Body fat assessment in women. Special considerations.

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Sports Medicine 13 (4): 245-269, 1992 0112-1642/92/0004-0245/$12.50/0 © Adis International Limited. All rights reserved. SPOll17

Body Fat Assessment in Women

Special Considerations

James A. Vogel and Karl E. Friedl Occupational Physiology Division, US Army Research Institute of Environmental Medicine, Natick, Massachusetts, USA I

Contents 245 246 24"'

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25 259 260 260

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Summary

Summary I. Gender Differences l.l Gender Values 1.2 Two-Compartment Model Assumptions 1.3 Regional Distributions of Body Fat 2. Androgenicity 3. Ethnicity 4. Obesity 5. Reproductive Cycle 5.1 Menstrual Cycle 5.2 Amenorrhoea 5.3 Pregnancy and Lactation 5.4 Aging and Menopause 6. Anthropometric Prediction 6.1 Body Mass Index 6.2 Generalised Equations 6.3 Predictive Equations for Athletes 7. Conclusions

Methods of in vivo body fat estimation are based on simple assumptions about body composition which work reasonably well for men, while estimations in women have been largely extrapolated from the male studies so that women are treated as men with just more of the same fat. Compared to men, fat regulation in women is considerably more elaborate , with more and different sites for storage and a larger proportion of fat distributed to the extremities and in subcutaneous locations. Thus, a ratio of waist-to-hips girth which reflects increasing fatness in men only specifies 2 different extremes of a broader spectrum of possibilities for fat distribution in women . This complicates anthropometric prediction of total fatness and clearly limits the generalisability of any female equations. Anthropometric methods are further confounded by difficulties in the criterion methods against which they are developed. For example, the validity

1 The views, opinions, and findings contained in this art icle are those of the authors and should not be construed as an official Department of the Army posit ion, policy or decision.

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Sports Medicine 13 (4) 1992

of assumptions about the fractional contributions of bone mineral and body water to fat-free mass and density may not hold through the reproductive cycles. Women athletes involved in weight-bearing or strength training may increase bone mineral content above average values but if they become amenorrhoeic, bone mineral density may fall significantly below average values. Fit premenopausal women distribute fat differently and have a higher bone mineral content than unfit postmenopausal women. Genetic factors which also affect criterion method assumptions in men are superimposed on these additional complications in women. Body fat in female athletes extends across almost the entire range of female fatness, with some of the lowest measurements in distance runners and body builders which fall into the normal male range, but also with some relatively high values in swimmers and strength athletes, which would classify these women as obese by male standards. Thus, total body fat reflects a more complex regulation and has a different meaning to health and performance in women than it does for men. Predictive equations for women athletes should be developed with a view to the specific group and ultimate purpose to which they will be applied.

Quantification of body composition has become an integral part of the assessment of nutritional status and physical development in sports and in physically demanding occupations. For example, body fat may be assessed for its negative influence on endurance activities, and muscle mass, for its positive influence on strength or power events. The measurement of body composition, however, has posed significant obstacles, and in some circumstances, the application constitutes a misuse of the science when the assessment is no better than what could be obtained from bodyweight alone. Traditional laboratory procedures for the estimation of body fat such as hydrostatic weighing and total body water are comparatively cumbersome, expensive, and not always readily available, while more expedient methods, such as anthropometry and electrical impedance, suffer from concerns about validity and accuracy. Each method presents unique problems when applied to women; the first, because of assumptions which were based largely on male reference standards and physiology, and the second, because of the greater variety of sites of fat deposition and factors regulating fat deposition in women. The topics covered in this article include gender fat patterns and sex-specificfat, how well the malebased criterion method assumptions serve the prediction of fatness in women, variations in hydration and bone density with menopause, menstruation, pregnancy and lactation, and the modifying

factors of virilisation, and extremes of diet and exercise, with emphasis on athletes. This review addresses the effects of these female-specific factors and how they may influence the measurement of body fat in laboratory methods as well as fat patterning which affects anthropometric prediction techniques.

1. Gender Differences Gam (1957) made the early observation from a soft tissue x-ray technique (fat-shadow measurements) that 'women carry more fat on and less in their smaller frames' compared to men. More recent technology, such as computed tomography, has confirmed this difference in subcutaneous and visceral fat areas in men and women (Enzi et al. 1986; Kvist et al. 1986;Weits et al. 1988) [fig. 1]. Because women tend to be smaller and have less bone and muscle, absolute skinfold measurements for a given relative fatness are smaller than for men (Durnin & Womersley 1974), but this does not obviate the greater proportional distribution of fat to the surface. Women not only carry more of their fat on the outside, they also distribute more of it to the extremities than men, and this is reflected in the higher triceps and thigh skinfold thicknesses relative to trunk measures, such as the subscapular skinfold. It is also readily evident to any casual observer that women have an obligatory deposition of fat in gender-specific sites, most import-

247


1.1 Gender Values

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Fig. 1. Mean body fat percentages for 3 men and 3 women at 7 cross-sectional sites analysed by computed tomography (Weits et al. 1988).Site 0 represents the umbilicus and positive numbers indicate sites above the umbilicus; internal fat is indicated by solid circles and external fat by open circles (reprinted with the permission of International Journal of Obesity).

antly, the breasts, hips and thighs. Men do not have this obligatory storage and deposit excess fat in the abdominal region which, in healthy young women, is teleologically 'reserved' for the abdominal expansion which accompanies fetal growth. The differences are the basis for sex-specific anthropometric equations, where skinfold and many circumference measurements in women reflect toal fatness in a different way than they do for men. These differences also form the basis of a higher total body fat in women reflecting these obligatory fat stores which may have different physiological roles than excess storage fat, complicating the relationship of total fatness to physical performance, nutritonal status and health risk in women (fig. 2).

