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.
In the strict sense, the term sarcopenia (Greek sarx=flesh, penia=loss, literally: poverty of flesh) is used to define loss of muscle mass and strength that occur with advancing age.1 A decrease in muscle mass can, however, have many causes that are related to lifestyle (inactivity, inadequate diet), metabolic and neuroendocrine changes associated with ageing, diseases and obesity (for example, insulin resistance, inflammation, oxidative stress) or therapy (for example, cortisol, operation, chemotherapy). Although researchers and clinicians from different disciplines all use their individual terminology to describe lean mass depletion (sarcopenia in gerontology, sarcopenic obesity in obesity research and cachexia in oncology), there is an overlap in the underlying multifactorial pathophysiology of these phenotype descriptions (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 cachexia’ due to the masking effect of expanded adipose tissue that impedes the clinical identification of lean mass deficiency.2 On the other hand, an overlap between age-related sarcopenia and sarcopenic obesity is likely becoming more frequent considering the demographic change with an increasing number of elderly people and the obesity epidemic.3 Irrespective of the etiology, lean mass depletion is associated with impaired prognosis in patients with chronic disease and reduced life expectancy in healthy subjects as well as impaired quality of life and increased personal and health care costs.4, 5, 6
Besides the heterogeneous terminology used to characterize lean mass depletion, current diagnostic strategy differs among studies and even among consensus definitions.7 This is due to the diversity of body composition parameters used and discrepancies in the method of normal range-definition. These inconsistencies contribute to a great variety in prevalence estimates and prevent comparative evaluation of interventions directed against lean mass depletion. Commonalities in the pathophysiology, overlapping etiology and consequences justify a unique definition and terminology of lean mass depletion in several conditions such as ageing, obesity and disease. The aim of this review is to summarize basic determinants of a normal lean mass (age, gender, fat mass, body region) that need to be considered in a unique definition of lean mass depletion. In addition, we demonstrate limitations of different lean mass parameters as indices for skeletal muscle mass and discuss their appropriate normalization.
Parameters and indices used to determine lean mass depletion
Fat-free mass (FFM) is a widely used parameter that differs from lean mass because it is chemically defined as the sum of all non-lipid components of the body (including non fat components of adipose tissue), whereas the term lean mass is anatomically defined as the sum of all non-adipose tissues of the body (including essential lipids, for example, from cell membranes or central nervous system). Others describe lean mass as fat-free soft tissue mass (total body mass without FM and bone mineral mass) that can be measured using dual X-ray absorptiometry (DXA) and if derived from arms and legs is a more specific measure for skeletal muscle mass than FFM (which can be also derived from DXA by summing total bone mineral content and fat-free soft tissue mass8). Besides by DXA, FFM can be measured by densitometry (underwater weighing or air-displacement plethysmography) or bioelectrical impedance analysis. In order to normalize FFM for body size, it is either expressed as a percentage of body weight (%FFM) or by dividing FFM by height squared (fat-free mass index, FFMI=FFM (kg) per ht (m)2). FFM scales to height with a power of ~2 ranging from 1.86 in non-Hispanic white women to 2.32 in non-Hispanic black men.9 Scaling of FM to height shows a greater variability ranging from 1.51 in non-Hispanic white women to 2.98 in Mexican men. Both body compartments therefore scale to height rounded to a power of 2 as the nearest integer.9 The rationale for using FFMI and FMI instead of normalizing both compartments for body weight is illustrated in Figure 2. During follow-up, the same %FM can result either from a gain in FM or a loss in FFM (Figure 2a). In addition, people with the same %FM who differ in height have a different nutritional status (Figure 2b).
FFM is a heterogeneous compartment that comprises components with different functional importance like skeletal muscle mass, organ mass and parts of connective tissue. With respect to sarcopenia, the major interest is on skeletal muscle mass. Although muscle mass is the main constituent of total FFM, the use of FFMI as a proxy for skeletal muscle mass is not without limitation. This is evident from Swiss reference data showing an increase in FMI with advancing age, whereas FFMI remained fairly constant across age groups.10 Because the decrease in muscle mass with age is likely compensated by an increase in connective tissue (that is, an increase in the FFM component of adipose tissue) FFMI is insensitive to age-related changes in muscle mass.
Baumgartner et al.11 summed the lean (or fat-free) soft tissue mass of the four limbs from a DXA scan as appendicular skeletal muscle mass and defined a skeletal muscle index (SMI) as appendicular skeletal muscle mass/height2 (kg m−2). This would provide a more accurate measure of muscle mass, especially when considering that bone density differs by age, ethnicity and in response to medications and so on. However, this index also has limitations with increasing age or advancing body fat mass. Comparison of regional lean mass measured by DXA with skeletal muscle mass assessed by magnetic resonance imaging revealed that the contribution of skeletal muscle to appendicular lean soft tissue is lower at a higher degree of adiposity especially in women who store more adipose tissue at the limbs compared with men who proportionally store more adipose tissue at the trunk with increasing FMI.12 Thus with advancing adiposity (and possibly also with advancing age) the increase in connective tissue masks a 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 higher extra- to intracellular water ratio in post obese people that is indicative of a higher contribution of connective tissue to total lean mass.13,14
Validity of body composition methods to assess skeletal muscle mass is also affected by disturbancies in the hydration of FFM. Conditions like cancer cachexia, chronic heart or renal failure, morbid obesity and ageing are frequently accompanied by increased or decreased water content of FFM that violate the underlying assumptions of two-compartment methods for body composition analysis. An increase in hydration will thus lead to an overestimation of FFM by bioelectrical impedance analysis (owing to decreased resistance per body length) or deuterium dilution (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, see Muller et al.15).
Impact of fat mass on the identification of lean mass deficiency
Lean mass deficiency may occur in the presence of a normal or elevated fat mass. For example, the loss of lean body mass in rheumatoid cachexia is often compensated for by a gain in body fat.16 ‘Hidden cachexia’ may also occur owing to muscle wasting in obese cancer patients.2 On the other hand, metabolic disturbancies related to a high fat mass may also contribute to a loss in muscle mass (for example, insulin resistance, mitochondrial dysfunction, low grade inflammation and so on, Figure 1). Because a normal weight gain consists of fat as well as lean mass, overweight and obese patients are expected to have a higher FFMI or SMI when compared with normal weight individuals.17 Hence, normal values of lean mass as the sole basis for the diagnosis of lean mass deficiency likely lead to an underestimation of sarcopenia in overweight and obese subjects. In addition, a diagnosis of sarcopenic obesity that is often based on a body mass index (BMI) ⩾30 kg m−2 in addition to a low FFMI or SMI is also inaccurate because it does not take into account the relationship between FFM and FM.
Gilbert Forbes18 had developed an empirical non-linear equation that quantifies the fat-free proportion of a weight change as a function of the initial body fat (ΔFFM/ΔBW=10.4/(10.4+FM)) based on cross-sectional data in women. Thus, Forbes’s theory predicted that the contribution of FFM to an infinitesimal weight change depended only on the FM.19 A study in a larger sample of white and African-American men and women found a FM-FFM relationship very close to that originally described by Forbes (constant 9.7 instead of 10.4), absent of significant variability by ethnicity or sex.20 By contrast, in a very large and more diverse multiethnic population from NHANES survey Thomas et al.21 were able to show that the FFM-FM relationship varies with age, height, race and gender. This argues in favor of age-, gender- and ethnicity-specific reference values for FFMI or SMI per FMI. The relationship described by Forbes is also affected by body region in both genders. Figure 3 shows how much skeletal muscle in relation to adipose tissue contributes to each body region (arms, trunk, legs) with increasing quintile of FMI.12 With increasing FMI, the ratio of skeletal muscle to adipose tissue decreases faster at the trunk than corresponding ratios for the extremities in men. This is explained by the preferential accumulation of trunk adipose tissue with higher degrees of obesity in men. By contrast in women, body fat accumulates predominantly at the extremities.
