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Review

Reference Values for Skeletal Muscle Mass – Current Concepts and Methodological Considerations

by
Carina O. Walowski
1,
Wiebke Braun
1,
Michael J. Maisch
2,
Björn Jensen
2,
Sven Peine
3,
Kristina Norman
4,5,
Manfred J. Müller
1 and
Anja Bosy-Westphal
1,*
1
Institute for Human Nutrition and Food Science, Christian-Albrechts-University Kiel, 24105 Kiel, Germany
2
seca gmbh & co. kg., Hammer Steindamm 3-25, 22089 Hamburg, Germany
3
Institute for Transfusion Medicine, University Hospital Hamburg-Eppendorf, 20246 Hamburg, Germany
4
Department of Nutrition and Gerontology, German Institute of Human Nutrition, Potsdam-Rehbruecke, 14558 Berlin, Germany
5
Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, 13347 Berlin, Germany
*
Author to whom correspondence should be addressed.
Nutrients 2020, 12(3), 755; https://doi.org/10.3390/nu12030755
Submission received: 17 February 2020 / Revised: 6 March 2020 / Accepted: 9 March 2020 / Published: 12 March 2020
(This article belongs to the Special Issue Nutrition, Metabolic Status, and Body Composition)
Versions Notes

Abstract

:
Assessment of a low skeletal muscle mass (SM) is important for diagnosis of ageing and disease-associated sarcopenia and is hindered by heterogeneous methods and terminologies that lead to differences in diagnostic criteria among studies and even among consensus definitions. The aim of this review was to analyze and summarize previously published cut-offs for SM applied in clinical and research settings and to facilitate comparison of results between studies. Multiple published reference values for discrepant parameters of SM were identified from 64 studies and the underlying methodological assumptions and limitations are compared including different concepts for normalization of SM for body size and fat mass (FM). Single computed tomography or magnetic resonance imaging images and appendicular lean soft tissue by dual X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) are taken as a valid substitute of total SM because they show a high correlation with results from whole body imaging in cross-sectional and longitudinal analyses. However, the random error of these methods limits the applicability of these substitutes in the assessment of individual cases and together with the systematic error limits the accurate detection of changes in SM. Adverse effects of obesity on muscle quality and function may lead to an underestimation of sarcopenia in obesity and may justify normalization of SM for FM. In conclusion, results for SM can only be compared with reference values using the same method, BIA- or DXA-device and an appropriate reference population. Limitations of proxies for total SM as well as normalization of SM for FM are important content-related issues that need to be considered in longitudinal studies, populations with obesity or older subjects.

1. Introduction

Beyond the well-established role of ageing associated loss in skeletal muscle mass (SM) (primary sarcopenia) as a risk factor of frailty, morbidity and mortality in older people, a low SM is observed as a result of diseases like malignant cancer, chronic obstructive pulmonary disease, heart failure and renal failure (secondary sarcopenia [1]) and is also an emerging prognostic marker in a number of diseases [2,3,4,5,6,7,8,9,10,11,12]. The etiology for sarcopenia as a risk factor might be partly explained by the correlation between SM and cardiac, respiratory or immune function but remains to be investigated further in order to understand the preventative and therapeutic potential of SM. Muscle not only functions as the major tissue for insulin-stimulated glucose uptake, amino acid storage and thermoregulation, but is also secreting a large number of myokines that regulate metabolism in muscle itself as well as in other tissues and organs including adipose tissue, the liver and the brain [13,14]. The recent popularity of SM outpaced the interest in fat mass (FM) that only has a limited and inconsistent impact on morbidity and mortality [15,16]. The assessment of SM by segmentation of continuous whole body magnetic resonance imaging (MRI) is considered as the gold standard [17]. However, this method is too cumbersome and expensive for clinical practice and is even rarely used in studies with larger sample sizes [17,18]. Instead, single slices at different reference sites measured by MRI or obtained from routine computed tomography (CT) examinations are taken as a proxy for the total tissue volume (e.g., L3 muscle cross-sectional area [17,19]). Most commonly, dual X-ray absorptiometry (DXA) is used to assess appendicular lean soft tissue (ASM, the sum of lean soft tissue from both arms and legs) or fat-free mass (FFM, total lean soft tissue plus bone mineral mass or body weight minus FM) as a proxy for SM. More simple and even non-invasive, the output of bioelectrical impedance analysis (BIA) depends on the reference method used to generate the BIA algorithm and can be FFM [20], ASM, e.g., [21,22,23] or even SM, e.g., [24,25,26,27].
To facilitate comparison between studies and to evaluate individual results for SM in patients, it is important to understand the differences between parameters and cut-offs for SM. These differences are not only method inherent but also depend on characteristics of the study population (e.g., ethnicity, age and disease). Device-specific characteristics by different manufacturers determine the validity and precision of parameters for SM. In addition, the available reference values differ with respect to parametric normalization (linear regression or indexing) to account for body size. Further complexity to the definition of a normal SM is derived from the concept of sarcopenic obesity [28]. Since high levels of FM may adversely affect the quality and function of SM [29,30], a normal SM may also depend on the amount of FM.
Different professional associations have published definitions of sarcopenia based on an estimate of SM and impaired muscle strength and/or physical performance [31,32,33,34,35,36,37], but no consensus definition has yet been reached. The aim of this review is not to provide an optimal diagnosis of sarcopenia but to compare current definitions of a low SM considering the impact of the underlying methodological assumptions, limitations and normalization of SM parameters for height, weight, body mass index (BMI) or FM.

2. Methods

In order to identify reference values for SM, seven consensus reports were reviewed [31,32,33,34,35,36,37]. Further studies were identified through reference lists and a search for relevant articles based on the keywords “sarcopenia”, “low muscle mass”, “cut-off sarcopenia”, “reference value sarcopenia”, “sarcopenic obesity”. Only parameters of SM normalized for height, weight, BMI or FM were considered. To be included in this article, studies were required to contain the following information: method of SM assessment (device), cut-off points for SM and description of the reference population including geographical location, sample size, distribution between sexes and age (range and/or standard deviation (SD) ± mean). Only English language articles were considered. Therefore, 64 studies were identified that met the inclusion criteria. Main reasons for the exclusion of articles were duplicate analyses conducted on the same reference population (only the first published paper was included), a missing normalization of reference values, a sample size <200 subjects (sample size <200 subjects will not be representative for both sexes, all ages and BMI-groups), the use of anthropometric measures to determine a low SM and the adoption of previously published cut-offs regarding SM and obesity.

Study Characteristics

Studies that met the inclusion criteria were published between 1998 and 2019 and were performed in 21 countries. The sample size of the individual studies ranged from 200 to 38,099 subjects with an age range between 18 and >90 years. In 36 studies, the authors clearly indicated that the reference population included healthy individuals.

3. Results

Published cut-off points for a low SM normalized by height are presented in Table 1, Table 2 and Table 3 stratified by DXA, BIA and CT. In the majority of studies (14 of 32), SM was measured by DXA using lean soft tissue from the arms and legs normalized by height2 given as appendicular skeletal muscle mass index (ASMI) [22,38,39,40,41,42,43,44,45,46,47,48,49,50]. One study [40] used DXA-derived ASM to predict whole body SM measured by MRI using the equation by Kim et al. [51] that was validated in an ethnically diverse sample of healthy men and women. The range of published cut-off values for ASMI by DXA (without considering different classes of sarcopenia) was 5.86–7.40 kg/m2 in men and 4.42–5.67 kg/m2 in women.
With ten studies, the second most commonly used method underlying published SM reference values was BIA [21,22,23,24,25,26,52,53,54,55]. To measure SM by BIA, five studies have used the BIA-equation by Janssen et al. [56] to predict SM [24,25,26,53,55]. This BIA-equation was developed and cross-validated against whole body MRI in a sample of 269 Caucasian men and women aged 18 to 86 years with a BMI of 16-48 kg/m2 using a model 101B BIA analyzer (RJL Systems, Detroit, MI, USA) [56]. The authors reported that the BIA-equation is applicable for Caucasian, African-American, and Hispanic populations but has not been validated for the estimation of SM in Asian populations. One study calculated SM by multiplying BIA-derived FFM with a constant factor (0.566) derived from comparison with SM estimates by 24 h creatinine excretion in healthy subjects [52]. The range of cut-offs for ASMI by BIA was 6.75–7.40 kg/m2 in men and 5.07–5.80 kg/m2 in women, whereas cut-offs for skeletal muscle mass index (SMI) by BIA validated against MRI ranged between 7.70 and 9.20 kg/m2 in men and 5.67 and 7.40 kg/m2 in women (without considering severity of sarcopenia).
Nine studies used standard diagnostic CT to determine SM cut-off points for single slices [57,58,59,60,61,62,63,64,65]. Skeletal muscle area (SMA) at the level of the third lumbar vertebra (L3 SMA; L3 SMI = L3 SMA/height2, cm2/m2) was used in three studies on patients with cancer [62,64,65]. Cut-off points ranged between 36.00 and 43.20 cm2/m2 in men and 29.00 and 34.90 cm2/m2 in women. Six studies determined sex-specific cut-offs for SM by CT in healthy populations, thereof five in organ donors [57,58,59,60,61,63]. L3 SMI was used in four studies on healthy subjects [57,58,59,60] and three studies with a healthy reference group used CT imaging at the L3 level to measure the psoas muscle mass area (L3 PMA; L3 psoas muscle index (PMI) = L3 PMA/height2, cm2/m2) [57,61,63]. In healthy populations, cut-off values for L3 SMI ranged between 36.54 and 45.40 cm2/m2 in men and 30.21 and 36.05 cm2/m2 in women, whereas thresholds for L3 PMI were 2.63-6.36 cm2/m2 for men and 1.48–4.00 cm2/m2 for women.

Combination of Measures for Muscle mass and Obesity

Table 4 shows reference values of 34 publications for a low SM in combination with different measures of obesity. Cut-offs for a low SM were mostly determined by DXA or BIA, whereas only a few studies reported CT-defined cut-offs in combination with obesity criteria. SM parameters were commonly normalized for height squared or given as % of body weight. In addition, two studies adjusted ASM for BMI [66,67]. Alternative parameters were FM/FFM ratio [68], visceral fat area/thigh muscle area ratio (VFA/TMA) [69] and fat mass index (FMI) in combination with fat-free mass index (FFMI) [70].
Prado et al. [71] published CT-derived SMI cut-offs determined in a population of obese (BMI ≥ 30 kg/m2) Canadians with tumors of the respiratory or gastrointestinal tract. In 2013, this CT database was extended by Martin et al. [72] and low SM reference values were reported for subjects with normal weight and overweight according to BMI classifications. In both studies, optimal stratification was used to determine the threshold of mortality. Many studies adopted the criteria proposed by Prado et al. [71] and Martin et al. [72] (e.g., [73,74,75]). Only one further study developed BMI-dependent reference values for SM [76]. Although some studies referenced the cut-offs by Prado et al. [71], reported thresholds differ from the original work (e.g., [77,78]). These reported values were then cited in further studies [79].
In most studies, obesity was defined as BMI ≥ 30 kg/m2 [71,76,80,81]. Alternative BMI thresholds were 27.5 kg/m2 [82,83], 27 kg/m2 [84], 25 kg/m2 [72,85,86,87,88,89,90] or 23 kg/m2 [91]. Furthermore, sex and ethnic-specific waist circumference (WC) thresholds for central obesity were considered [44,84,92,93,94,95]. Other criteria include %FM [50,81,96,97,98,99,100,101], visceral fat area [73] or fat-muscle ratios like visceral fat area (VFA) to total abdominal muscle area (TAMA) [74].
Table 5 displays cut-offs and average values for body composition stratified into groups of subjects with underweight, normal weight, overweight and obesity. Cut-offs for FMIDXA were released by the National Health and Nutrition Examination Survey (NHANES; [102]) and respective BMI-dependent normal values for FFMIDXA were calculated as BMI minus FMI. For each given BMI displayed in Table 5, corresponding normal value for SMIMRI were calculated using a stepwise regression analysis (SMIMRI, men = 0.479 × FFMIDXA −0.017 × age + 0.683 and SMIMRI, women = 0.348 × FFMIDXA − 0.011 × age + 1.971) in a healthy Caucasian population. In addition, respective values for SMIBIA validated against MRI were generated based on a young and healthy Caucasian population using linear regression analysis (SMIBIA, men = 0.168 × BMI + 5.49 (R2 = 0.53, standard error of estimate (SEE) = 0.514) and SMIBIA, women = 0.159 × BMI + 3.72 (R2 = 0.61, SEE = 0.465)). Adjacent to the average SMIBIA (median) for each BMI, cut-offs with two SDs below the sex-specific mean of the young and healthy population were shown.

