Identification of Skeletal Muscle Mass Depletion Across Age and BMI

REVIEW Identification of skeletal muscle mass depletion across age and BMI groups in health and disease—there is need for a unified definition

A Bosy-Westphal1,2 and MJ Müller2

Although reduced skeletal muscle mass is a major predictor of impaired physical function and survival, it remains inconsistently diagnosed to a lack of standardized diagnostic approaches that is reflected by the variable combination of body composition indices and cutoffs. In this review, we summarized basic determinants of a normal lean mass (age, gender, fat mass, body region) and demonstrate limitations of different lean mass parameters as indices for skeletal muscle mass. A unique definition of lean mass depletion should be based on an indirect or direct measure of skeletal muscle mass normalized for height (fat-free mass index (FFMI), appendicular or lumbal skeletal muscle index (SMI)) in combination with fat mass. Age-specific reference values for FFMI or SMI are more advantageous because defining lean mass depletion on the basis of total FFMI or appendicular SMI could be misleading in the case of advanced age due to an increased contribution of connective tissue to lean mass. Mathematical modeling of a normal lean mass based on age, gender, fat mass, ethnicity and height can be used in the absence of risk-defined cutoffs to identify skeletal muscle mass depletion. This definition can be applied to identify different clinical phenotypes like sarcopenia, sarcopenic obesity or cachexia.

International Journal of Obesity (2015) 39, 379–386; doi:10.1038/ijo.2014.161

INTRODUCTION diversity of body composition parameters used and discrepancies In the strict sense, the term sarcopenia (Greek sarx = flesh, in the method of normal range-definition. These inconsistencies penia = loss, literally: poverty of flesh) is used to define loss of contribute to a great variety in prevalence estimates and prevent muscle mass and strength that occur with advancing age.1 A comparative evaluation of interventions directed against lean decrease in muscle mass can, however, have many causes that are mass depletion. Commonalities in the pathophysiology, over- related to lifestyle (inactivity, inadequate diet), metabolic and lapping etiology and consequences justify a unique definition and neuroendocrine changes associated with ageing, diseases and terminology of lean mass depletion in several conditions such as obesity (for example, insulin resistance, inflammation, oxidative ageing, obesity and disease. The aim of this review is to stress) or therapy (for example, cortisol, operation, chemotherapy). summarize basic determinants of a normal lean mass (age, Although researchers and clinicians from different disciplines all gender, fat mass, body region) that need to be considered in a use their individual terminology to describe lean mass depletion unique definition of lean mass depletion. In addition, we (sarcopenia in gerontology, sarcopenic obesity in obesity research demonstrate limitations of different lean mass parameters as and cachexia in oncology), there is an overlap in the underlying indices for skeletal muscle mass and discuss their appropriate multifactorial pathophysiology of these phenotype descriptions normalization. (Figure 1). For example an overlap between sarcopenic obesity and cancer cachexia may be observed in obese cancer patients. In these patients lean mass deficiency is referred to as ‘hidden PARAMETERS AND INDICES USED TO DETERMINE LEAN MASS cachexia’ due to the masking effect of expanded adipose tissue DEPLETION that impedes the clinical identification of lean mass deficiency.2 Fat-free mass (FFM) is a widely used parameter that differs from On the other hand, an overlap between age-related sarcopenia lean mass because it is chemically defined as the sum of all non- and sarcopenic obesity is likely becoming more frequent lipid components of the body (including non fat components of considering the demographic change with an increasing number adipose tissue), whereas the term lean mass is anatomically of elderly people and the obesity epidemic.3 Irrespective of the defined as the sum of all non-adipose tissues of the body etiology, lean mass depletion is associated with impaired (including essential lipids, for example, from cell membranes or prognosis in patients with chronic disease and reduced life central nervous system). Others describe lean mass as fat-free soft expectancy in healthy subjects as well as impaired quality of life tissue mass (total body mass without FM and bone mineral mass) and increased personal and health care costs.4–6 that can be measured using dual X-ray absorptiometry (DXA) and Besides the heterogeneous terminology used to characterize if derived from arms and legs is a more specific measure for lean mass depletion, current diagnostic strategy differs among skeletal muscle mass than FFM (which can be also derived from studies and even among consensus definitions.7 This is due to the DXA by summing total bone mineral content and fat-free soft

