Keywords:
older adults - cognitive dysfunction - frailty
Palavras-chave:
idoso - disfunção cognitiva - fragilidade
A slight decrease in cognitive function is expected during the ageing process. However,
cognitive impairment can occur when one's performance regarding memory, judgment,
language, and attention is lower than that expected for one's age and educational
level[1],[2]. Cognitive impairment can be caused by neurodegeneration, vascular problems and
metabolic problems. Nonetheless, chronic stress, depressive symptoms and anxiety can
contribute to a poorer mental performance during old age[3]. Lately, the poor physical function, such as frailty, is considered another strong
factor linked to cognitive impairment, seen that these conditions share similar pathophysiological
mechanisms on the cellular and systemic levels[4],[5].
Different theories and particular (but complementary) evaluations of frailty in older
adults have contributed to the health care, to the comprehensive geriatric and gerontological
assessment, and supported the interventions planning. The Cardiovascular Health Study
(CHS) defined frailty as a geriatric syndrome that could be assessed using the measurement
of five clinical criteria: unintentional weight loss, fatigue (exhaustion), muscle
weakness, slow gait/slowness and low levels of physical activity[6]. More recently, Morley et al. contributed to the definition of the clinical syndrome
as a multiple-cause condition that leads to vulnerability, functional dependence,
and death[7].
Other frailty theories and measures are also useful to predict cognitive impairment[8]. However, a physical examination using the CHS frailty clinical criteria[6] may indicate changes in cognitive functions. Systematic reviews and meta-analyses
confirmed the existence of a strong link between physical and cognitive impairment[5],[9],[10]. Although there is evidence associating frailty with cognitive impairment, a small
number of studies on this subject have been conducted considering the five clinical
criteria, individually, in low and middle-income countries.
In Brazil, an analysis of the FIBRA study described the criteria of slow gait speed
(slowness) and low grip strength (weakness) as the strongest measures associated with
cognitive performance among older adults[11]. A further FIBRA study analysis with 384 community-dwelling older adults confirmed
that frailty and specific cognitive domains are linked, with a poorer performance
as to time orientation and working memory prevalent among frail older adults[12]. Similar findings were observed in 737 participants of a study conducted in Rio
de Janeiro City[13] and in a multi-centric Brazilian study[14]. Older adults with frailty had consistently lower Mini Mental State Examination
(MMSE) scores compared to prefrail and robust older adults[13],[14]. A systematic review with 29,664 participants in 19 studies, which were mostly conducted
in Latin America, found that memory is the main function affected in older adults
with frailty, and slowness and weakness are the most prevalent frailty clinical criteria
in cognitively impaired older adults[15].
Despite the growing interest in investigating the association between cognitive and
physical status, further studies should be conducted with older adults living in low
and middle-income countries. Therefore, the aim of the present study was to analyze
the association between cognitive impairment and the clinical criteria for frailty
syndrome. We hypothesized that some frailty clinical criteria are strongly associated
with cognitive impairment in older adults. Additionally, we want to confirm whether
frailty presents a close association with cognitive impairment, compared to prefrailty
and robust older adults.
METHODS
Participants
The present cross-sectional study is part of a study entitled “Variables associated
with cognition in elderly caregivers” conducted by the Health and Ageing Group of
Universidade Federal de São Carlos involving individuals registered in the Family
Health Units of São Carlos City, São Paulo State, Brazil. São Carlos is in the Southeastern
region of the country and has an estimated population of 221,950 residents, among
whom 13% were aged 60 or older, according to the 2010 Brazilian census[16].
The study was conducted between April and December of 2014. The participant selection
process is described elsewhere[3],[17],[18], but a brief description follows. All community-dwelling older adults (age≥60 in
Brazil, as defined by the World Health Organization) registered at 18 primary healthcare
centers (n=1,188) in São Carlos City, Brazil, were contacted in person and invited
to participate in the survey. Individuals with auditory, visual or language limitations
that could constitute barriers to the data collection instruments were excluded. The
response rate was 59.1%. The survey was conducted with 351 community-dwelling older
caregivers and 351 community-dwelling older non-caregivers (total: 702 individuals)
registered with primary care services in rural and urban regions. For the present
study, 667 individuals were included, and the single criterion for entry was having
complete data available on demographics, cognitive and frailty status.
The present study was approved by the Human Research Ethics Committee of Universidade
Federal de São Carlos (certificate number: 517.182) and all participants signed an
informed consent. Household interviews were conducted by trained professionals in
the fields of Gerontology and Nursing.
