body composition by x-ray absorptiometry and bioelectrical impedance in chronic respiratory...

APPLIED NUTRITIONAL INVESTIGATION Nutrition Vol. 13, Nos. 1 l/12, 1997

Body Composition by X-ray Absorptiometry and Bioelectrical Impedance in Chronic

Respiratory Insufficiency Patients

CLAUDE PICHARD, MD,* URSULA G. KYLE, MS, RD,* JEAN-PAUL JANSSENS, MD,t LUC BURDET, MD,? THIERRY ROCHAT, MD,_F DANIEL OLIVIER SLOSMAN, MD,$ JEAN-WILLIAM FITTING, MD,? DANIEL THIEBAUD, MD,+ MICHEL ROULET, MD,§ JEAN-MARIE TSCHOPP, MD,I/ MICHEL LANDRY, MD,‘j AND YVES SCHUTZ, PHD#

From the Divisions of *Clinical Nutrition and Dietetics, i_Pneumology, gNuclear Medicine, SPediatrics, IRadiology, and #Physiology, University Hospitals, Geneva and Lausanne,

and IlValaisan Center of Pneumology, Geneva, Switzerland

Date accepted: 9 April 1997

ABSTRACT

Nutrition assessment is important during chronic respiratory insufficiency to evaluate the level of malnutrition or obesity and should include body composition measurements. The appreciation of fat-free and fat reserves in patients with chronic respiratory insufficiency can aid in designing an adapted nutritional support, e.g., nutritional support in malnutrition and food restriction in obesity. The purpose of the present study was to cross-validate fat-free and fat mass obtained by various bioelectric impedance (BIA) formulas with the fat-free and fat mass measured by dual-energy X-ray absorptiometry (DXA) and determine the formulas that are best suited to predict the fat-free and fat mass for a group of patients with severe chronic respiratory insufficiency. Seventy-five patients (15 women and 60 men) with chronic obstructive and restrictive respiratory insufficiency aged 45-86 y were included in this study. Body composition was calculated according to 13 different BIA formulas for women and 12 for men and compared with DXA. Because of the variability, calculated as 2 standard deviations, of 25.0 kg fat-free mass for women and 26.4 kg for men for the best predictive formula, the use of the various existing BIA formulas was considered not clinically relevant. Therefore disease-specific formulas for patients with chronic respiratory insufficiency should be developed to improve the prediction of fat-free and fat mass by BIA in these patients. Nutrition 1997;13:952-958. OElsevier Science Inc. 1997

Key words: bioelectric impedance analysis, BIA, dual energy x-ray absorptiometry, DXA, body composition, fat-free mass, fat mass, pulmonary disease, chronic respiratory insufficiency

INTRODUCTION rather than fat-free mass (which is mostlv muscle). Thus the

Patients with severe chronic insufficiency disorders are frequently limited in their daily activity because their lung disease causes them to be intolerant to physical efforts and to have an imbalance between food intake and nutritional needs. Under- and overnutrition can both affect quality of life and survival of patients with chronic pulmonary disease. Severe protein-calorie malnutrition leads to quantitative, qualitative, and functional muscular alterations.l,2 Muscle wasting can therefore affect muscular function, including respiratory function in patients with already limited respiratory reserves. On the other hand, overnutrition in extremely sedentary subjects can result in a preferential increase in fat mass

appreciation of fat-free and fat reserves in patients with chronic respiratory insufficiency can aid in designing an adapted nutrition support, e.g., nutrition support in malnutrition and food restriction in obesity. Nutrition assessment is therefore important and should include body composition measurements, because these measurements are based on objective rather than subjective criteria of nutritional status. Body composition can be measured by different techniques, i.e., hydrodensitometry, isotope dilution, total body potassium.’ However, these methods are not easily applicable in patients. More recent methods for the determination of the fat-free mass (FFM) and fat mass are dual-energy X-ray absorptiometry (DXA) and bioelectric impedance analysis (BIA). DXA has been

Correspondence to: Claude Pichard, MD, PhD, Head, Clinical Nutrition and Diettherapy, Geneva University Hospital, 121 I Geneva, Switzerland.