Table I depicts reference values for young adult nonathletic women compared to men based on a composite of reference sources. Women are characterised by a greater fat content and smaller fatfree (FFM) and skeletal muscle mass than men. Based on underwater weighing and the 2-compartment Siri equation, the fat content of the reference woman is 28%,or at least 10%body fat units greater than the reference man (Forbes 1987). A similar difference of 10% has been observed in high fitness, young West Point cadets, assessed by the Durnin-Womersley skinfold equations at 14% (males) and 24% (females), in 2 separate studies separated by a decade (Fults et a1. 1982; Friedl et a1. 1991). However, body fat below 20% is common among women athletes (Malina et a1. 1971; Sinning 1978; Wilmore et a1. 1977). Body fat percentages are generally lowest for endurance runners and bodybuilders (11 to 17%), somewhat higher for sprint and power athletes (14 to 20%) and highest for athletes in body-supported events (20 to 22%) [Eliot et a1. 1987; Fleck 1983; Graves et a1. 1987; Sinning 1978; Withers & Ball 1988; Withers et a1. 1987]. Obligatory fat in men is considered to be the lipid which is required for structural support and protection (e.g. perirenal fat), for nutritional support (e.g. pericardial fat), and additional amounts which can be chemically extracted from tissues rather than dissected as adipose tissue (e.g. components of cell membranes and intracellular substrates for biochemical processes). In women, additional sites are also obligatory (as a consequence of sex hormones) and may be essential for normal physiological function. Breast fat in women, for example, has been estimated to represent an average of3.5% ofthe total weight or about 10%of the sexspecific fat (Katch et a1. 1980). Along with less obligatory fat, men have a larger muscle mass and greater bone mass, accounting for a significantly larger FFM compared to women (82 versus 72% of bodyweight), with the divergence largely beginning at puberty.

248


Fit woman

Fig. 2. Dual energy x-ray absorptiornetry images of a physically fit woman and a sedentary young man. Although these two subjects have the same overall proportion of chemically definable fat (18 to 20%), there are substantial differences between excess fat in this man and the obligatory fat deposition in this woman. A man roughly equated to this woman subject in terms of fitness and health risk would have approximately 10%body fat.

1.2 Two-Compartment Model Assumptions The equations to convert from density to body fat (Siri 1961; Brozek et al. 1963) are based on assumptions derived primarily from body density measurements in a group of young men ('reference man') and later 'confirmed' on the basis of 3 White male cadavers ('reference body'). Water, protein and mineral were 73.8, 19.4 and 6.8%, respectively, of PPM in the 'reference man' (Brozek et al. 1963), supporting the initially recommended overall mean FFM density of 1.100 g/cm-' (Behnke et al. 1942), but slight differences between the 2 equations are magnified with increasing fatness, thus affecting

women more than men. While Siri's equation is based on assumptions of 1.100 g/cm 3 and 0.900 gj cm-' for the densities of fat-free and fat components, respectively, Brozek's equation is based on the 'reference body' relationship of 15.3% body fat and 1.064 g/cm-', Although these equations yield the same interpretation of body density for 'reference man' at 15% body fat, the differences between equations becomes progressively larger as density decreases, with about a I % body fat difference at 1.03 gjcm 3 (approximately 30%body fat) and a 2% body fat difference at 1.01 g/cm 3 (approximately 40% body fat). Thus, in the estimation of female body fat which commonly exceeds 30%


body fat, the equations are less interchangeablethan when applied to a normal population of men. These fixed value assumptions do not take into account the greater variability in the density of the FFM of women, and that this value is lower for many women compared to men because of reduced fractional body bone mineral content (Lohman et al. 1984b) and episodic increases in body water (Berl & Better 1979). Thus, there is a tendency to overestimate fatness from density in women. Although the dissectable bone was comparable at 20% of adipose tissue-free mass in women and men in the Belgian cadaver study (Clarys et al. 1984), bone mineral content as a fraction of FFM is estimated to be 6.0% in women compared to 6.6% in men (Lohman et al. 1984b), and individual variations in this component can have very large effects on the estimate of body fat from body density (Martin et al. 1989). Recently, Wang et al. (1989) have reported a range of 1.05 to 1.14 with a mean of 1.09 ± 0.02 g/cm-', in 184 White women, aged 19 to 89 years, using soft tissue and bone mineral dual photon absorptiometry and underwater weighing. Although the group of men reported in the same study had the same mean value of 1.09 g/cm ', also less than the assumed

249

value of 1.100 g/cm-', they had a narrower range of values (1.07 to 1.11 g/cm-'). Other 2-compartment models which have been used as criterion measures also suffer from assumptions about the FFM which are less tenable when applied to women. Total body potassium is used to estimate the FFM on the basis of a fixed fractional content of the total body potassium. On the basis of cadaver data this was set at 2.66g of 4oK/kg FFM (Forbes & Lewis 1956). However, on the basis of body water measurements, Behnke and Wilmore (1974) calculated lower and different values for men and women, with a constant of 2.28g 4oK/kg FFM for women; Forbes (1974) also calculated a value for women which was 6% lower than his original constant, but Morgan and Burkinshaw (1983) have found even this to be too high and more appropriate for men, where the 4oK-containing muscular component of the FFM represents a larger fraction. Total body water (TBW) measurements have also been used to estimate body fat based on the assumption that fat contains no water and the FFM in normally hydrated subjects is composed of a fixed proportion of water. The constant usually used, 73.2% water in the FFM, is an average value derived from studies of 6 different animal species

Table I. Reference body composition values for young adult nonathletic women and men (% of total body mass). Values are constructed from a composite of reference sources Variable Height (em) Body mass (kg) Body mass index Fat mass (kg) essential storage

Women

Men

Ratio (W/M)

163

177

0.92

60 22.2

75

0.80

23.6

0.94

16.8 (28%) 7.2 (12%)

13.5 (18%) 2.3(3%)

1.24

9.6 (16%)

11.3 (15%)

3.13 0.85

Fat-free mass (kg)

43.2 (72%)

skeletal muscle

21.6 (36%)

61.5 (82%) 33.8 (45%)

0.70 0.64

bone

7.2 (12%) 14.4 (24%)

11.3 (15%) 16.4 (22%)

0.64 0.88

Minimal weight (kg)

50.4

63.8

0.79

Waist/hip rati0 8

0.75

0.83

other

a Waist = abdominal circumference at the navel; hips = circumference at the greatest projection of the buttocks.

