
Gibson RS1
Principles of Nutritional
Assessment: Body Composition
3rd Edition
February 2023
Abstract
Most anthropometric methods used to assess body composition are based on the two componment model whereby the body consists of fat and fat-free mass. These two body components can be assessed indirectly from selected skinfold thickness and circumference measurements taken by standardized techniques. Several methods exist for estimating percentage body fat and/or total body fat. In the simplest method, skinfold thickness measurements, either singly or in combination, are used to assess body fat (as % or total). Alternatively, percentage body fat can be predicted from adiposity equations matched to the measured skinfolds and study population (by age, gender, ethnicity, activity level, etc). Arm-fat area, calculated from triceps skinfold thickness and mid-upper arm circumference (MUAC), is also used as a proxy for total body fat, although the equation used has some limitations. Both WHO international and population-specific reference data are available for triceps and subscapular skinfolds for children. Arm-fat area data are more limited, although data for U.S. children (1-20y) from the CDC2000 BMI growth chart sample have been compiled. Anthropometric variables from multiple anatomical sites are also used to estimate body density, from which the percentage of body fat, and subsequently, total body fat is calculated. The reliability of this method has been questioned based on comparisons of the derived percentage body fat estimates against those generated using the in vivo gold standard 4-component model which does not rely on any theoretical assumptions. Corrections that account for age, sex, disease state or nutritional status can now be applied to the density-based formulae and/or the empirical equations used to relate fat content to body density, and thus improve the final assessment of body composition. Recognition of the link between the distribution of body fat and risk of cardiovascular disease has prompted use of waist-hip ratio (WHR), and more recently, waist circumference (WC), as practical anthropometric surrogates for intra-abdominal visceral fat. Population-specific cutoffs for adults have been set to denote high WHR or WC indicative of abdominal obesity and cardiovascular risk. Increasingly, WC is being included along with BMI in all obesity surveillance studies. Fat-free mass can be estimated as body weight (kg) minus body fat from the adiposity or density-based methods outlined above. Alternatively, simpler methods include the measurement of MUAC, either alone or combined with triceps skinfold thickness to calculate arm mid-upper-muscle circumference (MUAMC) or arm muscle area (AMA). MUAC alone is used in emergencies to screen for severe acute malnutrition (SAM), whereas MUAMC and AMA can be used as proxies for muscle mass, and thus for the detection of sarcopenia in the elderly. AMA is preferable to MUAMC because it more adequately reflects the true magnitude of tissue changes. Calf circumference and hand grip strength as surrogate markers of skeletal muscle mass and strength respectively, are increasingly being used, in part because loss of both muscle mass and strength has been associated with several adverse health outcomes. Both measurements are recommended by the Asian and European Working Groups on Sarcopenia to identify at risk older adults. The measurements are also used in children and athletes to assess physical fitness. Adiposity has been identified as a confounder of calf circumference measurements, and BMI adjustment factors have been developed. Low calf circumference with any BMI can now be identified. Population-specific calf circumference cutoff values are available to detect low muscle mass in adults. Handgrip strength is said to be a better predictor of functional health outcomes than low muscle mass, and has been linked with alteration in physical performance in cross-sectional studies. Associations with all-cause mortality, cardiovascular mortality, and hospital readmissions have also been observed in prospective cohort studies. Hand grip strength is measured with a calibrated handheld dynamometer. The measurements depend on the model used, but dynamometer-specific cutoffs values are not yet applied. Current cutoffs for weak muscle strength vary across regions; population specific normative reference data are available for the elderly and across the life course. Finally, the cross-sectional nature of the normative reference data compiled for the anthropometric variables discussed limits their use for monitoring the trajectories of individuals and the degree to which causal and age-related inferences can be drawn. None of the anthropometric variables are sensitive enough to monitor small changes in body fat or fat-free mass that may arise after short-term nutritional support or deprivation. CITE AS: Gibson RS. Principles of Nutritional Assessment: Body Composition. https://nutritionalassessment.org/bodycomposition/Email: Rosalind.Gibson@Otago.AC.NZ
Licensed under CC-BY-SA-4.0
11.0 Anthropometric assessment of body composition
Most anthropometric methods used to assess body composition are based on a model in which the body consists of two chemically distinct components: fat and the fat-free mass, with the principle that if one of these components is determined, the other can be estimated. The amount and distribution of both body fat and the fat-free mass have important health outcomes in infants, children, and adults. Fat is the main long-term storage form of energy in the body, and alterations in body fat content provide indirect estimates of changes in energy balance. Most of the body fat is stored in adipose tissue, which is distributed in different proportions throughout the body. The pattern of distribution of adipose tissue is dependent on many factors including sex, age, race/ethnicity, genotype, diet, physical activity, and hormone levels. Adipose tissue is traditionally distributed into two main components, each with different metabolic characteristics: subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). Of the two components, visceral adipose tissue is a hormonally active component of total body fat tissue and an independent risk marker of cardiovascular and metabolic morbidity and mortality. Abnormally high deposition of VAT is known as visceral obesity (Neeland et al., 2019). Fat may also be present in areas of the body where fat is not physiologically stored, such as liver, pancreas, heart, and skeletal muscle. Fat surrounding these organs is termed ectopic fat, and its deposition might contribute to increased risk of atherosclerosis and type 2 diabetes (Neeland et al., 2019). The mechanisms whereby an excess of VAT is related to various health outcomes, as well as the tendency to deposit adipose tissue in ectopic depots are not fully understood; see Neeland et al., 2019 for more details. The fat-free mass consists of the skeletal muscle, non-skeletal muscle, organs, connective tissue, total body water, and the skeleton. Muscle is a major component of the fat-free mass and the primary site for glucose uptake and storage, as well as a reservoir of amino acids stored as protein. Loss of muscle mass is associated with several negative health outcomes, including delayed recovery from illness, slowed wound healing, reduced metabolic rate, and physical disability (Argilés et al., 2016). Patients at high risk of losing muscle are those who are experiencing weight loss through diseases or conditions associated with inflammatory components, and malnutrition (Cruz-Jentoft et al., 2019). Risk of death during infections is exacerbated by the loss of muscle mass in malnourished children (Briend et al., 2015). Aging may also lead to a loss of muscle mass. This condition is known as sarcopenia, and results in diminished quality of life, greater susceptibility to infection, and an increased risk of mortality (Deutz et al., 2019). The anthropometric measurements of body composition are fast, noninvasive, and require the minimum of equipment compared to laboratory techniques. Consequently, anthropometry has been the method most frequently used in the past in both routine clinical and public health settings. Increasingly, however, recognition of the limitations of anthropometry to assess body composition has led to the use of alternative laboratory methods to assess body composition such as bioelectrical impedance analysis (BIA) and dual energy X-ray absorptiometry (DXA) in these settings (Howell et al., 2018; Schubert et al., 2019). See Chapter 14 for details of these laboratory methods. In clinical practice, indices of body composition can be used to identify patients with chronic under‑ or overnutrition and to monitor long-term changes in body composition during nutritional support. In public health, they can identify individuals who are vulnerable to under‑ or overnutrition and help evaluate the effectiveness of nutrition intervention programs. Details of the standardized procedures used for anthropometric measurements of body composition, and the derivation of the more important indices and their limitations are summarized in this chapter and are given in detail in Lohman et al. (1988). Variability in body composition across populations associated with lifestyle, environment, genetics, and ethnicity has emphasized the need for population-specific reference data to interpret body composition indices (Wells, 2019). Hence, where appropriate, the interpretive criteria available for the assessment of body composition based on anthropometry, are also summarized. For a review of the methods used to develop reference values and cutoff points, the reader is advised to consult Chapter 1.11.1 Assessment of body fat
The body fat content is the most variable component of the body, differing among individuals of the same sex, height, and weight. Estimates of total body fat, together with the rate of change in the body fat content, are often used to assess the presence and severity of undernutrition. A large and rapid loss of body fat is indicative of severe negative energy balance. Small changes in body fat (i.e., < 0.5kg), however, cannot be measured accurately using anthropometry. On average, the fat content of women is higher than that of men, representing 26.9% of their total body weight compared with 14.7% for men (Table 11.1).Fat location | man | woman |
---|---|---|
Essential fat (lipids of the bone marrow, central nervous systems, mammary glands and other organs) | 2.1 | 4.9 |
Storage fat (depot) | 8.2 | 10.4 |
Subcutaneous | 3.1 | 5.1 |
Intermuscular | 3.3 | 3.5 |
Intramuscular | 0.8 | 0.6 |
Fat of thoracic and abdominal cavity | 1.0 | 1.2 |
Total fat | 10.5 | 15.3 |
Body weight | 70.0 | 56.8 |
Percentage fat | 14.7 | 26.9 |

11.1.1 Skinfold thickness measurements
Skinfold thickness measurements provide an estimate of the size of the subcutaneous fat depot, which, in turn, has been used to derive an estimate of total body adiposity. Such an estimate is based on seven assumptions shown in Figure 11.1, most of which are not true. For example, the relationship between subcutaneous and internal fat is nonlinear and varies with body weight and age: very lean subjects have a smaller proportion of body fat deposited subcutaneously than obese subjects. Moreover, variations in the distribution of subcutaneous fat occur with sex, race or ethnicity, and age (Wagner & Heyward, 2000). For a detailed discussion of the limitations of each of the seven assumptions depicted in Figure 11.1, see Provyn et al. (2011).
