Vol.:(0123456789)1 3
Aging Clinical and Experimental Research (2020) 32:1085–1092
https://doi.org/10.1007/s40520-019-01295-3
ORIGINAL ARTICLE
Malnutrition is associated with dynamic physical performance
Keenan A. Ramsey1
 · Carel G. M. Meskers1,2
 · Marijke C. Trappenburg3,4
 · Sjors Verlaan4
 · Esmee M. Reijnierse5
 ·
Anna C. Whittaker6
 · Andrea B. Maier1,5
 
Received: 28 May 2019 / Accepted: 25 July 2019 / Published online: 19 August 2019
© The Author(s) 2019
Abstract
Background  Malnutrition and poor physical performance are both conditions that increase in prevalence with age; however,
their interrelation in a clinically relevant population has not been thoroughly studied.
Aims  This study aimed to determine the strength of the association between malnutrition and measures of both static and
dynamic physical performance in a cohort of geriatric outpatients.
Methods  This cross-sectional study included 286 older adults (mean age 81.8, SD 7.2 years, and 40.6% male) who were
referred to geriatric outpatient mobility clinics. The presence of malnutrition was determined using the Short Nutritional
Assessment Questionnaire (SNAQ, cut-off ≥ 2 points). Measures of dynamic physical performance included timed up and
go (TUG), 4-m walk test, and chair stand test (CST). Static performance encompassed balance tests and hand grip strength
(HGS). Physical performance was standardized into sex-specific Z-scores. The association between malnutrition and each
individual measure of physical performance was assessed using linear regression analysis.
Results  19.9% of the cohort was identified as malnourished. Malnutrition was most strongly associated with CST and gait
speed; less strong but significant associations were found between malnutrition and TUG. There was no significant association
between malnutrition and HGS or balance.
Discussion  Physical performance was associated with malnutrition, specifically, dynamic rather than static measures. This
may reflect muscle power being more impacted by nutritional status than muscle strength; however, this needs to be further
addressed.
Conclusions  Malnutrition is associated with dynamic physical performance in geriatric outpatients, which should inform
diagnosis and treatment/prevention strategies.
Keywords  Malnutrition · Physical performance · Community dwelling · Aged · Older adults
Introduction
Malnutrition and poor physical performance are two highly
prevalent conditions in older adults that are associated with
poor health outcomes, such as higher morbidity, mortality,
and lower quality of life [1–4]. Both conditions may be
highly interrelated and may potentiate each other.
Muscle is a key linking substrate between malnutrition
and physical performance. Decreased muscle mass has been
found to be an outcome of malnutrition [5] and decreased
*	 Andrea B. Maier
	andrea.maier@mh.org.au
1
	 Department of Human Movement Sciences, @
AgeAmsterdam, Amsterdam Movement Sciences, Vrije
Universiteit Amsterdam, Amsterdam, The Netherlands
2
	 Department of Rehabilitation Medicine, Amsterdam
Movement Sciences, VU University Medical Center,
Amsterdam UMC, Amsterdam, The Netherlands
3
	 Department of Internal Medicine, Section of Gerontology
and Geriatrics, VU University Medical Center, Amsterdam
UMC, Amsterdam, The Netherlands
4
	 Department of Internal Medicine, Amstelland Hospital,
Amstelveen, The Netherlands
5
	 Department of Medicine and Aged Care, @AgeMelbourne,
The Royal Melbourne Hospital, University of Melbourne,
Melbourne Health, City Campus, Level 6 North, 300 Grattan
Street, Parkville, VIC 3050, Australia
6
	 School of Sport, Exercise and Rehabilitation Sciences,
University of Birmingham, Birmingham, UK
1086	 Aging Clinical and Experimental Research (2020) 32:1085–1092
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muscle power has been found to be a predictor of physical
performance [6]. A recent meta-analysis [7] found evidence
for a positive association between malnutrition and physical
frailty in community dwelling adults. Physical frailty, as
defined by Fried et al. [8], is a composite measure including
two physical performance measures. The associations
between malnutrition and individual measures of physical
performance may be important to understand.
Malnutrition has been defined “a state resulting from
lack of uptake or intake of nutrition that leads to altered
body composition (decreased fat free mass) and body cell
mass leading to diminished physical and mental function
and impaired clinical outcome from disease” and, in this
definition, is synonymous with undernutrition [9]. In a
chronic malnourished state, adipose tissue is used as the
body’s primary energy source in efforts to maximize muscle
preservation [10]. However, amino acids from muscle can
be required to provide 10% of energy needs for glycolytic
tissues and the brain, which leads to muscle protein catabolism
[11, 12]. Malnutrition is marked by muscle atrophy
and overall decline in body muscle mass [10, 11]. Skeletal
muscle constitutes the majority of protein-rich lean body
mass and its atrophy may impede its functions including
muscle strength [13], which is demonstrated by hand grip
strength (HGS) and muscle power [6], which may be more
reflected by dynamic physical performance tests: chair stand
test (CST), gait speed, and timed up and go (TUG). Major
organs, such as the heart and lungs, are not spared from
being sources of energy and broken down by muscle protein
catabolism, which can negatively impact cardiovascular fitness
[11, 14, 15] and would likely diminish the capacity for
dynamic physical performance. However, preservation of the
brain may fuel neural compensation in performing cognitive
aspects of physical performance tasks, such as maintaining
standing balance and turning around in TUG, and limit the
negative effects of malnutrition in performing these tests.