1Institute of Nutritional Medicine, University Hohenheim, Stuttgart, Germany and 2Institute of Human Nutrition and Food Science, Christian-Albrechts-University of Kiel, Kiel, Germany. Correspondence: Dr Professor A Bosy-Westphal, Institut für Ernährungsmedizin, Universität Hohenheim, Fruhwirthstr. 12 Stuttgart 70593, Germany. E-mail: [email protected] Received 13 March 2014; revised 4 August 2014; accepted 13 August 2014; accepted article preview online 1 September 2014; advance online publication, 23 September 2014 Diagnosis of lean mass depletion A Bosy-Westphal and MJ Müller 380 decrease in skeletal muscle at unchanged total lean soft tissue or FFM. Even in the post obese (weight reduced) state the composition of FFM remains altered. This is supported by a sarkopenic higher extra- to intracellular water ratio in post obese people that obesity is indicative of a higher contribution of connective tissue to total 13,14 ageing obese lean mass. life style (sedentary,low physical activity,diet) obese oncologic immunological (inflammation, ROS) Validity of body composition methods to assess skeletal muscle endocrine (GH↓ IGF-1↓ testosterone↓ patients mass is also affected by disturbancies in the hydration of FFM. mitochondrial dysfunction, insulin resistance) Conditions like cancer cachexia, chronic heart or renal failure, sarkopenia morbid obesity and ageing are frequently accompanied by inactivity bed rest cachexia increased or decreased water content of FFM that violate the protein intake ↓ immunological (inflammation, ROS) underlying assumptions of two-compartment methods for body age-related changes: therapy (surgery, chemotherapy) endocrine (GH↓ composition analysis. An increase in hydration will thus lead to an tumor factors testosterone ↓ overestimation of FFM by bioelectrical impedance analysis (owing insulin resistance insulin resistance) α ↓ immobilization to decreased resistance per body length) or deuterium dilution -motor-units (because calculation of FFM from total body water would require a higher hydration factor) and an underestimation by densitometry or DXA (because of similar densities of fat and water; for a review, elderly oncologic patients see Muller et al.15). Figure 1. A low muscle mass can be driven by ageing, disease or obesity with considerable overlap between the pathophysiology of the three. IMPACT OF FAT MASS ON THE IDENTIFICATION OF LEAN MASS DEFICIENCY tissue mass8). Besides by DXA, FFM can be measured by Lean mass deficiency may occur in the presence of a normal or densitometry (underwater weighing or air-displacement plethys- elevated fat mass. For example, the loss of lean body mass in mography) or bioelectrical impedance analysis. In order to rheumatoid cachexia is often compensated for by a gain in body normalize FFM for body size, it is either expressed as a percentage fat.16 ‘Hidden cachexia’ may also occur owing to muscle wasting in of body weight (%FFM) or by dividing FFM by height squared (fat- obese cancer patients.2 On the other hand, metabolic disturban- free mass index, FFMI = FFM (kg) per ht (m)2). FFM scales to height cies related to a high fat mass may also contribute to a loss in with a power of ~ 2 ranging from 1.86 in non-Hispanic white muscle mass (for example, insulin resistance, mitochondrial women to 2.32 in non-Hispanic black men.9 Scaling of FM to dysfunction, low grade inflammation and so on, Figure 1). Because height shows a greater variability ranging from 1.51 in non- a normal weight gain consists of fat as well as lean mass, Hispanic white women to 2.98 in Mexican men. Both body overweight and obese patients are expected to have a higher compartments therefore scale to height rounded to a power of 2 FFMI or SMI when compared with normal weight individuals.17 as the nearest integer.9 The rationale for using FFMI and FMI Hence, normal values of lean mass as the sole basis for the instead of normalizing both compartments for body weight is diagnosis of lean