mass in early Alzheimer disease and its association with brain atrophy. Arch Neurol 2010;67:428-433. 112. Bauer JM, Kaiser MJ, Sieber CC. Sarcopenia in nursing home residents. J Am Med Dir Assoc 2008;9:545-551. 113. Semprini R, Ragonese M, Poggi M, Franze A, Martorana A. Ageing as a Trait de Union between diabetes and dementia for frailty. CNS Neurol Disord Drug Targets 2013;12:520-524. 114. Panza F, Solfrizzi V, Logroscino G, Maggi S, Santamato A, Seripa D, et al. Current epidemiological approaches to the metabolic-cognitive syndrome. J Alzheimers Dis 2012;30 Suppl 2:S31-S75. 115. Inzitari M, Doets E, Bartali B, Benetou V, Di Bari M, Visser M, Volpato S, Gambassi G, Topinkova E, De Groot L, Salva A; International Association Of Gerontology And Geriatrics (IAGG) Task Force For Nutrition In The Elderly. Nutrition in the age-related disablement process. J Nutr Health Aging 2011;15:599-604. 116. Lourida I, Soni M, Thompson-Coon J, Purandare N, Lang IA, Ukoumunne OC, Llewellyn DJ. Mediterranean diet, cognitive function, and dementia: a systematic review. Epidemiology 2013;24:479– 489. 117. Tamura BK, Bell CL, Masaki KH, Amella EJ. Factors associated with weight loss, low BMI, and malnutrition among nursing home patients: A systematic review of the literature. JAMA 2013;14:649– 655. 118. Wirth R, Smoliner C, Sieber CC, Volkert D. Cognitive function is associated with body composition and nutritional risk of geriatric patients. J Nutr Health Aging 2011;15:706–710. 119. Talegawkar SA, Bandinelli S, Bandeen-Roche K, Chen P, Milaneschi Y, Tanaka T, Semba RD, Guralnik JM, Ferrucci L. A higher adherence to a mediterranean-style diet is inversely associated with the

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development of frailty in community-dwelling elderly men and women. J Nutr 2012;142:2161–2166. 120. Bollwein J, Diekmann R, Kaiser MJ, Bauer JM, Uter W, Sieber CC, Volkert D. Dietary quality is related to frailty in community-dwelling older adults. J Gerontol A Biol Sci Med Sci 2013;68:483–489. 121. Singh B, Parsaik AK, Mielke MM, Erwin PJ, Knopman DS, Petersen RC, Roberts RO. Association of Mediterranean diet with mild cognitive impairment and Alzheimer's disease: a systematic review and meta-analysis. J Alzheimers Dis 2014;39:271–282. 122. Mulero J, Zafrilla P, Martinez-Cacha A. Oxidative stress, frailty and cognitive decline. J Nutr Health Aging 2011;15:756–760. 123. Auyeung TW, Lee JS, Kwok T, Leung J, Ohlsson C, Vandenput L, Leung PC, Woo J. Testosterone but not estradiol level is positively related to muscle strength and physical performance independent of muscle mass: a cross-sectional study in 1489 older men. Eur J Endocrinol 2011;164:811-817. 124. Maggio M, Dall’Aglio E, Lauretani F, Cattabiani C, Ceresini G, Caffarra P, Valenti G, Volpi R, Vignali A, Schiavi G, Ceda GP. The hormonal pathway to cognitive impairment in older men. J Nutr Health Aging 2012;16:40-54. 125. Morley JE, Malmstrom TK. Frailty, sarcopenia, and hormones. Endocrinol Metab Clin North Am 2013;42:391-405. 126. Hunt KJ, Walsh BM, Voegeli D, Roberts HC. Inflammation in aging part 2: implications for the health of older people and recommendations for nursing practice. Biol Res Nurs 2010;11:253-260. 127. Puts MT, Visser M, Twisk JW, Deeg DJ, Lips P. Endocrine and inflammatory markers as predictors of frailty. Clin Endocrinol 2005;63:403-411. 128. Weaver JD, Huang MH, Albert M, Harris T, Rowe JW, Seeman TE. Interleukin-6 and risk of cognitive decline: MacArthur studies of successful aging. Neurology 2002;59:371-378. 129. Ma SL, Tang NL, Lam LC, Chiu HF. The association between promoter polymorphism of the interleukin-10 gene and Alzheimer’s disease. Neurobiol Aging 2005;26:1005-1010. 130. Canon ME, Crimmins EM. Sex differences in the association between muscle quality, inflammatory markers, and cognitive decline. J Nutr Health Aging 2011;15:695-698. 131. Fratiglioni L, Wang HX, Ericsson K, Maytan M, Winblad B. Influence of social network on

