Original                   Research Communications-general

Lean body mass estimation by bioelectrical impedance
analysis:...
SEGAL            ET       AL

(lab D). After              all experimental                  procedures           were expl...
LEAN          BODY                MASS              ESTIMATION                     BY     IMPEDANCE                       ...
SEGAL       ET AL
             90                                                                                         ...
LEAN            BODY          MASS       ESTIMATION                BY IMPEDANCE                                           ...
SEGAL                  ET AL
TABLES                                                                                       ...
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Lean body mass estimation by bioelectrical impedance
analysis: a four-site cross-Validation study1

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  1. 1. Original Research Communications-general Lean body mass estimation by bioelectrical impedance analysis: a four-site cross-Validation study13 Karen R Segal, EdD; Marta Van Loan, PhD; Patricia I Fitzgerald, PhD; James A Hodgdon, PhD; and Theodore B Van Itaiie, MD ABSTRACT This study validated further the bioelectrical impedance analysis (BIA) method for body composition estimation. At four laboratories densitometrically-determined lean body mass (LBMd) was compared with BIA in 1567 adults (1069 men, 498 women) aged 17-62 y and with body fat. Equations 3-56% for predicting LBMd from resistance measured by BIA, height2, weight, and age were obtained for the men and women. Application of each equation to the data from the other labs yielded small reductions in R values and small increases in SEES. Some regression coefficients differed among labs but these differences were eliminated after adjustment for differences among labs in the subjects’ body fatness. All data were pooled to LBMd: the resulting R values ranged from 0.907 Downloaded from www.ajcn.org by on January 11, 2008 derive fatness-specific equations for predicting to 0.952 with SEES of 1.97-3.03 kg. These results confirm the validity of BIA and indicate that the precision of predicting LBM from impedance can be enhanced by sex- and fatness-specific equations. AmfClinNutr l988;47:7-l4. KEY WORDS Body composition, densitometry, bioelectrical impedance analysis, lean body mass Infroduction sites in large samples of men and women who vary widely in age and body fat content. The use of bioelectrical impedance analysis (BIA) in body composition assessment has been investigated re- cently. BIA, a portable impedance analyzer (RJL Systems, Subjects and methods Detroit, MI), is a localized 50-kHz current-injection Subjects method that yields a measure of total body resistivity. Fifteen hundred sixty-seven subjects (1069 men, 498 women) The method is based on the principle that impedance to aged 17-62 y were studied in four laboratories located in four the electrical flow of an injected current is related to the different cities in the United States: San Francisco, CA (lab A); volume ofa conductor (the human body) and the square New York, NY (lab B); Natick, MA (lab C); and San Diego, CA of the length of the conductor (height). Hoffer et al (1) demonstrated that total body water (TBW) and lean body mass (LBM) were strongly correlated with height2/resis- I From the Division of Pediatric Cardiology (KRS), Mount Sinai tance, where body resistivity or impedance was measured School ofMedicine, New York, NY; the US Dept of Agriculture (MVL), with a tetrapolar electrode configuration. Western Human Nutrition Research Center, San Francisco, CA; the US Several recent studies (2-4) demonstrated strong cor- Army Research Institute ofEnvironmental Medicine (PIF), Natick, MA; relations between BIA (either height2/resistance measured the Naval Health Research Center(JAH), San Diego, CA; and the Obesity with BIA or LBM and TBW predicted from BIA with use Research Center (TBVI), St. Luke’s-Roosevelt Hospital Center, New of equations provided by the manufacturer) and TBW York, NY. 2 Supported in part by grants from the National Institutes of Arthritis, measured