INTRODUCTION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

A MEASUREMENT of the central, energy-exchanging mass of working tissue has long been sought as a standard for assessing nutritional status (40). The core reference for the central tissues of the body has, in the past, been identified as fat-free mass (FFM). However, the FFM is too broad, including extracellular water (ECW) and the structural bone matrix, which are largely involved with support and not direct oxidative energy turnover. On the other hand, body cell mass (BCM) consists of cellular components minus ECW and support tissue. The ability to measure BCM would provide a reference basis for the measurement of oxygen consumption, caloric requirements, basal metabolic rate, and work performance (51). From an ideal point of view, the intracellular water (ICW) most closely approximates the BCM (40, 51). This is because acute changes in body protein occur mainly in the cellular compartment (23), and changes in body protein are generally accompanied by changes in ICW (2).

The depletion of BCM and involuntary weight loss are common in human immunodeficiency virus (HIV) infection and are closely associated with morbidity and mortality (1, 26, 42). In fact, BCM loss has been observed to precede overt weight loss even in the early stages of HIV infection (42), and a critical level of BCM must be maintained for survival (26). Therefore, the ability to accurately monitor changes in BCM is essential to the successful treatment of individuals with HIV infection and acquired immunodeficiency syndrome. BCM can be estimated from total body potassium (TBK) measured with whole body counting, from ICW measured by dilution methods, or from total body nitrogen measured by neutron activation (16). However, whole body counting and neutron activation are expensive, and dilution methods are tedious and time consuming.

Theoretically, bioimpedance measurements can be used to estimate BCM noninvasively by measuring ICW; however, there is considerable debate concerning the best bioimpedance method to use. One group of investigators believes that ECW, ICW, and total body water (TBW) are best predicted using a bioimpedance spectroscopy (BIS) approach (11, 58). Others continue to advocate the approach first proposed by Thomasset in 1963 (53; see also Refs. 14, 19). Although various frequency combinations have since been used, Thomasset proposed the use of a fixed, single low-frequency (1-kHz) bioimpedance approach to measure ECW and a fixed, single high-frequency (100-kHz) bioimpedance approach to measure TBW. The ICW was computed as TBW minus ECW. A third group of investigators proposes that BCM can be adequately measured by a fixed, single-frequency 50-kHz measurement of impedance (Z) (43) or reactance (X) transformed into parallel X (X_P) (7, 25, 31). Whereas the latter two approaches rely on the derivation of prediction equations for TBW, ECW, and ICW using statistical methods, BIS provides a more direct measure of body water components. In fact, BIS is the technique from which all underlying theories of the bioimpedance method evolved and implies fitting measured spectral data to a biophysical model (44).

Basic theoretical and analytic bioimpedance principles. Biological cell membranes behave as capacitors (C_m), and Z is frequency dependent (9) (Fig. 1). With direct current or at the zero frequency, there is theoretically no conduction through biological cells, and Z is purely resistive (R) and a function of ECW [R at 0 frequency (R₀) or R_E]. With alternating current, C_m charges and discharges the current at the rate of the frequency; therefore, as frequency increases, the amount of ICW measured increases. At some infinitely () high frequency (>10,000 kHz or 10 MHz; Fig. 1), the charge and discharge of current through the cells become so fast that the effects of C_m become insignificant, Z becomes purely resistive (R), and both ECW and ICW are fully measured. Once R₀ or R_E and R are determined, ICW resistance (R_I) can be computed as 1/R_I = 1/R  1/R₀ (Fig. 2). At R₀ and R, the overall Z is independent of C_m; whereas, at the middle or characteristic frequency (f_c), the dependence on the value of C_m is at a maximum (Fig. 2). The f_c can also be defined as the frequency of maximum X (9). The f_c is an important term because it is computed from R_E, R_I, and C_m and thus changes with changes in ECW, ICW, or the cell membranes, respectively (30). The equation for f_c is 1/[2C_m(R_E + R_I)].

