Journals of Gerontology: Medical Sciences
cite as: J Gerontol A Biol Sci Med Sci, 2016, Vol. 71, No. 7, 935–940
doi:10.1093/gerona/glv197
Advance Access publication October 30, 2015
Research Article
Anthropometric Cut Points for Definition of Sarcopenia
Based on Incident Mobility and Physical Limitation in
Older Chinese People
Jean Woo1 and Jason Leung2
1
Department of Medicine & Therapeutics and 2The Jockey Club Centre for Osteoporosis Care and Control, The Chinese University of Hong
Kong, Shatin.
Address correspondence to Jean Woo, MD, 9/F Lui Che Woo Clinical Sciences Building, Department of Medicine & Therapeutics, Prince of
Wales Hospital, 30-32 Ngan Shing Street, Shatin, N.T. Hong Kong. E-mail: [email protected]
Received May 21, 2015; Accepted October 7, 2015
Decision Editor: Stephen Kritchevsky, PhD
Abstract
Background: The Foundation of the National Institutes for Health (FNIH) Sarcopenia Project derived cut points in appendicular lean mass
(ALM) and grip strength, in relation to mobility limitation defined as a walking speed less than 0.8 m/s.
Methods: Using data from the Mr and Ms Os cohort of 4,000 community-dwelling Chinese men and women aged 65 years and older and a
similar data-driven approach, we examined whether the cutoff values are the same for Chinese people using baseline walking speed, incident
physical limitation, and incident slow walking speed at 4 years. Physical limitation was determined by interviewer-administered questionnaire.
Height, weight, body composition (using dual-energy X-ray absorptiometry), grip strength, and walking speed were measured.
Results: Cutoff values identified by Classification and Regression Tree (CART) analysis for grip strength were less than 27 kg for men and less than
17 kg for women. The values for ALM were less than 15.61 kg in men and less than 12.42 kg in women; the values for ALM/body mass index (BMI)
were less than 0.72 in men and less than 0.47 in women. Using presence of physical limitation at 4 years as the outcome measure, cutoff values
identified by CART analysis for grip strength were less than 27 kg for men and less than 19 kg for women; for ALM, less than 15.65 kg for men and
less than 11.26 kg for women; for ALM/BMI, less than 0.69 for men and 0.52 for women. Cutoff values for grip strength were less than 28.5 kg for
men and less than 19 kg for women; for ALM, less than 17.61 kg for men and less than 10.84 kg for women; for ALM/BMI, less than 0.81 for men
and less than 0.53 for women.
Conclusions: Cutoff values may differ between ethnic groups as a result of differences in body size and lifestyles.
Keywords: Sarcopenia—Mobility limitation—Grip strength—Walking speed—Appendicular lean mass—Body mass index
Twenty years after the introduction of the concept of sarcopenia,
while there is agreement of its detection in clinical practice and the
potential for reversibility, there is no universal consensus on the
operationalized definition nor treatment (1). The need for universal definition of sarcopenia is all the more pressing in view of the
increasing number of potential pharmacological agents that may be
useful in treatment (2,3), as well as nonpharmacological interventions such as exercise regimes and nutritional supplements (4,5).
Sarcopenia definition has evolved from the initial criterion using
appendicular lean mass (ALM) in relation to young adult mean (6),
to various Consensus Panel definitions incorporating muscle strength
and physical performance measures in addition to lean mass (7–9).
Such definitions of sarcopenia predict incident disability, hospitalization, and death (10).
However, ethnic and geographic variations in muscle mass, muscle strength, physical performance measures, and cut point values
relating to incident physical limitation exist (11,12).
Another method is to determine cut points using data-driven
approach (12–14). Recently, the Foundation of the National Institutes
for Health (FNIH) Sarcopenia Project used data from a pooled sample of 26,625 predominantly Caucasian older people with a mean
age over 70 years in the United States to derive cut points in ALM
and grip strength, using data-driven approaches (13,15–17). The consortium pointed out that further validation in other populations and
© The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved.
For permissions, please e-mail: [email protected].