This cross-sectional inception cohort study aimed to
determine the strength of association between common clinically
used measures of malnutrition and measures reflecting
both dynamic and static physical performance in a population
of geriatric outpatients referred to a mobility clinic.
Methods
Study design
Older adults from two Dutch inception cohorts were
included: the Bronovo cohort and the Center of Geriatrics
Amsterdam (COGA) cohort. The Bronovo cohort included
community-dwelling older adults referred to the geriatric
outpatient mobility clinic (The Hague, The Netherlands)
from March 2011 to January 2012. The COGA cohort
included patients referred to the geriatric outpatient mobility
clinic (Amsterdam, The Netherlands) from January 2014
to December 2015. Individuals were included if they completed
a nutritional assessment questionnaire (Short Nutritional
Assessment Questionnaire or Mini Nutritional Assessment)
and completed at least one out of the five continuously
measured physical performance tests (4-m walk test, CST,
SPPB, TUG, or HGS). All patients underwent a comprehensive
geriatric assessment (CGA). Patient characteristics were
derived by questionnaires or obtained from medical records.
Questionnaires and measurements were performed as part
of CGA within routine care. The protocol and study design
of the Bronovo cohort [16] and the COGA cohort [17] have
been described extensively elsewhere.
Each study has been approved by the local ethical committees
and has been performed in accordance with the ethical
standards laid down in the 1964 Declaration of Helsinki.
The need for individual informed consent was waived as the
research was based on regular patient care.
Measures of malnutrition
The presence of malnutrition was determined in the Bronovo
cohort using the Short Nutritional Assessment Questionnaire
(SNAQ) score, including questions about unintentional
weight loss, decrease in appetite, and the use of supplemental
drinks or tube feeding over the last month and has been
further described elsewhere [18]. SNAQ scores range from
0 to 7 points with a score of 0–1 points is indicative of adequate
nutrition; a score of 2 points indicating moderate risk
of malnutrition; and a score ≥ 3 points indicating severe risk
of malnutrition. In the COGA cohort, the Mini Nutritional
Assessment (MNA) [19] was used to screen for malnutrition.
The MNA contains questions that can be used to calculate
an adapted SNAQ score, by adding the points from
the first two questions pertaining to unintentional weight
loss and decreased appetite, which are similar amongst the
two screening tools and excluding the question pertaining to
supplemental drinks or tube feeding. This calculation was
done in the COGA to convert the MNA to an adapted SNAQ
score with the same cut-off values as above mentioned being
applied to define malnutrition. A cut-off score of 2 points
or greater was used to determine the presence or absence of
malnutrition from the SNAQ score or the adapted SNAQ
score.
Physical performance measures
Measures of dynamic physical performance included 4-m
walk test from which gait speed can be obtained and chair
stand test (CST), as sub-tests of the Short Physical Performance
Battery (SPPB) [20] as well as the timed up and
go (TUG). The TUG is a dynamic test where participants
1087Aging Clinical and Experimental Research (2020) 32:1085–1092	
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are asked to stand up from a seated position without using
their hands, walk to a cone 3 m away, walk around the cone,
walk 3 m back, and return to the sitting position without
using their hands, as quickly as possible. The amount of
time needed to complete all of these tasks was measured in
seconds [21].
Measures of static physical performance encompassed the
third subtest of the SPPB, i.e., balance tests (side by side,
semi-tandem, and tandem) [20] as well as hand grip strength
(HGS). HGS represents an individual’s ability to squeeze a
handheld dynamometer as hard as possible with each hand
three times. The maximal HGS was recorded in kilograms
(kg) and used for analysis [22].
Statistical analysis
Descriptive statistics for continuous variables were presented
as mean and standard deviation (SD) when data were
distributed normally or as median interquartile range [IQR]
if the data had a skewed distribution. Dichotomous variables
were reported by the sample size/number (n) and the
percentage (%).
Prevalence of malnutrition according to the SNAQ score
and the adapted SNAQ score were calculated in the Bronovo
and COGA cohort, respectively, to test the validity of the
adapted SNAQ score and provide evidence that the removal
of the “supplemental drinks or tube feeding” question would
not have an impact on the results.
For each measure of physical performance, respectively,
individuals were included if they were able to perform
the test. Continuous physical performance measures were
standardized into sex-specific Z-scores to allow for direct
comparison of effect sizes of malnutrition with physical performance
tests. Variables with a skewed distribution were
log transformed prior to making Z-scores. Linear regression
analysis was used to study the association between malnutrition
with both dynamic and static measures of physical performance.