One might ask why we should pay attention to the ratio of muscle and fat when we want to determine whether a muscle mass is normal or low. The answer is that there is a physiological relationship between the two compartments that likely leads to functional impairment when the ratio is out of range. This means that the same muscle mass is worth less at a higher fat mass. To give an example, Figure 4 shows representative magnetic resonance imaging images of two young women with a similar skeletal muscle mass of 21 kg but great differences in the total volume of adipose tissue (24 vs 41 liter). Because there is evidence for impaired muscle quality in obesity,22,23 the women with a higher adipose tissue could have an altered fiber size, -number and fiber type, higher intramyocellular lipids, higher intermuscular adipose tissue (because of a positive association between total adipose tissue and intermuscular adipose tissue24) and an increased collagen content in skeletal muscle. These changes are known to be associated with metabolic disturbances (insulin resistance, mitochondrial dysfunction) and impaired neurological modulation of contraction. Additional impairments of muscle function in obesity involve limitations in physical performance.25,26 Given the same work load, energy expenditure and muscle force required for an obese person are higher27 leading to ‘dynapenic obesity’ characterized by a limitation of strength and increased disability. Finally, obesity also adversely affects the relationship between muscle and bone mass. The interaction of adipose tissue with muscle and bone involves endocrine regulation (fat-muscle-bone axis determined by adipokines, myokines-and osteokines-like osteocalcin) as well as commitment of progenitor mesenchymal stem cells in the bone marrow/adipose/muscle microenvironment into the osteoblastogenic, adipogenic or myogenic linage (for a review, see Ilich et al.28).
The simultaneous evaluation of adipose tissue and skeletal muscle mass can be done by combining appendicular SMIDXA or FFMI with FMI. Appendicular SM adjusted for height and fat mass using regression analysis prevented an underestimation of the prevalence of sarcopenia especially in overweight or obese people.29,30 Alternatively, lumbal SMICT and adipose tissue area can be used in cancer patients where computer tomography images are obtained for staging or diagnostic purposes. These images can be easily segmented into fat and muscle compartments using Hounsfield unit ranges from −29 to 150 for skeletal muscle, −190 to −30 for subcutaneous and intermuscular adipose tissue, and –150 to −50 for visceral adipose tissue.31 Skeletal muscle and adipose tissue areas at the lumbar vertebral landmark (L3) have been shown to correspond to whole-body tissue quantities in nonmalignant populations as well as cancer patients (for a review, see Mourtzakis et al.31).
Impact of age and gender on the lean to fat mass relationship
FFM (primarily skeletal muscle) progressively decreases up to 40% from 20 to 70 years of age,32, 33, 34, 35 whereas FM increases and reaches a maximum at ~60–70 years.32,33 To evaluate the relationship between FFMI and FMI with age in both genders a Hattori chart can be used (Figure 5, Schautz et al.12). The chart reveals that in men at the same adipositiy (quartile of FMI), lean mass increases up to the age group of 20–40 years and decreases thereafter (Figure 5a), whereas in women, at the same adiposity lean mass slightly increases only up to the age group of 16–20 years and sharply decreases thereafter (Figure 5b). The starting point as well as the increase in lean mass with increasing adiposity are gender-specific but the latter is similar between age groups in both genders.