Nutrition 13:952-958, 1997 OElsevier Science Inc. 1997 Printed in the USA. All rights reserved. ELSEVIER

0899-9007/97/$17.00 PI1 SO899-9007(97)00336-5

BODY COMPOSITION IN CHRONIC RESPIRATORY INSUFFICIENCY 953

TABLE I.

PHYSICAL CHARACTERISTICS AND DIAGNOSIS OF STUDY GROUP

Women (n = 15)

Men (n = 60)

Anthropometrics

Age (Y) Height (cm) Weight (kg) BMI (kg/m’)

60.8? 11.1 66.8 2 8.2 158.4 % 7.3 170.9 t 8.1 53.7 5 11.7 54.8 ? 9.3 21.7 -t 5.9 18.8 2 2.8

stable (nondecompensated) condition for a period of 22 mo. In addition, patients with a body mass index (BMI) 232 (kg/m2) were excluded because errors in prediction of fat mass are known to be increased.2’ Thirty-six percent of the total sample were under home oxygen therapy according to well-established criteria.l? Mean predicted maximal expiratory volume per second (FEV,) was 35 2 12% of the predictive values for age and sex; mean PO, was 8.6 ? 1.7 kPa (64.5 ? 12.8 mmHg) and Pcoz was 6.1 ? 0.8 kPa (45.8 2 6.0 mmHg). Maintenance medication included the- ophylline, inhaled or oral corticosteroids, and beta agonists. An- thropometric measurements (height, weight) and body composition assessment (BIA and DXA) were performed one after the other to ensure that the measurements were comparable. The study met the condition of local ethical rules.

Values are means -t SD. BMI, body mass index. Anthropometrics and Bioelectric Impedance

validated against independent methods including gamma neutron- activation model,“,5 total body potassium, and hydrodensitometry6 and is becoming one of the reference methods for body composition analysis, but requires sophisticated technology. On the other hand, BIA is easy. noninvasive, and inexpensive.’ Although BIA measurements have been validated in healthy adults,*~iO it is not fully documented whether it can predict FFM and fat mass equally well in patients with various pathologies that cause them to be excessively sedentary or to have important variations in body compartments.

Earlier studies found that the relationship between body impedance and body composition is dependent on height, age, sex, and cell mass.” A number of different formulas8~~~i2-iy have been published to estimate the FFM and fat mass based on BIA measurements. The purpose of the present study was to cross-validate FFM and fat mass obtained by various BIA formulas with the FFM and fat mass measured by DXA and determine the formulas that are best suited to predict the FFM and fat mass for a group of patients with severe chronic respiratory insufficiency who are older than 45 y of age. A validated respiratory disease-specific BIA formula would permit the use of BIA for sequential evaluation of muscle and fat reserves and changes in these parameters in patients with chronic respiratory insufficiency. As far as we are aware, this is the first study that compares the FFM and fat mass determined by a number of different BIA formulas to the DXA- determined FFM and fat mass in a large number of patients with severe respiratory insufficiency.

Height was measured to the nearest 0.5 cm. The height scale used for subjects in this study was the same for BIA and DXA measurements. Body weight was measured to the nearest 0.1 kg on a balance beam scale. BIA was used to determine FFM and fat mass via standard tetrapolar method as previously described.8,i2.‘3 Briefly, an electric current of 50 kHz and 0.8 mA was produced by a generator (Bio-Z, EugCdia, Paris, France) and applied to the skin using adhesive electrodes (Sentry Silver Sircuit, Sentry Medical Products, Irvine, CA, USA) placed on all right side limbs with patient in the decubitus dorsalis position as previously described.2a The Bio-Z generator used in this study has been validated against other bioimpedance generators (RJL Systems and Xitron) and has been determined to provide equivalent results. The skin was cleaned with 70% alcohol.

Body composition was calculated according to 13 different formulas for women and 12 for men, as identified in Table II. Schols et al.‘s formulaiy was chosen because it was developed for patients with COPD. The formula by Deurenberg et al.” was used to predict FFM and fat mass for patients over 60 y of age (women. n = 12; men, n = 52). Two formulas used by the manufacturer RJL Systems (Detroit, MI, USA) in their BIA instruments were included because these devices are widely distributed and the formulas are frequently used as part of the manufacturer’s pack- age. The other eight formulas including Deurenberg et al.,‘” Heit- manni Graves et a1.,15 Lukaski,*,i* Lukaski et al..‘” Segal et al., and Van Loan et a1.,14 were selected because they are frequently cited in the literature. One formula by Stolarczyk2s is specific for women who tend to have increased body fat (native American Indians).