Balance tests were dichotomized into two groups,
unable to maintain for 10 s and able to maintain for 10 s, and
were included in the analysis to determine the presence of
an association using a binary logistic regression. Results for
the linear regression analysis are presented as beta, or for the
logistic regression as odds ratios (OR), and 95% confidence
intervals with p values. Analyses were performed unadjusted
(crude model) and adjusted for age, sex, and the presence
of multimorbidity. Interpretation of the results of this linear
regression analysis with standardized outcome measures was
as follows: the effect size represents the average difference
in effect of the presence of malnutrition on the physical performance
test compared to the absence of malnutrition in
standard deviations.
All statistical analyses were conducted using SPSS (Statistical
Package for the Social Sciences), version 24.0 (SPSS
Inc. Chicago, IL, USA). A p value of less than 0.05 was
considered statistically significant. Visualization of results
was performed using GraphPad Prism 5.01.
Results
Participant characteristics
This study included 286 geriatric outpatients, with a mean
age of 81.8 years (SD 7.2). Characteristics of the outpatients
are shown in Table 1. Multimorbidity was present in 39.5%
(n = 107) of the outpatients. Mean body mass index (BMI)
was 25.4 kg/m2
(SD 4.3). The prevalence of malnutrition
was 19.9%.
Association between malnutrition and static
and dynamic measures of physical performance
Table 2 shows the associations between malnutrition and the
standardized measures of physical performance. Malnutrition
associated with the composite SPPB score as well as all
dynamic measures of physical performance. Malnourished
patients had a 0.53 SDs longer time to complete the CST,
0.49 SDs slower gait speed, 0.37 SDs longer time to complete
the TUG, and a 0.40 SDs worse score SPPB score. No
statistically significant association between malnutrition and
static measures of physical performance, i.e., HGS (Table 2)
and balance performance (Table 3) was found. Comparing
effect sizes, as shown in Fig. 1, the strongest association with
the presence of malnutrition was with gait speed and, after
adjustment, CST.
Discussion
In a cohort of geriatric outpatients, malnutrition as defined
by SNAQ score was most strongly associated with CST and
gait speed; less strong associations were found between malnutrition
and TUG and the compound SPPB score. There
was no significant association between HGS or balance.
Previous studies have classified physical performance into
two groups: dynamic physical performance (CST, gait speed,
and TUG) and static physical performance (standing balance
tests and HGS) [23]. In the present study, dynamic physical
performance was associated with malnutrition in contrast to
static measures, which did not show a significant association.
In considering the predominant physiological components
of the physical performance measures, it can be argued
that dynamic physical performance requires muscle power,
while static physical performance (HGS) relies on muscle
strength. Previous studies have shown that when assessing
functional decline, it is important to acknowledge not only
1088	 Aging Clinical and Experimental Research (2020) 32:1085–1092
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muscle strength (the ability to generate maximal muscle
force) but also muscle power (the product of the force and
velocity of muscle contraction) [6]. Muscle power has been
shown to decline more rapidly with increasing age than
strength, and to be a more discriminant predictor of functional
performance than muscle strength [6]. As malnutrition
Table 1  Characteristics of geriatric outpatients referred to mobility clinics
All variables are presented as n (%) unless indicated otherwise. SD, standard deviation; IQR, interquartile ratio; BMI, body mass index; SNAQ,
short mini nutritional assessment questionnaire; HGS, hand grip strength; CST, chair stand test; TUG, timed up and go; SPPB, short physical
performance battery
a
 Multimorbidity was defined as two or more diseases
b
 Malnutrition was determined from the SNAQ score using a cut-off of ≥ 2
c
 Balance tests were dichotomized into unable (0) and able (1) to maintain for 10 s
Measure Total Bronovo COGA
n = 286 Value n = 184 Value n = 102 Value
Age, mean (SD) 286 81.8 (7.4) 184 82.0 (7.3) 102 81.4 (7.6)
Male 286 116 (40.6) 184 74 (40.2) 102 42 (41.2)