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examination of alternative relevant outcomes are needed. Subsequently,
this definition using the FNIH cut points has been applied to a longitudinal study of 1,705 men aged 70 years or older in Sydney, Australia,
and sarcopenia classifications predicted incident disability, institutionalization, and mortality (18). A recent study in 1,566 men and women
aged 60–64 years found limited overlap in identification of sarcopenia
using the FNIH criteria compared with the European Working Group
on Sarcopenia in Older people, and only sarcopenia defined using the
FNIH criteria was associated with higher odds of slowness and walking difficulties (19). Cut points using ALM/body mass index (BMI)
identified people with difficulties in several domains of physical activity while low ALM/height2 did not (20).
Using data from the Mr and Ms Os cohort of community-dwelling Chinese men and women aged 65 years and older which began in
2000, we used Classification and Regression Tree (CART) models as
employed in the FNIH study to derive cutoff values for parameters
used for sarcopenia definition, using cross-sectional association with
walking speed less than 0.8 m/s as for the FNIH study to determine
whether the cutoff values are the same for Chinese people using the
same mobility disability outcome, as well as examining cut points
for incident physical limitation and incident slow walking speed as
alternative relevant outcomes.
Methods
This analysis uses data from the Mr and Ms Os cohort, consisting of 4,000 community-living Chinese men and women aged
65 years and older recruited for a study on osteoporosis and general health in Hong Kong between August 2001 and February 2003.
Participants responded to a general advertisement for health check.
The sample was stratified so that approximately 33% each would
be aged 65–69, 70–74, and 75+ years. They were invited to return
at 2 and 4 years for follow-up assessments. At baseline, information
regarding sociodemographic data, medical history, lifestyle habits,
and activities of daily living was collected. Measurements included
height, weight, body composition, and physical performance measures (21). Information regarding physical limitation was obtained
from interviewer-administered questionnaire and assessed using
the following two questions: do you have any difficulty in climbing
stairs (no, a little, a lot) and do you have difficulty in carrying out
household activities such as moving chairs or tables (no, a little, a
lot). Participants were categorized as having physical limitation if
the answer to either question was either a little or a lot, and no
limitation of the answer to both questions was no. Body composition was measured by dual-energy X-ray absorptiometry by using a
Hologic Delphi W4500 densitometer (Hologic Delphi, auto whole
body version 12.4; Hologic Inc., Bedford, MA). Total appendicular skeletal muscle mass was calculated as the sum of ALM minus
bone mineral content of both arms and legs. Grip strength was measured using a dynamometer (JAMAR Hand Dynamometer 5030JO;
Sammons Preston Inc., Bolingbrook, IL). Two readings were taken
from each side, and the average value between right and left was
used for analysis. Gait speed was measured using the best time in
seconds to complete a walk along a straight line 6 m long. A warmup period of less than 5 minutes was followed by two walks, and the
best time was recorded (12). The cohort was invited to re-attend for
repeat questionnaire interviews and physical measurements at 2 and
4 years. Due to participant attrition, at 4 years, data on physical limitation were only available from 1,516 men and 1,587 women; data
on walking speed were available from 1,560 men and 1,577 women.
Journals of Gerontology: MEDICAL SCIENCES, 2016, Vol. 71, No. 7
Statistical Analysis
All statistical analyses were performed using the statistical package
SAS, version 9.1.3 (SAS Institute, Inc., Cary, NC) except that CART
was done by R software (version 3.1.2). Two sample independent
t tests were used for continuous variables, while chi-squared tests
for categorical variables. We determined the baseline cut points
for grip strength, lean muscle mass (ALM), ALM/BMI, that corresponded to baseline walking speed less than 0.8 m/s, and presence of physical limitation and walking speed less than 0.8 m/s at
4 years for men and women separately, using CART analysis. In
the Sarcopenia FNIH project (13,15,17), different cut points were
obtained for Caucasians. Chinese cut points were determined by
CART to compare with those for Caucasians. CART analysis does
not assume a particular form of association between independent and dependent variables. It is useful in this study because (i)
predictors and cut points are chosen by optimizing discrimination
of the outcomes, (ii) number of cut points does not need to be
predefined, and (iii) CART can identify complex and unsuspected
interactions between important variables (19). CART analysis was
performed by using rpart in R software (version 3.1.2) and crossvalidation was used to remove the less important splits by assessing the change in prediction error. Performing the cross-validation,
the original sample is randomly partitioned into 10 equally sized
mutually exclusive subsamples (ie, each sample contained 90% of
the original pooled data). The tree was then applied to these 10
subsamples such that each contained 90% of the data. Prediction
error from each subsample was calculated. These 10 prediction
errors were used to calculate the empirical SE of the prediction
error. The tree was pruned to the most parsimonious model within
one standard prediction error from the best-fit model. The pruned
tree contains the final set of cut points.