4. Discussion

SM has evolved as the most promising body composition parameter associated with health risk in ageing and many chronic diseases [1]. Evaluation of SM is complicated by a variety of available methods that provide different outcome parameters as a proxy for total body SM. Therefore, it is important to have accurate reference values that apply to the patient or population under study as well as to the respective body composition method. In this review, we identified multiple published reference values for discrepant parameters of SM (Table 1, Table 2, Table 3 and Table 4), discussed the differences in the underlying assumptions and limitations as well as different concepts for normalization of SM parameters for height, weight, BMI or FM.
Imaging technologies are thought to provide the best assessment of SM. Briefly, segmentation of transversal images by special software (e.g., SliceOmatic Tomovision, version 4.3; Montreal, Québec, Canada) results in muscle areas that are multiplied by the correspondent slice thickness to calculate muscle volume [27] that is transformed to SM by assuming a constant density (1.04 kg/L) of adipose tissue-free SM [103]. Muscles at the head, hands and feet are commonly neglected in this approach. The precision of whole body SMMRI is high (intra-observer coefficient of variation = 1.8% [104]). Reference data for total SM based on the gold standard whole body MRI (Table 5) are scarce due to high costs and cumbersome image-segmentation [17,18]. However, whole body MRI was integrated in the assessment of current large and representative national databases like the UK biobank [105] or the national cohort (NAKO) in Germany [106]. Future evaluation of these databases will provide the basis of statistically derived normal values whereas prospective investigation of mortality or correlation with frailty, fracture risk, glucose or amino acid metabolism would allow to establish even more meaningful disease-specific cut-offs.
Instead of whole body imaging, reference values for L3 single slices are frequently published (Table 3 and Table 4), especially in patients where CT images are routinely applied for cancer staging. The use of these cut-offs may be specific for the population studied and transferability of the results to other patient groups needs to be investigated. Radiation exposure is a major limitation that confines the application of CT to individual transversal images or the secondary analysis of routine clinical measurements. As a further drawback, clinical CT protocols for L3 are not standardized across hospital sites. SMA at L1, L2, L4, L5, and the thoracic vertebra T12, T11, and T10 were reported to be suitable alternatives to SMA measured at L3 [58]. Nonetheless, there are also advantages of CT images with a high resolution and precision of the measurement. Most studies report the precision of single slice CT scan analysis to range between 1% and 2% [107]. Thus, automated segmentation is facilitated by using a characteristic range of Hounsfield units for fat-free muscle tissue [107,108]. CT can also differentiate individual muscle or muscle groups and can thus for example investigate the impact of pectoralis muscle area for survival at the Intensive Care Unit [12] because respiratory musculature may determine weaning from mechanical ventilation. On the other hand, characteristic changes in the Hounsfield distribution of muscle can reveal qualitative changes of the tissue (e.g., fatty infiltration or edema) that have been found to be of prognostic value [71].
DXA is the most commonly used method for assessment of SM (Table 1). Lean soft tissue at the arms and legs (ASM) is highly correlated with muscle volume derived from imaging studies (correlation coefficients ranging from 0.77 to 0.97 for both, whole body and regional scans [51,109,110,111,112,113,114,115]). However, only 44% of total lean soft tissue is derived from extremities (unpublished results) and only part of total lean soft tissue is SM. Therefore, SM measured by DXA is considerably higher when compared with muscle volume measured by imaging technologies [27,116]. Precision errors for total ASM are reported to be low (1–3%), device specific and depend on population characteristics like age or prevalence of obesity [117].
BIA can assess SM, ASM or FFM, depending on the reference method used to generate the BIA-algorithm. The choice of the BIA-algorithm not only depends on the desired target-parameter but also on the agreement between the BIA-device or reference population used to generate the BIA-algorithm and the BIA-device and patient characteristics to be evaluated [118]. However, in two studies, the equation by Janssen et al. [56] that is not suitable for Asians was used to predict SM in Asian populations [53,55] with only one study providing a validation in 41 Taiwanese people (age: 20–99 years; BMI: 17.6–34.6 kg/m2) [55]. Except for the study by Masanés et al. [26], all other studies used different BIA devices than Janssen et al. [56] (Table 2). Validity and precision of BIA results differ between manufacturers and depend on the hardware as well as the appropriate validation of the BIA-algorithm [119]. Discrepancies in the assumptions of the homogeneous bioelectrical model that lead to a higher measurement error occur with changes in hydration (e.g., edema) and with differences in body shape that are associated with aging (decreasing limb relative to trunk diameter), obesity (apple and pear shape of body fat distribution) and ethnicity (trunk to leg length, regional adiposity and muscularity). Therefore, segmental BIA that can measure the relative contribution of trunk and extremities to total body conductivity may help to reduce assumptions on body shape leading to an improved prediction compared with conventional wrist-ankle measurements [27]. The accuracy of phase-sensitive segmental BIA compared with MRI as a reference is clinically acceptable when whole body SM was assessed (two SDs: 11–12% for different ethnicities) but it was low when small compartments of the body were assessed (e.g., two SDs: 20–29% for the arms) [27].

4.1. Limitations of Proxies for Total Skeletal Muscle

Single SMA at L3 level turned out to be the best compromise site to assess volumes of total SM together with visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) (r = 0.832–0.986; p < 0.01 [17]). Furthermore, SMA at L3 is considered as a valid proxy for whole body FFM (r = 0.940; p < 0.001 [120]). Other authors reported high correlations between single abdominal SMA at L4-L5 intervertebral space and total SM (r = 0.710–0.920 [121]), whereas the use of PMI to determine whole body SM is controversial because psoas is a relatively small muscle. A good correlation between PMI and SMI measured by BIA in healthy 35 Asian liver donors (r = 0.737; p < 0.001) and a moderate correlation in 137 living donor liver transplantation recipients (r = 0.682; p < 0.001) were found [63]. Other authors argue that L3 PMA is not representative of total SM [122,123]. Despite acceptable correlations, the accuracy of single images is limited in individual cases. Likewise, it is well established that the correlation between BMI and FM is fairly good at the population level whereas at the individual level BMI is only a poor indicator of adiposity [124]. In addition, validity of the assessment of changes in SM during follow-up is limited by the use of individual images from L3 or mid-thigh. These images cannot be used as pars pro toto because of regional differences in changes of muscle volume with age or obesity (e.g., the contribution of SMMRI at the arms and legs to ASM tended to decrease at higher adiposity in both genders [104]).
Similarly, ASM has limitations to assess the change in total SM with ageing or overweight and obesity. Since lean soft tissue from the extremities also contains lean compartments from connective tissue (e.g., skin and adipose tissue), SM accounts for only about 50% of FFM in obesity [116]. ASM was therefore shown to overestimate appendicular SM assessed by MRI with increasing BMI [27]. In line with this finding, DXA was also shown to underestimate the age-related loss of thigh muscle mass in comparison with MRI [125]. Furthermore, DXA measures of change in lean mass before and 10-week after resistance training were only modestly associated with MRI measures of change in muscle volume [126].
In summary, the random error of single images or ASM as a proxy for total SM limits the applicability of these substitutes in individual cases and together with the systematic error limit the accurate detection of changes in SM.

4.2. Normalization of Skeletal Muscle Mass for Body Size and Obesity

Normalization of lean mass for weight is inappropriate because two people with the same %FFM who differ in height have a different nutritional status, with the taller person having a lower muscularity [127]. FFM has been shown to scale to height with a power of around two in different ethnicities, ranging from 1.86 in non-Hispanic white women to 2.32 in non-Hispanic black men [128]. Consequently, appropriate normalization of total SM, SM-area, ASM and FFM is performed for height2.
In addition to the physiologic increase in SM with height, there is also an increase in SM with weight gain that depends on the initial amount of FM [129]. The evaluation of SM may thus also depend on the amount of FM. With increasing obesity, adverse effects on myocyte metabolism, muscle tissue composition and peak force generation can be mediated via paracrine signaling of proinflammatory immune cells in intermuscular adipose tissue [30]. The same SM at a higher FM may also lead to a limitation of strength and increased disability because at the same work load, energy expenditure and muscle force are higher for a person with obesity [130]. In line with these mechanisms, patients with a low SM and a concomitant high FM were shown to have a higher morbidity and mortality when compared to patients with a high FM only (for review see [131]). However, it remains unclear whether the risk of a low SM and a high FM is additive or if the risk of a high FM is disproportionally higher at a concomitantly low SM.
Published definitions of sarcopenic obesity use BMI to assess overweight and obesity in combination with fixed cut-offs for a low SM that are derived from subjects with normal weight and/or overweight [72,76]. To the best of our knowledge, all current definitions disregard the relationship between fat and lean mass that can be investigated by applying the Forbes rule (energy partitioning, i.e., the fraction of energy lost or gained as protein, is a nonlinear function of FM [129]) or the Hattori chart (two dimensional plot of FMI vs. FFMI [132]). Table 5 provides novel BMI-dependent SMI cut-offs.
The combination of FFMI with FMI [133], %FM [6,8] or BMI [134] facilitate to investigate the proportional contribution of fat and lean compartments to health risk as well as their presumable interaction. An attractive alternative to the simultaneous use of two indices is integration of information on fat and lean compartments in one index as FM/FFM2. This index was proposed by Wells and Victoria who determined the appropriate power by which to raise the denominator from regressing FM on FFM [135]. The usefulness of this index needs to be investigated in future studies because it depends on a linear correlation between FM and FFM2, as well as on absence of heteroscedasticity.
Beyond diverse methods of normalization (e.g., appendicular lean mass (ALM) adjusted by BMI [66,67], FFM normalized for body surface area (FFMBSA = (weight [kg]0.425 × height [m]0.725) × 0.007184 [20])) heterogeneous outcome parameters (ASMI, SMI, L3 SMI, L3 PMI, FFMI) and a discrepant nomenclature for the same outcome parameter as well as different ways of reporting reference values hinder the comparison between studies. ASMI (i.e., appendicular skeletal muscle mass/height2) and SMI (total skeletal muscle mass/height2) were the most commonly used denominations within publications and therefore consistently applied in Table 1, Table 2, Table 3, Table 4 and Table 5. A great variety of different notations for the same outcome parameter were found for (a) SMI: e.g., skeletal muscle mass index, SMMI [52], muscle mass index, MMI [25,26], total skeletal muscle index, TSMI [53], total body skeletal muscle mass index, TBSMI [40] and also (b) ASMI: e.g., appendicular skeletal muscle mass index, ASMMI [136], appendicular muscle mass index, AMI (appendicular muscle mass (AMM)/height2) [54], relative appendicular skeletal muscle index, RASM [47,137], relative skeletal muscle mass index [138] and appendicular lean mass index (ALM/height2) [21]. In contrast to the heterogeneous nomenclature, some studies apply the same term “SMI” for different outcome parameters: e.g., ALM/BMI [66,67], ASM/height2 [46,139,140], ALM/height2 [141], ASM/body weight [53] and SM/body weight × 100 [25,137,142,143,144]. In cancer studies, SMI is normally defined as SMA/height2 [62,71,72]. Thus, a consistent nomenclature for proxies of SM is needed in order to facilitate comparison between studies.
Moreover, suitable reference values require an appropriate sample size ideally comprised of healthy or “normal” subjects (normative approach) or derive cut-offs from an older population or a group of patients (stratification approach). In addition, reference values can be reported using parametric methods, like Z-scores or 2 SDs below the mean, that rely on normal distribution of the data, on the absence of residual associations, and on constant variance of the normalized measurements throughout the entire sample (absence of heteroscedasticity, logarithmic transformation of the dependent variables or weighted regression models). In Table 1, Table 2, Table 3 and Table 4, most studies used cut-off thresholds for low SM on the basis of young healthy adults’ reference groups according to the recommendations proposed by the European Working Group on Sarcopenia in Older People [32]. The majority of these studies used two SDs below the means of healthy young subjects as a cut-off, e.g., [21,39,40,44,45,50] whereas other studies defined a low SM as one SD below the mean, e.g., [85,90,94,95]. Six articles stratified the cut-offs according to severity of a low SM [22,44,46,49,76,80]. One SM threshold was based on the fifth percentile [59] or on the 20th percentile [92] or on the 50th percentile [89]. Other studies used the sex-specific lowest quintiles [43], quartiles [47,62], tertiles [84], the lower two quintiles of the study population [98,100] or the lowest 20% of the distribution [38,42,48]. In one study, receiver operating characteristics analysis was used to develop SM cut-offs associated with physical disability [24]. In four studies, optimal stratification was used to determine the SM threshold of mortality risk in cancer patients [64,65,71,72]. Further diagnostic criteria applied classification and regression tree analysis [66,67].

5. Conclusions and Recommendations

In summary, published reference values for SM differ widely dependent on the outcome parameter and reference population. Results should consider the limitation of all proxies for total SM with respect to application in individual cases as well as for measurement of changes in SM. To facilitate comparison between results of different studies, authors should use a unified nomenclature for outcome parameters and indicate the device and software version of the body composition analyzer. In addition, the choice of body composition method should depend on the aim of the study. For assessment of changes in SM and evaluation of individual patients, a high precision is required that is, for instance, not fulfilled when segmental bioelectrical impedance is used to assess limb SM. The adverse effects of obesity on muscle quality and function may lead to an underestimation of sarcopenia in obesity and therefore requires normalization of SM for FM.

Author Contributions

Conceptualization, A.B.-W. and W.B.; methodology, C.O.W., A.B.-W. and W.B.; formal analysis, C.O.W., B.J. and W.B.; data curation (Table 5), B.J., S.P. and W.B..; writing—original draft preparation, A.B.-W., C.O.W. and W.B.; writing—review and editing, B.J., M.J.M. (Michael J. Maisch), M.J.M. (Manfred J. Müller), K.N. and S.P. All authors have read and agreed to the published version of the manuscript.

Funding

We acknowledge financial support by Land Schleswig-Holstein within the funding programme Open Access Publikationsfonds.

Conflicts of Interest

Michael Maisch and Björn Jensen are employed by seca gmbh & co. kg., Anja Bosy-Westphal serves a consultant for seca gmbh & co. kg. The other authors declare no conflict of interest.

Abbreviation

ALMappendicular lean mass
ASMappendicular skeletal muscle mass
ASMIappendicular skeletal muscle mass index
BIAbioelectrical impedance analysis
BMIbody mass index
BSAbody surface area
CARTclassification and regression tree analysis
CTcomputed tomography
DXAdual X-ray absorptiometry
FFMfat-free mass
FFMIfat-free mass index
FMfat mass
FMIfat mass index
FNIHFoundation for the National Institutes of Health
IOTFInternational Obesity Taskforce
Llumbar vertebra
L3third lumbar vertebra
MRImagnetic resonance imaging
NAnot available
NAKOGerman National Cohort
NHANESNational Health and Nutrition Examination Survey
NIHNational Institutes of Health
PMApsoas muscle area
PMIpsoas muscle index
SATsubcutaneous adipose tissue
SDstandard deviation
SEEstandard error of estimate
SMskeletal muscle mass
SMIskeletal muscle mass index
SMAskeletal muscle area
Tthoracic vertebra
TAMAtotal abdominal muscle area
TMAthigh muscle area
VATvisceral adipose tissue
VFAvisceral fat area
WCwaist circumference
WHOWorld Health Organization

