- Open Access
Physiological models of body composition and human obesity
© Levitt et al; licensee BioMed Central Ltd. 2007
- Received: 07 May 2007
- Accepted: 20 September 2007
- Published: 20 September 2007
The body mass index (BMI) is the standard parameter for predicting body fat fraction and for classifying degrees of obesity. Currently available regression equations between BMI and fat are based on 2 or 3 parameter empirical fits and have not been validated for highly obese subjects. We attempt to develop regression relations that are based on realistic models of body composition changes in obesity. These models, if valid, can then be extrapolated to the high fat fraction of the morbidly obese.
The analysis was applied to 3 compartment (density and total body water) measurements of body fat. The data was collected at the New York Obesity Research Center, Body Composition Unit, as part of ongoing studies. A total of 1356 subjects were included, with a BMI range of 17 to 50 for males and 17 to 65 for females. The body composition model assumes that obese subjects can be represented by the sum of a standard lean reference subject plus an extra weight that has a constant adipose, bone and muscle fraction.
There is marked age and sex dependence in the relationship between BMI and fat fraction. There was no significant difference among Caucasians, Blacks and Hispanics while Asians had significantly greater fat fraction for the same BMI. A linear relationship between BMI and fat fraction provides a good description for men but overestimates the fat fraction in morbidly obese women for whom a non-linear regression should be used. New regression relations for predicting body fat just from experimental measurements of body density are described that are more accurate then those currently used. From the fits to the experimental BMI and density data, a quantitative description of the bone, adipose and muscle body composition of lean and obese subjects is derived.
Physiologically realistic models of body composition provide both accurate regression relations and new insights about changes in body composition in obesity.
- Body Mass Index
- Obese Subject
- Body Density
- Total Body Water
- Regression Relation
The body fat fraction is an important determinant of the pharmacokinetics of most drugs and can become the dominant factor for highly lipid soluble compounds such as the volatile anesthetics [1, 2] or propofol . The body mass index (BMI) has become the standard parameter used to predict body fat when all that is known is the subject's weight and height. Although the limitations of BMI as a predictor of body fat are well recognized , it has become widely accepted because of its simplicity. The regression relations that are currently used to predict body fat from BMI are based on simple empirical fits to the data. We describe a new set of regression relations that are based on a realistic model of body composition. An advantage of using this model relation is that it can be extrapolated to the very high fat fractions of the morbidly obese. The body fat prediction relations that are currently used have been derived from studies of normal to moderately obese subjects and their applicability to the morbidly obese (BMI > 40) (e.g. for bariatric surgery) is uncertain. The purpose of this paper is to derive regression relations that are based on a model of body composition and extend the model to the morbidly obese. The model validity and the regression parameters are then estimated by applying the model to a large experimental database.
Summary of recent BMI regression equations used to predict percent body fat for men and women.
Men (N subjects)
Women (N subjects)
Gallagher et. al. 
.516 -8.64/BMI +0.0012 age (192)
.637 -8.64/BMI +0.0012 age (225)
Blacks and Asians differ
Jackson et. al. 
.0376 BMI -.0004BMI2 -.478 (296)
.0435 BMI-.0005BMI2 -.4624 (359)
Significant age and ethnic correlation
Deurenberg et. al. 
.01294BMI+.002 age -.193 (1976)
.01294BMI+.002 age -.08 (2516)
Gallagher et. al. 
Blacks differ significantly
Equation 2, which is simply an empirical polynomial fit to the data, is also not physiological, going to negative values at large BMI. In contrast, eq. 3 used by Gallagher et. al.  is valid in the limit of large BMI and can be derived from a model of body composition changes in obesity (see below). A major advantage of using this model is that the parameters in the equation quantify these body composition changes. For example, it will be shown that these parameters provide information about the relative fractions of fat and non fat of the extra weight in obese subjects.