Several CART models were run. First, each of the predictors—
grip strength, ALM, and ALM/BMI—was entered into separate
CART models and examined with different outcomes (baseline and
incident slowness, and incident physical limitation) independently.
Then, ALM and ALM/BMI were entered together. Finally, all three
predictors—grip strength, ALM, and ALM/BMI—were entered into
one model and were examined with different outcomes. The outcomes and predictors were further analyzed by using logistic regressions. The area under the curve (AUC) was used to measure the
concordance of predictive values with actual outcomes. AUCs were
compared using Wilcoxon tests.
Sensitivity analysis was performed to examine the predictive
power of the cut points in different groups including different age,
BMI, height, and health status. Heterogeneity between different
groups was accessed by including interaction term. All statistical
tests were two sided. A p value of less than .05 was considered statistically significant.
Results
Table 1 shows the baseline characteristics for the variables used in
the analysis for men and women.
Walking Speed Less Than 0.8 m/s at Baseline
Cutoff values identified by CART analysis for grip strength were
less than 27 kg for men, and less than 17 kg for women. The values
for ALM were less than 15.61 kg in men and less than 12.42 kg in
women; the values for ALM/BMI were less than 0.72 in men and
less than 0.47 in women (see Supplementary Figures 1–3). Table 2
shows the odds ratio (OR) for slow walking speed at baseline and
Journals of Gerontology: MEDICAL SCIENCES, 2016, Vol. 71, No. 7
AUC for these cut points. The ORs for men were all greater than 2,
the highest OR being observed for grip strength, followed by ALM
and lastly ALM/BMI. A different pattern was observed in women,
in that the OR for ALM/BMI was higher than ALM and similar to
grip strength. As for the FNIH project, sensitivity analysis was carried out for different strata of age, BMI, height, presence of cancer,
937
heart failure, chronic obstructive pulmonary diseases (COPD) and
diabetes (see Supplementary Table 5).
In men, significant ORs for all three parameters were observed
only in the 75+ age group; two weight categories (overweight and
obese); height more than or equal to 160.5 cm; and absence of cancer, congestive heart failure, COPD, or diabetes. For women (see
Supplementary Table 5), significant ORs for all three parameters
were only observed for those aged 70–74 years; the obese category;
and those without cancer, heart failure, COPD, or diabetes.
Table 1. Characteristics of Subjects
Frequency (%)/Mean (SD)
Age
65–69
70–74
75 or above
Mean
BMI
Underweight
(<18.5 kg/m2)
Normal weight
(18.5–22.9 kg/m2)
Overweight
(23–24.9 kg/m2)
Obese (≥25 kg/m2)
Mean
Height (cm)
Cancer
CHF
COPD
Diabetes
Male (N = 2,000)
Female (N = 2,000)
664 (33.20%)
708 (35.40%)
628 (31.40%)
72.39 (5.01)
669 (33.45%)
665 (33.25%)
666 (33.30%)
72.58 (5.36)
*
100 (5.00%)
115 (5.75%)
760 (38.00%)
711 (35.55%)
524 (26.20%)
476 (23.80%)
601 (30.05%)
23.45 (3.13)
163.10 (5.72)
87 (4.35%)
73 (3.65%)
232 (11.60%)
293 (14.65%)
713 (35.65%)
23.92 (3.45)*
150.90 (5.31)*
90 (4.50%)
78 (3.90%)
101 (5.05%)*
286 (14.30%)
Physical Limitation at 4 Years
Using presence of physical limitation at 4 years as the outcome measure, cutoff values identified by CART analysis for grip strength were
less than 27 kg for men and less than 19 kg for women; for ALM,
less than 15.65 kg for men and less than 11.26 kg for women; for
ALM/BMI, less than 0.69 for men and less than 0.52 for women
(see Supplementary Figures 4–6). Table 3 shows the OR for physical limitation at 4 years and AUC for these cut points. The risk for
physical limitation was significantly increased for all values below
the cut points; however there were gender differences. The ORs for
men were all greater than 2, with ALM/BMI having the highest OR
(3.14). However, for women, the ORs were lower (all below 2). The
predictive values were not high, the AUCs being all 0.55 or below.