- Triceps skinfold is measured at the midpoint of the back of the upper arm (Figure 11.2).
- Biceps skinfold is measured as the thickness of a vertical fold on the front of the upper arm, directly above the center of the cubital fossa, at the same level as the triceps skinfold.
- Subscapular skinfold is measured below and laterally to the angle of the shoulder blade, with the shoulder and arm relaxed. Placing the subject's arm behind the back may assist in identification of the site. The skinfold should angle 45° from horizontal, in the same direction as the inner border of the scapula (i.e., medially upward and laterally downward) (Figure 11.3A).
- Suprailiac skinfold is measured in the midaxillary line immediately superior to the iliac crest. The skinfold is picked up obliquely just posterior to the midaxillary line and parallel to the cleavage lines of the skin (Figure 11.3B).
- Midaxillary skinfold is picked up horizontally on the midaxillary line, at the level of the xiphoid process.

Measurement of triceps skinfold
The measurement of the triceps skinfold is performed at the midpoint of the upper right arm, between the acromion process and the tip of the olecranon, with the arm hanging relaxed. To mark the midpoint, the right arm is bent 90° at the elbow, and the forearm is placed palm down across the body. Then the tip of the acromion process of the shoulder blade at the outermost edge of the shoulder and the tip of the olecranon process of the ulna are located and marked. The distance between these two points is measured using a non-stretchable tape, and the midpoint is marked with a soft pen or indelible pencil, directly in line with the point of the elbow and acromion process (Figure 11.2). The right arm is then extended so that it is hanging loosely by the side. The examiner grasps a vertical fold of skin plus the underlying fat, 2cm above the marked midpoint, in line with the tip of the olecranon process, using both the thumb and forefinger. The skinfold is gently pulled away from the underlying muscle tissue, and then the caliper jaws are applied at right angles, exactly at the marked midpoint (Figure 11.5). The skinfold remains held between the fingers while the measurement is taken.
Precision of skinfold measurements
Within-examiner and between-examiner measurement errors can occur when measuring skinfolds, particularly for subjects with flabby, easily compressible tissue or with very firm tissue that is not easily deformed (Lukaski, 1987). Errors may also occur when measuring skinfolds in obese subjects (Forbes et al., 1988). Within-examiner errors can occur when the same examiner fails to obtain identical results on repeated skinfolds on the same subject; such errors are a function of the skinfold site, the experience of the examiner, and the fatness of the subject. Within-examiner measurement errors can be small when measuring triceps skinfolds, provided that training in standardized procedures is given; the errors in these circumstances typically range from 0.70‑0.95mm (Table 11.2).Skinfold measurement | no. of studies | Mean (mm) | Range (mm) |
---|---|---|---|
Within-observer TEM | |||
Biceps | 3 | 0.17 | 0.1–0.2 |
Triceps | 21 | 0.84 | 0.1–3.7 |
Subscapular | 19 | 1.26 | 0.1–7.4 |
Suprailiac | 10 | 1.16 | 0.1–3.2 |
Between-observer TEM | |||
Biceps | 8 | 0.84 | 0.2–2.1 |
Triceps | 28 | 1.06 | 0.2–4.7 |
Subscapular | 28 | 1.21 | 0.1–3.3 |
Suprailiac | 11 | 2.28 | 0.3–6.4 |
Skinfold measurement | Range (mm) |
---|---|
Within-examiner TEM | MGRS teams |
Triceps | 0.39-0.61 |
Subscapular | 0.29-0.41 |
Between-examiner TEM | |
Triceps | |
Longitudinal | 0.50-0.83 |
Cross-sectional | 0.46-0.85 |
Subscapular | |
Longitudinal | 0.42-0.69 |
Cross-sectional | 0.44-0.62 |
Trainee-trainer difference | |||
---|---|---|---|
Measurement | Good | Fair | Poor |
Height or length (mm) | 0–5 | 6–9 | 10–19 |
Weight (kg) | 0–0.1 | 0.2 | 0.3–0.4 |
Arm circ. (mm) | 0–5 | 6–9 | 10–19 |
skinfolds (any) (mm) | 0–0.9 | 1.0–1.9 | 2.0–4.9 |
Interpretive criteria for triceps and sub-scapular skinfolds
The WHO included triceps and subscapular skinfold thickness measurements in the construction of the Multicenter Child Growth Standard (MCGS) for young children aged 0‑5y. Children from six diverse countries (Brazil, China, India, Norway, Oman, and the USA) were included. To reduce the impact of environmental variation, only privileged healthy populations were selected (See Chapters 9 and 10 for more details). Charts based on sex-specific percentiles and Z‑scores for triceps-for-age (WHO MCGS Triceps) and subscapular-for-age (WHO MCGS Subscapular) are available for children 3mos‑5y. Details of the standardized methods used and the development of these reference data are available (de Onis et al., 2004). Age‑ and sex-standardized percentile reference curves for triceps and subscapular skinfold thicknesses have also been compiled for children of varying ages in several high-income countries (e.g., U.S., Spain, Poland) (Addo & Himes, 2010; Moreno et al., 2007; Jaworski et al., 2012). In the United States numerical data for the smoothed percentiles for triceps and subscapular skinfolds for U.S. girls and boys aged 1.50‑19.99y are available in Addo and Himes (2010). These reference data are based on the same population of children and adolescents used to construct the CDC 2000 growth curves for BMI-for-age (Kuczmarski et al., 2000a). Serrano et al. (2015) have cautioned the use of these U.S. skinfold percentiles for interpreting skinfolds from Hispanic American children and adolescents because schoolchildren from Spain, Argentina, Cuba, Venezuela and Mexico were found to have higher triceps and subscapular percentiles than those of the CDC reference (Addo & Himes, 2010; Kuczmarski et al., 2000a). Instead, Serrano et al. (2015) recommend using their triceps and subscapular skinfolds reference values for Hispanic American children. Increasingly, reference data based on anthropometric measures of adiposity based on skinfolds are becoming available from low and middle-income countries. Khadilkar et al. (2015) have published reference percentiles for triceps skinfold thickness for Indian children aged 5‑17y, whereas Pandey et al. (2008) provide percentiles for both triceps and subscapular skinfolds for urban Asian Indians aged 14‑18y. Again, these percentiles differed and were higher than those recorded for U.S. children. Even infants in South Asia appear to have subscapular skinfolds at birth that are higher than those for comparable birthweight Caucasian babies, despite having other body measurements that are smaller (Yajnik et al., 2003). Age‑ and sex-standardized percentile reference curves for triceps and subscapular skinfold thicknesses are especially useful in remote emergency settings, in bed-bound hospitalized patients, and when other medical conditions are present that preclude the evaluation of weight, height, and body composition (Heymsfield & Stevens, 2017).11.1.2 Assessing body fat with skinfolds
Skinfold measurements at a single or multiple sites can be used to estimate total body fat or percentage body fat. Calculation of percentage body fat is based on the assumption that fat mass is adjusted for body weight, even though percentage body fat is not fully independent of body size (Wells, 2014). Furthermore, high values for percentage body fat might reflect either high fat mass or low fat-free mass, as noted earlier (Wells, 2019). If a single skinfold measurement approach is used, it is critical to select the skinfold site that is most representative of the whole subcutaneous fat layer, because subcutaneous fat is not uniformly distributed about the body. Unfortunately, the most representative site is not the same for both sexes, nor is it the same for all ages, ethnicities, or degree of adiposity. Hence, it is not surprising that there is no general agreement as to the best single skinfold site as an index of total body fat. In the past, the triceps skinfold thickness has been the site most frequently selected by nutritionists for a single, indirect estimate of body fat. To account for the differing distribution of sub&sny;cutaneous fat, investigators often recommend taking one limb skinfold (right triceps) and one body skinfold measurement (right subscapular). For example, persons of African descent tend to have less subcutaneous fat in the extremities than in the trunk relative to Caucasians, irrespective of age and athletic status (Wagner & Heyward, 2000). More than 100 formulae have been developed to estimate percentage body fat from skinfold thickness measurements alone. The formulae have been established across varying populations, using numerous protocols with deviations in the skinfold sites measured (Lohman et al, 1988). Unfortunately, discrepancies have been reported when different formulae are applied on the same set of individuals. This finding has led to the proposal that the sum of skinfold sites (in mm) (preferably using eight sites) may provide a more accurate and reliable outcome of body fat than using an indirect method based on anthropometric-based prediction formulae (Kasper et al., 2021). The measurement of multiple skinfolds and not just a single skinfold to estimate body fat is particularly advisable when individuals are undergoing rapid and pronounced weight gain. Changes in the energy balance are known to alter the rate of fat accumulation differently among skinfold sites (Heymsfield et al., 1984)11.1.3 Body adiposity index
The body adiposity index (BAI) is a surrogate measure of adiposity which is calculated as: \[ \small \mbox{BAI (%fat)} = \frac {\mbox{Hip circumference}}{\mbox{(Height)}^{1.5}} − \mbox{18}\] The body adiposity index (BAI) was developed by Bergman et al. (2011) in part as a result of the inability of BMI to distinguish between fat and fat-free mass. Several studies of adults have reported positive correlations of BAI with BMI and waist circumference (Nickerson et al., 2015). Further, BAI has been validated as a measure of percentage body fat against both DXA (Johnson et al., 2012; Sun et al., 2021; Nickerson et al., 2015), and more recently the 4‑component model (Fedewa et al., 2019). The 4‑component model is considered the gold standard criterion method for measuring percentage body fat because the technique reduces the need for theoretical assumptions when calculating body composition outputs; information on body weight, body volume, total body water (TBW) and bone mineral mass are each collected separately (Wells, 2014). Nevertheless, results on the relative accuracy of BAI as a surrogate indicator of percentage body fat have been inconsistent, and appear to be dependent on sex (Johnson et al., 2012; Fedewa et al., 2019), race-ethnicity (Johnson et al., 2012; Ramírez-Vélez et al., 2016), level of adiposity (Bergman et al., 2011; Johnson et al., 2012; Sun et al., 2021) and activity level (Esco, 2013). More research employing prospective studies are needed to establish the predictive ability of BAI for various health outcomes in differing population groups.11.1.4 Arm-fat area
