Nutritional Assessment of Body Composition

3^rd Edition, July 2024

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-4.0
( PDF ).

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).

Table 11.1 Distribution of body fat in reference man and women. Data in kilograms. Weights for total fat and body weight in reference man and woman from Behnke (1969). Other weights from Allen et al. (1956), Alexander (1964), and Wilmore and Brown (1974).
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

Body fat is deposited in two major types of sites: one for essential lipids, and the other for storage of fat. Essential lipids are found in the bone marrow, central nervous system, mammary glands, and other organs and are required for normal physiological functioning; fat from these sites makes up about 9% (4.9kg) of body weight in reference woman and 3% (2.1kg) in reference man. Storage fat consists of inter‑ and intra-muscular fat, fat surrounding the organs (e.g., liver, heart, pancreas) and gastrointestinal tract, and subcutaneous fat (Lohman, 1981). The proportion of storage fat in males and females is relatively constant, averaging 12% of total body weight in males and 15% in females.

Of the total body fat, over one-third in reference man and woman is estimated to be subcutaneous fat. Body fat is expressed either in absolute terms (the weight of total body fat in kilograms) or as a percentage of the total body weight. There is, however, a lack of consensus about the usefulness of percentage body fat as an index of adiposity. Some investigators argue that percentage body fat over-adjusts for weight because it includes the fat mass component in both the numerator and denominator (Cole et al., 2008). A further limitation is that percentage fat is not fully independent of body size. High percentage fat values might reflect high adiposity or low lean mass (Wells, 2014).

In population studies, body fat is often assessed by anthropometry. In the past, body mass index has been the principal index used to predict excess adiposity; see Chapter 10 for more details. However, skinfold thickness determinations, either alone or in association with other anthropometric variables (e.g., limb girths and breadths), are also used to predict percentage body fat; over six hundred prediction equations have been developed. Associations between these anthropometric variables and percent body fat differ by many factors including gender, age, race / ethnicity, and level of adiposity so that prediction equations must be carefully matched with the population under study and standardized techniques used for the measurements (Provyn et al., 2011).

Figure 11.1. Flow chart of the transformation from skinfold to total body adiposity; eight possible steps (left) and possible assumptions (right). Modified from: Provyn et al. (2011)

More recently, the importance of the distribution of body fat has been emphasized. Numerous studies have reported correlations between the amount of intra-abdominal fat (i.e., visceral adipose tissue) and metabolic disturbances linked to the risk of cardiovascular disease (Neeland et al., 2019; Ross et al., 2020). These findings have led to the assessment of visceral adipose tissue as an independent risk marker of cardiovascular and metabolic morbidity and mortality (Hiuge-Shimizu et al., 2012). Waist-hip circumference ratio and, increasingly, waist circumference alone, are being used as anthropometric surrogates for intra-abdominal visceral fat (Sections 11.1.6 and 11.1.7).

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).

Figure 11.2. Location of the midpoint of the upper arm. Redrawn from Robbins et al. (1984).

The following skinfold sites, described in detail in Lohman et al. (1988), are commonly used:

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).

Figure 11.3. Location of the subscapular (A) and suprailiac (B) skinfold sites.

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.

Marked ethnic differences in adiposity based on skinfolds (as well as fat mass via BIA and DXA) have been reported. For example, greater subcutaneous fatness was reported for white boys compared to their black counterparts in the U.S. (Addo & Himes, 2010). These data were based on the population of healthy U.S. children aged 1‑20y used to construct the CDC 2000 BMI charts (Kuczmarski et al., 2000a). For these BMI charts, the weight data for children > 6y who participated in the NHANES III survey were excluded because the inclusion of these data shifted the upper percentile curves.

Race-ethnicity differences in skinfolds have also been reported in children living in the U.K. Adiposity levels were higher among South Asian children based on the sum of four skinfolds (biceps, triceps, subscapular and suprailiac), whereas black African Caribbean children had similar or lower adiposity levels than white Europeans (Nightingale et al., 2011). Clearly, race-ethnicity differences in fat patterning should be taken into account when interpreting results based on subcutaneous skinfolds.

Skinfold thickness measurements are best made using precision thickness calipers; they measure the compressed double fold of fat plus skin. As a result of the compression, they always underestimate actual subcutaneous fat thickness. The skinfold is always grasped at the marked site with the fingers on top, thumb below, and forefinger on the marked site. Three types of precision calipers can be used: Harpenden, Lange, and Holtain (Figure 11.4).

Figure 11.4. Harpenden (a), Lange (b), and Holtain (c) precision skinfold thickness calipers.

Precision calipers are designed to exert a defined and constant pressure throughout the range of measured skinfolds and to have a standard contact surface area or “pinch” area of 20‑40mm². The skinfold calipers must be recalibrated at regular intervals using a calibration block. Both the Harpenden and Holtain skinfold calipers, which have a standard jaw pressure of 10g/mm², give smaller skinfold values than Lange calipers, which are fitted with a lighter spring (Gruber et al., 1990). For example, values from Holtain calipers are about 2‑5mm (mean) lower than those obtained using the Lange calipers (Lohman et al., 1984). Hence, care must be taken to ensure the same precision calipers are used when examining secular trends in skinfold thicknesses.

For all the skinfold measurements, the subject should stand erect with the weight evenly distributed and feet together, shoulders relaxed, and arms hanging freely at the sides. The measurement technique is described in detail for the triceps skinfold, as the latter is the site most frequently used to obtain a single indirect measure of body fat; the technique used for the other skinfold sites is similar. There is no consensus as to whether the left or right side of the body should be used. In the WHO Multicentre Child Growth Reference Study, triceps and subscapular measurements were taken on the left side of the body (de Onis et al., 2004). A description of these measurement protocols are available in the WHO anthropometric training video. However, the current practice of the U.S. National Health and Nutrition Examination Surveys (NHANES) is that skinfold sites are measured on the right side of the body.