by isotope dilution and densitometrically de- Diabetes, and Digestive and Kidney Diseases (grant #AM-26687) and termined LBM. However, these studies have usually made by the Naval Medical Research and Development Command (work unit use of small or heterogeneous samples, and the repro- #M0096.OOl-1050). ducibility of the method between laboratories has not been 3 Address reprint requests to Dr Karen R Segal, Annenberg 3-45, determined by means of cross-validation studies. Mount Sinai School ofMedicine, Box 1201, 1 Gustave Levy Place, New The purpose of this study is to cross-validate the BIA York, NY 10029. method by comparing the relationship between BIA and Receivedianuary 12, 1987. densitometrically determined LBM at four geographical Accepted for publication March 24, 1987. Am J Clin Nuir l988;47:7-14. Printed in USA. © 1988 American Society for Clinical Nutrition 7
  2. 2. SEGAL ET AL (lab D). After all experimental procedures were explained to the tions to predict densitometrically determined LBM (LBMd) for subjects their written informed consent was obtained. The test each sample. Resistance, reactance, height2/resistance, weight, protocol was reviewed and approved by the institutional review height2, age, and sex (dummy coded with males, = 0, females board at each of the participating institutions. Each subject = 1) were offered as possible predictors. LBMd was used as the completed all measurementS on the same morning. Most of the dependent variable. The regressions were carried out in stepwise subjects were studied after an overnight (12 h) fast and those fashion. Before pooling the data from males and females, the who were not tested after an overnight fast were at least 3 h equality of the slopes for males and females was tested for sta- postabsorptive. tistical significance (1 1). A quadruple cross-validation of the equations for predicting Densitometry LBMd from BIA was carried out according to the procedure Body fat content and LBM were determined by densitometry. described by Lord and Novick (12): the best-fitting equation At lab B, body density was determined by hydrostatic weighing derived from each data set was applied to the other three data in a stainless steel tank in which a swing seat was suspended sets. For each data set statistical significance ofthe deviation of from a Chatillon 15-kg scale (Chatillon, New York, NY). At the regression of LBMd on LBM, cross-predicted with use of labs A, C, and D, the underwater weighing systems were mod- the equations derived from the other three laboratories, from ifications ofthe method ofAkers and Buskirk (5), which makes the line of identity was tested. This procedure was followed to use of force transducers. The subjects submerged beneath the test the significance of differences in the best-fitting regression surface of the water while expiring maximally and remained as lines among laboratories (1 1). motionless as possible at the point of maximal expiration for Additional statistical analyses ofthe data are described in the , 5 5 while underwater weight was recorded. After several prac- results section. The 0.05 level of significance was used for all tice trials to familiarize the subjects with the test procedure, 10 data analyses. Downloaded from www.ajcn.org by on January 11, 2008 trials were performed except in lab A where only 4 trials were performed. The estimated underwater weight was the highest Results value that was reproduced three times (6). In labs A, B, and C residual lung volume was estimated by means of the closed- The characteristics of the subjects are shown in Table circuit oxygen dilution method of Wilmore (7) with use of a I The population . varied widely with respect to age and Collins spirometer (Warren E Collins, Braintree, MA) and a body composition. The same predictor variables were se- Hewlett Packard Model 47302A (Hewlett-Packard, Cupertino, lected by the stepwise regression procedure in all four sets CA) or a Med-Science Model 505D nitrogen analyzer (Fiske Med-Science, St Louis, MO). In lab D, residual volume was of data: R, ht2, wt, age, and sex. The data from the men measured by the closed-circuit helium dilution method (8) with and women were