SUBJECTS AND METHODS DXA

Subjects

Seventy-five patients (15 women and 60 men) with respiratory disorders aged 45-86 y in stable clinical condition were included in this study. The physical characteristics of the patients are shown in Table I. Fifty-eight of the patients were diagnosed with chronic obstructive pulmonary disease (COPD), and 17 were patients with restrictive pulmonary disease (post tuberculosis syndrome, and kyphoscoliosis). Patients with COPD, scoliosis, and post tuberculosis syndrome have all been shown to have an increased oxygen cost of breathing and increased resting energy expenditurezO and are therefore at risk of peripheral and respiratory muscle wasting. It was therefore believed that these patients could be pooled for body composition purposes. Patients with neuromuscular diseases were excluded. Patients under 45 y of age as well as patients with decompensated cardiovascular. renal, or endocrine diseases were excluded. Patients with symptomatic edema and fluid retention were also excluded. Extracellular fluid retention is possible with COz retention in these patients; however, our patients were in

DXA is based on the principle that a double photon beam, generated by an X-ray source, can discriminate between the bone mineral content, and lean and fat soft tissues in reference to an externally calibrated standard. The advantage of this method is that, within a few minutes, total fat mass can be estimated directly rather than by subtracting the other body compartments.4 The reproducibility of the measurements by DXA is excellent.26 The FFM derived from DXA measurements correlates well with the FFM determined by hydrodensitometry and total body K measurements.4,26 All measurements were performed by Hologic QDR-2000 instrument (Hologic Inc., Waltham, MA, USA) and used the Enhanced Whole Body 5.54 software version.

Statistical Analysis

Patient characteristics (Table I) are expressed as means + SD. FFM and fat mass in Tables III and IV are reported as means ? SD. Pearson I values were calculated to test simple correlations between DXA and the various BIA formulas and are expressed as r ? standard error of the estimate (SEE), slope rt standard error

954 BODY COMPOSITION IN CHRONIC RESPIRATORY INSUFFICIENCY

TABLE IIA.

FORMULAS USED TO CALCULATE FAT-FREE MASS IN WOMEN

Name Parameter Formulas

RJL Systems-l R.JL Systems-2 Lukaski’* Lukaski8 Lukaski et al.‘j Van Loan et a1.14 Segal et al.9 Graves et al.” Deurenberg et aLI >60 y Heitmann16

Deurenberg et al.” Schols et aLI

Stolarczyk et al.*’

%FM FFM FFM FFM FFM FFM FFM FFM FFM FFM TBW FFM

TBW FFM FFM

[ 1 - [0.3981 * ht’/R + 0.3066 * wt + 0.0952999 * (ht - 100) + 0.7414]/wt] * 100 5.091 + 0.6483 * ht*/R + 0.1699 * wt 3.04 + 0.85 * ht*/R 4.917 + 0.821 */R -4.03 + 0.734 * ht*/R + 0.116 * wt + 0.096 * react + 0.878 * sex (M = 1, F = 0) 17.7868 + 0.0098 * (ht*) + 0.3736 * wt - 0.0238 *R -4.2921 * sex (M = 0, F = 1) - 0.1531 * age 10.4349 + 0.000646 * (ht’) - 0.01397 * R + 0.42087 * wt 5.49 + 0.475 * ht*/R + 0.295 * wt 0.671 * h&R + 3.1 * sex (M = 1, F = 0) + 3.9 TBW/O.72 * 100 11.03 + 0.266 * ht*/R + 0.186 * wt + 4.702 * sex (M = 1, F = 0) - 0.081 * age ht’/R * 0.34 + 0.1534 * ht + 0.273 * wt - 0.127 * age + 4.56 * sex (M = 1, F = 0) - 12.44 3.32 + 0.44 * ht*/R + 0.13 * weight TBW * 110.73 0.0012454 * ht’ - 0.04904 * R + 0.1555 * wt + 0.1417 * react - 0.0833 * age + 20.05