250

for which average measured values of the percentage water in FFM actually ranged from 69.9 to 76.3% (Pace & Rathbun 1945). Early cadaver studies also demonstrated a wide variation in this component, with values of77.5, 72.7 and 68.6% for the 3 'reference body' men (Brozek et al. 1963). Although 73% appears to be a reasonable average estimate for human subjects, just as 1.100 g/cm ' may be a reasonable average estimate for the density of the FFM, this component varies through the menstrual cycle (Berl & Better 1979; Bunt et al. 1989) and, thus, women may be even less accurately assessed than men under the assumption of a fixed value. These observations suggest that instead of deriving new coefficients to better predict the average body composition expected within each gender, age, and ethnic subgrouping, direct measurements of each of the major components should be made; this is most important for accurate assessment of female fat because of the greater variation compared to men. The technology is now more readily available for measuring total body bone mineral (TBBM) and total body water (TBW). This allows researchers to estimate body fat with a greater accuracy than previously available by taking into account the highly variable bone compartment. Coupled with density and body water measurements in a 4-compartment model (Allen et al. 1959; Heymsfield et al. 1990; Selinger 1977), the main density assumptions involve a much smaller fourth chemical component involving primarily protein. With the dual energy x-ray absorptiometry (DEXA) technology used for measuring TBBM, algorithms have also been developed which appear to estimate a soft tissue component (i.e. fat mass) with a greater precision and, perhaps, with a greater accuracy (because it accounts for the bone mineral component), than underwater weighing estimates can offer (Heymsfield et al. 1989, 1990; Mazess et al. 1990) [examples at fig. 2]. 1.3 Regional Distributions of Body Fat

The predominant region for fat storage in males is in the abdominal region, while women typically deposit fat in the hips and buttocks (Krotkiewski


et al. 1983) or, as Gam reminds us: 'men tend to belly, women to bum' (Gam et al. 1987). Vague (1956) first described these patterns as the 'android', or central and upper body fat patterning, commonly found in men, and the 'gynoid', or peripheral and lower body fat patterning, typically found in women. Android body fat patterning tends to reflect greater 'deep fat' or visceral fat storage. This upper body fatness is commonly described by a high waist-to-hips ratio (WHR) but the index has a different meaning in men and women. In men, WHR correlates with increasing fatness because of the tendency to store excess fat in the abdominal region. Because of the heterogeneity of fat storage in women, WHR correlates poorly with fatness, except perhaps as a discriminator of the very obese; however, it is useful in describing 2 principal fat distributions observed in women, where both 'android' and 'gynoid' patterns are common. Another expression which identified male-pattern fatness is the ratio of subscapular-to-triceps skinfolds thickness, referred to as a central (or truncal) versus extremity distribution (Baumgartner et al. 1986; Haffner et al. 1986). This expression has different physiological connotations than WHR (Bouchard et al. 1990; Haffner et al. 1987; Pouliot et al. 1990) since it reflects subcutaneous fat patterns only, while the waist circumference includes both 'deep' visceral fat and subcutaneous fat. Mueller et al. (1987) suggested a composite index using a ratio of waist-to-thigh circumferences which distinguishes central and upper body fat from extremity and lower body fat distribution, and Seidell et al. (1987) found that, in women, this index correlated better with computed tomographic-assessed intraabdominal fat than did WHR. By any of these assessments, a male pattern of fat distribution in women has physiological and health risk significance. The greatest importance to body composition assessments in physically demanding occupations may be the association between male-pattern fat distribution and muscularity, as first proposed by Vague (1956). In other words, the pattern offat distribution in women may be an important consideration in prediction or

251


interpretation of body composition from anthropometry. These regional variations are related to differences in the activity of the enzyme lipoprotein lipase and to adipocyte sensitivity to insulin (Arner et al. 1981; Evans et al. 1983). In women, compared to men, the enzyme is markedly more active in the femoral fat regions (Fried & Kral 1987) and, before menopause, this activity is greater in femoral fat than in breast or abdominal fat (RebuffeScrive et al. 1986) [fig. 3]. Recently, Arner et al. (1991) demonstrated that there was more lipase mRNA and more lipase activity in female than in male subcutaneous fat from normal weight subjects; in both sexes there was more RNA message in the abdominal compared to gluteal subcutaneous depots, but the enzyme activity was higher in gluteal compared to abdominal fat in women, and higher in abdominal compared to gluteal fat in men. Thus, post-translational factors are important in determining the expression of lipase activity and where fat will be stored. Regional differences in the regulation of this enzyme in women appear to account for many of the differences in female anIii

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thropometric measurements during different phases of maturation and reproductive life.