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occurrence of dementia: a community-based longitudinal study. Lancet 2000;355:1315-1319. 132. Wilson RS, Krueger KR, Arnold SE, Schneider JA, Kelly JF, Barnes LL, Tang Y, Bennett DA. Loneliness and risk of Alzheimer disease. Arch Gen Psychiatry 2007;64:234-240. 133. Bennett DA, Schneider JA, Tang Y, Arnold SE, Wilson R.S. The effect of social networks on the relation between Alzheimer’s disease pathology and level of cognitive function in old people: a longitudinal cohort study. Lancet Neurol 2006;5:406-412. 134. Schneider JA, Li JL, Li Y, Wilson RS, Kordower JH, Bennett DA. Substantia nigra tangles are related to gait impairment in older persons. Ann Neurol 2006;59:166-173. 135. Aggarwal NT, Wilson RS, Beck TL, Bienias JL, Bennett DA. Motor dysfunction in mild cognitive impairment and the risk of incident Alzheimer Disease. Arch Neurol 2006;63:1763–1769. 136. 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;347:1761–1768. 137. Buchman AS, Yu L, Boyle PA, Levine SR, Nag S, Schneider JA, Bennett DA. Microvascular brain pathology and late-life motor impairment. Neurology 2013;80:712-718. 138. Hausdorff JM, Buchman AS. What links gait speed and MCI with dementia? A fresh look at the association between motor and cognitive function. J Gerontol A Biol Sci Med Sci 2013;68:409-411. 139. Rebok GW, Carlson MC, Langbaum JB. Training and maintaining memory abilities in healthy older adults: traditional and novel approaches. J Gerontol B Psychol Sci Soc Sci 2007;62 Spec No 1:53-61. 140. Buchman AS, Bennett DA. Cognitive frailty. J Nutr Health Aging 2013;17:738-739. 141. Clegg A, Barber S, Young J, Forster A, Iliffe S. Do home-based exercise interventions improve outcomes for frail older people? Findings from a systematic review. Rev Clin Gerontol 2012;22:68–78. 142. de Vries NM, van Ravensberg CD, Hobbelen JS, Olde Rikkert MG, Staal JB, Nijhuis-van der Sanden MW. Effects of physical exercise therapy on mobility, physical functioning, physical activity and quality of life in community-dwelling older adults with impaired mobility, physical disability and/or multi-morbidity: a meta-analysis. Ageing Res Rev 2012;11:136–149. 143. Allen VJ, Methven L, Gosney MA. Use of nutritional complete supplements in older adults with dementia: systematic review and meta-analysis of clinical outcomes. Clin Nutr 2013;32:950–957.

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144. Stange I, Bartram M, Liao Y, Poeschl K, Kolpatzik S, Uter W, Sieber CC, Stehle P, Volkert D. Effects of a low-volume, nutrient- and energy-dense oral nutritional supplement on nutritional and functional status: a randomized, controlled trial in nursing home residents. J Am Med Dir Assoc 2013;14:628.e1–8. 145. Chan DC, Tsou HH, Yang RS, Tsauo JY, Chen CY, Hsiung CA, Kuo KN. A pilot randomized controlled trial to improve geriatric frailty. BMC Geriatr 2012;12:58. 146. van de Rest O, van der Zwaluw NL, Tieland M, Adam JJ, Hiddink GJ, van Loon LJ, de Groot LC. Effect of resistance-type exercise training with or without protein supplementation on cognitive functioning in frail and pre-frail elderly: Secondary analysis of a randomized, double-blind, placebocontrolled trial. Mech Ageing Dev 2014;136-137:85-93. 147. Langlois F, Vu TT, Kergoat MJ, ChasséK, Dupuis G, Bherer L. The multiple dimensions of frailty: physical capacity, cognition, and quality of life. Int Psychogeriatr 2012;24:1429-1436.

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f

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45 Table 1 Principal population- and hospital-based studies on the association of deficit accumulation/multidimensional operational definition of frailty or frailty instruments with late-life cognitive impairment and decline, dementia, Alzheimer’s disease (AD), and other different cognitive outcomes.

Reference

Study design and sample

Frailty and cognitive assessment

Principal results

Armstrong et al., 2010 [33]

Database of 23,952 patients receiving community care in the home or in long-term care institutions across Ontario, Canada, with subjects aged 81.7 ±7.4 years

CSHA Frailty Index, CHESS scale, and EFS and diagnosis of dementia (criteria not specified)

Among participants, 40% classified in the frailest category had a diagnosis of dementia compared to 11% of those in the least frail category

Sourial et al., 2012 [13]

Database from five different populationbased studies (2 EPESE sites, LASA, MHAS, and NuAge); a total of 14,217 individuals with mean age ranged from 72.4 to 75.6 years

Seven frailty domain measures (nutrition, physical activity, mobility, strength, energy, cognition, and mood) were selected from each study and dichotomized into presence or absence of a frailty marker

Among frailty markers consistently aggregated in the five samples, strength had the highest contribution overall in explaining differences among participants across the samples; mobility and energy followed as the next most discriminating markers; and nutrition and cognition appeared to be least discriminating

Rolfson et al., 2013 [34]

Population-based, crosssectional study; 388 subjects aged 74.0 ±7.0 years from the OPTIMA

Frailty was defined with a modified phenotype operalizionated with the CHS criteria, the EFS and a frailty index, while cognitive function was assessed using MMSE and a test of visuomotor speed

