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Percentiles for skeletal muscle index, area and radiation attenuation based on computed tomography imaging in a healthy Caucasian population

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Background/objectives: Muscle mass is a key determinant of nutritional status and associated with outcomes in several patient groups. Computed tomography (CT) analysis is increasingly used to assess skeletal muscle area (SMA), skeletal muscle index (SMI) and muscle radiation attenuation (MRA). However, interpretation of these muscle parameters is difficult since values in a healthy population are lacking. The aim of this study was to provide sex specific percentiles for SMA, SMA and MRA in a healthy Caucasian population and to examine the association with age and BMI in order to define age- and BMI specific percentiles. Subjects/methods: In this retrospective cross-sectional study CT scans of potential kidney donors were used to assess SMA, SMI and MRA at the level of the third lumbar vertebra. Sex specific distributions were described and, based on the association between age/BMI and muscle parameters, age, and BMI specific predicted percentiles were computed. The 5th percentile was considered as cut-off. Results: CT scans of 420 Individuals were included (age range 20-82 years and BMI range 17.5-40.7 kg/m2). Sex specific cut-offs of SMA, SMI and MRA were 134.0 cm2, 41.6 cm2/m2 and 29.3 HU in men and 89.2 cm2, 32.0 cm2/m2 and 22.0 HU in women, respectively. Correlations were negative between age and all three muscle parameters, positive between BMI and SMA/SMI and negative between BMI and MRA, resulting in age- and BMI specific percentiles. Conclusions: This study provides sex specific percentiles for SMA, SMI, and MRA. In addition, age- and BMI specific percentiles have been established.
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European Journal of Clinical Nutrition (2018) 72:288296
https://doi.org/10.1038/s41430-017-0034-5
ARTICLE
Percentiles for skeletal muscle index, area and radiation attenuation
based on computed tomography imaging in a healthy Caucasian
population
A. van der Werf1,2 J. A. E. Langius1,3 M. A. E. de van der Schueren1,4 S. A. Nurmohamed5
K. A. M. I. van der Pant6S. Blauwhoff-Buskermolen1N. J. Wierdsma1
Received: 14 February 2017 / Revised: 14 July 2017 / Accepted: 4 October 2017 / Published online: 15 December 2017
© The Author(s) 2018. This article is published with open access
Abstract
Background/objectives Muscle mass is a key determinant of nutritional status and associated with outcomes in several
patient groups. Computed tomography (CT) analysis is increasingly used to assess skeletal muscle area (SMA), skeletal
muscle index (SMI) and muscle radiation attenuation (MRA). However, interpretation of these muscle parameters is difcult
since values in a healthy population are lacking. The aim of this study was to provide sex specic percentiles for SMA, SMA
and MRA in a healthy Caucasian population and to examine the association with age and BMI in order to dene age- and
BMI specic percentiles.
Subjects/methods In this retrospective cross-sectional study CT scans of potential kidney donors were used to assess SMA,
SMI and MRA at the level of the third lumbar vertebra. Sex specic distributions were described and, based on the
association between age/BMI and muscle parameters, age, and BMI specic predicted percentiles were computed. The 5th
percentile was considered as cut-off.
Results CT scans of 420 Individuals were included (age range 2082 years and BMI range 17.540.7 kg/m2).
Sex specic cut-offs of SMA, SMI and MRA were 134.0 cm2, 41.6 cm2/m2and 29.3 HU in men and 89.2 cm2,
32.0 cm2/m2and 22.0 HU in women, respectively. Correlations were negative between age and all three muscle
parameters, positive between BMI and SMA/SMI and negative between BMI and MRA, resulting in age- and BMI specic
percentiles.
Conclusions This study provides sex specic percentiles for SMA, SMI, and MRA. In addition, age- and BMI specic
percentiles have been established.
Introduction
Muscle mass is a key determinant of nutritional status [1]
and loss of muscle mass characterizes the malnutrition
syndromes cachexia (disease related loss of muscle mass)
and sarcopenia (age related low muscle mass and function)
[24]. Muscle mass can be assessed with different body
*A. van der Werf
an.vanderwerf@vumc.nl
1Department of Nutrition and Dietetics, Internal Medicine, VU
University Medical Center, Amsterdam, The Netherlands
2Department of Medical Oncology, Internal Medicine, VU
University Medical Center, Amsterdam, The Netherlands
3Department of Nutrition and Dietetics, Faculty of Health, Nutrition
and Sport, The Hague University of Applied Sciences, The Hague,
The Netherlands
4Faculty of Health and Social Studies, Department of Nutrition,
Sports and Health, HAN University of Applied Sciences,
Nijmegen, The Netherlands
5Department of Nephrology, VU University Medical Center,
Amsterdam, The Netherlands
6Department of Internal Medicine, Renal Transplant and
Nephrology Unit, Academic Medical Center, Amsterdam, The
Netherlands
Electronic supplementary material The online version of this article
(https://doi.org/10.1038/s41430-017-0034-5) contains supplementary
material, which is available to authorized users.
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composition modalities. These modalities include bioelec-
trical impedance analysis, dual-energy X-ray absorptio-
metry (DXA) and computed tomography (CT) scan [5]. The
latter modality is increasingly used in research to evaluate
muscle mass [6], using a single slice at the level of the third
lumbar vertebra (L3). Cross-sectional skeletal muscle area
(SMA, cm2) at this level is highly correlated with total body
skeletal muscle mass [7,8]. Adjustment of SMA for height2
results in skeletal muscle index (SMI, cm2/m2), a measure
for relative muscle mass [9]. An advantage of CT analysis
over bioelectrical impedance analysis and DXA is the
possibility to differentiate between lean mass components
like organs and muscle. In addition, changes in muscle mass
and composition which are undetectable using other mod-
alities, can be detected [5,1012]. Furthermore, it provides
the ability to determine muscle radiation attenuation (MRA,
Hounseld Units (HU)), a measure of muscle quality which
is inversely related to muscle fat content [13]. Another
advantage is that abdominal CT scans are conducted as part
of routine care in several patient populations. In these
patient populations this method can be used for muscle
analysis without additional burden to the patient [5].
Recent studies have used CT analysis to evaluate muscle
mass in different patient groups, for example in intensive
care patients, cancer patients and patients undergoing sur-
gery [1419]. Most of these studies show that a low
muscle mass (either SMA or SMI) is associated with worse
outcomes compared to patients with a normalmuscle
mass. Whereas some of these studies have only investigated
the linear association between muscle mass and clinical
outcomes [18,19], other studies have created cut-off points,
distinguishing between lowand normalmuscle mass.
In many studies, cut-off values for a lowmuscle mass
were based on optimal stratication for survival [14,16,17,
20], resulting in different cut-off points between studies and
patient populations. Regarding MRA, the association with
survival has been analyzed with MRA as a continuous
variable [19,21] or as two groups [18,21]. One study
dened body mass index (BMI)-specic MRA
cut-off values associated with survival in cancer patients
[20].
Interpretation of the muscle parameters SMA, SMI, and
MRA is difcult since reference values in a healthy popu-
lation are lacking [22]. Moreover, these muscle parameters
are likely to be associated with sex, age [4,2325], BMI
[20,24,26] and ethnicity [13,2729]. These characteristics
may have to be taken into account while interpreting muscle
parameters. Therefore, the aim of this study is to provide
sex specic percentiles for SMA, SMI, and MRA in a
Caucasian population, measured by CT analysis at the L3
level, as well as examine the association with age and BMI
in order to dene age and BMI specic predicted
percentiles.