mass deficiency likely lead to an underestimation illustrated in Figure 2. During follow-up, the same %FM can result of sarcopenia in overweight and obese subjects. In addition, a either from a gain in FM or a loss in FFM (Figure 2a). In addition, diagnosis of sarcopenic obesity that is often based on a body mass people with the same %FM who differ in height have a different index (BMI) ⩾ 30 kg m − 2 in addition to a low FFMI or SMI is also nutritional status (Figure 2b). inaccurate because it does not take into account the relationship FFM is a heterogeneous compartment that comprises compo- between FFM and FM. nents with different functional importance like skeletal muscle Gilbert Forbes18 had developed an empirical non-linear mass, organ mass and parts of connective tissue. With respect to equation that quantifies the fat-free proportion of a weight sarcopenia, the major interest is on skeletal muscle mass. change as a function of the initial body fat (ΔFFM/ΔBW = 10.4/ Although muscle mass is the main constituent of total FFM, the (10.4+FM)) based on cross-sectional data in women. Thus, Forbes’s use of FFMI as a proxy for skeletal muscle mass is not without theory predicted that the contribution of FFM to an infinitesimal limitation. This is evident from Swiss reference data showing an weight change depended only on the FM.19 A study in a larger increase in FMI with advancing age, whereas FFMI remained fairly sample of white and African-American men and women found a constant across age groups.10 Because the decrease in muscle FM-FFM relationship very close to that originally described by mass with age is likely compensated by an increase in connective Forbes (constant 9.7 instead of 10.4), absent of significant tissue (that is, an increase in the FFM component of adipose variability by ethnicity or sex.20 By contrast, in a very large and tissue) FFMI is insensitive to age-related changes in muscle mass. more diverse multiethnic population from NHANES survey Baumgartner et al.11 summed the lean (or fat-free) soft tissue Thomas et al.21 were able to show that the FFM-FM relationship mass of the four limbs from a DXA scan as appendicular skeletal varies with age, height, race and gender. This argues in favor of muscle mass and defined a skeletal muscle index (SMI) as age-, gender- and ethnicity-specific reference values for FFMI or appendicular skeletal muscle mass/height2 (kg m − 2). This would SMI per FMI. The relationship described by Forbes is also affected provide a more accurate measure of muscle mass, especially when by body region in both genders. Figure 3 shows how much considering that bone density differs by age, ethnicity and in skeletal muscle in relation to adipose tissue contributes to each response to medications and so on. However, this index also has body region (arms, trunk, legs) with increasing quintile of FMI.12 limitations with increasing age or advancing body fat mass. With increasing FMI, the ratio of skeletal muscle to adipose tissue Comparison of regional lean mass measured by DXA with skeletal decreases faster at the trunk than corresponding ratios for the muscle mass assessed by magnetic resonance imaging revealed extremities in men. This is explained by the preferential that the contribution of skeletal muscle to appendicular lean soft accumulation of trunk adipose tissue with higher degrees of tissue is lower at a higher degree of adiposity especially in women obesity in men. By contrast in women, body fat accumulates who store more adipose tissue at the limbs compared with men predominantly at the extremities. who proportionally store more adipose tissue at the trunk with One might ask why we should pay attention to the ratio of increasing FMI.12 Thus with advancing adiposity (and possibly also muscle and fat when we want to determine whether a muscle with advancing age) the increase in connective tissue masks a mass is normal or low. The answer is that there is a physiological

