Original                       Research                            Communications-general

Body composition       in Pima ...
BIOELECTRICAL                                   RESISTANCE                       IN       PIMA           INDIANS          ...
596                                                                                                                       ...
BIOELECTRICAL                                      RESISTANCE                     IN     PIMA              INDIANS        ...
598                                                                                                    RISING     ET     A...
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Fat mass est in obesity

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Fat Mass Estimates in Obesity

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Fat mass est in obesity

  1. 1. Original Research Communications-general Body composition in Pima Indians: validation of bioelectrical resistance1’2 Russell Rising, Boyd Swinburn, Karen Larson, and Eric Ravussin ABSTRACF To assess the validity of bioelectrical resistance specific equations (9) increased correlations between FFM de- (BR) in an obese population, body composition was determined termined by hydrostatic weighing and FFM predicted by resis- by both hydrostatic weighing and by BR in 156 Pima Indian tance from 0.90 to 0.94 and from 0.89 to 0.93 for men and volunteers representing a wide range of body weight (46.1-202.6 women, respectively. kg) and body composition (1 1-52% fat). A predictive equation The degree ofobesity observed in the Pima Indian community was derived by use of data on height, BR, weight, age, and sex is far greater than that measured in the populations previously from 130 randomly selected volunteers and was applied to the used to validate the BR method. To validate this method in remaining 26 volunteers. When compared with the manufac- Pima Indians, FFM was estimated by both hydrostatic weighing turer’s software, the new equation increased correlations with and BR in 156 Pima Indians representing a wide range of body Downloaded from www.ajcn.org by on December 24, 2007 hydrostatic weighing for predicting percent body fat and fat-free weight (46. 1-202.6 kg) and body composition (1 1-52% body mass (FFM) from 0.70 to 0.92 and 0.79 to 0.97, respectively. fat). A new equation was derived in 130 Pima Indian volunteers The manufacturer’s software underestimated FFM by 5.3 ± 8.6 and was tested on the remaining 26 volunteers. To test its validity kg (P < 0.05) when compared with FFM derived from hydro- in field conditions, the effect offood and fluid ingestion on body static weighing whereas the new equation improved the accuracy composition by BR was also assessed in another 1 7 Pima Indian to -0. 1 ± 3.3 kg (NS). There were no significant effects of fluid volunteers. intake (700 mL) or breakfast consumption on body composition as determined by BR. BR represents a simple and accurate way Methods to assess body composition in Pima Indians with our newly de- rived equation. Am J C/in Nuir 199 1;53:594-8. Subjects KEY WORDS Bioelectrical impedance, obesity, fat-free One hundred fifty-six Pima Indian volunteers (92 males, 64 mass, fat mass females) were admitted to the Clinical Diabetes and Nutrition Section ofthe National Institutes of Health in Phoenix, AZ, for various studies of carbohydrate and energy metabolism over a Introduction 3-y period. All volunteers were fed a weight-maintenance diet (50% ofenergy as carbohydrate, 30% as fat, and 20% as protein) The Pima Indians are a relatively genetically homogeneous during their stay in the metabolic ward. Upon admission, all group of people living southeast of Phoenix, AZ. They have the volunteers were determined to be in good health by means of highest incidence of non-insulin-dependent diabetes in the world medical history, physical examination, electrocardiogram, blood and a very high