In men, significant ORs were observed for all three parameters only
in the following subgroups: weight 18.5–22.9 kg/m2, height less
than 165.54 cm, those without chronic cachectic diseases (cancer,
heart failure, COPD) or diabetes. Similar findings were observed in
women, with the exception that none of height group had significant
ORs for all three parameters (see Supplementary Table 6).
Walking Speed at 4 Years
Notes: BMI = body mass index; CHF = congestive heart failure;
COPD = chronic obstructive pulmonary disease.
*p Value <.05 of chi-square for categorical and t test for continuous variables.
Cutoff values for grip strength were less than 28.5 kg for men and
less than 19 kg for women; for ALM, less than 17.61 kg for men
Table 2. Relationship of Walking Speed <0.8 m/s at Baseline and Different Definitions for Sarcopenia
Walking Speed < 0.8 m/s
Male
Grip strength
≥27 kg
<27 kg (low)
ALM
≥15.61 kg
<15.61 kg (low)
ALM/BMI
≥0.7223
<0.7223 (low)
Female
Grip strength
≥17 kg
<17 kg (low)
ALM
≥12.42 kg
<12.42 kg (low)
ALM/BMI
≥0.4718
<0.4718 (low)
No
Yes
OR (95% CI)
N = 1,782
N = 218
1,574 (91.89%)
208 (72.47%)
139 (8.11%)
79 (27.53%)
1.0 (referent)
4.30 (3.15, 5.87)
1,663 (90.63%)
119 (72.12%)
172 (9.37%)
46 (27.88%)
1.0 (referent)
3.74 (2.57, 5.44)
1,574 (90.25%)
208 (81.25%)
N = 1,587
170 (9.75%)
48 (18.75%)
N = 413
1.0 (referent)
2.14 (1.50, 3.04)
1,473 (81.52%)
114 (59.07%)
334 (18.48%)
79 (40.93%)
1.0 (referent)
3.06 (2.24, 4.17)
1,231 (80.83%)
356 (74.63%)
292 (19.17%)
121 (25.37%)
1.0 (referent)
1.43 (1.12, 1.83)
1,532 (80.42%)
55 (57.89%)
373 (19.58%)
40 (42.11%)
1.0 (referent)
2.99 (1.96, 4.56)
AUC
0.6228
0.5721*
0.5517*
0.5597
0.5343
0.5311*
Notes: ALM = appendicular lean mass; AUC = area under the curve; BMI = body mass index; CI = confidence interval; OR = odds ratio.
*p Value <.05 for AUC of ALM or ALM/BMI comparing with grip strength.
Journals of Gerontology: MEDICAL SCIENCES, 2016, Vol. 71, No. 7
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and less than 10.84 kg for women; for ALM/BMI, less than 0.8
for men and less than 0.53 for women (see Supplementary Figures
7–9). The ORs for these cutoff values vary between 1.75 and 2.97,
the highest being grip strength in men. With the exception of grip
strength in men, all the AUC values were below 0.6 (Table 4). In the
subgroup analyses for men, significant ORs for all three parameters
were observed in the 75+ age group; the normal and overweight
group; height 165.55 cm and above; and absence of cancer, heart
failure, COPD, or diabetes. For women, significant ORs for all three
parameters were observed in the following groups: normal weight
Table 3. Relationship of Physical Limitation After 4 y and Grip Strength, ALM, and ALM/BMI
Physical Limitation After 4 y
Male
Grip strength
≥27 kg
<27 kg (low)
ALM
≥15.65 kg
<15.65 kg (low)
ALM/BMI
≥0.6933
<0.6933 (low)
Female
Grip strength
≥19 kg
<19 kg (low)
ALM
≥11.26 kg
<11.26 kg (low)
ALM/BMI
≥0.522
<0.522 (low)
No
Yes
N = 1,076
N = 490
OR (95% CI)
AUC
0.5445
983 (70.87%)
93 (51.96%)
404 (29.13%)
86 (48.04%)
1.0 (referent)
2.25 (1.64, 3.08)
1,028 (69.88%)
48 (50.53%)
443 (30.12%)
47 (49.47%)
1.0 (referent)
2.27 (1.50, 3.45)
1,040 (70.18%)
36 (42.86%)
N = 716
442 (29.82%)
48 (57.14%)
N = 871
1.0 (referent)
3.14 (2.01, 4.90)
616 (48.20%)
100 (32.36%)
662 (51.80%)
209 (67.64%)
1.0 (referent)
1.95 (1.50, 2.53)
676 (45.86%)
40 (35.40%)
798 (54.14%)
73 (64.60%)
1.0 (referent)
1.55 (1.04, 2.30)
611 (47.44%)
105 (35.12%)
677 (52.56%)
194 (64.88%)
1.0 (referent)
1.67 (1.28, 2.17)
0.5257
0.5323
0.5501
0.5140*
0.5380†
Notes: ALM = appendicular lean mass; AUC = area under the curve; BMI = body mass index; CI = confidence interval; OR = odds ratio.