40 kg FM 40 kg FM 60 kg FM 1.80 m

Disease, 40 kg immobilisation 60 kg Weight gain 60 kg FFM FFM FFM

b.w. = 80 kg b.w. = 100 kg b.w. = 120 kg

FM = 50% FM = 40% FM = 50%

2 2 2 FMI =12.3 kg/m FMI =12.3 kg/m FMI =18.3 kg/m FFMI =12.3 kg/m2 FFMI =18.5 kg/m FFMI =18.5 kg/m2

40 kg FM 2 m

40 kg FM 1.50 m 60 kg FFM 60 kg FFM

b.w. = 100 kg b.w. = 100 kg FM = 40% FM = 40% FMI =17.8 kg/m2 FMI =10 kg/m2

FFMI =27.6 kg/m2 FFMI =15 kg/m2 Figure 2. Rationale for using FFMI and FMI instead of %FM. (a) Theoretically, the same percentage of FM can result from a grain in FM (right image) or a loss in FFM (left image). The change in body composition is not reflected by %FM, whereas the increase in FMI (on the right) or the decrease in FFMI (on the left) clearly reveal discrepant nutritional states like obesity or hidden cachexia. (b) People with the same body weight and percentage of fat mass who differ in height have a different nutritional state (for example, elevated FMI = higher adiposity on the left and decreased FFMI = lower muscle mass on the right). relationship between the two compartments that likely leads to resistance, mitochondrial dysfunction) and impaired neurological functional impairment when the ratio is out of range. This means modulation of contraction. Additional impairments of muscle that the same muscle mass is worth less at a higher fat mass. To function in obesity involve limitations in physical performance.25,26 give an example, Figure 4 shows representative magnetic Given the same work load, energy expenditure and muscle force resonance imaging images of two young women with a similar required for an obese person are higher27 leading to ‘dynapenic skeletal muscle mass of 21 kg but great differences in the total obesity’ characterized by a limitation of strength and increased volume of adipose tissue (24 vs 41 liter). Because there is evidence disability. Finally, obesity also adversely affects the relationship for impaired muscle quality in obesity,22,23 the women with a between muscle and bone mass. The interaction of adipose higher adipose tissue could have an altered fiber size, -number tissue with muscle and bone involves endocrine regulation and fiber type, higher intramyocellular lipids, higher intermuscular (fat-muscle-bone axis determined by adipokines, myokines-and adipose tissue (because of a positive association between total osteokines-like osteocalcin) as well as commitment of progenitor adipose tissue and intermuscular adipose tissue24) and an mesenchymal stem cells in the bone marrow/adipose/ increased collagen content in skeletal muscle. These changes muscle microenvironment into the osteoblastogenic, adipogenic are known to be associated with metabolic disturbances (insulin or myogenic linage (for a review, see Ilich et al.28).

2.50 2.5 legs legs y = 3.719x-0.33 2 2.00 R = 0.97 2 trunk trunk y = 38.66x-1.03 R2 = 0.99 1.50 1.5 y = 28.46x-0.96 y = 3.387x-0.31 R2 = 0.97 R2 = 0.99 1.00 1

Skeletal muscle/Adipose tissue muscle/Adipose Skeletal 0.50 0.5 y = 3.018x-0.66 y =3.697x-0.84 R2 = 0.99 R2 = 0.97 0.00 0 0 5 10 15 20 0 5 10 15 20 FMI (kg/m2) FMI (kg/m2) Figure 3. Regional relationship between skeletal muscle to adipose tissue volume (by MRI) and fat mass index (FMI, kg m − 2). (a) In men, with increasing FMI the ratio of muscle/adipose tissue declines faster at the trunk when compared with the extremities. (b) In women, with increasing FMI the ratio of muscle/adipose tissue declines faster at the limbs when compared with the trunk. In summary, the relationship described by Forbes18 differs with respect to body region and gender: In women, with increasing adiposity relatively more fat is stored at the limbs whereas in men, more fat is preferentially stored at the trunk. (cross-sectional data from 117 healthy men and 146 women, Schautz et al.12).

Figure 4. MRI images at the level of the upper arms, the umblilicus and the thigh of two healthy young women with a similar skeletal muscle mass (SM) but a great difference in adipose tissue volume. This example is used to illustrate the argument that the deﬁnition of a normal muscle mass should depend on the amount of fat mass. Because a high fat mass is known to impair muscle quality (for example, fatty inﬁltration and insulin resistance) and muscle function (lower strength, higher disability) the same muscle mass is worth less at a higher adiposity.

