Human Body Composition and Muscle Mass

Chapter 1

Human Body Composition and Muscle Mass Krzysztof Duda1, Joanna Majerczak2, Zenon Nieckarz3, Steven B. Heymsfield4 and Jerzy A. Zoladz2 1

Intensive Care Unit, Cancer Institute, Krako´w Division, Krako´w, Poland, 2Department of Muscle Physiology, Chair of Physiology and Biochemistry, Faculty of Rehabilitation, University School of Physical Education, Krako´w, Poland, 3Experimental Computer Physics Department, Institute of Physics, Jagiellonian University, Krako´w, Poland, 4Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, United States

1.1 INTRODUCTION Body shape has attracted the attention of artists since the beginning of mankind. In Antiquity, proportions of the human body inspired artists, especially sculptors and painters. At that time, the so-called perfect proportions of the human body were defined (the “Polykleitos’ Canon” of the human figure). The greatest breakthrough in introducing human anatomy into art was made by Michelangelo Buonarroti (1475
1564, known as Michelangelo), a spectacular Renaissance artist whose work has been inspiring others until now (Hilloowala, 2009). In contrast to body shape, however, body composition that focuses on quantitative relationships between body components appeared in medicine in modern times, and nowadays it is an important branch of human biology (Wang et al., 1992). The components of body composition significantly change during a life span in the process of growing, ageing, pregnancy, or during disease (“non-interventional” chronic biological processes). Moreover, body composition is dependent to a major extent on two unavoidable, “interventional” activities, namely nutrition and physical activity. Both may significantly change body composition, mainly in such extreme conditions as that of malnutrition, overfeeding, immobilization, and prolonged strenuous physical training. Since body composition can independently influence health, it has become a matter of interest for various specialists in medical sciences—such as endocrinology, rheumatology, surgery, pediatrics, or geriatrics—who deal with a variety of medical conditions, including the metabolic syndrome, degenerative diseases, reaction to injury, osteoporosis, or sarcopenia. Studies on body composition seem to be particularly important for prediction, Muscle and Exercise Physiology. DOI: https://doi.org/10.1016/B978-0-12-814593-7.00001-3 © 2019 Elsevier Inc. All rights reserved.

prevention as well as management of obesity, type 2 diabetes, and cardiovascular disease—the latter being the main factor that increases morbidity and mortality in modern societies (Buskirk and Mendez, 1984; Duda, 2012; Lee et al., 2012; Aleman-Mateo and Ruiz Valenzuela, 2014; NCD Risk Factor Collaboration, 2016). Additionally, body composition is an important topic in sport sciences, not only when considering the selection of candidates for different sports disciplines, but also when evaluating the impact of training, recovery from injuries, and ageing on athletes. Moreover, monitoring body composition changes resulting from combined effects of microgravity and energy imbalance is one of the key problems to be considered during long-term space flight (Bartok et al., 2003; Smith et al., 2005). This is why this chapter will aim at presenting the current state of knowledge concerning human body composition, with a special focus on muscle mass.

1.2 THE ASSESSMENT OF THE SYSTEM AS A WHOLE From the beginning of humanity, people were interested in expressing length in standardized units. For this purpose, human body size variables such as the width of the human palm (lat. palmus), the length of the foot (lat. pes), the length of the ell (lat. cubitum, i.e., the distance from the elbow to the tip of the middle finger), and fathom (i.e., the span of man’s outstretched arms) have been used in daily life as units of length. Nowadays, although advanced techniques of determining body characteristics are available, some traditional, basic human body measures—such as body mass (BM) and body height 3

4 SECTION | I Skeletal Muscle Morphology

(with regard to gender and age), body circumferences (e.g., waist circumference, hip circumference), skinfold thickness (used to estimate regional adiposity), body surface, and body volume (BV)—are still in use in clinical practice as well as in large population studies.

1.2.1 Body Mass, Basal Metabolic Rate, and Total Daily Energy Expenditure BM is one of the fundamental physical characteristics of the human body. In physics, mass is the amount of “matter” that an object has, whereas weight (also referred to as the force of gravity) is the effect of the gravitational pull on the mass of the object and, according to Newton’s second law (see Eq. (1.1)), it is measured in newtons: F ðNÞ 5 M ðkgÞ 3 a ðm s22 Þ

(1.1)

where Newton (N) is the unit of force, M, mass, and a, acceleration (Sir Isaac Newton, 1687). However, weight is commonly expressed in kilograms, which consciously omits multiplication of mass by the gravitational acceleration, approximately constant on the entire surface of the Earth (average value: 9.81 m  s22). In this chapter, we used the term “body mass” (expressed in kg), whereas the term “weight” (expressed in N) was used only in the part dedicated to hydrodensitometry. BM measurement and its monitoring is the starting point for controlling the energy balance of the human body. The relationship between BM and basal metabolic rate (BMR) has been intensively examined by European physiologists and zoologists from the beginning of the 19th century. BMR—the steady-state rate of heat production by an entire organism under a set of standard conditions (an individual is adult, awake, but resting, stress-free, for at least 12 hours after his/her last meal, maintained at a temperature that elicits no thermoregulatory effect on heat production)—represents the minimal metabolic activity of all tissues in a body at rest (Rolfe and Brown, 1997). It is usually expressed as heat production (direct calorimetry) or oxygen consumption (indirect calorimetry) per unit of body size (Rolfe and Brown, 1997; Henry, 2005). BMR or easier-to-assess resting energy expenditure (REE) (typically evaluated with indirect calorimetry in thermoneutrality, supine position at least 4 hours after the last meal) in most sedentary individuals accounts for about 1 kcal per 1 kg of BM per hour and constitutes of about 60%
80% of total daily energy expenditure (TDEE). Two other components of TDEE are: the rather stable cost of diet-induced thermogenesis (DIT) (10%
12% of TDEE) and the most changeable energy cost of physical activity (physical activity energy expenditure, PAEE) (Lowell and Spiegelman, 2000; Heymsfield et al., 2012a).

Organs in the human body differ according to resting metabolic rate and they may be divided into organs with high or low metabolic rate (Elia, 1992; Gallagher et al., 1998; Wang et al., 2001; Heymsfield et al., 2012a). For example, the energy cost of high metabolic rate organs such as kidneys and heart is similar and amounts to B440 kcal per kg per day. In another high metabolic rate tissue such as brain it amounts to B240 kcal per kg per day, whereas the energy cost of skeletal muscle (SM) at rest (low metabolic rate organ) amounts to about 13 kcal per kg per day (Elia, 1992; Wang et al., 2001). The energy cost of organs with high metabolic rate (brain, kidneys, heart, endocrine glands, that weigh only about 3.5 kg, i.e., B5% of body weight of a standard man) constitutes about 60% of the REE. Organs with low metabolic rate such as: (1) SMs at rest, weighting about 28 kg (B40% of BM) accounts for about 20% of the REE; and (2) bones, fasciae, and extracellular fluid (ECF), weighting about 21 kg (B30% of BM) contribute to about 1% of the REE. Moreover, the energy cost of the digestive system, lungs and the immune system (that weight about 3.5 kg) accounts for 15% of the REE. The remaining part of the REE (about 4%) is completed by metabolism of adipose tissue weighting about 15 kg (B20% of BM). It should be underlined that, during strenuous physical exercise, SM metabolism can increase more than 100 times above its rate at rest and it constitutes about 90% of the total energy used by the human body (for overviews see Chapter 5: Muscle Energetics by Kemp and Chapter 18: Metabolic Transitions and Muscle Metabolic Stability: Effects of Exercise Training by Zoladz et al.). In clinical practice, it is important to know BMR (as minimum energy required to exist) to determine caloric needs for energy balance and body weight maintenance (Henry, 2005; Heymsfield et al., 2012b), including weight loss programs in obesity management. Although much of the BMR, which is a main component of TDEE, is accounted for by the activity of organs with high metabolic rate, variations in BMR are related mainly to differences in body size. One of the earliest formulas showing the relationship between BMR and BM was developed in 1932 by Max Kleiber (1893
1976) (Kleiber, 1932), the leader in animal nutrition and metabolism research. He showed that BM raised to three-fourth power is the most reliable basis for the prediction of the BMR of mature mammals (Eq. (1.2)): BMR 5 a 3 BM0:75

(1.2)

where BMR is basal metabolic rate (kcal per day), BM is body mass (kg), a is proportionality constant or normalizing coefficient (the intercept, when the equation is graphed in log
log coordinates, for mammals, the average value of “a” is 71.8), 0.75 is scaling exponent for

Human Body Composition Chapter | 1

mature mammals (the slope of regression line in log
log coordinates) (Lindsted and Schaeffer, 2002). Kleiber’s classic equation was formulated at the wholebody level. Wang et al. (2001) proposed a new perspective on Kleiber’s law by reconstructing it at the organ
tissue level. Interestingly, REE values of individual components (liver, brain, kidneys, heart, and remaining tissues) do not scale equally, but their combined formula was similar to that observed by Kleiber (Wang et al., 2001). In the past century, many formulas were used to predict BMR in clinical practice, including Harris and Benedict equations, Schofield, Roberston and Reid equations (see, e.g., in Heshka et al., 1993). Roberston and Reid equations are recommended for obese individuals, since most equations developed to predict BMR overestimate its value in this particular group (Heshka et al., 1993). A method of estimating BMR in larger groups of men and women belonging to varied age ranges (0
3, 3
10, 10
18, 18
30, 30
60, .60), based only on the BM and the so-called “Oxford equations” (Eqs. (1.3
1.8)), were presented by Henry (2005): BMR for men: 18
30 years old ðn 5 2821Þ: BMR ðkcal per dayÞ 5 545 1 16:0 3 BM ðkgÞ

(1.3)

30
60 years old ðn 5 1010Þ: BMR ðkcal per dayÞ 5 593 1 14:2 3 BM ðkgÞ

(1.4)

. 60 years old ðn 5 534Þ: BMR ðkcal per dayÞ 5 514 1 13:5 3 BM ðkgÞ

(1.5)

BMR for women: 18 2 30 years old ðn 5 1664Þ: BMR ðkcal per dayÞ 5 558 1 13:1 3 BM ðkgÞ 30 2 60 years old ðn 5 1023Þ: BMR ðkcal per dayÞ 5 694 1 9:74 3 BM ðkgÞ

(1.6)

(1.7)

. 60 years old ðn 5 334Þ: BMR ðkcal per dayÞ 5 569 1 10:1 3 BM ðkgÞ

5

where E represents total daily energy requirement (kcal per day) and BM represents body mass (kg). An important issue in TDEE is the assessment of the energy cost of physical activity. According to FAO/ WHO/UNU recommendations the physical activity level (calculated as TDEE/BMR) for sedentary and light activity lifestyles ranges between 1.40 and 1.69; for moderately active lifestyles between 1.70 and 1.99 and for strenuous or heavy leisure activity between 2.0 and 2.4 (Westerterp, 2013, 2017). Hence, TDEE might be expressed as a multiple of BMR or REE (measured by indirect calorimetry or calculated based on the prediction equations) by using adequate factor related to physical activity level. The generally accepted and indicated method of TDEE measurements is doubly labeled water (DLW) method, which allows the measurement of energy expenditure under daily living conditions including exercise and extreme environment (Westerterp, 2013, 2017). The DLW method (method of indirect calorimetry) is based on the difference between the apparent turnover rates of the hydrogen and oxygen of body water as a function of carbon dioxide production after a loading dose of water labeled with the stable isotopes of 2H and 18O (Westerterp, 2017). Based on this method Redman et al. (2014) presented normative equations to calculate TDEE for nonobese men and women using the following basic variables: BM, age, and sex. In this study involving a group of 217 healthy subjects (aged 21
50 years; BMI: 22
28 kg  m22), they showed that the mean TDDE amounts to 2443 6 397 kcal per day and is on average 20% (580 kcal per day) higher in men than in women (see Eq. (1.11)): TDEE ðkcal per dayÞ 5 1279 1 18:3 3 BM ðkgÞ 1 2:3 3 age ðyearsÞ
338 3 sex

(1.11)

where TDEE represents total daily energy expenditure (kcal per day), BM represents body mass (kg), and the sex variable may assume the following values: 1 5 female, 0 5 male.