Methods
This multicenter retrospective cross-sectional study was
conducted in two university hospitals in Amsterdam, the
Netherlands (VU University Medical Center (VUmc) and
Academic Medical Center (AMC)). All data have been
acquired as part of standard practice. The Medical Research
Involving Human Subjects Act does not apply to the study,
as conrmed by the Medical Ethics Committee of the
VUmc, and the study was conducted in accordance with the
Declaration of Helsinki.
Study population
A database consisting of potential living kidney donors was
used as a representation for a healthy population. All indi-
viduals were screened for potential living kidney donation
between 2006 and 2014. Medical evaluation for potential
kidney donors includes review of the medical history,
physical examination, blood- and urine tests and medical
imaging. Individuals were included in the study if (1) the
individual was considered to be healthy, i.e., when an
individual was medically approved as a kidney donor can-
didate (see Supplementary Table 1 for the exclusion criteria
for kidney donor candidates) [30]; (2) the individual had a
Caucasian background, and (3) a 120 kV non-contrast CT
scan eligible for assessment of SMA was available. To
dene cut-off values for sarcopenia, only individuals aged
2060 years old were included as representation of younger
adults.
Individual characteristics, assessed as part of the kidney
donor screening, were obtained from the medical record and
include sex, age at the time of CT scan, ethnicity, current
smoking, height, body weight, and comorbidity.
CT scan evaluation
CT scans were performed in individuals who were eligible
for kidney donation based on rst assessment by the
nephrologist or specialized nurse. Scans were performed
according to the local screening protocol of potential living
kidney donors. For the current study, the non-contrast CT
scan with the largest slice thickness (35 mm) was selected
and when not available, the 1.5 mm reconstruction was
selected. Other scanning parameters were as follows: 64-
row CT scanner (Sensation 64, Siemens, Forchheim, Ger-
many (VUmc) or CT Brilliance 64, Philips, Eindhoven,
Netherlands (AMC)); rotation time 0.5 s; pitch value 0.8
(VUmc) or 0.992 (AMC); collimation 64 × 0.6 mm; effec-
tive mAs 70 (VUmc) or 125 (AMC); reconstruction algo-
rithms were similar for all scans (kernel B30f (VUmc) and
lter B (AMC)). Scanners were calibrated (tolerance ± 4.0
HU) every 3 months using air-water phantoms. All scans
Percentiles for skeletal muscle index, area and radiation attenuation 289
Content courtesy of Springer Nature, terms of use apply. Rights reserved
were made in supine position. The transverse image at the
L3 level most clearly displaying both vertebral transverse
processes was selected. The selected image had to be of
sufcient quality for muscle analysis, meaning (1) no arte-
facts; (2) no cut-off of muscle, and (3) clear differentiation
between muscle and surrounding tissue.
Muscle parameters
Muscle parameters were measured on the selected CT slice
with SliceOmatic software V5.0 (Tomovision, Magog,
Canada). Muscle was identied based on anatomical fea-
tures and included the psoas, paraspinal and abdominal wall
muscles. Analyses were performed according to the Quality
Assurance and Training Manual Version 1.4 [31] using
threshold values of 29 to +150 HU for muscle tissue. An
example of an analyzed CT slice is shown in Fig. 1, where
analyzed muscle is delineated. The software
program computed SMA (cm2) by summing cross-sectional
muscle areas and multiplying by pixel surface area. SMI
(cm2/m2) was calculated by correcting SMA for height:
SMA (cm2)/height (m)2. MRA (HU) was determined by the
application by averaging the attenuation rate of the selected
pixels.
Statistics
Subject characteristics were described separately for men
and women, using percentages for categorical data and
mean (standard deviation) or median (interquartile range)
for respectively normally and not normally distributed
continuous data. Percentiles (p5, p10, p25, p50, p75, p90,
p95) were used to describe the distribution of SMA, SMI,
and MRA. This was done for the total study population and
for the individuals aged 2060 years. A low SMA, SMI,
and MRA was dened as a value below p5 [32,33]. For the
total study population, the correlation between age and
skeletal muscle parameters and between BMI and skeletal
muscle parameters was visualized with scatterplots and
tested with linear regression analyses. Since both age and
BMI were linearly related to all three skeletal muscle
parameters, a multivariate regression analysis was per-
formed. Interaction between age and BMI in predicting
skeletal muscle parameters was tested and included in the
analyses in case of signicant interaction. For each of these
parameters the 90% prediction interval was calculated based
on linear regression, with the lower bound of the interval
representing the predicted p5 value based on the correlation
with age and BMI within the total study population. These
predicted p5 values for SMI, SMA, and MRA were calcu-
lated for age by decade and for different BMI-groups
(1720, 2025, 2530, 3035 kg/m2). In addition, a 80%
prediction interval was calculated for the same subgroups to
compute predicted values for p10 (Supplementary Table 2).
If the number of individuals per age or BMI category
stratied by gender was below 5, the predicted p5 values for
these categories were not provided. Statistical analyses were
performed using Statistical Package for the Social Sciences
(SPSS, version 22.0. Armonk, NY). A p-value of <0.05 was
considered statistically signicant.
Results
Of the 692 individuals medically approved as a kidney
donor, 420 were eligible for inclusion in this study based on
personal and CT scan characteristics. Reasons for exclusion
are shown in the study owchart (Fig. 2).
Study population
The study population consisted of 420 healthy individuals,
of which 41% was male. The mean age in the total study
population was 53 ± 11 (range from 20 till 82) years old and
300 individuals were aged 2060 years. Mean BMI of the
total study population was 25.7 ± 3.5 kg/m2(range
17.540.7 kg/m2, Table 1).
Skeletal muscle area (SMA)
Mean SMA was 173.6 ± 25.1 cm2with a p5 of 134.0 cm2in
men and 113.4 ± 15.2 cm2with a p5 of 89.2 cm2in women.
Other percentiles and percentiles for the subgroup of indi-
viduals aged 2060 years are shown in Table 2. SMA was
lower with increasing age (R2=0.128, p< 0.001 in men
Fig. 1 Example of a CT slice at the level of the third lumbar vertebra
on which muscle was analyzedOf the selected muscle, both area and
mean radiation attenuation can be computed, to determine skeletal
muscle area (SMA) and the muscle radiation attenuation (MRA),
respectively
290 A. van der Werf et al.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
and R2=0.210, p< 0.001 in women). There was a sig-
nicant positive correlation between BMI and SMA, which
was stronger in men (R2=0.195) than in women (R2=
0.058). Scatter plots of age and SMA and of BMI and SMA
are included as supplementary material (Supplementary
Fig. 1). In men, there was no interaction between age and
BMI (p=0.390), while in women there was an interaction
(p=0.001), indicating a stronger negative correlation
between age and SMA with increasing BMI and a less
strong correlation between BMI and SMA with increasing
age. When age and BMI were included in the multivariate
regression model (including the interaction term for
women), explained variance (R2) was 0.326 in men and
0.335 in women. Age and BMI specic lower limits of
SMA are presented in Table 3.
Skeletal muscle index (SMI)
Mean SMI was 52.8 ± 7.4 cm2/m2in men and 40.2 ± 5.2
cm2/m2in women, with a p5 of 41.6 cm2/m2and 32.0 cm2/
m2, respectively (Table 2). The association between age and
SMI and between BMI and SMI is shown in Fig. 3. Both
were linearly associated in men and in women. There was a
negative linear correlation between age and SMI (explained
variance: R2=0.071, p< 0.001 in men and R2=0.078, p<
0.001 in women) and a positive correlation between BMI
and SMI (explained variance: R2=0.295, p< 0.001 in men
and R2=0.112, p< 0.001 in women). There was no inter-
action between age and BMI in men (p=0.655). In women,
there was an interaction (p=0.005) showing a stronger
negative correlation between age and SMA with increasing
BMI and a less strong correlation between BMI and SMA
with increasing age, as was the case for SMA. Multivariate
regression with age and BMI (including the interaction term
for women) resulted in an explained variance (R2) of SMI of
0.369 in men and 0.248 in women. Based on this model,
lower limits (p5) of SMI by age- and BMI group were
calculated (Table 3).