Married 284 104 (36.6) 182 71 (39.0) 95 33 (34.7)
Alcohol (> 1 glass/week) 277 129 (46.6) 184 85 (46.2) 93 51 (49.5)
BMI (kg/m2
), mean (SD) 268 25.6 (4.3) 170 25.7 (4.4) 98 25.4 (4.1)
Number of Medications, mean (SD) 273 5.8 (3.4) 179 5.3 (3.2) 94 7.0 (3.5)
Multimorbiditya
271 107 (39.5) 176 67 (38.1) 95 40 (42.1)
MMSE, median [IQR] 283 27.0 [25.0–29.0] 184 27.0 [24.0–29.0] 101 28.0 [26.0–29.0]
SNAQ score, median [IQR] 286 0.0 [0.0–1.0] 184 0.0 [0.0–1.0] 102 0.0 [0.0–2.0]
Unintentional weight loss 286 54 (18.8) 184 23 (12.5) 102 29 (28.4)
Decreased appetite 286 80 (27.9) 184 51 (27.7) 102 31 (30.4)
Malnutritionb
286 57 (19.9) 184 23 (12.5) 102 29 (28.4)
HGS (kg), mean (SD) 279 24.2 (9.0) 181 26.1 (8.4) 98 20.8 (8.9)
 Male 114 31.3 (8.0) 73 33.9 (6.1) 41 26.7 (8.8)
 Female 165 19.3 (5.8) 108 20.8 (5.0) 57 18.0 [13.0–20.0]
Gait speed (m/s), mean (SD) 268 0.8 (0.3) 174 0.8 (0.3) 94 0.8 (0.3)
CST time (s), median [IQR] 229 14.9 [11.7–20.0] 149 15.8 [11.6–20.1] 80 14.8 [12.1–20.0]
TUG (s), median [IQR] 235 16.1 [12.3–22.2] 160 15.8 [11.8–21.9] 75 16.5 [12.9–23.1]
SPPB score, mean (SD) 272 7.1 (3.3) 179 7.0 (3.4) 97 8.0 [5.0–10.0]
Balance: side by ­sidec
272 249 (91.5) 175 161 (92.0) 97 88 (90.7)
Balance: semi-tandemc
271 215 (79.3) 175 143 (81.7) 96 72 (75.0)
Balance: ­tandemc
268 96 (35.8) 175 98 (56.0) 93 36 (38.7)
Table 2  The association between malnutrition and standardized measures of physical performance in geriatric outpatients referred to mobility
clinics (n = 286)
*Statistically significant results
SPPB, short physical performance battery; LN, natural log; CST, chair stand test; TUG, timed up and go; HGS, hand grip strength; β, beta; OR,
odds ratio; CI, confidence interval
All continuous measures of physical performance were standardized and presented as sex-specific Z-scores. Variables with a skewed distribution
were log transformed prior to making Z-scores
Z SPPB Score Z LN CST Z Gait speed Z LN TUG​ Z HGS
Crude
 β (95% CI) − 0.42 (− 0.74, − 0.11)* 0.52 (0.18, 0.86)* − 0.56 (− 0.86, − 0.25)* 0.37 (0.03, 0.72)* − 0.27 (− 0.58, 0.04)
 p value 0.008 0.003 0.000 0.034 0.083
Model adjusted for age, sex, and multimorbidity
 β (95% CI) − 0.40 (− 0.70, − 0.10)* 0.53 (0.19, 0.87)* − 0.49 (− 0.78, − 0.20)* 0.37 (0.03, 0.70)* − 0.24 (− 0.54, 0.07)
 p value 0.009 0.003 0.001 0.032 0.131
1089Aging Clinical and Experimental Research (2020) 32:1085–1092	
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(undernutrition) creates a negative energy balance and is
associated with decreased energy expenditure [24], a malnourished
individual’s ability to supply the rapid burst of
energy required to exert a muscle power may be diminished.
Therefore, results may provide evidence for further investigation
into the impact of malnutrition on discrete measures
of muscle power as the physical performance tests in the
current study involve multiple systems and muscle functions.
Our study confirms the previously reported associations
between malnutrition and CST, gait speed, TUG, and SPPB
Score [25–32], with the added novel component of comparing
the effects of the associations. We expected that malnutrition
would show the strongest association with HGS as a
direct measure of muscle function, i.e., strength. However,
there is debate regarding the relationship between HGS and
nutritional status. Some studies have advocated for the added
value of HGS in nutritional assessment [33, 34], while others,
including the current study, provide evidence that HGS
may be of limited use as a predictor of nutritional status
[35–37]. A recent study showed that HGS and knee extension
strength (KES) have moderate-to-low agreement, indicating
that HGS should not be used to represent overall muscle
strength [38]. Furthermore malnutrition has been shown
to have a greater negative effect on KES, in comparison to
HGS [39]. These findings may further provide evidence that
upper body and lower body strength may be impacted by
nutritional status differently.
As hypothesized, none of three balance tests were associated
with malnutrition. A previous study showed that muscle
strength, measured by HGS and KES, rather than muscle
mass, were positively associated with ability to maintain
standing balance [40]. This result that balance was most
dependent on muscle strength may provide additional context
for the results present study as HGS, muscle strength,
was also not associated malnutrition.