(1.8)

The estimation of TDEE includes two major components: BMR and physical activity energy expenditure (Westerterp, 2013). Based on the FAO nutrition studies (FAO, 1957), two simplified empirical equations were developed for the first time to predict total daily energy requirements. Those equations are easy to use since they involve only BM measurements (Eqs. (1.9) and (1.10)): for men: E 5 152 3 BM0:73

(1.9)

and for women: E 5 123 3 BM0:73

(1.10)

1.2.2 Body Mass Index BM and body height allow one to calculate other measures frequently used in epidemiology and clinical research, namely the BMI and the body surface area (BSA). The BMI was introduced for the first time in wholebody assessment in 1832, by a Belgian polymath, Adolphe Quetelet (1796
1874), who was looking for an index of relative BM and introduced the Quetelet Index,

6 SECTION | I Skeletal Muscle Morphology

i.e., the ratio of BM in kilograms divided by the square of height in meters (Eq. (1.12)): BMI 5 BM 3 H22

(1.12)

where BM is body mass (kg) and H is height (m). Ancel Keys (1904
2004), an American pioneer in biostatistics and a physiologist, confirmed 140 years later the validity of the Quetelet Index in epidemiological studies and named it (in 1972) “body mass index” (Eknoyan, 2008). From then on, BMI has become a standard formula for establishing, heuristically, ideal BM. The BMI for adult underweight people is lower than 18.5 kg  m22; for normal weight people it ranges from 18.5 to 25 kg  m22, for the overweight from 25 to 30 kg  m22, and it is higher than 30 kg  m22 for the obesity. The BMI above 25 kg  m22 is associated with an increased the risk of morbidity and mortality. BMI may be understood as a simple sum of body fat mass (FM) and fat-free mass (FFM) component of BM (Eq. (1.13)), each of which divided by the square of height in meters (Van Itallie et al., 1990): BMI 5 FM 3 H22 1 FFM 3 H22

(1.13)

BMI is often used in obesity studies as a measure of FM, since a high correlation between BMI and total body fat as well as BMI and the percentage of body fat have been reported during childhood and in adult individuals. However, BMI is neither a specific marker of body fat or a good marker of abnormal fat accumulation (Adler et al., 2017) and its applicability as body fat marker is questionable, since individuals of the same age, height, and weight (hence the same BMI) can have different body shape, body composition, and metabolic profile. For example, Asian people have higher body fat percentage than Western populations with the same value of BMI (Choo, 2002). In children, BMI is not a good index of body fatness because of their growth. Hence, the calculated BMI should be compared against the percentile for children of the same sex and age (Reilly, 2010; Laurson et al., 2011). In other situations—when FM and FFM may get altered due to ageing, physical training, or several diseases—BMI alone might lead to false conclusions and should be used with caution. Therefore, it is proposed nowadays to extend the description of body composition with other measures, which are based on more advanced techniques and better describe FM and FFM in the human body. Recently, Peterson et al. (2017) found that in the group of non-Hispanic whites aged 8
29 (n 5 2285 participants) percent body fat scales to height with an exponent closer to 3. Therefore, they proposed tri-ponderal mass index (BM divided by height cube) as an alternative for BMI and more accurate measure of body fat for the group of nonHispanic white adolescents (aged 8
17 years).

1.2.3 Body Circumferences and Skinfolds Measurements It is generally accepted by clinicians and researchers that not total amount of adipose tissue, but rather the distribution of its excess correlates better with the risk of the occurrence of diabetes and/or cardiovascular disease. It has been agreed that individuals with fat distribution of the central type (android vel “apple shape”) are at greater health risk (greater prevalence of metabolic syndrome, arterial hypertension, heart disease, stroke, type 2 diabetes) than those with peripheral fat distribution (gynoid vel “pear shape”) (Vague, 1996). The use of imaging techniques (computed tomography, CT; magnetic resonance imaging, MRI) indicated that unhealthy “apple shape” is associated with an internal, visceral fat deposition rather than external subcutaneous fat depots (Browning et al., 2010; Schneider et al., 2010). Therefore, simple anthropometric indices that allow one to describe regional adiposity—such as waist circumference (WC), waist-hip ratio (WHR) and waist-to-height ratio (WHtR) —might be used as a screening tool to predict diabetes and cardiovascular disease. WC was found to strongly correlate with abdominal fat measurement by means of advanced imaging techniques. The WHtR —as another measure of relative fat distribution—was introduced by Japanese researchers in 1995 as predictor of coronary heart disease (Hsieh and Yoshinaga 1995a; Hsieh and Yoshinaga, 1995b) and it has received more attention in the past few years (RodeaMontero et al., 2014; Lo et al., 2016; Choi et al., 2017). WHtR corrects the WC for the height of individuals and, similarly to WC, it shows a strong positive correlation with abdominal fat measured by means of imaging techniques (Soto Gonza´lez et al., 2007). WHtR as a proxy for central obesity was found to be a better predictive marker of “early health risk” then BMI (Schneider et al., 2010; Ashwell et al., 2014; Ashwell and Gibson, 2016). The WHtR assuming the value of 0.5 (“keep your waist to less than half your height”) has the character of a global boundary. When exceeded, it indicates an increased risk across different age groups (also in children and adolescents) as well as sex and ethnic groups (Browning et al., 2010; Mehta, 2015) (Table 1.1). Skinfold measurements, which also belong to simple anthropometric measurements, are typically performed at 3
9 standard anatomical sites (e.g., “triceps,” “biceps,” “chest,” “subscapular,” “abdominal,” “suprailiac”), on the right side of the body, by means of caliper with constant pressure of 10 g mm22. The correct position of the calipers is critical for the accuracy of the measurement and the anatomical site should be accurately determined and then marked. The sum of skinfolds allows one to estimate (by means of an adequate equation) the amount of body fat (Jackson and Pollock, 1985).

Human Body Composition Chapter | 1

7

TABLE 1.1 Boundary Values of WC, WHR and WHtR Index Value

Waist Circumference (cm) Men

Women

Waist-hip Ratio Men

Waist-to-Height Ratio

Women

No “health risk”

,94

,80

,0.90

,0.85

,0.5

“Health risk”

$ 94 and # 102

$ 80 and # 88







$ 0.5 and ,0.6

Very high “health risk”

.102

.88

$ 0.90

$ 0.85

$ 0.6

1.2.4 Body Surface Area Accurate determination of BSA is the essential issue in several medical fields. The use of BSA enables standardization of certain physiological parameters, such as cardiac function or glomerular filtration. BSA is also used to assess drug dosage. Typically, in clinical practice, BSA is indirectly estimated on the basis of empirical formulas (Redlarski et al., 2016). Direct methods of BSA measurement—such as coating, surface integration, linear geometry, and touchless measurement (3D laser scanning) in the different groups of subjects (varied age, sexes, ethnic populations, different regions)—constitute the starting point for fitting model equations for the obtained data. The first measurements of BSA were made in England during experiments on insensible perspiration by anatomist William Cruishank (1745
1800) in 1778 and by surgeon John Abernethy (1764
1831) in 1793. Abernethy, by applying the coating method (with paper) and linear geometry, calculated BSA as 2700 square inches (which equals 1.74 m2 in the metric system) (Abernethy, 1793). Interestingly enough, both of them were searching for the proportion between hand area and BSA. Currently, it is agreed that the palm (i.e., the palmar surface area, which is the area between the interstyloid line and the palmar digital crease of each digit) represents 0.5% of the total BSA and the hand (i.e., the sum of the palmar surface area and the areas of the fingers and the thumb) represents around 0.8% of the total BSA. Both measures (hand and palm surface areas) are suitable for assessing the size of minor burns (,10% of total body surface) (Rhodes et al., 2013; Thom, 2017). In 1879, German physiologist Karl Meeh suggested, on the basis of geometric considerations, that the BSA of mammals could be expressed with the following equation (Eq. (1.14)): BSA ðm2 Þ 5 k 3 BM ðkgÞ2=3

(1.14)

where BM is the body mass, k is Meeh’s normalizing coefficient that varies slightly between species and

amounts to 0.1053 for humans (Meeh, 1879). Nowadays, this formula is used only in veterinary medicine. Meeh’s formula remained a standard of BSA assessment until 1916, when E.F. DuBois and D. DuBois (cousins) published a new formula for BSA assessment, where they introduced height (H) as a variable (Eq. (1.15)): BSA ðm2 Þ 5 0:007184 3 BM ðkgÞ0:425 3 H ðcmÞ0:725 ðthe originally used formÞ or BSA ðm2 Þ 5 0:20247 3 BM ðkgÞ0:425 3 H ðmÞ0:725 ðSI unitsÞ (1.15) The estimation of the model coefficient in BSA assessment turned out to be an important issue. As it was found out, DuBois’ formula underestimated BSA in obese patients by 3%
5% (Verbraecken et al., 2006). After almost 100 years, the DuBois and DuBois BSA equation was corrected (Shuter and Asiani, 2000), based on a greater number of examined persons and application of modern statistical methods (Eq. (1.16)): BSA ðm2 Þ 5 0:00949 3 BM ðkgÞ0:441 3 H ðcmÞ0:655 ðthe originally used formÞ or BSA ðm2 Þ 5 0:19376 3 BM ðkgÞ0:441 3 H ðmÞ0:655 ðSI unitsÞ (1.16) Since BSA scaling plays a key role in medicine—for example, in pharmacology, toxicology, cytotoxic chemotherapy, nephrology, transplantology, extracorpeal circulation, burns assessment and fluid resuscitation—many studies in subsequent years tried to find more precise BSA formulas based on more accurate methods (including three-dimensional (3D) laser scanning techniques) and higher numbers of subjects (see Redlarski et al., 2016). As 3D full scan is a very fast technique that takes from a dozen seconds up to several dozens, depending on the type of equipment, the number of objects tested is generally much higher than in previously applied methods.

8 SECTION | I Skeletal Muscle Morphology

It should be mentioned that the method is unable to recognize overlapping parts of human skin. Based on 3D full scanning measurements, Schlich et al. (2010) proposed the following formula for European men (n 5 49) aged 21
68 (Eq. (1.17)): BSA ðm2 Þ 5 0:000579479 3 BM ðkgÞ0:38 3 H ðcmÞ1:24 ðthe originally used formÞ or

(Eq. (1.21)), which is commonly accepted due to its precision and simplicity. The originally used form: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi H ðcmÞ 3 BM ðkgÞ 2 BSA ðm Þ 5 3600 or in SI units:

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi H ðmÞ 3 BM ðkgÞ BSA ðm Þ 5 36 2

BSA ðm2 Þ 5 0:1750 3 BM ðkgÞ0:38 3 H ðmÞ1:24 ðin SI unitsÞ (1.17) and for women (n 5 132) aged 20
84 (Eq. (1.18)): BSA ðm2 Þ 5 0:000975482 3 BM ðkgÞ0:46 3 H ðcmÞ1:08 ðthe originally used formÞ or BSA ðm2 Þ 5 0:1410 3 BM ðkgÞ0:46 3 H ðmÞ1:08 ðin SI unitsÞ

(1.21)

In clinical practice, the consequences of applying an inadequate BSA formula might be severe, including inappropriate drug dosage. The choice of an adequate BSA formula is important not only for children, but also for people from different geographical regions and for people with nonstandard body proportions, for example, in the case of obesity, cachexia, or massive bone structure (Redlarski et al., 2016).

(1.18) Similarly, Yu et al. (2003) proposed the following formula for a population of Taiwanese workers (Eq. (1.19)): BSA ðm Þ 5 0:015925 3 BM ðkgÞ 3 H ðcmÞ ðthe originally used formÞ or BSA ðm2 Þ 5 0:15925 3 BM ðkgÞ0:5 3 H ðmÞ0:5 ðin SI unitsÞ 2

0:5

0:5

1.2.5 Body Volume and Body Density The total BV is an indicator of body size, which is subsequently used to calculate body density (BD) (Eq. (1.22)): BD 5 BM 3 BV21

(1.19)

which was based on 3D measurements of a group of 3951 women and men, aged 20
91. Additionally, Yu et al. (2003) showed different coefficients dedicated to various subgroups, i.e., separately for men and women within different age ranges. Determination of BSA is an important issue from the point of view of diagnostic and therapeutic aspects of pediatric medicine, since BSA increases from 0.2 m2 at birth up to 1.73 m2 in adulthood. Only few formulas, however, have been validated for children (Feber and Kra´snicanova´, 2012). Haycock et al. (1978) developed a formula based on the measurements of a group of subjects, comprising the range from premature infants to adults, where (Eq. (1.20)): BSA ðm2 Þ 5 0:024265 3 BM ðkgÞ0:5378 3 H ðcmÞ0:3964 ðthe originally used formÞ or BSA ðm2 Þ 5 0:1506 3 BM ðkgÞ0:5378 3 H ðmÞ0:3964 ðin SI unitsÞ (1.20) According to the authors, this formula gives a good fit for all values of BSA within the range from less than 0.2 m2 up to over 2.0 m2. In 1987, Mosteller (1987) presented a simple formula for BSA calculation for adults, small children, and infants

(1.22)

and in consequence, body FM. BV can be assessed by the water-displacement technique, also called “underwater weighting” or “hydrodensitometry,” or the air-displacement technique, also called “air-displacement plethysmography” (Duren et al., 2008). Both techniques are time-consuming, laborious and requires demanding laboratory conditions. Hydrodensitometry is regarded as the most reliable of available techniques used to estimate BD. Archimedes’ principle is applied by comparing the mass of a subject in the air (Ma) with the “mass underwater” (Mw), which is calculated from the gravitational force (Fw) exerted on a submerged object according to the Newton’s law (Eq. (1.23)): Mw 5 Fw 3 g21

(1.23)

where g is gravitational acceleration of 9.81 m  s22. During underwater measurement, total expiration is necessary and account is taken of the residual gas volume remaining in the lungs (Vr), and an estimated volume of gas in the intestine (Vi). Temperature, which influences water density (WD), should be also taken into account. BD is calculated with the following equation (Eq. (1.24), Brodie et al., 1998): BD 5

Ma ððMa 2 Mw Þ=WDÞ 2 ðVr 1 Vi Þ

(1.24)

The volume of gas in the intestine (Vi) included in the calculation is usually assessed to amount to about

Human Body Composition Chapter | 1

100 mL, but this value should be increased for large adults and decreased for children. Underwater weighting (UWW)—considered to be the “golden standard” for BV measurements—is actually replaced by the DEXA method which does not require lung volume measurement for body fat determination. BV can be estimated with classic formulae. In 1959, Sendroy and Cecchini (1959), developed a formula (Eqs. (1.25) and (1.26)) based on the data collected for 446 men and adolescent boys [the ratio of BM (kg) to height (cm) is between 0.2 and 0.8] as: BV ðLÞ 5 BSA ðm2 Þ 3 60:20 3 ðBM=HÞ0:562

(1.25)

and for 113 adult women and adolescent girls (the ratio of BM to H is between 0.2 and 0.8) as: BV ðLÞ 5 BSA ðm Þ 3 62:90 3 ðBM=HÞ 2

0:578

(1.26)

BSA and BV can be assessed on the basis of digital data recorded with the computer tomography, magnetic resonance imaging, or 3D scanning methods. The main advantage of these techniques is shorter time of acquisition, resulting in less measurement noise.

1.3 BODY COMPOSITION AT VARIED LEVELS OF COMPLEXITY Since 1990s, a research team at Columbia University (St. Luke’s Roosevelt Hospital) has been developing a new concept of body composition research (Heymsfield and Waki, 1991; Wang et al., 1992; Wang et al., 2008). The so-called five-level model of body composition introduced by them (now widely accepted) organizes body components into a sequence of increasing complexity, namely: (1) the atomic level, where body composition is assessed in terms of the content of elements, including potassium, sodium, chlorine, phosphorus, calcium, nitrogen, and carbon; (2) the molecular level, at which chemical compounds such as fat, water, proteins, minerals, and glycogen are assessed; (3) the cellular level that accounts for the presence of cell membranes and describes extracellular and intracellular spaces; (4) the tissue
organ level, where the distribution of adipose, SM, bone and other tissues is described, and (5) the whole-body level, which describes the system as a whole (presented above) (Wang et al., 1992; Wang et al., 1998; Wang et al., 2008).

1.3.1 Body Composition at the Atomic Level Virtually 99% of BM is constituted by the mass of 6 elements, namely: oxygen (61%), carbon (23%), hydrogen (10%), nitrogen (2.6%), calcium (1.4%), and phosphorus (0.83%). The content of none of the remaining macroelements exceeds 0.5% of BM: potassium 0.4%, sulphur

9

0.3%, sodium and chloride 0.2% each, and magnesium 0.1% (Fig. 1.1). The atomic body composition is measured primarily with two techniques: the whole-body counting that measures natural body radioactivity (i.e., the measurement of natural 40K isotope) and the neutron activation analysis (NAA) that uses neutron flux to activate atomic nuclei (reaching excited state). The measurement of characteristic gamma radiation of radionuclides enables quantitative assessment of the content of elements—such as hydrogen, carbon, oxygen, nitrogen, sodium, calcium, phosphorous, and chlorine—in the human body (Kehayias et al., 1991; Mattsson and Thomas, 2006).

1.3.1.1 Total Body Nitrogen Nitrogen is one of the main body components, required for protein synthesis and production of several nitrogenous compounds such as hormones, neurotransmitters, and components of antioxidant defense. The measurement of TBN, using in vivo NAA, allows one to assess body protein content, while it is assumed that all body nitrogen is incorporated into proteins. There is a close relationship between TBN and body proteins: every 6.25 g of protein contains 1 g of nitrogen. Proteins are mainly located in FFM, hence the evaluation of TBN is an indirect measure of FFM, and especially SM mass. In healthy individuals (age range: from 24 to 72 years) TBN increases with BM and decreases with age, and it can be calculated with the following formula (Eq. (1.27)) developed on the basis of in vivo NAA measurements (Ryde et al., 1993): TBN ðkgÞ 5 1:42 kg 1 0:0109 3 BM ðkgÞ
ðA ðyearsÞ 3 0:008 kg year21 Þ
ðgender 3 0:46 kgÞ (1.27) (gender: male 5 0, female 5 1). It was postulated that the amount of nitrogen in FFM is biologically constant and the TBN/FFM relation can be formulated as follows (Eq. (1.28), Ryde et al., 1993): TBN ðkgÞ 5 0:031 3 FFM ðkgÞ
0:0009 kg

(1.28)

1.3.1.2 Total Body Potassium The measure of the total amount of potassium in the body [total body potassium (TBK)] is based on the activity of the natural 40K isotope (with 1.46 MeV gamma radiation) as the isotope constitutes 0.0118% of potassium ion. TBK amounts to about 47 and 36 mmol  kg21 in men and women, respectively. TBK increases with BM and body height (H), and decreases with age (A). According to the formula Eq. (1.29), (Wang et al., 1992), TBK might be estimated as follows:

10

SECTION | I Skeletal Muscle Morphology

FIGURE 1.1 Body composition at atomic level in the reference man. Based on the data from Snyder, W.S., et al., 1984. Report of the task group on Reference Man. Oxford, Pergamon Press; Wang, Z.M., et al., 1992. Am. J. Clin. Nutr. 56, 19
28.

The originally used form: TBK ðmmolÞ 5 77:8 1 27:3 3 BM ðkgÞ 1 11:5 3 H ðcmÞ

used to evaluate total body bone mineral content (see Eq. (1.58)).


21:9 3 A ðyearsÞ (1.29) or TBK ðmmolÞ 5 77:8 1 27:3 3 BM ðkgÞ 1 1150 3 H ðmÞ
21:9 3 A ðyearsÞ ðin SI unitsÞ TBK can be used to assess the body cell mass (BCM), as noticed by Francis D. Moore (1913
2001) in the mid20th century (see Section 1.5.3).

1.3.1.3 Total Body Calcium The total body calcium (TBCa) content can be measured in vivo by the delayed γ-NAA and amounts to about 1100 g in men and 800 g in women (Reid, 1986). Based on the TBCa and TBK, it is possible to calculate the total body phosphorus (TBPh, Eq. (1.30)), (Wang et al., 1992): TBPh ðkgÞ 5 0:456 3 TBCa ðkgÞ 1 0:022 3 TBK ðmolÞ (1.30) Since calcium constitutes a relatively constant fraction of bone minerals (38%
39%), its content can also be

1.3.2 Body Composition at the Molecular level Measurements at the level of chemical molecules concern water, fat, protein, salts and glycogen (Fig. 1.2).

1.3.2.1 Total Body Water At the chemical level, the two largest compartments of the system are water (approximately 60% of BM) and anhydrous fat (20%
30% of BM). Mean values of TBW have been reported to range from 38 to 50 L in men (B60% of BM), whereas in women, it is between 26 and 40 L (B50% of BM), (Chumlea et al., 2001). Women and elderly individuals have less body water, due to greater adiposity and lower muscle mass. TBW decreases with age. For instance, in individuals around 60 years of age, it comprises 55% of BM in case of males, and 45% in females. The TBW, determined on the basis of the dilution principle by means of labeled water isotopes (e.g., 2H2O, 3 H2O, H218O), was used as the starting point to derive equations that predict TBW from anthropometric measurements (Watson et al., 1980; Chumlea et al., 2001).

Human Body Composition Chapter | 1

11

FIGURE 1.2 Body composition at the molecular (chemical) level in the 70 kg reference man (expressed as percentage of body mass, % BM). FM, fat mass; TBW, total body water; TBPro, total body proteins; TBMin, total body mineral; and TBGly, total body glycogen. Based on the data from Snyder, W.S., et al., 1984. Report of the task group on Reference Man Oxford, Pergamon Press.

for black men (n 5 128):

Watson et al. (1980) formulated the following equations to calculate TBW (Eqs. (1.31) and 1.32): for men (n 5 458):

TBW ðLÞ 5 2 18:37
0:09 3 A ðyearsÞ 1 0:34 3 BM ðkgÞ 1 0:25 3 H ðcmÞ

TBW ðLÞ 5 2:447
0:09516 3 A ðyearsÞ 1 0:1074 3 H ðcmÞ 1 0:3362 3 BM ðkgÞ ðthe originally used formÞ

ðthe originally used formÞ

(1.34)

or TBW ðLÞ 5 2 18:37
0:09 3 A ðyearsÞ 1 0:34

(1.31)

3 BM ðkgÞ 1 25 3 H ðmÞ ðin SI unitsÞ

or TBW ðLÞ 5 2:447
0:09516 3 A ðyearsÞ 1 10:74 3 H ðmÞ

for white women (n 5 772):

1 0:3362 3 BM ðkgÞ ðin SI unitsÞ

TBW ðLÞ 5 2 10:50
0:01 3 A ðyearsÞ 1 0:20 3 BM ðkgÞ 1 0:18 3 H ðcmÞ

for women (n 5 265):

ðthe originally used formÞ

TBW ðLÞ 5 0:1069 3 H ðcmÞ 1 0:2466 3 BM ðkgÞ
2:097 ðthe originally used formÞ

or (1.32)

TBW ðLÞ 5 2 10:50
0:01 3 A ðyearsÞ 1 0:20 3 BM ðkgÞ 1 18 3 H ðmÞ ðin SI unitsÞ

or TBW ðLÞ 5 10:69 3 H ðmÞ 1 0:2466 3 BM ðkgÞ
2:097 ðin SI unitsÞ

(1.35) for black women (n 5 191):

Chumlea et al. (2001) presented the following race- and gender-specific formulas (Eqs. (1.33)
(1.36)) based on a larger group of adult subjects (age between 18 and 90): for white men (n 5 604): TBW ðLÞ 5 23:04
0:03 3 A ðyearsÞ 1 0:50 3 BM ðkgÞ
0:62 3 BMI

(1.33)

TBW ðLÞ 5 2 16:71
0:01 3 A ðyearsÞ 1 0:22 3 BM ðkgÞ 1 0:24 3 H ðcmÞ ðthe originally used formÞ or

(1.36)

12

SECTION | I Skeletal Muscle Morphology

TBW ðLÞ 5 2 16:71
0:01 3 A ðyearsÞ 1 0:22 3 BM ðkgÞ 1 24 3 H ðmÞ ðin SI unitsÞ Total body water (TBW) consists of intracellular (ICW) and extracellular water (ECW). Since almost all body potassium is located in the ICW and ECW compartments, assuming stable intra- and extracellular K1 concentration of 152 and 4 mmol  kg21 H2O, respectively, the ICW and ECW can be calculated (Eqs. (1.37) and (1.38)) if TBK (determined by the whole-body counting) and TBW (determined with the dilution method) are known (Wang et al., 2003; Silva et al., 2007): TBK ðmmolÞ
4 3 TBW ðkgÞ 148

(1.37)

152 3 TBW ðkgÞ
TBK ðmmolÞ 148

(1.38)

ICW ðkgÞ 5 and ECW ðkgÞ 5

It is worth highlighting that FFM hydration is strikingly stable in mammals; as noted already in 1945 by Pace and Rathbun (see in Wang et al., 1999). In a mature organism, hydration rests within the range between 70% and 75%, as confirmed by the formula for calculating the TBW in an adult human (Eq. (1.39), Ryde et al., 1993): TBW ðkgÞ 5 0:733 3 FFM ðkgÞ
0:44 kg

well-trained endurance athletes and in some extremely well-trained marathon runners can account for less than 5% of BM (Costill, 1986; Noakes, 2003). On the other hand in case of pathological obesity, body fat can constitute up to 50% of BM (Alema´n et al., 2017).

1.3.2.3 Total Body Protein Total body protein (TBPro) accounts for about 14%
16% of BM, that is, B11 kg in men and 9 kg in women. TBPro is comprised in BCM (B77%), but also in extracellular solids and ECF (B23%). As mentioned above, TBPro can be calculated on the basis of the TBN (determined by the NAA), on the assumption that every 6.25 g of protein contains 1 g of nitrogen (i.e., the nitrogen-to-protein ratio amounts to 0.16). TBPro can also be estimated on the basis of the value of TBK, measured by the whole-body counting (40K) method, and of the content of bone minerals assessed using the whole-body DEXA method (Eq. (1.40), Wang et al., 2003): TBPro ðkgÞ 5 0:00252 3 TBK ðmmolÞ 1 0:732 3 bone mineral ðkgÞ

(1.40)

(1.39)

1.3.2.4 Total Body Mineral 1.3.2.2 Total Body Fat There are no direct methods of in vivo evaluation of body fat. Fat can be determined by measuring the effect fat has on physical properties of the body, such as BD (measured by UWW, see Section 1.2.5) and body impedance (Kehayias et al., 1991). Rough evaluation of body fatness in clinical practice can be performed through easilyaccessible simple measures, namely BM, BMI, abdominal circumference, skinfold thickness measurements. The bioimpedance method—a low-cost and frequently used approach to body composition measurements—differentiates between FM, considered to be a non-conductor of electric charge, and FFM, considered to be a conducting volume that helps the passage of electric current, due to conductivity of electrolytes dissolved in body water (Lemos and Gallagher, 2017). Although bioimpedance is a simple, noninvasive approach to body composition measurements, it is not a reference method, as it relies on specific assumptions, the most important of which is constant body hydration (Lemos and Gallagher, 2017). Nowadays, methods acquiring higher precision—such as MRI, CT, DEXA—are implemented to determine body fat and muscle mass (Hellmanns et al., 2015). Body fat is one of the most changeable elements of body composition. It can account for 7%
10% of BM in

Total body mineral (TBMin) (B4.5% of BM) consists of bone minerals (BoM, B4% of BM) and soft-tissue minerals (STM, B0.5% of BM). According to Beddoe et al. (1984) TBMin can be estimated as 6.22% of FFM (Eq. (1.41)): TBMin ðkgÞ 5

0:0622 3 TBW 0:732

(1.41)

STM (B0.5 vs 0.38 kg, respectively, for men and women) is a small molecular component which consists of soluble minerals and electrolytes (6 main: K1, Na1, 2 Mg21, Cl2, H2 PO2 4 , HCO3 ) and is found in the extracellular and intracellular compartment of soft tissue (Wang et al., 2002). The whole-body STM can be measured in vivo by delayed-γ NAA and is estimated roughly to reach 400 mg, that is, 0.5% of BM (St-Onge et al., 2004). Its contribution to BD is very important, because of its high density reaching 3.317 g  cm23, which is higher than that of bone mineral (2.982 g  cm23) (Heymsfield et al., 1991). The ratios of STM to extracelullar water and to intracellular water are relatively stable in young adults and whole-body STM can be calculated from TBW mass (Eq. (1.42), Wang et al., 2008): STM ðkgÞ 5 0:0129 3 TBW ðkgÞ

(1.42)

Human Body Composition Chapter | 1

13

1.3.2.5 Total Body Glycogen

1.3.3.1 Extracelullar Fluid

Glycogen is the principal D-glucose storage polymer in muscles and liver. It is an important energy source during exercise. TBGly can be calculated (Eqs. (1.43) and (1.44)) from total body nitrogen (TBN), because the analysis of organ glycogen content indicated that TBGly can be estimated as 4.4% of total body proteins (Kehayias et al., 1991).

