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Body Composition and Physical Performance 1992. Pp. 175-184. Washington, D.C. National Academy Press 10 Body Composition, Morbidity, and Mortality William Cameron Chumiea and Richard N. Baumgartner INTRODUCTION Relationships among body composition and morbidity and mortality are complicated by several factors, including the accuracy and reliability of methods of measuring body composition and the effects of age, gender, race, genetic, environmental (for example, altitude, climate), and behavioral (for example, diet, smoking) factors. In discussing measures of body com- position, it is helpful to distinguish criterion from prediction methods, and direct measures from indirect estimates. Criterion methods measure physi- cal properties, chemical or anatomical constituents that are either direct measures of well-defined components (for example, total body water from deuterium dilution space), or they can be used to calculate indirect esti- mates of other components of body composition (for example, percent body fat [BF] from total body water). Prediction methods are generally based on measurements of less specific aspects of the body, such as circumferences, skinfold thicknesses, or bioelectric impedance. These variables must be used in equations that are calibrated against values from criterion methods. In the selection of criterion or prediction methods, consideration should be given as to what aspect of body composition is to be related to a disease. METHODS OF MEASURING BODY COMPOSITION Underwater weighing, from which body density is derived, continues to be considered the "gold standard" among the indirect criterion methods of 175
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76 WILLIAM CAMERON CHUMLEA AND RICHARD N. BAUMGARTNER estimating body composition, despite long-standing recognition of its limi- tations (Sir), 1961~. Other indirect criterion methods include potassium 40 counting, total body water from tritium or deuterium dilution, and total body carbon from neutron activation. In addition to technical errors of measurement, which are considered to be random, these methods may be subject to nonrandom and/or systematic errors due to deviations of individ- uals from the assumed proportionality values for body composition associ- ated with age, gender, race, and other factors. These errors can distort as well as attenuate associations with morbidity or mortality. In addition to technical errors of measurement, methods of predicting body composition also contain sampling errors, as well as errors associated with the limitations of the criterion method selected for calibration. The most commonly used prediction methods at present employ anthropometry and bioelectric impedance. Equations for predicting fat-free mass (FFM) and total BE using anthropometric and bioelectric variables have been de- veloped for young or middle-aged adults, many of which may be appropri- ate for use with military personnel (Barillas-Mury et al., 1987; Baumgartner et al., 1989; Chumlea et al., 1988; Hodgdon and Fitzgerald, 1987; Lukaski et al., 1985; Lukaski and Bolonchuk, 1987; Segal et al., 1985; Zillikens and Conway, 19871. However, most of these prediction equations have not been cross-validated properly to determine their accuracy when applied to popu- lations other than the ones used in development (Guo et al., 19894. Methods such as neutron activation, computed tomography (CT), dual photon absorptiometry (DPA), and magnetic resonance imaging (MRI) are invasive or require cumbersome, expensive equipment and specialized per- sonnel. As a partial result of these problems, reported reference data for these measures of body composition and their associations with risk factors are limited. CT and MRI are most useful as methods of regional body composition analysis and are among the only methods currently available for quantifying amounts of intraabdominal adipose tissue for which there may be considerable risk for several endocrine and metabolic diseases (Baum- gartner et al., 1987; Kvist et al., 1986; Larsson et al., 1984~. In contrast to CT, MRI does not involve exposure to ionizing radiation and is associated with little risk. MRI spectroscopic techniques can provide important infor- mation regarding the chemical composition as well as anatomical distribu- tion of muscle and fat. Photon absorptiometry is an accurate method of quantifying bone min- eral density and for estimating total body mineral and skeletal mass. Accu- rate estimates of bone mineral density and total body bone mineral are needed to adjust equations for estimating BE from body density. The cur- rent equations are subject to systematic errors since they assume that bone density and the proportion of FFM that is bone are constants, despite evi