The simultaneous evaluation of adipose tissue and skeletal prevalence of sarcopenia especially in overweight or obese 29,30 muscle mass can be done by combining appendicular SMIDXA or people. Alternatively, lumbal SMICT and adipose tissue area FFMI with FMI. Appendicular SM adjusted for height and fat mass can be used in cancer patients where computer tomography using regression analysis prevented an underestimation of the images are obtained for staging or diagnostic purposes. These

International Journal of Obesity (2015) 379 – 386 © 2015 Macmillan Publishers Limited Diagnosis of lean mass depletion A Bosy-Westphal and MJ Müller 383 images can be easily segmented into fat and muscle compart- subcutaneous or total body fat.36 The latter is also reflected by the ments using Hounsfield unit ranges from − 29 to 150 for skeletal increase in visceral adipose tissue volume per cm of waist muscle, − 190 to − 30 for subcutaneous and intermuscular adipose circumference with increasing age in both genders (Figure 6, tissue, and –150 to − 50 for visceral adipose tissue.31 Skeletal Bosy-Westphal et al.37). In addition, intermuscular,38 intramuscular muscle and adipose tissue areas at the lumbar vertebral landmark and intrahepatic fat are higher in older persons and associated (L3) have been shown to correspond to whole-body tissue with insulin resistance.39,40 quantities in nonmalignant populations as well as cancer patients (for a review, see Mourtzakis et al.31). REFERENCE VALUES FOR DETECTION OF LEAN MASS DEPLETION IMPACT OF AGE AND GENDER ON THE LEAN TO FAT MASS Generation of reference values for lean mass should ideally take RELATIONSHIP into account the aforementioned physiological determinants: FFM (primarily skeletal muscle) progressively decreases up to 40% gender, age, ethnicity and fat mass. However, many publications from 20 to 70 years of age,32–35 whereas FM increases and disregard the effect of age and fat mass and use a simple reaches a maximum at ~ 60–70 years.32,33 To evaluate the stratification of their own male and female study populations relationship between FFMI and FMI with age in both genders a based on cutoffs (for example, two s.d.’s below the mean or Hattori chart can be used (Figure 5, Schautz et al.12). The chart percentiles).7 Contrary to this stratification approach, a normative reveals that in men at the same adipositiy (quartile of FMI), lean approach is based on cutoffs derived from a healthy reference mass increases up to the age group of 20–40 years and decreases population (s.d., percentiles) and facilitates comparison of results thereafter (Figure 5a), whereas in women, at the same adiposity between studies. Sarcopenia can thus be defined as a FFMI, lean mass slightly increases only up to the age group of 16–20 appendicular or lumbal SMI more than two s.d.’s below the mean years and sharply decreases thereafter (Figure 5b). The starting for a young adult reference population (that would be ideally not point as well as the increase in lean mass with increasing adiposity only stratified for gender but also for fat mass). Criteria for a low are gender-specific but the latter is similar between age groups in lean mass can also be based on the amount of lean mass lower both genders. than expected for a given amount of fat mass using residuals from Aging is not only associated with an increase in FM and a linear regression models. Thus, the residual distribution derived decrease in muscle mass but also with a redistribution of both from the regression of appendicular skeletal muscle mass on FFM and FM characterized by a greater relative decrease in height and FM was used to define sarcopenia.41,42 The most appendicular than in trunk FFM (because the rate of decline in advanced reference values for FMI, FFMI and appendicular SMI are skeletal muscle exceeds the decline in organ mass), as well as a gender, age and ethnicity specific and have been published as greater relative increase in intraabdominal fat compared with 3rd, 5th, 25th, 50th, 75th, 95th and 97th percentiles derived from the NHANES population-based sample acquired with modern DXA fan beam scanners in 15 counties across the United States from 1999 through 2004.43 Corresponding high standard reference FMI (kg/m2) values are still lacking for the lumbal adipose tissue areaCT and 20 SMICT as well as more simple body composition techniques like BMI 30 FMI and FFMI by bioelectrical impedance analysis. Applying age- 40% FM fi 15 and fat mass-speci c reference values avoids the methodological BMI 25 limitations of FFMI and appendicular SMI that both show an boys 11-15y increase in connective tissue in relation to skeletal muscle mass 30% FM 12 10 BMI 20 boys 16-20y with advancing age or obesity. In this respect, DXA scans of the men 20-40y NHANES survey have been used to develop sex-specific mathe- 20% FM 5 matic models to predict FFM from a subject’s age, fat mass, height men 40-60y 10% FM (instead of normalization of FFM and FM for height) and ethnicity 21 men older 60y (download calculator at http://pbrc.edu/calculators/fatfreemass). 0 12 17 22 These prediction models are most useful to evaluate a lean mass FFMI (kg/m2) of a patient. A drawback of these reference values is that Asians have not been differentiated but were included in a group of non- FMI African-American people that includes all ethnic groups other (kg/m2) 50% FM than African-American subjects. However, Asians (especially