A significant relationship between visuomotor speed and frailty was found with both the frailty index and physical frailty, while a relationship independent of the MMSE score was only demonstrated in the frailty index. The relationship of visuomotor speed with frailty was not confirmed using a modified version of the EFS

Cross-sectional studies

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46 Schoufour et al., 2014 [38]

Population-based, crosssectional study; 1050 adults aged 50 years and over with all levels of intellectual disabilities from the HA-ID

Frailty was defined with a frailty index, while intellectual quotient scores, Vineland scores, and social emotional development were used to determine the level of intellectual disabilities

The least frail group was characterized by the absence of mobility and physical fitness limitations, relative independence, less specific medical problems, and less signs of depression/dementia

Longitudinal studies Strawbridge et al., 1998 [61]

Population-based, 1994 Frailty Measure with four items assessing cognitive longitudinal study; functioning 574 Alameda County Study respondents, aged 65-102 years

Frail persons reported reduced activities, poorer cognitive function, and lower life satisfaction. Cumulative predictors over the previous three decades included heavy drinking, cigarette smoking, physical inactivity, depression, social isolation, fair or poor perceived health, prevalence of chronic symptoms, and prevalence of chronic conditions

Rockwood et al., 2004 [19]

Population-based, longitudinal study; 9,008 older individuals from the CSHA aged 65 years and older

Among those participants described as mildly frail, 71.3% had functional impairment alone, 14.4% had cognitive impairment alone, and 14.3% had both. For those who were moderately or severely frail, coincident functional and cognitive impairments were more common, occurring in 28.1%

Rockwood et al., 2005 [20]

Population-based, CSHA Clinical Frailty Scale and 3MS longitudinal study; 2,305 older individuals from the CSHA aged 65 years and older

The operational and rules-based definition of frailty of the CSHA was based on the GSS, a scale combining aspects of cognitive and functional performance to describe various degrees of frailty; cognitive functions were also measured with the 3MS

Participants with higher scores on the CSHA Clinical Frailty Scale were more likely to be cognitively than those with lower scores. This frailty instrument performed better than measures of cognition, function or comorbidity in assessing risk for death

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f

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47 Rockwood et al., 2007 [30]

Population-based, CSHA Frailty Index, CSHA Clinical Frailty Scale, CHS longitudinal study; phenotype, and 3MS 728 institutionalized older adults in the second clinical examination cohort of the CSHA aged 65 years and older

All three frailty measures were significantly associated with an increased risk of mortality, disability and cognitive decline, measured with the modified 3MS. When pairs of frailty measures were included in the models, only the CSHA Frailty Index was associated with a higher risk of mortality and decline in the 3MS

Pilotto et al., 2009 [39]

Hospital-based, longitudinal study; 262 patients aged 65 years and older with a diagnosis of dementia

The MPI accurately stratified hospitalized elderly patients with dementia into groups at varying risk of short- and long-term all-cause mortality

Sourial et al., 2010 [12]

Three population-based, Seven domains of frailty were evaluated: nutrition, physical longitudinal studies; activity, mobility, strength, energy, cognition, and mood 839 individuals aged 75 years and older from the MUNS, 1,600 individuals aged 65 years and older from the CSHA, and 1,164 individuals aged 65 years and older from the SIPA

In two of these population-based studies (CSHA and MUNS) presence of deficits for all domains separated from absence of deficits. In the SIPA, there was separation in all domains except cognition

Mitnitski et al., 2011 [46]

Population-based, CSHA Frailty Index and five-year change in errors on 3MS longitudinal study (5 grouped into categories of 3 years); 9,266 individuals of the CSHA sample subjects aged 75.8±7.1 years

Frailty at baseline associated with cognitive change in men and women

Mitnitski et al., 2011 [35]

Population-based, CSHA Frailty Index, CSHA Clinical Frailty Scale, CHS frailty All measures of frailty at baseline associated longitudinal study (5 phenotype and five-year change in errors on 3MS grouped into with cognitive decline years); 2,305 individuals categories of 3 from the CSHA sample of subjects aged 83.1± 6.9 years

The CGA-based MPI and diagnosis of dementia according to the DSM-IV, NINCDS-ADRDA, and NINDS-AIREN criteria

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f

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48 Frailty Index of “non-traditional” risk factors for dementia and Frailty at baseline was associated with the diagnosis of dementia according to the DSM-III-R and incidence of AD and dementia of all types diagnosis of AD according to NINCDS-ADRDA criteria over 5-year and 10-year intervals

Song et al., 2011 [37]

Population-based, longitudinal study (5 and 10 years); 5,909 individuals from the CSHA aged 65 years and older

Matusik et al., 2012 [88]

A group of 86 residents CSHA Clinical Frailty Scale and cognitive function assessed 65 years and older, with MMSE living in two nursing homes, mean age: 83.8 ±8.3, longitudinal study (1 year)

Individuals with severe frailty and severe cognitive impairment as compared to all other patients, were significantly less probable (59% vs. 79%) to survive one year

Doba et al., 2012 [41]

Population-based, MDS cognitive performance scale. Self-reported cognitive longitudinal study (5 change and 5-year change in CSHA Clinical Frailty Scale years); 407 individuals from the Japanese Health Research Volunteer Study sample of subjects aged 78±5 years