References

  1. Bauer, J.; Morley, J.E.; Schols, A.M.W.J.; Ferrucci, L.; Cruz-Jentoft, A.J.; Dent, E.; Baracos, V.E.; Crawford, J.A.; Doehner, W.; Heymsfield, S.B.; et al. Sarcopenia: A Time for Action. An SCWD Position Paper. J. Cachexia Sarcopenia Muscle 2019, 10, 956–961. [Google Scholar] [CrossRef] [PubMed]
  2. Hanai, T.; Shiraki, M.; Nishimura, K.; Ohnishi, S.; Imai, K.; Suetsugu, A.; Takai, K.; Shimizu, M.; Moriwaki, H. Sarcopenia impairs prognosis of patients with liver cirrhosis. Nutrition 2015, 31, 193–199. [Google Scholar] [CrossRef] [PubMed]
  3. Lin, T.-Y.; Lim, P.-S.; Hung, S.-C. Impact of Misclassification of Obesity by Body Mass Index on Mortality in Patients with CKD. Kidney Int. Rep. 2018, 3, 447–455. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Caan, B.J.; Cespedes Feliciano, E.M.; Prado, C.M.; Alexeeff, S.; Kroenke, C.H.; Bradshaw, P.; Quesenberry, C.P.; Weltzien, E.K.; Castillo, A.L.; Olobatuyi, T.A.; et al. Association of Muscle and Adiposity Measured by Computed Tomography with Survival in Patients with Nonmetastatic Breast Cancer. JAMA Oncol. 2018, 4, 798–804. [Google Scholar] [CrossRef] [PubMed]
  5. Hopkins, J.J.; Reif, R.L.; Bigam, D.L.; Baracos, V.E.; Eurich, D.T.; Sawyer, M.B. The Impact of Muscle and Adipose Tissue on Long-term Survival in Patients with Stage I to III Colorectal Cancer. Dis. Colon Rectum 2019, 62, 549–560. [Google Scholar] [CrossRef]
  6. Huang, B.-T.; Peng, Y.; Liu, W.; Zhang, C.; Huang, F.-Y.; Wang, P.-J.; Zuo, Z.-L.; Liao, Y.-B.; Chai, H.; Huang, K.-S.; et al. Lean mass index, body fat and survival in Chinese patients with coronary artery disease. QJM Int. J. Med. 2015, 108, 641–647. [Google Scholar] [CrossRef] [Green Version]
  7. Medina-Inojosa, J.R.; Somers, V.K.; Thomas, R.J.; Jean, N.; Jenkins, S.M.; Gomez-Ibarra, M.A.; Supervia, M.; Lopez-Jimenez, F. Association Between Adiposity and Lean Mass with Long-Term Cardiovascular Events in Patients with Coronary Artery Disease: No Paradox. J. Am. Heart Assoc. 2018, 7. [Google Scholar] [CrossRef] [Green Version]
  8. Lavie, C.J.; De Schutter, A.; Patel, D.A.; Romero-Corral, A.; Artham, S.M.; Milani, R.V. Body Composition and Survival in Stable Coronary Heart Disease: Impact of lean mass index and body fat in the “obesity paradox”. J. Am. Coll. Cardiol. 2012, 60, 1374–1380. [Google Scholar] [CrossRef] [Green Version]
  9. Toledo, D.O.; Carvalho, A.M.; Oliveira, A.M.R.R.; Toloi, J.M.; Silva, A.C.; Francisco de Mattos Farah, J.; Prado, C.M.; Silva, J.M. The use of computed tomography images as a prognostic marker in critically ill cancer patients. Clin. Nutr. ESPEN 2018, 25, 114–120. [Google Scholar] [CrossRef]
  10. Kou, H.-W.; Yeh, C.-H.; Tsai, H.-I.; Hsu, C.-C.; Hsieh, Y.-C.; Chen, W.-T.; Cheng, H.-T.; Yu, M.-C.; Lee, C.-W. Sarcopenia is an effective predictor of difficult-to-wean and mortality among critically ill surgical patients. PLoS ONE 2019, 14, e0220699. [Google Scholar] [CrossRef] [Green Version]
  11. Moisey, L.L.; Mourtzakis, M.; Cotton, B.A.; Premji, T.; Heyland, D.K.; Wade, C.E.; Bulger, E.; Kozar, R.A.; Nutrition and Rehabilitation Investigators Consortium (NUTRIC). Skeletal muscle predicts ventilator-free days, ICU-free days, and mortality in elderly ICU patients. Crit. Care 2013, 17, R206. [Google Scholar] [CrossRef] [Green Version]
  12. Jaitovich, A.; Khan, M.M.H.S.; Itty, R.; Chieng, H.C.; Dumas, C.L.; Nadendla, P.; Fantauzzi, J.P.; Yucel, R.M.; Feustel, P.J.; Judson, M.A. ICU Admission Muscle and Fat Mass, Survival, and Disability at Discharge: A Prospective Cohort Study. Chest 2019, 155, 322–330. [Google Scholar] [CrossRef]
  13. Carson, B.P. The Potential Role of Contraction-Induced Myokines in the Regulation of Metabolic Function for the Prevention and Treatment of Type 2 Diabetes. Front. Endocrinol. 2017, 8, 97. [Google Scholar] [CrossRef] [Green Version]
  14. Lee, J.H.; Jun, H.-S. Role of Myokines in Regulating Skeletal Muscle Mass and Function. Front. Physiol. 2019, 10, 42. [Google Scholar] [CrossRef]
  15. Bigaard, J.; Frederiksen, K.; Tjønneland, A.; Thomsen, B.L.; Overvad, K.; Heitmann, B.L.; Sørensen, T.I.A. Body Fat and Fat-Free Mass and All-Cause Mortality. Obes. Res. 2004, 12, 1042–1049. [Google Scholar] [CrossRef]
  16. Lee, D.H.; Giovannucci, E.L. Body composition and mortality in the general population: A review of epidemiologic studies. Exp. Biol. Med. 2018, 243, 1275–1285. [Google Scholar] [CrossRef] [Green Version]
  17. Schweitzer, L.; Geisler, C.; Pourhassan, M.; Braun, W.; Glüer, C.-C.; Bosy-Westphal, A.; Müller, M.J. What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults? Am. J. Clin. Nutr. 2015, 102, 58–65. [Google Scholar] [CrossRef] [Green Version]
  18. Heymsfield, S.B.; Gonzalez, M.C.; Lu, J.; Jia, G.; Zheng, J. Skeletal muscle mass and quality: Evolution of modern measurement concepts in the context of sarcopenia. Proc. Nutr. Soc. 2015, 74, 355–366. [Google Scholar] [CrossRef] [Green Version]
  19. Jung Lee, S.; Janssen, I.; Heymsfield, S.B.; Ross, R. Relation between whole-body and regional measures of human skeletal muscle. Am. J. Clin. Nutr. 2004, 80, 1215–1221. [Google Scholar] [CrossRef] [Green Version]
  20. Bahat, G.; Saka, B.; Tufan, F.; Akin, S.; Sivrikaya, S.; Yucel, N.; Erten, N.; Karan, M.A. Prevalence of sarcopenia and its association with functional and nutritional status among male residents in a nursing home in Turkey. Aging Male 2010, 13, 211–214. [Google Scholar] [CrossRef]
  21. Krzymińska-Siemaszko, R.; Fryzowicz, A.; Czepulis, N.; Kaluźniak-Szymanowska, A.; Dworak, L.B.; Wieczorowska-Tobis, K. The impact of the age range of young healthy reference population on the cut-off points for low muscle mass necessary for the diagnosis of sarcopenia. Eur. Rev. Med. Pharmacol. Sci. 2019, 23, 4321–4332. [Google Scholar] [CrossRef]
  22. Alkahtani, S.A. A cross-sectional study on sarcopenia using different methods: Reference values for healthy Saudi young men. BMC Musculoskelet. Disord. 2017, 18, 119. [Google Scholar] [CrossRef] [Green Version]
  23. Yamada, M.; Nishiguchi, S.; Fukutani, N.; Tanigawa, T.; Yukutake, T.; Kayama, H.; Aoyama, T.; Arai, H. Prevalence of Sarcopenia in Community-Dwelling Japanese Older Adults. J. Am. Med. Dir. Assoc. 2013, 14, 911–915. [Google Scholar] [CrossRef]
  24. Janssen, I.; Baumgartner, R.N.; Ross, R.; Rosenberg, I.H.; Roubenoff, R. Skeletal Muscle Cutpoints Associated with Elevated Physical Disability Risk in Older Men and Women. Am. J. Epidemiol. 2004, 159, 413–421. [Google Scholar] [CrossRef]
  25. Tichet, J.; Vol, S.; Goxe, D.; Salle, A.; Berrut, G.; Ritz, P. Prevalence of sarcopenia in the French senior population. J. Nutr. Health Aging 2008, 12, 202–206. [Google Scholar] [CrossRef] [Green Version]
  26. Masanés, F.; Culla, A.; Navarro-Gonzalez, M.; Navarro-Lopez, M.; Sacanella, E.; Torres, B.; Lopez-Soto, A. Prevalence of sarcopenia in healthy community-dwelling elderly in an urban area of Barcelona (Spain). J. Nutr. Health Aging 2012, 16, 184–187. [Google Scholar] [CrossRef]
  27. Bosy-Westphal, A.; Jensen, B.; Braun, W.; Pourhassan, M.; Gallagher, D.; Müller, M.J. Quantification of whole-body and segmental skeletal muscle mass using phase-sensitive 8-electrode medical bioelectrical impedance devices. Eur. J. Clin. Nutr. 2017, 71, 1061–1067. [Google Scholar] [CrossRef]
  28. Prado, C.M.; Siervo, M.; Mire, E.; Heymsfield, S.B.; Stephan, B.C.; Broyles, S.; Smith, S.R.; Wells, J.C.; Katzmarzyk, P.T. A population-based approach to define body-composition phenotypes. Am. J. Clin. Nutr. 2014, 99, 1369–1377. [Google Scholar] [CrossRef] [Green Version]
  29. Akhmedov, D.; Berdeaux, R. The effects of obesity on skeletal muscle regeneration. Front. Physiol. 2013, 4. [Google Scholar] [CrossRef] [Green Version]
  30. Wu, H.; Ballantyne, C.M. Skeletal muscle inflammation and insulin resistance in obesity. J. Clin. Invest. 2017, 127, 43–54. [Google Scholar] [CrossRef]
  31. Cederholm, T.; Jensen, G.L.; Correia, M.I.T.D.; Gonzalez, M.C.; Fukushima, R.; Higashiguchi, T.; Baptista, G.; Barazzoni, R.; Blaauw, R.; Coats, A.; et al. GLIM criteria for the diagnosis of malnutrition—A consensus report from the global clinical nutrition community. Clin. Nutr. 2019, 38, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Cruz-Jentoft, A.J.; Baeyens, J.P.; Bauer, J.M.; Boirie, Y.; Cederholm, T.; Landi, F.; Martin, F.C.; Michel, J.-P.; Rolland, Y.; Schneider, S.M.; et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010, 39, 412–423. [Google Scholar] [CrossRef] [Green Version]
  33. Cruz-Jentoft, A.J.; Bahat, G.; Bauer, J.; Boirie, Y.; Bruyère, O.; Cederholm, T.; Cooper, C.; Landi, F.; Rolland, Y.; Sayer, A.A.; et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 2019, 48, 16–31. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Chen, L.-K.; Liu, L.-K.; Woo, J.; Assantachai, P.; Auyeung, T.-W.; Bahyah, K.S.; Chou, M.-Y.; Chen, L.-Y.; Hsu, P.-S.; Krairit, O.; et al. Sarcopenia in Asia: Consensus Report of the Asian Working Group for Sarcopenia. J. Am. Med. Dir. Assoc. 2014, 15, 95–101. [Google Scholar] [CrossRef]
  35. Chen, L.-K.; Lee, W.-J.; Peng, L.-N.; Liu, L.-K.; Arai, H.; Akishita, M. Recent Advances in Sarcopenia Research in Asia: 2016 Update From the Asian Working Group for Sarcopenia. J. Am. Med. Dir. Assoc. 2016, 17, 767.e1–767.e7. [Google Scholar] [CrossRef]
  36. Studenski, S.A.; Peters, K.W.; Alley, D.E.; Cawthon, P.M.; McLean, R.R.; Harris, T.B.; Ferrucci, L.; Guralnik, J.M.; Fragala, M.S.; Kenny, A.M.; et al. The FNIH Sarcopenia Project: Rationale, Study Description, Conference Recommendations, and Final Estimates. J. Gerontol. A Biomed. Sci. Med Sci. 2014, 69, 547–558. [Google Scholar] [CrossRef] [PubMed]
  37. Fielding, R.A.; Vellas, B.; Evans, W.J.; Bhasin, S.; Morley, J.E.; Newman, A.B.; Abellan van Kan, G.; Andrieu, S.; Bauer, J.; Breuille, D.; et al. Sarcopenia: An Undiagnosed Condition in Older Adults. Current Consensus Definition: Prevalence, Etiology, and Consequences. International Working Group on Sarcopenia. J. Am. Med. Dir. Assoc. 2011, 12, 249–256. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Imboden, M.T.; Swartz, A.M.; Finch, H.W.; Harber, M.P.; Kaminsky, L.A. Reference standards for lean mass measures using GE dual energy x-ray absorptiometry in Caucasian adults. PLoS ONE 2017, 12, e0176161. [Google Scholar] [CrossRef]
  39. Kruger, H.S.; Micklesfield, L.K.; Wright, H.H.; Havemann-Nel, L.; Goedecke, J.H. Ethnic-specific cut-points for sarcopenia: Evidence from black South African women. Eur. J. Clin. Nutr. 2015, 69, 843–849. [Google Scholar] [CrossRef]
  40. Alemán-Mateo, H.; Ruiz Valenzuela, R.E. Skeletal Muscle Mass Indices in Healthy Young Mexican Adults Aged 20–40 Years: Implications for Diagnoses of Sarcopenia in the Elderly Population. Sci. World J. 2014, 2014. [Google Scholar] [CrossRef] [Green Version]
  41. Gould, H.; Brennan, S.L.; Kotowicz, M.A.; Nicholson, G.C.; Pasco, J.A. Total and Appendicular Lean Mass Reference Ranges for Australian Men and Women: The Geelong Osteoporosis Study. Calcif. Tissue Int. 2014, 94, 363–372. [Google Scholar] [CrossRef] [PubMed]
  42. Marwaha, R.K.; Garg, M.K.; Bhadra, K.; Mithal, A.; Tandon, N. Assessment of lean (muscle) mass and its distribution by dual energy X-ray absorptiometry in healthy Indian females. Arch. Osteoporos. 2014, 9, 186. [Google Scholar] [CrossRef] [PubMed]
