Anthropometry and Methods of Body Composition Measurement for Research and Field Application in the Elderly

Anthropometry and methods of body composition measurement for research and ®eld application in the elderly

SB Heyms®eld1*, C NunÄez1, C Testolin1 and D Gallagher1

1Obesity Research Center, Department of Medicine, St Luke's-Roosevelt Hospital Center, Columbia University College of Physicians and Surgeons, New York, NY, USA

Evaluation of body composition is important in the study of human energy and protein metabolism as methods are available for quantifying energy stores and protein content at a single point in time; energy ± protein balance can be monitored over time; and dynamic measures of energy and protein metabolism can be referenced to body mass and related measurable components for between-individual comparisons. This review emphasizes the need for considering subject age when developing body composition component prediction models that are applied in elderly populations. An overview of body composition research is provided that emphasizes compartment and level de®nitions and interrelations. Two broad method categories, mechanistic and descriptive, are then critically examined in relation to their role in energy-protein metabolism and aging research. Our collective review indicates that all major body composition components are now measurable using one or more methods that are based on non age-dependent assumptions. We also found that some methods, particularly descriptive ®eld methods (eg anthropometry), may be based on age-sensitive assumptions and measurements and suggestions for future development of these methods are provided. Lastly, as body composition differences between races, cultures, and countries are now recognized, it would be useful to create international cooperative groups with the aim of developing widely applicable descriptive ®eld methods based on simple available techniques such as anthropometry and bioimpedance analysis. Descriptors: anthropometry; body composition; aging European Journal of Clinical Nutrition (2000) 54, Suppl 3, S26±S32

`Science is fundamentally an exercise in measurement.' Human body components and their organization Dr Harold Varmus, NIH Director, 1999. The ®rst area, body composition rules, involves component and level de®nitions and also includes the study of quanti- tative component relationships. The 35 main body com- Overview position components are organized into ®ve levels of Evaluation of human body composition relates to the topic increasing complexity, atomic, molecular, cellular, tissue- of this workshop in two respects: methods are available for system, and whole body. The ®rst four levels and the quantifying energy stores and protein content at a single respective main components at each level are depicted in point in time and energy ± protein balance can be monitored Figure 2 (Heyms®eld et al, 1998). over time; and dynamic measures of energy and protein Subjects who are weight stable over short time periods metabolism can be referenced to body mass and related (ie several weeks) are in a body composition `steady state' measurable components for between-individual compari- in which component mass balance approaches zero. An sons. The speci®c aim of this review is to emphasize the important aspect of body composition research is establish- need for considering subject age when developing body ing the component relationships that exist under these composition component prediction models that are applied conditions. These include, for example, the water content in elderly populations. of fat-free body mass (ie total body water=fat-free body mass) and the potassium content of fat-free body mass (ie Human body composition research total body potassium=fat-free body mass). These relation- The study of human body composition dates back several ships are important and interesting because of their intrinsic centuries, although major research interest evolved in the scienti®c value (Wang et al, 2000) and because they are early years following World War II (Wang et al, 1999). The often the formative basis of body composition model modern study of body composition encompasses three development (Wang et al, 1995). This is a critical area in areas Ð body composition rules, methodology, and altera- the study of aging as component relationships may change tions (Figure 1). Each of these areas interacts with the other with senescence, physical activity, and nutritional status. two, and this is particularly important in the study of aging. Body composition models developed in young subjects Alterations in body composition that occur with aging may, may therefore not be accurate in older individuals. in turn, modify both component relationships or `rules' and body composition methods. Body composition method organization *Correspondence: SB Heyms®eld, Weight Control Unit, 1090 Amsterdam Body composition methods can be broadly classi®ed Avenue, 14th Floor, New York, NY 10025, USA. as in vitro and in vivo (Wang et al, 1995; Heyms®eld E-mail: [email protected] et al, 1996). This review only examines available in vivo Methods of body composition measurement SB Heyms®eld et al S27 Mechanistic methods are often based on models that have an underlying physical or biological basis. The typical form of mechanistic models is C b Q where b is usually a model relating Q to C. Some typical models are the ratios of total body water and potassium to fat-free body mass (ie TBW=FFM 0.73 and TBK= Figure 1 The study of human body composition: three research areas. FFM 60 mmol=kg) (Wang et al, 1995). As with descrip- From Wang et al (1995), with permission. tive methods, age should be considered when developing mechanistic methods.