prevalence of obesity (1). In metabolic studies screening, and urine testing. Volunteers were screened for dia- an accurate assessment of body composition to estimate fat-free betes mellitus by an oral-glucose-tolerance test, according to the mass (FFM) and fat mass is important so that results can be procedure of the National Diabetes Data Group (10), but were expressed in terms ofthe volunteer’s metabolic size. Bioelectrical- not excluded from the study if they were diabetic. None was resistance (BR) analysis (2-5) offers an inexpensive, rapid, and taking any medication or had clinical evidence of illness apart safe method for estimating body composition without the need from obesity. for a hydrostatic weighing facility. Body composition of each volunteer was determined by hy- In general, studies validating BR analysis for determining body drostatic weighing (1 1), and percent body fat was calculated ac- composition used sample populations of various ages and in- cording to the equation of Keys and Brozek (12). FFM was cluding many racial groups (6, 7). These sample populations did not include obese individuals extremely (> 50% fat) or individ- uals from Native American Indian populations. Total body water I From the Clinical Diabetes and Nutrition Section, National Institute was found to be similar in young black and white men (8) al- of Diabetes and Digestive and Kidney Diseases, National Institutes of though blacks had a higher body density, possibly because of Health, Phoenix, AZ. greater bone mineral content in their FFM. In people with var- 2 Address reprint requests to R Rising, National Institutes of Health, ious degrees of obesity, fat-specific regression equations (9) were 4212 North 16th Street, Room 541-A, Phoenix, AZ 85016. derived by use of anthropometry to classify men and women Received April 27, 1990. into categories of> 20% and < 30% fat, respectively. These fat- Accepted for publication July 18, 1990. 594 Am iC/in Nuir 199 1;53:594-8. Printed in USA. © 1991 American Society for Clinical Nutrition
  2. 2. BIOELECTRICAL RESISTANCE IN PIMA INDIANS 595 TABLE I Predictive equation Physical characteristics of 156 Pima Indians in whom a prediction equation to estimate body composition by bioelectrical resistance was Ofthe 156 volunteers whose characteristics are shown in Table derived and tested 1 ( 123 nondiabetic, 33 diabetic), 1 30 (74 males, 56 females) were randomly selected (13) for the derivation ofa new predictive Predictive equation Testing of equation equation, whereas 26 (18 males, 8 females) were utilized for (n = 130) (n = 26) testing the equation. Percent body fat was calculated by use of the new prediction equation by subtracting FFM from the vol- Sex ratio (M:F) 74:56 18:8 unteer’s body weight to determine fat mass, then dividing fat Age(y) 30 ± 8 32 ±10 mass by total body weight, multiplied by 100. There were no Weight (kg) 99.3 ± 26.7 106.8 ± 24.8 significant differences in age, height, body weight, and body Height (cm) 166 ± 9 168 ± 9 Fat, hydrostatic (%) 34 ± 9 34 ± 8 composition between the subjects used to derive the new equa- Fat, resistance (%) 38 ± 9 39 ± 9 tion and those utilized for testing this equation. FFM, hydrostatic (kg) 63.5 ± 13.4 69.3 ± 13.5 FFM, resistance (kg) 58.6 ± 1 1.7 64.0 ± 12.9 Effect offluid intake To test the effect of hydration, resistance measurements were S SD. obtained according to the above protocol on another eight male Pima Indian volunteers (aged 35 ± 14 y, with 25 ± 10% body fat by hydrostatic weighing) 1 h before and 1 h after consuming calculated from the difference between the volunteers’ body 700 mL of fluid (water or diet soda). Percent body fat was cal- weight and their fat mass. BR was measured between 0700 and culated by use of the new prediction equation. The volunteers’ Downloaded from www.ajcn.org by on December 24, 2007 0800 after an 1 1-12-h fast. For the resistance measurement vol- physical characteristics