  43. Yu, R.; Wong, M.; Leung, J.; Lee, J.; Auyeung, T.W.; Woo, J. Incidence, reversibility, risk factors and the protective effect of high body mass index against sarcopenia in community-dwelling older Chinese adults: Sarcopenia incidence and its risk factors. Geriatr. Gerontol. Int. 2014, 14, 15–28. [Google Scholar] [CrossRef] [PubMed]
  44. Kim, Y.-S.; Lee, Y.; Chung, Y.-S.; Lee, D.-J.; Joo, N.-S.; Hong, D.; Song, G.; Kim, H.-J.; Choi, Y.J.; Kim, K.-M. Prevalence of Sarcopenia and Sarcopenic Obesity in the Korean Population Based on the Fourth Korean National Health and Nutritional Examination Surveys. J. Gerontol. A Biomed. Sci. Med. Sci. 2012, 67, 1107–1113. [Google Scholar] [CrossRef]
  45. Oliveira, R.J.; Bottaro, M.; Júnior, J.T.; Farinatti, P.T.V.; Bezerra, L.A.; Lima, R.M. Identification of sarcopenic obesity in postmenopausal women: A cutoff proposal. Braz. J. Med. Biol. Res. 2011, 44, 1171–1176. [Google Scholar] [CrossRef]
  46. Sanada, K.; Miyachi, M.; Tanimoto, M.; Yamamoto, K.; Murakami, H.; Okumura, S.; Gando, Y.; Suzuki, K.; Tabata, I.; Higuchi, M. A cross-sectional study of sarcopenia in Japanese men and women: Reference values and association with cardiovascular risk factors. Eur. J. Appl. Physiol. 2010, 110, 57–65. [Google Scholar] [CrossRef]
  47. Szulc, P.; Duboeuf, F.; Marchand, F.; Delmas, P.D. Hormonal and lifestyle determinants of appendicular skeletal muscle mass in men: The MINOS study. Am. J. Clin. Nutr. 2004, 80, 496–503. [Google Scholar] [CrossRef] [Green Version]
  48. Newman, A.B.; Kupelian, V.; Visser, M.; Simonsick, E.; Goodpaster, B.; Nevitt, M.; Kritchevsky, S.B.; Tylavsky, F.A.; Rubin, S.M.; Harris, T.B.; et al. Sarcopenia: Alternative Definitions and Associations with Lower Extremity Function. J. Am. Geriatr. Soc. 2003, 51, 1602–1609. [Google Scholar] [CrossRef] [Green Version]
  49. Tankó, L.B.; Movsesyan, L.; Mouritzen, U.; Christiansen, C.; Svendsen, O.L. Appendicular lean tissue mass and the prevalence of sarcopenia among healthy women. Metabolism 2002, 51, 69–74. [Google Scholar] [CrossRef]
  50. Baumgartner, R.N.; Koehler, K.M.; Gallagher, D.; Romero, L.; Heymsfield, S.B.; Ross, R.R.; Garry, P.J.; Lindeman, R.D. Epidemiology of Sarcopenia among the Elderly in New Mexico. Am. J. Epidemiol. 1998, 147, 755–763. [Google Scholar] [CrossRef]
  51. Kim, J.; Wang, Z.; Heymsfield, S.B.; Baumgartner, R.N.; Gallagher, D. Total-body skeletal muscle mass: Estimation by a new dual-energy X-ray absorptiometry method. Am. Clin. Nutr. 2002, 76, 378–383. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Bahat, G.; Tufan, A.; Tufan, F.; Kilic, C.; Akpinar, T.S.; Kose, M.; Erten, N.; Karan, M.A.; Cruz-Jentoft, A.J. Cut-off points to identify sarcopenia according to European Working Group on Sarcopenia in Older People (EWGSOP) definition. Clin. Nutr. 2016, 35, 1557–1563. [Google Scholar] [CrossRef] [PubMed]
  53. Chang, C.-I.; Chen, C.-Y.; Huang, K.-C.; Wu, C.-H.; Hsiung, C.A.; Hsu, C.-C.; Chen, C.-Y. Comparison of three BIA muscle indices for sarcopenia screening in old adults. Eur. Geriatr. Med. 2013, 4, 145–149. [Google Scholar] [CrossRef]
  54. Tanimoto, Y.; Watanabe, M.; Sun, W.; Sugiura, Y.; Tsuda, Y.; Kimura, M.; Hayashida, I.; Kusabiraki, T.; Kono, K. Association between sarcopenia and higher-level functional capacity in daily living in community-dwelling elderly subjects in Japan. Arch. Gerontol. Geriatr. 2012, 55, e9–e13. [Google Scholar] [CrossRef]
  55. Chien, M.-Y.; Huang, T.-Y.; Wu, Y.-T. Prevalence of Sarcopenia Estimated Using a Bioelectrical Impedance Analysis Prediction Equation in Community-Dwelling Elderly People in Taiwan. J. Am. Geriatr. Soc. 2008, 56, 1710–1715. [Google Scholar] [CrossRef]
  56. Janssen, I.; Heymsfield, S.B.; Baumgartner, R.N.; Ross, R. Estimation of skeletal muscle mass by bioelectrical impedance analysis. J. Appl. Physiol. 2000, 89, 465–471. [Google Scholar] [CrossRef] [Green Version]
  57. Ufuk, F.; Herek, D. Reference Skeletal Muscle Mass Values at L3 Vertebrae Level Based on Computed Tomography in Healthy Turkish Adults. Int. J. Geront. 2019, 13, 221–225. [Google Scholar]
  58. Derstine, B.A.; Holcombe, S.A.; Ross, B.E.; Wang, N.C.; Su, G.L.; Wang, S.C. Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population. Sci. Rep. 2018, 8, 11369. [Google Scholar] [CrossRef] [Green Version]
  59. van der Werf, A.; Langius, J.A.E.; de van der Schueren, M.A.E.; Nurmohamed, S.A.; van der Pant, K.A.M.I.; Blauwhoff-Buskermolen, S.; Wierdsma, N.J. Percentiles for skeletal muscle index, area and radiation attenuation based on computed tomography imaging in a healthy Caucasian population. Eur. J. Clin. Nutr. 2018, 72, 288–296. [Google Scholar] [CrossRef] [Green Version]
  60. Benjamin, J.; Shasthry, V.; Kaal, C.R.; Anand, L.; Bhardwaj, A.; Pandit, V.; Arora, A.; Rajesh, S.; Pamecha, V.; Jain, V.; et al. Characterization of body composition and definition of sarcopenia in patients with alcoholic cirrhosis: A computed tomography based study. Liver Int. 2017, 37, 1668–1674. [Google Scholar] [CrossRef]
  61. Kim, J.S.; Kim, W.Y.; Park, H.K.; Kim, M.C.; Jung, W.; Ko, B.S. Simple Age Specific Cutoff Value for Sarcopenia Evaluated by Computed Tomography. Ann. Nutr. Metab. 2017, 71, 157–163. [Google Scholar] [CrossRef] [PubMed]
  62. Sakurai, K.; Kubo, N.; Tamura, T.; Toyokawa, T.; Amano, R.; Tanaka, H.; Muguruma, K.; Yashiro, M.; Maeda, K.; Hirakawa, K.; et al. Adverse Effects of Low Preoperative Skeletal Muscle Mass in Patients Undergoing Gastrectomy for Gastric Cancer. Ann. Surg. Oncol. 2017, 24, 2712–2719. [Google Scholar] [CrossRef] [PubMed]
  63. Hamaguchi, Y.; Kaido, T.; Okumura, S.; Kobayashi, A.; Hammad, A.; Tamai, Y.; Inagaki, N.; Uemoto, S. Proposal for new diagnostic criteria for low skeletal muscle mass based on computed tomography imaging in Asian adults. Nutrition 2016, 32, 1200–1205. [Google Scholar] [CrossRef] [PubMed]
  64. Zhuang, C.-L.; Huang, D.-D.; Pang, W.-Y.; Zhou, C.-J.; Wang, S.-L.; Lou, N.; Ma, L.-L.; Yu, Z.; Shen, X. Sarcopenia is an Independent Predictor of Severe Postoperative Complications and Long-Term Survival After Radical Gastrectomy for Gastric Cancer: Analysis from a Large-Scale Cohort. Medicine 2016, 95, e3164. [Google Scholar] [CrossRef] [PubMed]
  65. Iritani, S.; Imai, K.; Takai, K.; Hanai, T.; Ideta, T.; Miyazaki, T.; Suetsugu, A.; Shiraki, M.; Shimizu, M.; Moriwaki, H. Skeletal muscle depletion is an independent prognostic factor for hepatocellular carcinoma. J. Gastroenterol. 2015, 50, 323–332. [Google Scholar] [CrossRef]
  66. Chiles Shaffer, N.; Ferrucci, L.; Shardell, M.; Simonsick, E.M.; Studenski, S. Agreement and Predictive Validity Using Less-Conservative Foundation for the National Institutes of Health Sarcopenia Project Weakness Cutpoints. J. Am. Geriatr. Soc. 2017, 65, 574–579. [Google Scholar] [CrossRef] [Green Version]
  67. Cawthon, P.M.; Peters, K.W.; Shardell, M.D.; McLean, R.R.; Dam, T.-T.L.; Kenny, A.M.; Fragala, M.S.; Harris, T.B.; Kiel, D.P.; Guralnik, J.M.; et al. Cutpoints for Low Appendicular Lean Mass That Identify Older Adults with Clinically Significant Weakness. J. Gerontol. A Biomed. Sci. Med. Sci. 2014, 69, 567–575. [Google Scholar] [CrossRef] [Green Version]
  68. Biolo, G.; Di Girolamo, F.G.; Breglia, A.; Chiuc, M.; Baglio, V.; Vinci, P.; Toigo, G.; Lucchin, L.; Jurdana, M.; Pražnikar, Z.J.; et al. Inverse relationship between “a body shape index” (ABSI) and fat-free mass in women and men: Insights into mechanisms of sarcopenic obesity. Clin. Nutr. 2015, 34, 323–327. [Google Scholar] [CrossRef]
  69. Lim, K.I.; Yang, S.J.; Kim, T.N.; Yoo, H.J.; Kang, H.J.; Song, W.; Baik, S.H.; Choi, D.S.; Choi, K.M. The association between the ratio of visceral fat to thigh muscle area and metabolic syndrome: The Korean Sarcopenic Obesity Study (KSOS). Clin. Endocrinol. 2010, 73, 588–594. [Google Scholar] [CrossRef]
  70. Atkins, J.L.; Whincup, P.H.; Morris, R.W.; Lennon, L.T.; Papacosta, O.; Wannamethee, S.G. Sarcopenic Obesity and Risk of Cardiovascular Disease and Mortality: A Population-Based Cohort Study of Older Men. J. Am. Geriatr. Soc. 2014, 62, 253–260. [Google Scholar] [CrossRef] [Green Version]
  71. Prado, C.M.; Lieffers, J.R.; McCargar, L.J.; Reiman, T.; Sawyer, M.B.; Martin, L.; Baracos, V.E. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: A population-based study. Lancet Oncol. 2008, 9, 629–635. [Google Scholar] [CrossRef]
  72. Martin, L.; Birdsell, L.; MacDonald, N.; Reiman, T.; Clandinin, M.T.; McCargar, L.J.; Murphy, R.; Ghosh, S.; Sawyer, M.B.; Baracos, V.E. Cancer Cachexia in the Age of Obesity: Skeletal Muscle Depletion Is a Powerful Prognostic Factor, Independent of Body Mass Index. J. Clin. Oncol. 2013, 31, 1539–1547. [Google Scholar] [CrossRef] [PubMed]
  73. Nishigori, T.; Tsunoda, S.; Okabe, H.; Tanaka, E.; Hisamori, S.; Hosogi, H.; Shinohara, H.; Sakai, Y. Impact of Sarcopenic Obesity on Surgical Site Infection after Laparoscopic Total Gastrectomy. Ann. Surg. Oncol. 2016, 23, 524–531. [Google Scholar] [CrossRef] [PubMed]
  74. Pecorelli, N.; Carrara, G.; De Cobelli, F.; Cristel, G.; Damascelli, A.; Balzano, G.; Beretta, L.; Braga, M. Effect of sarcopenia and visceral obesity on mortality and pancreatic fistula following pancreatic cancer surgery. Br. J. Surg. 2016, 103, 434–442. [Google Scholar] [CrossRef]
  75. Cushen, S.J.; Power, D.G.; Murphy, K.P.; McDermott, R.; Griffin, B.T.; Lim, M.; Daly, L.; MacEneaney, P.; O’ Sullivan, K.; Prado, C.M.; et al. Impact of body composition parameters on clinical outcomes in patients with metastatic castrate-resistant prostate cancer treated with docetaxel. Clin. Nutr. ESPEN 2016, 13, e39–e45. [Google Scholar] [CrossRef] [PubMed]
  76. Muscariello, E.; Nasti, G.; Siervo, M.; Di Maro, M.; Lapi, D.; D’Addio, G.; Colantuoni, A. Dietary protein intake in sarcopenic obese older women. Clin. Interv. Aging 2016, 133. [Google Scholar] [CrossRef] [Green Version]
  77. Antoun, S.; Baracos, V.E.; Birdsell, L.; Escudier, B.; Sawyer, M.B. Low body mass index and sarcopenia associated with dose-limiting toxicity of sorafenib in patients with renal cell carcinoma. Ann. Oncol. 2010, 21, 1594–1598. [Google Scholar] [CrossRef]
  78. Barret, M.; Antoun, S.; Dalban, C.; Malka, D.; Mansourbakht, T.; Zaanan, A.; Latko, E.; Taieb, J. Sarcopenia Is Linked to Treatment Toxicity in Patients with Metastatic Colorectal Cancer. Nutr. Cancer 2014, 66, 583–589. [Google Scholar] [CrossRef]
  79. Huillard, O.; Mir, O.; Peyromaure, M.; Tlemsani, C.; Giroux, J.; Boudou-Rouquette, P.; Ropert, S.; Delongchamps, N.B.; Zerbib, M.; Goldwasser, F. Sarcopenia and body mass index predict sunitinib-induced early dose-limiting toxicities in renal cancer patients. Br. J. Cancer 2013, 108, 1034–1041. [Google Scholar] [CrossRef] [Green Version]
  80. De Rosa, E.; Santarpia, L.; Marra, M.; Sammarco, R.; Amato, V.; Onufrio, M.; De Simone, G.; Contaldo, F.; Pasanisi, F. Preliminary evaluation of the prevalence of sarcopenia in obese patients from Southern Italy. Nutrition 2015, 31, 79–83. [Google Scholar] [CrossRef]
  81. Kemmler, W.; von Stengel, S.; Engelke, K.; Sieber, C.; Freiberger, E. Prevalence of sarcopenic obesity in Germany using established definitions: Baseline data of the FORMOsA study. Osteoporos. Int. 2016, 27, 275–281. [Google Scholar] [CrossRef] [PubMed]
  82. Kim, M.K.; Baek, K.H.; Song, K.-H.; Il Kang, M.; Park, C.Y.; Lee, W.Y.; Oh, K.W. Vitamin D Deficiency Is Associated with Sarcopenia in Older Koreans, Regardless of Obesity: The Fourth Korea National Health and Nutrition Examination Surveys (KNHANES IV) 2009. J. Clin. Endocrinol. Metab. 2011, 96, 3250–3256. [Google Scholar] [CrossRef] [Green Version]
