Sarcopenic obesity and cognitive performance
Clinical Interventions in Aging
sarcopenic obesity and cognitive performance
Magdalena I Tolea 1 2
0 Christine e. l ynn College of n ursing, l ouis and Anne g reen Memory and Wellness Center, Florida Atlantic University , Boca raton, Fl, UsA , USA
1 Charles e. s chmidt College of Medicine, Department of Integrated Medical s ciences, Comprehensive Center for Brain h ealth, Florida Atlantic University , Boca raton, Fl, UsA , USA
2 James e galvin
PowerdbyTCPDF(ww.tcpdf.org) stephanie Chrisphonte 1 Background: Sarcopenia and obesity both negatively impact health including cognitive function. Their coexistence, however, can pose an even higher threat likely surpassing their individual effects. We assessed the relationship of sarcopenic obesity with performance on global- and subdomain-specific tests of cognition. Patients and methods: The study was a cross-sectional analysis of data from a series of community-based aging and memory studies. The sample consisted of a total of 353 participants with an average age of 69 years with a clinic visit and valid cognitive (eg, Montreal Cognitive Assessment, animal naming), functional (eg, grip strength, chair stands), and body composition (eg, muscle mass, body mass index, percent body fat) measurements. Results: Sarcopenic obesity was associated with the lowest performance on global cognition (Est.Definition1=−2.85±1.38, p=0.039), followed by sarcopenia (Est.Definition1=−1.88±0.79, p=0.017) and obesity (Est.Definition1=−1.10±0.81, p=0.175) adjusted for sociodemographic factors. The latter, however, did not differ significantly from the comparison group consisting of older adults with neither sarcopenia nor obesity. Subdomain-specific analyses revealed executive function (Est.Definition1=−1.22±0.46 for sarcopenic obesity; Est.Definition1=−0.76±0.26 for sarcopenia; Est.Definition1=−0.52±0.27 for obesity all at p0.05) and orientation (Est.Definition1=0.59±0.26 for sarcopenic obesity; Est.Definition1=−0.36±0.15 for sarcopenia; Est.Definition1=−0.29±0.15 all but obesity significant at p0.05) as the individual cognitive skills likely to be impacted. Potential age-specific and depression effects are discussed. Conclusion: Sarcopenia alone and in combination with sarcopenic obesity can be used in clinical practice as indicators of probable cognitive impairment. At-risk older adults may benefit from programs addressing loss of cognitive function by maintaining/improving strength and
sarcopenia; obesity; sarcopenic obesity; cognition; cross-sectional studies
open access to scientific and medical research
Changes in body composition including a shift toward higher fat mass and decreased
lean muscle mass (MM) represent a significant public health concern among older
adults as they may lead to various negative health outcomes including cardiovascular
and neurodegenerative diseases. Higher body mass index (BMI) is inversely related to
global cognition and subdomains such as executive function and processing speed.1,2
In addition, higher levels of Alzheimer’s disease (AD) pathology3 and structural
brain changes have been found in obese adults regardless of their cognitive status.4,5
Finally, an elevated risk of late-life dementia has been reported in middle-aged obese
individuals6 suggesting a cumulative effect throughout the lifespan.7 The findings
are fairly consistent across various measures of obesity.6,8 However, while obesity in
early adulthood and middle age may increase risk for dementia, later in life it may not
pose the same risk.9 The exact mechanisms linking obesity to cognitive dysfunction
Clinical Interventions in Aging 2018:13 1111–1119 1111
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are yet to be determined, although several pathways including
sedentary behavior, inflammation, and vascular damage have
Sarcopenia or age-related loss of MM and function may
represent another, perhaps even more important, predictor of
reduced cognition. In epidemiologic studies, sarcopenia has
been linked to global cognitive impairment and dysfunction
18 in specific cognitive skills including memory, speed, and
l-20 executive function.10–13 National Health and Nutrition
Exami-Ju2 nation Survey (NHANES) data confirm the link between
1on sarcopenia and cognitive dysfunction in older adults and
sug702 gest inflammation as a contributing factor at least in women.13