FM, fat mass; FFM, fat-free mass; TBW, total body water; ht, height; wt. weight; R, resistance; react, reactance; M, male; F, female.

of the mean (SEM). Bland and Altmanz7 analysis was calculated according to methods previously described to assess the agreement between two measurements and reported as mean 2 SD. The difference between the values is plotted against their mean, the mean being the best available estimate of the true value. This analysis enables calculation of bias, that is the mean difference between the estimate of FFM by BIA and DXA, the 95% confi- dence interval for the bias, and the limits of agreement (2 SDS of the difference).

RESULTS

Tables III and IV show the correlation coefficients between DXA and BIA for the FFM and fat masses as calculated by different formulas reported in the literature. All the correlation coefficients between BIA formulas and DXA measurements were

highly significant (1. > 0.77, P < 0.001). However, there were substantial differences in the predictive capacity of each formula. Formulas giving a total mean difference between the two techniques of less than e2.0 kg of FFM were those developed by Van Loan and Mayclini4 and Deurenberg et al.]8 for both genders and Stolarczyk et alz5 for women. The correlation coefficients were >0.81 for the comparison of BIA- and DXA-derived FFM and fat masses. Figure 1 shows the agreement between FFMcBIA) as estimated by formula by Van Loan et al.14 and FFMcDXA) as evaluated using the analysis of Bland and Altman.27 Figure 1 shows that there is a tendency for the greater difference in the FFM as measured by BIA or DXA in men with higher weights. In addition, Deurenberg et al.‘si7 formula for subjects aged >60 y also resulted in acceptable correlations and mean differences for FFM and fat mass for the subjects included in this study.

TABLE IIB .

FORMULAS USED TO CALCULATE FAT-FREE MASS IN MEN

Name Parameter Formulas

RJL Systems-l RJL Systems-2 Lukaski” Lukaski’ Lukaski et aLI Van Loan et al. I4 Segal et a1.9 Graves et a1.l5 Deurenberg et al.” >60 y Heitmann16

Deurenberg et a1.18 Schols et al.19

%FM FFM FFM FFM FFM FFM FFM FFM FFM FFM TBW FFM

TBW FFM

(4.95/BD - 4.5) * 100 6.493 + 0.4936 * ht*IR + 0.3332 * wt 3.04 + 0.85 * h&R 5.214 + 0.827 * h&R -4.03 + 0.734 * ht’/R + 0.116 * wt + 0.096 * react + 0.878 * sex (M = 1, F = 0) 17.7868 + 0.00098 * (ht*) + 0.3736 * wt - 0.0238 *R -4.2921 * sex (M = 0, F = 1) - 0.1531 * age 9.3329 + 0.0007 * (ht’) - 0.022 * R + 0.629 * wt - 0.124 * age 5.32 + 0.485 * ht*/R + 0.338 * wt 0.671 * ht*/R + 3.1 * sex (M = 1, F = 0) + 3.9 TBWf0.72 * 100 11.03+0.266*ht2/R+0.186*wt+4.702*sex(M=1,F=0)-0.081*age wt * 0.34 + 0.1534 * ht + 0.273 * wt - 0.127 * age + 4.56 * sex (M = 1, F = 0) - 12.44 3.32 + 0.44 * ht*/R + 0.13 * weight TBW * l/O.73

FM, fat mass; FFM, fat-free mass; BD, body density; TBW, total body water; ht, height; wt, weight; R, resistance; react, reactance; M, male; F, female.


TABLE III.