  83. Lee, S.; Kim, T.-N.; Kim, S.-H. Sarcopenic obesity is more closely associated with knee osteoarthritis than is nonsarcopenic obesity: A cross-sectional study. Arthritis Rheum. 2012, 64, 3947–3954. [Google Scholar] [CrossRef] [PubMed]
  84. Ramachandran, R.; Gravenstein, K.S.; Metter, E.J.; Egan, J.M.; Ferrucci, L.; Chia, C.W. Selective Contribution of Regional Adiposity, Skeletal Muscle, and Adipokines to Glucose Disposal in Older Adults. J. Am. Geriatr. Soc. 2012, 60, 707–712. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Kwon, S.S.; Lee, S.-G.; Lee, Y.; Lim, J.-B.; Kim, J.-H. Homeostasis model assessment of insulin resistance in a general adult population in Korea: Additive association of sarcopenia and obesity with insulin resistance. Clin. Endocrinol. 2017, 86, 44–51. [Google Scholar] [CrossRef]
  86. Lee, Y.; Jung, K.S.; Kim, S.U.; Yoon, H.; Yun, Y.J.; Lee, B.-W.; Kang, E.S.; Han, K.-H.; Lee, H.C.; Cha, B.-S. Sarcopaenia is associated with NAFLD independently of obesity and insulin resistance: Nationwide surveys (KNHANES 2008–2011). J. Hepatol. 2015, 63, 486–493. [Google Scholar] [CrossRef]
  87. Oh, C.; Jho, S.; No, J.-K.; Kim, H.-S. Body composition changes were related to nutrient intakes in elderly men but elderly women had a higher prevalence of sarcopenic obesity in a population of Korean adults. Nutr. Res. 2015, 35, 1–6. [Google Scholar] [CrossRef]
  88. Chung, J.-Y.; Kang, H.-T.; Lee, D.-C.; Lee, H.-R.; Lee, Y.-J. Body composition and its association with cardiometabolic risk factors in the elderly: A focus on sarcopenic obesity. Arch. Gerontol. Geriatr. 2013, 56, 270–278. [Google Scholar] [CrossRef]
  89. Baek, J.; Park, D.; Kim, I.; Won, J.-U.; Hwang, J.; Roh, J. Autonomic dysfunction of overweight combined with low muscle mass. Clin. Auton. Res. 2013, 23, 325–331. [Google Scholar] [CrossRef]
  90. Baek, S.J.; Nam, G.E.; Han, K.D.; Choi, S.W.; Jung, S.W.; Bok, A.R.; Kim, Y.H.; Lee, K.S.; Han, B.D.; Kim, D.H. Sarcopenia and sarcopenic obesity and their association with dyslipidemia in Korean elderly men: The 2008–2010 Korea National Health and Nutrition Examination Survey. J. Endocrinol. Investig. 2014, 37, 247–260. [Google Scholar] [CrossRef]
  91. Lou, N.; Chi, C.-H.; Chen, X.-D.; Zhou, C.-J.; Wang, S.-L.; Zhuang, C.-L.; Shen, X. Sarcopenia in overweight and obese patients is a predictive factor for postoperative complication in gastric cancer: A prospective study. Eur. J. Surg. Oncol. 2017, 43, 188–195. [Google Scholar] [CrossRef] [PubMed]
  92. Moreira, M.A.; Zunzunegui, M.V.; Vafaei, A.; da Câmara, S.M.A.; Oliveira, T.S.; Maciel, Á.C.C. Sarcopenic obesity and physical performance in middle aged women: A cross-sectional study in Northeast Brazil. BMC Public Health 2016, 16, 43. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Hwang, B.; Lim, J.-Y.; Lee, J.; Choi, N.-K.; Ahn, Y.-O.; Park, B.-J. Prevalence Rate and Associated Factors of Sarcopenic Obesity in Korean Elderly Population. J. Korean Med. Sci. 2012, 27, 748–755. [Google Scholar] [CrossRef] [PubMed]
  94. Cho, Y.; Shin, S.-Y.; Shin, M.-J. Sarcopenic obesity is associated with lower indicators of psychological health and quality of life in Koreans. Nutr. Res. 2015, 35, 384–392. [Google Scholar] [CrossRef] [PubMed]
  95. An, K.O.; Kim, J. Association of Sarcopenia and Obesity with Multimorbidity in Korean Adults: A Nationwide Cross-Sectional Study. J. Am. Med. Dir. Assoc. 2016, 17, 960.e1–960.e7. [Google Scholar] [CrossRef]
  96. Bahat, G.; Kilic, C.; Topcu, Y.; Aydin, K.; Karan, M.A. Fat percentage cutoff values to define obesity and prevalence of sarcopenic obesity in community-dwelling older adults in Turkey. Aging Male 2018, 1–7. [Google Scholar] [CrossRef]
  97. Ishii, S.; Chang, C.; Tanaka, T.; Kuroda, A.; Tsuji, T.; Akishita, M.; Iijima, K. The Association between Sarcopenic Obesity and Depressive Symptoms in Older Japanese Adults. PLoS ONE 2016, 11, e0162898. [Google Scholar] [CrossRef] [Green Version]
  98. Kim, T.N.; Yang, S.J.; Yoo, H.J.; Lim, K.I.; Kang, H.J.; Song, W.; Seo, J.A.; Kim, S.G.; Kim, N.H.; Baik, S.H.; et al. Prevalence of sarcopenia and sarcopenic obesity in Korean adults: The Korean sarcopenic obesity study. Int. J. Obes. 2009, 33, 885–892. [Google Scholar] [CrossRef] [Green Version]
  99. Lee, J.; Hong, Y.; Shin, H.J.; Lee, W. Associations of Sarcopenia and Sarcopenic Obesity with Metabolic Syndrome Considering Both Muscle Mass and Muscle Strength. J. Prev. Med. Public Health 2016, 49, 35–44. [Google Scholar] [CrossRef]
  100. Gomez-Cabello, A.; Pedrero-Chamizo, R.; Olivares, P.R.; Luzardo, L.; Juez-Bengoechea, A.; Mata, E.; Albers, U.; Aznar, S.; Villa, G.; Espino, L.; et al. Prevalence of overweight and obesity in non-institutionalized people aged 65 or over from Spain: The elderly EXERNET multi-centre study: Adiposity and lifestyle in Spanish elderly. Obes. Rev. 2011, 12, 583–592. [Google Scholar] [CrossRef]
  101. Rolland, Y.; Lauwers-Cances, V.; Cristini, C.; van Kan, G.A.; Janssen, I.; Morley, J.E.; Vellas, B. Difficulties with physical function associated with obesity, sarcopenia, and sarcopenic-obesity in community-dwelling elderly women: The EPIDOS (EPIDemiologie de l’OSteoporose) Study. Am. J. Clin. Nutr. 2009, 89, 1895–1900. [Google Scholar] [CrossRef] [PubMed]
  102. Kelly, T.L.; Wilson, K.E.; Heymsfield, S.B. Dual Energy X-Ray Absorptiometry Body Composition Reference Values from NHANES. PLoS ONE 2009, 4, e7038. [Google Scholar] [CrossRef] [PubMed]
  103. Snyder, W.S.C.; Cook, M.J.; Nasset, E.S.; Karhansen, L.R.; Howells, G.P.; Tipton, I.H. Report of the Task Group on Reference Men; Pergamon Press: Oxford, UK, 1975. [Google Scholar]
  104. Schautz, B.; Later, W.; Heller, M.; Müller, M.J.; Bosy-Westphal, A. Total and regional relationship between lean and fat mass with increasing adiposity—Impact for the diagnosis of sarcopenic obesity. Eur. J. Clin. Nutr. 2012, 66, 1356–1361. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  105. Linge, J.; Borga, M.; West, J.; Tuthill, T.; Miller, M.R.; Dumitriu, A.; Thomas, E.L.; Romu, T.; Tunón, P.; Bell, J.D.; et al. Body Composition Profiling in the UK Biobank Imaging Study. Obesity 2018, 26, 1785–1795. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  106. Bamberg, F.; Kauczor, H.-U.; Weckbach, S.; Schlett, C.L.; Forsting, M.; Ladd, S.C.; Greiser, K.H.; Weber, M.-A.; Schulz-Menger, J.; Niendorf, T.; et al. Whole-Body MR Imaging in the German National Cohort: Rationale, Design, and Technical Background. Radiology 2015, 277, 206–220. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  107. MacDonald, A.J.; Greig, C.A.; Baracos, V. The advantages and limitations of cross-sectional body composition analysis. Curr. Opin. Support. Palliat. Care 2011, 5, 342–349. [Google Scholar] [CrossRef] [PubMed]
  108. Prado, C.M.M.; Heymsfield, S.B. Lean Tissue Imaging: A New Era for Nutritional Assessment and Intervention. J. Parenter. Enter. Nutr. 2014, 38, 940–953. [Google Scholar] [CrossRef] [Green Version]
  109. Visser, M.; Fuerst, T.; Lang, T.; Salamone, L.; Harris, T.B. Validity of fan-beam dual-energy X-ray absorptiometry for measuring fat-free mass and leg muscle mass. Health, Aging, and Body Composition Study--Dual-Energy X-ray Absorptiometry and Body Composition Working Group. J. Appl. Physiol. 1999, 87, 1513–1520. [Google Scholar] [CrossRef] [Green Version]
  110. Hansen, R.D.; Williamson, D.A.; Finnegan, T.P.; Lloyd, B.D.; Grady, J.N.; Diamond, T.H.; Smith, E.U.; Stavrinos, T.M.; Thompson, M.W.; Gwinn, T.H.; et al. Estimation of thigh muscle cross-sectional area by dual-energy X-ray absorptiometry in frail elderly patients. Am. J. Clin. Nutr. 2007, 86, 952–958. [Google Scholar] [CrossRef] [Green Version]
  111. Zhao, X.; Wang, Z.; Zhang, J.; Hua, J.; He, W.; Zhu, S. Estimation of Total Body Skeletal Muscle Mass in Chinese Adults: Prediction Model by Dual-Energy X-Ray Absorptiometry. PLoS ONE 2013, 8, e53561. [Google Scholar] [CrossRef] [Green Version]
  112. Freda, P.U.; Shen, W.; Reyes-Vidal, C.M.; Geer, E.B.; Arias-Mendoza, F.; Gallagher, D.; Heymsfield, S.B. Skeletal Muscle Mass in Acromegaly Assessed by Magnetic Resonance Imaging and Dual-Photon X-Ray Absorptiometry. J. Clin. Endocrinol. Metab. 2009, 94, 2880–2886. [Google Scholar] [CrossRef] [PubMed]
  113. Bridge, P.; Pocock, N.A.; Nguyen, T.; Munns, C.; Cowell, C.T.; Forwood, N.; Thompson, M.W. Validation of Longitudinal DXA Changes in Body Composition From Pre- to Mid-Adolescence Using MRI as Reference. J. Clin. Densitom. 2011, 14, 340–347. [Google Scholar] [CrossRef] [PubMed]
  114. Bredella, M.A.; Ghomi, R.H.; Thomas, B.J.; Torriani, M.; Brick, D.J.; Gerweck, A.V.; Misra, M.; Klibanski, A.; Miller, K.K. Comparison of DXA and CT in the Assessment of Body Composition in Premenopausal Women with Obesity and Anorexia Nervosa. Obesity 2010, 18, 2227–2233. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  115. Bilsborough, J.C.; Greenway, K.; Opar, D.; Livingstone, S.; Cordy, J.; Coutts, A.J. The accuracy and precision of DXA for assessing body composition in team sport athletes. J. Sports Sci. 2014, 32, 1821–1828. [Google Scholar] [CrossRef] [PubMed]
  116. Jensen, B.; Braun, W.; Geisler, C.; Both, M.; Klückmann, K.; Müller, M.J.; Bosy-Westphal, A. Limitations of Fat-Free Mass for the Assessment of Muscle Mass in Obesity. Obes. Facts 2019, 12, 307–315. [Google Scholar] [CrossRef] [PubMed]
  117. Buckinx, F.; Landi, F.; Cesari, M.; Fielding, R.A.; Visser, M.; Engelke, K.; Maggi, S.; Dennison, E.; Al-Daghri, N.M.; Allepaerts, S.; et al. Pitfalls in the measurement of muscle mass: A need for a reference standard. J. Cachexia Sarcopenia Muscle 2018, 9, 269–278. [Google Scholar] [CrossRef]
  118. Bosy-Westphal, A.; Schautz, B.; Later, W.; Kehayias, J.J.; Gallagher, D.; Müller, M.J. What makes a BIA equation unique? Validity of eight-electrode multifrequency BIA to estimate body composition in a healthy adult population. Eur. J. Clin. Nutr. 2013, 67, S14–S21. [Google Scholar] [CrossRef] [Green Version]
  119. Dehghan, M.; Merchant, A.T. Is bioelectrical impedance accurate for use in large epidemiological studies? Nutr. J. 2008, 7, 26. [Google Scholar] [CrossRef] [Green Version]
  120. Mourtzakis, M.; Prado, C.M.M.; Lieffers, J.R.; Reiman, T.; McCargar, L.J.; Baracos, V.E. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl. Physiol. Nutr. Metab. 2008, 33, 997–1006. [Google Scholar] [CrossRef]
  121. Shen, W.; Punyanitya, M.; Wang, Z.; Gallagher, D.; St.-Onge, M.-P.; Albu, J.; Heymsfield, S.B.; Heshka, S. Total body skeletal muscle and adipose tissue volumes: Estimation from a single abdominal cross-sectional image. J. Appl. Physiol. 2004, 97, 2333–2338. [Google Scholar] [CrossRef] [Green Version]
  122. Rutten, I.J.G.; Ubachs, J.; Kruitwagen, R.F.P.M.; Beets-Tan, R.G.H.; Olde Damink, S.W.M.; Van Gorp, T. Psoas muscle area is not representative of total skeletal muscle area in the assessment of sarcopenia in ovarian cancer. J. Cachexia Sarcopenia Muscle 2017, 8, 630–638. [Google Scholar] [CrossRef] [Green Version]
  123. Baracos, V.E. Psoas as a sentinel muscle for sarcopenia: A flawed premise. J. Cachexia Sarcopenia Muscle 2017, 8, 527–528. [Google Scholar] [CrossRef]
  124. Müller, M.J.; Braun, W.; Enderle, J.; Bosy-Westphal, A. Beyond BMI: Conceptual Issues Related to Overweight and Obese Patients. Obes. Facts 2016, 9, 193–205. [Google Scholar] [CrossRef]