Muscle radiation attenuation (MRA)
MRA was higher in men (mean 38.4 ± 5.6 HU) than in
women (mean 33.3 ± 6.8 HU). The p5 was 29.3 HU in men
and 22.0 HU in women (Table 2). There was a negative
Table 1 Characteristics of the study population
All subjects
(n=420)
Men (n=
174)
Women (n
=246)
Age (years) 53 ± 12 52 ± 12 54 ± 11
Ethnicity*
Dutch 395 (94.0%) 163
(93.7%)
232 (94.3%)
European non-Dutch 11 (2.6%) 5 (2.9%) 6 (2.4%)
Non-European 14 (3.3%) 6 (3.4%) 8 (3.3%)
Currently smoking (%) 113 (26.7%) 53 (30.5%) 60 (24.4%)
Anthropometric characteristics
Height (cm) 174 ± 10 182 ± 8 168 ± 6
Weight (kg) 78.9 ± 14.6 86.2 ± 12.3 72.0 ± 11.0
Body mass index (kg/
m2)
25.7 ± 3.5 26.1 ± 3.3 25.5 ± 3.7
Comorbidity for which use of maintenance medication
Hypertension 47 (11.2%) 21 (12.1%) 26 (10.6%)
Hyperlipidemia 21 (5.0%) 6 (3.4%) 15 (6.1%)
Asthma/COPD
/allergic rhinitis
15 (3.6%) 3 (1.7%) 12 (4.9%)
Depression/anxiety
disorder
30 (7.1%) 8 (4.6%) 22 (8.9%)
Hypothyroidism 17 (4.0%) 3 (1.7%) 14 (5.7%)
Other 38 (9.0%) 15 (8.6%) 23 (9.3%)
Characteristics are described using numbers (percentages) or mean ±
standard deviation
*Non-European Caucasians include Turkish (5), Moroccan (5),
Egyptian (1), Russian (1), Iranian (1), and Australian (1)
The ve most prevalent comorbidities are reported separately, other
include other comorbidities for which use of systemic maintenance
medication (except oral contraceptives)
COPD chronic obstructive pulmonary disease
Fig. 2 Study owchart showing
the selection of eligible
individuals with a CT scan
eligible for muscle analysis
Percentiles for skeletal muscle index, area and radiation attenuation 291
Content courtesy of Springer Nature, terms of use apply. Rights reserved
correlation between age and MRA, which was stronger in
women (R2=0.366, p< 0.001) than in men (R2=0.212, p
< 0.001). BMI was also negatively correlated with MRA
(R2=0.082, p< 0.001 in men and R2=0.156 p< 0.001 in
women). Scatter plots of age and MRA and of BMI and
MRA are shown in Supplementary Fig. 2. The was no
interaction between age and BMI (p=0.124 in men and p
=0.467 in women).The multivariate regression model with
age and BMI had a R2of 0.291 in men and 0.468 in women
for predicting MRA. Table 3shows the age- and BMI
specic lower limits.
Discussion and conclusion
This is the rst study describing percentiles for muscle
parameters measured by CT analysis at the L3 level in a
healthy Caucasian population. When p5 is considered as the
cut-off between low and normal, the sex specic cut-offs of
SMA, SMI and MRA are 134.0 cm2, 41.6 cm2/m2, and 29.3
HU in men and 89.2 cm2, 32.0 cm2/m2and 22.0 HU in
women, respectively. For the diagnosis of sarcopenia, the
SMI cut-off values in a healthy, younger population (2060
years old) are recommended, which is 43.1 cm2/m2in men
and 32.7 cm2/m2in women. Because both age and BMI are
associated with skeletal muscle parameters, sex specic cut-
off points for different age- and BMI categories are pro-
vided as well. The percentiles reported in this study facil-
itate interpretation of muscle parameters in disease and at
older age.
Although reference values are lacking for a healthy
Caucasian population, cut-offs in a healthy Asian popula-
tion have recently been dened in a study of Hamaguchi
et al. [34]. In this study, psoas muscle mass index was
assessed by CT analysis at the L3 level. The association
between psoas muscle mass index and SMI was analyzed in
a subgroup and was found to be moderate (r=0.682, p<
0.001). Psoas muscle mass index was 1.53-fold higher in
men than in women and a continuous decline in psoas
muscle mass index was seen in both men and women, with
a 1.20-fold higher muscle index in individuals <50 years vs.
50 years. In our Caucasian population, SMI was 1.31-fold
higher in men than in women and 1.08-fold higher in
individuals < 50 years vs. 50 years. Our ndings are in
line with Hamaguchisndings and conrm a higher SMI in
men compared to women, however the magnitude of the sex
and age specic proportions is different, which may be due
to ethnic specic differences [35] or differences in muscles
analyzed (psoas muscle vs. allmuscles at the L3 level,
respectively).
Another study describing SMI cut-offs was performed by
Mourtzakis et al. Appendicular muscle index (kg/m2) was
measured by DXA and SMI was assessed by CT analysis at
the L3 level in 31 cancer patients. Based on the association
between DXA and CT measurements, a regression equation
was computed. This regression equation was used to gen-
erate SMI cut-offs from previously dened DXA-based cut-
offs. The latter were dened as two standard deviation
below the mean value for healthy, non-Hispanic white
adults aged 1840 years (appendicular muscle index of
7.26 kg/m2in men and 5.45 kg/m2in women) [9]. This
resulted in a SMI cut-off of 55.4 cm2/m2in men and 38.9
cm2/m2in women [8]. In our study, the cut-off (p5) for low
muscle mass within the same age range is 44.7 cm2/m2in
men and 33.0 cm2/m2in women. These values are con-
siderably lower than the cut-offs dened by Mourtzakis
et al. This may be due to the fact that Mourtzakis et al.
indirectly calculated cut-offs using a regression equation
derived from a relatively small population, which induces a
margin of error. In addition, the relationship between DXA
derived appendicular muscle mass and CT derived SMI
may differ between cancer patients and a healthy population
and therefore the equation may not be extrapolated. Other
factors that may contribute to differences in cut-offs of
Table 2 Gender specic percentiles for skeletal muscle parameters for
the total study population and for the subgroup aged 2060 years
SMA (cm2) SMI (cm2/m2) MRA (HU)
Men Women Men Women Men Women
Aged 2082 years
Mean
±SD
173.6
± 25.1
113.4 ±
15.2
52.8
± 7.4
40.2 ± 5.2 38.4
± 5.6
33.3 ± 6.8
p5 134.0 89.2 41.6 32.0 29.3 22.0
p10 141.6 93.0 44.7 32.8 31.7 24.9
p25 154.2 102.8 47.7 36.4 34.9 28.5
p50 171.4 112.5 52.0 40.0 38.4 33.3
p75 190.1 124.0 58.0 43.3 42.3 38.8
p90 208.6 132.0 63.3 46.9 45.5 41.7
p95 216.9 138.9 67.1 48.9 48.0 43.6
Aged 2060 years
Mean
±SD
179.3
± 24.4
117.7 ±
14.4
53.9
± 7.1
41.2 ± 5.0 39.6
± 5.4
35.5 ± 6.0
p5 138.2 96.2 43.1 32.7 30.9 24.8
p10 146.3 99.8 45.9 34.5 32.8 27.7
p25 163.6 107.6 48.4 37.9 35.8 31.2
p50 178.9 117.9 53.2 40.9 39.4 36.2
p75 196.9 127.0 58.8 44.1 43.3 40.6
p90 212.2 135.4 64.8 47.7 46.4 42.8
p95 219.3 142.3 67.4 49.6 48.1 44.6
Percentiles are based on a healthy population of 174 men and 246
women aged 2082 years and 126 men and 174 women aged 2060
years. p5 is considered as the cut-off between low and normal SMI,
SMA, and MRA. SD standard deviation, SMI skeletal muscle index,
SMA skeletal muscle area, MRA muscle radiation attenuation, HU
hounseld units
292 A. van der Werf et al.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Mourtzakis et. al and our study are the fact that the DXA
cut-offs were based on another population (Mexican vs.