The calculated cross-sectional area of arm fat, derived from skinfold thickness and arm circumference measurements, has been used as an index of total body fat, especially in emergency settings. Arm-fat area correlates more significantly with total body fat (i.e., fat weight) than does a single skinfold thickness at the same site. In contrast, the estimation of percentage of body fat from arm fat area is no better than the corresponding estimation from the skinfold measurement, particularly in males (Himes et al., 1980). The advantage of using arm-fat area to estimate body fat is expected; more fat is needed to cover a large arm with a given thickness of subcutaneous fat than to cover a smaller arm with the same thickness of fat. Subcutaneous fat, however, is not evenly distributed around the limbs or trunk. For example, triceps skinfolds are consistently larger than the corresponding biceps skinfold, and, as a result, either the sum or the average of these should theoretically be used for the calculation of mid-upper-arm-fat area (Himes et al., 1980). In practice, however, limb fat area reference data are only available based on triceps skinfold and mid-upper-arm circumference (MUAC) measurements (Frisancho, 1990; Addo et al., 2017). Trained examiners using standardized techniques should be used for these measurements; for details see Sections 11.1.1 and 11.2.1.Calculation of arm-fat area
The equation for calculating mid-upper-arm-fat area is: \[ \small \mbox{AFA} = \mbox{(SKF } × \mbox{ MUAC/2)} − (π × \mbox{ (SKF})^{2}/4)\] where AFA = mid-upper-arm-fat area (mm2), MUAC = mid-upper-arm circumference (mm), and SKF = triceps skinfold thickness (mm). This equation is based on several assumptions, each of which may result in inaccuracies, leading to an underestimate of the degree of adiposity (Rolland-Cachera et al., 1997). The equation assumes that the limb is cylindrical, with fat evenly distributed about its circumference, and also makes no allowance for variable skinfold compressibility. This compressibility probably varies with age, sex, and site of the measurement, as well as among individuals, and is a source of error in population studies when equal compressibility of skinfolds is assumed. Arm-fat areas calculated from this equation were reported to agree within 10% to values measured by computerized axial tomography on normal weight adults. However, for obese subjects, differences were greater than 50% (Heymsfield et al., 1982). A correction for skinfold compressibility may be advisable in future studies. A simplified index has also been proposed, the upper-arm-fat estimate (UFE in cm2) \[ \small \mbox{UFE} = \mbox{MUAC } × \mbox{ TSF/2}\] This equation was validated by comparing arm-fat areas assessed by magnetic resonance imaging (MRI) and anthropometry in 11 obese and 17 control children. Both the traditional upper-arm-fat area and the upper-arm-fat estimate (UFE) were calculated. Results indicated that the UFE measurements were close to the MRI estimates (Rolland-Cachera et al., 1997). Nevertheless, this simplified index has had limited use.Interpretive criteria
Reference data for mid-upper-arm-fat area were compiled by Frisancho (1981) from the earlier NHANES I survey (1971‑1974) when smoothing techniques were not applied. More recently, Addo et al. (2017) have derived sex-specific percentile curves for arm fat area based on a wider age range of U.S children (i.e., 1‑20y) who were also included in the development of the CDC 2000 BMI growth charts (Kuczmarski et al., 2000a). For these charts, the measurements for all children between 1963 and 1994 were included, with the exception of those > 6y of age who were measured between 1988 and 1994. These children were excluded because of the rising prevalence of obesity (Kuczmarski et al., 2002). Figure 11.6 presents the arm-fat area percentiles for female US children and adolescents aged 1‑20y.
11.1.5 Calculation of body fat from anthropometric variables via body density
Measurements of anthropometric variables from multiple anatomical sites, including skinfolds, are also used to estimate body density from which the percentage of body fat, and subsequently total body fat are calculated. The method involves:- Determination of appropriate skinfolds and other anthropometric measurements for the prediction of body density; the selection of the sites depends on the age, sex, ethnicity/race, and population group under investigation
- Calculation of body density, using an appropriate prediction equation
- Calculation of percentage of body fat from body density using an empirical densitometric equation
- Calculation of total body fat and/or the fat-free mass:
C1 = 4.950, C2 = 4.500 (Siri,
1961)
C1 = 4.570, C2 = 4.142 (Brožek et al.
1963)
C1 = 5.548, C2 = 5.044 (Rathburn & Pace,
1945)
All assume the density of fat and the fat-free mass are constant by age and sex. Siri (1961) assumed that the densities of fat and the fat-free mass are 0.90 and 1.10kg/L respectively. Brožek et al. (1963) and Rathburn & Pace (1945) used the concept of a reference man of a specified density and composition. These equations came from the chemical analysis of a few adult cadaver dissections, animal data, and indirect estimates of fat-free mass in human subjects (Siri, 1961; Brožek et al., 1963; Heymsfield et al., 1991).
None of these classical empirical equations relating fat content to body density, however, are suitable in adult patients in whom the composition of fat-free mass may be abnormal. This will include patients undergoing hyperalimentation with high-sodium fluids, or with congestive heart failure or liver disease, as total body water content as a fraction of fat-free mass may be markedly higher in these patients, thus violating the assumption that the water content of the fat-free mass is constant (Heymsfield & Casper, 1987). In these circumstances, the density of fat-free mass is decreased. Not surprisingly, in patients with diseases associated with under-mineralisation, the density of fat-free mass is also decreased. Consequently, in all these patients, fatness will be overestimated (Wells & Fewtrell, 2006). More recent research has raised concerns over the assumption of constant properties for hydration and density of fat-free mass when these classical empirical equations are applied to assess body composition not only in patients with certain diseases, but also in healthy children and adolescents, the elderly, and those with obesity. Although fat has relatively uniform properties throughout the life course (zero water and a density of 0.9007kg/L), fat-free mass, in contrast, has different properties in children compared to adults. This arises because of chemical maturation of the fat-free mass during growth which results in higher levels of water and lower levels of mineral and proteins. Nevertheless, the adult-derived values for the density and hydration of fat-free mass and applied in the classical equations have often been used to study body composition in children. In an effort to improve the accuracy in the estimates of percentage body fat in children and adolescents based on the two-component model, Wells et al. (1999) measured the density and hydration of fat-free mass in children (n=41) aged 8‑12y using the 4‑component model which divides body weight into fat, mineral, and protein and overcomes the limitations associated with the assumptions of constant properties for hydration and fat-free mass density (Table 11.5).Age | Hydration (%) | Density (kg/L) | C1 | C2 |
---|---|---|---|---|
5 | 76.5 | 1.0827 | 5.36 | 4.95 |
6 | 76.3 | 1.0844 | 5.32 | 4.90 |
7 | 76.1 | 1.0861 | 5.28 | 4.86 |
8 | 75.9 | 1.0877 | 5.24 | 4.82 |
9 | 75.7 | 1.0889 | 5.21 | 4.79 |
10 | 75.5 | 1.0900 | 5.19 | 4.76 |
11 | 75.3 | 1.0911 | 5.16 | 4.73 |
12 | 75.2 | 1.0917 | 5.15 | 4.72 |
13 | 75.0 | 1.0920 | 5.14 | 4.71 |
14 | 74.8 | 1.0927 | 5.13 | 4.69 |
15 | 74.4 | 1.0942 | 5.09 | 4.66 |
16 | 74.0 | 1.0960 | 5.05 | 4.61 |
17 | 73.7 | 1.0978 | 5.02 | 4.57 |
18 | 73.5 | 1.0991 | 4.99 | 4.54 |
19 | 73.4 | 1.1000 | 4.97 | 4.52 |
20 | 73.3 | 1.1006 | 4.96 | 4.51 |
11.1.6 Waist-hip circumference ratio
The waist-hip circumference ratio (waist circumference divided by hip circumference) (WHR) is a simple method for distinguishing between fatness in the lower trunk (hip and buttocks) and fatness in the upper trunk (waist and abdomen areas). Lower trunk fatness (i.e., lower waist to hip ratio) is often referred to as “gynoid obesity” because it is more typical of females. Upper trunk or central fatness (higher waist to hip ratio) is called “android obesity” and is more characteristic of males. Nevertheless, obese men and women can be, and often are, classified into either group. The fat depots assessed by the WHR are mainly subcutaneous (external or outer) and visceral (internal or deep). Use of the WHR rose dramatically following several reports confirming that WHR separately or in combination with BMI was associated with increased risk of death, coronary heart disease and type 2 diabetes mellitus (Krotkiewski et al., 1983; Larsson et al., 1984). The application of new laboratory methods including computer tomography and magnetic resonance imaging has led to semi-quantitative estimates of the total fat stored within the abdomen (i.e., intra-abdominal fat). Ashwell et al. (1985) were the first investigators to show highly significant correlations between intra-abdominal fat (visceral adipose tissue) and the ratio of waist-to-hip circumference. Their findings led to the proposal that the metabolic complications of obesity shown to be associated with a high WHR, may be related specifically to the amount of intra-abdominal (visceral) fat. Recently, in a meta-analysis of 21 prospective cohort studies in which waist-hip ratio was measured as an indicator of abdominal obesity, the risk of cardiovascular disease rose continually with the increase in WHR when they exceeded a certain range (Xue et al., 2021). Based on these results the investigators advised that men should keep their WHR below 0.9 to maintain cardiovascular fitness, whereas women should keep their WHR as small as possible within the normal range.