  125. Maden-Wilkinson, T.M.; Degens, H.; Jones, D.A.; McPhee, J.S. Comparison of MRI and DXA to measure muscle size and age-related atrophy in thigh muscles. J. Musculoskelet. Neuronal Interact. 2013, 13, 320–328. [Google Scholar]
  126. Tavoian, D.; Ampomah, K.; Amano, S.; Law, T.D.; Clark, B.C. Changes in DXA-derived lean mass and MRI-derived cross-sectional area of the thigh are modestly associated. Sci. Rep. 2019, 9, 10028. [Google Scholar] [CrossRef]
  127. Bosy-Westphal, A.; Müller, M.J. Identification of skeletal muscle mass depletion across age and BMI groups in health and disease—There is need for a unified definition. Int. J. Obes. 2015, 39, 379–386. [Google Scholar] [CrossRef]
  128. Heymsfield, S.B.; Heo, M.; Thomas, D.; Pietrobelli, A. Scaling of body composition to height: Relevance to height-normalized indexes. Am. J. Clin. Nutr. 2011, 93, 736–740. [Google Scholar] [CrossRef]
  129. Forbes, G.B. Lean Body Mass-Body Fat Interrelationships in Humans. Nutr. Rev. 2009, 45, 225–231. [Google Scholar] [CrossRef]
  130. Hulens, M.; Vansant, G.; Claessens, A.L.; Lysens, R.; Muls, E. Predictors of 6-min walk test results in lean, obese and morbidly obese women. Scand. J. Med. Sci. Sports 2003, 13, 98–105. [Google Scholar] [CrossRef]
  131. Baracos, V.E.; Arribas, L. Sarcopenic obesity: Hidden muscle wasting and its impact for survival and complications of cancer therapy. Ann. Oncol. 2018, 29, ii1–ii9. [Google Scholar] [CrossRef]
  132. Hattori, K.; Tatsumi, N.; Tanaka, S. Assessment of body composition by using a new chart method. Am. J. Hum. Biol. 1997, 9, 573–578. [Google Scholar] [CrossRef]
  133. Schutz, Y.; Kyle, U.; Pichard, C. Fat-free mass index and fat mass index percentiles in Caucasians aged 18–98 y. Int. J. Obes. Relat. Metab. Disord. 2002, 26, 953–960. [Google Scholar] [CrossRef] [Green Version]
  134. Feliciano, E.M.C.; Kroenke, C.H.; Meyerhardt, J.A.; Prado, C.M.; Bradshaw, P.T.; Kwan, M.L.; Xiao, J.; Alexeeff, S.; Corley, D.; Weltzien, E.; et al. Association of Systemic Inflammation and Sarcopenia with Survival in Nonmetastatic Colorectal Cancer: Results From the C SCANS Study. JAMA Oncol. 2017, 3, e172319. [Google Scholar] [CrossRef] [PubMed]
  135. Wells, J.C.K.; Victora, C.G. Indices of whole-body and central adiposity for evaluating the metabolic load of obesity. Int. J. Obes. 2005, 29, 483–489. [Google Scholar] [CrossRef] [Green Version]
  136. Coin, A.; Sarti, S.; Ruggiero, E.; Giannini, S.; Pedrazzoni, M.; Minisola, S.; Rossini, M.; Del Puente, A.; Inelmen, E.M.; Manzato, E.; et al. Prevalence of Sarcopenia Based on Different Diagnostic Criteria Using DEXA and Appendicular Skeletal Muscle Mass Reference Values in an Italian Population Aged 20 to 80. J. Am. Med. Dir. Assoc. 2013, 14, 507–512. [Google Scholar] [CrossRef]
  137. Lee, W.-J.; Liu, L.-K.; Peng, L.-N.; Lin, M.-H.; Chen, L.-K. Comparisons of Sarcopenia Defined by IWGS and EWGSOP Criteria Among Older People: Results From the I-Lan Longitudinal Aging Study. J. Am. Med. Dir. Assoc. 2013, 14, 528.e1–528.e7. [Google Scholar] [CrossRef]
  138. Han, P.; Kang, L.; Guo, Q.; Wang, J.; Zhang, W.; Shen, S.; Wang, X.; Dong, R.; Ma, Y.; Shi, Y.; et al. Prevalence and Factors Associated with Sarcopenia in Suburb-dwelling Older Chinese Using the Asian Working Group for Sarcopenia Definition. J. Gerontol. A Biomed. Sci. Med. Sci. 2016, 71, 529–535. [Google Scholar] [CrossRef] [Green Version]
  139. Yuki, A.; Ando, F.; Otsuka, R.; Matsui, Y.; Harada, A.; Shimokata, H. Epidemiology of sarcopenia in elderly Japanese. J. Phys. Fit. Sports Med. 2015, 4, 111–115. [Google Scholar] [CrossRef] [Green Version]
  140. Ishii, S.; Tanaka, T.; Shibasaki, K.; Ouchi, Y.; Kikutani, T.; Higashiguchi, T.; Obuchi, S.P.; Ishikawa-Takata, K.; Hirano, H.; Kawai, H.; et al. Development of a simple screening test for sarcopenia in older adults. Geriatr. Gerontol. Int. 2014, 14, 93–101. [Google Scholar] [CrossRef]
  141. Cheng, Q.; Zhu, X.; Zhang, X.; Li, H.; Du, Y.; Hong, W.; Xue, S.; Zhu, H. A cross-sectional study of loss of muscle mass corresponding to sarcopenia in healthy Chinese men and women: Reference values, prevalence, and association with bone mass. J. Bone Miner. Metab. 2014, 32, 78–88. [Google Scholar] [CrossRef]
  142. Janssen, I.; Heymsfield, S.B.; Ross, R. Low Relative Skeletal Muscle Mass (Sarcopenia) in Older Persons Is Associated with Functional Impairment and Physical Disability. J. Am. Geriatr. Soc. 2002, 50, 889–896. [Google Scholar] [CrossRef] [Green Version]
  143. Wen, X.; Wang, M.; Jiang, C.-M.; Zhang, Y.-M. Are current definitions of sarcopenia applicable for older Chinese adults? J. Nutr. Health Aging 2011, 15, 847–851. [Google Scholar] [CrossRef]
  144. Zoico, E.; Di Francesco, V.; Guralnik, J.M.; Mazzali, G.; Bortolani, A.; Guariento, S.; Sergi, G.; Bosello, O.; Zamboni, M. Physical disability and muscular strength in relation to obesity and different body composition indexes in a sample of healthy elderly women. Int. J. Obes. Relat. Metab. Disor. 2004, 28, 234–241. [Google Scholar] [CrossRef] [Green Version]
Table
Table 1. Cut-off values and diagnostic criteria of a low muscle mass using dual X-ray absorptiometry (DXA).
Table 1. Cut-off values and diagnostic criteria of a low muscle mass using dual X-ray absorptiometry (DXA).
ReferenceDevice/SoftwareParameter/Cut-Off by GenderReference Group Characteristics (Mean ± SD)/Diagnostic Criteria (→)
Alkahtani (2017)Lunar iDXA General Electric machine, HealthcareASMI
Class I and Class II sarcopenia
men: 7.74 kg/m2 and 6.51 kg/m2		n = 232Saudi Arabians
menwomen
n2320
Age (y)27.1 ± 4.2
BMI (kg/m2)28.1 ± 5.5
→ Class I sarcopenia: 1 SD below the means for young, healthy adults
→ Class II sarcopenia: 2 SDs below the means for young, healthy adults
Imboden et al. (2017)GE Lunar Prodigy or iDXA(a) ASMI
men: 6.35 kg/m2
women: 4.92 kg/m2		(a) n = 1246US population
menwomen
n488758
Age (y)20 to 3920 to 39
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young adults
(b) ASMI
men: 7.40 kg/m2
women: 5.60 kg/m2		(b) n = 351US population
menwomen
n168183
Age (year)70 to 7970 to 79
BMI (kg/m2)NANA
→ sex-specific lowest 20% of study group
Kruger et al. (2015)Hologic Discovery-W,
software version 12.7 for Cape Town
QDR-4500A, software
version 12.5:7 for Soweto		(a) ASMI
women: 4.93 kg/m2		(a) n = 238Black South Africans
(Cape Town)
menwomen
n0238
Age (year) 25.8 ± 5.9
BMI (kg/m2) 29.8 ± 8.0
→ 2 SDs below the sex-specific means of young, healthy adults
(b) ASMI
women: 4.95 kg/m2
(b) n = 371Black South Africans (Soweto)
menwomen
n0371
Age (year) 35.1 ± 3.2
BMI (kg/m2) 28.8 ± 6.2
→ 2 SDs below the sex-specific means of young, healthy adults
Alemán-Mateo & Ruiz Valenzuela (2014)DPX-MD+, GE LunarASMI
men: 5.86 kg/m2
women: 4.72 kg/m2
SMI
men: 6.63 kg/m2
women: 5.22 kg/m2
SM was predicted using Kim’s equation (Kim et al., 2002)		n = 216Mexicans
menwomen
n13680
Age (year)27.3 ± 5.028.2 ± 5.6
BMI (kg/m2)25.7 ± 3.623.2 ± 3.1
→ 2 SDs below the sex-specific means of young, healthy adults
Gould et al. (2014)DPX-L scanner, software version 1.31; Lunar or Prodigy Pro, LunarASMI
men: 6.94 kg/m2
women: 5.30 kg/m2		n = 682study performed in southeastern Australia
menwomen
n374308
Age (year)20 to 3920 to 39
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young adults
Marwaha et al. (2014)Prodigy Oracle, GE Lunar Corp.(a) ASMI
women: 4.42 kg/m2		(a) n = 469Indians
menwomen
n0469
Age (year) 20 to 39
BMI (kg/m2) NA
→ 2 SDs below the sex-specific means of young adults
(b) ASMI
women: 5.11 kg/m2		(b) n = 1045Indians
menwomen
n01045
Age (year) 44.0 ± 17.1
BMI (kg/m2) 25.0 ± 5.2
→ sex-specific lowest 20% of study group
Yu et al. (2014)Hologic Delphi W4500 densitometer, auto whole body version 12.4ASMI
men: 6.52 kg/m2
women: 5.44 kg/m2		n = 4000Chinese (Hong Kong)
menwomen
n20002000
Age (year)72.5 ± 5.272.5 ± 5.2
BMI (kg/m2)23.7 ± 3.323.7 ± 3.3
→ lowest quintile
Kim et al. (2012)Hologic Discovery-WASMI
Class I and Class II sarcopenia
men: 7.50 kg/m2 and 6.58 kg/m2
women: 5.38 kg/m2 and 4.59 kg/m2		n = 2513Koreans
menwomen
n12451268
Age (year)31.0 ± 5.530.8 ± 5.6
BMI (kg/m2)24.0 ± 3.422.1 ± 3.5
→ Class I sarcopenia: 1-2 SDs below the sex-specific means for young, healthy adults
→ Class II sarcopenia: 2 SDs below the sex-specific means for young, healthy adults
Oliveira et al. (2011)DPX-L, Lunar Radiation CorporationASMI
women: 5.0 kg/m2		n = 349Brazilians
menwomen
n0349
Age (year) 29.0 ± 7.5
BMI (kg/m2) 23.5 ± 4.5
→ 2 SDs below the sex-specific means of young, healthy adults
Sanada et al. (2010)Hologic QDR-4500A scanner, software version 11.2:3ASMI
Class I and Class II sarcopenia
men: 7.77 kg/m2 and 6.87 kg/m2
women: 6.12 kg/m2 and 5.46 kg/m2		n = 529Japanese
menwomen
n266263
Age (year)28.2 ± 7.428.0 ± 7.0
BMI (kg/m2)23.0 ± 3.020.8 ± 2.6
→ Class I sarcopenia: 1 SD below the sex-specific means for young, healthy adults
→ Class II sarcopenia: 2 SDs below the sex-specific means for young, healthy adults
Szulc et al. (2004)Hologic 1000WASMI
men: 6.32 kg/m2		n = 845study performed in France
menwomen
n8450
Age (year)64.0 ± 8.0
BMI (kg/m2)28.0 ± 3.7
→ lowest quartile
Newman et al. (2003)QDR 4500A, Hologic, Inc.ASMI
men: 7.23 kg/m2
women: 5.67 kg/m2
Values recommended by the International Working Group on Sarcopenia (Fielding et al., 2011)		n = 2984study performed in USA (41% Blacks)
menwomen
n14351549
Age (year)73.6 ± 2.973.6 ± 2.9
BMI (kg/m2)27.4 ± 4.827.4 ± 4.8
→ sex-specific lowest 20% of study group
Tankó et al. (2002)QDR4500A scanner, Hologic, software version V8.10a:3 and DPX scanner, Lunar Radiation, software versions 3.1 and 3.2(a) ASMI
women: 6.10 kg/m2
(b) ASMI
women: 5.40 kg/m2		n = 216 womenDanes
menwomen
n0216
Age (year) 30.4 ± 5.3
BMI (kg/m2) NA
→ (a) 1-2 SDs below the sex-specific means for young, healthy, premenopausal women
→ (b) 2 SDs below the sex-specific means for young, healthy, premenopausal women
Baumgartner et al. (1998)Lunar DPXASMI
men: 7.26 kg/m2
women: 5.45 kg/m2		n = 229US population
(non-Hispanic white men and women)
menwomen
n107122
Age (year)28.7 ± 5.129.7 ± 5.9
BMI (kg/m2)24.6 ± 3.824.1 ± 5.4
→ 2 SDs below the sex-specific means of young, healthy adults
ASMI, appendicular skeletal muscle mass index; BMI, body mass index; DXA, dual X-ray absorptiometry; NA, not available; SD, standard deviation; SM, skeletal muscle mass; SMI, skeletal muscle mass index.
Table
Table 2. Cut-off values and diagnostic criteria of a low muscle mass using bioelectrical impedance analysis (BIA).
Table 2. Cut-off values and diagnostic criteria of a low muscle mass using bioelectrical impedance analysis (BIA).
ReferenceDevice/SoftwareParameter/Cut-Off by GenderReference Group Characteristics (Mean ± SD)/Diagnostic Criteria (→)
Krzymińska-Siemaszko et al. (2019)InBody 170 analyzer, Biospace Co.ASMI
men: 7.35 kg/m2 (20–30 y), 7.38 kg/m2 (18–40 y, 18–39 y, 20–35 y), 7.40 kg/m2 (20–39 y, 20–40 y)
women: 5.51 kg/m2 (20–30 y), 5.56 kg/m2 (18–40 y), 5.53 kg/m2 (18–39 y), 5.59 kg/m2 (20–39 y), 5.60 kg/m2 (20–40 y), 5.58 kg/m2 (20–35 y)
Authors recommended the highest cut-off points, i.e., 5.60 kg/m2 in women and 7.40 kg/m2 in men		n = 1512study performed in Poland (Caucasians)
menwomen
n635877
Age (year)24.2 ± 5.328.4 ± 6.8
BMI (kg/m2)NANA
total n for men and women depends on age range
→ 2 SDs below the sex-specific means of young, healthy adults
Alkahtani (2017)Tanita MC-980MA, Tanita Corporation
Inbody 770, Inbody Co.		ASMI
Class I and Class II sarcopenia
men: 8.68 kg/m2 and 7.45 kg/m2
ASMI
Class I and Class II sarcopenia
men: 7.29 kg/m2 and 6.42 kg/m2		n = 232Saudi Arabians
menwomen
n2320
Age (year)27.1 ± 4.2
BMI (kg/m2)28.1 ± 5.5
→ Class I sarcopenia: 1 SD below the means for young, healthy adults
→ Class II sarcopenia: 2 SDs below the means for young, healthy adults
Bahat et al. (2016)Tanita BC 532 model body analysis monitorSMI
men: 9.2 kg/m2
women: 7.4 kg/m2
SM (kg) = 0.566 x FFM		n = 301study performed in Turkey
menwomen
n187114
Age (year)26.8 ± 4.525.9 ± 4.7
BMI (kg/m2)25.5 ± 3.622.4 ± 3.4
→ 2 SDs below the sex-specific means of young, healthy adults
Chang et al. (2013)Tanita BC-418ASMI
men: 6.76 kg/m2
women: 5.28 kg/m2
SMI
men: 7.70 kg/m2
women: 5.67 kg/m2
SM by Janssen et al. (2000) equation		n = 998Taiwanese
menwomen
n498500
Age (year)23.1 ± 3.023.1 ± 2.7
BMI (kg/m2)22.2 ± 3.120.2 ± 2.6
→ 2 SDs below the sex-specific means of young, healthy adults
Yamada et al. (2013)Inbody 720, Biospace Co.ASMI
men: 6.75 kg/m2
women: 5.07 kg/m2		n = 38,099Japanese
menwomen
n19,79718,302
Age (year)18 to 4018 to 40
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young adults
Masanés et al. (2012)RJL Systems BIA 101SMI
men: 8.25 kg/m2
women: 6.68 kg/m2
SM by Janssen et al. (2000) equation		n = 230study performed in Spain
menwomen
n110120
Age (year)28.6 ± 5.028.2 ± 6.0
BMI (kg/m2)24.6 ± 2.621.9 ± 2.2
→ 2 SDs below the sex-specific means of young, healthy adults
Tanimoto et al. (2012)Tanita MC-190ASMI
men: 7.0 kg/m2
women: 5.8 kg/m2		n = 1719Japanese
menwomen
n838881
Age (year)26.6 ± 6.728.5 ± 7.3
BMI (kg/m2)22.4 ± 3.220.8 ± 2.9
→ 2 SDs below the sex-specific means of young, healthy adults
Chien et al. (2008)Maltron BioScan 920SMI
men: 8.87 kg/m2
women: 6.42 kg/m2
SM by Janssen et al. (2000) equation		n = 200Taiwanese
menwomen
n100100
Age (year)26.7 ± 5.727.6 ± 5.9
BMI (kg/m2)23.2 ± 3.520.6 ± 2.5
→ 2 SDs or more below the sex-specific means of young, healthy adults
Tichet et al. (2008)Impedimed multifrequency analyserSMI
men: 8.60 kg/m2
women: 6.20 kg/m2
SM by Janssen et al. (2000) equation		n = 782French people
menwomen
n394388
Age (year)30.2 ± 6.129.2 ± 6.3
BMI (kg/m2)23.9 ± 3.022.5 ± 3.4
→ 2 SDs below the sex-specific means of young, healthy adults
Janssen et al. (2004)Valhalla 1990B Bio-Resistance Body Composition AnalyzerSMI
moderate and severe sarcopenia
men: 8.51–10.75 kg/m2 and ≤8.50 kg/m2
women: 5.76–6.75 kg/m2 and ≤5.75 kg/m2
SM by Janssen et al. (2000) equation		n = 4499 US population
(non-Hispanic White, non-Hispanic Black and Mexican American)
menwomen
n22232276
Age (year)70.0 ± 7.071.0 ± 8.0
BMI (kg/m2)26.6 ± 4.327.0 ± 5.5
→ receiver operating characteristics
ASMI, appendicular skeletal muscle mass index; BIA, bioelectrical impedance analysis; BMI, body mass index; FFM, fat-free mass; NA, not available; SD, standard deviation; SM, skeletal muscle mass; SMI, skeletal muscle mass index.
Table
Table 3. Cut-off values and diagnostic criteria of a low muscle mass using computed tomography (CT).
Table 3. Cut-off values and diagnostic criteria of a low muscle mass using computed tomography (CT).
ReferenceDevice/SoftwareParameter/Cut-Off by GenderReference Group Characteristics (Mean ± SD)/Diagnostic Criteria (→)
Ufuk & Herek (2019)lumbar CT images
(16-detector row, Brilliance)		CT L3 SMI
men: 44.98 cm2/m2
women: 36.05 cm2/m2
CT L3 PMI
men: 2.63 cm2/m2
women: 2.02 cm2/m2		n = 270healthy Turkish population
menwomen
n134136
Age (year)44.3 ± 11.245.0 ± 8.6
BMI (kg/m2)26.4 ± 3.525.4 ± 3.6
→ 2 SDs below the sex-specific means of young adults
Derstine et al. (2018)lumbar CT images
(GE Discovery or LightSpeed scanner)		(a) CT L3 SMI
men: 45.4 cm2/m2
women: 34.4 cm2/m2		(a) n = 727healthy US population
menwomen
n317410
Age (year)18 to 4018 to 40
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young adults
(b) CT T10 SMI
men: 28.8 cm2/m2
women: 20.4 cm2/m2		(b) n = 278healthy US population
menwomen
n122156
Age (year)18 to 4018 to 40
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young adults
(c) CT T11 SMI
men: 27.6 cm2/m2
women: 19.2 cm2/m2		(c) n = 577healthy US population
menwomen
n241366
Age (year)18 to 4018 to 40
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young adults
(d) CT T12 SMI
men: 28.8 cm2/m2
women: 20.8 cm2/m2		(d) n = 700healthy US population
menwomen
n299401
Age (year)18 to 4018 to 40
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young adults
(e) CT L1 SMI
men: 34.6 cm2/m2
women: 25.9 cm2/m2		(e) n = 724healthy US population
menwomen
n315409
Age (year)18 to 4018 to 40
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young adults
(f) CT L2 SMI
men: 40.1 cm2/m2
women: 30.4 cm2/m2		(f) n = 726healthy US population
menwomen
n315411
Age (year)18 to 4018 to 40
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young adults
(g) CT L4 SMI
men: 41.3 cm2/m2
women: 34.2 cm2/m2		(g) n = 704healthy US population
menwomen
n305399
Age (year)18 to 4018 to 40
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young adults
(h) CT L5 SMI
men: 39.0 cm2/m2
women: 30.6 cm2/m2		(h) n = 506healthy US population
menwomen
n211295
Age (year)18 to 4018 to 40
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young adults
van der Werf et al. (2018)lumbar CT images
(64-row CT scanner, Sensation 64, Siemens or CT Brilliance 64, Philips)		CT L3 SMI
men: 44.6 cm2/m2
women: 34.0 cm2/m2		n = 300healthy Caucasian population
menwomen
n126174
Age (y)20 to 6020 to 60
BMI (kg/m2)NANA
→ 5th percentile
Benjamin et al. (2017)lumbar CT images
(Discovery 750 HD 64-row spectral CT scanner)		CT L3 SMI
men: 36.54 cm2/m2
women: 30.21 cm2/m2		n = 275healthy Asian Indians
menwomen
n139136
Age (year)32.2 ± 9.832.2 ± 9.8
BMI (kg/m2)24.2 ± 3.224.2 ± 3.2
→ 2 SDs below the sex-specific means of young adults
Kim et al. (2017)lumbar CT images
(64-slice multidetector CT scanner, Brilliance 64, Philips Healthcare)		CT L3 PMI
men: 5.92 cm2/m2 (20–39 y), 4.74 cm2/m2 (40–49 y), 4.22 cm2/m2 (50–59 y), 3.74 cm2/m2 (60–69 y), 3.32 cm2/m2 (70–89 y)
women: 4.0 cm2/m2 (20–39 y), 2.88 cm2/m2 (40–49 y), 2.43 cm2/m2 (50–59 y), 2.20 cm2/m2 (60–69 y), 1.48 cm2/m2 (70–89 y)		n = 1422study performed in Korea
menwomen
n550872
Age (year)52.4 ± 12.053.3 ± 12.2
BMI (kg/m2)24.5 ± 3.122.8 ± 3.2
total n for men and women depends on age range
→ 2 SDs below the sex-specific means of young, healthy adults
Sakurai et al. (2017)lumbar CT imagesCT L3 SMI
men: 43.2 cm2/m2
women: 34.6 cm2/m2 		n = 569 patients with gastric cancerstudy performed in Japan
menwomen
n396173
Age (year)66.7 ± 11.266.7 ± 11.2
BMI (kg/m2)22.0 ± 3.422.0 ± 3.4
→ lowest sex-specific quartile
Hamaguchi et al. (2016)lumbar CT images
(Aquilion 64, Toshiba Medical Systems)		CT L3 PMI
men: 6.36 cm2/m2
women: 3.92 cm2/m2		n = 230healthy Asian population
menwomen
n116114
Age (year)20 to 4920 to 49
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young adults
Zhuang et al. (2016)lumbar CT imagesCT L3 SMI
men: 40.8 cm2/m2
women: 34.9 cm2/m2		n = 937 patients with gastric cancerstudy performed in China
menwomen
n730207
Age (year)64.0 ± 15.064.0 ± 15.0
BMI (kg/m2)21.9 ± 3.021.9 ± 3.0
→ optimal stratification
Iritani et al. (2015)lumbar CT imagesCT L3 SMI
men: 36.0 cm2/m2
women: 29.0 cm2/m2		n = 217 patients with hepatocellular carcinomastudy performed in Japan
menwomen
n14671
Age (year)27 to 9027 to 90
BMI (kg/m2)13.4 to 35.913.4 to 35.9
→ optimal stratification
BMI, body mass index; CT, computed tomography; L, lumbar vertebra; L3, third lumbar vertebra; NA, not available; PMI, psoas muscle index; SD, standard deviation; SMI, skeletal muscle mass index; T, thoracic vertebra.
Table
Table 4. Cut-off values that combine measures of muscle mass and obesity.
Table 4. Cut-off values that combine measures of muscle mass and obesity.
ReferenceDevice/SoftwareParameter/Cut-Off by GenderReference Group Characteristics (Mean ± SD)/Diagnostic Criteria (→)
Prado et al. (2008)CT imagesCT L3 SMI:
men: ≤52.4 cm2/m2
women: ≤38.5 cm2/m2
+
BMI ≥ 30 kg/m2		n = 250 obese patients with cancers of the respiratory tract and gastrointestinal locationsstudy performed in Canada
menwomen
n136114
Age (year)64.6 ± 10.263.2 ± 10.5
BMI (kg/m2)33.9 ± 4.434.7 ± 4.3
→ optimal stratification
Martin et al. (2013)CT imagesCT L3 SMI:
men: <43 cm2/m2
women: <41 cm2/m2
for BMI < 25 kg/m2
men: <53 cm2/m2
for BMI ≥ 25 kg/m2		n = 1473 patients with cancers of the respiratory tract and gastrointestinal locationsstudy performed in Canada
menwomen
n828645
Age (year)64.7 ± 11.264.8 ± 11.5
BMI (kg/m2)26.0 ± 4.925.1 ± 5.8
→ optimal stratification
Muscariello et al. (2016)BIA
(RJL 101, Akern SRL)		(a) SMI + BMI < 25 kg/m2
Class I and Class II sarcopenia
women: 7.4 and 6.8 kg/m2		(a) n = 313study performed in Italy
menwomen
n0313
Age (year) 28.5 ± 7.6
BMI (kg/m2) 24.1 ± 2.5
→ Class I sarcopenia: 1 SD below the sex-specific means of young adults
→ Class II sarcopenia: 2 SDs below the sex-specific means of young adults
(b) SMI + BMI ≥ 30 kg/m2
Class I and Class II sarcopenia
women: 8.3 and 7.3 kg/m2
SM by Janssen et al. (2000) equation		(b) n = 361study performed in Italy
menwomen
n0361
Age (year) 30.9 ± 7.9
BMI (kg/m2) 35.1 ± 4.6
→ Class I sarcopenia: 1 SD below the sex-specific means of young adults
→ Class II sarcopenia: 2 SDs below the sex-specific means of young adults
Nishigori et al. (2016)CT imagesCT L3 SMI (Prado et al. 2008):
men: ≤52.4 cm2/m2
women: ≤38.5 cm2/m2
+
visceral fat area (VFA) ≥100 cm2 in both sexes		reference group characteristic CT L3 SMI see Prado et al. (2008)
Pecorelli et al. (2016)CT images(a) CT L3 SMI (Prado et al. 2008):
men: ≤52.4 cm2/m2
women: ≤38.5 cm2/m2
+
(b) visceral fat area/total abdominal muscle area ratio (VFA/TAMA)
men & women: 3.2		(a) reference group characteristic CT L3 SMI see Prado et al. (2008)
(b) n = 202 patients with resectable pancreas, periampullarystudy performed in Italy
menwomen
n10894
Age (year)66.8 ± 10.766.8 ± 10.7
BMI (kg/m2)23.6 ± 3.723.6 ± 3.7
→ optimal stratification
Kwon et al. (2017)DXA
(Discovery QDR 4500, Hologic)		ASM (as % of body weight)
men: 30.98%
women: 24.81%
+
BMI ≥ 25 kg/m2 (based on the definition in the Asian-Pacific region)		n = 3550Koreans
menwomen
n16681882
Age (year)20 to 3920 to 39
BMI (kg/m2)NANA
→ 1 SD below the sex-specific means of young adults
Chiles Shaffer et al. (2017)DXA
(Lunar Prodigy Advance with GE EnCore 2006 version 10.51.0006)		ASM adjusted for BMI
men: <0.725 kg/m2
women: <0.591 kg/m2
n = 545study performed in US
menwomen
n287258
Age (year)79.2 ± 7.277.7 ± 7.3
BMI (kg/m2)27.2 ± 3.827.0 ± 5.2
→ CART analysis
An & Kim (2016)DXA
(Discovery-W, Hologic)		ASM (as % of body weight)
men: 30.1%
women: 21.2%
+
WC ≥ 90 cm in men
WC ≥ 80 cm in women
(sex-specific cut-off for Asians)		n = 5944study performed in Korea
menwomen
n25023334
Age (year)20 to 3920 to 39
BMI (kg/m2)NANA
→ 1 SD below the sex-specific means of young adults
Cho et al. (2015)(a) DXA
(Discovery-W, Hologic)		(a) ASM (as % of body weight)
men: 30.3%
women: 23.8%
+
WC ≥ 90 cm in men
WC ≥ 85 cm in women		(a) n = 4987Koreans
menwomen
n21232864
Age (year)20 to 3920 to 39
BMI (kg/m2)NANA
→ 1 SD below the sex-specific means of young, healthy adults
Oh et al. (2015)DXA
(Lunar Corp.)		ASM (as % of body weight)
men: 44%
women: 52%
+
BMI ≥ 25 kg/m2 		n = 1746Koreans
menwomen
n748998
Age (year)20 to 3920 to 39
BMI (kg/m2)NANA
→ 1 SD below the sex-specific means of young, healthy adults
Lee et al. (2015)DXA
(Discovery QDR 4500, Hologic)		ASM (as % of body weight)
men: 32.2%
women: 25.5%
+
BMI ≥ 25 kg/m2 (based on the criteria of the Asian-Pacific region)		n = 2200Koreans