The different regression relations are then tested by comparing their predictions with a large database of body fat measurements based on the 3C (body density and total body water) approach. Although 3C or 4C (body density, total body water and mineral mass) provide the most accurate methods of measuring body fat, 2C estimates based just on the single measurement of body density remain the simplest and least expensive approach. Simple density measurements provide an approach to multiple measurements over short times (e.g. during weight loss after bariatric surgery ) when total body water measurements become problematic. Another purpose of this analysis is to use the models obtained from the BMI versus fat relations to obtain a more accurate set of 2C parameters relating body fat to experimental measurements of body density.
Subjects were participants in previously reported [7–9] and ongoing clinical studies at the New York Obesity Research Center at St. Luke's-Roosevelt Hospital, New York. The subjects were all ambulatory adults, age 18 years or over, without any clinically significant diagnosed medical conditions. All participants signed an informed consent approved by the hospital's Institutional Review Board.
Age and BMI distribution for subjects in different ethnic groups.
Age Range; ave (SD)
BMI Range; ave (SD)
18–97; 39.9 (18.3)
18.59–50.3; 25.6 (4.3)
18 – 90; 45.97 (18.0)
17.07–65.75; 25.05 (6.34)
20–86; 48.6 (17.0)
17.27–41.9; 26.2 (3.9)
18–88; 50.15 (17.5)
17.07–61.02; 29.04 (6.48)
20–74; 37.5 (17.4)
20.08–47.9; 26.6 (5.4)
20–87; 42.18 (16.8)
18.84–57.46; 27.31 (7.70)
28–89; 53.3 (18.1)
17.79–30.25; 23.9 (3.0)
26–88; 54.26 (17.5)
17.18–28.44; 21.73 (2.83)
20–79; 47.0 (14.9)
18.41–41.7; 27.3 (4.7)
20–85; 46.99 (15.5)
19.8–43.5; 28.48 (5.0)
Measurement of body fat
where Dres is the "residual mass density" . Values of Dres determined by Silva et. al.  were used (males: Dres = 1.555; females: Dres = 1.566). Although Silva et. al. found a significantly different value of Dres for Causasian versus African American women, the difference was very small and only the average female value was used so that no ethnic difference in body fat calculations were introduced at this stage.
Total body water was measured by tritium dilution as previously described . The tritium spaces were converted to total body water (kg) using a correction factor for non-aqueous hydrogen exchange and water density at 36°C (TBW = 3H2O × 0.95 × 0.994). The within person day-to-day coefficient of variation is 1.5%. Body density and volume were measured by underwater weighing using standard methods with a technical error of 0.0020 g/cm3. Residual lung volume was estimated after immersion in a sitting position by means of the closed-circuit O2 dilution method . The body mass index (BMI) was defined as weight/height2, with weight in kg and height in meters.
Ethnic dependence of body fat versus BMI relation
Comparison between different ethnic groups is complicated because body fat has a non-linear dependence on both age and BMI (see results) and the age distribution of the subjects differs significantly for the different ethnic groups. The approach we used to compare Black, Hispanic, Puerto Rican and Asians versus Caucasians was to pick an age and BMI range for the ethnic group and then choose an age and BMI range for the Caucasian group that had approximately the same average age and BMI. For most comparisons, the ethnic age range was 20 to 52 and the BMI range was 20 to 34. A smaller BMI range was used for Asians. Also, because Hispanic women had an unusual age distribution, an age range of 20 to 60 was used.
Physiological models of body composition
The fat fraction for Model II (eq.(8)) depends on 3 adjustable parameters: 1) BMI0, the BMI of the lean reference subject; 2) f0, the fat fraction of the lean reference subject; and 3) f1, the fat fraction of the additional weight of the obese subject.
Fat fraction dependence on body density
All statistical significance tests represent the standard two tailed t-test with unequal variance. All of the parameter optimization was carried out using the Statistics option in Maple (Maplesoft) which minimizes the mean square residual error (MSR) (= sum(Xmodel-Xexp)2/N). The average error of an individual measurement is roughly equal to the square root of the MSR. Both linear and non-linear optimization was used. For optimization of the non linear equations it was essential to select initial starting values in approximately the correct range.
Sex, age and ethnic dependence of BMI versus body fat fraction
Caucasian males: Dependence of fat fraction on age for two BMI ranges.