Available methods Mechanistic All of the methods in this category are presently formulated on mechanistic models.

In vivo neutron activation. Nitrogen, carbon, hydrogen, phosphorus, sodium, chlorine, calcium and oxygen are all measurable in vivo by a group of methods referred to as neutron activation analysis. A source emits a neutron stream that interacts with subject tissues. The resulting decay products of activated elements can be counted by detectors and elemental mass established. By linking ele- Figure 2 Some of the main components at the ®rst four body composi- ments with molecular level components using simultaneous tion levels. From Heyms®eld et al (1998), with permission. equations, mechanistic models can be prepared for all major molecular level components (Heyms®eld et al, 1996). For example, C, N and Ca can be used to solve methods. In vivo methods can be further classi®ed accord- for total body fat, protein and bone mineral mass (Kehayias ing to any one of several descriptors: measurement location et al, 1991). Although limited in availability, neutron (laboratory=®eld); measurement frequency (static=dyna- activation analysis is extremely valuable in body composi- mic); mathematical function type (model=descriptive); tion research as there are no presently known age or and portion of the body evaluated (regional=whole-body). gender-dependencies of currently applied models. More- The primary organization paradigm applied in this review over, components such as total body protein are best is based upon mathematical function type. evaluated using neutron activation methods (Chettle & All in vivo methods are by necessity indirect (Wang Fremlin, 1984). et al, 1995). That is, some measurable somatic or physical Neutron activation methods thus afford the opportunity property is exploited in quantifying the component of to quantify molecular level components without potential interest. A useful and informative approach for organizing age-related bias in derived estimates. body composition methods is based on mathematical function type. The basic formula for estimating components (C) in vivo is Whole-body 40K counting. A small and constant percen- 40 C f Â Q tage of total body potassium (TBK) is radioactive ( K) and emits a characteristic g-ray. With appropriate shielding where f is mathematical function and Q is measurable from background, this g-ray can be counted using scintilla- quantity. Measurable quantities include various properties tion detectors. As the ratio of 40Kto39K is known and (eg tissue conductivity) and other components. Two broad constant, 39K and `total body potassium' can be estimated categories of mathematical function are recognized, accordingly. descriptive and mechanistic. All of the body's potassium is within the fat-free body Descriptive mathematical functions have the general mass component and the TBK=FFM ratio is reasonably form stable in the same subject over time and between different C a b Q subjects of the same gender. This allows development of mechanistic models based on assumed TBK ratios for sex where a and b are regression line intercepts and slopes, and potentially age (Forbes, 1987). respectively. A reference method is selected for measuring An example of the relationship between TBK and fat- the component of interest in a well-characterized subject free body mass is shown in Figure 3. The subjects are group. The property or predictor component (ie Q) is also healthy weight stable adults and TBK and fat-free body measured in the subjects and regression analysis is then mass were measured using 40K whole-body counting and used to develop the component estimation model. A key dual-energy X-ray absorptiometry (DXA), respectively. point is that descriptive models are population speci®c. There is a strong correlation between TBK and fat-free Thus, if methods are to be applied in the elderly, they must body mass (r 0.97), although the regression line intercept be developed and cross validated in an appropriate older is signi®cantly different from zero. Multiple regression population. analysis with TBK as dependent variable reveals that, in

European Journal of Clinical Nutrition Methods of body composition measurement SB Heyms®eld et al S28 for estimating the important visceral adipose tissue compartment (Abate et al, 1994).