View larger version (48K): [in this window] [in a new window]   Fig. 1.   Frequency vs. impedance (Z) measured on 1 subject pre- and post-Oxandrin therapy. Resistances (R) at 0 frequency (R0) and infinity (R) are represented as 0.001 and 100,000 kHz, respectively. R0 and R were derived by modeling.

View larger version (18K): [in this window] [in a new window]   Fig. 2.   R vs. reactance (X) measured on 1 subject pre- and post-Oxandrin therapy. R0 and R were derived from modeling. X at R0 and R was set at 0. Characteristic frequency (fc) is frequency of maximum X.

Comparison of BIS and single-frequency approaches. Some studies comparing the BIS and single-frequency approaches report no advantage of BIS in predicting ECW, TBW, and ICW (43). These conclusions, however, are based on the prediction of absolute volume using correlation, standard error of estimate (SEE), and bias statistics. It is well known that ECW, ICW, and TBW are highly intercorrelated and that Z measured at any frequency can equally predict the absolute volume of each body water compartment (11, 43, 58). This intercorrelation would be expected because ECW and ICW are generally tightly regulated and comprise TBW. Detection of any systematic error (E_sys) that would impair the measurement of change in fluid compartments by a particular bioimpedance method would be obscured by the high intercorrelation of the variables when only absolute volume is predicted. For this reason, the ability to measure change should be the test of the validity of a bioimpedance method. Furthermore, correlation and SEE statistics should not be used to assess change, because neither of these statistics is sensitive to scaling; thus the predicted change could be significantly different from the actual change. For example, the correlation and SEE for the set of numbers 10, 15, 20, and 25 compared with the set 5, 7.5, 10, and 12.5 would be a perfect 1 and 0, respectively, despite the 50% difference between the sets.

	SUBJECTS AND METHODS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

Study design. This study was part of a larger clinical evaluation of HIV-infected individuals who were undergoing treatment for weight loss with the anabolic steroid oxandrolone (Oxandrin, BTG Pharmaceuticals, Iselin, NJ). Oxandrolone is an orally administered testosterone derivative (2-oxa analog of 17-methyldihydrotestosterone) that was approved by the FDA more than 30 years ago "as adjunctive therapy to promote weight gain after weight loss following extensive surgery, chronic infections, or severe trauma, and in some patients who without definite pathophysiologic reasons fail to gain or to maintain normal weight" (3). Dose-dependent anabolic effects of oxandrolone in normal healthy subjects were demonstrated by decreased nitrogen excretion and increased protein synthesis with an anabolic potency 6.3 times that of methyltestosterone (17). Oxandrolone has been used successfully to treat growth failure (Turner's syndrome) in children (48) and has been safely used to treat protein-energy malnutrition in alcoholic liver disease (4, 5, 35, 36). Oxandrolone has also been shown to significantly increase body weight (Wt) and muscle function after burn injury (12).

Subjects. Subjects were recruited from an outpatient clinic with a population of ~600 HIV-infected patients. Any individual with a history of weight loss, who was receiving standard antiretroviral therapy and whose primary care physician prescribed Oxandrin therapy, was recruited to participate in the study. Twenty-one subjects (20 men, 1 woman) between the ages of 27 and 56 yr, with a mean age of 41 ± 7.7 yr, completed this study. Of these, 20 subjects (19 men, 1 woman) were maintained on an Oxandrin dose of 20 mg/day, whereas one subject's dose was reduced to 10 mg/day after 1 mo. At the time of baseline assessment, the subjects had experienced an average weight loss of 9%. Ethnic distribution of the subject population was 72% (15 subjects) Caucasian, 14% (3 subjects) Hispanic, and 14% (3 subjects) African-American. The study protocol was approved by the Human Subjects Committee of The University of Arizona, and written, informed consent was obtained from all participants.