Caucasian) and in a different period (19861992 vs.
20062014), which might be related to a difference in
lifestyle and physical activity [36].
In several studies, cut-off values for muscle parameters
using CT analysis have been dened based on optimal
stratication for mortality [14,16,17,20]. The established
cut-offs in our study facilitate comparison of muscle para-
meters in patient groups with muscle parameters in healthy
individuals. For instance, Prado et al. dened cut-offs for
lumbar SMI associated with mortality in obese (BMI 30
kg/m2) patients with a solid tumor (n=250). The cut-off
was 52.4 cm²/m² in men and 38.5 cm²/m² in women, with
15% of the patients having a SMI below this sex specic
value [16]. The mortality based SMI cut-offs as dened by
Prado et al. correspond to our predicted p5 in healthy men
(Table 3)andp10 in healthy women (Supplementary
Table 2) within the same BMI range. This implies that men
with a solid tumor are at higher mortality risk when SMI is
below the p5, while women are already at higher mortality
risk when their muscle mass is below p10 of the healthy
population. Also in other studies in which outcome based
cut-offs have been dened, the comparison with healthy
individuals would be interesting and makes it possible to
assess the prevalence of low muscle parameters.
When using the percentiles reported in the current study,
a few considerations should be taken into account. Because
Table 3 Predicted p5 values for
skeletal muscle parameters for
different age- and BMI
categories in men and women*
Men Women
BMI (kg/m2) All BMIs 1720 2025 2530 3035 All BMIs 1720 2025 2530 3035
Age (years)
SMI (cm2/m2)
All ages 32.8 37.9 44.0 50.1 28.6 31.3 34.5 37.5
2029 44.9 37.4 42.5 48.7 54.8 36.7 28.5 33.7 39.6 45.1
3039 43.4 35.9 41.0 47.2 53.3 35.3 28.7 32.8 37.6 42.2
4049 41.8 34.3 39.4 45.6 51.7 33.9 28.8 31.8 35.6 39.2
5059 40.2 32.7 37.7 43.9 50.0 32.3 28.7 30.9 33.5 36.1
6069 38.6 31.0 36.1 42.3 48.4 30.7 28.5 29.9 31.4 32.9
7079 36.9 29.3 34.4 40.6 46.7 28.9 28.2 28.8 29.3 29.5
SMA (cm2)
All ages 109.8 123.7 140.8 157.3 83.6 90.5 98.5 105.9
2029 153.0 131.4 145.4 162.6 179.3 111.2 88.2 102.7 119.4 134.7
3039 146.1 124.3 138.3 155.5 172.2 104.9 86.8 97.9 111.2 123.7
4049 139.0 117.1 131.2 148.3 165.0 98.3 85.1 93.1 102.9 112.3
5059 131.8 109.8 123.8 141.0 157.7 91.5 83.0 88.2 94.4 100.6
6069 124.5 102.3 116.4 133.6 150.3 84.5 80.7 83.1 85.9 88.4
7079 116.9 94.8 108.8 126.0 142.7 77.3 78.0 78.0 77.3 75.9
MRA (HU)
All ages 33.1 31.3 28.9 26.3 27.9 25.1 21.5 17.9
2029 35.5 39.4 37.6 35.2 32.7 34.6 38.6 36.3 33.4 30.4
3039 33.5 37.4 35.6 33.2 30.7 31.1 35.3 33.0 30.1 27.1
4049 31.4 35.4 33.6 31.2 28.7 27.5 31.9 29.7 26.8 23.8
5059 29.4 33.3 31.5 29.1 26.7 23.9 28.6 26.3 23.4 20.4
6069 27.3 31.2 29.4 27.0 24.6 20.2 25.2 22.9 20.0 17.1
7079 25.1 29.1 27.3 24.9 22.5 16.6 21.7 19.5 16.6 13.7
SMI skeletal muscle index, SMA skeletal muscle area, MRA muscle radiation attenuation, HU hounseld
units. BMI body mass index
Predicted p5 values for the age and BMI categories are based on a regression equation, derived from 174
men and 246 women. For each category, the middle value within the category range is used, for instance
values for the age category 5059 years and BMI category 3035 kg/m2are predicted values of age 55 years
and BMI 32.5 kg/m2. In women, the regression equation for SMI and SMA included an interaction term for
age and BMI, because of interaction between these variables in predicting SMI and SMA
*Predicted p10 values are provided as supplementary table (Supplementary Table 2)
Percentiles for skeletal muscle index, area and radiation attenuation 293
Content courtesy of Springer Nature, terms of use apply. Rights reserved
SMA is not adjusted for height2, this parameter should only
be used when total body skeletal muscle mass is relevant,
for instance as indicator for body reserves of protein[14].
For diagnosis of low skeletal muscle mass, SMA should be
adjusted for height2(SMI). Regarding the study population,
it should be noted that all individuals were potential kidney
donors who were screening extensively, thus the health
status of the study population may be higher than the health
status of the general population. For instance, individuals
with diabetes mellitus are not represented within our study
population. The absence of individuals with diabetes might
have led to an overestimation of muscle parameters, since
diabetes mellitus is associated with a lower muscle mass
and a reduced MRA [13,37,38]. In addition, a BMI > 35
kg/m2was an absolute contraindication for kidney donation.
Therefore, the study population includes only 7 individuals
with a BMI > 35 kg/m2(who were accepted for donation
after weight loss) and percentiles for this BMI category
cannot reliably be extrapolated from this study population.
Because muscle parameters may differ between ethnicities
Fig. 3 Association between age and SMI (upper scatter plots) and between BMI and SMI (lower scatter plots). All associations were signicant
(p<0.001). SMI skeletal muscle index, BMI body mass index
294 A. van der Werf et al.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
[13,2729], the percentiles are representative for the Cau-
casian population, but could probably not be extrapolated to
other ethnicities. CT scans used for muscle measurements
should preferably be non-contrast scans performed at
120 kV since these factors may inuence measurement
outcomes [39,40]. Using other software programs than the
software program used in the current study (SliceOmatic)
may give slightly different results. However, SMA shows
excellent intersoftware-agreement and thus results of studies
using different software programs may reliably be com-
pared. [41] More research is needed to dene reference
values based on larger study population with a broader BMI
range, in other ethnic groups, as well as to determine the
effect of technical parameters on measurement outcomes.
In conclusion, this study is the rst to describe sex spe-
cic percentiles for the muscle parameters SMI, SMA, and
MRA measured by CT analysis at the L3 level derived from
a healthy population. Because both age and BMI were
associated with muscle parameters, sex, age, and BMI-
specic values have been established. These percentiles will
facilitate interpretation of muscle parameters in disease.