2. Androgenicity Upper body fat patterning is independently associated with increased androgenicity and obesity (Evans et al. 1988; Seidell et al. 1989). Thus, hirsute women and women with polycystic ovaries are characterised by an increased WHR (Evans et al. 1988; Hartz et al. 1984; Rebuffe-Scrive et al. 1989). This patterning difference not only confounds the development of anthropometric equations for the assessment of fatness in women, but also forms the basis of physiological differences which include increased male-associated health risks for cardiovascular disease and diabetes, and may signify greater bone mineral and skeletal muscle mass in these women. Excess fatness may be additive to the effects of androgenicity on abdominal fat distribution in women (Dunaif & Graf 1989; Evans et al. 1983). Abdominal fat is also more likely to metabolise androstenedione to androgens instead of estrogens, as the lower body fat does (Killinger et al. 1987; Kir-

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Fig. 3. Lipoprotein lipase activity in adipose tissue from femoral, abdominal, and mammary regions in premenopausal and postmenopausal women (Rebuffe-Scrive et al. 1985) and in premenopausal women in follicular and luteal menstrual phases, pregnancy, and lactation (Rebuffe-Scrive et al. 1986). Femoral lipase activity is significantly higher than abdominal lipase activity in premenopausal women except during lactation (Rebuffe-Scrive et al. 1986). In postmenopausal women, this femoral lipase activity is diminished (Rebuffe-Scrive et al. 1985). These data are redrawn from the original reports.

252

Fig. 4 The effect of fat patterning in obese women on the percentage conversion of androstenedione to estrone, a weak estrogen. The numbers between figures indicate the percentage conversion of androstenedione to estrone in primary cell culture, expressed per 106 adipose stromal cells (Killinger et al. 1987). In abdominal (upper body) fat, androstenedione is preferentially converted to 5a-reduced androgens instead of estrogens. Thus, the fat patterning shown to the right reflects greater androgenicity and this may also be associated with other aspects of virilisation, including greater muscle mass (reprinted with the permission of Steroids).

schner et al. 1990) [fig. 4] and obese women without androgen disorders still have increased androgen production (Samojlik et al. 1984) and do not increase estrogen production as obese males do (Zumoff et al. 1981). It may be for this reason that at the upper extremes of female obesity there is a shift to an abdominal pattern of fat distribution (Forbes 1990). The net result for these women with increasing upper body fatness, is an increased risk of cardiovascular disease (Despres et al. 1989; Lapidus et al. 1984, 1989) and diabetes (Lundgren et al. 1989; Pasquali et al. 1990), probably as a series of consequences initiated by the increased portal delivery of fatty acids to the liver (Rebuffe-Scrive et al. 1990). However, women can increase body fat by 30kg or more over men before they demonstrate the same types of metabolic derangements as men (Krotkiewski et al. 1983),possibly reflecting the initially lower amount of visceral fat in women (fig. 5). The reduction in bone mass which would be expected in women with amenorrhoea is not observed in women with hirsutism and amenorrhoea, and this has been suggested to be an offsetting benefit of the elevated androgens (Dixon et al. 1989). Male pattern obesity in women is tentatively linked to increased muscle and bone mass through


other aspects of androgen action. For example, strength athletes tend to have a more centralised fatness (associated with the androgenic pattern and greater fat-free mass) while swimmers are at the opposite extreme with extremity fat patterning (Mueller et al. 1982). Krotkiewski & Bjorntorp (1986) found that women with upper body obesity increased their lean body mass while losing fat, and improved their metabolic status (reduced serum lipids, insulin, and blood pressure) in a physical training programme. Thus, these women were more like the men on the same programme, while the lower body obese women demonstrated none of these benefits and actually increased body fat. An even more interesting finding of their study was that abdominal obese women also had a muscle fibre composition similar to that of obese men, with a larger proportion of fast-twitch skeletal muscle fibres than the women with a lower body fat dis-

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Total adiposity

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Fig.5. The relationship between intra-abdominal fat and total fat volumes in men and women assessed by computed tomography. Intra-abdominal fat is proportionally increased in men with increasing total adiposity, while women can add 30kg of fat before it begins to appear as intra-abdominal fat (Kvist et al. 1986) [reprinted with the permission of American Journal of Clinical Nutrition].


tribution. Using a statistical approach with principal components analysis, Baumgartner et al. (1986) arrived at the conclusion that a central (truncal) fat distribution across genders, age, and ethnicity, is associated with male sex hormone characteristics of greater internal fat, greater muscle and increased bone mass, as noted with increasing trunk fat in males during adolescence (Deutsch et al. 1985).

3. Ethnicity Two aspects of body composition are highly dependent on ethnicity: greater fractional bone mineral and an increased tendency to a central subcutaneous fat distribution. These differences have been observed in some ethnic groups compared to White women, and even in a north-to-south cross section of European women. It probably is not coincidental that these same trends have been associated with increased androgenicity. In the European Fat Distribution Study, Seidell et al. (1990) found variations in serum free testosterone concentrations which were positively correlated with the WHR. Mediterranean women, with the highest free testosterone, had the highest waist-to-thigh circumference ratios and lowest triceps-to-subscapular skinfold ratios, compared to northern European women who had lower androgen levels and more of a lower body and extremity fat distribution. Hediger and Katz (1986) also demonstrated this relationship of androgenicity and abdominal fatness in a Black female population. The male pattern of central fat distribution involving greater internal fat also has a strong genetic component. Inheritance was estimated by Bouchard et al. (1988) to account for 20 to 30% of the variance in fatness and fat distribution in French Canadians, with a much smaller genetic component estimated for the variance in subcutaneous fat. Stem and Haffner (1986) reported that a central pattern of fat distribution in Mexican-American women was significantly correlated with the percentage of Native American genetic admixture, and this was independent of body mass index. A