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.

Figure 11.5 Measurement of the triceps skinfold in the upright position using the Harpenden caliper. Redrawn from: Robbins et al. (1984).

When using the Lange, Harpenden, or Holtain calipers, pressure must be applied to open the jaws before the instrument is placed on the skinfold; the jaws will then close under spring pressure. As the jaws compress the tissue, the caliper reading generally diminishes for 2‑3s, and then the measurements are taken. Skinfolds should be recorded to 0.1mm on the Harpenden and Holtain skinfold calipers and to 0.5mm on the Lange.

Triceps skinfold measurements can also be made with the subject lying down. The subject lies on the left side with legs bent, the head supported by a pillow, and the left hand tucked under the pillow. The right arm rests along the trunk, with the palm down. The measurement is taken at the marked midpoint of the back of the upper right arm, as described above. The examiner should be careful to avoid parallax errors by bending down to read the calipers while taking the measurements (Chumlea et al.,1984).

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).

Table 11.2 Reported values for within-observer and between-observer technical error of the measurement (TEM) for skinfold measurements. Data from Ulijaszek & Kerr (1999).
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

Between-examiner errors arise when two or more examiners measure the same subject and skinfold site; such errors are usually larger than within-examiner errors, but they can be reduced to not more than 2mm with training and care (Burkinshaw et al., 1973). Within‑ and between-examiner measurement errors tend to be greater if very large (> 15mm) or small (< 5mm) skinfolds are measured (Edwards et al., 1955).

Table 11.2 lists some reported values for both within- and between-examiner technical error of the measurement (TEM) (Chapter 9) for biceps, triceps, subscapular, and suprailiac skinfold measurements, compiled by Ulijaszek and Kerr (1999). Consult Chapter 9 on how to measure TEM.

Within‑ and between examiner TEMs for triceps and subscapular skinfolds were also calculated in the WHO Multicentre Growth Reference Study (MGRS) (de Onis et al., 2004); the values are shown in (Table 11.3). As expected, the range for the between-examiner TEM for both the longitudinal and cross-sectional components of the MGRS from the six country sites was larger than the range for the within-examiner TEM for these two skinfolds. For more details, see WHO (2006).

Zerfas (1985) has evaluated the measurement error for skinfolds from any site using a repeat-measures protocol and recommended target values for the differences between the trainee and a criterion

Table 11.3 Reported values in mm for the within-examiner and between-examiner technical error of the measurement (TEM) for the routine MGRS data. Longitudinal measurements were made by the follow-up team during the longitudinal component. Cross-sectional data are from the MGRS cross-sectional component.
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

anthropometrist; the target training values are shown in (Table 11.4). A difference of more than 5mm between the measurements of the criterion anthropometrist and the trainee indicates a gross error related to the reading or recording; a difference between the measurement of the criterion anthropometrist and the trainee of 0.0‑0.9mm indicates that the trainee has reached an acceptable level of proficiency in the measurement technique.

In the WHO Multicentre Growth Reference Study, measurements for triceps and subscapular skinfolds were taken on each child by two trained and standardized anthropometrists. Their values were then compared to ensure that the duplicate measurements were within the maximum allowable difference, designated as 2.0mm for each skinfold (de Onis et al., 2004).

Sports anthropometrists have set target values for training which also include skinfolds and arm circumference measurements (Gore et al., 1996); these could be adopted by nutritionists. Suggested target values are expressed as TEM (as a percentage), and for skinfolds are 7.5 (level 1) and 5.0 (levels 2 and 3). Criterion anthropometrists should be expected to achieve a %TEM of 5.0 for skinfolds.

Table 11.4 Evaluation of measurement error in anthropometric measurements. After Zerfas (1985). Differences greater than those noted under “Poor” are taken to indicate a gross error. Data from Ulijaszek & Kerr (1999).
	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

Secular trends in adiposity across populations have been examined by measuring triceps and subscapular skinfold thicknesses. However, in a sample of > 45,000 U.S. adults participating in the NHANES surveys conducted from 1988‑1994 through 2009‑2010, Freedman et al. (2017) concluded that it is unlikely that skinfold thicknesses could be used to monitor trends in obesity. The changes in the measured skinfold thicknesses were small and fell within the technical error of the respective skinfold measurements.

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.

Figure 11.6 Upper-arm-fat-area-for-age percentiles for female US children and adolescents aged 1‑20y. Redrawn from Addo et al. (2017).

In addition to age and sex, height was also found to influence the ranking of mid-arm-fat area in this study. Hence, prediction equations for height-for-age adjusted Z‑scores for arm-fat area-for-age are also reported for males and females; see Addo et al. (2017) for more details. However, there is evidence that population-specific reference data are needed for arm-fat area. Oyhenart et al. (2019a) compared the arm-fat area values of children aged 4‑14y in Argentina with the U.S. reference data (Addo et al., 2017). The higher mean values of arm-fat area for 3^rd, 50^th, and 97^th percentiles were indicative of greater adipose tissue for Argentinian boys and girls than for comparably aged U.S. children.