ECF (Fig. 1.3B) surrounds, protects and immunoactively washes all body cells (BCM) (Moore et al., 1963). ECF consists mainly of ECW, a small amount of protein and ECF minerals. Extracellular fluid volume (ECFV) is divided into two parts (see Fig. 1.3B): (1) extravascular, interstitial fluid volume (ISFV), which is the primary source of lymph and constitutes about 20% of BM and (2) intravascular fluid volume, that is, plasma volume (PV), amounting to 4% of BM (Eq. (1.45)).

TBGly ðgÞ 5 TBN ðgÞ 3 6:25 3 0:044

(1.43)

TBGly ðgÞ 5 TBN ðgÞ 3 0:275

(1.44)

or

The glycogen content can be also assessed both in the SM and liver using 13Carbon magnetic resonance spectroscopy (13C MRS) (Stephenson et al., 2013; Heinicke et al., 2014).

1.3.3 Body Composition at the Cellular Level Body composition at the cellular level comprises cellular mass and extracellular space. BCM is the sum of all the body cells, whereas extracellular space contains the ECF and extracelullar solids (ECS) (Fig. 1.3A). Determination of these spaces is particularly useful in many clinical procedures, especially during surgical treatment (Mazariegos et al., 1992). Healing concepts related to this level of body composition were introduced by an American surgeon, Francis D. Moore (1913
2001) (Moore et al., 1963).

ECFVð24% of BMÞ 5 ISFV ð20% of BMÞ 1 PVð4% of BMÞ (1.45) ECF possess high dynamics in functional terms: every 3 hours PV is exchanged with ISFV as the result of the volume exchange across the microcirculation (i.e., abundant exchange of fluids, proteins, and cells between intravascular plasma and extravascular interstitial fluid). This exchange provides renal ultrafiltration (B180 L daily), secretion of digestive juices (B10 L daily) and production of lymph (B3 L postnodal lymph daily). The importance of interstitial space (“third spacing”) as a part of extracellular space has been recently reevaluated. In vivo microscopy studies (confocal laser endomicroscopy) revealed that interstitial space (macroscopically visible fluid-filled spaces within tissues) might be recognized as new, functionally relevant anatomical structure defined by a complex lattice of thick collagen bundles, which are lined on one side by fibroblast-like cells (CD34 positive) (Benias et al., 2018). This part of fluid-filled

FIGURE 1.3 (A) Body composition at the cellular level. FM, fat mass; BCM, body cells mass; ECF, extracellular fluid; ECS, extracellular solids (based on the data from Wang et al., 2003). (B) Body composition at the cellular level with two main fluid barriers: cellular membrane (osmotic) and vascular wall (hydrostatic and osmotic). FM, fat mass; ICS, intracellular solids; ICF, intracellular fluid; ECF, extracellular fluid; ECS, extracellular solids; ISF, interstitial (intercellular) fluid; and PV, plasma volume. Based on the data from Wang, Z., et al., 2003. Am. J. Clin. Nutr. 78, 979
984.

14

SECTION | I Skeletal Muscle Morphology

interstitial space (part of submucosa, subdermis, subfascia, vascular adventitia) has important implication for tissue function and pathology including, for example, fibrosis and metastasis. Interestingly, the marginal part of interstitial space adheres through capillary wall with endothelial glycocalyx layer (Fig. 1.3B). These hydrated and neighboring layers cooperate in maintaining tissue fluid homeostasis (Jacob et al., 2016). Assessment of the ECFV can be performed using dilution method (with e.g., NaBr) and/or NAA through the measurement of total body chlorine (Ellis, 2000). Based on the dilution method data, it is estimated that ECFV constitutes about 1/3 of TBW and/or about 20% of BM, while the neutron activation method data show a higher value of ECFV, namely about 40% of TBW and 24% of BM. ECF can be assessed with simple formulas based solely on BM, namely (Eq. (1.46)): ECF ðkgÞ 5 0:135 3 BM ðkgÞ 1 7:35 kg

(1.46)

or Bird’s formula (Eq. (1.47)) used in nephrology, toxicology, pharmacology, which accounts for body mass and height (Bird et al., 2003): ECFV ðLÞ 5 0:02154 3 BM ðkgÞ0:6469 3 H ðcmÞ0:7236 ðthe originally used formÞ (1.47) or ECFV ðLÞ50:60323BM ðkgÞ0:6469 3H ðmÞ0:7236 ðin SI unitsÞ ECF is a rather stable water compartment over a life span, though TBW drops by 10%
15% in older adults, while the ratio of ECW to ICW increases a little (owing to reduced BCM) (Allison and Lobo, 2004). However, ageing can impair both parts of ECF by lowering the rate of exchanges between interstitial fluid and plasma. These disturbances in the dynamics of water balance can affect physical exercise capacity and may augment negative consequences of dehydration, hemorrhage, hypothermia, or hyperthermia in elderly people (Olsen et al., 2000; Duda, 2012).

energy metabolism (Moore et al., 1963). BCM consists of intracellular water, proteins, intracellular minerals, and polysaccharides (Wang et al., 2003) (Fig. 1.3A and Fig. 1.4). There is no method, which can directly measure BCM. BCM can be calculated as ICW/0.70, where 0.70 is the mean hydration of BCM (Wang et al., 1999). The body cells compartment can be assessed also on the basis of measurements of components characteristic for the intracellular part, such as potassium. According to classic Moore’s formula (developed in 1963), BCM can be presented as (Eq. (1.48)): BCM ðkgÞ 5 k 3 Ke ðmmolÞ

(1.48)

where Ke is total body exchangeable potassium measured by 42K, and k is the reciprocal of the potassium content of BCM. Assuming that Ke is equivalent to TBK, BCM can also be presented as equation (1.49): BCM ðkgÞ 5 0:0083 3 TBK ðmmolÞ

(1.49)

The use of 42K to estimate TBK has some limitations, including relatively high radiation exposure and underestimation of TBK by 3%
10%. Cohn et al. (1985) were the first to propose a TBKindependent BCM prediction model, in which BCM can be derived from FFM (Eq. (1.50)), where FFM at the cellular level is the sum of BCM, ECF, and ECS (see Fig. 1.3A): BCM 5 FFM
ðECF 1 ECSÞ

(1.50)

and at the molecular level, FFM is the sum of TBW, TBPro, and bone mineral (BoM). Hence, BCM can be presented as (Eq. (1.51)): BCM 5 ðTBW 1 TBPro 1 BoMÞ
ðECF 1 ECSÞ (1.51) Implementation of new methods in body composition analysis (DEXA) enabled researchers to discern additional two components in FFM, namely STM and glycogen (Wang et al., 2004). Therefore, according to Wang et al. (2004), BCM can be derived more accurately from the following equation (Eq. (1.52)): BCM 5 ðTBW 1 TBPro 1 BoM 1 STM 1 glycogenÞ

1.3.3.2 Extracelullar Solids ECSs make up 8% of BM and consist of bone mineral (B3 kg i.e., 4% of BM), protein (B2 kg, i.e., 3% of BM and 1/5 of TBProt) and a small quantity of glycogen (0.3
0.5 kg) (Kehayias et al., 1991).

1.3.3.3 Body Cell Mass BCM—the sum of cells in the body—is the most precious part of a living organism, called “the body engine” involved in the biochemical processes and


ðECF 1 ECSÞ (1.52) where at the molecular level TBW can be measured by the 3H2O or 2H2O dilution method; TBPro can be calculated from the TBN measured by prompt-γ NAA (TBPro 5 6.25 3 TBN); BoM can be measured by DEXA; STM can be calculated from TBW (STM 5 0.0129 3 TBW); glycogen can be calculated from TBPro (glycogen 5 0.044 3 TBPro) or from TBN (glycogen 5 0.275 3 TBN).

Human Body Composition Chapter | 1

15

FIGURE 1.4 Mean ( 6 SD) body composition (% of BM) in healthy men and women at the age around 50, acquired with tritium dilution, promt-γ in vivo NAA, DEXA and whole-body 40K counting BM, body mass; BMI, body mass index; TBW, total body water; TBK, total body potassium; TBN, total body nitrogen; BCM, body cell mass; ECF, extracellular fluid; ECS, extracellular solids; FM, fat mass; ICW, intracellular water, and Gly, glycogen. Based on the data from Wang, Z., et al., 2003. Am. J. Clin. Nutr. 78, 979
984.

At the cellular level, ECF can be calculated from the ECW measured by NaBr dilution (ECF 5 (1/0.98) 3 ECW, where 0.98 is the mean hydration of ECF). ECSs (the sum of ECS protein and bone mineral) can be calculated from the equation (Eq. (1.53)): ECS 5 1:732 3 BoM

(1.53)

Based on the above-mentioned measurements, Wang et al. (2004) developed and validated a new TBK/BCM model on the basis of physiological associations between TBK and TBW at the cellular level of body composition. The value of the TBK/BCM ratio was estimated to reach 109.1 mmol  kg21, and it was stable across sex groups and it was almost identical to the value measured in healthy subjects by Cohn’s BCM prediction model (109.0 6 10.9 mmol  kg21). Therefore, a simplified and validated version of the TBK/BCM formula according to Wang et al. (2004) can be presented as (Eq. (1.54)): BCM ðkgÞ 5 0:0092 3 TBK ðmmolÞ

(1.54)

It is known that BCM decreases in response to injury, while extracellular space initially increases. Also the therapeutic availability of these spaces differs in clinical practice: it is easier to change the size and content of the ECF (and its intravascular component, i.e., plasma), while it is more difficult to influence the intracellular content (for example in parenteral nutrition). It is worth noting that immobilization and malnutrition following injury/surgery creates favorable conditions for the catabolic processes of cellular mass, predominantly muscle mass, that becomes the basic source of amino acids used in the processes of healing and immunity (Meynial-Denis, 2016).

1.3.4 Body Composition at the Tissue
Organ Level Body composition at the tissue
organ level comprises three specific tissues, namely adipose tissue (20% and 30% of BM, respectively, for men and women), muscle

16

SECTION | I Skeletal Muscle Morphology

FIGURE 1.5 Body composition at the tissue
organ (anatomic) level in 70 kg reference man (presented as percentage of body mass, % BM). AT, adipose tissue; SM, skeletal muscle; Bo, bone; B, blood with red marrow; OT, other tissues. Based on the data from Snyder, W.S., et al., 1984. Report of the task group on Reference Man. Oxford, Pergamon Press; Wang, Z.M., et al., 1992. Am. J. Clin. Nutr. 56, 19
28.

tissue (42% and 38%, respectively, for men and women), and bone tissue (B7% of BM). The sum of the remaining body tissues accounts for about 30% of BM (Fig. 1.5) and comprises, among others, circulating blood together with red bone marrow (B8% of BM). The assessment of adipose tissue and SM tissue is particularly important in ageing, obesity and wasting diseases.

1.3.4.1 Adipose Tissue In vertebrates, fat is mainly located in specialized cells that are recognizable as adipocytes (Ottaviani et al., 2011). Adipose tissue (AT) is a remarkably complex organ, which is not only responsible for calorie storage, but plays a major role in nutrient homeostasis, as it is a source of circulating fatty acids during fasting and, as recently underlined, an important endocrine organ (Rosen and Spiegelman, 2014). Apart from the presence of fat in adipocytes, ectopic lipid accumulation can be present in non-adipocyte cells, as a result of adipose tissue expandability. According to AT expandability theory, once the adipose tissue fat storage is limited, lipids begin to accumulate in other tissues, such as SM. As demonstrated, accumulation of ectopic lipid—especially of lipid intermediates (diacylglycerols, fatty-acyl- CoAs, ceramides) in SMs (IMAT; intermuscular adipose tissue)—is associated with insulin resistance that precedes the onset of type 2 diabetes by decades (Moreno-Indias and Tinahones, 2015; Brøns and Grunnet, 2017). Adipose tissue in the human body might be assessed using simple anthropometric measurements (see above). However, only advanced medical imaging techniques— such as MRI, CT—allow one to determine precisely adipose tissue distribution (Wald et al., 2012; Kullberg

et al., 2017). In this group of imaging techniques, MRI is the only available non-radiation technique that allows in vivo quantification of adipose tissue and its subdepots. As far as adipose tissue is concerned, two basic areas of fat distribution are recognized based on imaging techniques, namely visceral fat (VAT; visceral adipose tissue) and subcutaneous fat (SAT; subcutaneous adipose tissue). The VAT depot—the fat stored in the central part of the body—can be further divided into intraperitoneal (in mesentery and omentum) and extraperitoneal (intraabdominal and intrapelvic) component (Shen et al., 2003). In turn, the SAT can be divided into deep and superficial depots (Kullberg et al., 2017). Medical imaging techniques enabled visualization of the IMAT as well (Fig. 1.6). The differentiation between VAT and SAT is of particular importance, since visceral fat portends a greater risk of diabetes, cardiovascular disease, hypertension and certain cancers (Bergman et al., 2006; Kim et al., 2017). Based on DEXA and MRI measurements, it was established that total adipose tissue (TAT) in human body is higher in women than in men. In middle-aged women (B38 years old, BMI 24.4 kg  m22), TAT amounts to about 22.3 liters, whereas in middle-aged men (B38 years old, BMI 26.3 kg  m2), it amounts to about 19.6 L (Schweitzer et al., 2015). Moreover, the distribution of adipose tissue differs significantly in both sexes. In middle-aged men, the amount of VAT is higher than in middle-aged women (B2.0 and 0.9 L, respectively, for men and women), whereas the amount of SAT is lower in men (B17.0 and 21.0 L, respectively, for men and women) (Schweitzer et al., 2015).