253

combination of high body mass index and high subscapular-to-triceps skinfold thickness in girls predicts a high risk for adult centralised obesity (Rolland et al. 1990); thus, even if not genetically predetermined, a central fat distribution in women is established at an early age. Other characteristic fat patterns are clearly genetic traits, such as the tendency to deposit excess fat in the buttocks region (steatopygia), a characteristic of some Black African tribes which is especially manifested in the women. This occurs either in only a small subgroup of Black women or its expression occurs only in older Black women, because a large hip circumference does not emerge as a characteristic of young Black US soldiers; in fact, the distribution of hip circumference measures is lower than that for non-Hispanic White and Hispanic women in this group and there are no differences in WHR (Fitzgerald et al. 1986; Friedl et al. 1989). Truncal versus extremity subcutaneous fat patterning is different and requires more careful evaluation of anthropometric equations which may emphasise one part of this distribution. Table II summarises data bearing on this point taken from a sample of unselected women, 18 to 39 years of age, from the US Army (Fitzgerald et al. 1986). Comparing the 3 predominant ethnic groups represented in this sample, each with similar percentage-bodyfat, trunk-to-extremity skinfold ratios were higher for Hispanic and Black women, while WHR was not different among the groups. This is consistent with the data of Cronk & Roche (1982) who found smaller triceps skinfold thicknesses in young Black women even though they had a higher median body mass index than White women; Zillikens and Conway (1990) also found higher trunkto-extremity ratios in Blacks. Malina et al. (1983) found larger subscapular skinfolds but no difference in triceps skinfolds in Mexican-Americans, compared to non-Hispanic Whites, and Haffner et al. (1988) found higher ratios in Mexican-Americans but, like the others, there was no significant difference in WHR. The 10 to 20% greater bone mineral in Black men and women (Cohn et al. 1977; Luckey et al.


254

Table II. Comparison of anthropometric variables (mean ± SO) reflecting regional fat patterns in 3 racial groupings of women aged 18 to 39 years (derived from data of Vogel et al. 1988) Variable

White

Black

Hispanic

Sample size

162

121

24

% Body fat a• Body mass index (kg/m 2)

28.8 ± 6.3 22.9 ± 2.9

26.9 ± 5.8 22.9 ± 2.5

28.7 ± 4.7 23.5 ± 2.1

Circumferences (cm) waist hips

74.1 ± 8.0 95.5 ± 6.6

72.0 ± 7.5 94.8 ± 7.2

73.3 ± 6.0 95.1 ± 5.1

Skinfolds (mm) subscapular triceps

13.6 ± 5.4 17.7 ± 6.1

13.8 ± 5.5 16.1 ± 5.9

15.7 ± 4.6 16.6 ± 5.2

0.78 ± 0.06 1.38 ± 0.14

0.76 ± 0.05 1.33 ± 0.19

0.77 ± 0.06 1.36 ± 0.12

0.80 ± 0.30 0.90 ± 0.46 0.52 ± 0.25

0.90 ± 0.28 1.14 ± 0.55 0.63 ± 0.32

1.00 ± 0.32 1.11 ± 0.80 0.64 ± 0.24

Circumference ratios waisl/hip ratio waist/thigh ratio' Skinfold ratios

subscapularjtrlceps" subscapular/suprailiac'" subscapular/thigh" a Measured by underwater weighing. 1-way analysis of variance between ethnic groups: p

< 0.05; ..

1989; Trotter & (Hixon 1974) introduces a significant underestimate in the interpretation of underwater weighing results with the standard 2-compartment models. However, in Blackmales, Schutte et al. (1984) demonstrated that anthropometric estimations of fatness derived from White male populations were still valid (compared to total body water estimates). The same results would be expected for Black women. A similarly higher bone mineral content (compared to White women) may also be present in other populations, including Polynesian women (Reid et al. 1986) and, based on the differences in vertebral fracture rates (Bauer & Deyo 1987), it can be speculated that Hispanic women also have higher bone mineral content than non-Hispanic White women. Most recently, Ortiz et al. (1990) reported that Black women have not only higher proportions of bone mineral in the FFM, but also have a higher total body potassium/ FFM; thus, other criterion methods involving 2compartment assumptions may not be readily transferable across ethnic groups.

P < 0.01; ... P

< 0.001.

4. Obesity Obesity presents several problems to the measurement of body composition, including technical difficulties in measuring subjects underwater or with calipers and inaccuracy, resulting from violations of the assumptions of the usual models and anthropometric equations. Although many of these problems are not unique to women, obesity is a problem which involves women more than men, with women between the ages of 25 and 34 at twice the risk of men for a major weight gain (Williamson et al. 1990). In the United States, more than half of older women in some ethnic minorities are reportedly obese (Gillum 1987), at a rate which is double the rate for non-Hispanic White women. Thus, assessment of body composition in some groups of women will necessarily involve obese subjects. Additionally, some athletes will be misclassified as obese by some of the methods commonly used. Obesity is most frequently defined on the basis


of a weight-for-height index, usually by body mass index (BMI, in kg/m 2 ), although some investigators have suggested that the normal weight-forheight relationship in women is better described by weight/height 1.5 (Gillum 1987; Knapik et al. 1983; Micozzi et al. 1986). Body mass index is adequate for most clinical uses where the purpose is to generally establish that a patient is obese and to give an approximate quantitation of that obesity. Garrow and Webster (1985) have suggested that, especially in view of the difficulty in obtaining an accurate assessment of fatness in obese women, BMI is a very suitable alternative to more sophisticated methods which may be wrong anyway because of inappropriate assumptions, and others have reached the same conclusion in their studies (Leonhardt et al. 1987; Shephard et al. 1985). On the other hand, this may be inadequate for athletes, particularly when fat-free mass is higher than the norms to which these values are usually compared. Furthermore, BMI disproportionately classifies women according to both their ethnicity and socioeconomic status (Flegal et al. 1988; Stern et al. 1984)and the relationship between overweightness and obesity-related disease is still not the same in US minorities as for the better studied obese White American women (Dowling & Pi-Sunyer 1991; Kumanyika 1990). Body mass index is also not well suited to performance-based body composition standards as the strongest women tend to be the biggest even though they may not be the fattest (Friedl & Vogel, in preparation). Skinfold measurement in obese women is difficult because of problems in locating the site, inability to lift the subcutaneous layer and form a fold, folds exceeding the opening of the calipers, and variation in the compressibility of the fold (Kuczmarski et al. 1987). These problems may be overcome with ultrasound measurements of the subcutaneous fat thickness; however, the difficulties in measuring obese women go beyond this technical point. Most anthropometric prediction equations for body fat are typically derived from populations within the normal range of body composition and are not well suited for use at the upper ranges of body fat in obese individuals. Further-

255

more, skinfold equations underpredict body fat in obese subjects, probably because of the curvilinear relationship between skinfolds and density (Durnin & Womersley 1974; Jackson 1984). Allen et al. (1956) found that the proportion of superficial fat increased with total body fat so that only 25% of total fat was subcutaneous in lean subjects, while more than 60% was subcutaneous in obese subjects. With increasing fatness, truncal skinfolds increase more rapidly than extremity skinfolds, as reflected in the positive reltionship between fatness and subscapular/triceps skinfolds (Gam et al. 1982). Besides avoiding some technical difficulties, trunk circumferences may estimate the internal 'deep' fat, and this male pattern fatness becomes more important in obese women. Krotkiewski et al. (1983) found that abdominal fat increased linearly with excess fat in women above a threshold of 30kg of absolute fat weight; unlike the relationship in men, below this threshold, fat was distributed elsewhere. Forbes (1990) found that two-thirds ofa group of obese subjects were at least 1 standard deviation above the mean WHR for normal women, and 42% were more than 2 standard deviations above the mean. In a US Army sample, where the upper third (by weight-for-height) of young women in the national population have been excluded, abdominal measurements are the principal discriminators of fatness only for women in the fattest decile (fig. 6). At lower levels of fatness, there is a greater heterogeneity of sites for excess fat deposition, and multiple regression analyses yield hip circumference and bodyweight as the predictors of fatness. A major difficulty in establishing accurate anthropometric predictive equations for fatness in obese women lies with the methodological problems and reduced validity of the assumptions in the criterion methods against which these must be calibrated. Evans et al. (1989) have attempted to circumvent some of the technical problems in underwater weighing of morbidly obese women by using a head-out submersion procedure; others have tried performing underwater weighing to functional residual capacity (Thomas & Etheridge 1980) or to total lung capacity (Warner et al. 1986), in-

256


Average (em)

k

Neck

30.9

4.95

Shoulders

99.9

16.00

Chest

75.1

12.03

Waist

68.5

10.97

Abdomen (navel)

71.0

11.37

Hips

93.1

14.92

Thigh

52.7

8.44

Flexed biceps

26.7

4.28

23.0

3.68

14.7

2.36

34.4

5.51

34.2

5.48

Forearm

0< 18%

Wrist

.28-34%

Knee

e> 34%

Calf

-15

-10

-5

0

5

10

15

Percentage of reference soldier (female)

Fig. 6. Behnke 'somatogram' illustrating circumference distributions in the fattest decile (> 34% body fat, by underwater weighing), at the second to fifth deciles (28 to 34% body fat), and in the leanest decile « 18% body fat) of a sample of 237 female soldiers, compared to 'reference female soldier' (reference values and percentages of total circumferences 'k' are shown at right; the divisor value is 6.24). The predominance of abdominal fat patterning in the fattest decile of women may reflect a consistent fat deposition in this region, occurring after some threshold of fatness is reached, while excess fat placement at other sites is more varied.

stead of to residual volume. Garrow et al. (1979) used a head-out but enclosed combination plethysmograph-underwater weighing tank. When they compared fat mass change using this and several other methods to a criterion measure of direct and indirect calorimetry, they found that it yielded results within about 4kg of fat loss, comparable to that estimated from total body water, but significantly more accurate than the assessment from total body potassium measurements. They concluded that the major error comes from the assumptions inherent in the methods. With weight loss, there may be a delay in subcutaneous fat loss compared to deep abdominal fat; if true, this would be reflected in an overprediction of fatness from surface anthropometry. King and Katch (1986) examined weight loss in 26 obese young women and found that weight loss up to

2.3kg in a 14-week programme involving diet and exercise could be predicted by surface anthropometry, but greater weight losses were not reflected in comparable skinfold reductions. Gam et al. (1987) demonstrated that weight losses and weight gains are reflected by skinfold changes in abdominal subcutaneous fat sites (reflected as the highest mm/kg weight change) instead of subscapular and triceps sites. This is also consistent with observations that weight loss produces the greatest alterations in fat topography in upper body (or centralised) obese women (DBO) [Casimirri et al. 1989], suggesting that fat must be preferentially lost from abdominal sites, even in women. However, Wadden et al. (1988) found that lower body obese (LBO) women lost more weight than the DBOs, and they lost it from both upper and lower body sites, while DBO women lost pri-

257


marily from the upper body. A third study of obese premenopausal women found that the distribution of body fat (e.g. upper and lower body) was not a significant determinant of weight loss success (Vansant et al. 1988). These studies suggest that anthropometric equations will be least suitable to assessment of fat loss in DBO women, if they do not include abdominal or trunk fat assessments.