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BODY COMPOSlTlON, MORBlDlTY, AND MORTALITY 177 dence for their variability with factors including gender, ethnicity, and age (Lohman, 19864. Presently, there is a paucity of information on bone densi- ty for non-White individuals. DPA has the capability of directly estimating soft tissue composition for the whole body or body segments. The recent development of dual-energy x-ray absorptiometry (DEXA) with whole body scanning at relatively low cost may make this an important criterion method for body composition in the future (Mazess et al., 19841. There are many methods of measuring body composition, and the choice depends upon the experimental setting. Briefly, in a field setting one is generally limited to anthropometry, that is, skinfolds and circumferences, possibly some limited densitometry equipment, body water estimates de- pending on access to a laboratory for analysis, and more recently bioelectric impedance. All of these methods can have large measurement errors or limited specificity depending on the sample studied. For example, in young adults, the present gold standard of underwater weighing is estimated to have at best, a minimum residual error of 2.5 percent for estimates of per- cent BE (Behnke and Wilmore, 19741. This error, however, is likely to be greater in most settings because the accuracy of underwater weighing de- pends on the performance of the subject and the quality of the equipment. The use of an easily accessible water tank and a stable seat or platform suspended from load cells rather than spring scales will improve perfor- mance and accuracy of underwater weighing. If validated, DEXA could be the method of choice in the future, because it can provide both regional and whole body estimates of fat, lean mass, and bone mass at a relatively low cost. Because DEXA is passive and involves very low exposure to ionizing radiation, it is appropriate for repeated observations. ACCURACY OF MEASUREMENTS Body composition can be measured with increasingly greater accuracy than in the past. However, we are still hampered by the way measurement values are converted into amounts of bone, muscle, and fat. A major con- cern in this area is the validity of the assumptions underlying estimates of body composition. To date, most studies of body composition have used the simple two-compartment model or Siri's equation (19611. This equation divides the body into fat and FFM on the basis of body density from under- water weighing. Siri's equation is based on the assumptions that the densi- ties of fat and FFM are 0.9 g/ml and 1.10 g/ml, respectively (Pace and Rathburn, 19451. The density of fat varies little among individuals across age, but the density of FFM can vary substantially among individuals de- pending on the relative proportions of its constituents, mainly water, pro- tein, and osseous and nonosseous mineral and the age of the person (Lohm
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78 WILLIAM CAMERON CHUMLEA AND RICHARD N. BAUMGARTNER an, 19861. A variation of plus or minus 0.02 g/ml away from the assumed value of 1.10 g/ml for the density of FFM can translate into an error of plus or minus 5 percent BE for an individual with a body density of 1.05 g/ml. These errors can be compounded due to reported greater variations in body water amount and bone mineral content among individuals with differences in age, race, and gender, which affect body density. Also, individuals who are physically fit tend to have higher bone mineral content and as a result, may have artificially low percent BE values when calculated using Siri's equation. USING A FOUR-COMPARTMENT MODEL FOR STUDIES OF BODY COMPOSITION The problems of estimating body composition can be improved by using a four-compartment model, which is now considered necessary for studies de- termining body composition. The equation for this model is as follows: 1/D = F/df + TBW/dw + B/db + C, where 1/D is the sum of the volumes (fractions of weight/density) for fat (F), total body water (TBW), total bone mineral (B) and protein plus small amounts of nonosseous mineral and glycogen (C). In comparison to the two-compartment model, the volume of FFM is broken into three constituents: water, bone mineral, and protein. Other nonosseous minerals and carbohydrates that are only a small fraction of FFM (about 1.5 percent in young adults) are lumped together with the protein fraction. Water is the largest fraction of the fat-free body and is assumed to be about 73 percent of the fat-free volume in young adults. However, studies show that this percentage is somewhat higher in women and increases with levels of adiposity (Noppa et al., 1980; Pierson et al., 1982; Steen et al., 1977, 1979~. An increase in the amount of water will decrease the overall density of the FFM, but an increase in bone mineral content will increase the density. The fraction of FFM composed of pro- tein, nonosseous mineral, and carbohydrate is assumed to be relatively con- stant, but because of changes in these body tissues, such as an increase in connective tissue with age, and possible gender and racial differences, this assumption may be questionable. Before this model can be applied widely, however, it is necessary to establish the amount of difference among gender and racial groups that exists in the densities of the body components. With- out this information, estimates or predictions of body composition will be subject to significant errors of unknown magnitude when applied to unrep- resentative samples. Thus, our knowledge of body composition is limited when applied to women or members of non-White racial or ethnic groups.