Aging is not only associated with an increase in FM and a decrease in muscle mass but also with a redistribution of both FFM and FM characterized by a greater relative decrease in appendicular than in trunk FFM (because the rate of decline in skeletal muscle exceeds the decline in organ mass), as well as a greater relative increase in intraabdominal fat compared with subcutaneous or total body fat.36 The latter is also reflected by the increase in visceral adipose tissue volume per cm of waist circumference with increasing age in both genders (Figure 6, Bosy-Westphal et al.37). In addition, intermuscular,38 intramuscular and intrahepatic fat are higher in older persons and associated with insulin resistance.39,40
Reference values for detection of lean mass depletion
Generation of reference values for lean mass should ideally take into account the aforementioned physiological determinants: gender, age, ethnicity and fat mass. However, many publications disregard the effect of age and fat mass and use a simple stratification of their own male and female study populations based on cutoffs (for example, two s.d.’s below the mean or percentiles).7 Contrary to this stratification approach, a normative approach is based on cutoffs derived from a healthy reference population (s.d., percentiles) and facilitates comparison of results between studies. Sarcopenia can thus be defined as a FFMI, appendicular or lumbal SMI more than two s.d.’s below the mean for a young adult reference population (that would be ideally not only stratified for gender but also for fat mass). Criteria for a low lean mass can also be based on the amount of lean mass lower than expected for a given amount of fat mass using residuals from linear regression models. Thus, the residual distribution derived from the regression of appendicular skeletal muscle mass on height and FM was used to define sarcopenia.41,42 The most advanced reference values for FMI, FFMI and appendicular SMI are gender, age and ethnicity specific and have been published as 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 values are still lacking for the lumbal adipose tissue areaCT and SMICT as well as more simple body composition techniques like FMI and FFMI by bioelectrical impedance analysis. Applying age- and fat mass-specific reference values avoids the methodological limitations of FFMI and appendicular SMI that both show an increase in connective tissue in relation to skeletal muscle mass with advancing age or obesity.12 In this respect, DXA scans of the NHANES survey have been used to develop sex-specific mathematic models to predict FFM from a subject’s age, fat mass, height (instead of normalization of FFM and FM for height) and ethnicity (download calculator at http://pbrc.edu/calculators/fatfreemass).21 These prediction models are most useful to evaluate a lean mass of a patient. A drawback of these reference values is that Asians have not been differentiated but were included in a group of non-African-American people that includes all ethnic groups other than African-American subjects. However, Asians (especially Chinese) are known to have a shorter leg length and Afro Americans have a longer leg length compared with Caucasians.44,45 Because appendicular skeletal muscle accounts for 75% of the total-body skeletal muscle, subjects with shorter legs are expected to have a lower skeletal muscle mass at a given height.
An additional method-inherent limitation of reference values obtained by DXA and bioelectrical impedance analysis is that the output of these techniques is analyzer specific and varies with different manufacturers and software types.46,47
A less statistical definition is the employment of the health risk-related BMI classification from WHO (25 kg m−2 for overweight and 30 kg m−2 for obesity)48 in order to generate FMI cutoffs by assigning FMI values corresponding to each BMI classification threshold (normal, overweight, obese class 1and so on).43 Although this method has also been applied to establish cutoffs for waist circumference49 as well as age-and ethnicity-specific cutoffs for %FM,33 respective information on FFMI or SMI cutoffs has not been published yet.
Figure 7 summarizes three basic concepts how to define skeletal muscle mass depletion. The second concept is more physiological than the first because it takes into account the relationship between muscle and fat mass. The third concept shows a vision for future research that seeks to define cutoffs using functional consequences of skeletal muscle mass depletion.
Applications for evaluation of a normal composition of weight change and implications for future research
Evaluation of a normal composition of weight gain as FFM and FM is important when studying the effect of different pharmacological treatments on body composition (for example, cortisol, PPARγ-agonists, megestrolacetat, anti-retrovirals, myostatin antagonists, testosterone and growth hormone and so on). Likewise, the evaluation of a healthy weight loss with different energy deficits or dietary macronutrient composition requires knowledge on the expected normal values for changes in fat and lean mass. Individual deviations from predicted changes in FFM (according to the model by Thomas et al.21) can be used to evaluate changes 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 outcome in interventions targeted for sarcopenic elderly or obese people as well as cachectic patients. A precondition for interpreting changes in energy partitioning is understanding its determinants (for example, energy balance, composition of energy intake, type, amount and duration of physical activity, metabolic and endocrine parameters, for a review, see Heymsfied et al.17). In addition, the impact of weight cycling on body composition is still a matter of debate. A disproportional regain in fat mass has been observed in young people recovering from severe starvation (for a review, see, Dulloo et al.50). whereas unequivocal findings have been obtained in obese patients.51 There might, however, be a sex-specific redistribution of adipose tissue with weight regain in obese patients (for example, an increased propensity of lipid storage in subcutaneous trunk or limb depots in men and women, respectively). When compared with weight regain, the regain in adipose tissue of the extremities was disproportionally higher in women, whereas men showed a preferential regain in abdominal subcutaneous adipose tissue.51 This complies well with the preferential deposition of subcutaneous limb adipose tissue with increasing FMI in women or trunk adipose tissue in men.12 Furthermore, a redistribution of lean mass may occur with weight cycling. In both genders, reconstitution of skeletal muscle at the trunk during weight regain seems to lag behind the extremities.51 These findings are supported by Byrne et al.52 who found a preferential regain of lean soft tissueDXA at the limbs compared with the trunk in weight regaining obese African American and white women.
Lean mass deficiency is a symptom not a diagnosis. Irrespective of the underlying etiology, lean mass depletion can be associated with fatigue, impaired strength, endurance, power and thermoregulation that contribute to the diagnosis and prognosis in different diseases like cancer, metabolic syndrome or frailty. Different conditions associated with a low lean mass have commonalities in the underlying causes of ‘sarcopenia’ and would therefore benefit from a unified definition and reference values. A unique definition of lean mass depletion should be ethnicity specific and based on a measure of skeletal muscle mass normalized for height (FFMI, appendicular SMI, lumbal SMI) in combination with FM. 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, severe obesity or weight reduced obese patients due to an increased contribution of connective tissue to lean mass. In the absence of risk-defined cutoffs, mathematical modeling of a normal lean mass based on age, gender, fat mass and height can be used to identify different phenotype characteristics like sarcopenia, sarcopenic obesity or cachexia.
Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F et al. Sarcopenia: European consensus on definition and diagnosis. Age Ageing 2010; 39: 412–423.
Fearon K, Arends J, Baracos V . Understanding the mechanisms and treatment options in cancer cachexia. Nat Rev Clin Oncol 2013; 10: 90–99.
Houston DK, Nicklas BJ, Zizza CA . Weighty concerns: the growing prevalence of obesity among older adults. J Am Diet Assoc 2009; 109: 1886–1895.
Visser M, Schaap LA . Consequences of sarcopenia. Clin Geriatr Med 2011; 27: 387–399.
Rizzoli R, Reginster JY, Arnal JF, Bautmans I, Beaudart C, Bischoff-Ferrari H et al. Quality of life in sarcopenia and frailty. Calcif Tissue Int 2013; 93: 101–120.
Parr EB, Coffey VG, Hawley JA . 'Sarcobesity': a metabolic conundrum. Maturitas 2013; 74: 109–113.
Prado CMM, Wells JCK, Smith SR, Stephan BCM, Siervo M . Sarcopenic obesity: A critical appraisal of the current evidence. Clin Nutr 2012; 31: 583–601.
Hansen RD, Raja C, Aslani A, Smith RC, Allen BJ . Determination of skeletal muscle and fat-free mass by nuclear and dual-energy X-ray absorptiometry methods in men and women aged 51–84 y. Am J Clin Nutr 1999; 70: 228–233.
Heymsfield SB, Moonseong H, Thomas D, Pietrobelli A . Scaling of body composition to height: relevance to height-normalized indexes. Am J Clin Nutr 2011; 93: 736–740.
Schutz Y, Kyle UUG,, Pichard C . Fat-free mass index and fat mass index percentiles in Caucasians aged 18-98 y. Int J Obes 2002; 26: 953–960.
Baumgartner R, Koehler K, Gallagher D, Romero L, Heymsfield SB, Ross RR et al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol 1998; 147: 755–763.
Schautz B, Later W, Heller M, Müller MJ, 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.
Elia M, Stubbs RJ, Henry CJ . Differences in fat, carbohydrate, and protein metabolism between lean and obese subjects undergoing total starvation. Obes Res 1999; 7: 597–604.
Das SK, Roberts SB, Kehayias JJ, Wang J, Hsu LK, Shikora SA et al. Body composition assessment in extreme obesity and after massive weight loss induced by gastric bypass surgery. Am J Physiol Endocrinol Metab 2003; 284: E1080–E1088.
Müller MJ, Bosy-Westphal A, Lagerpusch M, Heymsfield SB . Use of balance methods for assessment of short-term changes in body composition. Obesity (Silver Spring) 2012; 20: 701–707.
Summers GD, Deighton CM, Rennie MJ, Booth AH . Rheumatoid cachexia: a clinical perspective. Rheumatology (Oxford) 2008; 47: 1124–1131.
Heymsfield SB, Cristina Gonzalez MC, Shen W, Leanne Redman L, Thomas D . Weight loss composition is one-fourth fat-free mass: a critique of this widely cited rule. in press Obes Rev 2014; 15: 310–321.
Forbes GB . Lean body mass-body fat interrelationships in humans. Nutr Rev 1987; 45: 225–231.
Hall KD . Body fat and fat-free mass interrelationships. Forbes’s theory revisited. Br J Nutr 2007; 97: 1059–1063.
Broyles ST, Bouchard C, Bray GA, Greenway FL, Johnson WD, Newton RL et al. Consistency of fat mass—fat-free mass relationship across ethnicity and sex groups. Br J Nutr 2011; 105: 1272–1276.
Thomas D, Das SK, Levine JA, Martin CK, Mayer L, McDougall A et al. New fat free mass - fat mass model for use in physiological energy balance equations. Nutr Metab (Lond) 2010; 7: 39.
Barbat-Artigas S, Filion ME, Plouffe S, Aubertin-Leheudre M . Muscle quality as a potential explanation of the metabolically healthy but obese and sarcopenic obese paradoxes. Metab Syndr Relat Disord 2012; 10: 117–122.
Stenholm S, Harris TB, Rantanen T, Visser M, Kritchevsky SB, Ferrucci L . Sarcopenic obesity: definition, cause and consequences. Curr Opin Clin Nutr Metab Care 2008; 11: 693–700.
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.
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 men and women aged 70 to 79: The Health, Ageing and Body Composition Study. J Am Geriatr Soc 2002; 50: 897–904.
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 exercise in non-obese, healthy adults. Int J Obes (Lond) 2011; 35: 1095–1103.
Hulens M, Vasnsant G, Claessens AL, Lysens R, Muls E . Predictors of 6-minute walk test results in lean, obese and morbidly obese women. Scand J Med Sci Sports 2003; 13: 98–105.
Ilich JZ, Kelly OJ, Inglis JE, Panton LB, Duque G, Ormsbee MJ . Interrelationship among muscle, fat, and bone: connecting the dots on the cellular, hormonal and whole body levels. Ageing Res Rev 2014; 15: 51–60.
Newman AB, Kupelian V, Visser M, Simonsick E, Goodpaster B, Nevitt M, Kritchevsky SB, Tylavsky FA, Rubin SM, Harris TB . Health ABC Study Investigators Sarcopenia: alternative definitions and associations with lower extremity function. J Am Geriatr Soc 2003; 51: 1602–1609.
Domiciano D, Figueiredo C, Lopes J, Caparbo V, Takayama L, Menezes P, Bonfak E, Pereirak R . Discriminating sarcopenia in community-dwelling older women with high frequency of overweight/obesity: the Sao Paulo Ageing & Health Study (SPAH). Osteoporos Int 2012; 24: 595–603.
Mourtzakis M, Prado CM, Lieffers JR, Reiman T, McCargar LJ, Reiman T et al. 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.
Baumgartner RN, Stauber PM, McHugh D, Koehler KM, Garry PJ . Cross-sectional age differences in body composition in persons 60+ years of age. J Gerontol A Biol Sci Med Sci 1995; 50: M307–M316.
Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y . Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 2000; 72: 694–701.
Flynn MA, Nolph GB, Baker AS, Martin WM, Krause G . Total body potassium in aging humans: a longitudinal study. Am J Clin Nutr 1989; 50: 713–717.
Muller DC, Elahi D, Tobin JD, Andres R . The effect of age on insulin resistance and secretion: a review. Semin Nephrol 1996; 16: 289–298.
Beaufrere B, Morio B . Fat and protein redistribution with aging: metabolic considerations. Eur J Clin Nutr 2000; 54 (suppl): S48–S53.
Bosy-Westphal A, Booke CA, Blöcker T, Kossel E, Goele K, Later W et al. Measurement site for waist circumference affects its accuracy as an index of visceral and abdominal subcutaneous fat in a Caucasian population. J Nutr 2010; 140: 954–961.