Ave age (SD)
Ave Fat Fraction
18 – 24
18 – 26
26 – 33
0.113 (.048) (NS)
34 – 84
0.150 (.059) (p < .01)
24 – 44
21 – 30
31 – 48
0.189 (.074) (NS)
49 – 97
0.264 (.077) (p < .01)
Caucasian females: Dependence of fat fraction on age for three BMI ranges.
Ave age (SD)
Ave Fat Fraction
17 – 22
18 – 30
30 – 49
0.220 (.058) (p < .05)
49 – 89
0.278 (.053) (p < .01)
22 – 25.9
18 – 33
33 – 51
0.28 (.057) (p < .01)
52 – 88
0.34 (.061) (p < .01)
26 – 56
21 – 45
45 – 61
.42 (.060) (NS)
62 – 90
0.40 (.055) (NS)
Ethnic dependence of BMI versus fat fraction for males.
Age range (ave)
BMI range (ave)
Ave Fat Fract. (SD)
28 – 54 (36.4)
20 – 52 (36.5)
20 – 34 (26.33)
0.173 (0.075) (NS)
20 – 52 (37.5)
20 – 34 (26.13)
0.192 (0.066) (p < .05)
20 – 52 (31.5)
20 – 34 (25.14)
20 – 52 (31.0)
20 – 34 (26.04)
0.169 (0.072) (NS)
28 – 54 (35.6)
20 – 30 (24.68)
20 – 52 (36.6)
20 – 30 (23.97)
0.19 (.071) (p < .01)
Ethnic dependence of BMI versus fat fraction for females.
Age range (ave)
BMI range (ave)
Ave Fat Fract. (SD)
20 – 57 (37.6)
22 – 34 (25.49)
20 – 52 (37.96)
20 – 34 (26.66)
0.31 (0.07) (NS)
20 – 60 (36.7)
20– 34 (25.64)
0.302 (0.09) (NS)
20 – 52 (36.02)
20 – 29 (26.29)
0.33 (0.058) (p < .05)
23 – 53 (35.6)
17 – 25 (21.85)
23 – 53 (36.7)
17 – 27 (21.25)
0.262 (.068) (p = 0.07)
Linear multi-regression relations for fat fraction versus age and BMI
Linear (eq. (16))
Non-linear Model I (eq. (9))
Male Puerto Rican
Female Caucasian +Hispanic+Black
Female Puerto Rican
Linear versus non-linear model fits for fat fraction versus BMI
Prediction of fat fraction from BMI for Caucasian + Black + Hispanic subjects.
Linear eq. (1)
Model I eq. (7)
Model II eq. (8)
Male: 18 – 89
Male: 18 – 31
Male: 32 – 50
Male: 51 – 89
Female: 18 – 90
Female: 18 – 31
Female: 32 – 50
Female: 51 – 90
A multivariable form of Model I that incorporated a linear age dependence (eq.(9)) was also fit to the male and female subjects. The optimal parameter values and the MSR for the different ethnic groups are listed on the right side of Table 7.
Fat fraction versus density
Prediction of fat fraction from body density for Caucasian + Black + Hispanic subjects.
Male: 18 – 89
Male: 18 – 31
Male: 32 – 50
Male: 51 – 89
Height versus weight relationship
Age and ethnic dependence of BMI versus fat fraction relationship
All the body fat fraction measurements used here are based on 3C model measurements of body density and total body water. This decision was made not only because the 3C model has the largest database but because DEXA measurements of bone or fat density are problematical for morbidly obese subjects [11–13]. The 3C method assumes a constant value of D res , the density of the non-fat and non-water body compartments (see eq.(4)). One can estimate the error introduced by using the 3C model from the analysis of Silva et. al. . They found, for example, a value of D res of 1.555 ± 0.024 for men. Using extremes of D res of + and - one standard deviation (1.579, 1.531) yields a fat fraction range of 0.168 to 0.176 ((eq.(4)). This error is small and probably within the range of the experimental errors in body density and total body water measurement. A major assumption of this analysis is that this constant value of Dres is valid for the morbidly obese subjects. There is no direct support for this assumption. Indirect support for this is provided by the observation that the plots of fat fraction versus either BMI using Model II (fig. 2) or density (fig. 5) do not show any obvious deviations at high fat fractions.
There is clear age dependence in the fat fraction versus BMI relation. For example, for males, the oldest age range subjects have about 60% more fat then the youngest for the same BMI (Table 3). For females the age dependence is less marked and more dependent on the BMI range. There is a roughly linear increase in fat fraction with age for women with a BMI < 26. However, for BMI in the range of 26 to 56 (average BMI of about 32), there was no significant age dependence (Table 4).
There was no significant difference in the relationship between fat fraction and BMI for Caucasians, Blacks and Hispanics for either males (Table 5) or females (Table 6) and these three ethnic groups were combined. This differs from previous studies that have found significant differences between Blacks and Caucasians [5, 17–19]. In contrast, both Asians and Puerto Ricans have significantly greater fat fraction then Caucasians for the same BMI and age (Tables 5 and 6). This is a consistent observation for Asians [5, 19]. The Puerto Rican result has not been previously reported.
Regression relations for predicting fat fraction from BMI
This analysis describes a new set of regression relations based on an analysis of 1356 subjects, the largest database currently reported. A comparison of the simple linear and the non-linear physiologically based regression models are shown in figs. 2, 3 and 4. Table 8 compares the mean square residual error (MSR) of the different models. The non-linear Models I and II deviate significantly from the linear fit only for values of BMI greater than about 50. Since all the men have BMI < 51 (figs. 2 and 3), there is no clear statistical advantage of Model I or II over the linear fit for men. The linear prediction in fig. 2 overestimates the fat fraction of the two men with the largest BMI (47.9, 50.3), suggesting that the non-linear regression may be superior for morbidly obese men. However, a larger data set will be required to prove this. For the women with BMI > 50, the predictions of Model I or II are clearly superior to that of the linear fit (figs. 2 and 4) and the MSR for Model II is about 25% smaller than the MSR for the linear fit to the same data (Table 8).
Table 8 compares the linear and Model I and II fits for subjects grouped in specific age ranges. For practical prediction of fat from BMI, it is preferable to use a general multi-regression relation that directly includes the age. Table 7 lists the parameters and MSR for the age dependent forms of the linear (eq.(16)) and Model I (eq.(9)) regressions for the different ethnic groups. The addition of this linear age dependence to Model I adds another adjustable parameter that weakens the physiological interpretation of the other parameters. The parameters listed in Table 7 for the age dependent Model I should be regarded simply as a set of empirical fitting parameters. The best estimate of the actual values of the physiological parameters is provided by the data listed in Table 8 which does not include the extra age dependent parameter in the data fitting.
The corresponding regression parameters for Asians and Puerto Ricans are listed in Table 7.
The limitations of using the BMI to predict body fat fraction are well known  and there can be large individual variations, depending primarily on body muscle mass. The mean square residual error (MSR) values of the current analysis provide one measure of the accuracy of the fat prediction (the "average" error is equal to the square root of the MSR). For Caucasian + Hispanic + Black ethnic males the MSR for the linear age dependent prediction is 0.00285, corresponding to an average error of about 0.053 in the value of the fat fraction predicted based just on the subject's age and BMI. For females the age dependent Model I fit is better, with an MSR of 0.00235, corresponding to an average error of about 0.048 in the value of fat fraction predicted from BMI and age for the entire BMI range (17 to 65).
Regression relations for predicting fat fraction from body density
Although the measurement of fat fraction using just the body density (2C model) is less accurate than the 3C method, it has the advantage of simplicity. It is particularly useful for following the time course of weight change (e.g. after bariatric surgery ) where the frequent measurements complicate body water measurements. Table 9 lists the optimal least square regression parameters (a and b, eq.(12)) for predicting fat from body density for Caucasian + Black + Hispanic males and females in different age classes. These predictions are described by the black lines in figs. 5, 6, 7.
Three different sets of regression coefficients (a and b) due to Siri  and Brozek et. al.  have been previously used to predict fat fraction from body density (eqs. (13) – (15)). Figures 5 , 6, 7 and Table 9 compare these predictions with the optimal predictions using the least square values of a and b. The MSR using these older parameters sets are from 40% to 300% larger then the MSR using the optimal values of a and b.
The Siri and Brozek relations were based on small data sets of relatively normal weight subjects. The new least square values of a and b listed in Table 9 for male and female subjects in different ages ranges should provide much more accurate relations for predicting fat fraction just from experimental measurements of body density. For men, there is a consistent age dependence in these parameters, with both a and b decreasing linearly with age. For females, the age dependence of the a and b parameters is small. The average MSR for males and females of about 0.0006 corresponds to an average error about 0.024 in the value of the fat fraction predicted using just the body density.
Physiological models for fat fraction as a function of BMI or body density
The same model of body composition was used to derive the relationship between fat fraction versus BMI and fat fraction versus body density. The basic assumption is that, as a subject with a given height gains weight, the extra weight has a constant, fixed composition with a fat fraction f1 and a density d1. This is clearly an approximation because one would expect that in severe obesity, as the subject becomes increasingly sedentary, there should be accompanying changes in both bone and muscle mass along with other pathological changes . However, the good fit of Model II for a BMI range of 17 to 65 (fig. 4) suggests that this is a roughly valid supposition.
The two models used to derive the fat fraction versus BMI differ in the body composition that is assumed for the lean "reference" subject to which the extra weight is added: for Model I, the reference subject is fat free, while, for Model II, the reference subject has a fat fraction f0. For young males (fig. 3), Model II is clearly superior to Model I which underestimates the fat fraction at large BMI. The difference between the two models is smaller for older males (fig.3) and females (fig. 4).
From the Model II fit to the fat fraction versus BMI data, the values of the fat fraction of the lean "reference" subject (f0) and of the extra weight (f1) can be determined. These values are listed in Table 9 for the different male and female age groups. For all males combined, the value of f1 is 0.644. This is nearly identical to the value of 0.64 determined by Brozek et. al.  from experimental weight gain or loss data in male subjects. For females the value of f1 is larger, about 0.73.
The model for fat fraction versus body density relates the two regression parameters (a and b) to four physiological parameters (f0, f1, d0, and d1, eq.(12)). Using the values of f0 and f1 determined from the fat fraction versus BMI regression (Table 8), these two equations for a and b can be solved for d0 and d1. Table 9 lists the assumed values of f0 and f1 and the corresponding values of d0 and d1 for the different male and female age dependent density regression relations.
Body composition of standard male and female.
Reference body composition
Extra body composition
The most uncertain value used to derive the composition in Table 10 is the value of the fat fraction of adipose tissue. A number of studies have suggested that the fat fraction of adipose tissue increases in obese subjects, possibly as a result of an increase in the individual adipose cell volume [21–23]. In Table 10 it has been assumed that adipose tissue is 80% fat in the lean references subject and 85% fat in the extra weight in obese subjects.
Table 10 provides a quantitative description of the composition of the extra weight that is gained (or lost) in obese subjects. This consists of the extra adipose tissue, plus the additional muscle and bone that is required to support this adipose tissue. In males, about 20% of this "extra" weight is muscle, while in females muscle is only about 10% of this extra weight. In both males and female, bone represents about 4% of the extra weight. This model, which uses a fixed composition of the "extra" tissue, provides a good description for the entire range of BMI (16 to 65) for females (see figs. 2 and 4). This suggests that the "extra" weight in morbidly obese subjects (BMI > 50) does not differ qualitatively from that of moderately obese subjects.
Table 10 lists the composition in terms of the weight fraction. This can be converted to absolute weight as follows. For a person with height H and weight W, the "reference" body weight = W0 = BMI0 × H2(eq.(6)) and the "extra" weight = W1 = W - W0, where BMI0 for Model II is listed in Table 8. Multiplying the "reference" weight fraction by W0 and the "extra" weight fraction by W1 gives the absolute tissue weights.
By making additional assumptions about the extracellular (ECW) and intracellular (ICW) water fraction of the different tissues (), one can predict the changes in the corresponding water compartments in obesity. It is well recognized that the ratio ECW/ICW increases in obesity [7, 25, 26]. This is because the "extra" weight in obese subjects is primarily adipose tissue (Table 10) whose water is almost all extracellular. In a comparison of the ECW and ICW compartments in obese versus matched non-obese controls, Waki et. al  reported that this ratio increased from 0.63 in non-obese to 0.81 in obese females. This is similar to the predictions using the model data in Table 10 (assuming that the extracellular water weight fraction is 0.091 for muscle and 0.15 for the "extra" adipose tissue).
In addition to the model assumptions about body composition, another assumption in the derivation of eq. (6) is that the body weight of the "reference" subjects scales as heightN, where N = 2. This is the basic assumption underlying the use of BMI as a parameter for obesity. A large, extensive review of the height versus weight relationship  found an average value of N of 1.92 for males and 1.45 for females. However, this review analyzed the relation between height and total body weight while eq. (6) assumes only that the "reference" weight scales as heightN. A more relevant test is to determine the height-weight relationship for the lean subjects in this current study, assuming that these lean subjects correspond to the "reference" subjects. Figure 8 shows a log-log plot of height versus weight for lean males (fat fraction < 0.15) and females (fat fraction < 0.24). For both males and females, the average value of N is close to 2 (1.96 for males and 1.95 for females).
Using a data base of 1356 subjects, new regression relations for predicting body fat fraction from either BMI or body density are derived. Although a linear regression provides a good fit to the BMI versus fat fraction for men, the linear fit significantly overestimate the fat fraction of highly obese women (BMI > 50). For women, a non-linear regression based on a physiological model of body composition provides an accurate prediction of fat fraction for the entire BMI range. In addition, regression relations for predicting fat fractions just from experimental measurements of body density are described that are much more accurate then those that are currently used. Based on the parameters obtained from the models for BMI and body density, a quantitative estimate is derived of the body composition (e.g. bone, muscle, adipose) of the standard lean "reference" subject and of the "extra" weight added in obese subjects.
- Levitt DG: PKQuest: volatile solutes - application to enflurane, nitrous oxide, halothane, methoxyflurane and toluene pharmacokinetics. BMC Anesthesiol 2002,2(1):5. 10.1186/1471-2253-2-5View ArticleGoogle Scholar
- Levitt DG: Heterogeneity of human adipose blood flow. BMC Clin Pharmacol 2007, 7: 1. 10.1186/1472-6904-7-1View ArticleGoogle Scholar
- Levitt DG, Schnider TW: Human physiologically based pharmacokinetic model for propofol. BMC Anesthesiol 2005,5(1):4. 10.1186/1471-2253-5-4View ArticleGoogle Scholar
- Prentice AM, Jebb SA: Beyond body mass index. Obes Rev 2001,2(3):141-147. 10.1046/j.1467-789x.2001.00031.xView ArticleGoogle Scholar
- Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y: Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 2000,72(3):694-701.Google Scholar
- Carey DG, Pliego GJ, Raymond RL: Body composition and metabolic changes following bariatric surgery: effects on fat mass, lean mass and basal metabolic rate: six months to one-year follow-up. Obes Surg 2006,16(12):1602-1608. 10.1381/096089206779319347View ArticleGoogle Scholar
- Silva AM, Wang J, Pierson RN Jr., Wang Z, Heymsfield SB, Sardinha LB, Heshka S: Extracellular water: greater expansion with age in African Americans. J Appl Physiol 2005,99(1):261-267. 10.1152/japplphysiol.01317.2004View ArticleGoogle Scholar
- Pierson RN Jr., Wang J, Heymsfield SB, Russell-Aulet M, Mazariegos M, Tierney M, Smith R, Thornton JC, Kehayias J, Weber DA, et al.: Measuring body fat: calibrating the rulers. Intermethod comparisons in 389 normal Caucasian subjects. Am J Physiol 1991,261(1 Pt 1):E103-8.Google Scholar
- Gallagher D, Visser M, Sepulveda D, Pierson RN, Harris T, Heymsfield SB: How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 1996,143(3):228-239.View ArticleGoogle Scholar
- Silva AM, Shen W, Wang Z, