Cross-Sectional Association Between Cognitive Frailty and White Matter Hyperintensity Among Memory Clinic Patients

Abstract

Background:

Cognitive frailty (CF) is defined as simultaneous presence of physical frailty (PF) and cognitive impairment among older adults without dementia. Although white matter hyperintensities (WMH) as expressions of cerebral small vessel disease are associated with physical and cognitive decline and could manifest as CF, this association remains yet to be clarified.

Objects:

To clarify the association between CF and WMH among memory clinic patients.

Methods:

The subjects of this cross-sectional study were 121 cognitively normal (CN) and 212 mildly cognitively impaired (MCI) patients who presented to the Memory Clinic at the National Center for Geriatrics and Gerontology of Japan. PF status was defined based on the definition proposed by Fried and colleagues. CF was defined as simultaneous presence of pre-PF or PF and MCI. WMH volumes were measured using an automatic segmentation application. Multiple liner regression analyses with adjustment for cardiovascular risk factors were performed.

Results:

Of all subjects, 77 (63.6%) and 22 (18.2%) CN patients and 132 (62.3%) and 65 (30.7%) MCI patients were categorized into pre-PF and PF, respectively. Multiple liner regression analysis showed that those with CF had higher WMH volumes than those without (β= 0.23). When categorized into six groups according to PF and cognitive status, the PF/CN (β= 0.15), pre-PF/MCI (β= 0.41), and PF/MCI (β= 0.34) groups had higher WMH volumes than the non-PF/CN group.

Conclusions:

This study showed increased WMH volumes in CF and PF, indicating that WMH could be one of the key underlying brain pathologies of CF.

Keywords

Cognitive frailty frailty syndrome mild cognitive impairment physical frailty white matter hyperintensity

INTRODUCTION

Physical frailty (PF) and cognitive impairment are common health problems in older adults, and simultaneous presence of PF and cognitive impairment (clinical dementia rating = 0.5) is proposed as “cognitive frailty (CF)” in 2013 by the international consensus group comprised of experts from the International Academy of Nutrition and Aging (IANA) and the International Association of Gerontology and Geriatrics (IAGG) [1]. Conceptually, CF is considered to be a state of reduced cognitive reserve which, unlike physiological brain aging, is supposed to be caused not by neurodegenerative disorders but by physical factors [1]. Moreover, as with the concept of PF, CF is also characterized by its potential for reversibility. CF could represent a useful target for early intervention against dependency and dementia in older adults [1]. Recently, the new operational definition, which includes two subtypes, “reversible” CF and “potential reversible” CF, has been proposed [2]. This new operational definition includes subjects with pre-PF and those at pre-clinical stage of dementia such as subjective cognitive decline and positive biomarkers [2]. Although there is as yet no consensus on the definition of CF, several studies demonstrated that CF would be a risk factor of disability, poor quality of life, death, and incident dementia [3].

The underlying mechanisms of CF remain unclear. The close association between PF and cognitive impairment suggests the presence of an underlying mechanism common to these conditions, which may include clinical markers (e.g., older age, lower education and income, alcohol intake, cardiovascular risk factors, and nutritional problems) and inflammatory/immunity markers (e.g., C-reactive protein [CRP], interleukin-6, fibrinogen, homocysteine, and cortisol) [4, 5]. In this context, these clinical and inflammatory markers have been reported as risk factor for white matter hyperintensities (WMH) recognized as one of the cerebral small vessel diseases [6, 7]. WMH can be observed as hyperintensities in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images. The underlying pathology of WMH reflects demyelination and axonal loss as a consequence of chronic ischemia in a majority of older adults. To date, clear evidence exists that WMH causes cognitive decline, in particular executive functions, speed and motor control, and attention, and increases the risk of dementia [8 –10]. Current evidence also suggests that, besides cognitive decline and dementia, WMH is associated with geriatric syndromes including physical dysfunction, falls, and urinary dysfunction [9, 10]. Therefore, WMH, which is associated with both physical and cognitive dysfunction, is considered as one of the key underlying mechanisms of CF [11]. However, no studies have investigated the association of CF with WMH.