treated separately because the regression use of a Collins Model 3002 modular lung analyzer (Warren E coefficients ofthe best-fitting regression lines were signif- Collins). In labs B, C, and D, two trials were performed while icantly different for men and women. The best-fitting the subjects assumed a sitting position that duplicated body po- equations for each laboratory are shown in Table 2. Re- sition in the tank during underwater weighing. Residual volume sistance and height2, individually, were better predictors at lab A was measured in the water at the time ofthe underwater of LBMd than the calculated height2/resistance, as deter- weighing. Body density was calculated from the formula of mined by greater correlation coefficients and smaller SEES. Goldman and Buskirk (9) and percent body fat was derived The residuals were analyzed and found to be randomly from body density by use ofthe Siri equation (10): percent body fat = (4.95/density) - 4.5. LBM is the difference between total body weight and fat weight, where fat weight equals total body weight multiplied by percent body fat. TABLE 1 Characteristics of the subjects (mean ± SD) Bioe!ectrical impedance analysis LabA LabB LabC LabD Total body resistivity was measured with a four-terminal por- (n = 96) (n = 99) (n = 490) (n = 404) table impedance analyzer (RJL Systems, Detroit, MI). Mea- surements were made while the subjects lay comfortably on a Men stretcher with the limbs abducted from the body. Current-injector Age 32± 9 26± 8 34± 8 32± 7 electrodes were placed just below the phalangeral-metacarpal Weight(kg) 75±12 79±12 79±12 88±13 joint in the middle of the dorsal side of the right hand and just Height(cm) 178± 8 179± 7 175± 7 179± 7 below the transverse (metatarsal) arch on the superior side of LBM(kg)* 61± 8 66± 7 62± 8 67± 8 the right foot. Detector electrodes were placed on the posterior Percent fat 18 ± 7 16 ± 8 22 ± 7 23 ± 8 side of the right wrist, midline, with the prominent pisiform Resistance(Q) 485±63 459±47 442±55 432 ±49 bone on the medial (fifth phalangeal) side and ventrally across LabA LabB LabC LabD the medial ankle bone ofthe right ankle with the foot semiflexed. (n = 64) (n = 81) (n = 224) (n = 141) Resistance (R) to the flow ofa 50-kHz injected current was mea- sured on a 0- l000-( scale and reactance (Xc) was measured on Women a 0-20041 scale. Empirically derived formulas provided by the Age 35± 9 29±10 24± 5 27± 6 manufacturer of the instrument were used to calculate es- Weight(kg) 59± 8 71±23 61± 8 63± 9 timated LBM. Height(cm) 165± 8 165± 7 163± 6 164± 7 LBM (kg)* 43 ± 6 48 ± 7 44 ± 6 45 ± 5 Statistical analyses Percent fat* 27 ± 8 29 ± 12 28 ± 6 27 ± 8 Resistance(Q) 587±58 551±68 554±62 559±68 Multiple regression analyses were applied to the data from each laboratory to derive best-fitting multiple regression equa- S Determined from hydrodensitometry. LBM = lean body mass.
  3. 3. LEAN BODY MASS ESTIMATION BY IMPEDANCE 9 TABLE 2 ofidentity. Reductions in the correlation coefficients and Best-fitting equations for predicting lean body mass for each lab and increases in the SEEs resulting from application of the all labs pooled equations derived at other laboratories compared with vnab1e Lab A Lab B Lab C Lab D All labs the best-fitting equations were minimal. However, as Men shown in Table 3, differences in regression equations were Height2 0.00109 0.00124 0.00122 0.00140 0.00132 found among some ofthe laboratories. For the men these Resistance -0.01607 -0.06626 -0.03736 -0.06336 -0.04394 differences were attributable to differences among the lab- Weight 0.41004 0.26261 0.31973 0.26079 0.30520 oratories in body fat content: The lab C and lab D men Age -0. 15407 -0.22776 -0.13038 -0. 15634 -0.16760 were significantly fatter than the lab A and lab B men. Intercept 8.14874 41.35041 19.77883 32.29519 22.66827 R 0.9$ I 0.907 0.882 0.896 0.898 When adjustment was made statistically for differences SEE 3.28 2.91 3.62 3.49 3.61 in body fat content among the four labs, differences among regression equations were eliminated. Specifically, LBMCI vuiable Lab A Lab B Lab C Lab D All Labs was regressed on body fat and residualized LBMd values Women were obtained. The residualized LBMd, purged of any Height2 0.00112 0.00114 0.000942 0.00103 0.00108 Resistance -0.03797 -0.02502 -0.01410 -0.02578 -0.02090 relationship with body fat, was used as the dependent Weight 0.21110 0.18856 0.31153 0.22280 0.23199 variable and stepwise regressions were carried out for each Age -0. 