*p Value <.05 for AUC of ALM or ALM/BMI comparing with grip strength.
†
p Value <.05 for AUC of ALM/BMI comparing with ALM.
Table 4. Relationship of Walking Speed <0.8 m/s After 4 y and Different Definitions for Sarcopenia
Walking Speed < 0.8 m/s After 4 y
Male
Grip strength
≥28.5 kg
<28.5 kg (low)
ALM
≥17.61 kg
<17.61 kg (low)
ALM/BMI
≥0.8006
<0.8006 (low)
Female
Grip strength
≥19 kg
<19 kg (low)
ALM
≥10.84 kg
<10.84 kg (low)
ALM/BMI
≥0.5269
<0.5269 (low)
No
Yes
OR (95% CI)
N = 1,256
N = 304
1,057 (84.42%)
199 (64.61%)
195 (15.58%)
109 (35.39%)
1.0 (referent)
2.97 (2.25, 3.93)
955 (82.97%)
301 (73.59%)
196 (17.03%)
108 (26.41%)
1.0 (referent)
1.75 (1.34, 2.29)
779 (84.49%)
477 (74.76%)
N = 1,041
143 (15.51%)
161 (25.24%)
N = 536
1.0 (referent)
1.84 (1.43, 2.37)
895 (70.25%)
146 (48.18%)
379 (29.75%)
157 (51.82%)
1.0 (referent)
2.54 (1.97, 3.28)
1,012 (66.62%)
29 (50.00%)
507 (33.38%)
29 (50.00%)
1.0 (referent)
2.00 (1.18, 3.38)
850 (69.56%)
191 (53.80%)
372 (30.44%)
164 (46.20%)
1.0 (referent)
1.96 (1.54, 2.50)
AUC
0.6001
0.5578*
0.5749
0.5763
0.5131*
0.5612†
Notes: ALM = appendicular lean mass; AUC = area under the curve; BMI = body mass index; CI = confidence interval; OR = odds ratio.
*p Value <.05 for AUC of ALM or ALM/BMI comparing with grip strength.
†
p Value <.05 for AUC of ALM/BMI comparing with ALM.
Journals of Gerontology: MEDICAL SCIENCES, 2016, Vol. 71, No. 7
and absence of cancer, congestive heart failure, COPD, or diabetes
(see Supplementary Table 7).
Further CART Models
By putting ALM and ALM/BMI together into a model, both were
finally included in all six models (three outcomes with two sexes).
ALM was the first node for baseline slowness while ALM/BMI was
the first node for incident slowness and incident physical limitation
in both men and women. For models with grip strength, ALM and
ALM/BMI together, grip strength was the first node for all models
(see Supplementary Figures 10–12). The cut points for grip strength
in the model with ALM and ALM/BMI are the same as that for grip
strength alone. Putting three parameters (grip strength, ALM, and
ALM/BMI) together, in men, baseline slowness only included grip
strength and ALM. Baseline slowness in women and incident physical limitation in men and women included grip strength and ALM/
BMI. However, incident slowness in men and women only included
grip strength.
Discussion
This study validated the FNIH approach to the definition of sarcopenia. However, there are similarities and differences in cut
points derived in a Chinese population compared with those from
the FNIH Sarcopenia project. By using baseline slowness, the cut
points for grip strength, ALM, and ALM/BMI in men were close to
that for Caucasians (<27.5, <16.2, and <0.74 kg, respectively), with
0.07–0.22 SD smaller than that for Caucasians. The discrepancy of
cut points between Hong Kong Chinese and Caucasian women is
larger, with 0.27–0.55 SD difference (the cut points of Caucasian
women: <18 kg for grip strength, <11.5 kg for ALM, and <0.51 for
ALM/BMI).