Hydrodensitometry=air plethysmography. An important early advance was the introduction by Behnke and his colleagues of a re®ned accurate method of measuring body volume by underwater weighing (Going, 1996). Today there are a number of additional methods for measuring body volume, including air displacement plethysmography (McCrory et al, 1995). Behnke ®rst suggested and others later re®ned the now- classic `two-compartment' model for estimating total body fat and fat-free body mass (Behnke et al, 1942). Body volume is used in this model with the assumption of known and assumed constant densities of fat and fat-free body Figure 3 Total body potassium (TBK) vs fat-free body mass (FFM) in mass. While fat, or triglyceride, has a stable density in all 190 healthy adults. The univariate regression model is shown in the ®gure mammals so far studied, the density of fat-free body mass and the multiple regression model is presented below the ®gure. can vary depending on the proportions of water, protein, and minerals (Lohman, 1986). addition to fat-free body mass, sex and age are signi®cant Hydration and bone mineral mass change from birth predictor variables. Two important points arise from this onward and the density of the fat-free body mass compart- example. First, this example highlights the critical impor- ment is thus likely not `constant' across the lifespan. tance of considering age in model development. That is, the Substantial changes in the density of fat-free body mass multiple regression formula suggests that the ratio of TBK in adults were not observed by Visser et al (1997). to fat-free body mass is lower in older subjects compared to Baumgartner and colleagues, however, suggest that sub- their younger counterparts. Second, even though mechan- clinical edema and osteoporosis of variable degree in the istic TBK models (ie assumed stable TBK=FFM ratio) for elderly may produce large individual differences in the estimating fat-free body mass have been used for half a density of fat-free body mass (Baumgartner et al, 1995). century (Forbes, 1987), the highly signi®cant (r 0.98) The classic two-compartment mechanistic `body descriptive multiple regression model shown in the ®gure volume' method is thus susceptible to model errors when suggests that more than likely a descriptive model would applied in elderly populations. give equally good or even more accurate fat-free body mass estimates. A likely explanation for these observations is a Total body ¯uids. Dilution methods are available for change with age in the proportion of organs and tissues that quantifying body ¯uid and water spaces (Forbes, 1987). constitute fat-free body mass. The most important of these methods is dilution of stable or radio-labeled water (Schoeller, 1996). There is a long-standing observation that the hydration Magnetic resonance imaging=computed tomography. Al- of fat-free body mass is stable in mammals and humans in though available for over 25 y, computed tomography (CT) particular with TBW=FFM 0.73 (Figure 4; Pace & was only minimally applied in body composition research Rathburn, 1945). A two-compartment model for estimating because of expense and radiation exposure (Sjostrom, fat-free body mass from measured total body water can be 1991). The introduction of magnetic resonance imaging developed from this assumed stable hydration of about 0.73 (MRI) and the technical re®nements that followed led to the (Schoeller, 1996). Fat mass can then be calculated as body widespread use of this important method in body composi- weight minus fat-free body mass. tion research (Fowler et al, 1991). Although still costly, There are a number of studies that have examined the MRI studies are safe and instruments are available in most constancy of fat-free body mass hydration as a function of medical centers. age (Schoeller, 1996; Wang et al, 1999). Hydration is high The importance of both CT and MRI is that both very early in life and declines to a mature level in early methods produce cross-sectional images of tissue-system adulthood (Figure 5). There is then no or only a small level components at pre-de®ned anatomic locations. Image measured increase in hydration from maturity onward. As analysis software then allows estimation of the adipose with the related hydrodensitometry two compartment tissue, skeletal muscle, and other tissue-system level com- model, the two-compartment total body water model for ponent pixels. Pixels, or `picture elements', translate to estimating fat-free body mass may thus be inaccurate in respective tissue areas. Acquiring images at prede®ned individual or groups of elderly subjects. intervals and then integrating tissue component areas per- mits reconstruction of whole-body components such as skeletal muscle mass (Ross et al, 1992; Kvist et al, Dual-energy X-ray absorptiometry (DXA). The DXA 1986). CT and MRI can now quantify the mass of all method evolved from earlier single and dual photon major organs and tissues. absorptiometry methods for evaluating bone mineral These two imaging methods are based on mechanistic (Mazess et al, 1990). DXA systems share in common an models that have no known age dependence. CT and MRI X-ray source that, after appropriate ®ltration, emits two also afford the important and novel opportunity to quantify effective photon energy peaks (Figure 6). The attenuation tissue-system level components. Lastly, both imaging of the two energy peaks relative to each other depends on methods provide the only presently fully accepted approach the elemental content of tissues through which the photons

European Journal of Clinical Nutrition Methods of body composition measurement SB Heyms®eld et al S29

Figure 4 Fat-free body mass (FFM) hydration vs log body mass in nine mammals (left). Total body water vs FFM observed in nine human cadavers (right). From Wang et al (1999c), with permission.

Figure 7 Mass fraction of four elements (H, C, N, O) in three components (lean soft tissue, fat, and bone mineral). Residual includes remaining elements including calcium. Figure 5 Fat-free body mass hydration versus age. The data points represent mean values from actual previous published studies and the smooth curve is a derived function based on the theoretical model of Wang DXA fat and bone mineral estimates. Excessive or de®cient et al. From Wang et al (1999c) with permission. ¯uid volume would be appropriately registered as changes in lean soft tissue (Pietrobelli et al, 1998). Accordingly pass. Bone, fat, and lean soft tissues are relatively rich in there is no reason to speci®cally challenge the validity of calcium=phosphorus, carbon, and oxygen, respectively DXA body composition measurements in healthy elderly (Figure 7; Pietrobelli et al, 1996). DXA systems are populations. designed to separate pixels based on appropriate models In addition to availability, low per-subject cost, and and relative attenuation into bone mineral, fat, and lean soft safety, DXA provides the opportunity to reproducibly tissue (Figure 6). There are no known factors, including measure the mass of total body and regional components hydration effects that signi®cantly in¯uence the validity of such as bone mineral that could not easily be measured in the past.

Multi-component models. The limitations of two-compartment methods led Siri (1961), Anderson (1963), and others to propose more complex multicomponent methods. There are now several well accepted multicomponent methods (Table 1) that are designed to quantify three or more components in addition to those mentioned earlier for DXA, MRI and CT. Several generalizations regarding multicomponent methods are important when considering application of these methods to elderly populations. First, adding more measurements and components to the developed model usually reduces model error (Guo et al, 1996). For example, purported changes with aging in fat-free body mass hydra- Figure 6 Simpli®ed schematic of DXA main features (left) and three- tion would cause model errors for total body water and component model (right). hydrodensitometry two-compartment methods. These

European Journal of Clinical Nutrition Methods of body composition measurement SB Heyms®eld et al S30 Table 1 Mechanistic models for estimating total body fat mass (kg) based on measured body weight and volume

Model Measurable properties Known component (s)

Two-compartment fat 4.956BV 7 4.506BW BV, BW none Three-compartment fat 2.0576BV 7 0.7866TBW 7 1.2866BW BV, BW TBW Four-compartment fat 6.3866BV 3.9616mineral 7 6.096BW BV, BW mineral fat 2.756BV 7 0.7146TBW 1.1486mineral 7 2.056BW BV, BW TBW, mineral fat 2.756BV 7 0.7146TBW 1.1296Mo 7 2.0376BW BV, BW TBW, Mo fat 2.5136BV 7 0.7396TBW 0.9476Mo 7 1.796BW BV, BW TBW, Mo

Abbreviations: BV, body volume (L); BW, body weight (kg); Mo, bone mineral; TBW, total body water (kg). Modi®ed from Heyms®eld et al (1996) with permission.

model errors are minimized by measuring both body volume and total body water as proposed in the three- compartment model of Siri (1961). Combinations of body volume, total body water, and bone mineral mass now permit development of four or more component models (Heyms®eld et al, 1996). The second generalization is that with increasing num- bers of model components, measurement error increases (Guo et al, 1996). That is, each measurement of a component or property is accompanied by measurement error and this error is propagated to the ®nal component estimate. Multicomponent methods are important because they afford the opportunity to quantify more components of biological interest with less potential age-related model error. Figure 8 Sum of four skinfolds (SF) vs subcutaneous (S) adipose tissue (AT) mass derived by MRI in 190 healthy adults. The multiple regression model is presented below the ®gure. Descriptive An important observation arising from review of mechan- The relationship between summed skinfold thickness istic methods is that some, although possibly not all, of the and MRI-derived subcutaneous adipose tissue is presented available methods provide accurate component estimates in Figure 8. The correlation is quite strong, suggesting that regardless of subject age. Hence, there are appropriate the measured skinfolds are collectively a good measure of available reference methods for preparing descriptive subcutaneous adiposity. However, multiple regression ana- body composition equations in elderly populations. lysis, as shown in the ®gure, indicates that age is a Although there are several available descriptive methods, signi®cant independent variable in prediction of subcuta- two will be reviewed here as examples, anthropometry and neous adipose tissue after controlling ®rst for skinfold sum. bioimpedance analysis (BIA). Older subjects have more subcutaneous adipose tissue for the same skinfold sum than do young subjects. Gender did Anthropometry. Anthropometric methods apply skinfold not enter as a signi®cant independent variable in the model. thickness, circumference, and other somatic measurements One possible explanation for this observation is that sub- to the assessment of component mass. Estimates of total cutaneous adipose tissue distribution changes with aging body fat (Durnin & Womersley, 1974), skeletal muscle and therefore the selected skinfold sum translates to a (Doupe et al, 1997), and other components are based on different total subcutaneous adipose tissue mass in old prediction models usually developed using simple or multi- subjects than it does in young subjects. Whatever the ple regression analysis. A simple example reveals some of the important factors that should be considered when developing methods for use in elderly populations. Subjects were healthy adult men and women varying widely in age and body weight. Each subject had a head-to- toe MRI scan consisting of about 40 ± 50 slices at 4 cm increments. Scans were analyzed for adipose tissue in each slice and subcutaneous, visceral and total body adipose tissue were calculated from the collective subject images. Subjects also had four skinfolds measured: biceps, triceps, subscapular, and iliac crest (Durnin & Womersley, 1974). The four skinfolds were summed as a measure of collective skinfold thickness. These four skinfold sites and the sum of the four measured skinfolds are used in the classic Figure 9 Sum of four skinfolds (SF) vs total adipose tissue (AT) mass Womersley ± Durnin anthropometric method of estimating derived by MRI in 190 healthy adults. The multiple regression model is total body fat (Durnin & Womersley, 1974). presented below the ®gure.

European Journal of Clinical Nutrition Methods of body composition measurement SB Heyms®eld et al S31 explanation, age enters the model as a signi®cant predictor Bioimpedance analysis. The BIA method is widely variable. applied in the ®eld as a means of estimating fat-free body Extending this analysis to the next step, the association mass (Baumgartner et al, 1990), total body water, and total between skinfold sum and total body adipose tissue is body fat (ie body weight 7 fat-free body mass). BIA presented in Figure 9. Again, the correlation is quite systems generate a single frequency alternating current strong and supports the link between measured skinfold and a pair of receiver electrodes (Foster & Lukaski, thickness and total body adiposity. However, multiple 1996) detects the voltage drop across a measured tissue regression analysis also reveals a signi®cant age term in bed. Multiple frequency BIA systems are also available the total body adipose tissue regression model presented (Foster & Lukaski, 1996) as are systems that measure in the ®gure. A portion of the age term presence must impedance, resistance, reactance, and phase angle in spe- by necessity carry over from the age-related skinfold ci®c body segments and regions (Tan et al, 1997). sum=subcutaneous adipose tissue relation presented for Most simple BIA systems include body composition the subjects in Figure 8. However, there is another age- software based upon descriptive models. Virtually all related effect that is easily observed in the data. It has been comprehensive published BIA prediction formulas include known for some time that older subjects have a larger an age term (Lukaski et al, 1985; Houtkooper et al, 1996). visceral adipose tissue mass than do young subjects If lean tissues and their associated electrolyte containing (Borkan et al, 1982). Skinfolds, which provide a measure ¯uids are the primary electrical conductors, what explains of subcutaneous adipose tissue, cannot account for varia- the presence of age terms in various component prediction tion in visceral adipose tissue when estimating total body models? The answer to this question is not entirely clear, adiposity. This possibility was explored by plotting the although several explanations are that: weakly conducting ratio of subcutaneous adipose tissue to total adipose tissue adipose tissue increases with age and may contribute to versus age in these subjects (Figure 10). It is clear that in measured impedance in the elderly (Baumgartner et al, both men and women, but particularly in men, the fraction 1998); lean, particularly skeletal muscle, distribution may of total adipose tissue sequestered in the subcutaneous change with age, thus altering the nature of electrical compartment becomes lower with greater age. pathways (Nunez et al, 1999); and muscle, the main These observations provide only an example of what has conductor tissue in arms and legs, may change in composi- been recognized for some time: adipose distribution, both tion and speci®c resistivity with age. While these are within the subcutaneous compartment and between the speculations, the importance of age-related effects is that subcutaneous and visceral compartments, varies with age any developed BIA descriptive component prediction (Borkan et al, 1982). Descriptive anthropometric methods models should be developed and cross-validated in elderly must therefore be carefully developed and cross-validated subjects if they are to be applied in this population. for speci®c use in older subjects. An important consideration with respect to all body composition methods, particularly the ones that are descrip- Conclusion tive, is the source and nature of subjects on whom predic- Evaluating the amount of and changes in energy stores and tion formulas are developed. In particular, a prediction protein content is fundamental to the study of aging in formula developed on one ethnic or age group may not relation to energy and protein metabolism. Quantifying the be accurate when applied to another ethnic or age group. mass of `metabolically active' compartments is also an For example, in a recent study we observed signi®cant important aspect of aging research. There are now a differences in body fat, after controlling ®rst for age, number of available body composition reference methods gender, and body mass index, between Caucasian subjects that do not rely on age-dependent models and that can serve in the USA and UK, and Asian subjects in Japan (Gallagher as standards for developing descriptive body composition et al, 2000). Others have made similar observations when methods. carrying out cross-ethnic and=or cross-country studies In summary: (Roche, 1995). Again, this highlights the potential population speci®city of developed descriptive body composition 1. Body composition changes with age and associated methods. processes such as immobility and malnutrition. 2. Some currently assumed stable component relationships may actually change with age. 3. All major body composition components are measurable using one or more methods that are based on models that are not dependent on age-related assumptions. 4. Some methods, particularly descriptive ®eld methods, may be based on age-sensitive assumptions and measurements. These and other descriptive methods should be developed using component reference methods chosen for their insensitivity to subject age. 5. As body composition differences may exist between races, cultures, and countries, it would be useful to create international cooperative groups that develop widely applicable descriptive ®eld methods based on methods such as anthropometry and BIA.

Figure 10 Fraction of total adipose tissue (AT) as subcutaneous adipose Acknowledgements ÐThis work was supported by National Institutes of tissue (SAT) vs age in 190 healthy adults. Health grant PO1-DK 42618.

European Journal of Clinical Nutrition Methods of body composition measurement SB Heyms®eld et al S32 References Lohman TG (1986): Applicability of body composition techniques and constants for children and youths. Exerc. Sport Sci. Rev. 14, 325 ± 357. Abate N, Burns D, Peshock RM, Garg A & Grundy SM (1994): Estimation Lukaski HC, Johnson PE, Bolonchuk WW & Lykken GI (1985): Assess- of adipose tissue mass by magnetic resonance imaging: validation ment of fat-free mass using bioelectrical impedance measurement of the against dissection in human cadavers. J. Lipid Res. 35, 1490 ± 1496. human body. Am. J. Clin. Nutr. 41, 810 ± 817. Anderson EC (1963): Three-component body composition analysis based Mazess RB, Barden HS, Bisek JP & Hanson J (1990): Dual-energy x-ray on potassium and water determination. Ann. NY Acad. Sci. 110, absorptiometry for total-body and regional bone-mineral and soft-tissue 189 ± 212. composition. Am. J. Clin. Nutr. 51, 1106 ± 1112. Baumgartner RN, Chumlea WC & Roche AF (1990): Bioelectric impe- McCrory MA, Gomez TD, Bernauer EM & Mole PA (1995): Evaluation of dance for body composition. Exercise Sport Science Review 18, a new air displacement plethysmograph for measuring human body 193 ± 224. composition. Med. Sci. Sports Exerc. 27, 1686 ± 1681. Baumgartner RN, Heyms®eld SB & Roch AF (1995): Human body Nunez C, Gallagher D, Grammes J, Baumgartner RN, Ross R, Wang ZM, composition and chronic disease. Obes. Res. 3, 73 ± 95. Thorton J & Heyms®eld SB (1999): Bioimpedance analysis: potential Baumgartner RN, Ross R, & Heyms®eld SB (1998): Does adipose tissue for measuring lower limb skeletal muscle mass. JPEN 23, 96 ± 103. in¯uence bioelectrical impedance in obese men and women? J. Appl. Pace N & Rathbun EN (1945): Studies on body composition (III): the body Physiol. 84, 257 ± 262. water and chemically combined nitrogen content in relation to fat Behnke AR, Feen BG & Welham WC (1942): The speci®c gravity of content. J. Biol. Chem. 158, 685 ± 691. healthy men. J. Am. Med. Assoc. 118, 495 ± 498. Pietrobelli A, Formica C, Wang ZM & Heyms®eld SB (1996): Dual- Borkan GA, Gerzof SG & Robbins AH (1982): Assessment of abdominal fat energy X-ray absorptiometry body composition model: review of content by computerized tomography. Am. J. Clin. Nutr. 36, 172 ± 177. physical concepts. Am. J. Physiol. 271, 941 ± 951. Chettle DR & Fremlin JH (1984): Techniques of in vivo neutron activation Pietrobelli A, Wang ZM, Formica C & Heyms®eld SB (1998): Dual- analysis. Phys. Med. Biol. 29, 1011 ± 1043. energy X-ray absorptiometry: fat estimation errors due to variation in Doupe MB, Martin AD, Searle MS, Kriellaars DJ & Giebrecht GG (1997): soft tissue hydration. Am. J. Physiol. 274, E808 ± E816. A new formula for population-based estimation of whole body muscle Roche AF (1995): Body composition of ethnic groups in the US. Asia mass in males. Can. J. Appl. Physiol. 22, 598 ± 608. Paci®c J. Clin. Nutr. 4, 63 ± 67. Dumin JV & Womersley GA (1974): Body fat assessed from total body Ross R, Leger L, Morris D, de Guise J & Guardo R (1992): Quanti®cation density and its estimation from skinfold thickness measurements on 481 of adipose tissue by MRI: relationship with anthropometric variables. men and women aged 16 to 72 y. Br. J. Nutr. 32, 77 ± 97. J. Appl. Physiol. 72, 787 ± 795. Forbes GB (1987): Human Body Composition. New York: Springer. Schoeller DA (1996): Hydrometry. In: Human Body Composition, ed. AF Foster KR & Lukaski HC (1996): Whole body impedance Ð what does it Roche, SB Heyms®eld, TG Lohman, pp 25 ± 44. Champaign IL: Human measure? Am. J. Clin. Nutr. 64, 388S ± 396S. Kinetics. Fowler PA, Fuller MF, Glasby CA, Foster MA, Cameron GG, McNeil G & Siri WE (1961): Body composition from ¯uid spaces and density: analysis Maughan RJ (1991): Total and subcutaneous AT distribution in women: of methods. In: Techniques for Measuring Body Composition, ed. H the measurement of distribution and accurate predication of quantity by Brozek, A Henschel, pp 223 ± 244. Washington DC: NAS Ð National using magnetic resonance imaging. Am. J. Clin. Nutr. 54, 18 ± 25. Research Council. Gallagher D, Heyms®eld SB, Heo M, Jebb S, Murgatroyd P & Sakamoto Y Sjostrom L (1991): A computer-tomography based multicomponent body (2000): Body mass index guidelines: corresponding % fat standards composition technique and anthropometric predictions of lean body based on three country study (in press). mass, total and subcutaneous adipose tissue. Int. J. Obes. 15, 19 ± 30. Going SB (1996): Densitometry. In: Human Body Composition, ed. AF Tan YX, Nunez C, Sun Y, Zhang K, Wang ZM & Heyms®eld SB (1997): Roche, SB Heyms®eld, TG Lohman, pp 3 ± 24. Champaign IL: Human New electrode system for rapid whole-body and segmental bioimpe- Kinetics. dance assessment. Med. Sci. Sports Exerc. 29, 1269 ± 1273. Guo SS, Chumlea WC & Cockram DB (1996): Use of statistical methods Visser M, Gallagher D, Deurenberg P, Wang J, Pierson RN & to estimate body composition. Am. J. Clin. Nutr. 64, 428S ± 435S. Heyms®eld SB (1997): Density of fat-free body mass: relation- Heyms®eld SB, Wang ZM & Withers R (1996): Multicomponent mole- ship with race, age, and level of body fatness. Am. J. Physiol. 272, cular-level models of body composition analysis. In: Human Body 781 ± 787. Composition, ed. AF Roche, SB Heyms®eld, TG Lohman, pp Wang Z, Wang ZM & Heyms®eld SB (1999a): History of the study of 129 ± 148. Champaign, IL: Human Kinetics. human body composition: a brief review. Am. J. Hum. Biol. 11, Heyms®eld SB, Baumgartner RN, Ross R, Allison DB & Wang ZM 157 ± 165. (1998): Evaluatiion of total and regional body composition. In: Hand- Wang ZM, Deurenberg P, Wang W, Pietrobelli A, Baumgartner R, book of Obesity, ed. GA Bray, C Bouchard, WPT James, chap 3, pp Heyms®eld SB (1999b): Hydration of fat-free body mass: review and 41 ± 77. New York: Marcel Dekker. critique of a classic body composition constant. Am. J. Clin. Nutr. 69, Houtkooper LB, Lohman TG, Going SB & Howell WH (1996): Why 833 ± 841. bioelectrical impedance analysis should be used for estimating adipos- Wang ZM, Deurenberg P, Wang W, Pietrobelli A, Baumgartner RN & ity. Am. J. Clin. Nutr. 64, 436S ± 448S. Heyms®eld SB (2000): Hydration of fat-free body mass: new physio- Kehayias JJ, Heyms®eld SB, LoMonte AF, Wang J & Pierson RN (1991): logical modeling approach. Am. J. Physiol. (in press). In vivo determination of body fat by measuring total body carbon. Am. Wang ZM, Heshka S, Pierson RN, Heyms®eld SB (1995): System- J. Clin. Nutr. 53, 1339 ± 1344. atic organization of body composition methodology: overview with Kvist H, Sjostrom L & Tylen U (1986): Adipose tissue volume determina- emphasis on component-based methods. Am. J. Clin. Nutr. 61, tions in women by computer tomography: technical considerations. Int. 457 ± 465. J. Obes. 10, 53 ± 67.

European Journal of Clinical Nutrition