menwomen
n9601240
Age (year)20 to 3020 to 30
BMI (kg/m2)NANA
→ 1 SD below the sex-specific means of young, healthy adults
Baek et al. (2014)DXA
(Lunar Corp.)		ASMI
men: 6.96 kg/m2
women: 4.96 kg/m2
ASM (as % of body weight)
men: 30.65%
women: 23.90%
+
BMI ≥ 25 kg/m2 (IOTF-proposed classification of BMI for Asia)		n = 4192Koreans
menwomen
n16992493
Age (year)20 to 3920 to 39
BMI (kg/m2)NANA
→ 1 SD below the sex-specific means of young, healthy adults
Cawthon et al. (2014)DXA
(QDR 4500, Hologic 2000, Lunar Prodigy)		ASM adjusted for BMI
men: <0.789
women: <0.512
recommended by FNIH (Studenski et al., 2014)		n = 11,270study performed in US
menwomen
n75823688
Age (year)65 to 8065 to 80
BMI (kg/m2)NANA
→ CART analysis plus sensitivity analyses
Chung et al. (2013)(a) DXA
(fan-beam technology, Lunar Corp.)		(a) ASM (as % of body weight)
men: 32.5%
women: 25.7%
+
BMI ≥ 25 kg/m2 (IOTF-proposed classification of BMI for Asia)		(a) n = 2781study performed in Korea
menwomen
n11551626
Age (year)20 to 3920 to 39
BMI (kg/m2)NANA
→ 1 SD below the sex-specific means of young, healthy adults
Hwang et al. (2012)DXA
(Discovery-W, Hologic)		ASM (as % of body weight)
men: 29.53%women: 23.20%
+
WC ≥ 90 cm in men
WC ≥ 85 cm in women
(Korean abdominal obesity criteria; Lee et al., 2007)		n = 2269Koreans
menwomen
n10031266
Age (year)30.7 ± 5.531.0 ± 5.5
BMI (kg/m2)24.1 ± 3.522.1 ± 3.6
→ 2 SDs below the sex-specific means of young adults
Lee et al. (2012)DXA
(Discovery-W, Hologic)		ASM (as % of body weight)
men: 26.8%
women: 21.0%
+
BMI ≥ 27.5 kg/m2		n = 2113Koreans
menwomen
n9021211
Age (year)20 to 4020 to 40
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young, healthy adults
Kim et al. (2012)DXA
(Discovery-W, Hologic)		ASM (as % of body weight)
Class II sarcopenia
men: 29.1%
women: 23.0%
ASMI
Class II sarcopenia
men: 6.58 kg/m2
women: 4.59 kg/m2
+
WC ≥ 90 cm in men (Lee et al., 2007)
WC ≥ 85 cm in women		n = 2513Koreans
menwomen
n12451268
Age (year)31.0 ± 5.530.8 ± 5.6
BMI (kg/m2)24.0 ± 3.422.1 ± 3.5
→ 2 SDs below the sex-specific means of young, healthy adults
Kim et al. (2011)DXA
(Lunar Corp.)		ASM (as % of body weight)
men: 29.5%
women: 23.2%
+
BMI ≥ 27.5 kg/m2		n = 2392study performed in Korea
menwomen
n10541338
Age (year)20 to 4020 to 40
BMI (kg/m2)NANA
→ 2 SDs below the sex-specific means of young, healthy adults
Kim et al. (2009)DXA
(Discovery A, Hologic)		(a) ASMI
men: 8.81 kg/m2
women: 7.36 kg/m2
+
(b) FM
men: 20.21%
women: 31.71%		n = 526Koreans
menwomen
n198328
Age (year)52.2 ± 14.451.2 ± 14.8
BMI (kg/m2)25.2 ± 3.123.9 ± 3.7
→ (a) lower two quintiles
→ (b) two highest quintiles
Rolland et al. (2009)(a) DXA
(Lunar DPX, Lunar Corp.)		(a) ASMI
women: 5.45 kg/m2 (Baumgartner et al., 1998)
+		(a) n = 122US population
(non-Hispanic white men and women)
menwomen
n0122
Age (year) 29.7 ± 5.9
BMI (kg/m2) 24.1 ± 5.4
→ 2 SDs below the sex-specific means of young, healthy adults
(b) DXA
(QDR 4500 W, Hologic)		(b) FM
women: 40%		(b) n = 1308study performed in France
menwomen
n01308
Age (year) ≥75
BMI (kg/m2) NA
→ 60th percentile of the healthy study sample
Baumgartner et al. (1998)DXA
(Lunar DPX, Lunar Corp.)		(a) ASMI
men: 7.26 kg/m2
women: 5.45 kg/m2
+
(b) FM
men: 27%
women: 38%		n = 229US population
(non-Hispanic white men and women)
menwomen
n107122
Age (year)28.7 ± 5.129.7 ± 5.9
BMI (kg/m2)24.6 ± 3.824.1 ± 5.4
(a) → 2SDs below the sex-specific means of young, healthy adults
(b) → >sex-specific median
Bahat et al. (2016); Bahat et al. (2018)BIA
(Tanita-BC532)		(a) SMI
men: 9.2 kg/m2
women: 7.4 kg/m2
SM (kg) = 0.566 × FFM
+		(a) n = 301study performed in Turkey
menwomen
n187114
Age (year)26.8 ± 4.525.9 ± 4.7
BMI (kg/m2)25.5 ± 3.622.4 ± 3.4
→ 2 SDs below the sex-specific means of young, healthy adults
(b) FM
men: 27.3%
women: 40.7%		(b) n = 992study performed in Turkey
menwomen
n308684
Age (year)75.2 ± 7.275.2 ± 7.2
BMI (kg/m2)27.7 ± 4.330.7 ± 5.6
→ above 60th percentile
Ishii et al. (2016)(a) BIA
(Tanita MC-190)		(a) ASMI
men: 7.0 kg/m2
women: 5.8 kg/m2
+		(a) n = 1719Japanese
menwomen
n838881
Age (year)26.6 ± 6.728.5 ± 7.3
BMI (kg/m2)22.4 ± 3.220.8 ± 2.9
→ 2 SDs below the sex-specific means of young, healthy adults
(b) BIA
(InBody 430, Biospace)		(b) FM
men: 29.7%
women: 37.2%		(b) n = 1731Japanese
menwomen
n875856
Age (year)≥ 65≥ 65
BMI (kg/m2)NANA
→ highest quintile
Moreira et al. (2016)BIA
(InBody R20, Biospace)		ASMI
women: 6.08 kg/m2
+
WC ≥ 88 cm in women (Brazilian obesity guidelines)		n = 491study performed in Northeast Brazil (Whites, Blacks, Pardo)
menwomen
n0491
Age (year) 50.0 ± 5.6
BMI (kg/m2) 29.0 ± 4.8
→ 20th percentile
Kemmler et al. (2016)BIA
(InBody 770, Biospace)		(a) ASMI
women: 5.66 kg/m2		(a) n = 689study performed in Germany (Caucasians)
menwomen
n0689
Age (year) 18 to 35
BMI (kg/m2) NA
→ 2 SDs below the sex-specific means of young, healthy adults
(b) ASMI
women: 5.99 kg/m2
+
BMI ≥ 30 kg/m2 (NIH)
FM ≥ 35% (WHO)		(b) n = 1325study performed in Germany (Caucasians)
menwomen
n01325
Age (year) 76.4 ± 4.9
BMI (kg/m2) 26.7 ± 4.3
→ lowest quintile
Lee et al. (2016)BIA
(InBody 720, Biospace)		(a) SMI (as % of body weight)
men: 38.2 %
women: 32.2%
SM by Janssen et al. (2000) equation
+		(a) n = 273study performed in Korea
menwomen
n157116
Age (year)25.5 ± 2.926.1 ± 4.6
BMI (kg/m2)24.1 ± 3.020.7 ± 2.6
→ 2 SDs below the sex-specific means of young, healthy adults
(b) FM
men: 25.8%
women: 36.5%		(b) n = 309study performed in Korea
menwomen
n85224
Age (year)70.7 ± 6.366.4 ± 7.2
BMI (kg/m2)NANA
→ two highest quintiles
Biolo et al. (2015)BIA
(Human IM-Plus, DS, Dieto System, BIA 101, Akern Srl, Tanita BC418MA, Tanita Corp.)		FM/FFM ratio > 0.8n = 200study performed in Italy and Slovenia
menwomen
n89111
Age (year)48.0 ± 12.051.0 ± 12.0
BMI (kg/m2)35.6 ± 6.235.5 ± 5.4
De Rosa et al. (2015)BIA
(Human IM Plus II–DS Medical)		SMI
moderate and severe sarcopenia
men: 8.44–9.53 kg/m2 and ≤8.43 kg/m2
women: 6.49–7.32 kg/m2 and ≤6.48 kg/m2
SMI (as % of body weight)
moderate and severe sarcopenia
men: 28.8–35.6% and ≤28.7%
women: 23.1–28.4% and ≤23.0%
SM by Janssen et al. (2000) equation
+
BMI ≥ 30 kg/m2		n = 500Italians
menwomen
n100400
Age (year)27.0 ± 7.025.0 ± 6.0
BMI (kg/m2)25.8 ± 5.725.2 ± 5.7
→ moderate sarcopenia: within 1 to 2 SDs below the sex-specific means of young, healthy adults
→ severe sarcopenia: 2 SDs below the sex-specific means of young, healthy adults
Atkins et al. (2014)BIA
(Bodystat 500, Bodystat Ltd.)		FFMI
men: ≤16.7 kg/m2
FFM (equation by Deurenberg et al., 1991)
+
FMI > 11.1 kg/m2		n = 4045study performed in UK (> 99 % white Europeans)
menwomen
n40450
Age (year)60 to 79
BMI (kg/m2)NA
→ lowest two-fifths of FFMI
Baek et al. (2013)BIA
(InBody 520, Biospace)		ASMI
men: 10.70 kg/m2
women: 8.60 kg/m2
+
BMI > 25 kg/m2 (WHO definition)		n = 1150study performed in Korea
menwomen
n618532
Age (year)43.6 ± 11.543.6 ± 11.5
BMI (kg/m2)24.6 ± 3.324.6 ± 3.3
→ 50th percentile of healthy study sample
Gomez-Cabello et al. (2011)BIA
(Tanita BC 418-MA)		(a) SMI
men: 8.61 kg/m2
women: 6.19 kg/m2
(b) FM
men: 30.33%
women: 40.9%
SM by Janssen et al. (2000) equation		n = 3136Spaniards
menwomen
n6782198
Age (year)72.4 ± 5.572.1 ± 5.2
BMI (kg/m2)NANA
→ (a) two lower quintiles
→ (b) two highest quintiles
Lou et al. (2017)CT imagesCT L3 SMI (Zhuang et al., 2016)
men: ≤40.8 cm2/m2
women: ≤34.9 cm2/m2
+
BMI ≥ 23 kg/m2 (WHO definition for Asians)		Predefined cut-off values for sarcopenia and obesity
Ramachandran et al. (2012)CT images
(Somatom Sensation 10 CT scanner)		adjusted thigh muscle area:
men: 110.7 cm2
women: 93.8 cm2
+
(1) BMI ≥ 27 kg/m2
(2) WC ≥ 102 cm for men
WC ≥ 88 cm for women		n = 539study performed in US
menwomen
n280259
Age (year)71.1 ± 0.471.1 ± 0.4
BMI (kg/m2)NANA
→ lowest sex-specific tertile
Lim et al. (2010)CT images
(Brilliance 64, Philips)		Visceral fat area (VFA)/thigh muscle area (TMA)
men: 0.93
women: 0.90		n = 264Koreans
menwomen
n126138
Age (year)20 to 8820 to 88
BMI (kg/m2)NANA
→ VFA/TMA median higher 50th percentile of the healthy study sample
ASM, appendicular skeletal muscle mass; ASMI, appendicular skeletal muscle mass index; BMI, body mass index; BIA, bioelectrical impedance analysis; CART, classification and regression tree analysis; CT, computed tomography; DXA, dual X-ray absorptiometry; FFM, fat-free mass; FFMI, fat-free mass index; FM, fat mass; FMI, fat mass index; FNIH, Foundation for the National Institutes of Health; IOTF, International Obesity Taskforce; L3, third lumbar vertebra; NA, not available; NIH, National Institutes of Health; SD, standard deviation; SM, skeletal muscle mass; SMI, skeletal muscle mass index; TAMA, total abdominal muscle area; TMA, thigh muscle area; VFA, visceral fat area; WC, waist circumference; WHO, World Health Organization.
Table
Table 5. Generation of cut-offs for SMI (corresponding to BMI thresholds) based on FFMI.
Table 5. Generation of cut-offs for SMI (corresponding to BMI thresholds) based on FFMI.
BMI
(kg/m2)		FMIDXA (kg/m2)
(Kelly et al., 2009)		FFMIDXA (kg/m2)
(Modified according to Kelly et al., 2009)		SMIMRI (kg/m2)
(1.5 T Siemens Avanto MRI Scanner)		SMIBIA_median (kg/m2)
(mBCA 515, Seca)		SMIBIA_-2SDs (kg/m2)
(mBCA 515, Seca)
Caucasian men<18.5<2.915.6 8.6>7.6
>25>6.019.09.859.7>8.7
>30>8.921.110.7110.5>9.5
>35>11.923.112.1511.4>10.3
>40>15.025.013.6712.2>11.2
Caucasian women<18.5<4.913.66.656.7>5.7
>25>9.215.87.497.7>6.8
>30>12.917.18.158.5>7.6
>35>16.818.28.999.3>8.4
>40>20.619.49.7410.1>9.2
BMI, body mass index; FMIDXA, fat mass index by dual X-ray absorptiometry (QDR 4500A fan beam densitometer (Hologic, Inc., Bedford, MA, Hologic Discovery software version 12.1)); FFMIDXA, fat-free mass index by dual X-ray absorptiometry; SMIMRI, skeletal muscle mass index by magnetic resonance imaging calculated by stepwise regression analysis (n = 410, 219 women (age: 38 ± 13 years, BMI: 27.7 ± 6.5 kg/m2) and 191 men (age: 41 ± 14 years, BMI: 27.7 ± 5.0 kg/m2) (detailed description of the segmentation procedure given elsewhere (Schautz et al., 2012)); SMIBIA_median, skeletal muscle mass index by bioelectrical impedance analysis given as median calculated by linear regression analysis (n = 529, 264 women (27 ± 6 years, BMI: 23.9 ± 3.6 kg/m2) and 265 men (28 ± 6 years, BMI: 25.2 ± 3.2 kg/m2) (detailed description of the BIA measurement procedure given elsewhere (Bosy-Westphal et al., 2017)); SMIBIA_-2SDs, skeletal muscle mass index by bioelectrical impedance analysis given as 2 SDs below the sex-specific mean calculated as linear regression analysis.

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Walowski, C.O.; Braun, W.; Maisch, M.J.; Jensen, B.; Peine, S.; Norman, K.; Müller, M.J.; Bosy-Westphal, A. Reference Values for Skeletal Muscle Mass – Current Concepts and Methodological Considerations. Nutrients 2020, 12, 755. https://doi.org/10.3390/nu12030755

AMA Style

Walowski CO, Braun W, Maisch MJ, Jensen B, Peine S, Norman K, Müller MJ, Bosy-Westphal A. Reference Values for Skeletal Muscle Mass – Current Concepts and Methodological Considerations. Nutrients. 2020; 12(3):755. https://doi.org/10.3390/nu12030755

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Walowski, Carina O., Wiebke Braun, Michael J. Maisch, Björn Jensen, Sven Peine, Kristina Norman, Manfred J. Müller, and Anja Bosy-Westphal. 2020. "Reference Values for Skeletal Muscle Mass – Current Concepts and Methodological Considerations" Nutrients 12, no. 3: 755. https://doi.org/10.3390/nu12030755

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