5. Reproductive Cycle The effects of the female reproductive cycle on regional fat deposition introduce significant variation in body composition relationships which are assumed to be constant in 2-compartment models. These include physiological changes through the menstrual cycle and in the absence of normal menses as well as changes through pregnancy and lactation, and later, aging and menopause. 5.1 Menstrual Cycle Body weight fluctuations are commonly observed during the menstrual cycle and are attributable to water retention (Thorn et al. 1938). Increased body water will decrease the average density of the FFM and thus give the appearance of a higher body fat in the interpretation of density measurements, while giving the appearance of a lower body fat content because of an overestimate of the FFM estimated from total body water measurements. Although estrogens, because of their salt-retaining properties, have been proposed to be the cause of water retention and bloating associated with the estrous cycle, the source of this and other premenstrual symptoms has not been identified (Schmidt et al. 1991). At least a part of the change is probably due to an increase in mineralocorticoids which also occurs in the luteal phase (Berl & Better 1979). Byrd and Thomas (1983) found insignficant mean changes in density in groups of women during the cycle, although individual changes were noted. However, Bunt et al. (1989) found that density and body fat varied significantly from 1.043 to 1.037 g/cm ' and 24.8 to 27.6%, respectively, in a group of women who fluctuated between their highest and

lowest body weights by an average 2.2kg in their menstrual cycles. Most, but not all, of this could be accounted for by a change in measured body water content from 74.5 to 75.9% of FFM. Gleichauf & Roe (1989) found that FFM determined by electrical impedance changed through various phases of the menstrual cycle, possibly as a result of changes in sodium intake. 5.2 Amenorrhoea Body composition in the female athlete has received considerable attention due to the suggested link between amenorrhoea and oligomenorrhoea and low bodyweight, body fat and FFM in this group (Carlberg et al. 1983; Dale et al. 1979; Feicht et al. 1978). For a comprehensive review of athletic amenorrhoea see Loucks and Horvath (1985). The study of the relationship of amenorrhoea to body fat may, itself, be confounded by factors which are altered such as a reduced bone mineral content. Groups of oligo- or amenorrhoeic athletes matched to eumenorrhoeic athletes for such factors as age, height, athletic events and extent of training, had significantly less body mass, percentage body fat, fat weight and FFM than normally menstruating athletes (Carlberg et al. 1983). However, no threshold level of fat or FFM could be established for amenorrhoea as had earlier been proposed by Frisch and McArthur (1974), and this may be a problem of inadequate caloric intake rather than low body fat (Loucks et al. 1991). One of the difficulties in properly assessing this low fat athletic population is in the identification of anorexic patients who are drawn to high volume training as part of their disorder. One difference will be the level of performance achieved, and this is highlighted by the finding that elite Olympic contenders do not have a higher incidence of amenorrhoea than other women (Glass et al. 1987). Bone mineral content will be lower in those athletes with amenorrhoea (Drinkwater et al. 1984; Lindberg et al. 1984; Marcus et al. 1985) as it is in anorexia nervosa patients (Mazess et al. 1989) and this confounds body fat assessment by underwater weighing. Trained women have higher bone mineral dens-

258

ities than untrained women; the extent of this influence from non-weight-bearing training, such as swimming, and the extent to which it counters amenorrhoeic bone loss is still relatively undefined (Smith & Gilligan, 1990). Although amenorrhoeic runners have a higher risk of stress fractrure, in postmenopausal women who are simlarly estrogendeficient, bone loss is at least partially reversed or slowed by weightbearing exercise (Dalsky et al. 1988; Johnston et al. 1985; Stillman et al. 1986). At the extreme ofanorexia nervosa, total body bone mineral (TBBM) has been measured at approximately 10% less than normal weight women, even though muscle mass may be similar and fat mass is greatly reduced (Mazess et al. 1989). This difference in the proportion of TBBM/FFM leads to significant overprediction of fatness in anorexic women (approximately 5% body fat units) using underwater weighing, and for the same reason, large errors may occur in the assessment of amenorrhoeic runners and other athletes. At the opposite extreme, resistance training, such as weightlifting, has been shown to increase bone mineral content compared to either endurance trained athletes or nonathletes (Heinrich et al. 1990). Risser et al. (1990) showed higher bone densities in vertical weightbearing event athletes (volleyball and basketball) as compared to swimmers and nonathletes. Such an increase in TBBM will produce an underestimate of fatness in these women with underwater weighing. 5.3 Pregnancy and Lactation With pregnancy, the most significant deviation from density assumptions, is in an increased water content of the FFM. In pregnancy and lactation, there is a redistribution of fat as lipid is accumulated and later lost. These are important considerations in the study of fat gain and loss through pregnancy especially since anthropometric prediction is likely to be the main tool available in these assessments, where more invasive or strenuous methods may not be practical or medically advisable. Nearly half of the weight gain in pregnancy is


from water. Using tritiated water, Forsum et al. (1989) measured an 18% increase in total body water (5.7kg) in a group of women who gained an average of 12.7kg weight during pregnancy. Using Siri's helium dilution method for body density measurement and deuterium dilution for total body water measurement, Seitchik (1967) found an increase in TBW/FFM from 74% in nonpregnant women to 76.6% in near-term women, and this gradually decreased to 75.1 %in women 6 to 9 weeks postpartum and back to control patient values after 12 weeks. Thus, there is little correlation between weight gain and the amount of fat gained during pregnancy because of the large changes in hydration status. Skinfold thicknesses, however, can be useful in the prediction offat change during pregnancy (Forsum et al. 1989). Pipe et al. (1979) found that skinfolds correlated well with changes in fatness through pregnancy in a group of women followed serially with body water and body potassium (40K) measurements; their finding was that maternal fatness peaked in the second trimester and began to decrease before parturition. At least 1 group has attempted to follow body composition changes through pregnancy using electrical impedance with separate determinations of extracellular fluid spaces and total body water (Dumont & Thoomasset 1980). During pregnancy and lactation, there is a differential fat deposition and mobilisation. The enzyme and lipid metabolism studies of RebuffeScrive et al. (1985) demonstrate that lipoprotein lipase activity, already highest in the femoral region in women, increases further during pregnancy (fig. 3). During lactation, fat mobilisation from this region is markedly enhanced because lipase activity specifically decreases, while abdominal wall lipoprotein lipase activity remains undiminished (Rebuffe-Scrive et al. 1985). Thus, thigh fat in women appears to be primarily mobilised under hormonal influences present during lactation. This has been attributed to an effect of prolactin (Steingrimsdottir et al. 1980; Zinder et al. 1974). Using skinfolds as indicators, Taggart et al. (1967) and Forsum et al. (1989) demonstrated that