CORRELATIONS AND MEAN DIFFERENCE FOR FAT-FREE MASS AND FAT MASS MEASURED BY BIOELECTRICAL IMPEDANCE ANALYSIS (BIA) VERSUS DUAL ENERGY X-RAY ABSORPTIOMETRY (DXA) IN WOMEN (A’ = 15) AGE 245 Y

BIA formula

Mean difference FFM and FM measured bq Fat-free mass Fat mass fat-free mass DXA (hold type) or BIA

r Slope t SEM SEE r Slope ? SEM SEE (kg)** FFM (kg)? FM (kg)’

DXA RJL Systems- I RJL Systems-2 Lukaski” Lukaski’ Lukaski et al.” Van Loan et al.‘” Segal et al.’ Graves et al.” Deurenberg et al.“?; Heitmann’(’ Deurenberg et al. Ix Schols et al.‘” Stolarczyk et al.‘5

0.11 0.66 t 0. I6 3.00 0.94 0.82 0.93 t 0.18 3.48 0.95 0.66 0.87 + 0.27 5.30 0.94 0.66 0.85 2 0.26 5.13 0.94 0.77 0.90 i- 0.21 4.01 0.96 0.9 I 1.05 f 0.13 2.58 0.97 0.88 0.90 2 0.14 2.64 0.97 0.8X 0.96 + 0.14 2.66 0.96 0.84 I .20 2 0.32 3.69 0.80 0.90 0.84 t 0.1 I 2.15 0.97 0.94 0.93 -c 0.10 1.9 0.98 0.84 0.90 2 0.16 3.19 0.95 0.79 0.90 2 0.19 3.77 0.96

I .05 t 0. IO 3.45 I.08 + 0.10 3.39 1.32 Z 0.13 4.47 I.31 i- 0.13 4.30 1.23 2 0.10 3.40 0.83 2 0.06 2.06 0.77 2 0.05 1.68 0.91 2 0.08 2.54 I.lX + 0.37 3.71 0.92 i- 0.07 2.18 0.97 ? 0.06 I .90 1.07 -c 0.10 3.17 1.20 * 0.10 3.27

35.8 f 5.2 4.0 t- 3.4 39.8 2 4.5 7.3 2 3.4 43.1 i- 5.9 5.1 2 5.0 40.9 2 6.8 5.7 r+_ 5.0 41.5 Z 6.6 5.3 + 3.9 41.1 ? 6.1

-0.7 t 2.5 35.1 t- 6.0 5.4 -c 2.6 41.2 i- 5.3 6.7 lr 2.6 42.5 I 5.6 0.1 2 3.5 34.9 2 6.3 2.5 % 2.2 38.3 z 4.8

ml.9 t 1.9 33.9 1 5.2 5.1 -+ 3.1 40.‘) 5 5.6 0.1 + 3.7 35.7 i 5.9

17.9 + 8.9 13.9 % 10.0 10.6 2 IO.2 12.8 ? 12.5 12.3 i- 12.4 12.6 t Il.5 18.6 + 7.6 12.5 2 7.1 I I.2 5 x.5 15.3 5 5.7 15.4 -+ 8.4 19.X +- 8.X 12.x t IO.0 18.0 + I1 2

FFM, fat-free mass; FM, fat mass; r, correlation coefficient: SEM, standard error of the mean: SEE, standard error of the estimate.

* FFM,,,,, - FFM,oxA, after Bland and Altman.” + Values are means -+ SD. $ For subjects over 60 y of age (n = 8).

Segal et al.‘9 formula gave good results for men with regard to the bias and correlation coefficients (0.83 for FFM and 0.84 for fat mass) but cannot be recommended because the slope of the regression line was very high for FFM (1.47 ? 0.13) and very low for fat mass (0.37 ? 0.03). This would result in a significant overestimation of FFM and underestimation of fat mass in subjects with a fat mass of >20% body fat. Indeed the formula underestimated the fat mass by up to 207~ in some subjects.

Intermediary results of a mean difference of 2.1-4. I kg for the FFM were obtained with formulas of RJL System I and Heitmann for men and women.lh

The formula specifically developed for COPD patients by Schols et al.‘” resulted in mean differences for FFM of 5. I -+ 3. I kg in women and 3.8 2 3.1 kg in men in our subjects and was judged to be inadequate.

The remaining six formulas for women and four formulas for

TABLE IV

CORRELATIONS AND MEAN DIFFERENCE FOR FAT-FREE MASS AND FAT MASS MEASURED BY BIOELECTRICAL IMPEDANCE ANALYSIS (BIA) VERSUS DUAL ENERGY X-RAY ABSORPTIOMETRY (DXA) IN MEN (N = 60) AGE 245 Y

BIA formula

Mean difference FFM and FM measured by Fat-free mass Fat mass fat-free mash DXA (bold type) or BIA

Y Slope ? SEM SEE r Slope + SEM SEE (kg)“+ FFM (kg)-; FM (kg)?

DXA RJL Systems- I 0.89 1.21 ? 0.08 RJL Systems-2 0.88 1.27 + 0.09 Lukaski” 0.77 1.26 -+ 0.14 LukdskiX 0.77 1.23 t 0.13 Lukaski et al.” 0.78 1.01 + 0.10 Van Loan et al.” 0.85 I.18 i 0.10 Sepal et al.” 0.83 1.47 % 0.13 Graves et al.li 0.88 1.27 ? 0.09 Deurenberg et al.“% 0.81 0.‘)‘) t- 0. I I Heitmannlh 0.85 0.93 t 0.08 Deurenberg et al.lx 0.87 1.10 2 0.08 Schols et al.‘” 0.86 I.19 t 0.09

2.7 I 0.92 2.89 0.90 4.44 0.86 4.33 0.87 3.42 0.9 I 3.09 0.9 I 4.20 0.84 2.86 0.90 3.36 0.90 2.43 0.94 2.58 0.93 3.05 0.90

0.71 ir 0.04 2.04 0.74 2 0.05 2.54 I.09 ? 0.09 4.53 1.10 t 0.08 4.38 I .07 i- 0.06 3.39 0.66 * 0.04 2.10 0.37 t 0.03 1.65 0.74 + 0.05 2.49 I.10 2 0.08 3.29 0.84 + 0.04 2.12 0.78 2 0.04 2.12 0.91 + 0.06 3.1

4.9 -c 4.2 9.9 2 6.7 3.3 t- 2.8 48.2 Ii- 5.8 6.6 t- 5.3 8.0 5 3.1 52.9 -+ 6.1 1.9 f s.7 6.6 t- 4.5 51.5 -k h.Y 3.3 t 8.7 7.5 2 4.4 52.4 + 6.7 2.4 z 8.7 6.2 23.4 51.1 z 5.4 3.7 + x.1

-0.6 + 3.2 44.3 -? 5.8 10.5 i 4.9 -0.4 5 4.6 44.5 ? 7.5 10.3 2 3.0

6.6 -c 3.1 51.5 + 6.0 3.3 2 S.6 0.4 2 3.3 45.3 2 5.6 9.3 rt_ 7.5 4.0 ? 2.4 4X.9 f 4.6 5.9 + 6.2

-0.7 2 2.6 44.2 2 5.3 IO.6 + 5.7 3.8 ? 3.1 4x.7 t 5.x 6.1 2 7.0

FFM, fat-free mass; FM. fat mass: r, correlation coefficient; SEM. standard error of the mean: SEE. standard error of the estimate. * FFM,,,,, - FFM,DXA) after Bland and Ahman.*’ t Values are means % SD. f For subjects over 60 y of age (n = 8).


men showed a mean difference in the FFM ranging from 5.1 +- 3.1 to 8.0 ? 3.1 kg. They were judged too unreliable in this patient group. These variations represented between 10.6 I 9.9% and 14.4 i 4.7% of the total fat mass.

DISCUSSION

The availability of portable BIA instruments in recent years has made body composition measurements easy to perform and relatively inexpensive. These instruments have resulted in a dra- matic increase in the clinical application of BIA and have greatly facilitated the assessment of nutritional status of healthy and ill individuals.7Js There remains, however, some concern about the validity of BlA in specific diseases. For instance, a change in body composition is anticipated in patients with pulmonary disease with increasing age, and increasing severity of the disease can result in severe disability and lack of mobility. The accuracy of formulas published in the literature, however, depends on a number of criterias, including age, height, sex, and cell mass.’ I The purpose of this study was to test the relationship between FFM and fat mass estimated in women and men with different BIA formulas reported in the literature, and DXA-derived FFM and fat mass in a large group of patients with severe pulmonary diseases.