The adjustment for lean mass using BMI proposed by the FNIH
project to certain extent adjust for body size and fat mass (13) and
clearly narrows the difference between Caucasian and Chinese populations, suggesting that future studies where ethnic differences may
act as a confounder may consider using this ratio instead of absolute
ALM values. The findings confirm that there are ethnic and gender
differences in the use of cut points for the definition of sarcopenia, and
that these values may not be applicable for those with chronic wasting
diseases such as heart failure or COPD or diabetes, since poor predictive values are seen in the presence of these diseases. Furthermore, the
applicability of these parameters in very old age groups are uncertain,
since few cohorts will have sufficient numbers of such people.
The use of the FNIH approach towards sarcopenia definition
is logical and evidence based. However, population variations in
anthropometry and lifestyle habits are obstacles in applying universal
cutoff values. At the same time, there are cost constraints in carrying out large-scale cross-sectional and longitudinal studies to establish cut points for individual populations. Such an approach may be
necessary if incremental value for using this approach versus that of
Consensus Panel definitions or screening instruments could be established and shown to be high. CART models identify subgroups which
most greatly discriminate the outcome, providing the cut point which
will result in the highest AUC and ORs. Among the three parameters,
grip strength had the highest AUC and the cut point was most discriminatory (also being the first node among three parameters), and
the cut point of ALM/BMI was better than ALM alone.
However, a recent analysis using the Mr and Ms Os dataset
to examine this point showed that with respect to predicting incident physical limitation and mortality, the FNIH criteria, various
939
Consensus Panel criteria, and brief screening instruments all have
similar performance, the AUCs being between 0.6 and 0.7 (21).
While all the cut point values derived in this study also predicted
10-year mortality, all the AUC values were below 0.6.
There are limitations in the use of prospective data to derive cut
points to be used as criteria for sarcopenia definitions. In any followup studies, the default rate would be higher among frailer individuals, and this may introduce a bias towards cutoff values being higher.
However, it may be argued that prospective data have an advantage
over cross-sectional data. Furthermore, it is difficult to take into
account all the potential confounders such as lifestyle factors. From
the point of view of comparison, this dataset has a major advantage
in that it used the same protocol as that for the US Os cohort which
was one of seven cohorts used for the FNIH Sarcopenia Project.
Conclusion
While the findings of this analysis support the derivation of cutoff
values using a data-driven approach, actual cutoff values may differ
between ethnic groups as a result of differences in body size and
lifestyles. This may be explored with further analysis of longitudinal
data from populations of diverse ethnicity.
Supplementary Material
Please visit the article online at http://gerontologist.oxfordjournals.org/ to
view supplementary material.
Funding
This study was supported by the Hong Kong Jockey Club Charities
Foundation.
Conflict of Interest
The authors declare no conflict of interest.
References
1. Michel JP. Sarcopenia: there is a need for some steps forward. J Am Med
Dir Assoc. 2014;15:379–380. doi:10.1016/j.jamda.2014.03.016
2. Cohen S, Nathan JA, Goldberg AL. Muscle wasting in disease: molecular
mechanisms and promising therapies. Nat Rev Drug Discov. 2015;14:58–74.
doi:10.1038/nrd4467
3. Laosa O, Alonso C, Castro M, Rodriguez-Manas L. Pharmaceutical interventions for frailty and sarcopenia. Curr Pharm Des. 2014;20:3068–3082.
4. Landi F, Marzetti E, Martone AM, Bernabei R, Onder G. Exercise as a
remedy for sarcopenia. Curr Opin Clin Nutr Metab Care. 2014;17:25–31.
doi:10.1097/MCO.0000000000000018
5. Paddon-Jones D, Leidy H. Dietary protein and muscle in older persons. Curr Opin Clin Nutr Metab Care. 2014;17:5–11. doi:10.1097/
MCO.0000000000000011
6. Baumgartner RN, Koehler KM, Gallagher D, et al. Epidemiology of sarcopenia among the elderly in New Mexico [erratum appears in Am J Epidemiol 1999;149(12):1161]. Am J Epidemiol. 1998;147(8):755–763.