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BODY COMPOSITION, MORBIDITY, AND MORTALITY ASSOCIATIONS AMONG BODY COMPOSITION, DISEASE AND DEATH Bone 179 Until recently, bone has been the largest unknown in body composition due to our inability to quantify it accurately and noninvasively. The associ- ations among bone and disease or death are not ones that usually affect individuals in the age range of most military personnel. With the use of DEXA, however, the potential exists for identifying young adults with low or falling amounts of bone mineral content or bone density who are at risk for osteoporosis or fractures due to physical stress in their military occupa- tional specialty. Fat-Free Mass Differences between individuals in the quantity and quality of FFM result in variations in physical ability and performance. However, there is little or no information that associates FFM with disease or death except for the changes that occur during weight loss or in association with eating disorders. With greater numbers of women in the military, the incidence of eating disorders and dieting problems could be expected to increase. These problems can be associated with potentially harmful losses of FFM in some individuals. Individuals who gain FFM or attempt to lose BE should be made aware that changes in FFM are accompanied by concurrent and corre- sponding changes in adipose tissue. The link in these changes may be due to the extragonadol aromatization of androgens to estrogens in muscle as well as adipose tissue (Segal et al., 19871. Excess Adipose Tissue The vast majority of the associations among body composition and morbidity and mortality relate to excess adipose tissue or fat. The main impact of these associations tends to be on the cardiovascular system, al- though the effects on an individual can be modified or compounded by environmental and genetic factors. Fat or lipid is a pervasive component of the body, but in regard to morbidity and mortality, it can be viewed more in terms of amounts and distribution of adipose tissue and of-the concentra- tions of various lipid molecules in the blood. High concentrations of total cholesterol, triglycerides, and the low-density lipoproteins are significantly associated with the occurrence of cardiovascular disease and increased risk of death due to myocardial infarction or stroke (Angel and Roncari, 1978; Bray, 1987; Hubert et al., 19831.
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180 WILLIAM CAMERON CHUMLEA AND RICHARD N. BAUMGARTNER Adipose tissue is either subcutaneous or internal. The location of the tissue may be associated with the type of lipid stored, the metabolic activity of the tissue, the size and number of adipocytes, its response to diet and age, and its ease of measurement (Bray, 1987; Kaplan, 1989~. The amounts and distribution of subcutaneous and internal adipose tissue are related to an individual's risk for cardiovascular disease, diabetes mellitus, hyperten- sion, and some forms of cancer (Haines et al., 1987; Kaplan, 1989; Larsson et al., 1984; Selby et al., 1989; Shimokata et al., 1989; Sparrow et al., 1986~. Many of these associations are confounded by the effects of smok- ing, diet, levels of physical activity, and genetic susceptibility. Measuring Body Fat The simplest measure of BF is weight. Individuals with above normal weights for their age and stature tend to have greater than normal levels of BF either in absolute amounts or in the percentage of the body that is fat (percent BF). These individuals are considered overweight and obese, but there can also be individuals who are overweight and not obese and individ- uals who are not overweight but are obese. Other convenient measures or indices of obesity are weight over stature squared or the body mass index (BMI), skinfold thicknesses, and ratios of body circumferences. There are numerous reports of the statistical relationships between body weight, rela- tive weight, skinfold thicknesses, weight for stature, or the BMI and risk for cardiovascular disease. In most of these analyses, the data have come from large population studies such as Framingham, the first and second National Health and Nutrition Examination Surveys and several large insurance in- dustry studies (Donahue et al., 1987; Hubert et al., 1983; Keys, 1989; Neser et al., 1986; Selby et al., 19891. All of these indices, however, do not have the same relationships with risk for disease or death. There is still some controversy depending on the measurement used, on the person's age, and on smoking habits. Those individuals with extreme levels of BMI are at risk, and those individuals with significant weight gains are at increased risk. Recently, Segal and coworkers (1987) reported that weight and BMI are not as important for the individual as it would appear. Weight and BMI are useful measures to describe levels of obesity indirectly in large samples, but for the individual, the amount and distribution of total BF is indepen- dently related to cardiovascular disease risk factors (Segal et al., 19871. Distribution of Body Fat in Adults Vague (1956) first reported that in adults the pattern of adipose tissue distribution differed by gender and that the masculine distribution was more closely related to endocrine and metabolic diseases. In the early 1980s,
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BODY COMPOSITION, MORBIDITY, AND MORTALITY 181 these facts were noticed again by Kissebah and coworkers (1982) who relat- ed the adipose tissue distribution or the waist-hip ratio to levels of cardio- vascular risk. This ratio attempts to describe an individual with a large waist circumference compared to a small hip circumference, that is, the masculine type, with the converse consisting of large hips to a small waist, or the feminine type. The masculine type or centripetal form tends to be produced by large deposits of internal adipose tissue, while the feminine type is due to large deposits of subcutaneous adipose tissue. This simple difference between internal and subcutaneous adipose tissue deposits is also related to differing levels of risk. The masculine or centripetal pattern is strongly associated with increased glucose intolerance resulting in non-in- sulin-dependent diabetes, heart disease, hypertension, and stroke and an increased risk for premature mortality (Bray, 1987; Donahue et al., 1987; Haines et al., 1987; Larsson et al., 1984; Seidell et al., 1985; Selby et al., 1989~. Individuals with the masculine pattern tend to have increased con- centrations of saturated fat within the internal adipose tissue deposits, high- er triglycerides, and lower high-density lipoprotein (HDL) cholesterol blood levels regardless of their gender (Baumgartner et al., 1987; Kaplan, 1989; Leclerc et al., 1983; Sedgwick et al., 1984; Segal et al., 1987; Wing et al., 19891. It has also been observed that smokers, even though they may be thin, have a greater waist-to-hip ratio than do nonsmokers who may have higher body weights. Upon the cessation of smoking, the body configura- tions of the smokers tend to move toward that of the feminine pattern with a smaller waist-to-hip ratio (Shimokata et al., 1989~. The primary problem with the use of the waist-hip ratio has been in measuring the circumferences at accepted locations. Much of the literature is confusing because someone's waist measurement is someone else's hip measurement. If the ratio is to be used, suitable landmarks for the measure- ments need to be identified and adhered to strenuously. Fortunately, the association between waist circumference and internal adipose deposits has been confirmed by computed tomography (Baumgartner et al., 1988; Kvist et al., 1986~. The increased availability of MRI combined with spectro- graphic analysis will provide further detail about the amounts and chemical content of internal adipose tissue. Thus, it appears that one of the major problems of BE and disease is primarily one of the deposition of internal adipose tissue. Upper body, centripetal, or masculine type of adipose tissue deposition is the major contributor to the risk of overweight or obesity. With weight reduction, and corresponding decreases in the amounts of in- ternal adipose tissue, many of the risks for cardiovascular disease are re- duced accordingly. Much of the work relating fat patterning and risk for disease has in- volved White women. There are only a few studies of men or Blacks except what has been reported from the national health surveys. There are
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82 WlLLlAM CAMERON CHUMLEA AND RICHARD N. BAUMGARTNER possible ethnic or racial differences in the levels of thresholds for risk or in the patterning of adipose tissue. These differences are being explored in Mexican-Americans where the waist-hip ratio is the preferred measure, but skinfold thickness ratios may be significant (Haffner et al., 1986, 1987; Reichley et al., 19871. Because of the diverse ethnic background of U.S. military personnel, the use of any single criterion for risk should be dis- cussed carefully. SUMMARY Body composition is an interdependent, multifaceted quantity. It is not yet possible to describe and quantify the tissues in the body with consistent levels of accuracy. It is hopeful that in the near future this goal will be attained in laboratory settings, but clinical or field procedures may remain relatively inaccurate and subject dependent. One can, however, determine when the distribution of tissues in the body's composition shifts toward a greater-than-normal level of fat or adipose tissue. In an individual with such a condition, the risk for disease and early death increases, but the magnitude of the shift relative to the threshold for the increased risk is affected by the age, gender, race, and living habits of the individual. Some of this change may be a normal manifestation of age, but it is evident that increased amounts of internal adipose tissue in the abdomen put one at the greatest health risk. ACKNOWLEDGMENT This work was supported by Grant HD-12252 and AG-08510 from the National Institutes of Health, Bethesda, Maryland. REFERENCES Angel, A., and D. A. K. Roncari. 1978. Medical complications of obesity. Can. Med. Assoc. J. 119:1408-1411. Barillas-Mury, C., C. Vettorazzi, S. Molina, and O. Pineda. 1987. Experiences with bioelectri- cal impedance analysis in young children: Sources of variability. Pp. 87-90 in In Vivo Body Composition Studies, K. J. Ellis, S. Yasumura, and W. D. Morgan, eds. London: The Institute of Physical Sciences in Medicine. Baumgartner, R. N., A. F. Roche, W. M. Chumlea, R. M. Siervogel, and C. J. Glueck. 1987. Fatness and fat patterns: Associations with plasma lipids and blood pressures in adults, 18 to 57 years of age. Am. J. Epidemiol. 126:614-628. Baumgartner, R. N., S. B. Heymsfield, A. F. Roche, and M. Bernardino. 1988. Abdominal composition quantified by computed tomography. Am. J. Clin. Nutr. 48:936-945. Baumgartner, R. N., W. C. Chumlea, and A. F. Roche. 1989. Estimation of body composition from bioelectric impedance of body segments. Am. J. Clin. Nutr. 50:221-226. Behnke, A. R., and J. H. Wilmore. 1974. Evaluation and regulation of body build and composi- tion. Englewood Cliffs, N.J.: Prentice-Hall.
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BODY COMPOSITION, MORBIDITY, AND MORTALITY 183 Bray, G. A. 1987. Obesity and the heart. Mod. Concepts Cardiovasc. Dis. 56:67-71. Chumlea, W. C., R. N. Baumgartner, and A. R. Roche. 1988. Specific resistivity used to estimate fat-free mass from segmental body measures of bioelectric impedance. Am. J. Clin. Nutr. 48:7-15. Donahue, R. P., E. Bloom, R. D. Abbott, D. M. Reed, and K. Yano. 1987. Central obesity and coronary heart disease in men. Lancet i:821-824. Guo, S., A. R. Roche, and L. Houtkooper. 1989. Fat-free mass in children and young adults predicted from bioelectric impedance and anthropometric variables. Am. J. Clin. Nutr. 50:435-443. Haffner, S. M., M. P. Stern, H. P. Hazuda, J. Pugh, J. K. Patterson, and R. M. Malina. 1986. Upper body and centralized adiposity in Mexican American and non-Hispanic whites: Relationship to body mass index and other behavioral and demographic variables. Int. J. Obes. 10:493-502. Haffner, S. M., M. P. Stern, H. P. Hazuda, J. Pugh, and J. K. Patterson. 1987. Do upper-body and centralized adiposity measure different aspects of regional body-fat distribution? Diabetes 36:43-51. Haines, A. P., J. D. Imeson, and T. W. Meade. 1987. Skinfold thickness and cardiovascular risk factors. Am. J. Epidemiol. 126:86-94. Hodgdon, J. A., and P. I. Fitzgerald. 1987. Validity of impedance predictions at various levels of fatness. Hum. Biol. 59:281-298. Hubert, H. B., M. Feinleib, P. M. McNamara, and W. P. Castelli. 1983. Obesity as an indepen- dent risk factor for cardiovascular disease. Circulation 67:968-977. Kaplan, N. M. 1989. The deadly quartet: Upper body obesity, glucose intolerance, hypertri- glyceridemia, and hypertension. Arch. Intern. Med. 149:1514-1520. Keys, A. 1989. Longevity of man: Relative weight and fatness in n~iddle age. Ann. Med. 21: 163-168. Kissebah, A. H., N. Vydelingum, R. Murry, D. J. Evans, A. J. Hartz, R. K. Kalkhoff, and P. W. Adams. 1982. Relation of body fat distribution to metabolic complications of obesity. J. Clin. Endocrinol. Metabol. 54:254-260. Kvist, H., L. Sjostrom, and U. Tylen. 1986. Adipose tissue volume determinations in women by computed tomography: Technical considerations. Int. J. Obes. 10:53-67. Larsson, B., K. Svardsudd, L. Welin, L. Wilhelmsen, P. Bjorntorp, and G. Tibblin. 1984. Abdominal adipose tissue distribution, obesity and risk of cardiovascular disease and death: 13-year follow up of participants in the study of men born in 1913. Br. Med. J. 288: 1401-1404. Leclerc, S., C. Bouchard, J. Talbot, R. Gauvin, and C. Allard. 1983. Association between serum high-density lipoprotein cholesterol and body composition in adult men. Int. J. Obes. 7:555-561. Lohman, T. G. 1986. Applicability of body composition techniques and constants for children and youths. Exerc. Sport Sci. Rev. 14:325-357. Lukaski, H. C., and W. W. Bolonchuk. 1987. Theory and validation of the tetrapolar bioelectri- cal impedance method to assess human body composition. Pp. 410-414 in In Vivo Body Composition Studies, K. J. Ellis, S. Yasumura, and W. D. Morgan eds. London: The Institute of Physical Sciences in Medicine. Lukaski, H. C., P. E. Johnson, W. W. Bolonchuk, and G. I. Lykken. 1985. Assessment of fat- free mass using bioelectrical impedance measurements of the human body. Am. J. Clin. Nutr. 41:810-817. Mazess, R. B., W. W. Peppler, and M. Gibbons. 1984. Total body composition by dual-pho- ton (153Gd) absorptiometry. Am. J. Clin. Nutr. 40:834-839. Neser, W. B., J. Thomas, K. Semenya, D. J. Thomas, and R. F. Gillum. 1986. Obesity and hypertension in a longitudinal study of black physicians: The Meharry cohort study. J. Chronic Dis. 39: 105- 113.
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84 WILLIAM CAMERON CHUMLEA AND RICHARD N. BAUMGARTNER Noppa, H., M. Andersson, C. Gengtsson, C. Bengtsson, A. Bruce, and B. Isaksson. 1980. Longitudinal studies of anthropometric data and body composition: The population of women in Goteborg, Sweden. Am. J. Clin. Nutr. 33:155-162. Pace, N., and E. N. Rathburn. 1945. Studies on body composition, III. The body water and chemically combined nitrogen content in relation to fat content. J. Biol. Chem. 158:685- 691. Pierson, R. N., J. Wang, E. W. Colt, and P. Neumann. 1982. Body composition measurements in normal man: The potassium, sodium sulfate and tritium spaces in 58 adults. J. Chronic Dis. 35:419-428. Reichley, K. B., W. H. Mueller, G. L. Hanis, S. K. Joos, B. R. Tulloch, S. Barton, and W. J. Schull. 1987. Centralized obesity and cardiovascular disease risk in Mexican Americans. Am. J. Epidemiol. 125:373-386. Sedgwick, A. W., A. H. Davidson, R. E. Taplin, and D. W. Thomas. 1984. Relationships between weight change and changes in blood pressure and serum lipids in men and women. Int. J. Obes. 8:343-353. Segal, K. R., B. Gutin, E. Presta, J. Wang, and T. B. Van Itallie. 1985. Estimation of human body composition by electrical impedance methods: A comparative study. J. Appl. Phys- iol. 58: 1565-1571. Segal, K. R., A. Dunaif, B. Gutin, J. Albu, A. Nyman, and F. X. Pi-Sunyer. 1987. Body composition, not body weight is related to cardiovascular disease risk factors and sex hormone levels in men. J. Clin. Invest. 80:1050-1055. Seidell, J. C., J. C. Bakx, E. De Boer, P. Durenberg, and J. G. A. J. Hautvast. 1985. Fat distribution of overweight persons in relation to morbidity and subjective health. Int. J. Obes. 9:363-374. Selby, J. V., G. D. Freidman, and C. P. Quesenberry. 1989. Precursors of essential hyperten- sion. Am. J. Epidemiol. 129:43-53. Shimokata, H., D. C. Muller, and R. Andres. 1989. Studies in the distribution of body fat. J. Am. Med. Assoc. 261:1169-1173. Siri, W. E. 1961. Body composition from fluid spaces and density: Analysis of methods. Pp. 224-244 in Techniques for Measuring Body Composition, J. Brozek and A. Henschel, eds. Washington, D. C.: National Academy of Sciences. Sparrow, D., G. A. Borkan, S. G. Gerzof, C. Wisniewski, and C. W. Silbert. 1986. Relation- ship of fat distribution to glucose tolerance. Diabetes 35:411-415. Steen, B., A. Bruce, B. Isaksson, T. Lewin, and A. Svanborg. 1977. Body composition in 70- year-old males and females in Goteborg, Sweden: A population study. Acta Med. Scand. 611(suppl):87-112. Steen, G. B., B. Isaksson, and A. Svanborg. 1979. Body composition at 70 and 75 years of age: A longitudinal population study. J. Clin. Exp. Gerontol. 1:185-200. Vague, J. 1956. The degree of masculine differentiation of obesities: A factor determining predisposition to diabetes, atherosclerosis, gout and uric calculus. Am. J. Clin. Nutr. 4:20-34. Wing, R. R., C. H. Bunker, L. H. Kuller, and K. A. Matthews. 1989. Insulin, body mass index and cardiovascular risk factors in premenopausal women. Arteriosclerosis 9:479-484. Zillikens, M. C., and J. M. Conway. 1987. Estimation of lean body mass in black adults by total body impedance. Fed. Proc. 46:1335.
Representative terms from entire chapter: