What Does BIA Measure?

Precisely what BIA measures in terms of electrical and biological parameters is not known and probably varies somewhat from person to person. Instruments for BIA introduce into the body a known amount of current (I ), of about 800 µ A, most often at a frequency of 50 kHz. The current passes between two electrodes, often called the source and sink (or detector), and generates voltages between different points in the body volume according to Ohm’s law. In present practice, the electrodes usually are located on the wrist and ankle. The current flows through all conduct­ing material present in the body in the path between the source and sink electrodes. Because living tissue constitutes a volume conductor, the physical carriers of the current are predominantly charged ions, such as sodium or potassium ions, which are able to move within the volume. Conductivity within such materials as blood and urine is high, that of muscle is intermediate, and that of such materials as bone, fat, or air is low. Current will flow predominantly through materials with higher conductivities. There is a lower resistance to current flow in regions where the conductor has a larger cross-sectional area, such as the body trunk, and a higher resistance in regions with a smaller cross-sectional area, such as the forearm. Finally, there is less resistance to current where there are fewer obstacles, such as cell membranes, that form barriers to charge movement.

The actual parameter measured with BIA is the voltage (V ) that is produced between two electrodes located most often at sites near to, but different from, the sites where current is introduced. The measurement normally is expressed as a ratio, V/I, which is also called impedance (Z ). The measuring instrument is therefore called a bioelectrical impedance analyzer. Impedance has two components, resistance (R) and reactance (X ). In BIA the resistance is nominally about 250 Ω , and reactance is about 10 percent of that amount, so the magnitude of Z is similar to that of R. In many BIA reports, Z and R are used as if they are interchange­able, although Z = (R² + X²)^1/2.

In practice the current magnitude, about 800 µ A, is chosen to be small enough so as not to be perceived by the subject, but large enough to produce voltages that are above interfering “noise.”

It is important to keep in mind that BIA measures only this end-to-end voltage across the entire path between the voltage-sensing elec­trodes. This voltage is the energy expended per unit of charge for the total current path (e.g., the total path from wrist to ankle). As such, the voltage or impedance measurement does not provide any direct information with respect to the amount of current traveling through intracellular versus extracellular volumes, in blood versus muscle, or in fat versus fat-free media. Similarly, the measurement provides no direct information as to the length, areas, or volumes of the seg­ments of the body through which it passes. Relationships between impedance and other variables of interest such as TBW, FFM, or body fat have been established as statistical correlations with imped­ance for a particular population rather than on a biophysical basis. Although such statistical relationships may be established and found to be useful for a particular well-defined group, they cannot be abso­lute in view of the fact that the current diffuses throughout the con­ducting volume and makes use of any and all conductive paths that are available in the specific person at the particular time of measurement.

Because the current is greater in those paths with lower resistance, the paths will, in general, differ from person to person because of dif­ferences in body size, shape, electrolytes, fluid distribution, or other aspects of body composition, and will vary in the same person from time to time as these characteristics change. Almost any change in body size, shape, or composition will have at least a small effect on impedance.

Although the current paths are not known from the BIA measure­ments, plausible inferences can be made from known body structure. Much of the current will flow through the most highly conducting mate­rials, such as fluids containing electrolytes. Relatively small percentages of change in the sizes or conductivities in fluid compartments will likely cause a significant change in the resulting impedance. Conversely, even large changes in the volume or conductivity of high-resistance regions, such as bone or fat, will not affect the BIA measurement very much.

A limitation in interpretation for a particular subject is that the most significant paths are not known. Furthermore, because cross-sectional area is an important determinant of resistance, the overall resistance may be seen as a series combination of a number of shorter segments, such as arm, trunk, and leg. The overall resistance will be most affected by those segments of the current flow path that have the highest seg­ment resistance, that is, the arm and leg, rather than the trunk. The consequence is that sensitivity to changes in the conductive properties in the trunk will be less than the sensitivity to changes in the arm or leg.

Explanations of the BIA method often begin with a discussion of a special, simplified volume that has a uniform cross-section, such as a cylinder, and that is filled with a homogeneous conducting material of resistivity (ρ). It is then observed that the end-to-end resistance (R) is the resistivity times the length (L), divided by the cross-sectional area (A) (i.e., R = rL/A). Multiplying the right side of the equation by L/L gives R = ρL²/Vol, where Vol is the volume. Rearranging gives Vol = ρL²/R, an equation that allows one to determine the volume, for this special case, if ρ, L, and R are measured.

The major assumptions that underlie this simplified example and the resulting equation, namely uniform cross-sectional area and homogeneous conductivity, are not fulfilled in humans, not even approximately. Correspondingly, this equation as such is not used in BIA instruments, so no values for resistivity (ρ) are identified or used. Rather, the equation has been used by analogy to suggest a statistical parameter that can be used in humans, Ht²/R, where height (Ht) replaces length (L). This parameter is then used as one of the inde­pendent variables in a statistical regression procedure, and its degree and form of association with some output of interest, such as TBW, are evaluated. In other words, as used with humans, the BIA measurement is not a direct measure of any biological output quantity of interest, such as fat, on the basis of a physical or biophysical model or reason­ing. Rather, resistance simply becomes one element of a statistical evaluation, and may or may not be found significant in relation to a particular output variable in a particular population.

Discussions of BIA reports often include a discussion of “equations.” These equations are those describing the statistical relationships found for a particular population and are not derived from biophysical reason­ing, although plausible arguments in their support are often provided. Because the impedance or resistance that is the product of the BIA measurement is affected by numerous physical and biophysical vari­ables, most of these populations are chosen to be similar in many of their attributes. This is done so that the BIA result then can be corre­lated with the remaining attributes that are allowed to vary. The con­sequence is that each equation is useful only for subjects who closely match the reference population used in the original derivation of the equation.

How Safe Is It?

Users of the BIA procedure consider it safe because of several fac­tors. One factor is that currents at a frequency of 50 kHz are reported to be unlikely to stimulate electrically excitable tissues, such as nerves or cardiac muscle. Another is the absence of any reports of untoward events induced by BIA, even in the course of thousands of individuals undergoing measurement. A third factor is that relatively small cur­rent magnitudes are involved, less than 1 mA, which are less than the threshold of perception. Furthermore, the use of batteries or low-voltage power sources greatly diminishes risks from macroshock. At the same time, not all safety issues have been explicitly or formally evaluated, particularly issues that might arise when additional devices are involved (such as an implanted cardiac defibrillator) or in a hospital environment where electrical interference is more likely. In addition, so far as the panel is aware, there exist no formal safety standards for BIA instruments. Also, introduced current in the BIA procedure is larger in magnitude than are leakage currents allowed for some other medical devices, such as electrocardiograph machines (although that leakage involves the 60-Hz power-line frequency, which is thought to have more intrinsic risk).

The panel neither heard nor identified any particular reason why the BIA measurement is other than safe. However, the panel thought that a more systematic assessment of all safety-related issues would be in order in view of the obvious public interest in this issue and because the panel was not advised of any comprehensive assessment that has presently been completed. Furthermore, a review of issues of electro­magnetic compatibility may also be in order, in regard to both inter­ference with the BIA measurement and interference by the BIA measurement with other devices that might be in the vicinity. It is wise to advise anyone with an implanted defibrillator to avoid BIA evaluation until this issue has been reviewed, because even small currents could potentially provoke an incorrect defibrillator response.

Application to Total Body Water

An often-cited basis for BIA states that the volume (Vol) of a cylindrical conductive object is related to its length and resistance by the expression, Vol = ρL²/R, where L is the object’s length, R is resistance, and ρ is a resistivity constant. The electrical current passing through the body is primarily limited to electrolyte solutions; therefore, the only direct biological parameter measurable by BIA is related to the body’s water content. At low frequencies (e.g., <1 kHz), cell membranes impede current flow through the intracellular space, whereas no such barriers limit extracellular current flow. However, at higher frequencies (e.g., >100 kHz), the signal penetrates cell membranes and passes through all fluids. Thus, in principle, it would appear possible to estimate TBW by modeling the body as comprising smaller components and then per­forming a summation of water content over those components (limbs, trunk, etc.). Unfortunately, this approach, based only on biophysical principles, is beyond the scope of present BIA instruments. It is thus necessary to correlate whole-body BIA measurements to other meas­ures of TBW.

BIA can be calibrated against TBW by isotopic dilution techniques using ²H₂O, ³H₂O, or H²¹⁸O with the following relationship:

TBW = α • Ht²/R + c where Ht is the subject’s height, R is the resistance obtained by single-frequency BIA (usually 50 kHz), α is a proportionality constant specific for a given subject population, and c is a constant. The correlation between TBW and Ht²/R can be better than 0.95 in normal subjects varying in age from infancy to adulthood. In some studies, this cor­relation is slightly improved by the addition of an independent weight term in the following expression:

TBW = α • Ht²/R + b • Wt + c. The standard error of the estimate of TBW by carefully performed BIA is generally less than 2 liters of water, or less than 4 percent error for a nominal 50 liters of TBW.

It is commonly assumed that a 50-kHz signal penetrates cell mem­branes and freely passes through all fluids. Unfortunately, this assump­tion is known to be false; the current is carried by extracellular fluid plus some component of intracellular fluid. Furthermore, the human body is poorly approximated by the cylinder required by the relation­ships outlined above, and the BIA measurement is disproportionately sensitive to limb versus trunk water content. For these reasons, it is apparent that the correlation between 50-kHz BIA resistance measure­ments and isotopic dilution TBW differs slightly in conditions in which there are disturbances in the distribution of extracellular versus intra­cellular water or limb versus trunk water. Thus, optimal accuracy in predicting TBW for a given subject will be achieved by using a prediction equation validated for subjects with overall body composition similar to that of the subject.

Nature of the Prediction of Adiposity From Bioelectrical Impedance Variables

There is no direct theoretical relationship between resistance and/or reactance and relative body fatness (percentage of body mass as fat). Estimation of adiposity from BIA therefore is based on empirical relationships in samples of experimental subjects and requires calcula­tions involving assumptions at several steps. BIA most closely estimates body water, as has been discussed. The next step in the estimation of body composition is the estimation of FFM from TBW. Fat mass (FM) is then estimated as the difference between body weight and FFM, and relative body fat is calculated accordingly.

Characteristics of Published Validation Studies

Validation studies have been undertaken on a number of cohorts of healthy adults, with recent and more limited data available on neo­nates, children, youth, and the elderly. Most of the studies relating BIA measures to a criterion method were conducted with White Euro­pean or North American non-Hispanic White subjects. More limited data are available on Hispanics, non-Hispanic Blacks, Asians, and Native Americans.

The criterion methods used in most of the published studies have been TBW by isotope dilution and body density by the under­water weighing method. A few studies have used other criterion methods including multicompartment methods. The accuracy of prediction equations for estimating FFM, FM, and relative body fat depends in part on the accuracy of measurement of the criter­ion variable. The equation for estimating the percentage of fat from body density assumes that the density of the FFM is constant (1.1 g/cm³). Determination of FFM from TBW assumes a constant hydration level of FFM of 73 percent within and across individuals. Changes in body composition during growth, aging, disease, and obesity result in deviations from the assumed density and hydra­tion of FFM used to derive the Siri equation.¹ This observation has prompted some investigators to recommend that age-specific predictive equations be used for FFM; this issue remains contro­versial in light of the relative disadvantages for practical use of a proliferation of prediction equations. Clearly, disease and severe obesity result in body composition changes that render inapplicable the prediction equations that are derived from healthy adults of normal weight.

In a variety of studies of healthy adults and children, the empir­ical relationships among TBW, FFM, and percentage of body fat and BIA variables are significant, with lower predictive value and higher error terms in predicting percentage of body fat than predicting TBW or FFM. Validation and cross-validation studies predicting FFM derived by body density equations from BIA measures (usually Ht²/R) have usually resulted in prediction errors for young adults of 5 percent or less (coefficient of variation). These errors reflect the sum of the error of measurement of the criterion method, the (usually smaller) error in the BIA measurement, errors in measurement of height and weight, and errors attributable to the prediction equation. The larger errors occur in populations having a greater proportion of relatively fatter individuals. Errors may be substantially larger for individuals who differ from the reference populations. The impedance index Ht²/R has typically been the best single predictor of these body composition variables; inclusion of information on reactance and on body weight provides some improve­ment in predictive ability.

Use of age- and gender-specific values for the density of FFM for children, youth, and the elderly and use of multicomponent criterion models that include TBW and total body mineral, in addition to body density, provide more accurate criterion estimates of FFM and therefore of percentage of body fat. This improvement is due to a reduction in the overall error in the prediction equation by reducing the error attributable to the criterion method.

Ideally, prediction equations should be cross-validated on indepen­dent samples or at least with statistically accepted techniques for split­ting samples for cross-validation purposes; thus far, a minority of studies in the published literature have provided cross-validation data.

Use of BIA to Predict Body Cell Mass

Body cell mass (BCM) represents the active metabolic component of the body and is thus an important component of body composition. An accurate assessment of BCM would provide the clinician with use­ful information for nutritional management of patients. Although BIA is highly correlated with TBW, limited data are available to assess how well such data can estimate BCM. BCM consists of intracellular fluid and solids. Therefore, one would expect a high correlation of measures made by BIA. Data are limited, but suggest that BIA may have the poten­tial to estimate BCM (in healthy adults). It is not clear whether reliable of BCM can be made in other population groups such as hospitalized patients or whether BIA can reliably detect changes in BCM.

Use of BIA to Predict Adiposity in Severely Obese Individuals

Because of widespread usage of BIA to estimate levels of adiposity in obesity, it is worth detailing the reasons why predictions of fatness in severely obese subjects using existing prediction equations must be viewed with caution. The geometric proportions of obese individuals compared with those of leaner individuals are such that a greater pro­portion of body mass and body water is accounted for by the trunk in relation to the extremities; the trunk, however, contributes a relatively minor amount to total body impedance. This situation would tend to result in overestimation of body fat using standard equations. If the assumptions of body density are used in the prediction equation, then body fat may be underestimated. The net effect in severely obese individuals is highly individual and generally unpredictable.

Use of BIA in Measuring Changes in Body Fat in Individuals

Available information indicates that BIA is not useful in measuring acute changes in body fat in individuals, although it can characterize longer term changes in groups of subjects. Measuring change by defini­tion produces a larger error term by virtue of dependence on two (rather than one) imperfect measures, the error from which may be additive.

Among individuals at the extreme of leanness (5–6 percent body weight as fat), such as in some athletes and patients with wasting diseases, changes in the percentage of body fat may be less than the error of measurement.

Regional Fat Distribution

In some individuals, the basic assumptions underlying the pro­cedure are not met sufficiently to render conventionally performed total body BIA appropriate as a measure of TBW and subsequent estimates of FFM and adiposity. These conditions include significant body asymmetry as in amputations, unilateral hemiparesis, and neuro­muscular conditions that produce localized changes in perfusion or tissue atrophy. BIA should only be applied to these populations after appropriate validation.

Technological Research

There are more than 30 different manufacturers of bioimpedance machines worldwide. These manufacturers use different equations for converting the raw data of impedance or reactance to estimates of body composition. As a consequence, the same types of measurements have been extrapolated to widely different estimates of body composition in both lean and obese subjects. There has been difficulty in obtaining the actual equations incorporated into the software of the machines and the data from which these equations were derived. The availability of this information would help provide a more uniform application of BIA in research and clinical settings. Additional research is warranted to examine the relationships of the components of body composition among different technologies and to establish correlates of such body composition measures with health risk and other clinical or nutritional parameters.

Some BIA reports, particularly more recent ones, use the reactance as well as the resistance components of impedance, incorporate meas­urements taken at multiple frequencies, or use measurements made at multiple sites or at sites other than the wrist and ankle. It is plaus­ible, although unproven, that reactance and changes with frequency are tied to intracellular versus extracellular fluid. No consensus has yet been reached as to the value of such procedures, which add com­plexity as well as new information. Future research may show that the use of such information can tie BIA measurements more closely tothe underlying biophysical structure.

Basic Science

An important need in the basic science of this technology is to investigate the biophysical pathways of the currents. The basic science underlying this technology is simple; however, application to humans involves a complex volume conductor shape, heterogeneous tissue types, extracellular and intracellular volumes, and changes over time. There­fore, additional research that more completely and precisely links BIA measurements (either as currently used or with newer multifrequency, multisite forms) to the underlying physiological and biophysical structure will help to place BIA technology on a much stronger scientific basis.

Clinical Research

A major need in clinical research is the establishment of reference norms to improve data interpretation. These norms are needed in humans with and without disease across the life span. Further investigations are clearly needed in measuring different human somatotypes. For example, the disproportionate distribution of body weight in individuals with truncal obesity most likely affects impedance measurements. More research is needed to evaluate the possible diagnostic and treatment benefits of using BIA assessment of body composition to classify sub­types of obesity. There is little information on this topic. Moreover, BIA measurements in populations with varied ratios of extremity length and truncal size should provide valuable information on the contribution of different body parts to total BIA measurements.

An important characteristic of BIA is its ability to estimate TBW. Recent studies suggest that measurements of TBW may be further refined to determine quantities and distributions of intracellular and extracellular water. It may be possible to differentiate between extra­cellular and intracellular water by combining BIA measurements at low frequency with those at high frequency. In addition to measuring resistance, BIA instruments also provide a measurement of reactance. Although the biophysical significance of reactance is poorly under­stood, it appears to relate to the extracellular/intracellular water ratio. The potential to differentiate between extracellular and intra­cellular water in various pathophysiologic conditions is an exciting potential application for BIA awaiting future development. Measure­ments of the distribution of body water, in addition to its amount, would be of potential value to clinicians when managing fluid require­ments in patients with critical illness, renal and liver disease, and cardiac failure.

Conventional BIA technology does not provide information on regional distribution or patterning of body fat, a variable that has been shown to relate more strongly to some health risks than does overall level of adiposity. Investigations on segmental impedance (separately measuring impedance of the limbs and trunk) must be regarded as a purely research application at this time, but studies should be conducted to determine the value of BIA in the assessment of body fat patterning.

Epidemiological Research

Additional studies are needed to determine whether population-specific equations may be needed to predict adiposity in the elderly, the very lean, and the obese as well as to determine whether racial/ ethnic backgrounds affect the validity of predictions. Given that the prevalence of obesity is higher in Hispanics, Native Americans, and non-Hispanic Blacks than in the non-Hispanic White popula­tions on whom most of the prediction equations have been devel­oped, one might expect poorer predictive value for these ethnic minority populations. To be of maximum value, studies should include the more complex (multifrequency) impedance measure­ments, multicomponent criterion methods accounting for TBW and total body mineral as well as body density, and statistically accepted cross-validation methods. Ideally, cross-validation should take place on independent samples.

The existing archival data from the National Health and Nutri­tion Examination Survey (NHANES III) that was conducted between 1988 and 1994 includes more than 16,000 measurements by BIA in a nationally representative sample of noninstitutionalized civilians in the United States who are 12 years of age or older. Resistance and reactance data from the survey are included in the NHANES III data­base, along with anthropometric data on weight, height, extremity circumferences, diameters, and skin-fold thicknesses. At a minimum, the BIA data, height, and weight can be applied in equations to esti­mate TBW. Additional anthropometric information and other clinical variables might be used to improve estimates of TBW if valid prediction equations are developed for specific demographic subgroups. Additional validation studies using multicompartment models to test how well BIA-derived estimates of TBW predict FFM and adiposity are needed. Data presented to the panel from the Framingham Heart Study and the Fram­ingham Offspring Study provided some validation of BIA methodologies for body composition assessment in elderly men and women.

The NHANES III includes non-Hispanic White, non-Hispanic Black, and Mexican-American men and women in adequate numbers to allow the development of nationally representative normative data on body composition for the U.S. population. NHANES III BIA data are poten­tially extremely useful to examine the relation of body composition estimates from this technique to clinical risk factors such as blood pressure, blood lipids, and glucose intolerance. This will determine, in part, whether BIA provides additional information on disease risks compared with other techniques of body composition assessment. The availability of longitudinal followup of the NHANES III cohort would provide valuable information relating body composition derived from BIA to clinical outcome. It is possible that BIA measurements may cor­relate positively with adverse clinical outcomes.

BIA estimates of body composition should be directly compared with other field techniques of body composition such as skin-fold thicknesses and other anthropometric measurements. Data on the validity of BIA measurements performed in settings such as clinicians’ offices and health clubs would provide valuable information on the effective applications of this technology in nonresearch settings.

Consensus Development Panel

• John L. Rombeau, M.D.
  Conference and Panel Chair
  Professor of Surgery
  Hospital University of Pennsylvania
  Philadelphia, Pennsylvania
• Linda Bandini, Ph.D.
  Assistant Professor of Pediatrics
  Department of Pediatrics
  Tufts University School of Medicine
  Boston, Massachusetts
• Roger Barr, Ph.D.
  Professor of Biomedical Engineering
  Associate Professor of Pediatrics
  Department of Biomedical Engineering
  Duke University
  Durham, North Carolina
• Dennis M. Bier, M.D.
  Director, Children's Nutrition Research Center
  Professor of Pediatrics
  Department of Pediatrics
  Baylor College of Medicine
  Houston, Texas
• Bruce Ryan Bistrian, M.D., Ph.D.
  Co-Director, Nutrition Support Service
  New England Deaconess Hospital
  Professor of Medicine
  Harvard Medical School
  Boston, Massachusetts
• Steven N. Blair, P.E.D., F.A.C.S.M.
  Director of Epidemiology and Clinical Applications
  Cooper Institute for Aerobics Research
  Dallas, Texas
• Cynthia L. Gaboury, M.D.
  Assistant Professor of Medicine
  Division of Nephrology and Hypertension
  Department of Medicine
  Oregon Health Sciences University
  Portland, Oregon
• Melvin D. Gerald, M.D., M.P.H.
  President
  District of Columbia Academy of Family Physicians
  Washington, District of Columbia
• Gail Harrison, Ph.D.
  Professor
  Department of Community Health Sciences
  UCLA School of Public Health
  Los Angeles, California
• Donald F. Kirby, M.D.
  Associate Professor of Medicine
  Chief, Section of Nutrition
  Medical College of Virginia Hospitals
  Richmond, Virginia
• Donna H. Mueller, Ph.D., R.D.
  Assistant Professor of Nutrition and Food Sciences
  Department of Bioscience and Biotechnology
  Drexel University
  Philadelphia, Pennsylvania
• Bruce W. Patterson, Ph.D.
  Assistant Professor
  Department of Surgery
  University of Texas Medical Branch
  Metabolism Laboratory
  Shriners Burns Institute
  Galveston, Texas
• June Stevens, Ph.D.
  Associate Professor
  Department of Nutrition/Epidemiology
  University of North Carolina
  Chapel Hill, North Carolina

Speakers

• William Cameron Chumlea, Ph.D.
  "Mechanical and Physiological Modifiers of Bioelectrical Impedance Analysis Measurements"
  Fels Professor
  Division of Human Biology and Pediatrics
  Department of Community Health
  Wright State University School of Medicine
  Yellow Springs, Ohio
• Paul Deurenberg, Ph.D.
  "Why Bioelectrical Impedance Analysis Should Not Be Used for Estimating Adiposity"
  Department of Human Nutrition
  Wageningen Agricultural University
  Wageningen, The Netherlands
• Kenneth R. Foster, Ph.D.
  "Whole-Body Impedance: What Does It Measure?"
  Associate Professor
  Department of Bioengineering
  University of Pennsylvania
  Philadelphia, Pennsylvania
• Shumei S. Guo, Ph.D.
  "Use of Statistical Methods to Best Estimate Body Composition or Adiposity"
  Professor
  Division of Human Biology
  Department of Community Health
  Wright State University School of Medicine
  Yellow Springs, Ohio
• David Heber, M.D., Ph.D.
  "Use of Bioelectrical Impedance Analysis Measurements in the Private Clinician's Office"
  Professor of Medicine
  Chief, Division of Clinical Nutrition
  Director, UCLA Clinical Nutrition Research Unit and UCLA Nutrition and Obesity Program
  UCLA School of Medicine
  Los Angeles, California
• Steven B. Heymsfield, M.D.
  "Techniques Used in the Measurement of Body Composition"
  "Overview of Bioelectrical Impedance Analysis Measurements for the Estimation of Body Cell Mass and Total Body Water"
  Professor of Medicine
  College of Physicians and Surgeons
  Columbia University
  Deputy Director, Obesity Research Center
  St. Luke's Roosevelt Hospital Center
  New York, New York
• James A. Hodgdon, Ph.D.
  "Use of Bioelectrical Impedance Analysis Measurements as Predictors of Performance"
  Head
  Human Performance Department
  Naval Health Research Center
  San Diego, California
• Linda B. Houtkooper, Ph.D., R.D.
  "Why Bioelectrical Impedance Analysis Should Be Used for Estimating Adiposity"
  Nutrition Specialist
  Department of Nutritional Sciences
  College of Agriculture
  University of Arizona
  Tucson, Arizona
• Danny O. Jacobs, M.D., M.P.H.
  "Use of Bioelectrical Impedance Analysis Measurements in the Clinical Management of Critical Illness"
  Associate Professor
  Department of Surgery
  Harvard Medical School
  Department of Surgery
  Brigham and Women's Hospital
  Boston, Massachusetts
• Donald P. Kotler, M.D.
  "Use of Bioelectrical Impedance Analysis Measurements in the Clinical Management of Patients With AIDS or the Wasting Syndrome"
  Associate Professor of Medicine
  College of Physicians and Surgeons
  Columbia University
  Gastroenterology Division
  St. Luke's Roosevelt Hospital Center
  New York, New York
• Robert J. Kuczmarski, Dr.P.H., R.D.
  "Use of Bioelectrical Impedance Analysis Measurements as Part of a National Nutrition Survey"
  Division of Health Examination Statistics
  National Center for Health Statistics
  Centers for Disease Control and Prevention
  Hyattsville, Maryland
• Robert F. Kushner, M.D.
  "Clinical Characteristics Influencing Bioelectrical Impedance Analysis Measurements"
  "Use of Bioelectrical Impedance Analysis Measurements in the Clinical Management of Patients Undergoing Dialysis"
  Associate Professor of Clinical Medicine
  Division of Gastroenterology
  Department of Medicine
  University of Chicago
  Chicago, Illinois
• Lawrence A. Leiter, M.D., F.R.C.P.(C.), F.A.C.P.
  "Use of Bioelectrical Impedance Analysis Measurements in Patients With Diabetes"
  Director, Lipid Clinic and Staff Endocrinologist
  St. Michael's Hospital
  Associate Professor
  Departments of Medicine and Nutritional Sciences
  University of Toronto
  Toronto, Ontario, Canada
• Henry C. Lukaski, Ph.D.
  "Biological Parameters Considered in the Derivation of the Bioelectrical Impedance Analysis"
  Research Leader for Mineral Nutrient Functions
  Grand Forks Human Nutrition Research Center
  Agricultural Research Service
  U.S. Department of Agriculture
  Grand Forks, North Dakota
• Nile M. Oldham
  "Overview of Bioelectrical Impedance Analyzers"
  Electronics Engineer
  Electricity Division
  Electronics and Electrical Engineering Laboratory
  National Institute of Standards and Technology
  U.S. Department of Commerce
  Gaithersburg, Maryland
• Paul B. Pencharz, M.B., Ch.B., Ph.D., F.R.C.P.(C.)
  "Use of Bioelectrical Impedance Analysis Measurements in the Clinical Management of Malnutrition"
  Professor, Departments of Paediatrics and Nutritional Sciences
  University of Toronto
  Division of Gastroenterology and Nutrition
  The Hospital for Sick Children
  Toronto, Ontario, Canada
• Ronenn Roubenoff, M.D., M.H.S., F.A.C.P.
  "Use of Bioelectrical Impedance Analysis Measurements in a Prospective Epidemiological Study"
  Associate Professor of Medicine and Nutrition, Scientist II
  Jean Mayer Human Nutrition Research Center
  U.S. Department of Agriculture
  Tufts University
  Boston, Massachusetts
• Paul R. Schloerb, M.D.
  "Use of Bioelectrical Impedance Analysis Measurements in the Clinical Evaluation of Patients With Liver Disease"
  Professor of Surgery
  University of Kansas Medical Center
  Kansas City, Kansas
• Karen R. Segal, Ed.D.
  "Use of Bioelectrical Impedance Analysis Measurements as an Evaluation for Participating in Sports"
  Associate Professor
  Departments of Pediatrics and Physiology
  Cornell University Medical College
  New York Hospital
  New York, New York
• Barbara J. Zarowitz, Pharm.D., F.C.C.P.
  "Use of Bioelectrical Impedance Analysis Measurements in Association With Pharmacokinetics"
  Clinical Manager
  Department of Pharmacy Services
  Henry Ford Hospital
  Adjunct Associate Professor of Pharmacy Practice
  College of Pharmacy and Allied Health Professions
  Wayne State University
  Detroit, Michigan