Acknowledgements AvdW, JL, MdvdS, SB, NW contributed to the
study design, analyses and writing the article. SN and KP recruited the
study population, contributed to data collection and to writing the
article. The authors acknowledge everyone who has contributed to this
project. We would especially like to thank dr. M.R. Meijerink, drs. P.
F.C. Groot and dr. M. van de Wiel for their radiological, technical and
statistical support, respectively.
Competing interests The authors declare that they have no competing
interests.
Open Access This article is licensed under a Creative Commons
Attribution-NonCommercial-ShareAlike 4.0 International License,
which permits any non-commercial use, sharing, adaptation,
distribution and reproduction in any medium or format, as long as
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. If you remix, transform, or build upon this article
or a part thereof, you must distribute your contributions under the same
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article are included in the articles Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not
included in the articles Creative Commons license and your intended
use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright
holder. To view a copy of this license, visit http://creativecommons.
org/licenses/by-nc-sa/4.0/.
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Supplementary resources (4)

Data
January 2018
Anne van der Werf · Jacqueline Langius · M. A. E. de van der Schueren · S. A. Nurmohamed · Nicolette J Wierdsma
Data
January 2018
Anne van der Werf · Jacqueline Langius · M. A. E. de van der Schueren · S. A. Nurmohamed · Nicolette J Wierdsma
Data
January 2018
Anne van der Werf · Jacqueline Langius · M. A. E. de van der Schueren · S. A. Nurmohamed · Nicolette J Wierdsma
Data
January 2018
Anne van der Werf · Jacqueline Langius · M. A. E. de van der Schueren · S. A. Nurmohamed · Nicolette J Wierdsma
... Patients' weight was classified according to the World Health Organization's (WHO) weight classes 27 . Patients were classified as sarcopenic based on their MRA using the sex, age, and body mass index (BMI) specific 5th percentile of van der Werf et al. 28 . As different SMI cut-offs have been suggested and are applied for Caucasian populations in the literature, SMI values were categorized as sarcopenic according to three widely used systems: the sex-specific cut-offs of Prado et al. 29 , the sex-specific cut-offs of Martin et al. 30 , which for men also consider the BMI, and the sex-, age-, and BMI-specific percentiles of van der Werf et al. 28 . ...
... Patients were classified as sarcopenic based on their MRA using the sex, age, and body mass index (BMI) specific 5th percentile of van der Werf et al. 28 . As different SMI cut-offs have been suggested and are applied for Caucasian populations in the literature, SMI values were categorized as sarcopenic according to three widely used systems: the sex-specific cut-offs of Prado et al. 29 , the sex-specific cut-offs of Martin et al. 30 , which for men also consider the BMI, and the sex-, age-, and BMI-specific percentiles of van der Werf et al. 28 . ...
... In detail: according to the cut-off values for the SMI of Prado et al. 29 18 men and six women were sarcopenic, by the cut-off for the SMI of Martin et al. 30 17 men and seven women. 11 male patients and three female patients were below the sex-, age-, and BMI-specific 5th percentile for the SMI proposed by van der Werf et al. 28 , while five male and three female patients were below the 5th percentile for the MRA. An overview of the sex-specific distribution of sarcopenia classification results and variance of fat as well as muscle parameters at L3 is provided in Fig. 2 Fig. 3). ...
Article
Full-text available
As most COVID-19 patients only receive thoracic CT scans, but body composition, which is relevant to detect sarcopenia, is determined in abdominal scans, this study aimed to investigate the relationship between thoracic and abdominal CT body composition parameters in a cohort of COVID-19 patients. This retrospective study included n = 46 SARS-CoV-2-positive patients who received CT scans of the thorax and abdomen due to severe disease progression. The subcutaneous fat area (SF), the skeletal muscle area (SMA), and the muscle radiodensity attenuation (MRA) were measured at the level of the twelfth thoracic (T12) and the third lumbar (L3) vertebra. Necessity of invasive mechanical ventilation (IMV), length of stay, or time to death (TTD) were noted. For statistics correlation, multivariable linear, logistic, and Cox regression analyses were employed. Correlation was excellent for the SF (r = 0.96) between T12 and L3, and good for the respective SMA (r = 0.80) and MRA (r = 0.82) values. With adjustment (adj.) for sex, age, and body-mass-index the variability of SF (adj. r2 = 0.93; adj. mean difference = 1.24 [95% confidence interval (95% CI) 1.02–1.45]), of the SMA (adj. r2 = 0.76; 2.59 [95% CI 1.92–3.26]), and of the MRA (adj. r2 = 0.67; 0.67 [95% CI 0.45–0.88]) at L3 was well explained by the respective values at T12. There was no relevant influence of the SF, MRA, or SMA on the clinical outcome. If only thoracic CT scans are available, CT body composition values at T12 can be used to predict abdominal fat and muscle parameters, by which sarcopenia and obesity can be assessed.
... Participants with major organ dysfunction (Appendix B) or malignancy were excluded. Subjects aged 20 to 60 years were grouped as the reference group to develop the cutoff points of L1MI [28]. Subjects over 60 years of age were categorized as the older group [29]. ...
... The main difficulty is that CT is usually used in patients with specific diseases and rarely in healthy individuals. Therefore, the data for the L3MI criteria were mainly derived from the studies of healthy donors of organs for transplantation [14,28] or explorations of other measurement criteria, such as BIA or dual energy X-ray absorptiometry in ill populations [15]. The majority of these studies had limited case numbers, which might have affected the predictive power. ...
Article
The muscle index of the first vertebra (L1MI) derived from computed tomography (CT) is an indicator of total skeletal muscle mass. Nevertheless, the cutoff value and utility of L1MI derived from low-dose chest CT (LDCT) remain unclear. Adults who received LDCT for health check-ups in 2017 were enrolled. The cutoff values of L1MI were established in subjects aged 20-60 years. The cutoff values were used in chronic obstructive pulmonary disease (COPD) patients to determine muscle quantity. A total of 1780 healthy subjects were enrolled. Subjects (n = 1393) aged 20-60 years were defined as the reference group. The sex-specific cutoff values of L1MI were 26.2 cm2/m2 for males and 20.9 cm2/m2 for females. Six subjects in the COPD group (6/44, 13.6%) had low L1MI. COPD subjects with low L1MI had lower forced expiratory volume in one second (0.81 ± 0.17 vs. 1.30 ± 0.55 L/s, p = 0.046) and higher COPD assessment test scores (19.5 ± 2.6 vs. 15.0 ± 4.9, p = 0.015) than those with normal L1MI. In conclusion, LDCT in health assessments may provide additional information on sarcopenia.
... Recent studies have focused on developing reference diagnostic cut-off values among the normal population. For people under 60 years old, the cut-off SMI value ranged between 40 and 45 in male and 30 and 35 in female (Supplement Table 1) [66][67][68][69][70][71][72]. However, the population characteristics were different between these studies, and determination of normal reference cut-off values for different population characteristics using larger series of data via an AI-assisted approach may fasten the development of standardized assessment. ...
... Supplement Table S1 Normal reference and cut-off for sarcopenia [58,66,[68][69][70][71][72]. (Supplementary Materials) ...
Article
Full-text available
Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence- (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice. Sarcopenia is a prognostic factor for patients with cancer, and confirming its presence helps to recognize those patients at the greatest risk, which provides a guide for designing individualized cancer treatments. In this review, we examine the recent literature (2017-2021) on AI-assisted image assessment of body composition and sarcopenia, seeking to synthesize current information on the mechanism and the importance of sarcopenia, its diagnostic image markers, and the interventions for sarcopenia in the medical care of patients with cancer. We concluded that AI-assisted image analysis is a reliable automatic technique for segmentation of abdominal adipose tissue. It has the potential to improve diagnosis of sarcopenia and facilitates identification of oncology patients at the greatest risk, supporting individualized prevention planning and treatment evaluation. The capability of AI approaches in analyzing series of big data and extracting features beyond manual skills would no doubt progressively provide impactful information and greatly refine the standard for assessing sarcopenia risk in patients with cancer.
... However, the employed body composition method is trained using images from various patient populations and with various acquisition protocols (e.g., with and without contrast agents). It is likely that the performance of the method will generalize to other cohorts, because muscle composition is relatively similar among cohorts (31)(32)(33). However, this hypothesis should be confirmed in future studies, especially in groups with extreme or deviating muscle composition. ...
Article
Full-text available
Background Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method.Methods This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap).ResultsIncluded patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of −0.69 [−6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74–0.82] and a within-subject CV of 11.2% [95% CI: 10.2–12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [−24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was −0.55 [1.71–2.80] cm2.Conclusion Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.
Thesis
La cachexie cancéreuse est une complication fréquente du cancer aux conséquences dramatiques sur la survie des patients. Ce syndrome se caractérise par une diminution de la masse musculaire avec ou sans atteinte de la masse grasse dans un contexte inflammatoire et hypercatabolique. L'identification des facteurs responsables de son développement est un élément majeur de la prise en charge des patients. Notre groupe a montré précédemment une prévalence importante des altérations de la dépense énergétique de repos (DER) dans une population hétérogène de patients atteints de cancer. L'hyper et l'hypométabolisme, définis comme une DER mesurée différant notablement de la DER calculée, étaient associés à une diminution de l'efficacité et de la tolérance aux traitements et de la survie. Nous nous sommes donc interrogés sur la signification de ces anomalies de la DER et sur leurs déterminants, et en particulier l'effet de l'âge qui pourrait fragiliser les patients. Dans un premier temps, nous avons caractérisé les effets sur la survie des altérations du métabolisme énergétique dans une population homogène de patients atteints de cancer broncho-pulmonaire non à petites cellules. Cette étude incluant 144 patients a montré une augmentation du risque de décès de 9 % pour chaque augmentation de 10 % du rapport DER mesurée/DER calculée. Nous montrons également que le seuil de DER mesurée/DER calculé le plus discriminant en matière de survie est 120 % avec un hazard ratio de 2,16 pour la mortalité. Nous nous sommes ensuite intéressés aux déterminants de la DER et en particulier à l'effet de l'âge sur cette DER dans une étude prospective incluant 44 patients atteints de cancer broncho-pulmonaires non à petite cellules. L'analyse multivariée montre que les déterminants principaux de la DER chez ces patients sont le sexe et le poids alors que l'âge n'a pas d'influence significative. En revanche, la DER, lorsqu'elle est rapportée à la masse maigre, n'est plus associée ni au poids ni au sexe mais de façon complexe avec la CRP et la TSH. Ainsi, les statuts inflammatoires et thyroïdiens apparaissent comme les principaux déterminants de la réponse métabolique au cancer. Nos résultats montrent également une corrélation inverse entre la DER rapportée à la masse maigre et le pourcentage de couverture des besoins énergétiques par le patient témoignant d'une incapacité à compenser l'augmentation de leurs besoins énergétiques. Ces deux études soulignent l'importance de l'évaluation, parallèlement à la DER, de la masse maigre et de son évolution chez ces patients. Si le scanner au niveau de la troisième vertèbre lombaire est la méthode de choix chez les patients atteints de cancer, nous avons voulu évaluer les performances de deux méthodes a priori plus adaptée pour le suivi : l'impédancemétrie bioélectrique (BIA) et le rapport créatinine/cystatine C (CC), chez 44 patients atteints de cancers variés. Ces deux méthodes sont bien corrélées avec le scanner en L3 mais avec pour la BIA des limites d'agrément larges. La sensibilité et la spécificité de ces techniques pour la détection d'une masse maigre insuffisante sont modestes, meilleures pour le CC que pour la BIA et meilleures chez l'homme que chez la femme. Une régression linéaire avec étude des modèles imbriqués permet d'améliorer significativement la prédiction du rapport créatinine/cystatine chez l'homme (sensibilité : 89,5 %, spécificité : 100 %). Nos résultats montrent l'importance pronostique de la mesure de la DER et de l'évaluation de la composition corporelle chez le patient atteint de cancer. Néanmoins, nous pouvons nous interroger sur l'impact que la DER peut ou doit avoir sur la décision thérapeutique. Une meilleure connaissance des mécanismes à l'origine de l'altération du métabolisme énergétique chez le patient atteint de cancer devrait permettre d'identifier de nouvelles cibles et stratégies thérapeutiques.
Article
Background Associating liver partition and portal vein ligation for staged hepatectomy induces rapid and effective hypertrophy of the future liver remnant to prevent postoperative liver failure. The aim of this study was to determine cofactors, including sarcopenia, influencing the kinetic growth rate, and subsequently future liver remnant, in terms of safety, complications, and posthepatectomy liver failure. Method Patients undergoing associating liver partition and portal vein ligation for staged hepatectomy between 2010 and 2020 were included in this study. Kinetic growth rate was defined as the quotient of the degree of hypertrophy and the time interval between the 2 steps. The sarcopenia muscle index was defined as the skeletal muscle area of both psoas major muscles normalized to the patient’s height. Results During the study period, 90 patients underwent associating liver partition and portal vein ligation for staged hepatectomy. The association between kinetic growth rate and posthepatectomy liver failure indicates a significant nonlinear effect (P = .02). The incidence of posthepatectomy liver failure significantly increased at a kinetic growth rate below 7% per week (31%) compared to patients with a kinetic growth rate >7%/wk (7%, P = .02). In patients with a low kinetic growth rate (<7%/wk), the sarcopenia muscle index was significantly lower compared to patients with a high kinetic growth rate (>7%/wk). Furthermore, a low sarcopenia muscle index and a high body mass index turned out to be independent risk factors for a low kinetic growth rate. Conclusion After the first step of the associating liver partition and portal vein ligation for staged hepatectomy procedure, a low kinetic growth rate (<7%/wk) increases the risk of posthepatectomy liver failure. The presence of a low sarcopenia muscle index and a high body mass index are profoundly correlated with clinically substantial impaired liver regeneration, which can result in increased liver dysfunction after associating liver partition and portal vein ligation for staged hepatectomy.
Article
Background: Cachexia is detrimental for patients with head and neck cancer (HNC). However, postoperative consequences of HNC cachexia remain unknown. Methods: A 2014-2019 retrospective review was performed of adults undergoing aerodigestive HNC resection with free tissue reconstruction. Propensity score matching using inverse probability of treatment weighting (IPTW) of cachectic and control groups was employed to adjust for covariate imbalances followed by binary logistic regression on postoperative outcomes. Results: Out of 252 total patients, 135 (53.6%) had cancer cachexia. The cohort was predominantly white (94.4%) males (65.1%) aged 61.5 ± 11.5 years with stage III-IV (84.1%) malignancy of the oral cavity (66.3%). After matching cohort pre- and intra-operative covariates using IPTW, cancer cachexia remained a strong, significant predictor of serious National Surgical Quality Improvement Program (NSQIP) complications (OR [95%CI] = 3.84 [1.80-8.21]) and major Clavien-Dindo complications (OR [95%CI] = 3.00 [1.18-7.60]). Conclusions: Cancer cachexia is associated with worse HNC free flap reconstruction outcomes.