..64 Other proposed pathways include behavioral,14 oxidative,15
.957 and metabolic mechanisms.16,17 In addition, reduced lean
y3b mass is associated with brain atrophy in AD18 as well as in
/om cognitively normal older populations.19 Baseline sarcopenia
.css predicts cognitive decline20 and development of AD and mild
re cognitive impairment (MCI).21 Neuropsychiatric symptoms
.vdoepw l.syeon are also more prevalent once MCI is diagnosed.22,23
w u Given these individual effects, presence of combined
/sw laon sarcopenia and obesity captured by the concept of sarcopenic
h ep obesity is likely to have an even stronger impact on
from roF tion. This hypothesis found empirical support in a recent
ed cross-sectional analysis of NHANES data, in which higher
load waist girth was linked to poor cognition, followed by low
onw MM and finally by the presence of both phenotypes due in
ingd part to higher insulin resistance in the these groups compared
gA to healthy older adults.24 In light of evidence that loss of
isnn muscle strength (MS) may have a stronger impact on health
itno outcomes than loss of MM,25 the current study was designed
trvee to investigate the association between sarcopenic obesity
lIan and cognitive function using two sarcopenia definitions that
ilicn incorporate measures of muscle function in addition to MM,
C and also to assess this association in relation to global as well
as specific aspects of cognition. Based on prior reports, we
hypothesized sarcopenia alone or in combination with obesity
to predict poorer performance on global- and domain-specific
aspects of cognition, in particular executive function.
Patients and methods
Study participants were adults enrolled in research studies
between 02/2012 and 03/2015. A detailed description of
these studies has been published previously.11 Briefly,
community dwelling adults aged 40 years residing in the
local catchment area (Manhattan, Queens, and Brooklyn)
were recruited via collaborations with local community
partners, word-of-mouth, educational seminars, and from
an in-house research registry to enroll in cognitive and
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functional studies. The protocol, which was approved by
the New York University Institutional Review Board and
signed by each participant, was similar across all studies in
terms of physical and cognitive assessments and other
collection procedures. Exclusion criteria were age 40 years,
nonfluency in English or Spanish, and active psychiatric
and neurological conditions that could impact physical and/
or cognitive performance or could otherwise interfere with
participation. A total of 353 participants with data on body
composition, physical function, and cognition were included
in this cross-sectional analysis. All study participants
provided written informed consent.
Performance on the Montreal Cognitive Assessment (MoCA)
test was used to measure global cognitive function. MoCA
can detect impairment in older adults with a sensitivity of
90% for MCI and 100% for mild AD26 and also assesses
several cognitive domains commonly affected in early AD,
including short-term memory, visuospatial skills, executive
function, attention and concentration, language, and
orientation for a total of 30 points. Higher scores indicate better
performance and scoring 26 can be used to define cognitive
impairment in community samples. An additional point is
added for those with 12 years or less of formal education to
account for differences in education. In addition, scores for
individual items were used to measure specific cognitive
skills including visuospatial/executive function by Trail
Making B, verbal abstraction, and clock drawing; language
by naming, sentence repetition, and word fluency; and
attention measured by finger tapping to letter, serial subtraction,
and digits forward and backward. Orientation to time and
place was measured by six items and short-term memory by
delayed recall of five nouns.
The animal naming test, another dementia screening test,
was used as a second cognitive measure of verbal fluency and
mental flexibility. As part of this test, participants are asked
to name as many animals as they can think of in 60 seconds.
A score of 14 is indicative of cognitive impairment.27 Trail
Making A (TMA), a measure of visual search speed,
scanning, and processing speed, was also included as an additional
cognitive performance measure independent of the MoCA.
While the definition of sarcopenic obesity as the combination
of sarcopenia and obesity is well accepted, its measurement is
arbitrary due in part to the various ways the two components
are measured. The definition of sarcopenia evolved overtime
from reduced MM to later on to include reduced muscle
function. Baumgartner was among the first to define sarcopenia
within the former context using dual X-ray absorptiometry
(DXA).28 Using similar methods to derive MM, measures
that adjusted for body size followed.29 More easily accessible
methods to measure MM in the clinical setting including
bioelectrical impedance (BIA) were later proposed.30 More
recently, reduced muscle function measured as low MS31 or
low MS and/or poor physical performance were added.32,33
Sarcopenia was measured in our study with the short
portable sarcopenia measure (SPSM) proposed by Miller et al
and validated for use in older populations.33 SPSM follows
the current sarcopenia diagnosis guidelines and incorporates
measures of MM, MS, and physical performance. Body
composition was measured in our study by BIA with the BC-558
Ironman Segmental Body Composition Monitor (Tanita
Corporation, Arlington Heights, IL, USA). Handgrip strength
was measured by dynamometry (Baseline® Digital Smedley
Spring Dynamometer; Patterson Medical, Warrenville, IL,
USA) in both hands and the mean expressed as kg/m was
used in data analysis. Participants were also asked to stand
five times from a seated position as fast as possible and the
time in seconds was recorded. We used sex-specific cutoff
points to divide participants in quintiles of lean BMI, grip
strength, and chair stands. A summary score was derived
using a weighted scheme in accordance to Miller et al’s
algorithm and sarcopenia was defined as the bottom two quintiles
of this summary score.33 A second formula included low MM
and/or low MS using the same cutoff. This second definition
was based on the notion of strength as a measure of muscle
function. However, given that muscle weakness may have a
stronger negative health impact than reduced lean mass, we
allowed an independent contribution of low strength to the
definition of sarcopenia in our study.
Obesity has been traditionally defined as BMI 30 kg/m2,34
although other body composition measures such as fat mass,
percent body fat (PBF), waist circumference (WC), and
waist-to-hip ratio are often used to capture the concept of
obesity using various cutoff points including sample median,28
highest two quintiles,35 or sex-specific preset values (eg, WC
102 cm for men and 88 cm for women).34 Obesity was
measured in the current study not only as BMI 30 kg/m2
but also as the top two quintiles of PBF to offset inherited
limitations of BMI as a weight measure.
Using these different sarcopenia and obesity definitions,
we created four sarcopenic obesity indexes covering
various aspects of physical function and body composition to
measure results’ reliability and help discern the contribution
of individual components/measures (eg, overall vs visceral
obesity). The first definition combines the lowest two
quintiles of SPSM and BMI 30 kg/m2. The second combines the
lowest two quintiles of SPSM and the highest two quintiles of
PBF. The third defines sarcopenic obesity as the lowest two
quintiles of MM and/or MS and BMI 30 kg/m2. The last
definition uses the lowest two quintiles of MM and/or MS and
the highest two quintiles of PBF. For each of these different
definitions, a 4-value predictor variable was created: 0=no
obesity no sarcopenia group (controls); 1=obesity group (O);
2=sarcopenia group (S); 3=sarcopenic obesity group (SO).
The control group was used as the reference group.
Age and race (white: 68.3%; black: 24.7%; other: 7%) were
used as covariates. Sex (female: 65.4%) was not included
since sex-specific estimates of MM, MS, chair stands, and
PBF were used to compute our predictor measure. Depressive
symptomatology was measured with the Hospital Anxiety
and Depression Scale (HADS).36 A cutoff of 11 was used to
indicate presence of depression.
Differences in age- and race-adjusted MoCA means across
the four classes of our predictor were analyzed using analysis
of covariance. In general, generalized linear models (GLM)
and total MoCA and its five subdomains were regressed on
the predictor using the four different definitions adjusted for
age and race. These models were run on animal naming as
a second outcome measure. Also, to account for the impact
of depression on physical and cognitive performance,37–38
we re-analyzed the relationships of interest controlling for
depression. All analyses were performed using SAS 9.3
(SAS Institute Inc., Cary, NC, USA). A p0.05 was used
to evaluate statistical significance.
Participants were on average 69 years old (8% were
55 years), female, and white (Table 1). Body
composition, strength, and cognitive data were (all three) available
in 90% of participants. Of these, 42% had sarcopenia
according to Miller’s definition, whereas 62% had
sarcopenia by the second definition. Using BMI 30 kg/m2, 32%
of participants were found to be obese, whereas 42% were
in the top two quintiles of PBF. Using definition 1 for our
predictor yielded the most conservative SO prevalence:
14.3%, while the highest prevalence (37.2%) was found
using definition 2.
In GLM models controlled for age and race, SO status
was associated with MoCA in a dose–response pattern.
Compared to controls, all other groups performed poorer on
MoCA. However, the “risk” of poor cognitive performance
was lowest in the O and highest in the SO, whereas the
S group performed in the middle (Table 2). For example,
while the reduction in the MoCA score had the magnitude
of 1.10 in the O group, the effect was almost triple in the
SO group (ie, −2.85) with a decrease of 1.88 units in the
S group (Table 2; Definition 1 column). This finding was
consistent across all four SO definitions investigated in this
study. However, the effects are somewhat stronger when PBF
rather than BMI is used to define obesity. Adjusted mean
differences in MoCA among the four comparison groups are
presented graphically in Figure 1 for the two SO definitions
using BMI 30 kg/m2 as evidence of obesity.
We next investigated the effect on individual cognitive
subdomains. The results of the models based on the two SO
definitions using BMI 30 kg/m2 (ie, definitions 1 and 3)
are presented in Table 3. We found evidence of a significant
negative association with executive function and orientation,
where performance was poorest in SO (eg, Est.=−1.22±0.46,
p=0.009), followed by S (Est.=−0.76±0.26, p=0.005), and
finally by O (Est.=−0.52±0.27, p=0.060) when compared
to controls using definition 1. Definitions 2 and 4 yielded
similar results (data not shown).
To determine findings’ reliability, the analysis was
repeated for animal naming. As can be observed in Table 4,
presence of SO as measured by the two definitions based on
PBF (ie, definitions 2 and 4) was associated with the lowest
cognitive performance. While the direction of association
was consistent with the MoCA findings, neither of its two
components managed to reach statistical significance
independently of each other. By contrast, BMI 30 kg/m2 failed to
distinguish between the four groups (data not shown). The SO
group also showed the greatest degree of impairment across
all groups when assessing TMA as an outcome. For example,
using definition 2, we found the SO group to perform the
poorest on TMA (Est.=15.49±8.26, p=0.062), followed by
the S group (Est.=15.04±6.97, p=0.032) and finally by the
O group (Est.=14.16±6.11, p=0.021) compared to the control
group. A longer time taken to complete TMA indicates a
higher likelihood of impairment. While the SO group was
only marginally different from the control group as indicated
by the p level of 0.062, in analyses using MM/function as
the sarcopenia measure of choice, the SO group performed
significantly poorer than the control group (Est.=23.32±9.38,
p=0.014 for definition 3; Est.=17.33±8.35, p=0.039 for
definition 4), in line with the MoCA and animal naming findings.
Finally, the effect of our predictor was not significantly
impacted when models were adjusted for depression,
particularly those models predicting executive function.
However, some evidence of confounding/mediation was found
for MoCA (Est.O=−0.79±0.81, p=0.329; Est.S=−1.42±0.79,
p=0.08; Est.SO=−2.33±1.47, p=0.114 for definition 1) and
orientation (Est.O=−0.13±0.19, p=0.515; Est.S=−0.19±0.17,
p=0.273; Est.SO=−0.38±0.23, p=0.097 for definition 3) when
BMI 30 kg/m2 was used.
Using a cross-sectional design, we found consistent
evidence to link SO to poor global cognitive performance in
community-dwelling older adults. This effect is best captured
by its sarcopenia component with obesity likely having an
additive effect. This effect extends to specific cognitive skills,
in particular executive function and orientation.
Understanding the mechanisms through which this syndrome may affect
cognition is important as it may inform efforts to prevent
cognitive decline in later life by targeting at-risk groups with
an imbalance between lean and fat mass.
The less consistent independent effect of obesity is not
surprising. Although obesity has been linked to cognitive
deficits,2 structural brain changes,4,5 and AD pathology,3 its
impact may be limited to earlier stages of adulthood,9 with
inconsistent reports in samples of older adults. While some
found obesity to be related to impaired cognitive function39
and cognitive decline,40 other reports showed improved
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cognitive performance41 and lower decline42 rate in obese
older adults. The choice of obesity measures may at least in
part explain these differential effects.9 In our study, when
measured by BMI 30 kg/m2, no effect of obesity
independent of that of sarcopenia was detected. However, when
measured using PBF, obesity was significantly associated
with reduced global cognitive performance in one
opera.vdoepw l.syeon t(iioe,nadleifziantiitoionnm4e).thAoldso(,ier,egdaerfdinleitsisonof2m)ebaustunreomtienntthmeeoththoedr,
/sw laon obesity was significantly associated with lower executive
tthp rsep function but not with other cognitive subdomains. To further
from roF test the hypothesis that the effect of obesity may be limited
ed to younger ages, we split our sample into age 65 years vs
laod age 65 years. Although an independent effect of obesity
now remained nonsignificant for both age groups, when combined
idgn with sarcopenia obesity was consistently linked to poorer
gA cognitive performance in the younger group, while in older
isnn adults, that held for some (eg, Est.SO=−0.95±0.39, p=0.018
itno for executive function and Est.SO=−2.71±1.13, p=0.017 for
trvee MoCA using definition 2; similar results found for
definilIna tion 4) but not all cognitive outcomes (ie, Est.SO=−2.39±1.58,
iilcn p=0.131 for MoCA and Est.SO=−0.89±0.62, p=0.150 for
C executive function using definitions 1 and 3). Given these
inconsistent findings in previous work as well as ours, it is
possible that in older age, the effect of being overweight/
obese is offset by the strong impact of other risk factors
including sarcopenia on cognition. Alternatively,
maintenance of higher fat mass may be protective to the aging brain,
guarding off against AD.22 However, our results suggest that
efforts to maintain a healthy weight should be made even in
later life as obesity may enhance the detrimental effect of
reduced MM and function.
The impact of sarcopenia on global cognitive
performance was consistent in our study, retaining its significance
whether defined based on Miller’s SPSM or as low MM
and/or MS. This finding is in line with previous reports,
which have consistently linked sarcopenia to poor cognitive
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function.11,18,20,21 For example, in a case–control study
comparing early AD patients with cognitively normal controls
in terms of lean mass, cognitive performance, and brain
volume, reduced lean mass was reported in the AD group
and associated with poor cognitive performance and brain
atrophy.18 In large epidemiologic studies of functional and
cognitive aging, a protective role of high handgrip strength
on cognitive health has been reported,20 slowing the rate of
cognitive decline and decreasing the risk of AD.21 Studies
using the newer sarcopenia diagnosis guidelines also report
a strong association with cognitive impairment.10 Although
an independent effect of sarcopenia was noted in
relation to MoCA, we failed to find a significant effect when
animal naming was investigated. It is possible that in the
absence of obesity, the cognitive impact of sarcopenia is
limited to global cognition and certain subdomains such as
executive function and speed of processing12,13 rather than
Supporting the results of the one study reporting on the
association between SO and cognitive functioning that we
are aware of,24 we found that the risk of poor global
cognition is even higher in older adults who present with low MM
and strength in the context of high fat mass. This is line with
dose–response associations with other negative health
outcomes including all-cause mortality.43 However, we found
a less consistent effect for obesity alone when sarcopenia is
not present. This differential finding as it relates to global
cognitive performance may stem from several differences
in methodologic and analytical approaches. While we used
high BMI and PBF to define obesity in our study, Levine
and Crimmins used WC, which may be a better measure of
visceral adiposity24 and therefore more likely to be associated
with negative health outcomes than other obesity measures,
particularly BMI.45 Another difference relates to the study
population, our participants being slightly younger (25%
were 76+ years), which may have diluted the effects at least
Further, our study contributes to the current body of
knowledge by identifying specific cognitive abilities that may
be impaired in individuals with evidence of SO. Executive
function is reduced in obese older adults,9 and improvement in
muscular function has been linked to enhancement of
executive function in senior adults.46 In our study, obesity and
sarcopenia were associated with lower executive function when
assessed independently and even more so when they occurred
together. Obesity may contribute to the risk of impaired
executive function through vascular, behavioral, metabolic,
and inflammatory mechanisms or can result from reduced
impulse control, self-monitoring, and goal-directed
behavior in individuals with impaired executive function47 with
a negative effect on the ability to maintain energy balance.2
Sarcopenia, in turn, has been linked to impairments in
abilities that relate to conflict resolution and selective attention.46
Other cognitive skills may be impaired in sarcopenic older
adults whether or not in the presence of obesity including
orientation to time and space. How and in what direction this
latter association may operate should be further investigated.
Impaired ability to place oneself in time and space can lead
to loss of independence and therefore identifying modifiable
risk factors can help this at-risk group age in place.
Several interrelated mechanisms may explain the
obesitysarcopenia-cognitive dysfunction link including decreased
participation in physical activity, low-grade chronic
inflammation, oxidative stress, and insulin resistance,48 all of
which being by-products of the aging process. Our study
was not designed to investigate potential mediators of the
SO-cognitive impairment association due to data
unavailability, although there is empirical support for the metabolic
pathways.24 These and other proposed mechanisms should
be continued to be investigated in the quest of finding the
most effective interventions to delay or prevent cognitive
impairment, muscle impairment, and/or adiposity.
Study limitations relate to the cross-sectional design, the
population studied, as well as other methodological issues.
Although we observed a consistent association between
sarcopenia with or without obesity in our study, due to the
cross-sectional nature of our study, we were unable to
investigate the direction of this association. Prospective studies
will be instrumental in assessing the predictive role of SO
on rate of cognitive decline and/or development of cognitive
impairment and dementia in older adults. Also, our body
composition measures may have led to an underestimation
of obesity. For example, PBF has been found to be
underestimated when using BIA as compared to DXA absorptiometry
in obese children.49 However, a high correlation between
these two methods of measuring body composition was
reported in middle-aged adults.33 In addition, depression,
which can have a negative impact on performance testing,
may confound the observed effects. However, a sensitivity
analysis restricted to participants with low levels of
depression (eg, HADS-D 11) yielded similar results especially
when PBF was used to measure obesity (Est.=−3.08±1.01,
p=0.003 and Est.=−3.30±1.03, p=0.002 SO vs controls when
Miller’s SPSM and low MM and/or strength, respectively,
were used). Finally, some concern may arise from the
investigation of global- and domain-specific cognitive outcomes
derived from one cognitive measure (ie, MoCA). We sought
to avoid this potential limitation by incorporating two
cognitive measures that were independent of the MoCA: animal
naming and TMA, with similar results. The screening nature
of the study under investigation precluded the inclusion of
a detailed neuropsychological battery to assess cognitive
function. The consistency of our results that held across
different SO definitions and cognitive function measures,
along with the relatively large sample of a racially diverse
population, and the use of established measures are positive
indicators that these reported associations are not due to
Sarcopenia, either alone or in the presence of obesity, can be
used in clinical practice to estimate potential risk of cognitive
impairment. BIA and grip strength by dynamometry can be
easily administered within the time constraints of a clinic visit,
and BMI is already usually collected as part of Annual Wellness
visits. Further research is needed to determine whether SO is a
simple correlate of cognitive performance or also has a role in the
processes that lead to cognitive loss and dementia. Sarcopenic
obese older adults may benefit from interventions designed to
lower the risk of cognitive loss by improving/countering the
age-related imbalance between MM and function.
Data collection and analysis were supported by grants
awarded to JEG by the National Institutes of Health
(R01 AG040211 and P30 AG008051), the Morris and Alma
Schapiro Fund, and the New York State Department of Health
All authors contributed toward data analysis, drafting and
critically revising the paper and agree to be accountable for
all aspects of the work.
The authors report no conflicts of interest in this work.
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