12953 -0.06498 -0. 14505 -0.01802 -0.06777 lab using resistance, height2, weight, and age as the in- Intercept 27.16729 19.25955 10.91436 18.29870 14.59453 dependent variables. The resulting regression equations R 0.891 0.942 0.876 0.861 0.889 were analyzed for statistical differences among labs. For SEE 2.51 2.31 2.15 2.42 2.43 Downloaded from www.ajcn.org by on January 11, 2008 both men and women (even though the differences among labs in the women’s body fat did not achieve statistical significance) no statistical differences among labs in the distributed and were uncorrelated with the predicted LBM regression coefficients were obtained when this adjustment values. for body fat was made. This confounding effect of body fatness on the prediction of LBMd supports a previous Quadruple cross-validation finding that the error in predicting LBMd from BIA was significantly related to obesity (4). The quadruple cross-validation of the equations for The relationship between LBM predicted from height2, predicting LBMd is shown in Table 3. The purpose of the resistance, weight, and age and LBMd for men and women quadruple cross-validation was to determine the repro- (all labs pooled but separate equations for men and ducibility across laboratories of the relationship between women) is shown in Figures 1 and 2. When the data are LBMd and LBM predicted from BIA and other variables. expressed as percent body fat, the correlations between LBMd was regressed on LBM predicted by each of the densitometrically determined percent body fat and pre- equations in order to determine whether the slopes and dicted percent body fat are r = 0.809 and r = 0.852 for intercepts differed from 1 and 0, respectively, indicating men and women, respectively, with SEEs of4.44% fat for that the regression lines differed significantly from the line men and 3.98% fat for women. TABLE 3 Quadruple cross-validation ofequations for predicting lean body mass (LBM): correlations (and SEE) between densitometrically determined LBM (LBMd) and LBM predicted by best-fitting equation from each lab and all labs pooled LabA LabB LabC LabD Men LBM (lab A equation) 0.911 (3.23) 0.854 (3.54)t 0.872 (3.75) 0.854 (4.08)ff LBM (lab B equation) 0.832 (4.34)t1 0.907 (2.86) 0.860 (3.92)tf 0.892 (3.54) LBM (lab C equation) 0.896 (3.47) 0.883 (3. 19) 0.882 (3.61) 0.884 (3.66) LBM (lab D equation) 0.853 (4.09)ff 0.902 (2.94) 0.871 (3.77)t 0.896 (3.48) LBM (all labs equation) 0.886 (3.62) 0.893 (3.06) 0.88 1 (3.63) 0.889 (3.58) LabA L.abB LabC LabD Women LBM (lab A equation) 0.891 (2.45) 0.936 (2.38)tf 0.834 (2.45)tt 0.841 (2.55)tf LBM (lab B equation) 0.878 (2.58) 0.942 (2.27) 0.854 (2.30) 0.856 (2.44) LBM (lab C equation) 0.859 (2.76) 0.932 (2.44)ff 0.876 (2.15) 0.832 (2.62) LBM (lab D equation) 0.868 (2.68) 0.936 (2.38)ff 0.853 (2.32) 0.861 (2.40) LBM (all labs equation) 0.872 (2.64) 0.940 (2.30) 0.866 (2.22) 0.856 (2.44) S Best-fitting results for each lab are indicated by italics. t Intercept significantly different from 0; p < 0.05. :l:Slope significantly different from 1; p < 0.05.
  4. 4. SEGAL ET AL 90 and vice versa were significantly different from LBMd 000 5 0 (Table 6). DO DO C 0 0 D*C* C Figures 3 and 4 show the relationship between LBMd CC0O* 0* 0 0 80- O ODD 0 0 and LBM predicted with use of the fatness-specific equa- *000*CDS 11 CS *0000000*C *0000000000 C * tions. The dispersion ofdata points is considerably smaller DDDO000000000* DO than when the generalized equations were applied (Figs 0’ 70- * *A000IHSO*ODCDO* DSS0N*0000000 C #{149}000000000000*OC 1 and 2). For men, the R value increased from 0.896 to C CC00000000* CC* A 00*0*0*00*000 0.938 and the SEE decreased from 3.62 to 2.84 kg with SI A 0*110000000S0* * E 60- *CC*0*00000*0* use of fatness-specific equations. For women, the multiple 0 *DC00000CXOC000C SD CDA C000*000110 ACC0000000COC C C A I k A correlation coefficient R increased from 0.889 to 0.930 C 0 SI 5Q C ACAC0000COC ACC*D*AC B=LabB and the SEE decreased from 2.43 to 1.95 kg with use of CCC A C *CCC - - fatness-specific equations. For the men when the data are -J CCC DLabD expressed as percent body fat, the correlation between C C * = Multiple data 40 densitometrically determined and predicted percent body points fat increases from 0.809 to 0.896 and the SEE decreases C r.896 from 4.44% fat to 3.35% fat with use ofthe fatness-specific 30 SEE =3.62kg equations. For the women the correlation between den- I I I I sitometrically determined percent body fat and predicted .. 