Variables and evaluations
-
Demographic characteristics: gender (male, female), age (continuous and age range), years of education (continuous
and education level), retirement (yes, no), place of residence (rural, urban), and
ethnicity (black/brown, white and others).
-
Activities of daily living (ADL): Functioning was assessed using the Lawton and Brody Scale to determine the degree
of independence on basic activities, such as performing housework, handling money,
using the telephone, administering medications, traveling, shopping and preparing
full meals. The total score ranges from seven (complete dependence) to 21 (complete
independence), with intermediate scores (8 to 20 points) indicative of partial dependence[19],[20].
-
Cognitive impairment: Cognitive screening was performed using the Mini Mental State Examination (MMSE),
the score of which ranges from 0 to 30[21]. The cutoff points were adjusted for different levels of formal education: <26 for
those with ≥nine years of schooling; <24 for those with five to eight years of schooling,
<22 for those with one to four years of schooling, and <17 for illiterate individuals[22]. Addenbrooke’s Cognitive Examination - Revised (ACE-R; score: 0-100) was also used
to assess global cognition[23],[24].
-
The frailty syndrome and criteria: The five frailty clinical criteria of the Cardiovascular Health Study were considered:
unintentional weight loss in the past year, fatigue in the past week, muscle weakness,
slowness and decreased physical activity levels when compared to the previous year.
Unintentional weight loss in the past year, fatigue in the past week and decreased
physical activity level were self-declared. Muscle weakness was assessed using a dynamometer
and slowness, with the time required to walk 4.6 meters. Based on Fried’s phenotype,
the number of criteria was used to determine the level of frailty: frail (from three
to five criteria), prefrail (one or two criteria) and robust/non-frail (negative for
all five criteria)[6].
Statistical analysis
The Statistical Package for the Social Sciences - SPSS software, version 21.0 program
was used for the data analysis. Descriptive statistics were performed to characterize
the overall sample and the sample stratified by cognitive status. The values for frequency
(n), percentage (%), mean and standard deviation (±) were calculated. The independent
t-test was used to compare means and the chi-square test was used to compare categorical
variables between groups according to gender ([Table 1]). The prevalence of simultaneous cognitive impairment and frailty was estimated
with 95% confidence intervals (95%CI). One-way ANOVA was used with Tukey’s post hoc
test for comparisons of MMSE scores between frailty levels ([Figure 1]).
Table 1
Characterization of participants stratified by cognitive status. São Carlos City,
Brazil, 2014.
|
Characteristic
|
Total (n=667)
|
Cognitively impaired (n=226)
|
Cognitively unimpaired (n=441)
|
p-value
|
|
Male
|
301 (54.8)
|
96 (42.5)
|
205 (46.5)
|
0.184b
|
|
Female
|
366 (57.8)
|
130 (57.5)
|
236 (53.5)
|
|
|
Age, mean (±)
|
71.3 (7.8)
|
73.2 (8.9)
|
70.4 (7.0)
|
<0.001a
|
|
60‒69 y.o., n (%)
|
328 (49.2)
|
95 (42.0)
|
233 (52.8)
|
REF
|
|
70‒79 y.o., n (%)
|
234 (35.1)
|
78 (34.5)
|
156 (35.4)
|
0.155b
|
|
≥80 y.o., n (%)
|
105 (15.7)
|
53 (23.5)
|
52 (11.8)
|
<0.001b
|
|
Education, mean (±)
|
3.6 (3.5)
|
3.1 (3.5)
|
3.9 (3.5)
|
0.006a
|
|
Illiterate, n (%)
|
147 (22.0)
|
56 (24.8)
|
91 (20.6)
|
0.477b
|
|
1‒4 y, n (%)
|
395 (59.2)
|
133 (58.8)
|
262 (59.4)
|
0.379b
|
|
5‒8 y, n (%)
|
62 (9.3)
|
14 (6.2)
|
48 (10.9)
|
0.065b
|
|
≥9 y, n (%)
|
63 (9.4)
|
23 (10.2)
|
40 (9.1)
|
REF
|
|
Retired, n (%)
|
512 (76.7)
|
180 (79.6)
|
332 (75.3)
|
0.121b
|
|
Rural residence, n (%)
|
166 (24.9)
|
48 (21.2)