Measurement of waist-hip ratio
WHO (2011) has recommended standardized protocols for the measurements of waist and hip circumference for international use. WHO (2011) considered the following elements when developing the protocols: anatomical placement of the measuring tape, its tightness, and the type of tape used; the subject's posture, phase of respiration, abdominal tension, stomach contents, and clothing. After an extensive review, WHO (2011) concluded that waist circumference should be measured at the midpoint between the tenth rib (i.e., the lowest rib margin) and the top of the iliac crest, using a stretch-resistant tape that provides a constant 100g tension. Hip circumference should be measured around the widest portion of the buttocks, with the tape parallel to the floor. To perform the waist-circumference measurement, the lowest rib margin is first located and marked with a felt tip pen. The iliac crest is then palpated in the midaxillary line and the top of the iliac crest is also marked. An elastic tape can then be applied horizontally at the mid-point between the lowest rib margin and the highest point of the iliac crest: it is tied firmly so that it stays in position around the abdomen about the level of the umbilicus. The elastic tape thus defines the level of the waist circumference, which can then be measured by positioning the stretch-resistant tape over the elastic tape (Jones et al., 1986). Alternatively, a washable marker can be used to landmark the location of the tape. The stretch-resistant tape used for the measurement should provide a constant 100g tension. This can be achieved through the use of a special indicator buckle that reduces differences in tightness. The subject should wear little clothing and be asked to stand erect with feet close together, arms at the side, with their body weight evenly distributed across the feet. The subject should be relaxed and asked to take a few deep, natural breaths. The measurement should be taken at the end of a normal expiration to prevent the subject from contracting their muscles or from holding their breath. The measurement is taken when the tape is parallel to the floor, and the tape is snug, but does not compress the skin. The reading is taken to the nearest millimeter. Each measurement should be repeated twice. If the two measurements are within 1cm of one another, the average should be calculated, but if the difference exceeds 1cm, then the two measurements should be repeated. For the hip circumference measurement, the subject should stand erect with arms at the side and feet together, with body weight equally distributed across the feet. The measurement should be taken with the stretch-resistant tape used for the waist circumference measurement at the point yielding the maximum circumference over the buttocks. The tape must be held parallel to the floor, touching the skin but not indenting the soft tissue. The measurement is taken to the nearest millimeter. Again, each measurement should be taken twice. If the two measurements are within 1cm of one another, the average should be calculated, but if the difference exceeds 1cm, then the two measurements should be repeated. The degree to which factors such as postprandial status, standing position, and depth of inspiration contribute to error in the measurement of waist-hip circumference ratio is uncertain.Interpretive criteria
Bjôrntorp (1987) was the first to suggest that waist-hip ratios > 1.0 for men and > 0.85 for women indicated abdominal fat accumulation and an increased risk of cardiovascular complications and related deaths. Subsequently, many countries and settings have identified sex-specific cutoff points for WHRs, some also recommending ethnically based cutoff points particularly for populations of Asian descent. Generally, most of the cutoffs chosen have been based on disease risk (e.g., cardiovascular disease, type 2 diabetes and risk factors of cardiovascular disease) and on hard outcomes such as mortality. Japan is an exception as their cutoffs are based on assessment of visceral adipose tissue from computerized tomography, and hence on the extent to which measurements predict intra-abdominal fat rather than disease risk (WHO, 2011). Currently, WHO (2011) define abdominal obesity and risk of metabolic consequences as a WHR > 0.90 for men and WHR > 0.85 for women. The cutoffs recommended by the U.S. Department of Health and Human Services for WHR are > 0.95 for men and > 0.80 for women. WHO (2011) emphasize that further studies are needed to establish whether cutoff points for WHRs should be specific to age and ethnicity, given the known ethnic variations in body fat distribution, especially in populations of Asian origin (Wagner & Heyward, 2000; Lear et al., 2010). Further, different contributions of muscle mass and bone structure, as well as stature and abdominal muscle tone, may all lead to different associations between WHR and abdominal fat accumulation.
11.1.7 Waist circumference
Studies have shown that compared with the WHR, waist circumference alone is more strongly associated with the amount of intra-abdominal fat (i.e., visceral fat tissue) (Snijder et al., 2006; Neeland et al., 2019). Moreover, with an increase in waist circumference, there is a corresponding increase of visceral adipose tissue, the fat depot known to convey the strongest health risk. For example, in a large study in 29 countries waist circumference and BMI were measured, and visceral adipose tissue assessed directly using computer tomography (Nazare et al., 2015). A global cardiovascular risk score was also calculated from the sum of eight individual risk factor subscores based on a series of clinical biomarkers, all measured in one laboratory. As shown in Figure 11.10, visceral adipose tissue increased significantly while liver attenuation (inversely correlated with liver fat, a depot of ectopic fat) decreased significantly across the waist circumference tertiles, within each of the three BMI categories. Further, the measured cardiometabolic risk score reflecting the number of cardiometabolic abnormalities, was significantly correlated to visceral adipose tissue, waist circumference, and BMI in men and women.
WC tertiles | |||
---|---|---|---|
T1 | T2 | T3 | |
Men - BMI> | |||
< 25kg/m2 | WC ≤ 84cm 2.1 ± 0.1 | 84
< WC ≤ 90cm 2.5 ± 0.1** | WC > 90cm 2.7 ± 0.1*** |
25kg/m2 to < 30kg/m2 | WC ≤ 95cm 2.7 ± 0.1 | 95 < WC ≤ 101cm 3.3 ± 0.1** | WC > 101cm 3.6 ± 0.1*** |
≥ 30kg/m2 | WC ≤ 108cm 3.7 ± 0.1 | 108 < WC ≤ 116cm 4.1 ± 0.1* | WC > 116cm 4.5 ± 0.1 **† |
Women - BMI> | |||
< 25kg/m2 | WC ≤ 76cm 1.5 ± 0.1 | 76
< WC ≤ 83cm 1.9 ± 0.1** | WC > 83cm 2.7 ± 0.1***††† |
25kg/m2 to < 30kg/m2 | WC ≤ 87cm 2.5 ± 0.1 | 87 < WC ≤ 93cm 3.3 ± 0.1*** | WC > 93cm 3.8 ± 0.1***†† |
≥ 30kg/m2 | WC ≤ 100cm 3.4 ± 0.1 | 100 < WC ≤ 108cm 3.8 ± 0.1 | WC > 108cm 4.6 ± 0.1**†† |
● Insulin resistance
● Impaired glucose tolerance
● Type 2 diabetes
● Cardiovascular disease
○ Hypertension
○ Heart failure
○ Coronary heart disease, myocardial infarctions
○ Valve diseases
○ Arrhythmias
● Respiratory diseases
○ Sleep apnoea
○ Chronic obstructive pulmonary disease
● Brain health
○ Stroke, necrosis
○ Reduced brain size
○ Reduced grey matter
○ Reduced cognitive function
○ Dementia
● Cancers
● Others
○ Reduced bone density
○ Polycystic ovary syndrome
○ HIV infection and
antiretroviral therapy as
both can contribute to the accumulation
of visceral adipose tissue and ectopic fat.