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:

\[ \small \mbox{Total body fat (kg)} = \mbox{body weight (kg) × % body fat / 100}\] \[ \small \mbox{Fat-free mass (kg)} = \mbox{body weight (kg) − total body fat (kg)}\]

Choice of appropriate anthropometric variables to estimate body density (Steps 1 and 2)

Many studies have investigated the best combination of skinfolds and other anthropometric measurements from which to derive a regression equation for the initial estimation of body density (Steps 1‑2), prior to the calculation of percentage body fat (Step 3) by applying the empirical equations of Siri (1956) or Brožek (1963).

Numerous prediction equations are available to estimate body density from anthropometric variables for population groups ranging from sedentary to athletic and from children to the elderly (Provyn et al., 2011). Rarely have the studies recommended the same combination of measurements. The selection of the most appropriate prediction equation should be based on the characteristics of the population on which the chosen equation was originally validated.

Several studies have examined the predictive accuracy and the applicability of the prediction formulae available for estimating body density and subsequently percentage body fat. For example, Provyn et al. (2011) reported that even when the chosen predictive formulae were matched to the characteristics of the population (e.g., age, gender, ethnicity, activity level) on which the equation was originally validated, the prediction formulae investigated (n=57) were not necessarily reliable tools for predicting percentage body fat in Caucasian adults. This conclusion was based on the percentage body fat estimates generated from dual-energy X‑ray absorptiometry (DXA), and confirmed earlier from body fat data (in g) generated from direct dissection of domestic porcine hind legs (Provyn et al., 2008). Hence, the application of these prediction formulae for estimating percentage body fat on age-matched, apparently healthy individuals remains questionable.

Certainly, these prediction formulae should not be used to predict body density in undernourished individuals, as there is a decreasing correlation between skinfold thickness and total body fat content with increasing severity of undernutrition. This change in correlation may arise from a shift of fat storage from the regions represented by the subscapular and triceps skinfolds to other subcutaneous sites. Alternatively, a shift from subcutaneous to deep visceral sites may occur (Spurr et al., 1981).

Calculation of percentage body fat from body density using empirical equations (Step 3)

In most cases, the final stage in the calculation of the percentage of body fat (F) from measurements of skinfolds and other anthropometric variables is the selection of an empirical densitometric equation relating fat content to body density (D). Several densitometric equations have been derived based on the two‑component model for body composition in which body weight is divided into fat and fat‑free mass, relying on assumptions that ignore inter-individual variability in the composition of fat-free mass. All the classical densitometric equations assume: (a) the density of the fat‑free mass is relatively constant; (b) the density of fat for normal persons does not vary among individuals; (c) the water content of the fat-free mass is constant; and (d) the proportion of bone mineral (i.e., skeleton) to muscle in the fat-free body is constant. All authors used the equation: \[ \small \mbox{%F} = \mbox{((C}_{1}/\mbox{D)} − \mbox{C}_{2}\mbox{) }×\mbox{ 100%} \] but different authors used different values for the density of fat and the fat-free mass and as a result the values for C₁ and C₂ differ slightly:

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).

Table 11.5 Median values for males for hydration, density, and constants (C₁ and C₂) for the paediatric version of Siri's equation, obtained by using the LMS (lambda-mu-sigma) method. Data from Wells et al. (2010) who also present comparable data for females.
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

They reported the measured fat-free mass density for the children to be significantly lower than the adult value (1.0864kg/L vs. 1.1kg/L), whereas that for the measured fat-free-mass hydration was higher (75.3% vs. 73.2%). In a later study comprising a larger sample of children (n=533) and wider age range (4‑23y), Wells et al. (2010) developed empirical reference data for density and hydration of fat-free mass for children from age 5‑20y based on the 4‑component model (i.e., body weight, total body water, bone mineral content, and body volume). Table 11.5 presents the median values for hydration, density, and constants (C₁ and C₂). In addition, they developed prediction equations for the density and hydration of the fat-free mass based on age, sex, and body mass index standard deviation score (BMI SDS) using their 4‑component measurements of body composition; see Wells et al. (2010) for more details.

Note that the values for C₁ and C₂ constants for the adult males (age 20y) shown here are similar to the corresponding values shown in the Siri equation above (i.e., 4.95 for C₁ and 4.50 for C₂), whereas for adult females, the corresponding values are slightly lower: 4.90 for C₁ and 4.44 for C₂ at 20 y. With the substitution of the age- and sex-specific C₁ and C₂ constants in Table 11.5 for the C₁ (4.95) and C₂ (4.50) constants in the Siri equation, the accuracy of the two-component model for estimating fat mass of a healthy pediatric population could be improved.

More recent research indicates that nutritional status should also be considered when selecting values for both the density and hydration of fat-free mass using a two-component model for body composition. Gutierrez-Martin et al. (2019) reported increasing values for hydration but decreasing values for the density of fat-free mass in the children with heavier BMIs (Figure 11.7), a trend that has been observed earlier in children (Haroun et al., 2005) and adults (Waki et al., 1991).

Figure 11.7 Values for hydration and density of fat free mass based on the four-component model and stratified by nutritional status grouped by BMI SD score for UK subjects aged 4‑22y. Redrawn and abbreviated from Gutierrez-Marin et al. (2019).

Consequently, these investigators developed a method whereby corrections for the density of fat-free mass could be made for children with obesity and thus improve the accuracy of the two-component model for estimating fat mass in obese children. For more details of the adjustment process, see Gutierrez-Martin et al. (2021).

Similar trends in the values for the hydration and density of fat-free mass compared to the classic adult values applied in the Siri densitometric equation have been observed among older adults aged > 60y; values for fat-free mass density were lower but higher for the hydration fraction. These measurements were reported in studies of both Hispanic Americans (Gonzalez-Arellanes et al., 2019). and obese Mexicans` (González-Arellanes et al., 2021) aged > 60y and were based on the 4‑component model. Their findings also suggest that modifying the assumptions regarding both the density and hydration values for fat-free mass applied in the classical densitometric empirical equations may also be appropriate for the elderly and in conditions of obesity.

Recognition for the need to modify these classical empirical equations which assume constant properties of fat-free mass (hydration and density) has led to an increase in the measurement of body composition in vivo using the 4‑component model. This method is considered the gold standard for measuring body composition because it reduces the need for theoretical assumptions. See Chapter 14 for more discussion of the in vivo methods used to measure body composition.

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.

Figure 11.8. Effect of age on waist-hip ratio (WHR) shown by the difference in WHR in older subjects relative to the WHR at age 25-34y. Pooled data from 19 male and 18 female populations in the second MONitoring trends and determinants in CArdiovascular disease (MONICA) survey. Unadjusted differences (GREY - adjusted only for population) and adjusted differences (BLACK - adjusted for population, height and body mass (BMI). Redrawn from Molarius et al. (1999).

Several studies in adults have shown that the WHR varies with age and the degree of overweight, in addition to sex (Stevens et al., 2010). Jones et al. (1986) measured the WHR of a semi-random, age-stratified sample of 4349 British Caucasian men 20‑64y. They noted that the ratio increased with both age (curvilinearly) and excessive weight. In the WHO multinational MONItoring of trends and determinants of CArdiovascular disease (MONICA) project (Molarius et al., 1999), the WHR was also reported to increase with age in both men and women (Figure 11.8), and to be higher in men than in women.

There is also some evidence that WHR varies with ethnicity. In the MONICA project, a standard protocol was used to measure waist and hip circumference in men and women age 25‑64y in 19 countries. Mean waist-hip ratio varied considerably among the study population, ranging from 0.87‑0.99 for men and from 0.76‑0.84 for women. To date, most of the evidence for race/ethnic differences relates to Asian adults in whom lower WHRs have been associated with an increased metabolic risk compared to Europeans, probably because of higher body fat and visceral adipose tissue (Lear et al., 2010).

Relationships between the WHR and age, sex, and race/ethnicity have also been investigated in children. In the U.S. NHANES III, mean WHR varied consistently with age, sex, and ethnic group in children and adolescents aged 4‑19y, as shown in Figure 11.9. Ratios were highest in Mexican American boys (Gillum, 1999).

Figure 11.9. Mean waist-to-hip circumference ratio in male and female children and young adults of three racial groups. NHANES III data (1988-1994). Redrawn from Gillum (1999).

A variety of adverse health outcome measures have been examined in relation to WHR, most of which have been based on cross-sectional studies using differing methods to measure WHR. Consequently, comparisons across studies are difficult, as emphasized by Lear et al. (2010). However, in a prospective cohort study involving 15062 participants from Norfolk, U.K., WHR appeared to have the best predictive value for cardiovascular disease and mortality compared with BMI and percentage body fat (Myint et al., 2014). In some prospective studies, WHR has been associated with a higher risk for all-cause and cardiovascular mortality particularly in women (Rost et al., 2018).

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.

Figure 11.10 Visceral adipose tissue area and liver attenuation by WC tertiles within each BMI category in men. Redrawn from Nazare et al. (2015), who also present data for women.

The validity of serial measurements of WHR to measure changes in intra-abdominal visceral fat over time is uncertain. For example, any beneficial reductions in abdominal fat will not be evident when a ratio such as WHR is used if both the numerator and denominator values change in response to treatment. Consequently, waist circumference alone is now the preferred index for monitoring loss of visceral adipose tissue, and is discussed below.

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.

Table 11.6 Cardiometabolic risk score (CMR score) values across tertiles of waist circumference (WC) within each of the 3 body mass index (BMI) categories
*p < 0.05, **p < 0.01, ***p < 0.0001, denote significantly different from the first WC tertile group within the same BMI category and
†p < 0.05, ††p < 0.01, †††p < 0.0001 denote significantly different from the middle WC tertile group within the same BMI category.
T1, T2 and T3 are the WC tertile groups. All statistical analyses were adjusted for age, ethnicity, physician’s specialty, smoking status and educational level. BMI = body mass index; CMR = cardiometabolic risk; WC = waist circumference. Data are CMR score means ±SEM. Data from Nazare et al. (2015).
	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**††

Table 11.6 presents a comparison of the cardiometabolic risk scores by waist circumference tertile groups in each of the three BMI categories. Note the increase in cardiometabolic risk score for both males and females in all three categories of BMI across the waist circumference tertile groups.

Data from numerous other epidemiological studies have also shown that visceral adipose tissue is an independent risk marker of cardiovascular and metabolic morbidity and mortality. A study by Hiuge-Shimizu et al. (2012) on visceral fat accumulation, measured on computer tomography scans in 12,443 Japanese subjects, indicated that an absolute visceral fat area of about 100cm² equated with an increased risk of factors associated with obesity-related cardiovascular morbidity. Moreover, this relationship was irrespective of gender, as shown in Figure 11.11, as well as age and BMI. The obesity-related cardiovascular risk factors assessed in this study were hyperglycemia, dyslipidemia, and elevated blood pressure.

Figure 11.11 Association between the mean visceral fat area (VFA) and obesity-related cardiovascular risk factors. Pale bars: subjects with less than 1.0 risk factors; darker bars: subjects with more than 1.0 risk factors. Redrawn from Hiuge-Shimizu et al.
These findings have highlighted the importance of measuring waist circumference along with BMI in clinical practice to assess the distribution of visceral (intra-abdominal) adipose tissue between individuals (Ross et al., 2020). Moreover, hip circumference is more difficult to measure than waist circumference. (2012).

Listed below are the health and metabolic abnormalities that have been associated with an excess deposition of visceral adipose tissue and ectopic fat irrespective of total adiposity estimated by BMI (Neeland et al., 2019).

● 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.

Note, however, that the evidence for a causal relation with some of these conditions is insufficient. For more details of the constellation of metabolic abnormalities associated with an excess of visceral adipose tissue, see Neeland et al. (2018).

More recently, with the development of medical imaging, the detection and measurement of fat in areas of the body where fat is not physiologically stored has also been made. These studies have shown that at any given BMI, excess visceral adiposity is often associated with an increased accumulation of fat in normally lean tissues such as the liver, pancreas, heart, and skeletal muscle, a condition termed ectopic fat deposition. As noted earlier, emerging evidence suggests that the deposition of ectopic fat might contribute to increased risk of atherosclerosis and cardiometabolic risk (Neeland et al., 2019).

The causal mechanisms whereby an excess of visceral adipose tissue is related to the cardiometabolic complications are not yet fully established. Three mutually exclusive scenarios have been proposed, and are reviewed by Neeland et al. (2019):

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)

Figure 11.12 Overview of potential role of functional and dysfunctional adipose tissue contributing to increased cardiometabolic risk. FFA = free fatty acid; VLDL = very-low-density lipoprotein cholesterol. Redrawn from Ross et al. (2020).

An overview of the potential role of functional and dysfunctional adipose tissue contributing to increased cardiometabolic risk is presented in Figure 11.12. In a healthy cardiometabolic profile, the ability of subcutaneous adipose tissue to expand through hyperplasia (generation of new fat cells) allows the safe storage of the excess energy from the diet into a properly expanding subcutaneous 'metabolic sink'. When this process becomes saturated or in a situation where adipose tissue has a limited ability to expand, there is a spillover of the excess energy, which must be stored in visceral adipose tissue as well as in normally lean organs such as the skeletal muscle, the liver, the pancreas, and the heart, a process described as ectopic fat deposition. Visceral adiposity is associated with a hyperlipolytic state resistant to the effect of insulin along with an altered secretion of adipokines including inflammatory cytokines, whereas a set of metabolic dysfuntions are specifically associated with increased skeletal muscle, liver, pancreas, and epicardial, pericardial, and intra-myocardial fat. For more discussion, see the consensus documents by the International Atherosclerosis Society (IAS) and International Chair of Cardiometabolic Risk (ICCR) Working Group on Visceral Obesity (Neeland et al., 2019; Ross et al., 2020).

Neeland et al. (2019) have also reviewed the response of visceral and ectopic fat to treatment. Briefly, both exercise and dietary interventions are reportedly associated with a substantial reduction in visceral adipose tissue independent of age, sex, and ethnic origin, and irrespective of amount or intensity of exercise. Moreover, randomized controlled trials that have reported lifestyle-induced reductions in visceral adipose tissue and thus waist circumference have also shown they are associated with improvements in cardiometabolic risk factors with or without corresponding weight loss.

These observations, taken together, emphasize the importance of developing simple clinically applicable tools, previously validated with imaging data, with the ability to monitor changes in visceral and ectopic fat over time (Neeland et al., 2019; Ross et al., 2020). In this way, the definition of high-risk overweight and obesity could be refined. In the meantime, Neeland et al. (2019) suggest that the addition of the measurement of plasma triglyceride concentrations to the measurements of waist circumference may be helpful as a screening tool to identify individuals likely to be characterized by the cluster of abnormalities of the metabolic syndrome, as long as validated waist circumference cutoff values are applied.

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).

Table 11.7 Waist circumference (cm) for adults above which cardiometabolic risk is elevated. Japanese waist circumference values are thresholds above which visceral adipose tissue volume is > 100cm³. The original data sources, along with this summary are given in Ross et al. (2020).
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

Most of the values in this table were derived from cross-sectional data rather than prospective studies using representative populations and were not considered in association with BMI. Of note is the wide range in high-risk waist circumference values for both adult men (80‑98cm) and women (80‑96cm).

In the future Ross et al. (2020) recommend conducting prospective studies using representative populations to address the need for BMI category-specific waist circumference cutoffs across different ages, and by sex and ethnicity. Such data have been developed only for Caucasian adults by Ardern et al. (2004) and are summarized in Table 11.8. These investigators reported that in both sexes, the use of BMI category-specific waist circumference cutoffs improved the identification of individuals at high risk of future coronary events. These results were confirmed in a later study in which the prognostic performance of the Ardern waist circumference values was compared with the traditional U.S. waist circumference cutoffs associated with high cardiometabolic risk (i.e., > 88cm for Caucasian women; > 102cm for Caucasian men). Again, stratification of waist circumference cutoffs by BMI substantially improved predictions of mortality compared with the traditional waist circumference cutoffs for U.S. Caucasian adults of both sexes (Bajaj et al., 2009).

Table 11.8 Waist circumference thresholds (cm) stratified by BMI for white individuals. Subjects with measurements higher than these values have a high risk of future coronary events (based on 10-year risk of coronary events or the presence of diabetes mellitus). Data from Ross et al. (2020).
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

Waist circumference is also a highly sensitive and specific marker of accumulation of central obesity in children. Several country-specific waist circumference percentile cutoffs for children have been developed (Goran & Gower, 1999; Nagy et al., 2014; Eisenmann, 2005; Serrano et al., 2021).

Recently, international age‑ and sex-specific waist circumference cutoffs to define central obesity for children and adolescents aged 6‑18y have also been developed (Xi et al., 2020). Based on data from 8 countries (Bulgaria, China, Iran, Korea, Malaysia, Poland, Seychelles, Switzerland), the chosen cutoff is the 90^th waist circumference percentile in children with normal body weight (based on BMI). This cutoff performed well to predict cardiovascular risk when based on available data from 3 countries (China, Iran, Korea) on the presence of three or more of six cardiovascular risk factors: systolic blood pressure, diastolic blood pressure, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL‑C), low density lipoprotein cholesterol (LDL‑C), and fasting glucose.

Table 11.9 Age- and sex-specific waist circumference (WC) for the 90^th percentile of WC during childhood. All estimates are calculated based on data excluding children with obesity, overweight or underweight based on the International Obesity Task Force BMI pediatric criteria. Data from Xi et al. (2020)
	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

The calculated 90^th percentile waist circumference values for children aged 6‑18y with normal weight (i.e., excluding those who were underweight, overweight, or obese) and based on the pooled data from 113,453 children in 8 countries, are shown in the sex-specific columns (Table 11.9). However, more research is needed to further evaluate the performance of the proposed age‑ and sex-specific 90^th percentile WC values in other populations. See Xi et al. (2020) for more details.

Figure 11.13 The prevalence of abdominal obesity and obesity measured in different studies. Changes in the prevalence of abdominal obesity (measured using WC) and general obesity (measured using BMI) measured in different studies during the time period indicated on the x axis. General obesity was defined as BMI ≥ 30kg/m². Abdominal obesity was defied as WC ≥ 88cm and ≥ 102cm for women and men, respectively. Years given (for example, 1962‑2000) indicate the years in which the data were colllected. F = female; M = male. Redrawn from Ross et al. (2020).

Finally, emerging evidence suggests the relative increases in waist circumference in adults are larger than the relative increases in BMI across populations (Visscher et al., 2015). This trend appears to be independent of age, and sex and ethnicity as shown in Figure 11.13 (Ross et al., 2020), and emphasizes that a single focus on BMI > 25 or > 30kg/m² is likely to mask a real increase in the obesity epidemic. Clearly, waist circumference should be included along with BMI in all obesity surveillance studies in the future to ensure the phenotype of obesity that conveys the greatest health risk (i.e., abdominal obesity) is identified. This recommendation was made by the International Atherosclerosis Society (IAs) and the International Chair on Cardiometabolic Risk (ICCR) working group on visceral obesity. In addition, the working group have emphasized the importance of research to refine the WC cutoffs for a given BMI category (Table 11.8) to optimize obesity risk stratification across age, sex, and ethnicity (Ross et al., 2020).

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).

Figure 11.14 Use of insertion tape to measure mid-upper arm-circumference. Redrawn from Robbins & Trowbridge (1984).

After locating the midpoint, the arm is extended so that it is hanging loosely by the side, with the palm facing inward. The tape is then wrapped gently but firmly around the arm at the midpoint (Figure 11.14), care being taken to ensure that the arm is not squeezed. MUAC is often measured at the same time as triceps skinfold. In the WHO Multicentre Growth Reference Study, MUAC (and skinfold) measurements were taken on the left side of the body (de Onis et al., 2004).

The MUAC measurement tapes are cheap, widely available, and easy to use so that cases of SAM can be readily identified at the community level. If necessary, MUAC can be measured with subjects in the recumbent position. In this case, a sandbag is placed under the elbow to raise the arm slightly off the surface of the bed (Chumlea et al.,1984). Measurements are taken to the nearest mm.

Precision of MUAC measurements, both within and between examiners, can be high, even if the subjects are obese, provided that trained examiners and standardized methods are used. High precision is critical as MUAC varies little at any given age, with measurements tending to form a narrow symmetrical distribution, so that even small errors are significant. In the WHO Multicentre Growth Reference Study, the maximum allowable difference for the duplicate measurements of arm circumference was 5.0mm (de Onis et al., 2004).

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.

Figure 11.15 Mid-upper-arm-circumference-for-age percentiles for female US children and adolescents aged 1‑20y. Redrawn from Addo et al. (2017).

It is noteworthy that for ages 2‑5y, median values for the MUAC curves for the US children were comparable to those of the WHO Multicentre Child Growth Study. Predictive equations for height-for-age adjusted MUAC Z‑scores for males and females are also available in Addo et al. (2017).

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).

Figure 11.16 Mid-upper-arm-muscle circumference.

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 5^th 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 85^th 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.

The cross-sectional areas of neurovascular tissue and the humerus are relatively small and ignored. The first three assumptions are those used in the calculations of mid-upper-arm-fat area and mid-upper-arm muscle circumference. Several investigators have reported the equation shown above overestimates arm-muscle area compared with values based on in vivo reference methods such as computer axial tomography (Heymsfield et al., 1982) or magnetic resonance imaging (Rolland-Cachera et al., 1997). As a result, revised equations have been developed. However, their use has been limited, in part because the corrected formulae have only been validated in selected population groups, and population-specific corrected equations may be needed.

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.

Note the percentile curves in Figure 11.17 represent the entire race-ethnicity groups that were represented in the survey, so that interpretation based on these percentile curves should take into account any potential effects of specific race-ethnicity.

Some local region-specific percentiles curves for AMA are available, including those for boys and girls aged 4‑14y in Argentina (Oyhenart et al., 2019a). The mean AMA values for 3^rd, 50^th, and 97^th percentiles are lower for the Agentinean children than their U.S. counterparts (Oyhenart et al., 2019b).

Prediction equations adjusted for the age-dependent effects of height on AMA have also been developed by Addo et al. (2017). These prediction equations may be especially helpful in populations with a high prevalence of stunted children or those in which even well-nourished children are shorter than the average height.

Reference values for AMA for U.S. adults (18‑74y) or an older subset aged > 50y have not been compiled from the U.S. NHANES III data. Earlier data on AMA percentiles (5^th through 95^th) are available based on the merged NHANES I and II data (Frisancho, 1990). These data are presented by age, sex, and race-ethnicity for persons 1‑74y; by height for boys and girls 2‑17y; and by age, sex, and frame size for adults aged 18‑74y. However, secular changes in AMA in U.S. males and females have been reported, so caution must be used when interpreting data based on these older reference data.

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

Figure 11.18 Measuring tape position for maximal calf circumference. From CDC Manual (Revised Dec. 2000)

The measurements are taken using a steel measuring tape while the subject is in a seated position. The tape is placed around the calf and moved up and down to locate the maximum circumference as shown in Figure 11.18. The tape must be held snugly but not tight and the measurement taken to the nearest 0.1cm. In the U.S. NHANES, measurements are performed on the right calf (CDC Manual, Revised Dec. 2000).

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).

Table 11.10 Reference and cutoff values for calf circumference according to sex, ethnicity, and race, from participants with normal BMI³
¹ Reference values defined as mean values from participants aged 18‑39y.
² values defined as −1 SD below the mean values.
³ BMI: 18.5‑24.9 kg/m²; for other BMI groups, use the adjusting factors for correction of calf circumference (Table 11.11). Data from González-Arellanes et al. (2021).
	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

Table 11.11 presents an example of the BMI adjustment factors (in cm), noted earlier, for calf circumference for males with BMIs (kg/m²) outside the normal range based on the entire lifespan (i.e., total sample) and by ethnicity and race. Corresponding adjustment factors for females are available in González-Arellanes et al. (2021). To apply the adjustment factors, add 4cm to the calf circumference measure for BMI < 18.5) or subtract 3, 7, or 12cm from the calf circumference measure for BMI 25‑29, BMI 30‑39, and BMI ≥ 40, respectively. These adjustment factors are derived from linear regression for calf circumference, adjusted by age, for BMI outside the 18.5‑24.9 (kg/m²).

Application of these BMI adjustment factors to the measured calf circumference and then comparison with the cutoff values given in Table 11.10 ensures the correct identification of low calf circumference under any BMI (Table 11.11).

Table 11.11 Adjustment factors for calf circumference by BMI for males outside the 18.5‑24.9 BMI range, calculated from linear regression of calf circumference, adjusted by age. Data from González-Arellanes et al. (2021), who also present comparable data for females.
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

Without these adjustments, raw calf circumference measurements would result in an underestimate of the prevalence of low calf circumference in those with BMI ≥ 25 and an overestimate in those with a BMI < 18.5.

By applying these BMI adjustment factors, a stronger correlation of calf circumference with function may be obtained, so that in the future, BMI-adjusted calf circumference measurements should be validated for predicting sarcopenia-related health outcomes such as functional impairment, falls, and mortality. In addition, use of these BMI adjustment factors and cut-offs should be employed in studies of adults in other countries (Bahat, 2021).

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,

Figure 11.19. Muscle strength and the life course. Redrawn from Cruz-Jentoft et al. (2019).

in a large study of U.K. persons aged 4‑90y, the prevalence of weak grip strength increased sharply with age, with a peak prevalence by age 80y of 23% in males and 27% in females (Dodds et al., 2014). In all age groups, hand grip strength is higher in males than females. Ethnic and regional variations in muscle strength may also occur, prompting studies to develop cutoffs for weak muscle strength in several geographic regions (Lera et al., 2018; Steiber, 2016; Auyeung et al., 2020).

Handgrip strength is said to be a better predictor of functional health outcomes than low muscle mass. Several functional health outcomes have been used to define optimum hand grip strength in the elderly. They include slow measured walking speed, self-reported difficulty in walking (Lauretani et al., 2003; Sallinen et al., 2011), limitations in Instrumental Activities of Daily Living, and altered physical performance. The latter may be evaluated through the Timed Up and Go test, 5-time chair stand test (> 12s), 6-meter walk (< 1.0m/s) or short physical performance battery (< 9) (Lera et al., 2018; Chen et al., 2020). Most of these relationships have been based on cross-sectional data. However, there have also been several prospective cohort studies. In some, lower handgrip strength at discharge from acute care hospitals has been associated with 30‑day readmission (Allard et al., 2016), whereas in others, associations of hand-grip strength with all-cause mortality and cardiovascular mortality have been reported (Lera et al., 2018; Leong et al., 2015; Wu et al., 2017; Rantanen et al., 2003; Steiber, 2016).

Studies have also focused on the effects of modifiable behaviors such as exercise training on hand grip strength to prevent or slow adverse changes in muscle strength (Labott et al., 2019). Bohaanon (2017) has cautioned that relatively large percentage changes in grip strength may be necessary to conclude with confidence that a real change has occurred in muscle strength over time in response to treatment or an intervention. The EWSOP2 group 1 have emphasized that more studies are needed to establish whether the current recommended cutoffs improve the prediction of outcomes most sensitive to response to treatment for sarcopenia (Cruz-Jentoft et al., 2019).

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.

Box 11.1 Standardized method for grip strength via Southampton protocol based on the JAMA dynamometer (Figure 11.20).

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.

From Roberts et al. (2011).

Figure 11.20 The Jamar hydraulic hand dynamometer.

Note that in the U.S. NHANES studies the Smedley dynamometer has been used. The U.S. protocol recommends standing unless participants are unable to stand unassisted, with full elbow extension rather than sitting with 90° elbow flexion as described in Box 11.1 The AWGS (2019) also recommend standing, where possible, with full elbow extension for the Smedley dynamometer but sitting with 90° elbow flexion when the the Jamar dynamometer is used (Chen et al., 2020).

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 25^th 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 25^th 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, 25^th, 50^th, 75^th, 90^th) 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.

Table 11.12 Normative values for grip strength for UK Males. Data from Dodds et al. (2014).
		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)

In the United States, data for height (mean, SD), weight (mean, SD), mean (SD) handgrip strength (kg) and the 5^th, 10^th, 50^th, 25^th and 50^th percentiles by side of hand (dominant and non-dominant), sex, and age (3‑17y) are presented (Bohannon et al., 2017). The cross-sectional data were from the US National Institutes of Health Toolbox project conducted in 2011. The standardized method for measuring hand grip strength recommended by Roberts et al. (2011) using a Jamar dynamometer, was used in this U.S. study.

Later, Wang et al. (2018) used the same U.S. dataset to compile reference values for US adults 18‑85y. Again, data for height, weight, hand grip strength (kg) and percentiles (10^th, 25^th, 50^th, 75^th and 90^th) by sex, side of hand (dominant and non-dominant) and 13 age groups (limited to 5-year spans, except the strata 18‑24y) are presented. No cutoffs for hand grip strength based on the data for children or adults were derived.

Table 11.13 Normative reference values for handgrip strength for German women 40‑44y. Abstracted from more comprehensive data for men and women aged 17‑90y. Steiber (2016)
¹ Mean −1 age-group-specific SD
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

In Germany, the normative reference values are based on a nationally representative sample of healthy participants aged 17‑90y. Mean values for handgrip strength across seven height groups (within each age group: 17‑19y, 20‑24y, 25‑29y, 30‑34y, 35‑39y, 40‑44y, 45‑49y) were calculated and presented as sex-specific reference values, stratified by age and body height. For example, the reference value for 40‑44y women with a height of 165‑169cm is 34.8kg; this value increases by about 1kg for every 5cm of additional height, as shown in Table 11.13 (Steiber, 2016).

In some regions, reference values of handgrip strength have been restricted to certain age groups. For example, in community-based Asian cohorts (Auyeung et al., 2020), only population data for those age > 60y were included, with the lowest quintile (kg) and mean (SD) kg by sex and age group presented, as shown in Table 11.14.

Table 11.14 Sex- and age-specific lowest quintile and means of handgrip strength (kg) in subjects age > 60y from community-based Asian cohorts. Data abstracted from Auyeung et al. (2020)
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)

In Chile, percentiles by sex and age group are also restricted to community-dwelling persons > 60y (Lera et al., 2018), whereas in Canada data from the 2007‑2009 Canadian Health Measures Survey included only children, with the mean and median values aged 7‑10y; 11‑14y; 15‑19y (Tremblay et al., 2010).

Note that because all these normative reference values are based on cross-sectional data, they are likely to underestimate individual decline and hence should not be used to monitor the trajectories of individuals. Moreover, as with all cross-sectional studies, such a design limits the degree to which causal and age-related inference can be drawn (Perna et al., 2015).

Acknowledgements

RSG would like to thank past collaborators, particularly my former graduate students, and is grateful to Michael Jory for the HTML design and his tireless work in directing the translation to this HTML version.

Gibson RS1 Principles of Nutritional Assessment: Body Composition

11.0 Anthropometric assessment of body compo­sition

11.1 Assessment of body fat

11.1.1 Skin­fold thick­ness measure­ments

Measurement of triceps skin­fold

Precision of skin­fold measure­ments

Interpretive criteria for triceps and sub-scapular skin­folds

11.1.2 Assessing body fat with skin­folds

11.1.3 Body adiposity index

11.1.4 Arm-fat area

Calculation of arm-fat area

Interpretive criteria

11.1.5 Calculation of body fat from anthropometric variables via body density

11.1.6 Waist-hip cir­cum­fer­ence ratio

Measurement of waist-hip ratio

Interpretive criteria

11.1.7 Waist cir­cum­fer­ence

Measurement of waist cir­cum­fer­ence

Interpretive criteria

11.2 Assessment of fat-free mass

11.2.1 Mid-upper-arm cir­cum­fer­ence

Measurement of mid-upper arm cir­cum­fer­ence

Interpretive criteria

11.2.2 Mid-upper-arm-muscle cir­cum­fer­ence

Calculation of mid-upper-arm-muscle cir­cum­fer­ence

Interpretive criteria

11.2.3 Mid-upper-arm-muscle area

Calculation of mid-upper-arm-muscle area

Interpretive criteria

11.2.4 Calf cir­cum­fer­ence

Measurement of calf cir­cum­fer­ence

Interpretive criteria

11.2.5 Hand grip strength

Measurement of hand grip strength

Interpretive criteria

Acknowledgements

Gibson RS¹ Principles of
Nutritional Assessment:
Body Composition

11.0 Anthropometric assessment of body composition

11.1.1 Skinfold thickness measurements

Measurement of triceps skinfold

Precision of skinfold measurements

Interpretive criteria for triceps and sub-scapular skinfolds

11.1.2 Assessing body fat with skinfolds

11.1.6 Waist-hip circumference ratio

11.1.7 Waist circumference

Measurement of waist circumference

11.2.1 Mid-upper-arm circumference

Measurement of mid-upper arm circumference

11.2.2 Mid-upper-arm-muscle circumference

Calculation of mid-upper-arm-muscle circumference

11.2.4 Calf circumference

Measurement of calf circumference