are presented in Table 2. To test the unteers were instructed to lie flat with their hands at their sides reproducibility of the resistance measurements, this procedure and with their thighs maintained apart. Electrodes (Conmed was repeated for 5 consecutive days. The mean CVs of the per- Corp, Utica, NY) were placed on the subjects and resistance cent body fat determinations over the 5-d period were 3.6% and measurements were obtained with a bioelectrical impedance an- 3.8%, before and after fluid intake, respectively. Because of the alyzer (model BIA-l03, RJL Systems, Inc, Detroit) according small variability ofthe repeated measurements, data were pooled to instructions provided by the instrument manufacturer. Percent for the 5 d (Table 2). body fat was calculated by using the manufacturer’s software (Bodycomp II, version 1. 1, RJL Systems, Inc). The instrument Effect ofbreakfast ingestion was calibrated periodically with a 50041 resistor provided by the Resistance measurements were also obtained on another nine manufacturer, and measurements were continued as long as the Pima Indian volunteers (eight males, one female) (aged 30 ± 10 instrument readings remained within 2 2 of the calibrated re- y, with 28 ± 9% body fat by hydrostatic weighing) 1 h before sistor. The experimental protocol was approved by the NIDDK and 1 h after eating breakfast. Their physical characteristics are Clinical Research Subpanel and the Tribal Council of the Gila shown in Table 3. The measurements were repeated for 2 con- River Indian Community. Written informed consent was ob- secutive days. Percent body fat was calculated by use ofthe new tamed from the volunteers. All physical characteristics of the prediction equation, according to the above procedure. Because volunteers are expressed as mean ± SD. the mean CVs ofthe percent body fat were only 2. 1% and 2.8% TABLE 2 Physical characteristics and mean changes in resistance (AR) and percent body fat (iWat) for eight volunteers before and after 700 mL of fluid intake Fluid status Before After Subject Height Weight R Fat R Fat iR Fat cm kg l % Q % (1 % I 177 122.6 380 41 392 42 +12 +1 2 169 131.6 393 39 402 39 +9 0 3 165 73.5 490 18 511 19 +21 +1 4 170 80.5 449 15 460 16 +11 +1 5 173 91.6 482 25 489 25 +7 0 6 170 76.8 503 15 493 15 -10 0 7 163 52.7 604 3 616 3 +12 0 8 172 105.0 477 34 481 34 +4 0 i±SD 170±4 91.7±26.5 472±70 24± 13 481±70 24±13 8±9t 0±1 C Results are pooled from five repeated measurements. t Significantly greater than the value before fluid, P < 0.05 (paired I test).
  3. 3. 596 RISING ET AL TABLE 3 Physical characteristics and mean changes in resistance (SR) and percent body fat (Fat) for nine volunteers before and after breakfast5 Breakfast consumption Before After Subject Height Weight R Fat Weight R Fat R Fat cm kg % kg t % t % 1 178 118.4 500 41 118.8 490 41 -10 0 2 177 123.2 382 42 123.8 377 42 -5 0 3 168 59.6 588 7 59.3 609 8 +21 +1 4 164 85.2 499 22 85.4 500 22 +2 0 5 172 118.7 435 41 118.3 429 40 -6 -1 6 159 86.6 606 35 87.0 612 35 +6 0 7 176 92.0 560 27 92.4 530 26 -30 -1 8 170 116.7 384 39 117.4 385 39 +1 0 9 173 138.1 439 53 138.6 427 53 -12 0 i±SD 171 ±6 104.3±24.7 488±84 34± 14 104.5±24.9 484±88 34± 13 -4± 14 0± 1 S Results are pooled from two repeated measurements. Downloaded from www.ajcn.org by on December 24, 2007 before and after the standardized breakfast, respectively, mean determined by hydrostatic weighing. Statistical significance of results over the 2 d are presented in Table 3. differences between pooled means before and after hydration and breakfast, respectively, were assessed by paired I test. Data analysis All data were analyzed by use ofthe StatisticalAnalysis System Results package (SAS Institute, Cary, NC). Stepwise multiple-regression analysis (forward selection technique) was used to derive the Figure 1 shows the relationship between percent body fat (by best predictive equation with FFM, determined by hydrostatic