There is no gold standard for the diagnosis, screening,
or definition of malnutrition, so the results may be highly
dependent on the measure used to define malnutrition. The
SNAQ, which was used in this study, adopts a broad definition
of malnutrition that specifically reflects undernutrition
by emphasis on unintentional weight loss and loss
of appetite [9, 18, 41]. The SNAQ is often used in outpatient
settings due to its practical ease, and has moderate
sensitivity and high specificity in the current population
with respect to diagnostic accuracy [41, 42]. However,
previous studies have found the use of different nutritional
screening tools which can give different prevalence estimates
of malnutrition in the same cohort and different
Table 3  The association between malnutrition and balance in geriatric
outpatients referred to mobility clinics (n = 286)
OR, odds ratio; CI, confidence interval
a
 Balance tests were dichotomized into unable to maintain for 10 s (0)
and able to maintain for 10 s (1)
Balance ­testsa
Side by side Semi-tandem Tandem
Crude
 OR (95% CI) 0.66 (0.25,
1.76)
0.67 (0.33,
1.34)
0.97 (0.51, 1.80)
 p value 0.406 0.251 0.913
Model adjusted for age, sex, and multimorbidity
 OR (95% CI) 0.69 (0.23,
2.02)
0.67 (0.31,
1.43)
1.02 (0.51, 2.04)
 p value 0.497 0.294 0.957
Fig. 1  The association between
malnutrition and standardized
measures of physical
performance stratified by type
(dynamic vs. static) in geriatric
outpatients adjusted for age,
sex, and multimorbidity. Bars
represent the difference of each
physical performance test in
outpatients with the presence
of malnutrition compared to
those without. Lower Z-score
represents worse physical
performance. Asterisks indicate
significance at 0.05 level
(* = p < 0.05). SE, standard
error; CST, chair stand test;
TUG, timed up and go; HGS,
hand grip strength; SPPB, short
physical performance battery
1090	 Aging Clinical and Experimental Research (2020) 32:1085–1092
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validities [43, 44]. While the unintentional weight loss
and decreased appetite questions in the SNAQ and MNA
are similar, they are structured differently, which may have
caused a difference between SNAQ and adapted SNAQ.
Furthermore, because the SNAQ screens for malnutrition
rather than diagnosing it, this study uses the terms “malnutrition”
and “risk of malnutrition” interchangeably, as
is often the case in previous studies.
To our knowledge, this is the first study to compare the
effects of the association of malnutrition with different
measures of physical performance in geriatric outpatients.
With the methods used in this cross-sectional study, it is
difficult to distinguish the underlying systems involved
in physical performance that may have been affected by
malnutrition because of mutual interaction. Future studies
should aim to identify these systems as well as factors that
may influence the relationship between malnutrition and
physical performance. There is clinical need to expand on
these findings and further characterize the interrelation
between the nutritional and physical state in older adults.
This study shows that physical performance measures
have individual value in relation to malnutrition and
should not be used on their own interchangeably to represent
physical performance; the present results suggest that
focusing on measures of dynamic physical performance
and muscle power would be most informative in terms of
reviewing the physical impact of nutritional status. Furthermore,
future studies should distinguish and examine
the association between malnutrition different outcomes
related to muscle function, including muscle power, muscle
strength, and muscle mass.
Acknowledgements  We thank Prof. Dr. Gerard Jan Blauw for his
contribution in collecting the data. This project has received funding
from the European Union’s Horizon 2020 research and innovation
programme under the Marie Sklodowska-Curie Grant Agreement No.
675003. http://www.birmi​ngham​.ac.uk/panin​i.
Author contributions  All authors meet the criteria for authorship stated
in the Uniform Requirements for Manuscripts submitted to Biomedical
Journals. Please see the following detail of the authors’ contributions:
Study concept and design: KAR, CGMM, MCT, ACW, and ABM.
Acquisition of data: EMR, CGMM, and ABM. Data analysis: KAR.
Contributed to data analysis: CGMM, MCT, ACW, and ABM. Interpretation
of data: KAR, CGMM, MCT, and ACW, ABM. Drafting of
the manuscript: KAR. Critical revision of the manuscript for important
intellectual content: CGMM, MCT, SV, ACW, and ABM. All authors
read and approved the final manuscript.
Funding  This study was supported by the PANINI programme (Horizon
2020, Marie Curie, Sklodowska, Innovative Training Network, No.
675003) and PreventIT (European Union’s Horizon 2020 Research and
Innovative Programme, No. 689238). The funders had no role in the
study design, data collection and analysis, interpretation of data, or
preparation of the manuscript.
Data availability  The data sets generated during and/or analysed during
the current study are available from the corresponding author on
reasonable request.
Compliance with ethical standards 
Conflict of interest  The authors have no conflicts of interest to declare.
Human and animal rights  This study has been approved by the local
ethical committees and has been performed in accordance with the
ethical standards laid down in the 1964 Declaration of Helsinki.
Informed consent  The need for individual informed consent was
waived as the research was based on regular patient care.