Song MY, Ruts E, Kim J, Janumala I, Heymsfield S, Gallagher D . Sarcopenia and increased adipose tissue infiltration of muscle in elderly African American women. Am J Clin Nutr 2004; 79: 874–880.
Albu JB, Kovera AJ, Allen L, Wainwright M, Berk E, Raja-Khan N et al. Independent association of insulin resistance with larger amounts of intermuscular adipose tissue and a greater acute insulin response to glucose in African American than in white nondiabetic women. Am J Clin Nutr 2005; 82: 1210–1217.
Cree MG, Newcomer BR, Katsanos CS, Sheffield-Moore M, Chinkes D, Aarsland A et al. Intramuscular and liver triglycerides are increased in the elderly. J Clin Endocrinol Metab 2004; 89: 3864–3871.
Newman AB, Kupelian V, Visser M, Simonsick E, Goodpaster B, Nevitt M et al. Sarcopenia: alternative definitions and associations with lower extremity function. J Am Geriatr Soc 2003; 51: 1602–1609.
Dufour AB, Hannan MT, Murabito JM, Kiel DP, McLean RR . Sarcopenia definitions considering body size and fat mass are associated with mobility limitations: the Framingham Study. J Gerontol A Biol Sci Med Sci 2013; 68: 168–174.
Kelly TL, Wilson KE, Heymsfield SB . Dual energy X-Ray absorptiometry body composition reference values from NHANES. PLoS One 2009; 4: e7038.
Eveleth PB, Tanner JM . Worldwide Variation in Human Growth2nd ednCambridge University Press: Cambridge, 1990; 397.
Deurenberg P, Deurenberg Yap M, Wang J, Lin FP, Schmidt G . The impact of body build on the relationship between body mass index and percent body fat. Int J Obes Relat Metab Disord 1999; 23: 537–542.
Hull H, He Q, Thornton J, Javed F, Allen L, Wang J, Pierson RN Jr, Gallagher D . iDXA, Prodigy, and DPXL dual-energy X-ray absorptiometry whole-body scans: a cross-calibration study. J Clin Densitom 2009; 12: 95–102.
Bosy-Westphal A, Danielzik S, Dörhöfer RP, Piccoli A, Müller MJ . Patterns of bioelectrical impedance vector distribution by body mass index and age: implications for body-composition analysis. Am J Clin Nutr 2005; 82: 60–68.
World Health Organisation. Obesity: preventing and managing the global epidemic. Report of a WHO consultation Geneva, 3–5 June 1997 WHO: Geneva, 1998.
Lean MEJ, Han TS, Morrison CE . Waist circumference indicates the need for weight management. BMJ 1995; 311: 158–161.
Dulloo AG, Jacquet J, Montani JP . Pathways from weight fluctuations to metabolic diseases: focus on maladaptive thermogenesis during catch-up fat. Int J Obes Relat Metab Disord 2002; 26 (Suppl 2): S46–S57.
Bosy-Westphal A, Schautz B, Lagerpusch M, Pourhassan M, Braun W, Goele K et al. Effect of weight loss and regain on adipose tissue distribution, composition of lean mass and resting energy expenditure in young overweight and obese adults. Int J Obes (Lond) 2013; 37: 1371–1377.
Byrne NM, Weinsier RL, Hunter GR, Desmond R, Patterson MA, Darnell BE et al. Influence of distribution of lean body mass on resting metabolic rate after weight loss and weight regain: comparison of responses in white and black women. Am J Clin Nutr 2003; 77: 1368–1373.
The studies by the authors were funded by a grant of the Germany Ministry of Education and Research (BMBF 0315681), the German Research Foundation (DFG Bo 3296/1-1) and the BMBF Kompetenznetz Adipositas, Core domain ‘Body composition’ (Körperzusammensetzung; FKZ 01GI1125).
The authors declare no conflict of interest.
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Bosy-Westphal, A., Müller, M. 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 39, 379–386 (2015). https://doi.org/10.1038/ijo.2014.161
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