Procedures. Subjects came to the Metabolic Monitoring Laboratory at The University of Arizona for comprehensive assessment of body composition before initiation and after termination of Oxandrin therapy. To standardize testing conditions and to avoid acute changes in hydration status, subjects were instructed to abstain from vigorous exercise for at least 12 h and to abstain from alcohol and caffeine consumption for 48 h before assessment. Subjects reported to the laboratory on test days in the morning, after an 8- to 12-h fast.

Anthropometric assessment. At each visit, Wt was measured to the nearest 0.1 kg with a digital platform scale [Kubota model K-10-300L-A, Chugai Boyeki (America), Commack, NY]. Subjects were instructed to wear the same lightweight clothing at each visit. Standing height (Ht) was measured to the nearest 0.5 cm with a stadiometer (Narragansett Machine, Providence, RI).

Dilution volume. Criterion estimates of TBW and ECW were derived from ²H and bromide dilution, respectively. While still in a fasted state and after baseline urine and venous blood samples were collected, subjects were given a weighed dose of deuterium oxide (²H₂O; 99.8 atom percent; Isotec, Miamisburg, OH) equivalent to 0.15 g/kg TBW. To calculate the dose, an estimate of TBW was obtained by single-frequency bioimpedance analysis by using the Kushner et al. (29) equation. Immediately afterward, the subjects drank a measured dose of a sodium bromide (NaBr; Sigma Chemical, St. Louis, MO) solution, providing 1.0 ml of 3% (wt/vol) solution/kg body wt. Dose solutions were administered from paper cups and were followed by a 30-ml wash with deionized, distilled water. A 3-h equilibration period, during which subjects did not ingest anything, was allowed for ECW determination by bromide dilution. A second blood sample was drawn at 3 h postbromide dosing, and a light snack was provided immediately after the second blood draw. At 5 and 6 h after dosing with ²H₂O, urine samples were collected for TBW determination by ²H dilution. Blood samples were collected in 10-ml tiger-top vacutainer tubes and centrifuged at 3,000 rpm for 20 min to separate serum. Two 2.5-ml aliquots of each serum sample and three 5-ml aliquots of each urine sample were stored frozen at 80°C in airtight cryogenic vials until analysis.

Bioimpedance measurements. After the measurement of Wt and Ht and ingestion of the ²H₂O and NaBr doses, fasting subjects assumed a supine position for 30 min. The effects of orthostatic fluid shifts on Z can be considerable (i.e., 3% on recumbence, an additional 2% after 10 min, and an additional 4% after 4 h) (28). Thus the duration of recumbency before bioimpedance measurements are performed will affect the prediction of absolute volume, but, for estimation of fluid volume changes, the important thing is that repeated measurements be performed at identical time points during the recumbent period. This error was minimized by taking all measurements at the same 30-min mark after recumbence. With the use of the standard wrist-to-ankle measurement protocol (27), a Z measurement was made with a multiple-frequency bioimpedance device (model 4000B or 4200; Xitron Technologies, San Diego, CA). The multiple-frequency devices were confirmed to be in calibration according to manufacturer instructions. Three patients had baseline measurements taken with the 4000B device. All subsequent measurements were performed with the newer 4200 device. Comparison of R and X measurements by the two devices for seven subjects and 35 data points (frequencies 5, 50, 100, 200, and 500 kHz) showed no significant differences. Data were transmitted directly from the analyzer to a personal computer and controlled by the software programs supplied with the devices. All measurements were taken on the right side of the body by using disposable electrodes (IS4000; Xitron Technologies).

Data analysis. To evaluate the fit to the Cole model, the total weighted least squared error was computed by the modeling routine. The fit error is expressed as a ratio between conformance to model to accuracy specification of the device. A ratio of 1 would indicate that conformance to model was equal to device performance. The expected accuracy at each measured frequency is established as a pair of arrays (Z and ). The error ratio is then established at each modeling point by dividing the modeling error by the corresponding stored array error. Additional statistical analyses were carried out by using SPSS version 8.0 software (SPSS, Chicago, IL). For descriptive statistics, means and SDs were computed. Repeated-measures ANOVA was used to compare pre- and post-Oxandrin therapy Wt and dilution-determined TBW, ECW, and ICW volumes.