|
118 (26.8)
|
REF
|
|
Urban residence, n (%)
|
501 (75.1)
|
178 (78.8)
|
323 (7.2)
|
0.070b
|
|
Black/Brown, n (%)
|
200 (30.0)
|
82 (36.3)
|
118 (26.8)
|
NA
|
|
White, n (%)
|
461 (69.1)
|
144 (63.7)
|
317 (71.9)
|
NA
|
|
Others, n (%)
|
6 (0.9)
|
|
6 (1.4)
|
NA
|
|
Lawton ADL Scale, mean (±)
|
16.8 (4.0)
|
14.7 (4.6)
|
17.8 (3.1)
|
<0.001a
|
|
Independent, n (%)
|
147 (22.0)
|
35 (15.5)
|
112 (25.4)
|
REF
|
|
Partially dependent, n (%)
|
493 (73.9)
|
169 (74.8)
|
324 (73.5)
|
0.002b*
|
|
Completely dependent, n (%)
|
27 (4.0)
|
22 (9.7)
|
5 (1.1)
|
|
|
ACE-R, mean (±)
|
58.6 (20.7)
|
41.7 (18.4)
|
67.3 (16.0)
|
<0.001a
|
|
MMSE, mean (±)
|
21.8 (5.2)
|
16.8 (4.8)
|
24.4 (3.3)
|
<0.001a
|
|
Unintentional weight loss, n (%)
|
165 (24.7)
|
65 (28.8)
|
100 (22.7)
|
0.0522
|
|
Fatigue, n (%)
|
169 (25.3)
|
87 (38.5)
|
82 (18.6)
|
<0.001b
|
|
Weakness, n (%)
|
268 (30.2)
|
116 (51.3)
|
152 (34.5)
|
<0.001b
|
|
Slowness, n (%)
|
154 (23.1)
|
90 (39.8)
|
64 (14.5)
|
<0.001b
|
|
Low physical activity, n (%)
|
343 (51.4)
|
133 (58.8)
|
210 (47.6)
|
0.004b
|
|
Robustness, n (%)
|
140 (21.0)
|
26 (11.5)
|
114 (25.9)
|
REF
|
|
Pre-frailty, n (%)
|
363 (54.4)
|
112 (49.6)
|
251 (56.9)
|
0.003b
|
|
Frailty, n (%)
|
164 (24.6)
|
88 (38.9)
|
76 (17.2)
|
<0.001b
|
|
Cognitive impairment+frailty, n (%)
|
88 (13.2)
|
|
|
|
|
Cognitive impairment+prefrailty, n (%)
|
112 (16.8)
|
|
|
|
aSudent’s t-test; bchi-square; REF: reference category; NA: variable not compared; MMSE: Mini Mental
State Examination; ACE-R: Addenbrooke’s Cognitive Examination-Revised; ADL: Activities
of daily living; *Partially dependent/completely dependent analyzed together.
Figure 1 Box plot of performance on Mini Mental State Examination among robust, older adults
with pre-frailty and frailty (n=667). São Carlos City, Brazil, 2014.**p≤0.01. MMSE:
Mini Mental State Examination.
Multinomial regression models were run to analyze the associations between frailty
syndrome/criteria (independent variable) and cognitive impairment (dependent variable).
Crude models were run to determine associations between age (continuous), education
(continuous), gender (reference: male), place of residence (reference: rural), degree
of dependence on ADL (reference: independent), unintentional weight loss, fatigue,
weakness, slowness, low physical activity (reference: absence of criteria), prefrailty
and frailty (reference: no frailty). Variables with a p-value≤0.20 were selected for
the adjusted regression remodel. The first model ([Table 2]) included all criteria as controlling variables in the same model. Prefrailty (Model
B) and frailty (Model C) were incorporated independently in adjusted models. Associations
with a p-value≤0.05 were considered statistically significant.
RESULTS
Among the 702 participants, 35 were excluded from the analysis due the missing data
on education, cognitive and frailty status. The sample consisted of similar proportions
of women (54.8%) and men (45.2%). Women tended to be younger (mean difference: -1.6
years; p=0.012), more independent regarding ADL (w: 33.6% vs. m: 8%) and fewer were
retired compared to men (w: 64.5% vs. m: 91.7%). Women also had higher proportions
of slowness (w: 27% vs. m: 18.3%) and low physical activity compared to men (w: 54.9%
vs. m: 47.2%).
No differences between women and men were found regarding the prevalence of cognitive
impairment ([Table 1]). Cognitively impaired older adults tended to be older (mean difference: 2.8 years;
p<0.001) than those with normal cognition. Regarding performance on ADL, 15.5% of
cognitively impaired and 25.4% of older adults with normal cognitive were completely
independent. Regarding frailty, except for unintentional weight loss, all criteria
were more prevalent in older adults with cognitive impairment.