Planning Committee

• Van S. Hubbard, M.D., Ph.D.
  Chairperson and Chief
  Nutrition Sciences Branch
  National Institute of Diabetes and Digestive and Kidney Diseases
  Bethesda, Maryland
• Elsa Bray
  Program Analyst
  Office of Medical Applications of Research
  National Institutes of Health
  Bethesda, Maryland
• William Cameron Chumlea, Ph.D.
  Fels Professor
  Division of Human Biology
  Wright State University School of Medicine
  Yellow Springs, Ohio
• Leslie Curtis
  Office of Health Research Reports
  National Institute of Diabetes and Digestive and Kidney Diseases
  National Institutes of Health
  Bethesda, Maryland
• Carolyn E. Derrer
  Chief
  Gastroenterology and Renal Devices Branch
  Food and Drug Administration
  Rockville, Maryland
• James E. Everhart, M.D.
  Program Director
  Epidemiology and Data Systems
  Division of Digestive Diseases and Nutrition
  National Institute of Diabetes and Digestive and Kidney Diseases
  National Institutes of Health
  Bethesda, Maryland
• John H. Ferguson, M.D.
  Director
  Office of Medical Applications of Research
  National Institutes of Health
  Bethesda, Maryland
• Sanford A. Garfield, Ph.D.
  Director, Special Programs
  Division of Diabetes, Endocrinology, and Metabolic Diseases
  National Institute of Diabetes and Digestive and Kidney Diseases
  National Institutes of Health
  Bethesda, Maryland
• Robert Garrison, Ph.D.
  Branch Chief
  Field Studies and Biometry Branch
  Division Epidemiology and Clinical Application
  National Heart, Lung and Blood Institute
  National Institutes of Health
  Bethesda, Maryland
• Gilman D. Grave, M.D.
  Chief, Endocrinology Nutrition and Growth Branch
  National Institute of Child Health and Human Development
  National Institutes of Health
  Bethesda, Maryland
• William H. Hall
  Director of Communications
  Office of Medical Applications of Research
  National Institutes of Health
  Bethesda, Maryland
• Tamara Harris, M.D., M.S.
  Chief, Geriatric Epidemiology Office
  Epidemiology, Demography and Biometry Program
  National Institute on Aging
  National Institutes of Health
  Bethesda, Maryland
• Steven B. Heymsfield, M.D.
  Professor of Medicine
  College of Physicians and Surgeons
  Columbia University
  Director, Obesity Research Center
  St. Luke's Roosevelt Hospital Center
  New York, New York
• Henry C. Lukaski, Ph.D.
  Research Leader for Mineral Nutrient Functions
  Grand Forks Human Nutrition Research Center
  Agricultural Research Service
  U.S. Department of Agriculture
  Grand Forks, North Dakota
• Paul B. Pencharz, M.B., Ch.B., Ph.D., F.R.C.P.(C.)
  Professor, Departments of Paediatrics and Nutritional Sciences
  University of Toronto
  Division of Gastroenterology and Nutrition
  The Hospital for Sick Children
  Toronto, Ontario, Canada
• Susan M. Pilch, Ph.D.
  Health Scientist Administrator
  Diet and Cancer Branch
  National Cancer Institute
  National Institutes of Health
  Rockville, Maryland
• John L. Rombeau, M.D.
  Conference and Panel Chair
  Professor of Surgery
  Hospital University of Pennsylvania
  Philadelphia, Pennsylvania
• Richard Williams
  Biomedical Engineer
  Office of Device Evaluation
  Food and Drug Administration
  Rockville, Maryland
• Susan Yanovski, M.D.
  Division of Digestive and Diseases and Nutrition
  National Institute of Diabetes and Digestive and Kidney Diseases
  National Institutes of Health
  Bethesda, Maryland

Conference Sponsors

• The National Institute of Diabetes and Digestive and Kidney Diseases
  Phillip Gorden, M.D.
  Director
• The NIH Office of Medical Applications of Research
  John H. Ferguson, M.D.
  Director

Cosponsors

• The National Institute of Child Health and Human Development
  Duane F. Alexander, M.D.
  Director
• The National Institute on Aging
  Richard J. Hodes, M.D.
  Director
• The National Heart, Lung, and Blood Institute
  Claude Lenfant, M.D.
  Director
• The U.S. Department of Agriculture
  Mike Epsy
  Secretary