The aim of this study was therefore to clarify the association of CF with WMH among memory clinic outpatients. In this study, we defined CF as simultaneous presence of pre-PF or PF and cognitive impairment [1, 2]. Identifying common underlying mechanisms of CF, PF, and cognitive impairment may help develop effective strategies for preventing progression of disability and dementia among older adults.

METHODS

Design and subjects

The subjects of this cross-sectional study were outpatients first presenting to the Memory Clinic at the National Center for Geriatrics and Gerontology (NCGG) of Japan during the period from October 2010 to January 2017. We included the 333 patients (aged 65–89 years) who were clinically diagnosed as cognitively normal (CN, n = 121) or mildly cognitively impaired (MCI, n = 212). MCI was diagnosed based on the criteria of the National Institute on Aging-Alzheimer’s Association (NIA/AA) workgroups [12]. All patients completed comprehensive geriatric assessment, brain magnetic resonance imaging (MRI), and blood tests at presentation. The Ethics Committee of the NCGG approved the study protocol. The purpose, nature, and potential risks of the study were fully explained to the subjects, and all subjects gave written informed consent before participating in the study.

Definition of physical frailty and cognitive frailty

We defined PF based on the frailty phenotype proposed by Fried et al in the Cardiovascular Health Study [13]. The components of frailty phenotype include shrinking, weakness, slowness, self-reported exhaustion, and low physical activity. Shrinking was defined as the lowest quintile for body mass index (BMI ≤19.5). Weakness was defined as low hand grip strength measured with a digital force gauge (ZP-500N; Imada, Toyohashi, Japan). Low hand grip strength was defined as hand grip strength less than 18 kg in women and 26 kg in men [14]. Subjects answering “yes” to the question “Do you think you walk slower than before?” were considered as having slowness. Exhaustion was defined as a negative response to the following question in the geriatric depression scale [15]: “Do you feel full of energy?”. Low physical activity was defined as those subjects who did not engage in physical exercise at least once a week. Subjects meeting none of the criteria were classified as non-PF, those who met 1-2 criteria as pre-PF, and those who met 3–5 criteria as PF [13]. Then, in this study, pre-PF or PF individuals with MCI were considered to represent CF [1, 2].

Imaging data and evaluation of white matter hyperintensity

All patients underwent 1.5 T brain MR imaging (Siemens Avanto, Munich, Germany; or Philips Ingenia, Eindhoven, The Netherlands) with T1- and T2-weighted and FLAIR sequence. The individual intracranial volume (IC), brain parenchyma (PAR), and WMH volume were quantified using the automatic Software for Neuro-Image Processing in Experimental Research (SNIPER) segmentation application (Department of Radiology, Leiden University Medical Center, Netherlands). The protocols for brain MRI and SNIPER have been described in detail elsewhere [16, 17]. In analyses, the WMH volume was divided by the PAR volume to adjust for brain atrophy (WMH/PAR, %).

Other variables

Information on the subjects’ age, sex, education, and BMI were obtained from their clinical charts. We assessed global cognitive function and basic activities of daily living were assessed by using the Mini-Mental State Examination (MMSE) [18] and Barthel Index [19], respectively. Cardiovascular factors assessed included hypertension, diabetes mellitus, dyslipidemia, coronary artery disease, smoking status (current smoker or not), and drinking status (drinking daily or not), given that these factors are considered to play a key role in the etiology of WMH [6]. Moreover, in blood testing, all subjects were assessed for their estimated glomerular filtration rate (eGFR), homocysteine, brain natriuretic peptide (BNP), and C-reactive protein (CRP) values.

Statistical analysis

In univariate analyses, those with CF and those without were examined for differences in clinical characteristics and WMH/PAR by using the Kruskal-Wallis test and χ² test. Additionally, in order to clarify the association of PF and cognitive status with WMH/PAR in detail, subjects were categorized into six groups: non-PF/CN; pre-PF/CN; PF/CN; non-PF/MCI; pre-PF/MCI; and PF/MCI. Then, the 6 groups were also examined for differences in clinical characteristics and WMH/PAR by using the Kruskal-Wallis test and χ² test.

In multivariate analyses, to clarify the association of CF with WMH, multiple liner regression analysis was performed, where presence or absence of CF was entered as an independent variable. WMH/PAR was entered as a dependent variable. Age, sex, education, and other confounding variables (smoking status, drinking status, hypertension, diabetes mellitus, dyslipidemia, coronary artery disease, eGFR, homocysteine, BNP, and CRP) were entered as covariates. Moreover, to minimize the bias for each patient’s head size, IC volume was also entered as a covariate. WMH/PAR, homocysteine, BNP, and CRP were log-transformed before regression analysis, given that these variables showed skewed distributions. Moreover, to clarify the association of PF and cognitive status with WMH/PAR in detail, multiple liner regression analysis was also performed, with PF and cognitive status (reference group = non-PF/CN) as independent variables.

All statistical analyses were carried out by using STATA 14.2 (Stata Corp, College Station, Texas, USA). p values <0.05 were considered statistically significant.

RESULTS

The mean age of the subjects was 74.7±5.5 years, and 206 (61.9%) subjects were females. The mean MMSE scores of CN and MCI were 28.5±1.8 and 25.4±2.5, respectively. Of all subjects, 63.6% and 18.2% in CN and 62.3% and 30.7% in MCI were shown to represent pre-PF and PF, respectively. Of all subjects, therefore, 197 (59.2%) subjects were shown to represent CF.

Characteristics of subjects with and without cognitive frailty

Table 1 shows the characteristics of the subjects. The values were compared between those with CF and those without. There were differences in age, education, MMSE, drinking status, homocysteine, BNP, and PF components, except for shrinking and exhaustion, among the two groups. Those with CF had lower IC and PAR volumes and higher WMH and WMH/PAR volumes than those without (Table 1).

Table 1

Clinical characteristics of the subjects with and without CF

	Overall N = 333	With CF N = 197	Without CF N = 136	p
Age, y	74.7 (5.5)	75.5 (5.6)	73.4 (5.2)	<0.001
Female	206 (61.9)	120 (60.9)	86 (63.2)	0.668
Education, y	11.6 (2.6)	11.2 (2.5)	12.3 (2.5)	<0.001
Body mass index, kg/m²	22.1 (3.2)	21.9 (3.3)	22.6 (2.9)	0.102
MMSE	26.5 (2.7)	25.3 (2.5)	28.2 (2.1)	<0.001
Barthel Index	99.1 (3.6)	98.9 (4.2)	99.4 (2.6)	0.623
Hypertension	149 (44.7)	94 (47.7)	55 (40.4)	0.189
Diabetes mellitus	54 (16.2)	33 (16.8)	21 (15.4)	0.750
Dyslipidemia	120 (36.0)	64 (32.5)	56 (41.2)	0.105
Coronary artery disease	8 (2.4)	5 (2.5)	3 (2.2)	0.846
Current smoking	24 (7.2)	18 (9.1)	6 (4.4)	0.101
Daily drinking	68 (20.4)	33 (16.8)	35 (25.7)	0.046
eGFR, ml/min/1.73 m²	68.6 (15.6)	68.4 (17.6)	69.0 (12.3)	0.453
Homocysteine, μmol/L	9.6 (8.1, 11.5)	10.0 (8.3, 11.7)	9.0 (7.8, 10.8)	0.007
BNP, pg/mL	26.6 (14.2, 47.9)	30.0 (17.5, 55.1)	22.0 (13.4, 37.1)	0.001
CRP, mg/L	0.05 (0.03, 0.11)	0.05 (0.03, 0.10)	0.06 (0.03, 0.12)	0.351
Hand grip strength, kg
Male	30.8 (7.3)	28.9 (7.3)	33.8 (6.4)	<0.001
Female	20.1 (5.0)	19.4 (4.7)	21.1 (5.2)	0.002
Frailty components
Slowness	191 (57.4)	128 (65.0)	63 (46.3)	0.001
Weakness	90 (27.0)	68 (34.5)	22 (16.2)	<0.001
Shrinking	68 (20.4)	47 (23.9)	21 (15.4)	0.061
Exhaustion	177 (53.2)	112 (56.9)	65 (47.8)	0.103
Low physical activity	66 (19.8)	53 (26.9)	13 (9.6)	<0.001
MR imaging
IC, mL	1427.3 (127.4)	1416.0 (127.0)	1443.7 (126.5)	0.044
PAR, mL	1122.6 (123.3)	1108.2 (122.4)	1143.5 (122.1)	0.008
WMH, mL	8.9 (11.5)	11.0 (13.2)	5.8 (7.7)	<0.001
WMH/PAR, %	0.8 (1.0)	1.0 (1.1)	0.5 (0.7)	<0.001

Data are presented as n (%), mean (standard deviation), or median (interquartile range). BNP, brain natriuretic peptide; CF, cognitive frailty; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; IC, intracranial; MMSE, Mini-Mental State Examination; PAR, parenchyma; WMH, white matter hyperintensities.

Supplementary Table 1 shows the characteristics of subjects according to PF and cognitive status. There were differences in age, education, BMI, MMSE, dyslipidemia, homocysteine, BNP, and PF components among the 6 groups (Supplementary Table 1). PAR, WMH, and WMH/PAR volumes were significantly different between 6 groups, and the WMH/PAR volume was shown to be highest among those with MCI and PF (Supplementary Table 1 and Supplementary Figure 1).

Association between cognitive frailty and white matter hyperintensities

Table 2 shows the results of multiple regression analysis, where presence or absence of CF was entered as an independent variable. As a result, those with CF showed a significantly higher WMH/PAR volumes compared to those without (coefficient, 0.57; standard error [SE], 0.13; p < 0.001). The other factors associated with WMH/PAR volumes were older age (coefficient, 0.06; SE, 0.01; p < 0.001) and higher CRP (coefficient, 0.16; SE, 0.06; p = 0.013). To allow interpretation of the association of CF with WMH/PAR volumes, these coefficients were back-transformed into the original metric [20]. Multiple regression analysis thus showed that WMH/PAR volumes were 76.6% [(e^0.569 - 1)×100] higher for those with CF than for those without. Additionally, a 1-year increase in age was related to a 6.3% [(e^0.061 - 1)×100] increase in WMH/PAR volume. A 1% increase in CRP was related to a 0.2% [(1.01)^0.158 – 1]×100] increase in WMH/PAR volume. Moreover, multiple regression analysis, which included PF and cognitive status (reference group = non-PF/CN) as independent variables, showed that the PF/CN (coefficient, 0.77; SE, 0.17; p = 0.026), pre-PF/MCI (coefficient, 1.05; SE, 0.27; p < 0.001), and PF/MCI (coefficient, 1.06; SE, 0.29; p < 0.001) groups had significantly higher WMH/PAR volumes compared to the non-PF/CN group (Supplementary Table 2).

Fig.1

Distribution of WMH/PAR among subjects with and without CF. CF, cognitive frailty; PAR, parenchyma; WMH, white matter hyperintensities.

Table 2

Association of cognitive frailty with WMH (log transformed)

	N = 333
	Coefficient (SE)	β	p
Cognitive frailty	0.57 (0.13)	0.23	<0.001
Age	0.06 (0.01)	0.28	<0.001
Female	0.04 (0.17)	0.02	0.821
Education	0.03 (0.03)	0.06	0.267
Current smoking	0.24 (0.26)	0.05	0.357
Daily drinking	0.09 (0.17)	0.03	0.601
Hypertension	0.23 (0.14)	0.09	0.096
Diabetes mellitus	–0.11 (0.17)	–0.03	0.517
Dyslipidemia	–0.22 (0.14)	–0.09	0.113
Coronary artery disease	0.26 (0.42)	0.03	0.534
eGFR	0.00 (0.00)	–0.06	0.308
Homocysteine (log transformed)	–0.03 (0.21)	–0.01	0.881
BNP (log transformed)	0.08 (0.08)	0.06	0.314
CRP (log transformed)	0.16 (0.06)	0.13	0.013
IC	0.00 (0.00)	0.07	0.295

BNP, brain natriuretic peptide; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; IC, intracranial; SE, standard error; WMH, white matter hyperintensities.

DISCUSSION

The present study investigated the association between CF and WMH among patients presenting to the Memory Clinic. This study is the first report demonstrating greater WMH volumes in patients with CF than in those without CF.

While a significant link has been widely reported between WMH and cognitive impairment in previous studies [8 , 21], the association between PF and WMH remains to be fully elucidated. Recently, among 176 community-dwelling older adults without dementia, the WMH volume was shown to vary between the frailty phenotypes and to be higher in frail persons than in non-frail persons [22]. In the Tasmanian Study of Cognition and Gait population-based study, WMH was shown to be associated with higher frailty index (FI) values in 388 older adults, while this index included cognitive function and comorbidities (such as hypertension) [23]. In this study, there was no significant association between WMH and the frailty phenotypes [23]. Among 87 patients presenting to the university hospital, multidimensional FI including cognitive function was shown to be associated with WMH as determined by the modified Fazekas scale [24]. However, some studies showed disparate results and found marginal [25, 26] or no significant association between WMH and PF [27, 28]. These inconsistent results may be due to differences in the target populations and the operational definitions used for frailty. In some studies that found no association, the focus was on younger subjects who were less frail and had less WMH burden [27, 28]. Indeed, Siejka et al. reported that a greater than moderate WMH burden likely had the strongest association with frailty [23]. In the current study focused on the Memory Clinic patients, PF subjects had higher WMH volumes compared to non-PF subjects among patients without MCI even after adjustment for confounding factors that are considered to play a key role in the etiology of WMH. Moreover, our study was the first to demonstrate increased WMH volumes in CF and even in pre-PF patients with cognitive impairment. This finding suggests that WMH may be among the key common underlying mechanisms of PF and cognitive impairment.

To date, PF has been reported to increase a risk of incident dementia, in particular vascular dementia or non-Alzheimer’s disease (AD) [29 –31]. Moreover, several recent studies also demonstrate a significant link between CF and incident dementia [32 –35], especially vascular dementia [32]. Additionally, a very recent population-based study operationalized a biopsychosocial frailty model (PF plus psychosocial frailty) and showed that this new construct demonstrated increases in the risk of incident dementia, particularly vascular dementia, during 3.5-year and 7-year follow-up. These previous studies indicate that various frailty phenotypes may be closely associated with cognitive decline and dementia due to vascular pathologies. Our finding that PF or CF subjects have higher volumes of WMH is consistent with these previous studies, given that WMH is associated with a higher risk of incident stroke and dementia [37].

In contrast, a recent study that focused on 91 patients with amnestic MCI, a higher FI was associated with a higher risk of conversion to AD [38]. Additionally, in the Gait and Brain study, combined slow gait and cognitive impairment predicted cognitive decline and incident dementia [39]. In this study, almost all subjects progressing to dementia were diagnosed with AD [39]. Moreover, the level of frailty has been reported to be associated with AD pathologies [40 –42]. These results indicate that heterogeneous conditions involving AD pathologies as well as non-neurodegenerative disease may manifest as CF. Further studies investigating the relevance of neurodegenerative disease including AD may deepen our understanding of CF.

The present study has several limitations that should be noted and addressed in future studies. First, since our study was a cross-sectional study, the temporal association between WMH and CF remains unclear. Second, given the focus on outpatients presenting to the Memory Clinic, the sample size of subjects with CN was relatively small in this study, and the study results may not be readily generalizable to other populations. Moreover, the study did not assess other cerebral small vessel diseases including cerebral microbleeds and perivascular space. Despite these limitations, however, the present study is the first to demonstrate a significant link between CF and WMH among the Memory Clinic patients.