259

5.4 Aging and Menopause

Suprailiac - - Scapula - - Costal --------Mid-thigh -*-* Knee-cap - - - Triceps * * * * *

30

E

.s c ~

20

..", ~

Ul

E

E .E c :>2

CJ)

t

Partus

B

A

"1

I

DE

C

I

J

I

G

F

I

I

I

I

•

Months

Fig.7. Skinfold measurements before pregnancy (A), through pregnancy, and up to the first 6 months post partum (G) [Forsum et al. 1989]. Note the increase in triceps skinfo1d measurements in the lactational period as other subcutaneous sites are thinning out (reprinted with the permission of American Journal of Clinical Nutrition)

fat was added predominantly in the central region during pregnancy although increases were also seen in the mid-thigh site; this is reversed in lactation. While fat is being mobilised from the thigh in the early postpartum period, there is a redistribution of fat to the arm, breast and upper trunk (Steingrimsdottir et al. 1980). Quandt (1983) found that an increase in upper arm fat and subscapular fat folds was common for postpartum women during lactation even as bodyweight decreased. Brewer et al. (1989) also found this effect in lactating mothers, with an increase in triceps skinfolds at 3 months post partum. Forsum et al. (1989) found the same increase in triceps skinfold thickness, while the thickness of 5 other sites steadily decreased (fig. 7). The fat distributions appear to return to prepregnancy measurements sometime after 6 months post partum.

One of the best appreciated effects of menopause on body composition is a substantial reduction in bone mineral content. This is primarily due to the estrogen withdrawal (Johnston et al. 1985), although chronological age is also a factor. Thus, Nilas & Christiansen (1987) found a 15%decrease in bone mineral in a group of postmenopausal 53year-old women compared to premenopausal 53year-old women. This decline in bone mineral is the same for young women who undergo oophorectomy at an early age (Richelson et al. 1984) and is an expected consequence of prolonged amenorrhoea as previously discussed. Postmenopausal women receiving long term estrogen treatment had 20% greater bone mineral than nonusers at age 73, a total bone mineral content which would be expected in a 60-year-oldwoman (Ettinger et al. 1985). This advantage of the estrogen replacement is markedly reduced if the woman smokes (Jensen et al. 1985). Thus, the effect of estrogen deficiency following natural or surgical menopause can be as large as the 20% difference in bone mineral reported for Black compared to non-Hispanic White subjects; while premenopausal Black women may tend to be underestimated for relative body fat from underwater weighing, postmenopausal White women will tend to be overestimated. Regional fat changes may also be attributable to the hormonal events at menopause. Forbes (1990) found that, after menopause, women climb above an average WHR of 0.8 (measured at the umbilicus and the greatest protrusion ofthe buttocks); this is in contrast with the men he studied, where WHR averaged above 0.8 and tends to climb with age (and increasing fatness) [fig. 8]. Den Tonkelaar et al. (1989) also found a higher WHR in postmenopausal women, with fat distribution appearing more like that of men, and estrogen replacement therapy for postmenopausal women preserved a premenopausal-type WHR (Den Tonkelaar et al. 1989; Lindberg et al. 1989). Smoking by premenopausal women increases WHR to a postmenopausal-type WHR (den Tonkelaar et al. 1989), possibly through its antiestrogenic effect. Without question, older


260

0.95

..

0.90 J:

+9

0.85

~~.f,,~f.... 99

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Many of the special considerations in female fat deposition revolve around specific functions in support of reproduction, thus, pregnant and postpartum women rearrange fat stores to support lactation; beyond their reproductive years, postmenopausal women shift from gluteofemoral fat to more of a male pattern abdominal fat distribution. Such variability in fat distribution substantially affects the anthropometric assessment of fat in women. Anthropometric prediction of body composition should be used with caution or even avoided if the population may include atypical fat patterning due to ethnicity or androgenicity. Assessment of body fat in women by criterion methods is also more complicated, with assumptions about principal body compartments which are more variable in women than men (e.g. bone mineral reductions in amenorrhoea and menopause, body water changes through pregnancy, and even possibly within the menstrual cycle). The assumptions for 2-compartment models using density, total body water, or total body potassium are more variable and tend to be different for women compared to the values originally established on the basis of male cadavers. Thus, more so than in men, it is important to directly assess each of these components (e.g. using multicompartment models) or use methods which are relatively independent of them (soft-tissue DEXA or some anthropometric equations). Women athletes may have unusually high (in strength sports) or low levels of bone mineral (in runners) which will negate the validity of the 2-compartment body fat estimation, although generalised prediction equations developed against underwater weighing (e.g. Jackson et al. 1980) which have not emphasised this subgroup of women may be valid even when bone mineral content deviates from average values. Selection of anthropometric prediction equations for nonpregnant premenopausal women should incorporate skinfold and/or circumference sites that reflect the primary storage sites of excess fat, thigh and hip. Each of the currently available methods assesses women differently, emphasising different fat depots or relying on different assumptions, and body fat measured by different methods in women can-

265


not be readily equated. Because of the relatively higher FFM and lower body fat in female athletes, equations developed specifically for this group are advisable. Anthropometric prediction equations should be developed, if possible, against a 4-compartment criterion reference model.

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ic

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Correspondence and reprints: Dr James A. Vogel, Occupational Health and Performance Directorate, US Army Research Institute of Environmental Medicine, Natick, MA 01746-5007, USA.

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