	RESULTS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

Study findings of fluid compartment change after Oxandrin therapy indicate that both TBW and ICW increased considerably (P < 0.0005; repeated-measures ANOVA), whereas ECW did not change (Table 1). Consequently, ECW/TBW decreased. The data fit the Cole model with high precision as evidenced by the low total least squares fit error ratio (Table 2). The fit error ratio for the pre-Oxandrin therapy data does not include the three subjects measured at baseline with the Xitron 4000B device. The routine that was used to model the 4000B data does not display a fit error ratio. However, the fit was rated as good, which means that the fit error ratio equaled 1, and the correlation of fit for these three subjects was >0.997. Consistent with theory, R_E was greater than R at 5 kHz and continued to decrease with increasing frequency. Interestingly, C_m increased as would likely occur with an increase in intracellular hydration (44). The data used for evaluating the various bioimpedance equations are shown in Table 2.

Prediction of absolute body water volume. As reported previously, the prediction of absolute ECW, TBW, and ICW volume was similar for all methods tested. Because all 5-kHz ECW equations provided similar predictions, only the ECW_{5 R (H)} equation (20) is reported. Similarly, the two 100-kHz TBW equations evaluated (14, 50) resulted in predictions similar to those obtained using 50 and 200 kHz. Thus only the predictions from the TBW_{50 Z (K)} (25), TBW_{200 R (H)} (20), and TBW_{500 R (H)} (19) equations are reported. The single-frequency equations used for the predictions being reported are shown in Table 3. Correlation and SEE for ECW estimates by all methods ranged from 0.78 to 0.92 and 1.04 to 1.70 liter, respectively (Table 4). Subtraction of the ICW_{50 X (K)}-predicted ICW from the TBW_{50 Z (K)}-predicted TBW produced the best correlation and SEE for ECW (Table 4). For the prediction of absolute TBW volume, correlation and SEE for all methods ranged from 0.90 to 0.97 and 1.15 to 2.09 liter, respectively (Table 4). The TBW_{50 Z (K)} equation predicted TBW with the highest correlation and lowest SEE.

Prediction of body water volume change. The correlation of ECW was similar for the BIS and ECW_{5 R (H)} methods and slightly higher for the TBW_{50 Z (K)}  ICW_{50 X (K)} approach (Table 5). The bromide-determined ECW was small, and all methods detected this. The ECW predicted by BIS was different (P = 0.022) from that estimated by the dilution method. All methods exhibited E_sys, because zero (a perfect answer) was not a possible outcome. For every method, the error was of positive polarity (Table 5; Fig. 3).

View larger version (12K): [in this window] [in a new window]   Fig. 3.   Systematic error in the various Z methods for predicting extracellular water (ECW; A), total body water (TBW; B), and intracellular water (ICW; C). Methods are described as follows. A: BIS, bioimpedance spectroscopy-predicted ECW, TBW, or ICW (11); 5, ECW predicted by R at 5 kHz (20); 50 Z  50 X, ECW predicted by difference between 50-kHz Z-predicted TBW and 50-kHz parallel X-predicted ICW (25). B: 50 Z, TBW predicted by Z at 50 kHz (25); 200, TBW predicted by R at 200 kHz (20); 500, TBW predicted by R at 500 kHz (19). C: 50X, ICW predicted by parallel X at 50 kHz (25); 50Z, ICW predicted by Z at 50 kHz (43); 200  5, ICW predicted by difference between 200-kHz R-predicted TBW and 5-kHz R-predicted ECW (20); 500  5, ICW predicted by difference between 500-kHz R-predicted TBW and 5-kHz R-predicted ECW (19, 20).

	DISCUSSION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

R would be expected to change when ECW and ICW change, but it was of interest that C_m increased. In vitro studies have shown C_m to be inversely related to cell membrane thickness (9); therefore, an increase in C_m suggests swelling of the cells and thinning of the membranes (44). Such a finding appears consistent with the theory of Häussinger et al. (21) that an increase in cellular hydration acts as an anabolic proliferative signal. However, not only are there many factors that can affect C_m, but it can change when there is no change in ICW. For example, C_m increased 45% from pre- to posthemodialysis with virtually no change in ICW (32). De Lorenzo et al. (11) discussed the difficulties of using C_m to estimate ICW (BCM). Furthermore, its use appears unnecessary because ICW can be determined. Although repeat measurements of C_m on healthy adults and a simulated circuit showed no significant differences (34), the accuracy of determining C_m is dependent on the accuracy of the model fit. Evaluation of the accuracy of fit to the Cole model has been fully discussed by De Lorenzo et al. For repeated measures, the CV for C_m in this study was 5%.

Because it is difficult to relate f_c, defined as the frequency of maximum X, to a Z plot, it is useful to conceptualize f_c as defined by Schwan (49). He defined f_c as the frequency corresponding to Z at the midpoint between Z at frequencies zero and infinity (Fig. 1). At any frequency other than zero and infinity, the proportion of ICW volume measured varies with f_c, and f_c varies when ECW, ICW, or C_m change (30, 32). Thus predictions of ECW using anything other than R₀ should be less sensitive to ECW, because a portion of ICW will be included in the measurement. Theory (9), mathematical simulation, and initial in vivo results suggest that this is so (32). In this study, the BIS prediction of ECW was statistically different from the criterion. However, the random error effect on the measurement of E_sys was 0.17 liter or 43% of the measured average change of 0.40 liter (Table 5). Furthermore, the random error (RMSE) in each measure of change was, for all bioimpedance methods, larger (0.72-0.82 liter) than the average dilution ECW. Therefore, ECW was too small to draw any conclusions from the findings of this study (Table 5). It is important to consider that an E_{avg chg} of 0.4 liter (Table 5; Fig. 3) represents only 2% of a total ECW volume of 17.5 liters (Table 1). The BIS method has been used successfully to predict ECW in comparison with bromide dilution (15), with net fluid balance during surgery (52), and with volume removed by ultrafiltration during dialysis (60). However, it needs to be clearly established whether it performs better than the fixed low-frequency (e.g., 5-kHz) approach. Gudivaka et al. (18) reported that only an equation using Cole model term R_E accurately predicted ECW. All predictions were corrected by 1.6% based on the assumption that change in plasma albumin affects the total ECW volume. Coincidentally, 1.6% was effectively the same as the magnitude of the under- and overprediction of ECW using the Xitron ECW equation. Because the ECW equation published by Xitron (34) corrects for mixture effects, the additional 1.6% correction applied by Gudivaka et al. was, in effect, a double correction.

The TBW_{500 R (H)} method predicted TBW well; however, these results need to be qualified. The f_c values for the subjects in this study ranged from 30 to 66 kHz and are similar to the 40-60 kHz measured for healthy subjects (11). The range of f_c values obtained makes the difference between Z at 500 kHz and infinity small (Table 2; Fig. 1). Had the f_c values been higher, less ICW would have been measured, and the sensitivity of 500 kHz to TBW would have been reduced. This is supported by the finding that the 50- and 200-kHz methods did not accurately predict TBW. It has long been known that f_c changes considerably when tissue hydration changes (30). For example, f_c values >200 kHz have been reported in hemodialysis patients (11), and f_c values >500 kHz have been reported in young children with severe diarrheal disease (37). Although it is unsound to fit a theory to a result, the ratio of the measurement frequency to f_c may be useful for exploring the cause for the results of this study. For a measurement frequency of 200 kHz and an f_c of 50 kHz, the ratio is 4 (200/50). This suggests that, to achieve the same results when f_c is 125 kHz, a measurement frequency of 500 kHz would be required. Following this reasoning, if f_c is 300 kHz, a measurement frequency of 1.2 MHz would be required to produce the same poor prediction of TBW that 200 kHz yielded in this study. The fact that the 500-kHz equation (19) accurately predicted TBW may be due in part to the similarity of f_c values for the subjects of this study (Table 2) to the 40- to 60-kHz values determined for healthy subjects (11). This suggests that a measurement frequency-to-f_c ratio of 10 (500/50) may be needed to accurately predict TBW by using a single-frequency measurement. If this were so, a measurement frequency of 2 MHz would be needed if f_c were 200 kHz. However, using a very high single frequency to reduce the error introduced by f_c would still be problematic, because a twofold increase in frequency increases measurement error by a factor of 4. Thus a measurement at 1 MHz is four times less accurate than a measurement at 500 kHz. This partially explains why multiple-regression analyses provide the best TBW predictions at frequencies between 200 and 500 kHz and why accuracy decreases progressively at frequencies >500 kHz (13). Although the BIS method correctly predicted TBW in this study, it did have E_sys (Table 5; Fig. 3). Because TBW is predicted as ECW plus ICW, the E_sys in the predicted TBW may have simply been carried over from ECW and ICW. The P value for BIS-predicted TBW was only 9% above the threshold for considering the results different, whereas the effect of random error on the measurement of E_sys was 0.373 liter or 13% of the measured average change of 2.8 liters (Table 5). Although the TBW predicted by BIS was not different from the criterion (P = 0.109), further research is needed to confirm these findings. An E_{avg chg} of 0.65 liter is only a 1.6% error in 40 liters of TBW (Table 1).

Although it has recently been reported that a multiple-regression equation including X_P accurately predicted ICW in healthy, nonobese subjects (18), we found that the ICW_{50 X (K)} and ICW_{50 Z (P)} methods predicted ICW poorly. This discrepancy may be at least partly explained by the fact that Gudivaka et al. (18) used an  of 0.05 to test for differences between methods, which can increase the risk of a type II error in a situation in which sample sizes and the changes measured are small. An evaluation of how well a method performed is not possible without the actual P values for each method. Furthermore, from a physics standpoint, at any single frequency, the effect of a change in ECW, ICW, or C_m on X cannot be discerned; therefore, any prediction of ICW using X_P would be circumstantial (32). The ICW predicted by the fixed high- and low-frequency methods [TBW_{200 R (H)}  ECW_{5 R (H)} and TBW_{500 R (H)}  ECW_{5 R (H)}] was also considered statistically different from the criterion. A poor single-frequency prediction of TBW or ECW would result in a poor prediction in ICW because it is computed as TBW minus ECW by these methods. In addition, a change in f_c causes errors in the predicted ECW and TBW in opposite directions, thereby magnifying ICW error (Table 5). When f_c increases, a fixed low frequency (5 kHz) becomes closer to zero, and a fixed high frequency (500 kHz) becomes further from infinity. The opposite would occur when f_c decreases (Fig. 2). In this study, the changes in f_c were very small, but f_c can change by as much as 50% (11). Such a large change in f_c would cause large errors in the predicted ICW. Although the ICW predicted by TBW_{500 R (H)}  ECW_{5 R (H)} was different (P = 0.093) from that determined by criterion methods, it is important to consider these results carefully. The probability (P = 0.093) that the results achieved were true was only slightly (7%) below the significance level for considering the results equal. On the other hand, the effect of random error on the measurement of E_sys was 0.30 liter or 12-13% of the measured average change of 2.4 liters (Table 5). As the P value becomes smaller and the null hypothesis becomes more unreasonable, however, the point at which results are accepted or rejected is subjective. As such, the findings of this study do not lead to a firm rejection of the TBW_{500 R (H)}  ECW_{5 R (H)} method.

Albeit the strong prediction of TBW using 500 kHz suggests otherwise, the prediction of TBW using a single high-frequency measurement should be poor because the R_E and R_I differ by a factor of 3 (44). To predict TBW volume, it must be assumed that TBW resistivity is constant. Such an assumption seems tenuous considering that a simple change in the ECW/ICW can alter TBW resistivity.

These results support the BIS or modeling approach. The implication is that the BIS method predicted BCM better than the other methods did solely because it is based on the Cole model. To explore if this were true, multiple-regression analysis was used to predict ICW pre-Oxandrin therapy by using variables Ht, Wt, and R_I. According to the common single-frequency model used, Ht²/R, not R alone, should correlate to water volume; thus multiple regression was also performed by using variables Ht²/R_I and Wt. Wt alone can be predictive of ICW, but it is often an additive term in published multiple-regression equations (25) and was therefore included. The resulting equations were used to predict ICW pre- and post-Oxandrin therapy. The ICW was computed, and two-tailed paired t-tests were performed ( = 0.10). The equation using Ht²/R_I and Wt performed best. The predicted ICW volume was similar to that obtained from the other methods (Table 4) with pre- and post-Oxandrin correlations of 0.77 and 0.88 and SEEs of 2.27 and 1.99, respectively, but the prediction of ICW was poor (P = 0.015). We did not log transform R_I before running the regression analysis as suggested by Kotler et al. (25). It was not clear from the literature how to replicate the procedure, and it did not appear valid because X_P predicted BCM poorly (Table 5). The implication of these findings is that a mathematical modeling approach that solves for R₀ and R and accounts for any mixture effects should be used (11, 33).

The underlying basis for the mixture theory equations developed by Xitron (33) is that the relationship between Z and body water should be explained scientifically rather than randomly by using multiple-regression analysis. Use of a physical model (the Cole model) is an important first step, but, according to theory, the Z-to-body water association is complex and nonlinear because of conductor (ECW and ICW) and nonconductor (fat and bone) interactions. As such, Cole model terms R_E and R_I are model terms and not simply ECW and ICW R values, respectively. To accurately predict ECW and ICW volume, mixture effects must be taken into account. The results of this study seem to support this premise, because the prediction of BCM solely by the Cole model was poor. The mixture theory equations used in this study have been fully described (11). Gudivaka et al. (18) recently reported that the ICW mixture equation published by Xitron (34) predicted ICW poorly, and that only a multiple-regression equation, including Cole model term R_I, accurately predicted ICW. The equation tested was the original equation developed by Xitron, which assumes a linear relationship between ECW/ICW and TBW resistivity, whereas the present equation in the Xitron software, developed in 1993, assumes a nonlinear relationship. De Lorenzo et al. (11) discussed the differences between equations.

It is generally understood that FFM is not an ideal measure of BCM because it includes ECW and solids (40). Van Loan et al. (55) recently reported the successful prediction of FFM using bioimpedance. The FFM determined by BIS accurately reflected the nitrogen balance change in wasted acquired immunodeficiency syndrome patients after gonadal hormone therapy (55). The first successful prediction of ECW, TBW, and FFM with the use of BIS methods was reported in 1992 (34). The BIS FFM equation developed by Xitron is (D'_ECW V_ECW) + (D'_ICW V_ICW), where V_ECW and V_ICW are ECW and ICW volumes, and D'_ECW and D'_ICW are the apparent densities of the ECW and ICW and their associated materials, respectively. The D'_ECW (1.45 g/cm³ for men, 1.48 g/cm³ for women), and D'_ICW (1.31 g/cm³ for men, 1.23 g/cm³ for women) terms were adjusted by obtaining the best fit using FFM, predicted from dilution volumes, against the densitometrically determined FFM (56). The traditional single-frequency 50-kHz R-predicted TBW estimate of FFM (TBW/0.73) assumes that the ECW-to-ICW, protein-to-ICW, and water-to-bone relationships are fixed (59). The Xitron FFM method is theoretically less affected by changes in ECW/ICW and would better account for changes in body protein, assuming protein/ICW remained fixed. However, it is still dependent on assuming a fixed water-to-bone relationship. The above D'_ECW and D'_ICW coefficients are weighted so that the extracellular compartment influences the prediction more than the intracellular compartment does. This is probably because the early multifrequency devices made by Xitron predicted ECW better than ICW. With the use of the above constants, if ECW and bone do not change, the Xitron FFM method would reasonably predict BCM. However, FFM will never be a valid measure of BCM, because a change in bone or water changes the assumed water-to-bone relationship and causes error. On the other hand, the BIS ICW-to-BCM relationship is only dependent on ICW/protein.

The BIS methodology used in this study has been successfully validated in other clinical populations (15, 22, 57), and there is evidence that ECW and ICW differences can be detected between patient groups (24, 57). Whereas these previous findings add strength to the findings of this study, it is unknown whether the results of this study are applicable to other populations. Hannan et al. (19) reported that the BIS method did not accurately predict ECW and TBW, but the potential cause was not discussed. Hannan et al. (personal communication) have observed f_c values approaching 1 MHz, and this is consistent with the recent report of Meyer et al. (37). With an f_c of 1 MHz, which is equal to the highest frequency measured, an accurate determination of R would be extremely difficult because there would be data on only one side of the semicircle to fit (Fig. 2). Even with these constraints, however, it appears that ECW can still be predicted accurately (15).

When f_c becomes very high, it is doubtful that either BIS or a fixed, single high-frequency measurement will accurately predict ICW. If an accurate bioimpedance measurement were possible over a range of frequencies up to 5 MHz, accurate computation of the Cole model might be possible when f_c is very high. Without consistent use of good protocol, the bioimpedance technique can also be adversely affected by inaccurate electrode placement, geometry differences, orthostatic fluid shifts, inappropriate limb abduction, changes in ion concentration, changes in core and skin temperature, and changes in vascular perfusion. Whereas many of the above factors can be controlled, there may be instances when they cannot. Clinicians should have a thorough understanding of the sources and magnitude of potential error in the Z method. A detailed description of the error sources in the Z method can be found in the papers by Kushner et al. (28) and Scharfetter et al. (45). Each clinical population presents unique limitations for the method. For example, when body water is not evenly distributed in the body segments, as in ascites, the wrist-ankle approach used in this study would not yield valid results (46). Furthermore, when changes in body water occur faster than equilibration can occur in the body segments, as would be the case with infusion and ultrafiltration, a segmental approach must be used (52, 60).

In conclusion, the results of this study indicate that the ICW_{50 X (K)}, ICW_{50 Z (P)}, and TBW_{200 R (H)}  ECW_{5 R (H)} methods predict ICW poorly. The ICW predicted by TBW_{500 R (H)}  ECW_{5 R (H)} was different from the criterion. However, the effect of random error on the measurement of E_sys was 12-13% of the average measured ICW with the use of this method. This was two times larger than the difference between the obtained and critical threshold statistics. Although the TBW_{500 R (H)}  ECW_{5 R (H)} method predicted ICW with only marginal accuracy, it can be neither clearly rejected nor accepted by the results of this study. However, because of potential variations in f_c and the considerable E_sys inherent in this method, the utility of the TBW_{500 R (H)}  ECW_{5 R (H)} method is questionable. E_sys indicates a poor underlying basis that will not be corrected by simply regressing equations against larger data sets (6, 25). Random error decreases with sample size, but E_sys does not. The BIS mixture theory approach convincingly provided the best prediction of ICW (BCM). This was particularly evident when individual results were compared. It can be concluded that the BIS measurement is useful as a field method for monitoring BCM in HIV-infected populations.