The prevalence of simultaneous condition cognitive impairment and frailty was 13.2%
(95%CI 11-16) and the prevalence of concurrent cognitive impairment and prefrailty
was 16.8 (95%CI 14-20). [Figure 1] displays the MMSE scores among the levels of frailty. The mean MMSE score was 23.9±3.8
among robust individuals. Prefrail individuals had a poorer MMSE score compared to
robust individuals (mean difference: -1.5; p<0.01), and the mean difference in the
frail group compared to robust individuals was -5.2 (p<0.01).
As shown in [Table 2] and Figure 2, only fatigue/exhaustion and slowness remained associated with cognitive
impairment in the model controlled for age, education, place of residence, dependence
on ADL and other frailty criteria. Individuals with fatigue were 1.1 times more likely
to exhibit cognitive impairment, when compared to those without this criterion. Moreover,
individuals with slowness were 2.6 times more likely to exhibit cognitive impairment
([Table 2]; Model A). Frailty was more linked to cognitive impairment than prefrailty. The
chances of cognitive impairment increased up to 330% in individuals with frailty (Model
B) and 70% in individuals with prefrailty, when compared to robust individuals (Model
C).
Table 2
Crude and adjusted regression models of association between criteria for frailty (Model
A), prefrailty (Model B), frailty (Model C), and cognitive impairment (n=667). São
Carlos City, Brazil, 2014.
|
Variables
|
Crude model
|
Adjusted model
|
|
|
OR
|
95% CI
|
p-value
|
OR
|
95% CI
|
p-value
|
|
Model A
|
|
|
|
|
|
|
|
No Unintentional weight loss (ref)
|
1.0
|
|
|
1.0
|
|
|
|
Unintentional weight loss
|
1.3
|
0.9‒1.9
|
0.085
|
0.9
|
0.6‒1.4
|
0.877
|
|
No Fatigue (ref)
|
1.0
|
|
|
1.0
|
|
|
|
Fatigue
|
2.7
|
1.9‒3.9
|
<0.001
|
2.1
|
1.4‒3.2
|
<0.001
|
|
No Weakness (ref)
|
1.0
|
|
|
1.0
|
|
|
|
Weakness
|
2.0
|
1.4‒2.7
|
<0.001
|
1.3
|
0.9‒1.9
|
0.143
|
|
No Slowness (ref)
|
1.0
|
|
|
1.0
|
|
|
|
Slowness
|
3.8
|
2.6‒5.6
|
<0.001
|
2.6
|
1.7‒4.0
|
<0.001
|
|
No Low physical activity (ref)
|
1.0
|
|
|
1.0
|
|
|
|
Low physical activity
|
1.5
|
1.1‒2.1
|
0.006
|
1,2
|
0.8‒1.8
|
0.190
|
|
Model B
|
|
|
|
|
|
|
|
Non-frailty (ref)
|
1.0
|
|
|
1.0
|
|
|
|
Prefrailty
|
1.9
|
1.2‒3.1
|
0.006
|
1.7
|
1.0‒2.8
|
0.033
|
|
Model C
|
|
|
|
|
|
|
|
Non-frailty (ref)
|
1.0
|
|
|
1.0
|
|
|
|
Frailty
|
5.0
|
3.0‒8.5
|
<0.001
|
4.3
|
2.4‒7.8
|
<0.001
|
p-values in bold: statistically significant. For each model (A, B, C), age and education
(continuous), gender (ref: male), setting (ref: rural), and ADL performance (ref:
independent) were controlling variables.
DISCUSSION
One third of the participants presented cognitive impairment, one quarter was frail,
and one half was prefrail. The analyses confirmed that frailty was strongly associated
with cognitive impairment and fatigue, and that slowness seemed to be the clinical
criteria associated with cognitive impairment.
The prevalence of cognitive impairment in the population-based SABE study in Brazil
was 7.9%[25]. In another study, the proportion of elderly people with some degree of cognitive
impairment was 13.6%[26]. Similar prevalence rates of frailty were found in other middle-income and low-income
countries. In studies conducted in Colombia, the prevalence of frailty was 12.2%[27],[28]. In Taiwan, the prevalence of frailty and prefrailty was 4.9% and around 40%, respectively[29]. A systematic review analyzing 19 studies held in Latin America found that the prevalence
of cognitive impairment ranged from 16 to 25%, and frailty was present in 10% of the
population[15]. The proportion of cognitive impairment in studies may vary due to the profile of
older adults in the sample, as well as the measures and cut-off points employed. In
the present study, most participants had less than five years of schooling and the
full version of MMSE was used. Moreover, clinical frailty criterion of low physical
activity was more prevalent, which can be explained by the demographics, characterized
as female and older, which are conditions associated with physical inactivity[30].
