10.2: Assessment of body fat (11.1) (2025)

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).

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).

• 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).

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.

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).

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:

\[\text { BAI }(\% \mathrm{fat})=\frac{\text { Hip circumference }}{(\text { Height })^{1.5}}-18\nonumber\]

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.

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.

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:

\[\begin{aligned}
& \text { Total body fat }(\mathrm{kg})=\text { body weight }(\mathrm{kg}) \times \% \text { body fat } / 100 \\
& \text { Fat-free mass }(\mathrm{kg})=\text { body weight }(\mathrm{kg})-\text { total body fat }(\mathrm{kg})
\end{aligned}\nonumber\]

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:

\[\% \mathrm{~F}=\left(\left(\mathrm{C}_1 / \mathrm{D}\right)-\mathrm{C}_2\right) \times 100 \%\nonumber\]

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:

\[\begin{aligned}
& C_1=4.950, C_2=4.500(\text { Siri, 1961 }) \\
& C_1=4.570, C_2=4.142(\text { Brožek et al. 1963 }) \\
& C_1=5.548, C_2=5.044 \text { (Rathburn \& Pace, 1945) }
\end{aligned}\nonumber\]

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).

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 C₁and 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).

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.

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).

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).

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.

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 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.

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)

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.

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.

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.

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).