Subjective cognitive changes at baseline were significantly associated with development of frailty at follow up. There was no significant association between MDS and development of frailty at follow up

Sourial et al., 2013 [14]

Population-based, 129 combinations of 7 frailty markers (cognition, energy, longitudinal study (6 mobility, mood, nutrition, physical activity, and strength) years); two cohorts from the EPESE of 3,210 and 3,447 subjects aged 65 years and older

In predicting 6-year incidence of disability, the “best model” in each cohort was found to be a model including between 5 and 7 frailty markers including cognition, mobility, nutrition, physical activity and strength

CSHA = Canadian Study of Health and Aging; CHESS = Changes in Health, End-Stage Disease and Signs and Symptoms; EFS = Edmonton Frail Scale; EPESE = Established Population for the Epidemiological Study of the Elderly; LASA = Longitudinal Aging Study Amsterdam; MHAS = Mexican Health and Aging Study; NuAge = Nutrition as a determinant of successful aging; OPTIMA = Oxford Project To Investigate Memory and Aging;

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49 CHS = Cardiovascular Health Study; MMSE = Mini Mental State Examination; HA-ID = Healthy Aging and Intellectual Disability study; 3MS = modified Mini Mental State Examination; CGA = Comprehensive Geriatric Assessment; MPI = Multidimensional Prognostic Index; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders-IV; NINCDS-ADRDA = National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer’s Disease and Related Disorders Association; NINDS-AIREN = National Institute of Neurological Disorders and Stroke-Association Internationale pour la Recherche et l’Enseignement en Neurosciences; MUNS = Montreal Unmet Needs Study; SIPA = French acronym of the System of Integrated Services for Older Persons study; DSM-III-R = Diagnostic and Statistical Manual of Mental Disorders-III revised; MDS = Minimum Data Set

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f

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50

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51

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f

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52 Table 2 Principal population- and hospital-based studies on the association of physical/phenotypic/biological operational definition of frailty with late-life cognitive decline, mild cognitive impairment (MCI), dementia, Alzheimer’s disease (AD), vascular dementia (VaD), and other different cognitive outcomes.

Reference

Study design and sample

Frailty and cognitive assessment

Principal results

Avila-Funes et al., 2011 [42]

Population-based, crosssectional study; 475 community-dwelling subjects aged 70 years and older from the Mexican Study of Nutritional and Psychosocial Markers of Frailty

Physical frailty phenotype operalizionated slightly modifying the CHS criteria, cognitive impairment was assessed with MMSE, and IST and considered as an additional frailty criterion

Low physical activity and cognitive impairment appeared to be the more important contributors of functional disability

O’Halloran et al., 2011 [43]

Cross-sectional convenient sample of 384 community dwelling participants over 60 years from Ireland

Physical frailty phenotype operalizionated with the CHS criteria and SART Task

More errors and greater variability in performance speed in frail compared to nonfrail participants

Jacobs et al., 2011 [44]

Population-based, cross- Physical frailty phenotype operalizionated with the CHS sectional and criteria and MMSE longitudinal study (5 years); 840 individuals from the Jerusalem Longitudinal Cohort Study sample of subjects aged 85 years

Frailty status was significantly associated with cognitive impairment. After adjustment, frailty alone was predictive of subsequent 5year mortality

Cross-sectional studies

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53 Lee et al., 2011 [45]

Population-based, crosssectional and longitudinal study (6 years); 4000 community-dwelling older Chinese > 65 years

Cognitive impairment was evaluated with the CSI-D and physical frailty phenotype operalizionated modifying the CHS criteria, summarizing the performance into a composite physical frailty score

Cognitive impairment was independently associated with higher physical frailty with adjustment for age, physical activity level, and appendicular muscle mass. Increased physical frailty was associated with higher 6year mortality

NíMhaoláin et al., 2011 [46]

Participants were recruited through the cross-sectional ECAD study from an Irish tertiary center; 115 patients were assessed, of which 95 with a diagnosis of AD and 20 with a diagnosis of MCI mean age was 74 years

Physical frailty phenotype operalizionated slightly modifying the CHS criteria and probable AD diagnosed according to the NINCDS-ADRDA criteria and MCI according to international consensus criteria

Frailty was a distinct entity measurable in AD and MCI that correlates with age and increasing comorbid illness rather than markers of cognitive decline and illness severity

NíMhaoláin et al., 2012 [47]

Participants were recruited through the cross-sectional ECAD study from an Irish tertiary center; 115 patients were assessed, of which 95 with a diagnosis of AD and 20 with a diagnosis of MCI mean age was 74 years

Physical frailty phenotype operalizionated slightly modifying the CHS criteria and probable AD diagnosed according to the NINCDS-ADRDA criteria and MCI according to international consensus criteria. Cognitive function was evaluated with MMSE, neuropsychiatric symptoms were assessed with the NPI, and health-related quality of life with the DEMQOLProxy

Frailty and neuropsychiatric symptoms appeared to be the determinants of healthrelated quality of life in the earlier stages of cognitive impairment. Functional limitation predicted health-related quality of life in the later stages of cognitive impairment