Article
Background: There is currently no specific equation for estimating glomerular filtration rate (GFR) in Chinese children with chronic kidney disease (CKD). The commonly used equations are less robust than expected; we therefore sought to derive more appropriate equations for GFR estimation. Methods: A total of 751 Chinese children with CKD were divided into 2 groups, training group (n = 501) and validation group (n = 250). In the training group, a univariate linear regression model was used to calculate predictability of variables associated with GFR. Residuals were compared to determine multivariate predictability of GFR in the equation. Standard regression techniques for Gaussian data were used to determine coefficients of GFR-estimating equations after logarithmic transformation of measured GFR (iGFR), height/serum creatinine (height/Scr), cystatin C, blood urea nitrogen (BUN), and height. These were compared with other well-known equations using the validation group. Results: Median 99mTc-DTPA GFR was 90.1 (interquartile range: 67.3-108.6) mL/min/1.73 m2 in training dataset. Our CKD equation, eGFR (mL/min/1.73 m2) = 91.021 [height(m)/Scr(mg/dL)/2.7]0.443 [1.2/Cystatin C(mg/L)]0.335 [13.7/BUN (mg/dL)]-0.095 [ 0.991male] [height(m)/1.4]0.275, was derived. This was further tested in the validation group, with percentages of eGFR values within 30% and 15% of iGFR (P30 and P15) of 76.00% and 48.40%, respectively. For centres with no access to cystatin C, a creatinine-based equation, eGFR (mL/min/1.73 m2) = 89.674 [height(m)/Scr(mg/dL)/2.7]0.579 [ 1.007male] [height(m)/1.4]0.187, was derived, with P30 and P15 73.60% and 49.20%, respectively. These were significantly higher compared to other well-known equations (p < 0.05). Conclusion: We developed equations for GFR estimation in Chinese children with CKD based on Scr, BUN and cystatin C. These are more accurate than commonly used equations in this population.
Article
In critical care, low muscle mass is proposed as a risk factor for adverse outcomes that may be modified by nutrition. However, health care providers, including physicians and registered dietitians, may not routinely consider this risk factor in screening, assessing, or designing interventions. A literature search was conducted to compare clinical outcomes in critically ill adult patients with and without low muscle mass upon admission. This narrative review identified a statistically significant association between low muscle mass and increased risk of mortality and length of stay. Health care providers should consider screening for low muscle mass upon admission, as this may inform practice and improve clinical outcomes.
Article
Objectives This study aimed to compare the assessment of skeletal muscle area (SMA in cm²), skeletal muscle index (SMI in cm²/m²) and skeletal muscle density (SMD in HU) between third lumbar vertebra (L3) and thigh landmarks, and the agreement in diagnosing low muscle mass and low SMD (L3 as the reference method). Methods Multicenter cross-sectional study including healthy individuals (≥ 18 years old) of both sexes, who had an elective CT exam including abdominal and pelvic regions. CT images were analyzed to evaluate SMA, SMI and SMD. Muscle abnormalities (low SMA, SMI, and SMD) were defined as values below the 5th percentile from a subsample of healthy young individuals (n=111; 18-39 years; 55,9% female). Correlation coefficients, Bland-Altman graphs and receiver operating characteristic (ROC) curves were calculated for the total sample and stratified by sex and age. Results A total of 268 individuals (44.3 ± 15.2 years) were evaluated (53% female). Significant (p <0.001 for all analysis) and strong correlations between SMA (rho = 0.896), SMI (rho = 0.853), and SMD (rho = 0.864) compared to L3 and thigh landmarks were observed. For the ROC curves, similar AUC values were obtained for males (0.981), females (0.895), younger (0.902) and older adults (0.894). Conclusions Muscle characteristics between L3 and thigh landmarks have a strong correlation. This suggests that images of the thigh can be used to characterize muscle characteristics. Image acquisition and analysis of thigh region is simpler, with less radiation exposure, and consequently more appropriate for longitudinal analysis.
Article
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Low skeletal muscle area (SMA) and muscle radiation attenuation (MRA) have been associated with poor prognosis in various patient populations. Both non-contrast and contrast CT scans are used to determine SMA and MRA. The effect of the use of a contrast agent on SMA and MRA is unknown. Therefore, we investigated agreement between these two scan options. SMA and MRA of 41 healthy individuals were analysed on a paired non-contrast and contrast single CT scan, and agreement between paired scan results was assessed with use of Bland–Altman plots, intraclass correlation coefficients (ICCs), standard error of measurements (SEM) and smallest detectable differences at a 95% confidence level (SDD95). Analyses were stratified by tube voltage. Difference in SMA between non-contrast and contrast scans made with a different tube voltage was 7·0 ± 7·5 cm2; for scans made with the same tube voltage this was 2·3 ± 1·7 cm2. Agreement was excellent for both methods: ICC: 0·952, SEM: 7·2 cm2, SDD95: 19·9 cm2 and ICC: 0·997, SEM: 2·0 cm2, SDD95: 5·6 cm2, respectively. MRA of scans made with a different tube voltage differed 1·3 ± 11·3 HU, and agreement was poor (ICC: 0·207, SEM: 7·9 HU, SDD95: 21·8 HU). For scans made with the same tube voltage the difference was 6·7 ± 3·2 HU, and agreement was good (ICC: 0·682, SEM: 5·3 HU, SDD95: 14·6 HU). In conclusion, SMA and MRA can be slightly influenced by the use of contrast agent. To minimise measurement error, image acquisition parameters of the scans should be similar.
Article
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Background: The association between body composition (e.g. sarcopenia or visceral obesity) and treatment outcomes, such as survival, using single-slice computed tomography (CT)-based measurements has recently been studied in various patient groups. These studies have been conducted with different software programmes, each with their specific characteristics, of which the inter-observer, intra-observer, and inter-software correlation are unknown. Therefore, a comparative study was performed. Methods: Fifty abdominal CT scans were randomly selected from 50 different patients and independently assessed by two observers. Cross-sectional muscle area (CSMA, i.e. rectus abdominis, oblique and transverse abdominal muscles, paraspinal muscles, and the psoas muscle), visceral adipose tissue area (VAT), and subcutaneous adipose tissue area (SAT) were segmented by using standard Hounsfield unit ranges and computed for regions of interest. The inter-software, intra-observer, and inter-observer agreement for CSMA, VAT, and SAT measurements using FatSeg, OsiriX, ImageJ, and sliceOmatic were calculated using intra-class correlation coefficients (ICCs) and Bland-Altman analyses. Cohen's κ was calculated for the agreement of sarcopenia and visceral obesity assessment. The Jaccard similarity coefficient was used to compare the similarity and diversity of measurements. Results: Bland-Altman analyses and ICC indicated that the CSMA, VAT, and SAT measurements between the different software programmes were highly comparable (ICC 0.979-1.000, P < 0.001). All programmes adequately distinguished between the presence or absence of sarcopenia (κ = 0.88-0.96 for one observer and all κ = 1.00 for all comparisons of the other observer) and visceral obesity (all κ = 1.00). Furthermore, excellent intra-observer (ICC 0.999-1.000, P < 0.001) and inter-observer (ICC 0.998-0.999, P < 0.001) agreement for all software programmes were found. Accordingly, excellent Jaccard similarity coefficients were found for all comparisons (mean ≥ 0.964). Conclusions: FatSeg, OsiriX, ImageJ, and sliceOmatic showed an excellent agreement for CSMA, VAT, and SAT measurements on abdominal CT scans. Furthermore, excellent inter-observer and intra-observer agreement were achieved. Therefore, results of studies using these different software programmes can reliably be compared.