1.3.4.2 Skeletal Muscle Tissue Muscle mass is the largest anatomically homogeneous component of BM. In mammals, it varies considerably, from 24% to even 61% of BM (Grand, 1978). In humans, muscle mass changes during life span; it constitutes 21% of birth mass, reaches about 42% of BM in adults, and then falls down to 27% in elderly subjects (Lee et al., 2001). In humans, muscle mass consists of approximately 640 muscles that can generate mechanical force and power. As a result, working through a complex musculoskeletal and tendinous system, muscles perform various actions, including: (1) maintaining body position and balance (including positioning of the head that—from the superclass Tetrapoda onwards—became increasingly mobile); (2) developing various forms of movement, including breathing, locomotion and defense behaviors, as well as acquisition and intake of food, communication with gestures, voice, and facial expressions; and (3) transporting loads on the back or by traction in the case of domestic four-legged animals, or by carrying them in the hands or on the back in the case of humans. Some

Human Body Composition Chapter | 1

17

FIGURE 1.6 MRI cross-section of the thigh of (A) healthy, untrained individual (aged 22; BMI: 26.9 kg  m22); (B) middle-distance runner/beginner (aged 22; BMI: 25.5 kg  m22); (C) high class middle-distance runner (aged 21; BMI: 20.1 kg  m22). Note the difference in the thickness of the subcutaneous fat layer (A, B, and C, white color) in the muscle cross-section.

traditional forms of carrying loads, where loads are distributed axially, over the entire length the spine and back muscles, are worth mentioning. This occurs when loads are carried directly on the head (e.g., in Africa), or on the back, with a strap placed over the top of the head (tumpline system). Broad and flexible headbands, often made of local plant fiber, were commonly used in the past. In Mexico, they are known as mecapal and in Nepal as namlo. This mode of transport uses muscles of the neck (especially suboccipital neck muscles) and spine, which results in their chronic strain, but it frees the hands and relieves shoulder muscles. The weights carried are arranged vertically. During high mountain expeditions, they can reach even 50 kg. It is worth noting that the center of gravity of carriers is elevated, their gait is very slow and their loaded muscles start to resemble tortoise muscles (Marconi et al., 2006). Apart from different forms of movement, SMs participate in heat production in resting metabolism and, even more importantly, in exercise metabolism, as well as in shivering and non-shivering thermogenesis. It should be highlighted that there is a developmental relationship between tissues that are sources of non-shivering and contractible response. Namely, the shivering response which accounts for chemical heat production is the domain of particular fat cells, i.e., brown adipose tissue (BAT) cells. The results of genomic and cell lines research suggest that, unlike the yellow (white) adipose cells of WAT (white adipose tissue), which are derived from the lateral plate mesoderm, BAT cells have a common origin with SM cells and are derived from the paraxial mesoderm (Timmons et al., 2007; Enerback, 2009; Seale and Lazar, 2009). Muscle mass—the largest component of cell mass— also fulfills other metabolic functions: (1) it is the main source and “assimilator” of delivered and released potassium and hydrogen ions (McDonough et al., 2002); (2) it is an endocrine organ that can produce and secrete myokines, special cytokines produced by SMs, especially during exercise (Benatti and Pedersen, 2015; Karstoft and Pedersen, 2016); and (3) it is a key site for glucose uptake and storage (insulin-mediated blood glucose clearance),

that is, it is the crucial tissue for maintaining blood glucose control (Meyer et al., 2002). Since muscle proteins make up 50% to 75% of all proteins of the human body, SMs play an important role as an amino acids “bank” (Rennie et al., 1986), especially with respect to glutamine—the main amino acid in SMs that is used as a key source of energy for the immune system (Meynial-Denis, 2016; Wang et al., 2017). As an important source of amino acids in free form (about 200 g), SMs—mainly postural ones—are necessary in healing processes as well as in immune responses after surgery, multiple organ trauma, major burns, chemotherapy, radiotherapy and sepsis (Debes et al., 2014; Porter et al., 2016). In such high catabolic conditions, glutamine becomes a “conditionally” essential amino acid (MeynialDenis, 2016; Bro¨er and Bro¨er, 2017). Therefore, SM mass becomes an important issue, when determining eligibility of a patient to surgery as well as in the process of convalescence (recuperation) after planned (surgery) or unplanned injury (Makary et al., 2010; Gine-Garriga et al., 2014; Bouaziz et al., 2015). Nowadays, the socalled prehabilitation, which includes physical exercise (endurance and muscle conditioning exercises), and refeeding protocols are recommended as a procedure of preparation for planned surgery (Makary et al., 2010; Gine-Garriga et al., 2014; Bouaziz et al., 2015). Total SM mass differs in men and women. It is lower in women (B22 kg, i.e., 34% of BM in women aged 18
29) than in men (B34 kg, i.e., 42% of BM in men aged 18
29) (Janssen et al., 2000). The evaluation of SM mass is especially important in diagnosing age-related sarcopenia. Starting from the 3rd decade, ageing is accompanied by a decrease in SM mass. However, a decrease in SM amounting to about 1.9 and 1.1 kg per year, for men and women, respectively, is noticeable from the 5th decade of life (Janssen et al., 2000). It is worth noting that the age-dependent loss of SM mass and increase in overall body adiposity—which mainly refers to an increase in VAT both in men and women (up to about 4.0 and 2.5 L, for men and women, respectively) (Shen et al., 2009)—coincide with greater

18

SECTION | I Skeletal Muscle Morphology

fat accumulation in SM (Goodpaster et al., 2001), which leads to an increased risk of insulin resistance (Goss and Gower, 2012). Sarcopenia is a syndrome characterized by progressive loss of SM mass and strength, which involves the risk of such adverse outcomes as physical disability, poor quality of life, and death (Cruz-Jentoft et al., 2010). The European Working Group on Sarcopenia in Older People (EWGSOP) recommend that the diagnosis of sarcopenia should be based on the presence of both low SM mass and low muscle function. Therefore, the diagnosis requires documentation of criterion 1 (low SM mass) and either criterion 2 (low muscle strength) or criterion 3 (low physical performance) (Cruz-Jentoft et al., 2010). Evaluation of muscle mass in vivo has a history of nearly two hundred years (Heymsfield et al., 2014). Measurement techniques have been changing, starting from determining muscle metabolites in daily urine (e.g., urinary creatinine excretion), through whole-body counting of the content of the natural 40K isotope, bioelectrical impedance analysis to DEXA, CT and MRI (Peterson and Braunschweig, 2016). Among biochemical methods measurement of creatinine excretion has been found to correlate quite well with muscle mass determined using MRI (Clark et al., 2014). Muscle contains 95% of body creatine pool and has no capacity to synthesize creatine, which is produced in the liver and kidney and transported into SM. Creatine then is converted to creatinine by an irreversible reaction and excreted to urine, hence enrichment of urine creatinine provides a measure of SM creatine and thus whole-body creatine pool size as well as SM mass. The isotope-labeled creatine dilution method (methyl-D3 creatine, D3-creatine) gives an opportunity to measure total body creatine pool size for the determination of total body muscle mass by the enrichment of D3creatinine in urine (Clark et al., 2014). Until now, however, no technique has been developed to directly measure functional muscle mass. Whole-body MRI—as the only one non-radiation technique available—is nowadays a “gold standard” for the assessment of SM and AT volumes (Tothill and Stewart, 2002; Schweitzer et al., 2015). To put it in simple words, whole-body MRI imaging enables identification of bones, AT and SM based on the differences in gray-level pixels (Fig. 1.6). If the resolution of recorded images is known, the area of muscle and fat in each image can be manually selected or computed automatically on the basis of the threshold selection in gray-level pixels or watershed algorithm. Multiplication of the total area of muscle (the sum of muscle areas indicated on each image) by the distance between MRI slices allows one to calculate the muscle volume unit, which should be converted into mass by means of the following relationship: Muscle mass 5 Muscle volume unit 3 SM density, where SM

density equals to 1.04 kg  L21 (see Snyder et al., 1984) or 1.06 kg  L21 (see Dawson et al., 2013). Whole-body MRI measurements used to assess SM and AT volumes may be replaced with the examination of a selected slice (lower number) to ensure faster and easier assessment of SM and AT in diagnosing, for instance, sarcopenic obesity (Schweitzer et al., 2015). Both MRI and CT can precisely discern SMs, but due to high cost and limited access to equipment—they are used mainly in research. DEXA is one of the techniques most widely used to simultaneously quantify bone, lean soft tissue, and FM. It is based on differential X-ray attenuation of fat, bone minerals and fat-free soft tissue. A typical whole-body scan takes approximately 10
20 min and exposes the subject to around 0.20
0.37 μSv of radiation, which is about 1/100 of the dose used in a typical chest X-ray procedure. Estimation of fat and lean soft tissue is based on inherent assumptions regarding tissue levels of hydrogen, carbon, electrolytes, and minerals. These assumptions vary, dependent on manufacturer’s software (Roubenoff et al., 1993; Laskey, 1996). In clinical practice and in epidemiological studies, the bioimpedance analysis is often used (Lemos and Gallagher, 2017) as an inexpensive, easy to use method of differentiation between FM and FFM, and it may be recommended as an alternative to DEXA for identifying age-dependent body composition changes (Cruz-Jentoft et al., 2010). Total body SM mass might also be assessed on the basis of simple anthropometric measures, like BM and body height (Lee et al., 2000). The formula presented below for healthy, nonobese individuals (n 5 244, BMI , 30 kg  m22) was cross-validated with MRI for total SM volume measurements (Eq. (1.55)): SM ðkgÞ 5 0:244 3 BM ðkgÞ 1 7:80 3 H ðmÞ
0:098 3 age ðyearsÞ 1 6:6 3 sex 1 race
3:3 (1.55) where sex (female 5 0, male 5 1); race 5 0 for white and Hispanic, 21.2 for Asian, 1.4 for African American. Skeletal muscle mass can be divided into trunk and appendicular (arms and legs) musculature. The appendicular lean soft tissue, a marker of SM mass (ASM), can be calculated as the sum of the non-bone and non-FM of the four limbs from a DEXA scan, and corrected for height (ASM 3 height22). This index is called SM mass index (SMI) and it is used as a diagnostic criterion for sarcopenia (Baumgartner et al., 1998). SMI two standard deviations below the mean SMI of young male and female reference groups was defined as a gender-specific cut point for sarcopenia, which were proposed according to Baumgartner et al. (1998) at 5.45 kg  m22 for women and 7.26 kg  m22 for men.

Human Body Composition Chapter | 1

It needs to be mentioned that more than 50% of SM mass is located in lower body parts, predominantly at the thigh, and only a fifth of the mass is in upper body part (Janssen et al., 2000). The muscle mass at the thigh is the main determinant of human locomotion and power generating capabilities, and it assures independence in life, which is especially important in the elderly. In exercise physiology, the thigh volume or quadriceps muscle volume is used for normalization of strength, power measurements (Jones and Rutherford, 1987; Sargeant, 2007) as well as muscle oxygen consumption measurements in humans (Andersen and Saltin, 1985). In clinical and epidemiological studies, estimation of thigh volume can be performed through simple, inexpensive anthropometric measurements (skinfold thickness and girths). The anthropometric measurements of thigh muscle rely on several assumptions, namely: (1) estimation of the thickness of SAT by means of calipers provides an accurate estimate of SAT; (2) the limb is circular and the SAT forms an annulus; (3) intermuscular fat and bone volumes are negligible; (4) a limited number of measurement positions can be used to predict the total volume (Tothill and Stewart, 2002). Based on the anthropometric measurement, thigh volume can be calculated with the formula [Eq. (1.56), see Jones and Pearson, 1969; Radegran et al., 1999]: V ðcm3 Þ 5 length 3 ð12 πÞ
1 3 ðC12 1 C22 1 C32 Þ
ðS
0:4Þ 3 2
1 3 length 3 ðC1 1 C2 1 C3 Þ 3 3
1 (1.56) where length (cm) is the estimated thigh length from the greater trochanter to the lateral femoral epicondyle; C1, C2, C3 (cm) refer to the proximal, middle and distal circumferences, measured 10 cm above the middle, at the middle and 10 cm below the middle of the segment of the muscle, and S (cm) is the skinfold thickness of the thigh. The anthropometric prediction of muscle volume is biased due to variability of adipose tissue accumulation. In the group of middle-aged adults (B40 years old), the thigh muscle volume derived from the aforementioned formula was estimated at the level of B6488 cm3, whereas 1 H-MRI assessment yielded B3855 cm3 (mean bias: 2407 cm3) (Layec et al., 2014). Although anthropometric measurements overestimate muscle volume (B20%
40%), compared to MRI, they provide a good index of muscle volume in adults (Tothill and Stewart 2002; Layec et al., 2014). A high correlation between muscle volume estimated by anthropometric and MRI measurements made it possible to establish a formula aimed at correcting the value of thigh muscle volume obtained with anthropometric measurements (Eq. (1.57)), and at acquiring a more reliable result (as found in MRI studies) (Knapik et al., 1996; Tothill and Stewart, 2002; Layec et al., 2014):

19

Thigh muscle volume ðcm3 Þ 5 0:866 3 V ðanthropometricÞ ðcm3 Þ
1750

(1.57)

where V anthropometric is the thigh muscle volume derived from Eq. (1.56).