Khandelwal et al., 2012 [49]

Hospital-based, crossPhysical frailty phenotype operalizionated with the CHS sectional study; 250 criteria and cognitive function evaluated with MMSE older hospitalized patients, mean age: 66.4 ±6.3 years

In frail older patients, lower mean level of haemoglobin, higher chance of congestive heart failure, and lower mean MMSE score were found, with a 19.3% of cognitive impairment associated with frailty

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54 Jurschik et al., 2012 [50]

Population-based, cross- Physical frailty phenotype operalizionated with the CHS sectional study; 628 criteria and Pfeiffer’s Test for cognitive function. Cognitive individuals from impairment was ≥ 3 errors community dwelling adults aged 75+ in Spain; mean age: 81.3± 5 years

Cognitive impairment was 20% in frail population compared to 5.3% in non-frail older subjects

Langlois et al., 2012 [51]

Cross-sectional study; 83 community dwelling adults aged between 69 and 81 recruited through advertisement in newspapers and locally

Presence of railty affected executive function and processing speed. No effect on working memory, episodic memory or abstract verbal reasoning

Macuco et al., 2012 [52]

Population-based, cross- Physical frailty phenotype operalizionated with the CHS sectional study; 487 criteria and cognitive function was evaluated with MMSE individuals from the FIBRA study sample (Brazil) of subjects aged 65 years and over

Yassuda et al., 2012 [53]

Population-based, cross- Physical frailty phenotype operalizionated with the CHS Frail older adults performed lower than nonsectional study; 487 criteria. Cognitive function was evaluated with MMSE and the frail in most cognitive variables individuals from the BCSB FIBRA study sample (Brazil) of subjects aged 65 years and over

Evenhuis, et al., 2012 [72]

Population-based, cross- Physical frailty phenotype operalizionated with the CHS sectional study; 848 criteria, while intellectual quotient scores were used to adults aged 50 years and determine the level of intellectual disabilities over with all levels of intellectual disabilities from the HA-ID

Frail if present 2 of the following 3 criteria: physical frailty phenotype with the CHS criteria or CSHA Frailty Index or low score on MPP. MMSE and evaluation of the following cognitive domains: verbal abstract reasoning, episodic memory, working memory, speed of processing and executive function

Frail older adults had significantly worse performance on the MMSE

The univariate analyses showed significant associations between (pre)frailty and Down syndrome, dementia, care setting, motor disability, and severe intellectual disability

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55 Subra et al., 2012 [54]

Participants were Physical frailty phenotype operalizionated with the CHS recruited crosscriteria and cognitive function and dementia evaluated with sectionally through the MMSE and CDR Platform for Evaluation of Frailty and Prevention of disability in a French tertiary center; 160 older subjects, mean age: 82.7 years

In this frail population, cognitive impairment was observed in 33.1% (MMSE< 25), dementia (measured by the CDR) was observed in 11.6% of the population, whereas subjects with MCI (CDR=0.5) were 65.8%

Shimada et al., 2013 [55]

Population-based, cross- Physical frailty phenotype operalizionated slightly modifying sectional study; 5,104 the CHS criteria and MCI diagnosed according to international older individuals from consensus criteria the OSHPE sample of subjects aged 65 years or older, mean age 71 years

The overall prevalence of frailty, MCI, and frailty and MCI combined was 11.3%, 18.8%, and 2.7%, respectively. A significant relationships between frailty and MCI was also found

McCough et al., 2013 [56]

Participants were recruited through the cross-sectional RALLI, a randomized controlled trial of psychosocial and exercise interventions for sedentary older adults with amnestic MCI; 201 patients, mean age: 84.2±5.7 years

Lower performance on dimensions of physical frailty was associated with worse performance on the ADAS-Cog. In particular, slower usual gait speed was associated with elevated severity of cognitive impairment and worse performance within all dimensions of memory, attention, and executive function.

Ferrer et al., 2013 [57]

Participants were Physical frailty phenotype operalizionated with the CHS recruited through the criteria, cognitive function was evaluated with MMSE cross-sectional Octabaix a randomized controlled trial in communitydwelling individuals; 273 patients aged 86 years

Physical frailty dimensions adapted from the CHS criteria included slow gait speed, low physical activity, and low grip strength. Severity of cognitive impairment was measured with the ADAS-Cog. Cognitive functions were evaluated with TMT-A, TMT-B, WMS-R Logical Memory, and the delayed Word Recall subitem of the ADAS-Cog

The overall prevalence of frailty and cognitive impairment (MMSE< 24) and frailty and dementia combined was 55.4%, and 26.8%, respectively

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f

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56 Kulmala et al., 2014 [73]

Population-based, crosssectional study; 654 persons aged 76–100 years (mean 82 ±4.6 years) from the GeMS study, Kuopio, Finland

Physical frailty phenotype operalizionated with the CHS criteria, diagnosis of dementia, AD, and VaD according to the DSM-IV criteria and cognitive impairment evaluated with MMSE

Frail persons were almost 8 times more likely to have cognitive impairment, 8 times more likely to have some kind of dementia, almost 6 times more likely to have VaD, and over 4 times more likely to have AD than persons who were robust

Han et al., 2014 [58]

Population-based, cross- Physical frailty phenotype operalizionated with the CHS sectional study; 10,388 criteria and cognitive function was assessed using the Korean nationally representative version of the MMSE sample aged 65 years and older from the 2008 Living Profiles of Older People Survey, South Korea

Frail subjects showed a higher percentage of cognitive impairment , with some gender differences (55.8% in men, 35.2% in women). Cognitive impairment was associated with a higher likelihood of frailty in communitydwelling older men and women

Runzer-Colmenares et al., 2014 [59]

Hospital-based, crosssectional study; 311 men and women aged 60 years and older from the from the Geriatrics Service of the Peruvian Navy Medical Center

Physical frailty phenotype operalizionated with the CHS criteria and cognitive function was assessed with the CDT

Frail subjects showed a higher percentage of cognitive impairment (41.9%) , although cognitive function was not significantly associated with frailty

Fried et al., 2001 [8]

Population-based, longitudinal study; 5,745 older individuals from the CHS, aged 65 years and older

CHS frailty phenotype and MMSE

Lower cognition was associated with the frailty physical phenotype (despite exclusion of those with MMSE < 18)

Ottenbacher et al., 2005 [62]

Population-based, longitudinal study (1 year); 621 Mexican American men and women aged 70 and older from the HEPESE

MMSE and physical frailty phenotype operalizionated slightly The baseline MMSE total score was modifying the CHS criteria based on four frailty components significantly predictive of frailty at one-year and excluding physical activity follow-up in men, but not in women

Longitudinal studies

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f

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57 Buchman et al., 2007 [63]

Population-based, longitudinal study (3 years); 823 older persons (mean age: 80.4 years) without dementia who participated in the Rush Memory and Aging Project

Physical frailty phenotype operalizionated slightly modifying the CHS criteria and based on four frailty components. Diagnoses of AD and DLB were made according to the NINCDS-ADRDA and the Report of the Consortium on DLB International Workshop. The MMSE was used to describe the cohort, while scores on other 19 neuropsychological tests were used to create a composite measure of global cognitive function. The CIM was used for diagnostic classification purposes only

Both baseline level of frailty and annual rate of change in frailty were associated with an increased risk of incident AD. Furthermore, the level of frailty and rate of change in frailty were also associated with the rate of cognitive decline

Buchman et al., 2008 [75]

Population-based, longitudinal study; brain autopsies from 165 deceased participants from the Rush Memory and Aging Project

Physical frailty phenotype operalizionated slightly modifying the CHS criteria and based on four frailty components. Diagnoses of AD and DLB were made according to the NINCDS-ADRDA and the Report of the Consortium on DLB International Workshop criteria. Neuropathological measures of AD pathology, Lewy bodies, and cerebral infarcts were also obtained

Physical frailty proximate to death was related to level of AD pathology on postmortem examination but was not related to the presence of cerebral infarcts or Lewy body disease. This association was similar in persons with and without dementia

Samper-Ternent et al., 2008 [64]

Population-based, Physical frailty phenotype operalizionated slightly modifying longitudinal study (10 the CHS criteria based on four frailty components and years); excluding physical activity and MMSE 1,370 noninstitutionalized Mexican American men and women aged 65 years and older from the H-EPESE with a MMSE ≥ 21 at baseline

A statistically significant association between frailty and subsequent decline in cognitive function over a 10-year period was found in older Mexican Americans

Sarkisian et al., 2008 [65]

Population-based, longitudinal study; 1,118 high-functioning subjects aged 70-79 years from the MacArthur Study of Successful Aging

Two subdimensions of frailty were identified, and cognitive impairment was part of a frailty subdimension including slower gait, weaker grip, and lower physical activity, further increasing evidence that physical performance tests are sensitive indicators of cognitive impairment, and further supporting the hypothesis that cognitive impairment may be intrinsic to frailty

Physical frailty phenotype operalizionated slightly modifying the CHS criteria. Cognitive function was assessed using reliable tests of language, executive function, spatial ability, and verbal and nonverbal memory

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f

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58 Avila-Funes et al., 2009 [18]

Population-based, cross- Physical frailty phenotype operalizionated slightly modifying sectional and the CHS criteria, MMSE, and IST. Diagnosis of dementia longitudinal study (4 according to the DSM-IV criteria years); 6,030 older individuals aged 65-85 years from the ThreeCity Study

Frail individuals with cognitive impairment have a higher risk of IADL and ADL disability and of incident hospitalization and dementia than subjects with none of these conditions, even after adjusting for potentially confounding variables

Boyle et al., 2010 [66]

Population-based, longitudinal study (12 years); 761 older persons (mean age: 79 years) without cognitive impairment at baseline who participated in the Rush Memory and Aging Project

Higher level of physical frailty predicted the development of MCI and is associated with an accelerated rate of cognitive decline in older persons

Raji et al., 2010 [80]