7. Morley JE, Abbatecola AM, Argiles JM, et al.; Society on Sarcopenia,
Cachexia and Wasting Disorders Trialist Workshop. Sarcopenia with
limited mobility: an international consensus. J Am Med Dir Assoc.
2011;12:403–409. doi:10.1016/j.jamda.2011.04.014
8. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, et al.; European Working Group on
Sarcopenia in Older People. Sarcopenia: European consensus on definition
and diagnosis: report of the European Working Group on Sarcopenia in
Older People. Age Ageing. 2010;39:412–423. doi:10.1093/ageing/afq034
940
9. Chen LK, Liu LK, Woo J, et al. Sarcopenia in Asia: consensus report of the
Asian Working Group for Sarcopenia. J Am Med Dir Assoc. 2014;15:95–
101. doi:10.1016/j.jamda.2013.11.025
10. Bianchi L, Ferrucci L, Cherubini A, et al. The predictive value of the EWGSOP definition of sarcopenia: results from the InCHIANTI Study. J Gerontol A Biol Sci Med Sci. 2015:1–6. doi:10.1093/gerona/glv129
11.Woo J, Arai H, Ng TP, et al. Ethnic and geographic variations in muscle mass, muscle strength and physical performance measures. European
Geriatr Med. 2014;5:155–164. doi:10.1016/j.eurger.2014.04.003
12.Woo J, Leung J, Sham A, Kwok T. Defining sarcopenia in terms of risk
of physical limitations: a 5-year follow-up study of 3,153 Chinese men
and women. J Am Geriatr Soc. 2009;57:2224–2231. doi:10.1111/j.15325415.2009.02566.x
13.Studenski SA, Peters KW, Alley DE, et al. The FNIH sarcopenia project:
rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci. 2014;69:547–558. doi:10.1093/
gerona/glu010
14.Correa-de-Araujo R, Hadley E. Skeletal muscle function deficit: a
new terminology to embrace the evolving concepts of sarcopenia
and age-related muscle dysfunction. J Gerontol A Biol Sci Med Sci.
2014;69(5):591–594. doi:10.1093/gerona/glt208
15.Alley DE, Shardell MD, Peters KW, et al. Grip strength cutpoints for the
identification of clinically relevant weakness. J Gerontol A Biol Sci Med
Sci. 2014;69:559–566. doi:10.1093/gerona/glu011
Journals of Gerontology: MEDICAL SCIENCES, 2016, Vol. 71, No. 7
16.Dam TT, Peters KW, Fragala M, et al. An evidence-based comparison of
operational criteria for the presence of sarcopenia. J Gerontol A Biol Sci
Med Sci. 2014;69:584–590. doi:10.1093/gerona/glu013
17.McLean RR, Shardell MD, Alley DE, et al. Criteria for clinically relevant
weakness and low lean mass and their longitudinal association with incident mobility impairment and mortality: the foundation for the National
Institutes of Health (FNIH) sarcopenia project. J Gerontol A Biol Sci Med
Sci. 2014;69:576–583. doi:10.1093/gerona/glu012
18.Hirani V, Blyth F, Naganathan V, et al. Sarcopenia is associated with incident disability, institutionalization, and mortality in community-dwelling
older men: the Concord Health and Ageing in Men Project. J Am Med Dir
Assoc. 2015;16:607–613. doi:10.1016/j.jamda.2015.02.006
19.Cooper R, Bann D, Wloch EG, Adams JE, Kuh D. “Skeletal muscle function deficit” in a nationally representative British birth cohort in early
old age. J Gerontol A Biol Sci Med Sci. 2015;70:604–607. doi:10.1093/
gerona/glu214
20.Spira D, Buchmann N, Nikolov J, et al. Association of low lean mass with
frailty and physical performance: a comparison between two operational
definitions of sarcopenia-data from the Berlin Aging Study II (BASE-II).
J Gerontol A Biol Sci Med Sci. 2015;70:779–784. doi:10.1093/gerona/
glu246
21.Woo J, Leung J, Morley JE. Defining sarcopenia in terms of incident
adverse outcomes. J Am Med Dir Assoc. 2015;16:247–252. doi:10.1016/j.
jamda.2014.11.013