20 girls 11-15y Chinese) are known to have a shorter leg length and Afro BMI 30 Americans have a longer leg length compared with Cauca- 44,45 15 BMI 25 40% FM girls 16-20y sians. Because appendicular skeletal muscle accounts for 75% of the total-body skeletal muscle, subjects with shorter legs are women 20-40y 10 BMI 20 expected to have a lower skeletal muscle mass at a given height. 30% FM women 40-60y An additional method-inherent limitation of reference values obtained by DXA and bioelectrical impedance analysis is that the 5 20% FM women older 60y output of these techniques is analyzer specific and varies with 46,47 0 10% FM different manufacturers and software types. 12 17 22 A less statistical definition is the employment of the health risk- − 2 FFMI(kg/m2) related BMI classification from WHO (25 kg m for overweight and 30 kg m − 2 for obesity)48 in order to generate FMI cutoffs by Figure 5. Hattori chart showing the relationship between fat-free − 2 − 2 assigning FMI values corresponding to each BMI classification mass index (FFMI, kg m ) and fat mass index (FMI, kg m ) 43 measured by air-displacement plethysmography different stratified threshold (normal, overweight, obese class 1and so on). a b Although this method has also been applied to establish cutoffs into age groups and quartiles of FMI ( ) for male and ( ) for female 49 subjects. (1737 healthy Caucasians, 947 females, 790 males, age: for waist circumference as well as age-and ethnicity-specific 36 ± 20 years (11–84 years), BMI: 26 ± 6kgm− 2 (13.5–52.5 kg m − 2, cutoffs for %FM,33 respective information on FFMI or SMI cutoffs Schautz et al.12). See text for details. has not been published yet.

© 2015 Macmillan Publishers Limited International Journal of Obesity (2015) 379 – 386 Diagnosis of lean mass depletion A Bosy-Westphal and MJ Müller 384 Figure 7 summarizes three basic concepts how to define evaluation of a healthy weight loss with different energy deficits skeletal muscle mass depletion. The second concept is more or dietary macronutrient composition requires knowledge on the physiological than the first because it takes into account the expected normal values for changes in fat and lean mass. relationship between muscle and fat mass. The third concept Individual deviations from predicted changes in FFM (according shows a vision for future research that seeks to define cutoffs to the model by Thomas et al.21) can be used to evaluate changes using functional consequences of skeletal muscle mass depletion. in body composition. Instead of most commonly used absolute changes in FFM and FM, a favorable modulation of energy partitioning during weight gain or weight loss should be a major APPLICATIONS FOR EVALUATION OF A NORMAL outcome in interventions targeted for sarcopenic elderly or obese COMPOSITION OF WEIGHT CHANGE AND IMPLICATIONS FOR people as well as cachectic patients. A precondition for interpret- FUTURE RESEARCH ing changes in energy partitioning is understanding its determi- Evaluation of a normal composition of weight gain as FFM and FM nants (for example, energy balance, composition of energy intake, is important when studying the effect of different pharmacologi- type, amount and duration of physical activity, metabolic and cal treatments on body composition (for example, cortisol, PPARγ- endocrine parameters, for a review, see Heymsfied et al.17). agonists, megestrolacetat, anti-retrovirals, myostatin antagonists, In addition, the impact of weight cycling on body composition is testosterone and growth hormone and so on). Likewise, the still a matter of debate. A disproportional regain in fat mass has