1.3.4.3 Bone Tissue Bone mass and mineral density increases during childhood, reaching the highest value around 20
25 years of age, and declines thereafter at an annual, relatively constant average rate of B1% (Reid, 1986). After menopause in women, however, a faster rate of bone mass decrease is observed (Reid, 1986, see also the Chapter 20: The Role of Exercise on Fracture Reduction and Bone Strengthening by Kemmler and von Stengel). Bone mass can be evaluated using the whole-body DEXA method, which differentiates fat, bone minerals and lean soft tissue (see Section 1.3.4.2). Evaluation of total body bone mineral content with the DEXA method is widely used to assess the risk of osteoporosis in adults (see Chapter 20: The Role of Exercise on Fracture Reduction and Bone Strengthening, by Kemmler and von Stengel) and to monitor bone mass changes during childhood (Horlick et al., 2004). Total bone mass can also be assessed by the total body calcium (TBCa) measurement (using the NAA method), since calcium constitutes about 38%
39% of bone mineral (Reid, 1986). Based on the calcium assessment, total body bone mineral content can be calculated with the following equation (Eq. (1.58), Ellis et al., 1996): BoM ðgÞ 5 3:22 3 TBCa ðgÞ
51:4

(1.58)

1.4 BASICS OF BODY COMPARTMENTALIZATION The analysis of body composition can be performed by applying many classic and advanced technical methods (Lemos and Gallagher, 2017). The simplest, 2compartment (2-C) model of body composition allows one to discern fat and FFM that comprises all the remaining tissues. This oldest model has been used in body composition research for over 60 years. The 3compartment (3-C) model divides body weight into FM, TBW and fat-free dry mass (FFDM), whereas the most popular 4-compartment model (4-C) of body composition discerns FM, TBW, bone mineral mass, and residual mass (RM, i.e., mainly protein, some non-bone minerals and glycogen). The more complex 6compartment (6-C) model, thanks to implementation of advanced techniques (DEXA, NAA), allows one to discern proteins, STMs, and glycogen in addition to body

20

SECTION | I Skeletal Muscle Morphology

FIGURE 1.7 Two-, three-, four-, five-, and six-compartment models of body composition in the 70-kg reference man. FFDM, fat-free dry mass; BoM, bone minerals; STM, soft tissue minerals; Gly, glycogen. Based on the data from Wang, Z., et al., 2003. Am. J. Clin. Nutr. 78, 979
984.

fat, TBW, and bone minerals (Fig. 1.7) (Wang et al., 1998; Ellis, 2000).

1.4.1 Two-Compartment Model of Body Composition The 2-C model distinguishes only two virtual body components: FM and FFM, and it is based on the measurement of total BD. In the 2-C model, BM can be presented as sum of body FM and FFM (BM 5 FM 1 FFM), whereas BV is presented as sum of VFM and VFFM. As a consequence, after performing a few simple mathematical transformations, BD may be expressed as (Eq. (1.59)): 1 fFM fFFM 5 1 BD DFM DFFM

(1.59)

where fFM is a fraction of body FM, fFFM is a fraction of body FFM, DFM is density of fat, and DFFM is density of FFM. Assuming that the sum of fFM and fFFM in the 2-C model equals to 1, it follows that (Eq. (1.60)): 1 fFM ð12fFM Þ 5 1 BD DFM DFFM

(1.60)

According to Brozek et al. (1963), these densities are equal to 0.9007 g  cm23 and 1.100 g  cm23 for fat density and FFM density, respectively. Finally, if only total BD,

measured using one of the known methods (hydrodensitometry, the most common one, or others, such as the airdisplacement plethysmography or 3D scanning) one can calculate fat index (fFM) as a fraction of body weight (Eq. (1.61)): fFM 5

4:971 2 4:519 BD

(1.61)

This formula is close to the one proposed earlier by Siri (1956) (Eq. (1.62)): fFM 5

4:95 2 4:5 BD

(1.62)

A fraction of body fat (fFM) can be expressed as percentage (fFM 3 100%). Another approach to body fat assessment is based on the measurement of TBW and on the assumption that FFM consists of 73.8% of water. It means that TBW/FFM 5 0.738 and, finally, the fFM fraction can be calculated as (Eq. (1.63), Brozek et al., 1963):   fTBW fFM 5 1 2 (1.63) 0:738 Doubts concerning Brozek formula of body fat fraction follow from the criticism of the assumed density values (DFM, DFFM), which are based on analyses of not more than three male cadavers (Brozek et al., 1963).

Human Body Composition Chapter | 1

Despite this, these formulas are often used in clinical practice.

1.4.2 Three-Compartment Model of Body Composition The 3-C model distinguishes FM, TBW and fat-free dry mass (Siri, 1961). Hence, in this model, FFM is divided into two parts: its water content and the remaining solids (FFDM), predominantly proteins, and minerals. The model is based on measurements of both BD and TBW, while a constant mineral-to-protein ratio of 0.35 is assumed (Siri, 1961). It means that total minerals account for 7% of FFM (Withers et al., 1998). The densities (DFM, DTBW, DFFDM) of the three compartments (fat, water and body solids) at 36 C were assumed to reach 0.9007, 0.9937 (Lentner, 1981), and 1.569 g  cm23 (Withers et al., 1996), respectively. Assuming that fFM 1 fTBW 1 fFFDM 5 1, the following formula is obtained (Eq. (1.64)): 1 fFM fTBW fFFDM 5 1 1 BD DFM DTBW DFFDM

(1.64)

Hence, fFM can be calculated as follows (Eq. (1.65)):   2:115 TBW 2 0:780 3 fFM 5 2 1:348 (1.65) BD BM The 3-C model based on the estimate values of body solids density (DFFDM) would be incorrect in the case of patients with depleted body proteins and/or bone mineral mass (Ellis, 2000). Therefore, the estimation of body fat, based on the 3-C model can also be incorrect.

1.4.3 Four-Compartment Model of Body Composition The 4-C model distinguishes FM, TBW, bone minerals, and RM. Nowadays, the 4-C model is one of the most advanced and commonly used method of body composition analysis (Ellis, 2000). In the 4-C model of body composition, it is necessary to measure accurately the mass of two additional main compartments, namely the bone minerals and protein compartments. This is performed with two (when compared to the 2-C model) measurements, namely the NAA to assess body protein and the DEXA to assess bone mineral content. DEXA value for bone mineral compartment is rather commonly available, whereas the measurement of whole-body protein is not easily to take. Therefore, protein content is assumed to be proportional to bone mineral mass, irrespective of age and sex (Eq. (1.40), Wang et al., 2003). The 4-C model can be represented with the following formula (Eq. (1.66)):

1 fFM fTBW fBoM fRM 5 1 1 1 BD DFM DTBW DBoM DRM

21

(1.66)

where DBoM is bone mineral density, DRM is residual density [here; the sum of all fractions is one (fFM 1 fTBW 1 fBoM 1 fRM 5 1)]. If it is assumed that densities DBoM and DRM are 2.982 (Mendez et al., 1960) and 1.404 g  cm23 (Allen et al., 1959), respectively, than the fat fraction (fFM) can be calculated with the following equation (Eq. (1.67)):     2:513 TBW BoM 2 0:739 3 1 0:947 3 2 1:790 fFM 5 BD BM BM (1.67) We assume that 73% of the FFM is water in a normally-hydrated subject (Wang et al., 1999). In the 6-C model two additional compartments can be distinguished in the RM, that is, STMs and glycogen (Heymsfield et al., 1991; Wang et al., 1998) (Fig. 1.7).

1.5 CONCLUSIONS Measurements of body composition at different levels, as described above, provide a range of useful information of physiological and clinical importance. Developments of advanced techniques offer precise and accessible tools for the determination of body composition in humans, both in laboratory and in clinical conditions. We considered that two key parameters of body composition, namely muscle mass and body fat, should receive special attention both in health and disease. These two variables share a common feature, namely their strong involvement in modern chronic diseases such as sarcopenia, obesity, type 2 diabetes and cardiovascular disease. Therefore, their changes can provide diagnostic and prognostic information that is useful in the course of diseases.

ACKNOWLEDGMENT We kindly acknowledge Dr. Justyna Zapart-Bukowska and mgr Magdalena Guzik for their technical support in the preparation of this chapter.

REFERENCES Abernethy, J., 1793. An essay on the nature of the matter perspired and absorbed from the skin. Surgical and physiological essays. Part III. London 107
165. Adler, C., Steinbrecher, A., Jaeschke, L., Ma¨hler, A., Boschmann, M., Jeran, S., et al., 2017. Validity and reliability of total body volume and relative body fat mass from a 3-dimensional photonic body surface scanner. PLoS One 12, e0180201. Alema´n, J.O., Iyengar, N.M., Walker, J.M., Milne, G.L., Da Rosa, J.C., Liang, Y., et al., 2017. Effects of rapid weight loss on systemic and adipose tissue inflammation and metabolism in obese postmenopausal women. J. Endocr. Soc. 1, 625
637.

22

SECTION | I Skeletal Muscle Morphology

Aleman-Mateo, H., Ruiz Valenzuela, R.E., 2014. Skeletal muscle mass indices in healthy young Mexican adults aged 20-40 years: implications for diagnoses of sarcopenia in the elderly population. The Scientific World Journal 6, 672158. Available from: https://doi.org/ 10.1155/2014/672158. Allen, T.H., Krzywicki, H.J., Roberts, J.E., 1959. Density, fat, water, and solids in freshly isolated tissues. J. Appl. Physiol. 14, 1005
1008. Allison, S.P., Lobo, D.N., 2004. Fluid and electrolytes in the elderly. Curr. Opin. Clin. Nutr. Metab. Care 7, 27
33. Andersen, P., Saltin, B., 1985. Maximal perfusion of skeletal muscle in man. J. Physiol. 366, 233
249. Ashwell, M., Gibson, S., 2016. Waist-to-height ratio as an indicator of ‘early health risk’: simpler and more predictive than using a ‘matrix’ based on BMI and waist circumference. BMJ Open 6, e010159. Ashwell, M., Mayhew, L., Richardson, J., Rickayzen, B., 2014. Waistto-height ratio is more predictive of years of life lost than Body Mass Index. Plos One 9, e103483. Bartok, C., Atkinson, R.L., Schoeller, D.A., 2003. Measurement of nutritional status in simulated microgravity by bioelectrical impedance spectroscopy. J. Appl. Physiol. (1985) 95 225
232. Baumgartner, R.N., Koehler, K.M., Gallagher, D., Romero, L., Heymsfield, S.B., Ross, R.R., et al., 1998. Epidemiology of sarcopenia among the elderly in New Mexico. Am. J. Epidemiol. 147, 755
763. Beddoe, A.H., Streat, S.J., Hill, G.L., 1984. Evaluation of an in vivo prompt gamma neutron activation facility for body composition studies in critically ill intensive care patients: results on 41 normals. Metabolism 33, 270
280. Benatti, F.B., Pedersen, B.K., 2015. Exercise as an anti-inflammatory therapy for rheumatic diseases-myokine regulation. Nat. Rev. Rheumatol. 11, 86
97. Benias, P.C., Wells, R.G., Sackey-Aboagye, B., Klavan, H., Reidy, J., Buonocore, D., et al., 2018. Structure and distribution of an unrecognized interstitium in human tissues. Sci Rep. 27, 4947. Bergman, R.N., Kim, S.P., Catalano, K.J., Hsu, I.R., Chiu, J.D., Kabir, M., et al., 2006. Why visceral fat is bad: mechanisms of the metabolic syndrome. Obesity (Silver Spring) 14, 16S
19S. Bird, N.J., Henderson, B.L., Lui, D., Ballinger, J.R., Peters, A.M., 2003. Indexing glomerular filtration rate to suit children. J. Nucl. Med. 44, 1037
1043. Bouaziz, W., Schmitt, E., Kaltenbach, G., Geny, B., Vogel, T., 2015. Health benefits of cycle ergometer training for older adults over 70: a review. Eur. Rev. Aging Phys. Act. 12, 8. Brodie, D., Moscrip, V., Hutcheon, R., 1998. Body composition measurement: a review of hydrodensitometry, anthropometry, and impedance methods. Nutrition 14, 296
310. Bro¨er, S., Bro¨er, A., 2017. Amino acid homeostasis and signalling in mammalian cells and organisms. Biochem. J. 474, 1935
1963. Brøns, C., Grunnet, L.G., 2017. Skeletal muscle lipotoxicity in insulin resistance and type 2 diabetes: a causal mechanism or an innocent bystander? Eur. J. Endocrinol. 176, R67
R78. Browning, L., Hsieh, S.D., Ashwell, M., 2010. A systematic review of waist-to-height ratio as screening tool for the prediction of cardiovascular disease and diabetes: 0,5 could be a suitable global boundary value. Nutr. Res. Rev. 23, 247
269. Brozek, J., Grande, F., Anderson, J.T., Keys, A., 1963. Densitometric analysis of body composition: revision of some quantitative assumptions. Ann NY Acad. Sci. 110, 113
140.

Buskirk, E.R., Mendez, J., 1984. Sport science and body composition analysis; emphasis on cell and muscle mass. Med. Sci. Sports Exerc. 16, 584
593. Choi, D.H., Hur, Y.I., Kang, J.H., Kim, K., Cho, Y.G., Hong, S.M., et al., 2017. Usefulness of the waist circumference-to-height ratio in screening for obesity and metabolic syndrome among Korean children and adolescents: Korea National Health and Nutrition Examination Survey, 2010
2014. Nutrients 9. Available from: https://doi.org/10.3390/nu9030256. pii: E256. Choo, V., 2002. WHO reassesses appropriate body-mass index for Asian populations. Lancet 360, 235. Chumlea, W.C., Guo, S.S., Zeller, C.M., Reo, N.V., Baumgartner, R.N., Garry, P.J., et al., 2001. Total body water reference values and prediction equations for adults. Kidney Int. 59, 2250
2258. Clark, R.V., Walker, A.C., O’Connor-Semmes, R.L., Leonard, M.S., Miller, R.R., Stimpson, S.A., et al., 2014. Total body skeletal muscle mass: estimation by creatine (methyl-d3) dilution in humans. J. Appl. Physiol. (1985) 116, 1605
1613. Cohn, S.H., Vaswani, A.N., Yasumura, S., Yuen, K., Ellis, K.J., 1985. Assessment of cellular mass and lean body mass by noninvasive nuclear techniques. J. Lab. Clin. Med. 105, 305
311. Costill, D.L., 1986. Inside Running: Basics of Sports Physiology. Benchmark Press, Inc., Indianapolis, pp. 2
3. Cruz-Jentoft, A.J., Baeyens, J.P., Bauer, J.M., Boirie, Y., Cederholm, T., Landi, F., et al., 2010. European working group on sarcopenia in older people. Sarcopenia: European consensus on definition and diagnosis: report of the European working group on Sarcopenia in older people. Age Ageing 39, 412
423. Dawson, T.J., Webster, K.N., Lee, E., Buttemer, W.A., 2013. High muscle mitochondrial volume and aerobic capacity in a small marsupial (Sminthopsis crassicaudata) reveals flexible links between energyuse levels in mammals. J. Exp. Biol. 216, 1330
1337. Debes, C., Aissou, M., Beaussier, M., 2014. Le pre´habilitation. Pre´parer les patients a` la chirurgie pour ame´liorer la re´cupe´ration fonctionelle et re´duire la morbidite´ postope´ratoire (Pre-rehabilitation. Prepare patients for surgery to improve functional recovery and reduce postoperative morbidity). Ann Fr. Anesth. Reanim. 33, 33
40 (in French). Duda, K., 2012. Budowa i skład ciała człowieka w aspekcie starzenia (The structure and composition of the human body in the aspect of ˙ ˛d´z, J.A. (Eds.), aging). In: Marchewka, A., Da˛browski, Z., Zoła Fizjologia starzenia sie˛, profilaktyka i rehabilitacja. (Physiology of aging, prevention and rehabilitation). Wydawnictwo Naukowe PWN, Warszawa, pp. 60
86. , 2012, (in Polish). Duren, D.L., Sherwood, R.J., Czerwinski, S.A., Lee, M., Choh, A.C., Siervogel, R.M., et al., 2008. Body composition methods: comparisons and interpretation. J. Diabetes Sci. Technol. 2, 1139
1146. Eknoyan, G., 2008. Adolphe Quetelet (1796
1874)—the average man and indices of obesity. Nephrol. Dial. Transplant. 23, 47
51. Elia, M., 1992. Organ and tissue contribution to metabolic rate. In: Kinney, J., Tucker, H.N. (Eds.), Energy metabolism: tissue determinants and cellular corollaries. Raven Press, New York, pp. 61
79. Ellis, K.J., 2000. Human body composition: in vivo methods. Physiol. Rev. 80, 649
680. Ellis, K.J., Shypailo, R.J., Hergenroeder, A., Perez, M., Abrams, S., 1996. Total body calcium and bone mineral content: comparison of dual-energy X-ray absorptiometry with neutron activation analysis. J. Bone. Miner. Res. 11, 843
848.

Human Body Composition Chapter | 1

Enerback, S., 2009. The origins of brown adipose tissue. N. Engl. J. Med. 360, 2021
2023. FAO. Calorie Requirements, 1957. Report of the Second Committee on Calorie Requirements. Nutritional Studies No. 15. Food and Agriculture Organization, Rome. Feber, J., Kra´snicanova´, H., 2012. Measures of body surface area in children. In: Preedy, V.R. (Ed.), Handbook of Anthropometry, Physical Measures of Human Form in Health and Disease Vol. 1 Parts 1-6. Springer, pp. 1249
1257. Gallagher, D., Belmonte, D., Deurenberg, P., Wang, Z., Krasnow, N., PiSunyer, F.X., et al., 1998. Organ-tissue mass measurement allow modeling of REE and metabolically active tissue mass. Am. J. Physiol. 275, E249
E258. Gine-Garriga, M., Roque´-Figuls, M., Coll-Planas, L., Sirja`-Rabert, M., Salva`, A., 2014. Physical exercise interventions for improving performance-based measures of physical function in communitydwelling, frail older adults: a systematic review and meta-analysis. Arch. Phys. Med. Rehabil. 95, 753
769. Goodpaster, B.H., Carlson, C.L., Visser, M., Kelley, D.E., Scherzinger, A., Harris, T.B., et al., 2001. Attenuation of skeletal muscle and strength in the elderly: the Health ABC Study. J. Appl. Physiol. (1985) 90, 2157
2165. Goss, A.M., Gower, B.A., 2012. Insulin sensitivity is associated with thigh adipose tissue distribution in healthy postmenopausal women. Metabolism 61, 1817
1823. Grand, T.I., 1978. Adaptations of tissue and limb segments to facilitate moving and feeding in arboreal folivores. In: Montgomery, G.G. (Ed.), The Ecology and Arboreal folivores. Smiths. Inst. Press, Washington DC, pp. 231
241. Haycock, G.B., Schwartz, G.J., Wisotsky, D.H., 1978. Geometric method for measuring body surface area: a height-weight formula validated in infants, children, and adults. J. Pediatr. 93, 62
66. Heinicke, K., Dimitrov, I.E., Romain, N., Cheshkov, S., Ren, J., Malloy, C.R., et al., 2014. Reproducibility and absolute quantification of muscle glycogen in patients with glycogen storage disease by 13C NMR spectroscopy at 7 Tesla. PLoS One 9, e108706. Hellmanns, K., McBean, K., Thoirs, K., 2015. Magnetic resonance imaging in the measurement of whole body muscle mass: a comparison of interval gap methods. Radiography. 21, e35
e39. Henry, C.J., 2005. Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr. 8, 1133
1152. Heshka, S., Feld, K., Yang, M.U., Allison, D.B., Heymsfield, S.B., 1993. Resting energy expenditure in the obese: a cross-validation and comparison of prediction equations. J. Am. Diet. Assoc. 93, 1031
1036. Heymsfield, S.B., Waki, M., 1991. Body composition in humans: advances in the development of multicompartment chemical models. Nutr. Rev. 49, 97
108. Heymsfield, S.B., Waki, M., Kehayias, J., Lichtman, S., Dilmanian, F. A., Kamen, Y., et al., 1991. Chemical and elemental analysis of humans in vivo using improved body composition models. Am. J. Physiol. 261, E190
E198. Heymsfield, S.B., Thomas, D., Bosy-Westphal, A., Shen, W., Peterson, C.M., Mu¨ller, M.J., 2012a. Evolving concepts on adjusting human resting energy expenditure measurements for body size. Obes. Rev. 13, 1001
1014. Heymsfield, S.B., Thomas, D., Martin, C.K., Redman, L.M., Strauss, B., Bosy-Westphal, A., et al., 2012b. Energy content of weight loss: kinetic features during voluntary caloric restriction. Metabolism 61, 937
943.

23

Heymsfield, S.B., Adamek, M., Gonzalez, M.C., Jia, G., Thomas, D.M., 2014. Assessing skeletal muscle mass: historical overview and state of the art. J. Cachexia Sarcopenia Muscle 5, 9
18. Hilloowala, R., 2009. Michelangelo: anatomy and its implication in his art. Vesalius 15, 19
25. Horlick, M., Wang, J., Pierson Jr., R.N., Thornton, J.C., 2004. Prediction models for evaluation of total-body bone mass with dual-energy Xray absorptiometry among children and adolescents. Pediatrics 114, e337
e345. Hsieh, S.D., Yoshinaga, H., 1995a. Abdominal fat distribution and coronary heart disease risk factors in men
waist/height ratio as a simple and useful predictor. Int. J. Obes. 19, 585
589. Hsieh, S.D., Yoshinaga, H., 1995b. Waist/height ratio as a simple and useful predictor of coronary heart disease risk factors in women. Internal Medicine 34, 1147
1152. Jacob, M., Chappell, D., Becker, B.F., 2016. Regulation of blood flow and volume exchange across the microcirculation. Crit. Care 20, 319. Jackson, A.S., Pollock, M.L., 1985. Practical assessment of body composition. Phys. Sportsmed. 13, 76
90. Janssen, I., Heymsfield, S.B., Wang, Z., Ross, R., 2000. Skeletal muscle mass and distribution in 468 men and women aged 18
88 yr. J. Appl. Physiol. 89, 81
88. Jones, D.A., Rutherford, O.M., 1987. Human muscle strength training: the effects of three different regimens and the nature of the resultant changes. J. Physiol. 391, 1
11. Jones, P.R., Pearson, J., 1969. Anthropometric determination of leg fat and muscle plus bone volumes in young male and female adults. J. Physiol. 204, 63P
66P. Karstoft, K., Pedersen, B.K., 2016. Skeletal muscle as a gene regulatory endocrine organ. Curr. Opin. Clin. Nutr. Metab. Care 19, 270
275. Kehayias, J.J., Heymsfield, S.B., LoMonte, A.F., Wang, J., Pierson Jr., R.N., 1991. In vivo determination of body fat by measuring total body carbon. Am. J. Clin. Nutr. 53, 1339
1344. Kim, J.E., Dunville, K., Li, J., Cheng, J.X., Conley, T.B., Couture, C.S., et al., 2017. Intermuscular adipose tissue content and intramyocellular lipid fatty acid saturation are associated with glucose homeostasis in middle-aged and older adults. Endocrinol. Metab. (Seoul) 32, 257
264. Kleiber, M., 1932. Body size and metabolism. J. Agricultural. Sci. 6, 315
353. Knapik, J.J., Staab, J.S., Harman, E.A., 1996. Validity of an anthropometric estimate of thigh muscle cross-sectional area. Med. Sci. Sports Exerc. 28, 1523
1530. Kullberg, J., Hedstro¨m, A., Brandberg, J., Strand, R., Johansson, L., Bergstro¨m, G., et al., 2017. Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies. Sci. Rep. 7, 10425. Laskey, M.A., 1996. Dual-energy X-ray absorptiometry and body composition. Nutrition 12, 45
51. Laurson, K.R., Eisenmann, J.C., Welk, G.J., 2011. Body mass index standards based on agreement with health-related body fat. Am. J. Prev. Med. 41, S100
S105. Layec, G., Venturelli, M., Jeong, E.K., Richardson, R.S., 2014. The validity of anthropometric leg muscle volume estimation across a wide spectrum: from able-bodied adults to individuals with a spinal cord injury. J. Appl. Physiol. (1985) 116, 1142
1147. Lee, R.C., Wang, Z.M., Heo, M., Ross, R., Janssen, I., Heymsfield, S.B., 2000. Total body skeletal muscle mass: development and cross-

24

SECTION | I Skeletal Muscle Morphology

validation of anthropometric prediction models. Am. J. Clin. Nutr. 72, 796
803. Lee, R.C., Wang, Z.M., Heymsfield, S.B., 2001. Skeletal muscle mass and aging: regional and whole-body measurement methods. Can. J. Appl. Physiol. 26, 102
122. Lee, I.M., Shiroma, E.J., Lobelo, F., Puska, P., Blair, S.N., Katzmarzyk, P.T., 2012. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 380, 219
229. Lemos, T., Gallagher, D., 2017. Current body composition measurement techniques. Curr. Opin. Endocrinol. Diabetes Obes. 24, 310
314. Lentner, C., 1981. Geigy scientific tables, 8th ed. Units of Measurement: Body Fluids, Composition of the Body, Nutrition, vol. 1. CibaGeigy, Basel, p. 50. Lindsted, S.L., Schaeffer, P.J., 2002. Use of allometry in predicting anatomical and physiological parameters of mammals. Laboratory Animals 36, 1
19. Lo, K., Wong, M., Khalechelvam, P., Tam, W., 2016. Waist-to-height ratio, body mass index and waist circumference for screening paediatric cardio-metabolic risk factors: a meta-analysis. Obes. Rev. 17, 1258
1275. Lowell, B.B., Spiegelman, B.M., 2000. Towards a molecular understanding of adaptive thermogenesis. Nature. 404, 652
660. Makary, M.A., Segev, D.L., Pronovost, P.J., Syin, D., Bandeen-Roche, K., Patel, P., et al., 2010. Frailty as a predictor of surgical outcomes in older patients. J. Am. Coll. Surg. 210, 901
908. Marconi, C., Marzorati, M., Cerretelli, P., 2006. Work capacity of permanent residents of high altitude. High Alt. Med. Biol. 7, 105
115. Mattsson, S., Thomas, B.J., 2006. Development of methods for body composition studies. Phys. Med. Biol. 51, R203
R228. Mazariegos, M., Kral, J.G., Wang, J., Waki, M., Heymsfield, S.B., Pierson Jr., R.N., et al., 1992. Body composition and surgical treatment of obesity. Effects of weight loss on fluid distribution. Ann. Surg 216, 69
73. McDonough, A.A., Thompson, C.B., Youn, J.H., 2002. Skeletal muscle regulates extracellular potassium. Am. J. Physiol. Renal Physiol. 282, F967
F974. Meeh, K., 1879. Oberfla¨chenmessungen des menschlichen Ko¨rpers (Surface measurements of the human body). Z. Biol. 15, 425
485 (in Dutch). Mehta, S.K., 2015. Waist circumference to height ratio in children and adolescents. Clin. Pediatr. 54, 652
658. Mendez, J., Keys, A., Anderson, J.T., Grande, F., 1960. Density of fat and bone mineral of the mammalian body. Metabolism 9, 472
477. Meyer, C., Dostou, J.M., Welle, S.L., Gerich, J.E., 2002. Role of human liver, kidney, and skeletal muscle in postprandial glucose homeostasis. Am. J. Physiol. Endocrinol. Metab. 282, E419
E427. Meynial-Denis, D., 2016. Glutamine metabolism in advanced age. Nutr. Rev. 74, 225
236. Moore, F.D., Olesen, K.H., McMurrey, J.D., Parker, J.H.V., 1963. The Body Cell Mass and its Supporting Environment: Body Composition in Health and Disease. W. B. Saunders, Philadelphia and London. Moreno-Indias, I., Tinahones, F.J., 2015. Impaired adipose tissue expandability and lipogenic capacities as ones of the main causes of metabolic disorders. J. Diabetes Res. 2015, 970375. Mosteller, R.D., 1987. Simplified calculation of body-surface area. N. Engl. J. Med. 317, 1098. NCD Risk Factor Collaboration, 2016. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698

population-based measurement studies with 19.2 million participants. Lancet 387, 1377
1396. Noakes, T.D., 2003. Lore of running, 4th edition Human Kinetics Publishers, Champaign, Ill, pp. 104
105. Olsen, H., Vernersson, E., La¨nne, T., 2000. Cardiovascular response to acute hypovolemia in relation to age. Implications for orthostasis and hemorrhage. Am. J. Physiol. Heart Circ. Physiol. 278, H222
H232. Ottaviani, E., Malagoli, D., Franceschi, C., 2011. The evolution of the adipose tissue: a neglected enigma. Gen. Comp. Endocrinol. 174, 1
4. Peterson, S.J., Braunschweig, C.A., 2016. Prevalence of sarcopenia and associated outcomes in the clinical setting. Nutr. Clin. Pract. 31, 40
48. Peterson, C.M., Su, H., Thomas, D.M., Heo, M., Golnabi, A.H., Pietrobelli, A., et al., 2017. Tri-ponderal mass index vs body mass index in estimating Body Fat During Adolescence. JAMA Pediatr. 171, 629
636. Porter, C., Tompkins, R.G., Finnerty, C.C., Sidossis, L.S., Suman, O.E., Herndorn, D.N., 2016. The metabolic stress response to burn trauma: current understanding and therapies. Lancet 388, 1417
1426. Radegran, G., Blomstrand, E., Saltin, B., 1999. Peak muscle perfusion and oxygen uptake in humans: importance of precise estimates of muscle mass. J. Appl. Physiol. 87, 2375
2380. Redlarski, G., Palkowski, A., Krawczuk, M., 2016. Body surface area formulae: an alarming ambiguity. Sci. Rep. 6, 27966. Redman, L.M., Kraus, W.E., Bhapkar, M., Das, S.K., Racette, S.B., Martin, C.K., et al., 2014. Energy requirements in nonobese men and women: results from CALERIE. Am J Clin Nutr. 99, 71
78. Reid, D.M., 1986. Measurement of bone mass by total body calcium: a review. J. R. Soc. Med. 79, 33
37. Reilly, J.J., 2010. Assessment of obesity in children and adolescents: synthesis of recent systematic reviews and clinical guidelines. J. Hum. Nutr. Diet. 23, 205
211. Rennie, M.J., Hundal, H.S., Babij, P., MacLennan, P., Taylor, P.M., Watt, P.W., et al., 1986. Characteristics of a glutamine carrier in skeletal muscle have important consequences for nitrogen loss in injury, infection, and chronic disease. Lancet 2, 1008
1012. Rhodes, J., Clay, C., Phillips, M., 2013. The surface area of the hand and the palm for estimating percentage of total body surface area: results of a meta-analysis. Br. J. Dermatol. 169, 76
84. Rodea-Montero, E.R., Evia-Viscarra, M.L., Apolinar-Jime´nez, E., 2014. Waist-to-height ratio is a better anthropometric index than waist circumference and BMI in predicting metabolic syndrome among obese Mexican adolescents. Int. J. Endocrinol. 2014, 195407. Rolfe, D.F., Brown, G.C., 1997. Cellular energy utilization and molecular origin of standard metabolic rate in mammals. Physiol. Rev. 77, 731
758. Rosen, E.D., Spiegelman, B.M., 2014. What we talk about when we talk about fat. Cell 156, 20
44. Roubenoff, R., Kehayias, J.J., Dawson-Hughes, B., Heymsfield, S., 1993. Use of dual-energy x-ray absorptiometry in body-composition studies: not yet a “gold standard”. Am. J. Clin. Nutr 58, 589
591. Ryde, S.J., Birks, J.L., Morgan, W.D., Evans, C.J., Dutton, J., 1993. A five-compartment model of body composition in healthy subjects assessed using in vivo neutron activation analysis. Eur. J. Clin. Nutr. 47, 863
874. Sargeant, A.J., 2007. Structural and functional determinants of human muscle power. Exp. Physiol. 92, 323
331.

Human Body Composition Chapter | 1

Schlich, E., Schumm, M., Schlich, M., 2010. 3-D-body-scan als anthropometrisches Verfahren zur Bestimmung der spezifischen Ko¨rperoberfla¨che (3-D body scan as an anthropometric method for determining the specific body surface area). Erna¨hrungs Umschau 57, 178
183 (in Dutch). Schneider, H.J., Friedrich, N., Klotsche, J., Reper, L., Nauck, M., John, U., et al., 2010. The predictive value of different measures of obesity for incident cardiovascular events and mortality. J. Clin. Endocrinol. Metab. 95, 1777
1785. Schweitzer, L., Geisler, C., Pourhassan, M., Braun, W., Glu¨er, C.C., Bosy-Westphal, A., et al., 2015. 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. 102, 58
65. Seale, P., Lazar, M.A., 2009. Brown fat in humans: Turning up the heat on obesity. Diabetes 58, 1482
1484. Sendroy Jr, J., Cecchini, lP., 1959. Indirect estimation of body surface area and volume. J. Appl. Physiol. 14, 1000
1004. Shen, W., Wang, Z., Punyanita, M., Lei, J., Sinav, A., Kral, J.G., et al., 2003. Adipose tissue quantification by imaging methods: a proposed classification. Obes. Res. 11, 5
16. Shen, W., Punianytya, M., Silva, A.M., Chen, J., Gallagher, D., Sardinha, L.B., et al., 2009. Sexual dimorphism of adipose tisssue distribution across the life span: a cross-sectional whole-body magnetic resonance imaging study. Nutr. Metab. (Lond). 6, 17. Shuter, B., Asiani, A., 2000. Body surface area: DuBois and DuBois revisited. Eur. J. Appl. Physiol. 82, 250
254. Silva, A.M., Wang, J., Pierson Jr., R.N., Wang, Z., Spivack, J., Allison, D.B., et al., 2007. Extracellular water across the adult lifespan: reference values for adults. Physiol. Meas. 28, 489
502. Sir Isaac Newton, 1687. Philosophiae Naturalis Principia Mathematica. Siri, W.E., 1956. The gross composition of the body. Adv. Biol. Med. Phys. 4, 239
280. Siri, W.E., 1961. Body composition from fluid spaces and density. In: Brozek, J., Henschel, A. (Eds.), Techniques for Measuring Body Composition. National Academy of Sciences, Washington, DC, USA, pp. 223
244. Smith, S.M., Wastney, M.E., O’Brien, K.O., Morukov, B.V., Larina, I. M., Abrams, S.A., et al., 2005. Bone markers, calcium metabolism, and calcium kinetics during extended-duration space flight on the mir space station. J. Bone. Miner. Res. 20, 208
218. Snyder, W.S., Cook, M.J., Nasset, E.S., Karhausen, L.R., Howells, G.P., Tipton, I.H., 1984. Report of the task group on Reference Man Oxford, Pergamon Press. Soto Gonza´lez, A., Bellido, D., Bun˜o, M.M., Pe´rtega, S., De Luis, D., Martı´nez-Olmos, M., et al., 2007. Predictors of the metabolic syndrome and correlation with computed axial tomography. Nutrition 23, 36
45. Stephenson, M.C., Leverton, E., Khoo, E.Y., Poucher, S.M., Johansson, L., Lockton, J.A., et al., 2013. Variability in fasting lipid and glycogen contents in hepatic and skeletal muscle tissue in subjects with and without type 2 diabetes: a 1H and 13C MRS study. NMR Biomed. 26, 1518
1526. St-Onge, M.P., Wang, J., Shen, W., Wang, Z., Allison, D.B., Heshka, S., et al., 2004. Dual-energy x-ray absorptiometrymeasured lean soft tissue mass: differing relation to body cell mass across the adult life span. J. Gerontol. A. Biol. Sci. Med. Sci. 59, 796
800.

25

Thom, D., 2017. Appraising current methods for preclinical calculation of burn size - A pre-hospital perspective. Burns. 43, 127
136. Timmons, J.A., Wennmalm, K., Larsson, O., Walden, T.B., Lassmann, T., Petrovic, N., et al., 2007. Myogenic gene expression signature establishes that brown and white adipocytes originate from distinct cell lineages. Proc. Natl. Acad. Sci. U S A. 104, 4401
4406. Tothill, P., Stewart, A.D., 2002. Estimation of thigh muscle and adipose tissue volume using magnetic resonance imaging and anthropometry. J. Sports Sci. 20, 563
576. Vague, J., 1996. The degree of masculine differentiation of obesities: a factor determining predisposition to diabetes, atherosclerosis, gout, and uric calculous disease. Obes. Res. 4, 204
212. Van Itallie, T.B., Yang, M.U., Heymsfield, S.B., Funk, R.C., Boileau, R. A., 1990. Height-normalized indices of the body’s fat-free mass and fat mass: potentially useful indicators of nutritional status. Am. J. Clin. Nutr. 52, 953
959. Verbraecken, J., Heyning, P.V., Backer, W.D., Gaal, L.V., 2006. Body surface area in normal weight, overweight and obese adults. A comparison study. Metabolism Clin. Exp. 55, 515
524. Wald, D., Teucher, B., Dinkel, J., Kaaks, R., Delorme, S., Boeing, H., et al., 2012. Automatic quantification of subcutaneous and visceral adipose tissue from whole-body magnetic resonance images suitable for large cohort studies. J. Magn. Reson. Imaging. 36, 1421
1434. Wang, Z.M., Pierson Jr., R.N., Heymsfield, S.B., 1992. The five-level model: a new approach to organizing body-composition research. Am. J. Clin. Nutr. 56, 19
28. Wang, Z.M., Deurenberg, P., Guo, S.S., Pietrobelli, A., Wang, J., Pierson Jr., R.N., et al., 1998. Six-compartment body composition model: inter-method comparisons of total body fat measurement. Intern. J. Obesity 22, 329
337. Wang, Z., Deurenberg, P., Wang, W., Pietrobelli, A., Baumgartner, R. N., Heymsfield, S.B., 1999. Hydration of fat-free body mass: new physiological modeling approach. Am. J. Physiol. 276, E995
E1003. Wang, Z.M., O’Connor, T.P., Heshka, S., Heymsfield, S.B., 2001. The reconstruction of Kleiber’s law at the organ tissue level. J. Nutr 131, 2967
2970. Wang, Z., Pi-Sunyer, F.X., Kotler, D.P., Wielopolski, L., Withers, R.T., Pierson Jr., R.N., et al., 2002. Multicomponent methods: evaluation of new and traditional soft tissue mineral models by in vivo neutron activation analysis. Am. J. Clin. Nutr. 76, 968
974. Wang, Z., Shen, W., Kotler, D.P., Heshka, S., Wielopolski, L., Aloia, J. F., et al., 2003. Total body protein: a new cellular level mass and distribution prediction model. Am. J. Clin. Nutr. 78, 979
984. Wang, Z.M., St-Onge, M.P., Lecumberri, B., Pi-Sunyer, F.X., Heshka, S., Wang, J., et al., 2004. Body cell mass: model development and validation at the cellular level of body composition. Am. J. Physiol. 286, E123
E128. Wang, Z., Heymsfield, S.B., Pi-Sunyer, F.X., Gallagher, D., Pierson Jr., R.N., 2008. Body composition analysis: Cellular level modeling of body component ratios. Int. J. Body Compos. Res. 6, 173
184. Wang, K., Hoshino, Y., Dowdell, K., Bosch-Marce, M., Myers, T.G., Sarmiento, M., et al., 2017. Glutamine supplementation suppresses herpes simplex virus reactivation. J. Clin. Invest. 127, 2626
2630.

26

SECTION | I Skeletal Muscle Morphology

Watson, P.E., Watson, I.D., Batt, R.D., 1980. Total body water volumes for adult males and females estimated from simple anthropometric measurements. Am. J. Clin. Nutr. 33, 27
39. Westerterp, K.R., 2013. Physical activity and physical activity induced energy expenditure in humans: measurement, determinants, and effects. Front. Physiol. 4, 90. Westerterp, K.R., 2017. Exercise, energy expenditure and energy balance, as measured with doubly labelled water. Proc. Nutr. Soc. 77, 4
10. Withers, R.T., LaForgia, J., Heymsfield, S.B., Wang, Z.M., Pillans, R. K., 1996. Two-, three- and four-compartment chemical models of

body composition analysis. In: Norton, K.I., Olds, T.S. (Eds.), Anthropometrica. UNSW, Sydney, Australia, pp. 199
231. Withers, R.T., LaForgia, J., Pillans, R.K., Shipp, N.J., Chatterton, B.E., Schultz, C.G., et al., 1998. Comparisons of two-, three-, and fourcompartment models of body composition analysis in men and women. J. Appl. Physiol. (1985) 85, 238
245. Yu, C.Y., Lo, Y.H., Chiou, W.K., 2003. The 3D scanner for measuring body surface area: a simplified calculation in the Chinese adult. Appl. Ergon. 34, 273
278.