Patient Population

All patients studied had a severe chronic ventilatory defect (mean FEVl = 35 t 12% of predicted). These patients suffered, however, from obstructive and restrictive pulmonary diseases, with distinct repercussions on their nutritional status. Nutrition assessment by BIA or DXA is desirable because it permits the detection of low FFM in patients who may not be significantly below ideal body weight, but who have an excess of fat mass that may mask protein malnutrition. Severely obese patients (BMI 32 [kglm2]) were excluded from the study to eliminate a possible methodologic bias introduced by excessive fat mass in obese patients.”

Relevance of’ Comparing Two Methods qf Body Composition Analysis

The rationale was to be able to use, under clinical conditions, two methods for measuring body composition that measure independent parameters, i.e., electric variations (BIA) versus photon absorption (DXA). DXA estimates the fat mass without making assumptions related to lean tissue mass, potassium concentration or density that characterize traditional methods such as underwater weighing, densitometry or total body water techniques.‘y DXA measures the bone and soft tissue mass independently, then sep- arates the soft tissue mass into lean and fat mass. Although DXA is becoming one of the reference methods for measuring body composition,30 some discussion remains about comparability of different hardware and software, and previous studies have noted that caution must be exercised when making comparisons between studies if different hardware and software versions have been used.3im33 Tothill et al.“* found that percent fat mass as measured by Hologic DXA was not significantly different from that obtained by underwater weighing. They also noted that Hologic instruments reported lower fat mass than Lunar and Norland DXA. Snead et al.33 suggested that DXA underestimates the %fat mass in older and obese subjects. Modlesky et al.3J found fat mass and %fat mass were 3.3% and 4% greater with QDR-2000 than with QDR- 1000. In our study, we used the same type of machine and same software (Hologic QDR-2000 and Version Enhanced 5.54 software) for all measurements. At our institution we found that the percent fat mass was 2.2 ? 3.6% greater with this software version than with a previous version (whole body 5.35). There- fore, the problem of underestimation of fat mass appears to have been corrected with newer versions of the Hologic software. Some

doubt whether DXA accuracy still remains in the case of patho- logic hydration states.35

BIA measurements meet the criteria of “ease of use.” Indeed the advantage of BIA is that the portable instruments permit measurements to be performed equally well in field studies and at patients’ bedside, and allow for easy assessment and follow-up of nutritional status in patients who could not otherwise be evaluated. However, the limitation of this method arises from the difficulty in obtaining accurate results in a wide range of subjects. BIA has been cross-validated with hydrodensitometry,9 skinfold measurements,s6 and deuterium dilution in healthy (generally young) subjects.37,38 The method remains to be validated in patient pop- ulations with various diseases or diagnoses. Schols et a1.i9 developed prediction formulas for patients with pulmonary disease (n = 24 men and 8 women) by correlating BIA with deuterium dilution and skinfold anthropometry. The results of their study could not be confirmed in our patient population (Tables III and IV), despite the fact that the patients were similar in age, height, and weight. Schols correlated height2/resistance to deuterium-determined total body water. Although the slope of the equation was acceptable, their BIA formula overestimated FFM by 5. I 2 3.1 kg in women and 3.8 2 3.1 kg in men when compared with DXA. The patient sample (n = 32) in their study may have been too small to adequately control for patient variability. In addition, it should be noted that the gender of the subjects was not included in their formula. Gender-based formulas appear to improve the predict- ability of BIA formulas (Tables II-IV).ii. The lack of concor-

1 -121 20 30 40 so

n 60 kg FFM

p .1?I, 2 20 30 40 50 60

+2SD

kg FFM

FIG. 1. Assessment of agreement in men (top) and women (bottom) between FFM,m,, as calculated by Van Loan et a1.14 and FFM,,,,,. The ~__.. _, difference between FFM(a,,, and FFMo,,, is plotted against the mean of the FFM,,,,, and F’FM,,,,,. The mean difference and 2 SDS of the mean ,_._ _, difference are indicated. FFM, fat-free mass; BIA, bioelectric impedance analysis; DXA, dual energy x-ray absorptiometry.


dance noted between the results of Schols et ali’)J“ and those of our study is likely to be due to the different validation criterion (deuterium dilution versus DXA). The short-term reproducibility of BIA measurements was shown by Schols et al.‘” to be excellent in COPD patients.