ab VAT (cm3) VAT (cm3) 12000 women 45-78 y 12000 men 45-72 y y = 3E-05x4.079 y = 0.0115x2.8272 2 2 10000 R =0.54 R = 0.34 10000 men 19-44 y women 19-44 y 3.8178 y = 0.0141x2.5795 y = 9E-05x 8000 2 R2 = 0.62 8000 R = 0.67 boys 6-18 y girls 6-18 y y = 0.0006x3.2038 6000 2.6401 y = 0.0068x 6000 R2 = 0.85 R2= 0.80

4000 4000

2000 2000

0 0 20 40 60 80 100 120 140 20 40 60 80 100 120 140 waist circumference (cm) waist circumference (cm) Figure 6. Relationship between visceral adipose tissue volume (VAT) measured by MRI and waist circumference measured midway between the lowest rib and the iliac crest in children, adolescents and adults stratiﬁed by age groups and gender. (a) females and (b) males (data are taken from Bosy-Westphal et al.37).

1. SM ↓

Normative approach: Stratification approach: cut offs for SM based on a young, healthy stratification of the male and female study reference population with the same gender or population of each ethnicity according to and ethnicity (-2 SD, -1 SD, percentiles) SM and age (-2 SD, -1 SD, percentiles)

2. SM ↓ for a given FM

Mathematical modeling Stratification approach: stratification of the male and female study predicting a normal SM from height, or gender, age, FM and ethnicity using population of each ethnicity into FM-groups regression analysis and substratification of each FM-group into SM-percentiles

3. Health risk associated cut - offs for SM ↓ for a given FM

Indirect approach Direct approach derived from epidemiological data on BMI vs. requires age and gender-specific data on or mortality by estimating a muscle mass at the SM for a given FM vs. outcomes like BMI cut offs 25 and 30 in gender- and osteoporosis, strength, insulin sensitivitiy different age groups etc.

Figure 7. Three different strategies for identiﬁcation of lean mass depletion. The second strategy is better than the ﬁrst and the third strategy remains to be developed in future studies. (SM stands for a direct or indirect measure of skeletal muscle mass (for example, lumbal skeletal muscle index, appendicular skeletal muscle index or fat-free mass index), FM stands for fat mass index).

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Curr Opin Clin Nutr Metab Care 2008; phenotype characteristics like sarcopenia, sarcopenic obesity or 11:693–700. cachexia. 24 Gallagher D, Kuznia P, Heshka S, Albu J, Heymsfield SB, Goodpaster B, Visser M, Harris TB. Adipose tissue in muscle: a novel depot similar in size to visceral adipose tissue. Am J Clin Nutr 2005; 81:903–910. CONFLICT OF INTEREST 25 Visser M, Kritchevsky SB, Goodpaster BH, Newman AB, Nevitt M, Stamm E et al. Leg muscle mass and composition in relation to lower extremity performance in The authors declare no conflict of interest. men and women aged 70 to 79: The Health, Ageing and Body Composition Study. J Am Geriatr Soc 2002; 50:897–904. ACKNOWLEDGEMENTS 26 Peterson MD, Liu D, Gordish-Dressman H, Hubal MJ, Pistilli E, Angelopoulos TJ et al. Adiposity attenuates muscle quality and the adaptive response to resistance The studies by the authors were funded by a grant of the Germany Ministry of exercise in non-obese, healthy adults. 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