In a study involving 2,375 Chinese older adults, the estimated prevalence of frailty
with cognitive impairment was 1.8% and the estimated prevalence of prefrailty with
cognitive impairment was 8.9%[31]. Half of the population had completed high school and scored significantly higher
on the MMSE. Moreover, 61 participants were categorized with frailty, using the frailty
phenotype criteria, and the prevalence of cognitive frailty increased fivefold among
individuals aged 75 and older[32].
A Japanese study involving 4,207 participants found a 2.7% combined prevalence of
MCI and frailty (3% in women and 2.4% in men). This combination increased to 4.4%
among individuals with a low level of schooling. The regression analysis adjusted
by gender, age and education level showed that older adults with frailty had a 100%
increased chance of presenting MCI[33]. In a study involving Chilean older adults, individuals with frailty had 3.93 times
more chance of presenting MCI[34]. A study conducted in Brazil, with 51 prefrail and frail older adults used a similar
MMSE cut-off. Frailty was treated as the dependent variable and global cognition explained
up to 19% of the variation in the syndrome[35]. Furthermore, a longitudinal study demonstrated that 27.8% of non-frail individuals
will not experience cognitive decline, whereas only 2% of frail older adults improve
or stabilize their cognitive status[8].
Frailty criteria also seem to be associated with cognitive impairment. A longitudinal
survey involving 2,817 Japanese men showed that individual frailty factors were associated
with a 16 to 18% reduction in their global cognitive status[36]. In another study, slowness and physical exhaustion (fatigue) were associated with
a reduction in global cognition[37]. Slowness is the strongest frailty criterion associated to cognitive impairment,
and this association has been frequently seen in literature. Additionally, in this
study with 4,649 participants aged ≥50, prefrail individuals (n=1,444) had lower MMSE
scores than robust individuals (n=3,155), and frail individuals (n=90) had lower MMSE
scores compared to the other two groups[37]. A study involving 395 American older adults found than an increase in walking speed
was associated with a subsequent improvement in cognitive performance, especially
recalling[38]. This finding underscores the importance of measuring gait speed and other components
of frailty to identify older adults at risk of dysfunctional cognition and its determinants[39],[40].
In Brazil, the FIBRA study conducted in a low-income community used the same MMSE
cut-off as that used in the present study and found that weakness was associated with
global cognitive impairment, whereas slowness was specifically associated with a poorer
performance regarding verbal fluency and the clock drawing test[11].
Some studies suggest biological pathways that may occur in both cognitive impairment
and frailty. These mechanisms involve markers, such as sociodemographic clinical,
inflammatory/immunity, and laboratorial characteristics, as well as proteins, metabolism/oxidative
stress and genetics. Sociodemographic factors include advanced age, female gender,
widowhood, low formal education and financial income[5],[41]. Clinical factors, besides others, include cardiovascular conditions (diabetes,
dyslipidemias and hypertension), nutritional deficiencies (malnutrition and vitamin
D deficiency), functional dependence, hormonal dysregulation (reduction in testosterone
and insulin resistance), inflammation and neurotoxic accumulation of the protein beta-amyloid
in the brain, loss of neurons of the substantia nigra, symptoms of depression, use
of medications and other drugs, lifestyle and worse perception of health[5],[41].
The investigation of shared mechanisms in physiological conditions is a new field
of study, which limits hypothesizing the pathways of clinical frailty criteria and
the decline in cognitive functioning, despite the fact that the outcomes are known.
Both frailty and cognitive impairment are risk factors for future adverse outcomes,
such as dementia, disability, hospitalization and death. These outcomes have been
confirmed in Brazilian and non-Brazilian older adults[42],[43],[44],[45].
The major limitation of the present study was the non-evaluation of dementia, which
may affect the interpretation of results. The cross-sectional study design also limited
us from knowing causal effects. On the other hand, one of the strengths of the study
was the use of the MMSE with different cutoff points based on education level, which
lends credibility to the assessment of cognition among the participants.
In conclusion, older adults with frailty scored lower on the MMSE than those individuals
with prefrailty or robustness. Moreover, the prevalence of cognitive impairment, frailty
and prefrailty in the present sample is consistent with data reported in literature.
The frailty clinical criteria fatigue and slowness were associated to cognitive impairment;
slowness seems to be the strongest criteria associated with this condition. The present
findings contribute to the investigation of cognitive frailty in Brazil.