Open Access  This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://creat​iveco​
mmons​.org/licen​ses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate
credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
References
	 1.	 Saka B, Kaya O, Ozturk GB, Erten N, Karan MA (2010) Malnutrition
in the elderly and its relationship with other geriatric
syndromes. Clin Nutr 29:745–748. https​://doi.org/10.1016/j.
clnu.2010.04.006
	 2.	 Samson M (2000) Relationships between physical performance
measures, age, height and body weight in healthy adults. Age
Ageing 29:235–242. https​://doi.org/10.1093/agein​g/29.3.235
	 3.	 Chen CCH, Schilling LS, Lyder CH (2001) A concept analysis of
malnutrition in the elderly. J Adv Nurs 36:131–142. https​://doi.
org/10.1046/j.1365-2648.2001.01950​.x
	 4.	 Kubrak C, Jensen L (2007) Malnutrition in acute care patients: a
narrative review. Int J Nurs Stud 44:1036–1054
	 5.	 Jensen GL, Cederholm T, Correia MITD, Gonzalez MC, Fukushima
R, Higashiguchi T, de Baptista GA, Barazzoni R, Blaauw
R, Coats AJS, Crivelli A, Evans DC, Gramlich L, Fuchs-Tarlovsky
V, Keller H, Llido L, Malone A, Mogensen KM, Morley JE, Muscaritoli
M, Nyulasi I, Pirlich M, Pisprasert V, de van der Schueren
M, Siltharm S, Singer P, Tappenden KA, Velasco N, Waitzberg
DL, Yamwong P, Yu J, Compher C, Van Gossum A (2018) GLIM
criteria for the diagnosis of malnutrition: a consensus report from
the global clinical nutrition community. J Parenter Enter Nutr
43:32–40
	 6.	 Reid KF, Fielding RA (2012) Skeletal muscle power: a critical
determinant of physical functioning in older adults. Exerc Sport
Sci Rev 40:4–12. https​://doi.org/10.1097/JES.0b013​e3182​3b5f1​3
	 7.	 Verlaan S, Ligthart-Melis GC, Wijers SLJ, Cederholm T, Maier
AB, de van der Schueren MAE (2017) High prevalence of
physical frailty among community-dwelling malnourished older
adults—a systematic review and meta-analysis. J Am Med Dir
Assoc 18:374–382
	 8.	 Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener
J, Seeman T, Tracy R, Kop WJ, Burke G, McBurnie
MA (2001) Frailty in older adults: evidence for a phenotype. J
Gerontol Ser A Biol Sci Med Sci 56:M146–M156. https​://doi.
org/10.1093/geron​a/56.3.M146
	 9.	 Cederholm T, Barazzoni R, Austin P, Ballmer P, Biolo G, Bischoff
SC, Compher C, Correia I, Higashiguchi T, Holst M, Jensen GL,
1091Aging Clinical and Experimental Research (2020) 32:1085–1092	
1 3
Malone A, Muscaritoli M, Nyulasi I, Pirlich M, Rothenberg E,
Schindler K, Schneider SM, de van der Schueren MAE, Sieber
C, Valentini L, Yu JC, Van Gossum A, Singer P (2017) ESPEN
guidelines on definitions and terminology of clinical nutrition.
Clin Nutr 36:49–64. https​://doi.org/10.1016/j.clnu.2016.09.004
	10.	 Fischer M, Jevenn A, Hipskind P (2015) Evaluation of muscle
and fat loss as diagnostic criteria for malnutrition. Nutr Clin Pract
30:239–248. https​://doi.org/10.1177/08845​33615​57305​3
	11.	 Klein S, Jeejeebhoy KN (2002) The malnourished patient: nutritional
assessment and management. In: Feldman M, Friedman LS,
Sleisenger MH (eds) Sleisenger & Fordtran’s gastrointestinal and
liver disease, 7th edn. WB Saunders Co, Philidelphia, pp 265–285
	12.	 Bharadwaj S, Ginoya S, Tandon P, Gohel TD, Guirguis J, Vallabh
H, Jevenn A, Hanouneh I (2016) Malnutrition: laboratory markers
vs nutritional assessment. Gastroenterol Rep 4:272–280
	13.	 Vandewoude MFJ, Alish CJ, Sauer AC, Hegazi RA (2012)
Malnutrition-sarcopenia syndrome: is this the future of nutrition
screening and assessment for older adults? J Aging Res. https​://
doi.org/10.1155/2012/65157​0
	14.	 Filippatos GS, Anker SD, Kremastinos DT (2005) Pathophysiology
of peripheral muscle wasting in cardiac cachexia. Curr Opin
Clin Nutr Metab Care 8:249–254. https​://doi.org/10.1097/01.
mco.00001​65002​.08955​.5b
	15.	 Pieters T, Boland B, Beguin C, Veriter C, Stanescu D, Frans A,
Lambert M (2000) Lung function study and diffusion capacity in
anorexia nervosa. J Intern Med 248:137–142. https​://doi.org/10.
1046/j.1365-2796.2000.00712​.x
	16.	 Pasma JH, Bijlsma AY, Klip JM, Stijntjes M, Blauw GJ, Muller
M, Meskers CGM, Maier AB (2014) Blood pressure associates
with standing balance in elderly outpatients. PLoS ONE. https​://
doi.org/10.1371/journ​al.pone.01068​08
	17.	 Mol A, Reijnierse EM, Trappenburg MC, Van Wezel RJA, Maier
AB, Meskers CGM (2018) Rapid systolic blood pressure changes
after standing up associate with impaired physical performance in
geriatric outpatients. J Am Heart Assoc. https​://doi.org/10.1161/
jaha.118.01006​0
	18.	 Kruizenga HM, Seidell JC, de Vet HCW, Wierdsma NJ, van
Bokhorst-de van der Schueren MAE (2005) Development and
validation of a hospital screening tool for malnutrition: the short
nutritional assessment questionnaire (SNAQ©). Clin Nutr 24:75–
82. https​://doi.org/10.1016/j.clnu.2004.07.015
	19.	 Bauer JM, Kaiser MJ, Anthony P, Guigoz Y, Sieber CC (2008)
The mini nutritional ­assessment®
—its history, today’s practice,
and future perspectives. Nutr Clin Pract 23:388–396
	20.	 Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman
LF, Blazer DG, Scherr PA, Wallace RB (1994) A short physical
performance battery assessing lower extremity function: association
with self-reported disability and prediction of mortality and
nursing home admission. J Gerontol 49:M85–M94. https​://doi.
org/10.1093/geron​j/49.2.M85
	21.	 Podsiadlo D, Richardson S (1991) The timed “Up & Go”: a test
of basic functional mobility for frail elderly persons. J Am Geriatr
Soc 39:142–148. https​://doi.org/10.1111/j.1532-5415.1991.tb016​
16.x
	22.	 Reijnierse EM, de Jong N, Trappenburg MC, Blauw GJ, ButlerBrowne
G, Gapeyeva H, Hogrel JY, McPhee JS, Narici MV, Sipilä
S, Stenroth L, van Lummel RC, Pijnappels M, Meskers CGM,
Maier AB (2017) Assessment of maximal handgrip strength:
how many attempts are needed? J Cachexia Sarcopenia Muscle
8:466–474. https​://doi.org/10.1002/jcsm.12181​
	23.	 de Bruïne ES, Reijnierse EM, Trappenburg MC, Pasma JH, de
Vries OJ, Meskers CGM, Maier AB (2018) Diminished dynamic
physical performance is associated with orthostatic hypotension in
geriatric outpatients. J Geriatr Phys Ther. https​://doi.org/10.1519/
jpt.00000​00000​00018​6
	24.	 Carbone JW, McClung JP, Pasiakos SM (2012) Skeletal muscle
responses to negative energy balance: effects of dietary protein.
Adv Nutr 3:119–126. https​://doi.org/10.3945/an.111.00179​2
	25.	 Jacobsen EL, Brovold T, Bergland A, Bye A (2016) Prevalence of
factors associated with malnutrition among acute geriatric patients
in Norway: a cross-sectional study. BMJ Open 6:e011512. https​://
doi.org/10.1136/bmjop​en-2016-01151​2
	26.	 Kiesswetter E, Pohlhausen S, Uhlig K, Diekmann R, Lesser S,
Heseker H, Stehle P, Sieber CC, Volkert D (2013) Malnutrition
is related to functional impairment in older adults receiving home
care. J Nutr Heal Aging 17:345–350. https​://doi.org/10.1007/
s1260​3-012-0409-1
	27.	 van Rijssen NM, Rojer AGM, Trappenburg MC, Reijnierse EM,
Meskers CGM, Maier AB, de van der Schueren MAE (2018) Is
being malnourished according to the ESPEN definition for malnutrition
associated with clinically relevant outcome measures
in geriatric outpatients? Eur Geriatr Med 9:389–394. https​://doi.
org/10.1007/s4199​9-018-0057-z
	28.	 Landi F, Russo A, Liperoti R, Tosato M, Barillaro C, Pahor M,
Bernabei R, Onder G (2010) Anorexia, physical function, and
incident disability among the frail elderly population: results from
the ilSIRENTE study. J Am Med Dir Assoc 11:268–274. https​://
doi.org/10.1016/j.jamda​.2009.12.088
	29.	 Tramontano A, Veronese N, Giantin V, Manzato E, RodriguezHurtado
D, Trevisan C, De Zaiacomo F, Sergi G (2016) Nutritional
status, physical performance and disability in the elderly of
the Peruvian Andes. Aging Clin Exp Res 28:1195–1201. https​://
doi.org/10.1007/s4052​0-016-0591-9
	30.	 Helminen H, Luukkaala T, Saarnio J, Nuotio MS (2017) Changes
in nutritional status and associated factors in a geriatric post-hip
fracture assessment. Eur Geriatr Med 8:134–139. https​://doi.