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Background: A lack of agreement on definitions and terminology used for nutrition-related concepts and procedures limits the development of clinical nutrition practice and research. Objective: This initiative aimed to reach a consensus for terminology for core nutritional concepts and procedures. Methods: The European Society of Clinical Nutrition and Metabolism (ESPEN) appointed a consensus group of clinical scientists to perform a modified Delphi process that encompassed e-mail communication, face-to-face meetings, in-group ballots and an electronic ESPEN membership Delphi round. Results: Five key areas related to clinical nutrition were identified: concepts; procedures; organisation; delivery; and products. One core concept of clinical nutrition is malnutrition/undernutrition, which includes disease-related malnutrition (DRM) with (eq. cachexia) and without inflammation, and malnutrition/undernutrition without disease, e.g. hunger-related malnutrition. Over-nutrition (overweight and obesity) is another core concept. Sarcopenia and frailty were agreed to be separate conditions often associated with malnutrition. Examples of nutritional procedures identified include screening for subjects at nutritional risk followed by a complete nutritional assessment. Hospital and care facility catering are the basic organizational forms for providing nutrition. Oral nutritional supplementation is the preferred way of nutrition therapy but if inadequate then other forms of medical nutrition therapy, i.e. enteral tube feeding and parenteral (intravenous) nutrition, becomes the major way of nutrient delivery. Conclusion: An agreement of basic nutritional terminology to be used in clinical practice, research, and the ESPEN guideline developments has been established. This terminology consensus may help to support future global consensus efforts and updates of classification systems such as the International Classification of Disease (ICD). The continuous growth of knowledge in all areas addressed in this statement will provide the foundation for future revisions.
Article
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We employed a whole body magnetic resonance imaging protocol to examine the influence of age, gender, body weight, and height on skeletal muscle (SM) mass and distribution in a large and heterogeneous sample of 468 men and women. Men had significantly ( P < 0.001) more SM in comparison to women in both absolute terms (33.0 vs. 21.0 kg) and relative to body mass (38.4 vs. 30.6%). The gender differences were greater in the upper (40%) than lower (33%) body ( P < 0.01). We observed a reduction in relative SM mass starting in the third decade; however, a noticeable decrease in absolute SM mass was not observed until the end of the fifth decade. This decrease was primarily attributed to a decrease in lower body SM. Weight and height explained ∼50% of the variance in SM mass in men and women. Although a linear relationship existed between SM and height, the relationship between SM and body weight was curvilinear because the contribution of SM to weight gain decreased with increasing body weight. These findings indicate that men have more SM than women and that these gender differences are greater in the upper body. Independent of gender, aging is associated with a decrease in SM mass that is explained, in large measure, by a decrease in lower body SM occurring after the fifth decade.
Poster
Low skeletal muscle area (SMA) and mean muscle attenuation (MMA) have been associated with poor prognosis in various patient populations. To determine SMA and MMA, both non-contrast and contrast CT scans are used. The effect of the use of a contrast agent on SMA and MMA is unknown. Therefore, we investigated agreement between these two scan options.
Article
Objectives: To assess the impact of muscle composition and sarcopenia on overall survival in advanced epithelial ovarian cancer (EOC) after primary debulking surgery (PDS). Methods: Women with stage IIIC/IV EOC who underwent PDS with curative intent between 1/1/2006 and 12/31/2012 were included. Patient variables and vital status were abstracted. Body composition was evaluated in a semi-automated process using Slice-O-Matic software v4.3 (TomoVision). Skeletal muscle area and mean skeletal muscle attenuation were recorded. Associations with overall survival were evaluated using Cox proportional hazards models and recursive partitioning. Results: We identified 296 patients and 132 (44.6%) were classified as sarcopenic. The average mean skeletal muscle attenuation of the entire cohort was 33.4 Hounsfield units (HU). A multivariate model of overall risk of death included histology, residual disease, and mean skeletal attenuation. Among patients without residual disease, overall survival, but not progression free survival was significantly different between patients with low versus high mean skeletal attenuation (median survival, 2.8 vs. 3.3 years). Among patients with residual disease, overall survival was significantly different between patients with low versus high mean skeletal attenuation ≥36.40 vs. <36.40 HU (median survival, 2.0 vs. 3.3 years). Conclusions: Sarcopenia and low mean skeletal muscle attenuation are common in women undergoing PDS for advanced EOC. These factors are associated with poorer outcomes, and can be used in preoperative risk stratification and patient counseling. Further research into body composition and whether this risk factor can be altered via nutrition or fitness in this population is warranted.
Article
Objectives: Low skeletal muscle, referred to as sarcopenia, has been shown to be an independent predictor of lower overall survival in various kinds of diseases. Several studies have evaluated the low skeletal muscle mass using computed tomography (CT) imaging. However, the cutoff values based on CT imaging remain undetermined in Asian populations. Methods: Preoperative plain CT imaging at the third lumbar vertebrae level was used to measure the psoas muscle mass index (PMI, cm(2)/m(2)) in 541 adult donors for living donor liver transplantation (LDLT). We analyzed PMI distribution according to sex or donor age, and determined the sex-specific cutoff values of PMI to define low skeletal muscle mass. Results: PMI in men was significantly higher than observed in women (8.85 ± 1.61 cm(2)/m(2) versus 5.77 ± 1.21 cm(2)/m(2); P < 0.001). PMI was significantly lower in individuals ≥50 y than in younger donors in both men and women (P < 0.001 and P < 0.001, respectively). On the basis of the younger donor data, we determined the sex-specific cutoff values for the low skeletal muscle mass were 6.36 cm(2)/m(2) for men and 3.92 cm(2)/m(2) for women (mean - 2 SD). Conclusion: Data from healthy young Asian adults were used to establish new criteria for low skeletal muscle mass that would be applicable for defining sarcopenia in Asian populations.
Article
Objective: To provide a consensus-based minimum set of criteria for the diagnosis of malnutrition to be applied independent of clinical setting and aetiology, and to unify international terminology. Method: The European Society of Clinical Nutrition and Metabolism (ESPEN) appointed a group of clinical scientists to perform a modified Delphi process, encompassing e-mail communications, face-to-face meetings, in group questionnaires and ballots, as well as a ballot for the ESPEN membership. Result: First, ESPEN recommends that subjects at risk of malnutrition are identified by validated screening tools, and should be assessed and treated accordingly. Risk of malnutrition should have its own ICD Code. Second, a unanimous consensus was reached to advocate two options for the diagnosis of malnutrition. Option one requires body mass index (BMI, kg/m(2)) <18.5 to define malnutrition. Option two requires the combined finding of unintentional weight loss (mandatory) and at least one of either reduced BMI or a low fat free mass index (FFMI). Weight loss could be either >10% of habitual weight indefinite of time, or >5% over 3 months. Reduced BMI is <20 or <22 kg/m(2) in subjects younger and older than 70 years, respectively. Low FFMI is <15 and <17 kg/m(2) in females and males, respectively. About 12% of ESPEN members participated in a ballot; >75% agreed; i.e. indicated ≥7 on a 10-graded scale of acceptance, to this definition. Conclusion: In individuals identified by screening as at risk of malnutrition, the diagnosis of malnutrition should be based on either a low BMI (<18.5 kg/m(2)), or on the combined finding of weight loss together with either reduced BMI (age-specific) or a low FFMI using sex-specific cut-offs.