40 50 60 70 80 90 percent body fat increases from 0.852 to 0.909 and the Downloaded from www.ajcn.org by on January 11, 2008 LBM(BIA) (kg) SEE decreases from 3.98% fat to 3. 18% fat with use of the (Best fitting equation for all men) fatness-specific equations. The practical application of the fatness-specific equa- FIG 1. Relationship between densitometrically determined lean body tions is questionable since their use depends on prior mass (LBMd) and lean body mass (LBM) predicted from bioelectrical knowledge of an individual’s Further- body fat content. impedance analysis (BIA) and other indices with use of the best-fitting equation for the men in all four laboratories. more, the fatness-specific equations for predicting LBMd are the basis of categorizing the subjects with respect to densitometrically determined percent body fat, and it is important to note that percent fat and LBM are not truly Fatness-speafic prediction equations independent since both are derived from hydrodensitom- etry. Although for subjects who are obviously lean or obese Additional analyses were applied to the data to char- there would be no question as to which prediction is most acterize further the relationship between body fatness and appropriate, for subjects who are neither clearly lean nor the prediction of LBMd by BIA and to determine the appropriateness of fatness-specific equations. The total populations of men and women were divided randomly into two subsets for the purpose of cross-validation and each subset was divided into normal and obese groups 65- S S based on whether the subjects were less than or greater 55 than 20% and 30% body fat for men and women, respec- 60- S SB tively. For each of the two sets of normal and obese men 0’ S S A S and normal and obese women (total of eight subsets), an 55 - CD. 0 C DOD equation for predicting LBMd was derived. The same set *C *C* C O * *D*C*OSD S of variables (height2, resistance, weight, and age) entered SI E 50 - SCC #{149}0*0*00*SC *B** IHI C D 0 into all equations except that age did not enter into the *S**C*C*SCDS (I) C A***IHHI*** S equations for the two sets ofnormal women. The fatness- C **IHHI***C * A = Lab A . 45 - D *0*5*1155CC CD C specific equations and the cross-validation ofthese equa- C*****000**C B Lab B #{149} S*C*0000 SO C C = Lab C tions between the two randomly generated sets for each 40 - S*S**IIOOS*0C *005*5000CC 0 = Lab D -I sex and level of fatness are shown in Tables 4 and 5. Use C *B*DC*CDCC *C**CSCC 11 : Multiple data of these fatness-specific equations greatly improved the AACC *SC points 35 - #{149}CDC C accuracy of predicting LBMd: the multiple correlation CCC D r =889 coefficients were significantly increased and the SEES were SEE =2.43 kg 30 significantly reduced. No differences were found in the ii I I I I I I I I I regression coefficients between set 1 and set 2 of normal 30 40 50 60 70 men, obese men, normal women, or obese women, in- LBM(BIA) (kg) dicating the validity and reproducibility of fatness-specific equations (Table 5). However, significant differences were ( Bestfitting equation for all women) observed between the regression equations for the normal FIG 2. Relationship between densitometrically determined lean body and obese subjects for both men and women. Further- mass (LBMd) and lean body mass (LBM) predicted from bioelectrical more, the mean predicted LBM values obtained by ap- impedance analysis (BIA) and other indices with use of the best-fitting plying the normal group’s equations to the obese subjects equation for the women in all four laboratories.