- Visceral adipose tissue has metabolic properties that are distinct from subcutaneous adipose tissue;
- Excess visceral adipose tissue induces inflammation;
- Visceral adipose tissue is a marker of increased ectopic fat deposition (including hepatic and epicardial fat)
Measurement of waist circumference
A consensus on the optimal protocol for the measurement of waist circumference has not yet been reached. Currently two sites are used: (a) at the natural waist, i.e., mid-way between the tenth rib (the lowest rib margin) and the iliac crest (i.e., the superior border of the wing of the ilium), as proposed by WHO (2011) and (b) at the umbilicus level (van der Kooy & Seidell, 1993). In the future, adopting a standard approach by using the protocol described by WHO (2011) and described in Section 11.1.7, is recommended. In this way differences that might exist in absolute waist circumference measurements due to the difference in protocols will be avoided (Ross et al., 2020).Interpretive criteria
Waist circumference cutoffs in adults have been developed as simple surrogate markers to identify the increased risk associated with excess visceral adipose tissue (intra-abdominal fat). Consequently, measurements of waist circumference should be included routinely along with BMI by health practitioners in the evaluation and management of patients with overweight and obesity (Ross et al., 2020). In several countries a single cutoff threshold for white adults (> 102cm for men and > 88cm for women) is currently used to denote a high waist circumference, irrespective of BMI category (Molarius et al., 1999; Health Canada, 2003). These same sex-specific cutoffs have been proposed by WHO (2011). They were based on cross-sectional data in Caucasian adults in whom the specified sex-specific waist circumference cutoffs corresponded to a BMI of 30.0kg/m2, the BMI cutoff designated for obesity. Hence, they were not developed based on the relationship between waist circumference and adverse health risk (Ross et al., 2020). WHO (2011) recognized that population-specific cutoffs may be warranted in view of differences in the level of risk associated with a particular cutoff across populations, depending on levels of obesity and other risk factors for cardiovascular disease and type 2 diabetes. However, they emphasize that further prospective studies using representative populations are needed to understand the genetic and lifestyle factors that may be contributing to the reported regional variations in waist circumference (Lear et al., 2010). Consequently, to date, WHO (2011) have not recommended ethnicity-specific cutoffs for waist circumference. Nevertheless, ethnicity-specific cutoffs for waist circumference for adults have been developed by several investigators (Table 11.7); most have been optimized for the identification of adults with elevated cardiovascular risk, except those for Japanese adults, in whom a visceral adipose tissue volume > 100cm3 was applied (Hiuge-Shimizu et al., 2012).Ethnic Group | Men | Women |
---|---|---|
Japanese | ≥ 85 | ≥90 |
Jordanian | ≥ 98 | ≥ 96 |
Chinese | ≥ 80 | ≥ 80 |
Korean | ≥ 90 | ≥ 85 |
Tuisian | ≥ 85 | ≥ 85 |
Iranian | ≥ 89 | ≥ 91 |
Asian Indian | ≥ 90 | ≥ 80 |
BMI category (kg/m2) | Women | Men |
---|---|---|
Normal weight (18.5‑24.9) | ≥ 80 | ≥ 90 |
Overweight (25‑29.9) | ≥ 90 | ≥ 100 |
Obese I (30‑34.9 ) | ≥ 105 | ≥ 110 |
Obese II and III (≥35 ) | ≥ 115 | ≥ 125 |
WC Cutoffs for Adult Central Obesity | ||
---|---|---|
Males | Females | |
Age (y) | P90cm | P90cm |
6 | 58.7 | 57.9 |
7 | 60.7 | 60.0 |
8 | 62.9 | 62.3 |
9 | 65.3 | 64.9 |
10 | 67.8 | 67.5 |
11 | 70.4 | 70.0 |
12 | 72.8 | 72.2 |
13 | 75.0 | 74.1 |
14 | 77.0 | 75.5 |
15 | 78.8 | 76.5 |
16 | 80.3 | 77.2 |
17 | 81.8 | 77.8 |
18 | 83.2 | 78.4 |
11.2 Assessment of fat-free mass
The fat-free mass consists of the skeletal muscle, non-skeletal muscle, organs, connective tissue, total body water, and the skeleton (Earthman, 2015). Based on the two-component model, once total body fat has been estimated, then fat-free mass can be determined, as shown below: \[ \small \mbox{Total body fat (kg)} = \mbox{body weight (kg) × % body fat / 100} \] \[ \small \mbox{Fat-free mass} = \mbox{body weight (kg) − body fat (kg)} \] Therefore, the limitations outlined earlier when using anthropometric variables to assess percentage body fat based on the two-component model must also be considered when assessing fat-free mass. Recognition of these limitations is especially important when assessing fat-free mass in undernourished children with edema (Wells & Fewtrell, 2006; Girma et al., 2016), and obese subjects (Gutiérrez-Marín et al., 2021). In these circumstances, the assumptions used to convert from raw measurements to final body composition values (see Section 11.1.5) are often violated. Muscle is a major component of the fat-free mass and the primary site for glucose uptake and storage as well as a reservoir of amino acids stored as protein, as noted earlier. Assessment of muscle mass can therefore provide an index of the protein reserves of the body, which can be metabolized during periods of negative nitrogen balance. Loss of muscle mass is an important criterion for the definition of malnutrition (Cederholm et al., 2019), and for the diagnosis of sarcopenia in the elderly. Depleted muscle mass in malnourished children increases risk of mortality during infections (Briend et al., 2015), whereas in older adults, sarcopenia may be associated with several negative outcomes, including falls, fractures, and mobility disorders, cognitive impairments, and mortality (Cruz-Jentoft et al., 2019). Several simple, non-invasive anthropometric measurements based on mid-upper arm circumference (MUAC), either alone (Hu et al., 2021), or in combination with triceps skinfold thickness (i.e., arm-muscle circumference and arm-muscle area), are used as surrogates for muscle mass in both clinical and community settings. All three of these measurements have been shown to correlate with muscle mass assessed by in vivo laboratory-based methods such as bioelectrical impedance analysis (BIA) (Hu et al., 2021), DXA, or computed axial tomography (Heymsfield et al., 1982; Carnevale et al., 2018). As a result, they have also been used to predict changes in protein status in resource-poor settings, provided the changes are not small. More recently, calf circumference and hand grip strength have also been recommended as tools to assess the amount and strength of muscle mass and identify older people at risk for sarcopenia in clinical practice (Chen et al., 2020). These anthropometric measurements and indices derived from them are discussed below.11.2.1 Mid-upper-arm circumference
The arm contains both subcutaneous fat and muscle; a decrease in MUAC may therefore reflect a reduction in either muscle mass or subcutaneous tissue (or both). In some low-income countries, where the amount of subcutaneous fat is often small, changes in MUAC tend to parallel changes in muscle mass and, hence, are sometimes used as in indicators of severe and moderately severe undernutrition in young children (age < 5y) from resource-poor settings. Even in high-income settings, measurement of MUAC is recommended as one of the indicators of pediatric malnutrition by the Academy of Nutrition and Dietetics (AND) and the American Society for Parenteral and Enteral Nutrition (ASPEN) (Becker et al., 2014). MUAC measurements are particularly important for children whose weight may be affected by edema, ascites, or steroids in the lower extremities because of fluid retention (Mehta et al., 2013). Changes in the MUAC measurements can also be used to monitor progress during nutritional therapy. The measurement of MUAC requires a minimal amount of time and equipment, and changes in MUAC are easy to detect. Therefore, increasingly, MUAC is used in emergencies such as famines and refugee crises for screening children with severe acute malnutrition (SAM) or moderate acute malnutrition. In such situations, the measurement of weight or height may not be feasible, and ages of the children are often uncertain (de Onis et al., 1997). In addition, use of a weight-based nutritional assessment (e.g., WLZ) can be misleading in children with SAM who frequently have diarrheal disease accompanied by dehydration, which lowers the weight of a child. Modi et al. (2015) showed that MUAC outperformed weight-for-length Z‑score < −3 to identify SAM in children aged 6‑60mo with diarrhea. The use of fixed cutoffs to distinguish normal and malnourished children assumes that MUAC is relatively independent of age for these children. However, the age independence of MUAC has been questioned (Hall et al., 1993; Bern & Nathanail, 1995; WHO, 1995). This has led to the use of MUAC Z‑scores, that adjust for age and sex differences (Houssain et al., 2017). Research in Somalia suggested improved concordance in prevalence estimates for acute malnutrition using MUAC-for-age Z‑scores rather than MUAC alone (Custodio et al., 2018). However, Leidman et al. (2019) reported that the convergence with weight-for-height Z‑score data when MUAC-for-age Z‑score replaced MUAC alone was limited, based on data from population surveys from 41 countries. They concluded that the additional estimation of age, required when using MUAC-for-age Z‑score, especially in humanitarian settings, was not justified. They urged the need for further research on morbidity and mortality of children with low MUAC-for-age Z‑scores. Measurement of MUAC alone has also been used to detect overweight and obesity in children and adolescents in both low‑ and high-income settings due to the strong relation with body weight. Craig et al. (2014) concluded that MUAC may have potential for clinical and surveillance application as an accurate indicator of overweight and fatness in children and adolescence in place of BMI that requires measurements of both weight and stature. In their study of black South African children age 5‑14y, overweight was defined on the basis of BMI-for-age and overfatness from body fatness via bioelectrical impedance (BIA) estimates of body fatness. Talma et al. (2019) measured MUAC and BMI in children aged 2‑18y from the fifth Dutch Nationwide Growth Study. They also concluded that in studies when weight and stature measurements are impossible, MUAC can be used as an alternative and valid measure for detecting overweight and obesity. Certainly, the results of cross-sectional data from 12 countries representing five major geographic regions of the world suggest that MUAC may be a promising tool for obesity in resource-poor settings (Chaput et al., 2017). A major application of MUAC in older adults is as a surrogate for appendicular skeletal muscle mass, and the subsequent detection of sarcopenia (Pinheiro et al., 2020; Hu et al., 2021). MUAC is recommended because the measurement is less affected by fluid retention compared to the lower extremities, a condition that often occurs in older adults. Sarcopenia is characterized by decreases in muscle mass, as well as strength and function, all of which have multiple adverse health consequences, as noted earlier. Several studies have demonstrated that low MUAC is associated with an increased risk of all-cause mortality in adults (Weng et al., 2018). Some investigators have also used MUAC as a proxy for BMI measurements to classify adults as thin (Tang et al., 2020).Measurement of mid-upper arm circumference
Measurements of MUAC should be made using a flexible, non-stretch tape made of fiberglass or steel; alternatively, a fiberglass insertion tape can be used. The subject should stand erect and sideways to the measurer, with the head in the Frankfurt plane, arms relaxed, and legs apart. If the subject is wearing a sleeved garment, it should be removed or the sleeves should be rolled up. The measurement is taken at the midpoint of the upper arm, between the acromion process and the tip of the olecranon (Figure 11.14).
Interpretive criteria
To classify acute malnutrition in children (6‑59mos), a single MUAC cutoff irrespective of age (i.e., 125mm) is often used, as a proxy for low weight-for-height (i.e., WHZ < −2; wasting) (Leidman et al., 2019). WHO (2009) recommends a MUAC cutoff of < 115mm to diagnose children aged 6‑60mo with severe acute malnutrition (SAM), together with the presence of bilateral pitting edema, where possible. Following treatment, the WHO discharge criteria recommended for children with SAM are a MUAC cutoff of > 125mm and no edema for at least 2wks (WHO, 2009). The U.S Academy of Nutrition and Dietetics (AND) and the American Society for Parenteral and Enteral Nutrition (ASPEN) also recommends MUAC cutoffs to classify bedbound children aged 6‑60mos with undernutrition when measurements of weight and length or height are not feasible. For children classified as severely malnourished, a MUAC cutoff < 115mm is recommended, for moderately malnourished children, a MUAC cutoff of 115‑124mm, and for children at risk of malnutrition, a MUAC cutoff of 125‑134mm (Becker et al., 2014). Chaput et al. (2017) suggest a MUAC cutoff of about 25cm (250mm) for both boys and girls to identify obesity in children 9‑11y, based on their 12 country study data. However, from country-specific analyses, the cutoff value to identify obesity ranged from 23.2cm (boys in South Africa) to 26.2cm (girls in the UK; see Chaput et al. (2017) for more details. There is no consensus on an optimal MUAC cutoff for thinness among adults, with reported cutoff values ranging from 17.0cm to 25.1cm. This discrepancy is in part due to the use of different BMI cutoffs to define thinness (Philpott et al., 2021). Based on a meta-analyses stratified by gender, disease states, and geographies, a cutoff of < 24.0cm (240mm) has been claimed to adequately classify thinness across adult population groups (Tang et al., 2020). Likewise, there is no consensus on the MUAC cutoff to predict low muscle mass and diagnose sarcopenia in adults; cutoffs vary with sex, age, and possibly race-ethnicity. For community-dwelling Chinese adults > 50y, Hu et al. (2021) recommend MUAC cutoffs of < 28.6cm for men and < 27.5cm for women for predicting low muscle mass, and < 27cm for both sexes to identify sarcopenia. This cutoff was developed based on low muscle mass diagnosed using the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria. Whether these cutoffs are applicable for other race-ethnic groups warrants investigation. Concern that use of a fixed MUAC cutoff would over diagnose wasting among younger children and under-diagnose among older children because of the dependence of MUAC on age, as noted earlier, led WHO to recommend the use of MUAC Z‑scores, which adjust for age and sex differences. Consequently, WHO has developed MUAC-for-age reference data (Z‑scores and percentiles by sex) for children aged 3‑60mo for international use. The curves show both age-specific and sex-specific differences for boys and girls aged < 24mos. Numerical Z‑score tables and charts for boys are also available (WHO ICGS Arm Circ.). Reference ranges for MUAC percentiles are also available for US children and adolescents aged 1‑20y based on the same population used in the CDC body mass growth charts (Addo et al., 2017). The pecentiles for females are shown in Figure 11.15. The authors also provide percentiles for males and the necessary LMS coefficients to calculate Z-scores.
11.2.2 Mid-upper-arm-muscle circumference
The muscle circumference of the mid-upper arm is derived from measurements of both the MUAC and triceps skinfold thickness and is the calculated circumference of the inner circle of muscle surrounding a small central core of bone (Gurney & Jelliffe, 1973). Traditionally, in resource-poor settings, mid-upper-arm-muscle circumference (MUAMC) was used as a proxy for total body muscle mass and used to diagnose undernutrition in community surveys (Jelliffe, 1966). Strong correlations between calculated values for MUAMC and fat-free mass estimates based on reference in vivo methods such DXA (Carnevale et al., 2018) and computer tomography (Lambell et al., 2021) has led to the use of MUAMC as a proxy for muscle mass in the elderly (Akin et al., 2015; Landi et al., 2010). For example, in a prospective study of older men (60‑79y), MUAMC was significantly and inversely related to mortality, with the prediction of mortality being greater when MUAMC was combined with waist circumference (Wannamethee et al., 2007). In addition, in persons 80y or older, MUAMC was positively related to functional performance as well as survival. In this study, functional performance was assessed using the physical performance battery score based on three timed tests: 4‑m walking speed test, the balance test, and the chair stand test (Landi et al., 2010). Low MUAMC has also been shown to be associated with longer hospital stays in hospitalized adults. In a study by Pinto et al. (2021), such an association was independently related with undernutrition. MUAMC has also been used to detect low muscle mass in clinical and primary care settings where assessment of muscle mass using more direct in vivo methods such as computer tomography is not feasible (Gort-van Dijk et al., 2021). Nevertheless, it is important to realize that the mid-upper-arm-muscle circumference is a one-dimensional measurement, whereas mid-upper-arm-muscle area is two-dimensional, and mid-upper-arm-muscle volume is three dimensional. Consequently, if the volume of the mid-upper-arm muscle declines during undernutrition or enlarges following a program of nutritional support, the mid-upper-arm-muscle circumference change will be proportionally smaller than the change in the mid-upper-arm muscle area (Heymsfield et al., 1982). Hence, MUAMC is insensitive to small changes of muscle mass that might occur, for example, during a brief illness.Calculation of mid-upper-arm-muscle circumference
The equation for the calculation of mid-upper-arm-muscle circumference (MUAMC) is based on the same assumptions as those described for mid-upper-arm fat area (Section 11.1.4).
If MUAC = mid-upper-arm circumference, TSK = triceps skinfold, d1 = arm diameter,
and d2 = muscle diameter. Then:
TSK = 2 × subcutaneous fat (d1 − d2) and MUAC = π × d1.
MUAMC = π × d2 = π × [d1 − (d1 − d2)]
= π × d1 − π × (d1 − d2). Hence
MUAMC = MUAC − (π × TSK)
Note that this equation requires all measurements to be in the same units (preferably mm). As variations in skinfold compressibility are ignored, and as the triceps skinfold of females is generally more compressible than that of males, MUAMC in females may be underestimated (Clegg & Kent, 1967). As a further complication, the MUAMC equation does not account for between subject variation in the diameter of the humerus relative to MUAC (Frisancho, 1981).
Interpretive criteria
There are no reference ranges for MUAMC for children based on the WHO Multicentre Child Growth Study or for the U.S. children used to compile the CDC 2000 BMI growth charts (Kuczmarski et al., 2000a). Some population-specific reference data based on calculated MUAMC are available for Argentinian children aged 4‑14y (Oyhenart et al., 2019a). Kuczmarski et al. (2000b) compiled MUAMC reference data for adults from the U.S. NHANES III survey (1988‑1994), but only for those adults > 50y. Mean (SE) and selected percentile values of males and females for four age groups are available. During this time, values for MUAMC increased up to age 65y in women and up to middle age in men and then steadily decreased. However, secular-related changes in MUAC and triceps skinfold thickness have been reported in U.S. males and females, so caution must be used when comparing more recent MUAMC data with these earlier MUAMC reference data for U.S. adults (Frisancho, 1990).11.2.3 Mid-upper-arm-muscle area
Mid-upper-arm-muscle area (AMA) is said to be preferable to mid-upper-arm-muscle circumference as an index of total body muscle mass because it more adequately reflects the true magnitude of muscle tissue changes (Frisancho, 1981). Several studies have examined the validity of mid-upper-arm-muscle area by comparison with in vivo body composition reference methods. Magnetic resonance imaging (MRI) and computer tomography (CT) are considered the gold standard methods for in vivo assessment of muscle mass, although use of bioelectrical impedance (BIA) and DXA is increasing in clinical settings. Unfortunately, the validity of the calculated mid-upper-arm-muscle area (AMA) as a proxy for actual arm-muscle mass is dependent on the characteristics of the study population and the in vivo reference method used. For example, the traditional equation appears to overestimate AMA in obese patients and may not be appropriate for undernourished children (Heymsfield et al., 1982; Rolland-Cachera et al., 1997). Despite these limitations, calculated AMA as a proxy for arm-muscle mass has been used by several investigators. Pinto et al. (2021) reported that AMA, like MUAMC, was linked with length of hospital stay. In Caucasian adult patients with AMA values lower than the 5th percentile (i.e., indicative of depletion), the probability of being discharged from the hospital was lower. However, this finding has not been consistent in all studies (Luis et al., 2013). AMA has also served as an early indicator of deteriorating nutritional status in a longitudinal study of pediatric patients with cystic fibrosis (Ellemunter et al., 2021). AMA usually increases in adults up to age 65y in women and up to middle age in men, and then steadily decreases, as observed for MUAMC. Caution must be used, however, when interpreting estimates of AMA among adults who are obese or those with triceps skinfold thickness that exceeds the 85th age-and sex-specific percentiles. In such persons, estimates of AMA by anthropometry are said to overestimate AMA determined by computerized tomography, the degree of overestimation varying directly with the degree of adiposity (Forbes et al., 1988).Calculation of mid-upper-arm-muscle area
The following equation may be used to estimate mid-upper-arm-muscle area (AMA): \[ \small \mbox{Arm muscle area} = \mbox{(MUAC − (π × TSK))}^{2}\mbox{/4π}\] where MUAC = mid-upper-arm circumference and TSK = triceps skinfold thickness (Frisancho, 1981). Consistent units, preferably mm, should be used throughout. The index is based on the following assumptions:- The mid-upper-arm cross-section is circular.
- The triceps skinfold is twice the average adipose tissue rim diameter at the middle of the upper arm.
- The mid-upper-arm-muscle component is circular in cross-section.
- Bone atrophies in proportion to muscle wastage during protein-energy malnutrition.
Interpretive criteria
Age and sex-specific smoothed percentiles for AMA based on the same population of healthy U.S. children aged 1‑20y used to construct the CDC 2000 BMI charts (Kuczmarski et al., 2000a) and the 2010 skinfold thickness percentiles of Addo and Himes (2010) are shown in Figure 11.17 (Addo et al., 2017). The authors also provide the necessary LMS coefficients to calculate Z‑scores; see Addo et al. (2017) for more details.
11.2.4 Calf circumference
The quantity of skeletal muscle (i.e., muscle mass) changes over the life course: increasing during growth in childhood, stabilizing in midlife, after which muscle mass declines with ageing. In midlife in adults about 30% of the skeletal muscle is in the lower limbs, although after age 50y, the amount declines, with losses of ~1‑2% per year (Cruz-Jentoft et al., 2019). Calf circumference is often used as a surrogate marker of skeletal muscle mass. The measurement is simple and practical and was reported as the most used measurement in clinical practice to assess muscle mass, based on an international survey in 55 countries (Bruyere et al.,2016). Calf circumference has the added advantage of having a lower fat mass compared to other body sites. As a result, the impact of fat mass on the measurements will be less (Bahat, 2021). Calf circumference is used to diagnose sarcopenia in the elderly, a condition characterized by both low muscle mass and low muscle strength, which is associated with poor physical performance (Cruz-Jentoft et al., 2019). In older adults, sarcopenia is associated with falls, fractures and mobility disorders, cognitive impairments, and mortality, as noted earlier (Cruz-Jentoft et al., 2010; 2019). Several mechanisms may be involved in the onset and progression of sarcopenia, including protein synthesis, proteolysis, neuromuscular integrity and muscle fat content; for further details, see Cruz-Jentoft et al. (2019). However, other causes of sarcopenia besides ageing have been identified, including systemic disease, especially those invoking inflammation (e.g., cachexia), and malnutrition. For example, the Global Leadership Initiative on Malnutrition has included low muscle mass as an important criterion for the definition of malnutrition (Cederholm et al., 2019). The use of calf circumference as a proxy for muscle mass to identify older adults with or at risk for sarcopenia is supported by both the Asian Working Group for Sarcopenia (AWGS) (Chen et al., 2020), and the European Working Group on Sarcopenia in Older People (EWGSOP) (Cruz-Jentoft et al., 2019), especially in settings where no other muscle mass diagnostic methods are available. Several investigators have compared the performance of calf circumference as an indirect anthropometric marker of appendicular skeletal muscle mass against in vivo reference methods. Most of these studies have been on the elderly and have reported moderate to good correlations of calf circumference against direct measurements of skeletal muscle mass using reference methods such as dual-energy X-ray absorptiometry (DXA) (Kawakami et al., 2015) and bioelectrical impedance (Gonzalez-Correa et al., 2020). More recently, the use of calf circumference as an adequate predictor of skeletal muscle mass over the entire adult lifespan has been investigated (Santos et al., 2019). In this U.S. study of adults from the 1999‑2006 NHANES survey, calf circumference was verified as a satisfactory predictor of appendicular skeletal muscle mass based on DXA (Santos et al., 2019). The impact of age, sex, and ethnicity was also examined, given the earlier reports of their impact on skeletal muscle measurements (Rush et al., 2009; He et al., 2003). The U.S. adult age groups studied were: 18‑20y; 20‑39y;40‑59y; >60y and their ethnicities were: Mexican American; African America, White, Other. A prediction equation was developed based on calf circumference and age, sex, and ethnicity or self-reported race. In a later study employing the same NHANES 1999‑2006 dataset, calf circumference cutoff values for U.S. adults according to sex, ethnicity and race were defined as a marker to identify low muscle mass, validated by DXA measurements (Gonzalez et al., 2021). With these cutoff values, sarcopenia could be diagnosed, not only in older participants, but across the full adult lifespan. Factors confounding the calf circumference measurements across the entire adult lifespan were also identified. In addition to sex and ethnic or self-reported race identified earlier as confounders, BMI was also shown to be a very important confounder in this later study. Calf circumference values were reported to be lower for those with BMI < 18.5(kg/m2), but higher for those overweight or obese compared to those with a normal BMI, irrespective of age, ethnicity, or race. Further, the confounding effect of adiposity could be removed by applying BMI adjustment factors for calf circumference for those participants outside the normal-weight BMI range (i.e., BMI 18‑24.9) (see Table 11.11). Hence, by applying these BMI adjustment factors, the confounding effects of adiposity can be removed so that low calf circumference under any BMI can be identified.Measurement of calf circumference

Interpretive criteria
Cutoff points for calf circumference representing low muscle mass were first developed from studies in older persons. The cutoffs recommended by the Asian Working Group for Sarcopenia (AWGS) are < 34cm for males and < 33cm for females (Chen et al., 2020). Calf circumference cutoff values for U.S. adults ≥ 18y with a normal BMI (18.5‑24.9kg/m2) and defined by Gonzalez-Arellanes et al. (2021) are presented in Table 11.10. The cutoff values were determined by using 1 or 2 SDs below the mean (from a reference young population aged 18‑39y and of normal weight) for moderately low or severely low calf circumference values, respectively. The values based on 1 SD below the mean and indicative of a moderately low calf circumference are appropriate to detect low muscle mass in adults > 65y for sarcopenia diagnosis / screening (Table 11.10).