Why Some Formu1n.v Are Mow Approprint~ Than 0ther.v.~

In our group of patients, the formulas of Van Loan and May- clini-’ and Deurenberg et al. ix for both genders and Stolarczyk et alZ5 were judged to be more adequate since they predicted FFM and fat mass with relative low error in this population (Tables III and IV, Fig. I ). The common denominator of these three formulas is that they include age as a variable. Because most of the formulas are validated in young healthy adults, it is not surprising that introducing age as a parameter would improve the prediction in a population that is older than the mean age of the reference population. Deurenberg et al’s formulai also performed adequately because it was specifically designed for elderly subjects (Tables III and IV).

The formula published by Stolarczyk et al.2s also generated good correlations. This is rather surprising because it was developed for a very selective population of Native American Indian women that is characterized by marked adiposity (up to BMI 38.3 [kg/m’]). The formula performed better for mean fat mass than other formulas. We have no rational explanation for this correlation because the subjects in our study had a BMI at the low normal range. However, there is a possibility that inactivity as a result of debilitating illnesses could cause a disproportionate increase in the fat mass that could invalidate formulas for “normal” subjects. Webber et al.?(i suggested that BIA may underestimate the fat mass in the presence of excess intra-abdominal fat because a relatively small proportion of body resistance is accounted for by the trunk. Sedentary patients as those included in the present study might have increased intra-abdominal fat, even in the presence of normal body mass index.

However. the “best” formulas still yielded mean differences in the estimation of ‘%fat mass of I.8 5 5.I%> (range. -7.8 to +13.6’%) for women and 1.7 ? 5.2% (range, -13.5 to +14.9%) for men based on Van Loan and Mayclinr”: and 3.7 I 3.8% (range, -2.7 to + 12.7%) for women and I.7 ? 4.6% (range. -9.2 to + 13.3%) for men based on Deurenberg et al.rX (data not shown). Mean difference for Bfat mass for Stolarczyk et al? was -0.4 i- 5.1% (range, - I I .8 to + I .8’%) for women, Although the mean value was close to the reference method. the SDS were greater than what would be considered clinically acceptable. Svendsen et al.Ji noted that prediction of FFM in one population

cannot predict body fat mass in another population simply by subtracting the predicted FFM from the weight. This may be the reason for the underestimation of fat mass in most equations from the literature.

In the absence of disease-specific formulas for patients with severe respiratory disorders, it is suggested that formulas by Van Loan and Mayclin i4 be used for women and men. An alternative would be the formulas by Deurenberg et al.‘7 for patients over 60 y for both genders and Stolarczyk et al.‘” for women. However, disease-specific formulas for patients with respiratory disorders should be developed to improve the prediction of FFM and fat mass by BIA in these patients.

Although the results with existing formulas tend to show inadequate absolute values for FFM and fat mass, the BIA method may probably permit the use of the FFM and fat mass for indi- vidual patients when two or more measurements are available for the same patienta’ Under these circumstances the emphasis would be placed on the changes in FFM and fat masses, rather than on the absolute value of FFM and fat mass and the patient would be- comes his own control.

Conclusion

The use of BIA is becoming increasingly frequent in clinical practice. The interest of evaluating nutritional status by BIA measurement is that it permits easy following of the changes in FFM and fat mass that accompany changes in clinical status. A validated respiratory disease-specific BIA formula would permit the use of BIA for sequential evaluation of muscle and fat reserves and changes in these parameters in patients with chronic respiratory insufficiency. We used a number of different formulas taken from the literature to predict FFM,,,,, and fat mass~m,, and compare them with FFM(P,,, and fat massdlxA) in patients with severe chronic respiratory insufficiency of mixed origins. The variability in predicting the FFM by means of various formulas was substantially greater in the patients with chronic respiratory insufficiency than in healthy subjects. We found that none of the existing formulas developed for healthy subjects or patients per- mitted a clinically acceptable determination of the FFM and fat mass. We therefore conclude that BIA formulas specific for patients with chronic respiratory insufficiency still need to be developed.

ACKNOWLEDGMENTS

We are indebted to Giulio Conicella and Patrick Wagner for technical assistance.

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