  5. 5. LEAN BODY MASS ESTIMATION BY IMPEDANCE 11 TABLE 4 Fatness-specific equations for predicting LBM Normal Obese SetI Set2 Setl Setl Set2 SetI Variable (n=244) (n=228) +set2 (n=295) (n=302) +set2 Men HCight 0.00060171 0.00071366 0.00066360 0.00072092 0.001020 0.00088580 Resistance -0.01959 -0.02319 -0.02 1 17 -0.0542 1 -0.08245 -0.02999 Weight 0.65940 0.59597 0.62854 0.48291 0.38179 0.42688 Age -0. 14244 -0. 10545 -0. 12380 -0.0542 1 -0.08245 -0.07002 Intercept 8.73968 10.64701 9.33285 11.48504 16.69512 14.52435 R 0.948 0.943 0.946 0.937 0.939 0.937 SEE 2.50 2.44 2.47 2.97 3.04 3.03 Normal Obese Seti Set2 SetI SetI Set2 Sell Variable (n= 146) (n= 177) +set2 (n=99) (n=76) +set2 Women Height2 0.00060098 0.00066464 0.00064602 0.000955 14 0.00077596 0.00091186 Downloaded from www.ajcn.org by on January 11, 2008 Resistance -0.018 17 -0.01 121 -0.01 397 -0.01420 -0.01 560 -0.01466 Weight 0.40328 0.43868 0.42087 0.3 1 134 0.282 16 0.29990 Age - - - -0.07187 -0.06215 -0.07012 Intercept 15.26646 7.18338 10.43485 7.47371 14.20227 9.37938 R 0.839 0.903 0.907 0.953 0.954 0.952 SEE 1.90 2.00 1.97 2.06 1.81 1.97 obese, there is a need for a technique to determine which pendent of densitometrically determined body fatness, prediction equation is most applicable. The validity of would significantly improve the prediction of LBMd, an fatness-specific equations for predicting LBMd requires a independent group ofsubjects was studied. These subjects method of categorizing subjects that is objective and in- (88 men, 72 women) underwent hydrostatic weighing, BIA dependent ofdensitometry. Body mass index (BMI), body measurement, and skinfold thickness measurements. surface area (BSA), and percent ofdesirable body weight Percent fat was derived body from the sum of the biceps, according to the 1959 Metropolitan Life Insurance stan- triceps, suprailiac crest, and subscapular skinfolds with dards (13) were tested as possible classification criteria. use of the tabled values of Durnin and Womersely (14). Classification of the subjects by these indices did not sig- The subjects were divided into normal and obese groups nificantly improve the prediction ofLBMd: categorization based on whether anthropometrically determined percent ofthe subjects according to whether they had a BMI less fat was less than or greater than 20% for men and less than or greater than 26, were below or above the 50th than or greater than 30% for women. Compared with percentile ofBSA for their sex, or were less than or greater densitometrically determined percent fat, 82 of 88 men than 120% of desirable body weight did not lead to sig- (93%) and 66 of 72 women (92%) were correctly catego- nificant increases in the multiple correlation coefficients rized with respect to their level of fatness. To determine or significant reductions in the SEEs. Each of these three the effectiveness of this classification, for each subgroup criteria also was tested as a continuous variable: in separate (normal and obese men, normal and obese women) LBMd analyses, each ofthe indices was entered forcibly into the was compared with LBM estimated with use of the gen- regression equation to predict LBMd at the first step, and eralized equations and LBM estimated with use of the height2, resistance, weight, and age were entered block- fatness-specific equations. These results are shown in wise at the second step (1 1). Separate analyses were carried Table 7. Use ofthe generalized BIA equations significantly out for men and women. The resulting correlations and underestimated LBMd for both the normal men and nor- SEEs were not significantly different from those obtained mal women. However, the question remained as to by use ofthe generalized equations derived from all men whether use of the BIA fatness-specific equations signifi- and all women (individually) pooled together (Figs 1 and cantly improved the prediction of LBMd over an- 2), indicating that BSA, BMI, and percent desirable body thropometry alone. To answer this question, LBM weight did not significantly improve the prediction of values (LBMa) were derived from anthropometrically- LBMd. determined percent body fat: weight - (weight X percent fat). LBMd was regressed on LBMa and LBM determined Use ofanthropometry to categorize subjects with use of the fatness-specific equations with LBMa en- To test whether categorization ofsubjects into two levels tered forcibly at the first step. The significance ofthe entry of fatness on the basis of anthropometry, which is inde- of LBM derived with the fatness-specific BIA equations
  6. 6. SEGAL ET AL TABLES for men and from 2.63 to 2.09 kg for women). Thus, Cross-validation of fatness-specific equations for predicting lean body mass* anthropometrically determined percent fat can be used (LBM) reliably as the criterion for determining which fatness- specific BIA equation to apply. There is obviously no need Normal Normal All normal to prescreen subjects who are extremely lean or extremely seti set2 men obese. However, for subjects who are neither clearly lean Men nor obese, anthropometry may be useful in determining Normal men LBM (normal set I eq) 0.948 (2.50) 0.942 (2.45) - the optimal BIA prediction equation. It is important to LBM (normal set 2 eq) 0.947 (2.51) 0.943 (2.44) - note, however, that the fatness-specific BIA equations sig- LBM (obese men’s eq) - - 0.939 (2.60)t nificantly improve the prediction of LBMd over anthro- Obese Obese All obese pometry alone. sell set2 men Obese men Manufacturer’s prediction equation LBM (obese set 1 eq) 0.937 (2.97) 0.934 (.318) - LBM (obese set 2 eq) 0.933 (3.06) 0.939 (3.05) - Equations are provided with the BIA instrument for LBM (normal men’s eq) - - 0.932 (3.l3)t the prediction of LBM: for men LBM = 6.493 Normal Normal All normal + 0.4936(height2/resistance) + 0.332(weight); and for set 1 set 2 women women LBM 5.09 1 + 0.6483(height2/resistance) Women + 0. 1699(weight). The correlation between LBMd and Downloaded from www.ajcn.org by on January 11, 2008 Normal women LBM predicted with use of the manufacturer’s equation LBM (normal set I eq) 0.916 (1.90) 0.897 (2.05) - LBM(normalset2eq) 0.911 (1.93) 0.903(2.00) - was r = 0.857 (SEE = 3.70 kg) for men and r = 0.800 LBM (obese women’s UI) - - 0.897 (2.06)t (SEE = 3.18 kg) for women. However, the mean predicted LBM was significantly greater than LBMd for both men Obese Obese Allobese set 1 set 2 women (69.2 ± 8.52 kg predicted LBM vs 64.0 ± 8.2 kg LBMd) and women (47.3 ± 6.2 kg predicted LBM vs 44.6 ± 5.30 Obese women LBM (obese set 1 eq) 0.953 (2.05) 0.953 (1.97) - kg LBMd), indicating that the manufacturer’s equation LBM (obese set 2 eq) 0.952 (2.06) 0.954 (1.81) - systematically overestimated LBMd for both sexes. This LBM (normal women’s ui) - - 0.938 (2.20)t overestimation of LBMd by the manufacturer’s equation was most apparent in the obese men and women (Ta- 11 Best-fining equations for predicting LBM for normal and obese men and women denved from set 1 were applied to set 2 and vice versa. Measured LBM ble 6). (LBMd) was regressed on LBM predicted with use ofthe equation developed from the opposite t and correlations between measured and cross-predicted LBM and SEES are shown. Effect of fitness-specific equations is indicated by regression of Discussion LBMd on LBM cross predicted by applying obese subjects’ equation to normal subjects and vice versa. t Slope significantly different from 1, p < 0.05; intercept significantly different The results ofthis study confirm the validity ofthe BIA from 0, p< 0.05. method for predicting LBM in large heterogeneous sam- ples of men and women. In contrast to previous reports (2-4), height2 and resistance individually rather than at the second step was then determined. For both men height2/resistance were selected by the stepwise regression and women the addition ofLBM derived from the fatness- process. This finding was consistent among the four labs. specific BIA equations significantly improved the predic- Also, whereas in previous studies (3, 4) the slopes of the tion of LBMd: the change in the correlation coefficients prediction equations were not reliably different between (from R = 0.934 to 0.943 for men and from R = 0.923 men and women, in the present study significant differ- to 0.952 for women) was highly significant (p < 0.0001) ence in the regression coefficients were found between as were the reduction in the SEEs (from 2.53 to 2.22 kg men and women. It is possible that discrepancies between TABLE 6 Comparison of mean (±SD) densitometrically determined lean body mass (LBMd) with lean body mass (LBM) cross-p redicted with use of fatness- specific equations and RJL System’s equation Normal men Obese men Normal women Obese women LBMd 64. 12 ± 7.56 63.95 ± 8.63 44.54 ± 4.64 44.71 ± 6.36 LBM (manufacturer’s eq) 66.60 ± 7. 18 7 1 . 19 ± 8.95 46.56 ± 5.44 48.87 ± 7.04 LBM (obese men’s eq) 57.98 ± 5.90 LBM (normal men’s eq) 71.51 ± 10.1 1 LBM (obese women’s eq) 41.38 ± 3.91 LBM (normal women’s eq) 49.63 ± 7.79 S p < 0.01 vs LBMd.

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