Males | Females | |||||
---|---|---|---|---|---|---|
n | Reference1 mean ± SD | Cutoff2 −1 SD | n | Reference1 mean ± SD | Cutoff2 −1 SD | |
Total - All subjects | 1639 | 36.3 ± 2.2 | 34.1 | 1465 | 35.3 ± 2.3 | 33.0 |
White Non-Hispanic | 633 | 36.6 ± 2.2 | 34.4 | 656 | 35.6 ± 2.2 | 33.4 |
Black Non-Hispanic | 429 | 36.4 ± 2.2 | 34.2 | 279 | 35.3 ± 2.2 | 33.1 |
Mexican American | 428 | 34.9 ± 2.1 | 32.8 | 378 | 33.9 ± 2.3 | 31.6 |
Other races & ethnicities | 149 | 36.0 ± 2.1 | 33.9 | 152 | 34.6 ± 2.2 | 32.4 |
BMI Group (kg/m2) | Total All subjects | White Non- Hispanic | Black Non- Hispanic | Mexican- American | Other races & ethnicities |
---|---|---|---|---|---|
< 18.5 | +4.3 | +4.7 | +4.2 | +4.0 | +3.4 |
25‑29.9 | −3.4 | −3.4 | −3.4 | −3.1 | −3.5 |
30‑39.9 | −6.8 | −6.7 | −7.2 | −6.4 | −6.9 |
≥ 40 | −12.0 | −11.9 | −12.0 | −12.1 | −12.2 |
11.2.5 Hand grip strength
Research suggests that a measure of muscle mass such as calf circumference should be complemented with measures of muscle strength (Lauretani et al., 2003). Studies using magnetic resonance imaging have shown that muscle strength decreases by more than 50% in in the elderly (70‑82y) compared to that in young men (18‑30y). Moreover, only half the decrease in muscle strength that occurs with aging is accounted for by a decrease in the volume of muscle (Morse et al., 2005). Hand grip strength is the recommended technique for measuring muscle strength by the European Working Party on Sarcopenia in Older People (EWGSOP) (Cruz-Jentoft et al., 2019) and the Asian Working Group for Sarcopenia (AWGS) (Chen et al., 2020). It is the simplest and most inexpensive method to assess muscle strength and has been introduced by AWGS to identify “possible sarcopenia” with or without reduced physical performance in both community health care and prevention settings (Chen et al., 2020). However, more studies are needed to establish whether region-specific cutoffs for the diagnosis of sarcopenia based on hand grip strength are necessary. Hand grip strength is also used as a measure of physical fitness in children (Tremblay et al., 2010; Wick et al., 2021) and athletes (Leyk et al., 2007; Pizzigalli et al., 2017). Over the life course, hand grip strength increases, peaking in early adult life at a strength that is maintained through to midlife, after which it declines, with the loss accelerating through old age (Roberts et al., 2011). For example,
Measurement of hand grip strength
Accurate measurement of hand grip strength requires use of a calibrated handheld dynamometer. The hydraulic hand dynamometer Jamar measures grip force (kgf) and is accepted as the gold standard instrument. The Jamar dynamometer is small and portable, although relatively heavy (i.e.,1.5 lb), with a dial that reads force in both kilograms and pounds, and allows assessment to the nearest 1kg or 2.5 lb. In Asia, the spring-type dynamometer (Smedley) that detects the amount of spring tension (kgf) is more widely used. Data generated by these two devices are not comparable (Kim & Shinkai, 2017). Nevertheless, the AWGS 2019 recommend using either device, provided standard measurement protocols for the device are followed (Chen et al., 2020). Measurement of grip strength obtained by dynamometry appears to have good to excellent relative test-retest reliability, even among older adults (Bohannon, 2017). Dynamometers should be calibrated every 4‑6mos to maintain longitudinal validity. The methods used to measure grip strength vary. Consequently, Roberts et al. (2011) have developed a standardized method to increase the precision of measurements within any given study, and to facilitate comparison of results across studies. Nevertheless, other factors unrelated to muscle, such as motivation or cognition, may hamper the correct assessment of muscle strength. Details of this standardized method are outlined in Box 11.1.- Sit the participant comfortably in a standard chair with legs, back support and fixed arms. Use the same chair for every measurement.
- Ask them to rest their forearms on the arms of the chair with their wrist just over the end of the arm of the chair—wrist in a neutral position, thumb facing upwards.
- Demonstrate how to use the Jamar handgrip dynamometer to show that gripping very tightly registers the best score.
- Start with the right hand.
- Position the hand so that the thumb is round one side of the handle and the four fingers are around the other side. The instrument should feel comfortable in the hand. Alter the position of the handle if necessary.
- The observer should rest the base of the dynamometer on the palm of their hand as the subject holds the dynamometer. The aim of this is to support the weight of the dynamometer (to negate the effect of gravity on peak strength), but care should be taken not to restrict its movement.
- Encourage the participant to squeeze as long and as tightly as possible or until the needle stops rising. Once the needle stops rising the participant can be instructed to stop squeezing.
- Read grip strength in kilograms from the outside dial and record the result to the nearest 1kg on the data entry form.
- Repeat measurement in the left hand.
- Do two further measurements for each hand alternating sides to give three readings in total for each side.
- The best of the six grip strength measurements is used in statistical analyses so as to encourage the subjects to get as high a score as possible.
- Also record hand dominance, i.e. right, left or ambidextrous (people who can genuinely write with both hands) and the equipment model used.

Interpretive criteria
As noted earlier, although the measurements generated from dynamometers differ depending on the device used, currently dynamometer-specific cutoff values are not recommended because of insufficient comparative data (Chen et al., 2020). Differences also exist in both the methods and functional outcomes used to define cutoff values for dynamometry. For Asian elderly, the AWGS group recommends diagnostic hand grip cutoffs for weak muscle strength indicative of “possible sarcopenia” as < 28kg for men and < 18kg for women (Chen et al., 2020). These cutoff values are based on the lowest quintile for muscle strength based on seven community-based cohorts in East and Southeast Asia aged > 60y (Auyeung et al., 2020). For European elderly, the EWSOP2 group have defined hand grip cutoffs for weak muscle strength indicative of sarcopenia as < 27kg for men and < 16kg for women (Cruz-Jentoft et al., 2019). These are lower than the AWGS cutoffs, and are based on the U.K. data in which weak grip strength is based on strength at least 2.5 SDs below the gender-specific mean from a healthy and young U.K. reference population (Dodds et al., 2014). For Chilean community dwelling adults > 60y, the cutoffs proposed for weak muscle strength were < 27kg for men and < 15kg for women. These were based on the 25th percentile values (by sex) for hand grip strength for these elderly persons. Lera et al. (2018) also performed survival analysis on follow-up data of 9.2y in a subgroup of these elderly participants; hand grip values below 25th percentile were associated with an increased risk of all-cause mortality. For Germans aged 17‑90y, cutoff risks were < 33kg for men and < 21kg for women, defined as 2 SD below the sex-specific peak mean value for grip strength across the life course (Steiber, 2016). Thresholds for a critically weak grip strength associated with elevated mortality risk were defined as 1SD or more below the standardized mean hand grip strength. These cutoffs were defined using survival analysis from an 8y follow up on a restricted sample of older individuals aged 55‑90y. Several sets of region-specific normative reference values for hand grip strength are available. Examples across the life course from the U.K, U.S. Germany, and the U.S. are discussed briefly below. In the United Kingdom for example, percentiles (10th, 25th, 50th, 75th, 90th) by sex for grip strength based on cross-sectional observations in persons aged 4‑90y from 12 U.K. studies have been compiled (Dodds et al., 2014). A subset of these results are presented in Table 11.12.Centiles (kg) | |||||
---|---|---|---|---|---|
Age(y) | n | 10th | 50th | 90th | Mean (SD) |
10 | 3222 | 12 | 17 | 22 | 17.2 (4.1) |
20 | 354 | 30 | 40 | 52 | 41.5 (7.3) |
30 | 984 | 38 | 51 | 64 | 51.6 (9.6) |
40 | 880 | 38 | 50 | 63 | 50.3 (10.3) |
50 | 820 | 35 | 48 | 60 | 47.6 (10.1) |
60 | 2683 | 33 | 45 | 56 | 44.6 (9.2) |
70 | 3286 | 29 | 39 | 49 | 39.1 (8.1) |
80 | 1115 | 23 | 32 | 42 | 32.2 (7.3) |
90 | 431 | 16 | 25 | 33 | 24.7 (6.8) |
Height (cm) | Mean HGS (kg) | Threshhold1 (kg) |
---|---|---|
150‑154 | 31.5 | 25.3 |
155‑159 | 32.7 | 26.4 |
160‑164 | 33.7 | 27.4 |
165‑169 | 34.8 | 28.6 |
170‑174 | 35.8 | 29.6 |
175‑179 | 37.1 | 30.8 |
180‑184 | 38.0 | 31.8 |
Age (y) | n | Lowest quintile (kg) | Mean (SD) (kg) |
---|---|---|---|
Men | |||
60‑69.9 | 5319 | 32.7 | 37.9 (6.5) |
70‑79.9 | 5317 | 28.0 | 33.3 (6.3) |
≥ 80 | 1554 | 23.6 | 28.4 (6.2) |
Women | |||
60‑69.9 | 6384 | 20.0 | 23.6 (4.6) |
70‑79.9 | 6009 | 17.8 | 21.1 (4.5) |
≥ 80 | 1761 | 14.7 | 18.3 (4.5) |