Population-based, MMSE and hysical frailty phenotype operalizionated slightly longitudinal study (10 modifying the CHS criteria based on four frailty components years); 942 non-frail and excluding physical activity Mexican American men and women aged 65 years and older from the H-EPESE

Non-frail older Mexican Americans with low cognitive scores were significantly more likely to acquire one or more components of frailty over 10 years than those with higher cognitive scores

Aranda et al., 2011 [81]

Population-based, longitudinal study (2 years); 963 non-frail older subjects persons aged 75 years and older who participated in the H-EPESE

Respondents at risk of increasing frailty had lower levels of cognitive functioning at baseline

Physical frailty phenotype operalizionated slightly modifying the CHS criteria and based on four frailty components. Diagnoses of AD and MCI were made according to the NINCDS-ADRDA criteria and CSHA clinical criteria. The MMSE was used to describe the cohort, while scores on other 19 neuropsychological tests were used to create a composite measure of global cognitive function. The CIM was used for diagnostic classification purposes only

MMSE and physical frailty phenotype operalizionated slightly modifying the CHS criteria based on four frailty components and excluding physical activity

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f

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59 Buchman et al., 2011 [76]

Population-based, longitudinal study (4.2 years for AD incidence); 949 older persons (mean age: 79.7 ±7.3 years) without dementia, stroke or Parkinson’s disease who participated in the Rush Memory and Aging Project

Physical frailty phenotype operalizionated slightly modifying the CHS criteria and based on four frailty components. Diagnoses of AD was made according to the NINCDSADRDA. Other motor constructs were assessed with PSS and a motor tool based on nine strength measures and nine motor performances

All three constructs when considered individually were associated with risk of death, incident disability, and AD. When considered together, combinations of these constructs more strongly predicted adverse health outcomes and incident AD

Auyeung et al., 2011 [67]

Population-based longitudinal study (4 years); cognitively normal people aged 65 years and older and living in Hong Kong

BMI, grip strength, chair stand, step length, slowed walk, composite score on neuromuscular test and 4-year change in MMSE

Underweight, grip strength and chair stand predicted cognitive decline in men, while only grip strength predicted lower MMSE at follow-up in women

Solfrizzi et al., 2012 [48]

Population-based, longitudinal study (3 and 7 years); 2,581 individuals from the ILSA sample of 5,632 subjects aged 65-84 years

Physical frailty phenotype operalizionated slightly modifying the CHS criteria and diagnosis of dementia according to the DSM-III-R, NINCDS-ADRDA, and ICD-10 criteria

Lower cognition was associated with physical frailty. Frail demented patients were at higher risk of all-cause mortality over 3- and 7-year follow-up periods, but not of disability

Bilotta et al., 2012 [70]

Cross-sectional and longitudinal study (1 year); 109 participants attending an outpatient geriatric clinic in Italy aged 65 years and older (median age 84 years)

Physical frailty phenotype operalizionated with the SOF criteria and diagnosis of AD according to the DSM-IV-Text Revised

Among AD patients, 50% were frail, 28 were pre-frail and 22% were robust. Frail demented patients were at higher risk of all-cause mortality over 1-year follow-up period

Bilotta et al., 2012 [71]

Cross-sectional and longitudinal study (1 year); 265 participants attending an outpatient geriatric clinic in Italy aged 65 years and older (mean age 81.5 years)

Physical frailty phenotype operalizionated with the SOF criteria and diagnosis of dementia according to the DSM-IVText Revised

Dementia in 45% of frail participants compared to 32% in pre-frail and 33% robust,, although differences were not statistically significant

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f

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60 Cano et al., 2012 [87]

Population-based, longitudinal study (10 years); 1,815 Mexican American men and women aged 67 years and older from the HEPESE

Physical frailty phenotype operalizionated slightly modifying the CHS criteria and MMSE > 21

As MMSE score declined over time, the percent of frail individuals increased in a linear fashion. Frailty and cognitive impairment are independent risk factors for mortality after controlling for all covariates. When both cognitive impairment and frailty were added to the model, hazard ratio for individuals with cognitive impairment was no longer statistically significant

Avila-Funes et al., 2012 [77]

Population-based, longitudinal study (7 years); 5,480 older individuals aged 65-85 years from the ThreeCity Study

Physical frailty phenotype operalizionated slightly modifying the CHS criteria, MMSE, and IST. Diagnosis of dementia according to the DSM-IV criteria and VaD according to the NINDS-AIREN criteria

Frailty was marginally associated with greater risk of all types of dementia and was not associated with incident AD, but frailty status was independently associated with incident VaD

Solfrizzi et al., 2013 [78]

Population-based, Physical frailty phenotype operalizionated slightly modifying longitudinal study (3.5 the CHS criteria and diagnosis of dementia according to the years); 2,581 individuals DSM-III-R, NINCDS-ADRDA, and ICD-10 criteria from the ILSA sample of 5,632 subjects aged 65-84 years

Over a 3.5-year follow-up, frailty syndrome was associated with a significantly increased risk of overall dementia and, in particular, VaD, while the risk of AD or other types of dementia did not significantly change in frail individuals in comparison with subjects without frailty syndrome

Sampson et al., 2013 [90]

Hospital-based, longitudinal study (1 year); a cohort of 616 people over 70 years who had been admitted to the general hospital with acute medical illness

Waterlow Scale, a frailty marker, evaluating the risk of pressure sores, cognitive function assessed with MMSE, and diagnosis of dementia according to the DSM-IV criteria

In a 1-year follow-up, people with dementia had half the survival time of those without dementia. The effect of dementia on mortality was reduced after adjustment, particularly by the Waterlow score, a marker of frailty

Gray et al., 2013 [79]

Population-based, longitudinal study (6.5 years); 2,619 individuals from the ACT study sample of subjects aged 65 years and older

Physical frailty phenotype operalizionated with the CHS criteria and diagnosis of dementia according to the DSM-IV, diagnosis of possible and probable AD according to NINCDSADRDA criteria. Non-AD dementia consisted of all dementias not classified as possible or probable AD

Frailty was associated with higher risk of developing non-AD dementia but not AD. Although frailty was not associated with allcause dementia in the entire sample, an association did exist in participants with higher cognitive scores

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f

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61 Alencar et al., 2013 [68]

Participants were 207 community-dwelling individuals aged 65 years or older with and without cognitive impairment from the University Hospital of UFMG in Belo Horizonte, Brazil

Physical frailty phenotype operalizionated with the CHS criteria and cognitive function and dementia evaluated with MMSE and CDR

In a 1-year follow-up, frailty was associated with a subsequent decline in cognitive function when measured using the MMSE. No statistically significant differences among the different classifications of frailty were detected regarding the decline in cognitive function when assessed using the CDR

Forti et al., 2014 [89]

Population-based, Physical frailty phenotype operalizionated with SOF index and longitudinal study (7 cognitive function assessed with CDT years); 766 dementiafree individuals from the CSBA sample of 1,353 persons aged ≥ 65 years

Over a 7-year follow-up, the CDT may predict the mortality risk independently of the physical phenotype of frailty. The combination of the two measurements did not improve their individual prognostic abilities

Lee et al., 2014 [69]

Population-based, cross- Cognitive function was evaluated with MMSE and physical sectional and frailty phenotype operalizionated with the CHS criteria longitudinal study (2 years); 3,018 community-dwelling older Chinese > 65 years

Among prefrail participants, hospitalizations, older age, previous stroke, lower cognition, and osteoarthritis were risk factors associated with worsening to frail state or less improvement to robust state.

CHS = Cardiovascular Health Study; MMSE = Mini Mental State Examination; IST = Isaac Set Test; SART = Sustained Attention Response Time; CSI-D = Community Screening Instrument of Dementia; ECAD = Enhancing Care in Alzheimer’s Disease; NINCDS-ADRDA = National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer’s Disease and Related Disorders Association; NPI = Neuropsychiatric Inventory; CSHA = Canadian Study of Health and Aging; MPP = Modified Physical Performance; BCSB = Brief Cognitive Screening Battery; HA-ID = Healthy Aging and Intellectual Disability study;

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f

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62 CDR = Clinical Dementia Rating scale; OSHPE = Obu Study of Health Promotion for the Elderly; RALLI = Resources and Activities for Life-Long Independence; ADAS-Cog = Alzheimer’s Disease Assessment Scale-Cognitive Subscale; TMT-A = Trail-Making Test-A; TMT-B = Trail-Making Test-B; WMS-R = Wechsler Memory Scale revised; GeMS = Geriatric Multidisciplinary Strategy for the Good Care of the Elderly; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders-IV; CDT = Clock Drawing Test; H-EPESE = Hispanic Established Population for the Epidemiological Study of the Elderly; DLB = dementia with Lewy bodies; CIM = Complex Ideational Material; IADL = instrumental activities of daily living; ADL = activities of daily living; PSS = Parkinsonian Signs Score; BMI = body mass index; ILSA = Italian Longitudinal Study on Aging; DSM-III-R = Diagnostic and Statistical Manual of Mental Disorders-III revised; ICD-10 = International Statistical Classification of Diseases and Related Health Problems,10th revision; SOF = Study of Osteoporotic Fractures; NINDS-AIREN = National Institute of Neurological Disorders and Stroke-Association Internationale pour la Recherche et l’Enseignement en Neurosciences; ACT = Adult Changes in Thought; UFMG = Universidade Federal de Minas Gerais; CSBA = Conselice Study of Brain Aging

Rejuvenation Research nitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1 been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ f Page 63 of 67

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Rejuvenation Research Cognitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1637) This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.

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Rejuvenation Research Cognitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1637) This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof. Page 65 of 67

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Rejuvenation Research Cognitive Frailty - Epidemiological and Neurobiological Evidence of an Age-related Clinical Condition: A Systematic Review (doi: 10.1089/rej.2014.1637) This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof. Page 67 of 67

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Cognitive Frailty: A Systematic Review of Epidemiological and Neurobiological Evidence of an Age-Related Clinical Condition.

Advancing age is the focus of recent studies on familial and sporadic Alzheimer's disease (AD), suggesting a prolonged pre-clinical phase several deca...
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