org/10.1016/j.eurge​r.2017.02.002
	31.	 Reijnierse EM, Trappenburg MC, Leter MJ, Blauw GJ, De Van
Der Schueren MAE, Meskers CGM, Maier AB (2015) The association
between parameters of malnutrition and diagnostic measures
of sarcopenia in geriatric outpatients. PLoS ONE 10:e0135933.
https​://doi.org/10.1371/journ​al.pone.01359​33
	32.	 Tsutsumimoto K, Doi T, Makizako H, Hotta R, Nakakubo S, Makino
K, Suzuki T, Shimada H (2018) Aging-related anorexia and
its association with disability and frailty. J Cachexia Sarcopenia
Muscle 9:834–843. https​://doi.org/10.1002/jcsm.12330​
	33.	 Flood A, Chung A, Parker H, Kearns V, O’Sullivan TA (2014)
The use of hand grip strength as a predictor of nutrition status in
hospital patients. Clin Nutr 33:106–114. https​://doi.org/10.1016/j.
clnu.2013.03.003
	34.	 Norman K, Stobäus N, Smoliner C, Zocher D, Scheufele R,
Valentini L, Lochs H, Pirlich M (2010) Determinants of hand
grip strength, knee extension strength and functional status in
cancer patients. Clin Nutr 29:586–591. https​://doi.org/10.1016/j.
clnu.2010.02.007
	35.	 Byrnes A, Mudge A, Young A, Banks M, Bauer J (2018) Use of
hand grip strength in nutrition risk screening of older patients
admitted to general surgical wards. Nutr Diet 75:520–526. https​
://doi.org/10.1111/1747-0080.12422​
	36.	 Hu CL, Yu M, Yuan KT, Yu HL, Shi YY, Yang JJ, Li W, Jiang
HP, Li ZN, Xu HX, Ba Y, Wang KH, Li SY, Shi HP (2018) Determinants
and nutritional assessment value of hand grip strength in
patients hospitalized with cancer. Asia Pac J Clin Nutr 27:777–
784. https​://doi.org/10.6133/apjcn​.07201​7.04
	37.	 Garcia MF, Meireles MS, Führ LM, Donini AB, Wazlawik E
(2013) Relationship between hand grip strength and nutritional
assessment methods used of hospitalized patients. Rev Nutr
26:49–57. https​://doi.org/10.1590/S1415​-52732​01300​01000​05
	38.	 Yeung SSY, Reijnierse EM, Trappenburg MC, Hogrel JY, McPhee
JS, Piasecki M, Sipila S, Salpakoski A, Butler-Browne G, Pääsuke
1092	 Aging Clinical and Experimental Research (2020) 32:1085–1092
1 3
M, Gapeyeva H, Narici MV, Meskers CGM, Maier AB (2018)
Handgrip strength cannot be assumed a proxy for overall muscle
strength. J Am Med Dir Assoc 19:703–709. https​://doi.
org/10.1016/j.jamda​.2018.04.019
	39.	 Yeung SSY, Reijnierse EM, Trappenburg MC, Blauw GJ, Meskers
CGM, Maier AB (2018) Knee extension strength measurements
should be considered as part of the comprehensive geriatric
assessment. BMC Geriatr 18:130. https​://doi.org/10.1186/s1287​
7-018-0815-2
	40.	 Bijlsma AY, Pasma JH, Lambers D, Stijntjes M, Blauw GJ, Meskers
CGM, Maier AB (2013) Muscle strength rather than muscle
mass is associated with standing balance in elderly outpatients. J
Am Med Dir Assoc 14:493–498. https​://doi.org/10.1016/j.jamda​
.2013.02.001
	41.	 Neelemaat F, Kruizenga HM, de Vet HCW, Seidell JC, Butterman
M, van Bokhorst-de van der Schueren MAE (2008) Screening
malnutrition in hospital outpatients. Can the SNAQ malnutrition
screening tool also be applied to this population? Clin Nutr
27:439–446. https​://doi.org/10.1016/j.clnu.2008.02.002
	42.	 Schilp J, Kruizenga HM, Wijnhoven HAH, Leistra E, Evers AM,
van Binsbergen JJ, Deeg DJH, Visser M (2012) High prevalence
of undernutrition in Dutch community-dwelling older individuals.
Nutrition 28:1151–1156. https​://doi.org/10.1016/j.
nut.2012.02.016
	43.	 Bokhorst-de Van, van der Schueren MAE, Guaitoli PR, Jansma
EP, de Vet HCW (2014) Nutrition screening tools: does one size fit
all? A systematic review of screening tools for the hospital setting.
Clin Nutr 33:29–58. https​://doi.org/10.1016/j.clnu.2013.04.008
	44.	 Velasco C, García E, Rodríguez V, Frias L, Garriga R, Álvarez
J, García-Peris P, León M (2011) Comparison of four nutritional
screening tools to detect nutritional risk in hospitalized patients:
a multicentre study. Eur J Clin Nutr 65:269–274. https​://doi.
org/10.1038/ejcn.2010.243
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