resistance) calculated by use ofthe manufacturer’s software, and weighing as the dependent variable, and height (cm), body weight percent body fat (by hydrostatic weighing) in the 1 56 volunteers (kg), height/resistance (cm2/2), reactance (t1), body mass index used to derive and test a new predictive equation. At any given (kg/m2), age (y), body weight#{176}”, and sex (class variable; male = percent body fat measured by hydrostatic weighing, there is a 1 female , = 0) were entered as independent variables. Indepen- large range ofpercent body fat estimated from BR. In addition, dent variables were eliminated if they did not meet the signifi- BR overestimated percent body fat. From the multiple-regression cance level of P < 0. 1 5 for entry into the model. analysis with use of FFM (by hydrostatic weighing) as the de- To test the new prediction equation, percent fat and FFM pendent variable, Ht2/R, body weight, age, and sex were signif- values from both the manufacturer’s software and from the new icant determinants of FFM in Pima Indians. The following pre- prediction equation were regressed on percent fat and FFM, diction equation provides the best estimate of FFM (Table 4): FFM (kg) = 13.74 + 0.34 X (Ht2/R) 60 + 0.33 X (body weight) - 0.14 X (age) + 6.18 X (sex) C a where height is expressed in cm, resistance in , weight in kg, . 5 a) and age in y; a value of 1 is given for males and 0 for females. 0 In the 26 volunteers for whom the above new equation was U 40 applied, the coefficient of correlation and SEE between percent U a) body fat from resistance and percent body fat from hydrostatic a) 30 0 . .% > 20 .0 TABLE 4 0 Coefficient and SEE for the determinants of FFM (kg), by bioelectrical >, 10 0 resistance, in I 30 Pima Indians 0 r SEE F P 0 10 20 30 40 50 60 % Body fat by hydrostatic weighing Intercept(kg) 13.74 1.52 - 0.0001 Ht2/R (cm2/tI) 0.34 0.05 42.8 0.0001 FIG 1. Variation of percent body fat, by bioclectrical resistance (by use Body weight (kg) 0.33 0.02 324.8 0.0001 of the manufacturer’s software), compared with percent body fat, by Age (y) -0.14 0.03 18.3 0.0001 hydrostatic weighing, for 156 Pima Indians (P < 0.0001), SEE 5.04, Sex (0 or I ) 6. 18 0.9 1 44.4 0.0001 r = 0.83). Points plotted along the line of identity.
  4. 4. BIOELECTRICAL RESISTANCE IN PIMA INDIANS 597 weighing increased from 0.70 (SEE 6.89) when the manufac- 100 turer’s software was used, to 0.92 (SEE 3.22) when the new C 0 90 equation was used (Fig 2). Similar improvements in coefficients . 0 of correlation with the new equation were found when FFM 80 a. (predicted from resistance or determined by hydrostatic weigh- 0’ w . 5) 70 ing) were compared (Fig 3). Correlation coefficients for percent 5) L body fat and FFM, between resistance (new equation) and hy- C) C 60 #{149}S 0 U drostatic weighing, were not equal (0.92 and 0.97, respectively) 0 U) 50 because the variables used for the comparison (kg FFM and In C a) 0 percent body fat) were not directly proportional to each other. 40 0 For the 26 test volunteers, the new prediction equation im- U L 30 proved the mean accuracy of predicting FFM from -5.3 ± 8.6 0 0 50 60 70 80 90100 C) kg (P < 0.05 for difference from 0) to -0. 1 ± 3.3 kg (NS from a) a) 0) of the mean value (69.3 ± 1 3.5 kg) obtained by hydrostatic 0 .0 1( weighing. A similar magnitude of improvement was found for >‘ S .0 percent body fat with values decreasing from an overestimate In of 5 ± 7% (P < 0.05 for comparison with 0) to 0 ± 3% (NS for U) C 0 comparison with 0) of the mean value (34 ± 8%) obtained with E 0 hydrostatic weighing. a) 0 S a) L 0 In the eight subjects who participated in the fluid intake study, w Downloaded from www.ajcn.org by on December 24, 2007 the individual CV for five repeated resistance measurements 0 a) varied from 2.0% to 4.3% before fluid intake and from 2.8% to z 5.1% after fluid intake. Mean resistance increased slightly from 472 ± 70 121 h before fluid intake to 481 ± 70 12 (P < 0.05) 1 h after fluid intake (Table 2). This increase did not result in a 40 50 60 70 80 90 100 Fat-free moss by hydrostatic weighing (kg) FIG 3. Fat-free mass (kg), by bloelectrical resistance, (by use ofthe man- S ufacturer’s software) (upper panel) and the new prediction equation (lower C 0 panel) vs fat-free mass (kg), by hydrostatic weighing, in 26 Pima Indians a (P < 0.0001 ), SEE 8. 10 (r = 0.79) and 3.30 (r = 0.97) for the upper and lower panels, respectively). Points plotted along the line of identity. U) a) a) 0 C 0 U a significant change in percent body fat when estimated by resis- C’, tance. ‘I) C a) 0 For each volunteer in the breakfast study, mean resistance, 0 and therefore estimate of percent body fat, did not change in 0 response to breakfast consumption (Table 3). 0 a) a) 0 n Discussion >‘ BR, a simple and inexpensive method, does not accurately 0 estimate body composition in Pima Indians when the equations > 0 C 0 contained in the manufacturer’s software are used. Use of an 0 In 0 equation specific for Pima Indians improved the average accu- L4J racy of estimating FFM and percent body fat to within -0.1 ± 3.3 kg and 0 ± 3%, respectively, of the values obtained by a) z hydrostatic weighing. This new equation will provide a reason- ably accurate determination of body composition in this pop- ulation. Hydrostatic weighing is the most widely used standard for 10 20 30 40 50 60 determining body composition in humanc and is considered to % Body fat by hydrostatic weighing be the reference standard against which new methods are tested (1 1). We found hydrostatic weighing to be satisfactory (CV 7%) FIG 2. Percent body fat, by bioelectrical resistance (by use of the man- ufacturer’s software, upper panel) and by the new prediction equation (14). Therefore, the large variation in percent body fat estimated (lower panel) vs percent body fat, by hydrostatic weighing, in 26 Pima by BR at any given percent body fat, derived from hydrostatic Indians (P < 0.0001, SEE 6.89 (r = 0.70) and 3.22 (r = 0.92) for the weighing, indicates that the method is not suitable in Pima In- upper and lower panels, respectively). Points plotted along the line of dians when the manufacturer’s software is used. Indeed, other identity. equations have been derived from data on groups of subjects
  5. 5. 598 RISING ET AL from other races (6-8), but more importantly, not representing corporated, Detroit, for providing the equipment for the study; and most the degree of obesity found in Pima Indians. Therefore, a new importantly, the volunteers. equation needed to be derived for Pima Indians to easily estimate body composition in field conditions. We found that such a new References equation can greatly improve the measurement of body corn- position in the Pima Indian population. 1. Knowler WC, Peuitt DJ, Savage PJ, Bennet PH. Diabetes incidence The improvement in the accuracy ofdetermining body corn- in Pima Indians: contribution ofobesity and parental diabetes. Am position with BR in Pima Indians, by use ofan equation derived J Epidemiol l98l;l 13:144-56. in this population, confirms the studies of body results ofother 2. Nyboer J, Bango S. Barnett A, Halsey RH. Radiocardiograms-the composition in other populations (8, 9). Young black men had electrical impedance changes of the heart in relation to electrocar- diograms and heart sounds. J Clin Invest I940;l9:963. higher body densities than did young white men with the same 3. Thomasett A. Bioelectrical properties of tissue impedance. Lyon total body water content (8). Also, the accuracy of predicting Med l962;207: 107-18. FFM from resistance analysis was improved when subjects were 4. Hoffer EC, Meador CK, Simpson DC. Correlation of whole body grouped as normal or obese depending on their degree of obes- impedance with total body water volume. J Appi Physiol l969;27: ity (9). 531-4. There was no effect of fluid intake or breakfast ingestion on 5. Nyboeri, Liedtke Ri, Reid KA, Gessert WA. Nontraumatic electrical body composition estimated by BR. However, the small but detection of total body water and density in man. Med Jadertina significant increase in BR observed 1 h after fluid intake was l983;l5:38l-4. unexpected an increase because in the conductive medium (wa- 6. Segal KR, Gutin B, Presta E, Wang J, Van Italic TB. Estimation ter) theoretically should have reduced the resistance to current of human body composition by electrical impedance methods: a comparative study. J Appl Physiol l985;58: 1565-71. flow (5). It is possible that remeasuring resistance only 1 h after Downloaded from www.ajcn.org by on December 24, 2007 7. Lukaski HC, Johnson PE, Bolonchuk WW, Lykken 01. Assessment fluid ingestion does not allow enough time for complete fluid offat free mass using bio-electrical impedance measurements of the absorption and equilibration of the body water pool to occur. human body. Am J Clin Nutr 1985;41:810-7. Other investigators did not find a decrease in resistance in vol- 8. Schutte JE, Townsend EJ, HuggJ, Shoup RF, Malina RM, Blomqvist unteers rehydrated after an 18-h fast (15). On the other hand, CG. Density oflean body mass is greater in blacks than in whites. resistance did change according to water balance when measured J AppI Physiol l984;56: 1647-9. daily in obese woman who fasted for 14 d with limited fluid 9. Segal KR, Loan MV, Fitzgerald P1, Hodgdon JA, Van Itallie TB. intake, and were refed for an additional 5 d (16). Thus, major Lean body mass estimation by bioelectrical impedance analysis: a shifts in electrolyte and fluid balance may influence the results. four-site cross-validation study. Am J Clin Nutr 1988;47:7-l4. As implied by Segal et al (9), use of Ht2/R in our prediction 10. National Diabetes Data Group. Classification and diagnosis of di- abetes mellitus and other categories ofglucose intolerance. Diabetes equation may mask small changes in percent body fat with small l979;28: 1039-57. changes in resistance. Another prediction equation (not shown) 1 1. Goldman RF, Buskirk ER. A method of underwater weighing and was derived with a separate coefficient for resistance to determine the determination of body density. In: Brozek J, Herschel A, eds. ifthis type ofequation would show changes in percent body fat Techniques for measuring body composition. Washington, DC: Na- in response to fluid or breakfast intake. This latter equation did tional Academy of Sciences, 196 1:78-89. not show any effect of fluid or breakfast intake on body corn- 12. Keys A, Brozek J. Body fat in adult man. Physiol Rev l953;33:245- position. Also, coefficients were very similar to those presented 325. by Segal ci al (9). 13. Fishman GS, Moore LR. A statistical evaluation of multiplicate In conclusion, BR is an accurate and reproducible method congruential generators with modulus (2’-1). J Am Stat Assoc for determining body composition in Pima Indians of both sexes l982;77: 129-36. 14. Bogardus C, Lillioja S. Mott DM, Hollenbeck C, Reaven G. Rela- when a new predictive equation was used. Fluid or food ingestion tionship between degree of obesity and in vivo insulin action in shortly before resistance measurements does not affect the out- man. Am J Physiol l985;248 (Endocrinol Metab 1 l):E286-9 1. come of body composition determinations. 15. Stump CS, Houtkooper LB, Hewitt Mi, Going SB, Lohman TG. We thank Carol Lamkin and the nursing staff of the Clinical Diabetes Bioelectric impedance variability with dehydration and exercise. Med and Nutrition Section for their professional care of the volunteers and Sci Sports Exerc l987;20:S82 (abstr). for their assistance in performing the resistance measurements; Tom 16. Gray DS. Changes in bioelectrical impedance during fasting. Am J Anderson for his help with the hydrostatic weighing; RJL Systems In- Clin Nutr l988;48:l 184-7.

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