In conclusion, this study provides evidence for cross-sectional association between increased WMH and CF, indicating that WMH could be among the key underlying brain pathologies of CF. Further longitudinal studies are needed to confirm our findings.

Footnotes

ACKNOWLEDGMENTS

The authors thank the BioBank at National Center for Geriatrics and Gerontology for quality control of the clinical data. This work was supported by the Research Funding of Longevity Sciences (30-1) from the National Center for Geriatrics and Gerontology, and a Grant-in-Aid for JSPS Research Fellow (No. 17J03037) from Japan Society for the Promotion of Science. The funders had no role in study design, methods, data collections, analysis, and preparation of paper.

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

References

Kelaiditi

, Cesari

, Canevelli

, van Kan

, Ousset

, Gillette-Guyonnet

, Ritz

, Duveau

, Soto

, Provencher

, Nourhashemi

, Salva

, Robert

, Andrieu

, Rolland

, Touchon

, Fitten

, Vellas

(2013) Cognitive frailty: Rational and definition from an (I.A.N.A./I.A.G.G.) international consensus group. J Nutr Health Aging 17, 726–734.

Ruan

, Yu

, Chen

, Bao

, Li

, He

(2015) Cognitive frailty, a novel target for the prevention of elderly dependency. Ageing Res Rev 20, 1–10.

Sugimoto

, Sakurai

, Ono

, Kimura

, Saji

, Niida

, Toba

, Chen

, Arai

(2018) Epidemiological and clinical significance of cognitive frailty: A mini review. Ageing Res Rev 44, 1–7.

Panza

, Seripa

, Solfrizzi

, Tortelli

, Greco

, Pilotto

, Logroscino

(2015) Targeting cognitive frailty: Clinical and neurobiological roadmap for a single complex phenotype. J Alzheimers Dis 47, 793–813.

Sargent

, Nalls

, Starkweather

, Hobgood

, Thompson

, Amella

, Singleton

(2018) Shared biological pathways for frailty and cognitive impairment: A systematic review. Ageing Res Rev 47, 149–158.

Moroni

, Ammirati

, Rocca

, Filippi

, Magnoni

, Camici

(2018) Cardiovascular disease and brain health: Focus on white matter hyperintensities. Int J Cardiol Heart Vasc 19, 63–69.

Rostrup

, Gouw

, Vrenken

, van Straaten

, Ropele

, Pantoni

, Inzitari

, Barkhof

, Waldemar

, group

(2012) The spatial distribution of age-related white matter changes as a function of vascular risk factors–results from the LADIS study. Neuroimage 60, 1597–1607.

Prins

, Scheltens

(2015) White matter hyperintensities, cognitive impairment and dementia: An update. Nat Rev Neurol 11, 157–165.

Pantoni

, Fierini

, Poggesi

(2015) Impact of cerebral white matter changes on functionality in older adults: An overview of the LADIS Study results and future directions. Geriatr Gerontol Int 15(Suppl 1), 10–16.

10.

Debette

, Markus

(2010) The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: Systematic review and meta-analysis. BMJ 341, c3666.

11.

Morley

(2015) Cognitive frailty: A new geriatric syndrome? Eur Geriatr Med 6, 408–411.

12.

Albert

, DeKosky

, Dickson

, Dubois

, Feldman

, Fox

, Gamst

, Holtzman

, Jagust

, Petersen

, Snyder

, Carrillo

, Thies

, Phelps

(2011) The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 270–279.

13.

Fried

, Tangen

, Walston

, Newman

, Hirsch

, Gottdiener

, Seeman

, Tracy

, Kop

, Burke

, McBurnie

(2001) Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Med Sci 56, M146–156.

14.

Chen

, Liu

, Woo

, Assantachai

, Auyeung

, Bahyah

, Chou

, Chen

, Hsu

, Krairit

, Lee

, Liang

, Limpawattana

, Lin

, Peng

, Satake

, Suzuki

, Won

, Wu

, Zhang

, Zeng

, Akishita

, Arai

(2014) Sarcopenia in Asia: Consensus report of the Asian Working Group for Spenia. J Am Med Dir Assoc 15, 95–101.

15.

Yesavage

, Brink

, Rose

, Lum

, Huang

, Adey

, Leirer

(1982) Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res 17, 37–49.

16.

Ogama

, Yoshida

, Nakai

, Niida

, Toba

, Sakurai

(2016) Frontal white matter hyperintensity predicts lower urinary tract dysfunction in older adults with amnestic mild cognitive impairment and Alzheimer’s disease. Geriatr Gerontol Int 16, 167–174.

17.

Admiraal-Behloul

, van den Heuvel

, Olofsen

, van Osch

, van der Grond

, van Buchem

, Reiber

(2005) Fully automatic segmentation of white matter hyperintensities in MR images of the elderly. Neuroimage 28, 607–617.

18.

Folstein

, Folstein

, McHugh

(1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12, 189–198.

19.

Mahoney

, Barthel

(1965) Functional evaluation: The Barthel Index. Md State Med J 14, 61–65.

20.

Barrera-Gómez

, Basagaña

(2015) Models with transformed variables: Interpretation and software. Epidemiology 26, e16–e17.

21.

van Sloten

, Protogerou

, Henry

, Schram

, Launer

, Stehouwer

(2015) Association between arterial stiffness, cerebral small vessel disease and cognitive impairment: A systematic review and meta-analysis. Neurosci Biobehav Rev 53, 121–130.

22.

Avila-Funes

, Pelletier

, Meillon

, Catheline

, Periot

, Trevin

OFI

, Gonzalez-Colaco

, Dartigues

, Peres

, Allard

, Dilharreguy

, Amieva

(2017) Vascular cerebral damage in frail older adults: The AMImage Study. J Gerontol A Biol Sci Med Sci 72, 971–977.

23.

Siejka

, Srikanth

, Hubbard

, Moran

, Beare

, Wood

, Phan

, Callisaya

(2018) Frailty and cerebral small vessel disease: A cross-sectional analysis of the Tasmanian Study of Cognition and Gait (TASCOG). J Gerontol A Biol Sci Med Sci 73, 255–260.

24.

Jung

, Kim

, Yoon

, Choi

, Kim

(2014) Associations between frailty, retinal microvascular changes, and cerebral white matter abnormalities in Korean older adults. J Am Geriatr Soc 62, 2209–2210.

25.

Del Brutto

, Mera

, Cagino

, Fanning

, Milla-Martinez

, Nieves

, Zambrano

, Sedler

(2017) Neuroimaging signatures of frailty: A population-based study in community-dwelling older adults (the Atahualpa Project). Geriatr Gerontol Int 17, 270–276.

26.

Kant

IMJ

, de Bresser

, van Montfort

SJT

, Aarts

, Verlaan

, Zacharias

, Winterer

, Spies

, Slooter

AJC

, Hendrikse

(2018) The association between brain volume, cortical brain infarcts, and physical frailty. Neurobiol Aging 70, 247–253.

27.

Chen

, Chou

, Liu

, Lee

, Chen

, Wang

, Lin

(2015) Reduced cerebellar gray matter is a neural signature of physical frailty. Hum Brain Mapp 36, 3666–3676.

28.

Chung

, Chou

, Chen

, Liu

, Lee

, Chen

, Lin

, Wang

(2016) Cerebral microbleeds are associated with physical frailty: A community-based study. Neurobiol Aging 44, 143–150.

29.

Avila-Funes

, Carcaillon

, Helmer

, Carriere

, Ritchie

, Rouaud

, Tzourio

, Dartigues

, Amieva

(2012) Is frailty a prodromal stage of vascular dementia? Results from the Three-City Study. J Am Geriatr Soc 60, 1708–1712.

30.

Solfrizzi

, Scafato

, Frisardi

, Seripa

, Logroscino

, Maggi

, Imbimbo

, Galluzzo

, Baldereschi

, Gandin

, Di Carlo

, Inzitari

, Crepaldi

, Pilotto

, Panza

(2013) Frailty syndrome and the risk of vascular dementia: The Italian Longitudinal Study on Aging. Alzheimers Dement 9, 113–122.

31.

Gray

, Anderson

, Hubbard

, LaCroix

, Crane

, McCormick

, Bowen

, McCurry

, Larson

(2013) Frailty and incident dementia. J Gerontol A Biol Sci Med Sci 68, 1083–1090.

32.

Solfrizzi

, Scafato

, Seripa

, Lozupone

, Imbimbo

, D’Amato

, Tortelli

, Schilardi

, Galluzzo

, Gandin

, Baldereschi

, Di Carlo

, Inzitari

, Daniele

, Sabba

, Logroscino

, Panza

(2017) Reversible cognitive frailty, dementia, and all-cause mortality. The Italian Longitudinal Study on Aging. J Am Med Dir Assoc 18, 89.e81–89.e88.

33.

Feng

, Nyunt

, Gao

, Feng

, Lee

, Tsoi

, Chong

, Lim

, Collinson

, Yap

, Ng

(2017) Physical frailty, cognitive impairment, and the risk of neurocognitive disorder in the Singapore Longitudinal Ageing Studies. J Gerontol A Biol Sci Med Sci 72, 369–375.

34.

Shimada

, Makizako

, Lee

, Doi

, Lee

, Tsutsumimoto

, Harada

, Hotta

, Bae

, Nakakubo

, Harada

, Suzuki

(2016) Impact of cognitive frailty on daily activities in older persons. J Nutr Health Aging 20, 729–735.

35.

Shimada

, Doi

, Lee

, Makizako

, Chen

, Arai

(2018) Cognitive frailty predicts incident dementia among community-dwelling older people. J Clin Med 7, E250.

36.

Solfrizzi

, Scafato

, Lozupone

, Seripa

, Schilardi

, Custodero

, Sardone

, Galluzzo

, Gandin

, Baldereschi

, Di Carlo

, Inzitari

, Giannelli

, Daniele

, Sabbà

, Logroscino

, Panza

; Italian Longitudinal Study on Aging Working Group (2019) Biopsychosocial frailty and the risk of incident dementia: The Italian longitudinal study on aging. Alzheimers Dement 15, 1019–1028.

37.

Debette

, Schilling

, Duperron

, Larsson

, Markus

(2019) Clinical significance of magnetic resonance imaging markers of vascular brain injury: A systematic review and meta-analysis. JAMA Neurol 76, 81–94.

38.

Trebbastoni

, Canevelli

, D’Antonio

, Imbriano

, Podda

, Rendace

, Campanelli

, Celano

, Bruno

, de Lena

(2017) The impact of frailty on the risk of conversion from mild cognitive impairment to Alzheimer’s disease: Evidences from a 5-year observational study. Front Med (Lausanne) 4, 178.

39.

Montero-Odasso

, Barnes

, Speechley

, Muir Hunter

, Doherty

, Duque

, Gopaul

, Sposato

, Casas-Herrero

, Borrie

, Camicioli

, Wells

(2016) Disentangling cognitive-frailty: Results from the Gait and Brain Study. J Gerontol A Biol Sci Med Sci 71, 1476–1482.

40.

Buchman

, Schneider

, Leurgans

, Bennett

(2008) Physical frailty in older persons is associated with Alzheimer disease pathology. Neurology 71, 499–504.

41.

Wallace

, Theou

, Rockwood

, Andrew

(2018) Relationship between frailty and Alzheimer’s disease biomarkers: A scoping review. Alzheimers Dement (Amst) 10, 394–401.

42.

Wallace

LMK

, Theou

, Godin

, Andrew

, Bennett

, Rockwood

(2019) Investigation of frailty as a moderator of the relationship between neuropathology and dementia in Alzheimer’s disease: A cross-sectional analysis of data from the Rush Memory and Aging Project. Lancet Neurol 18, 177–184.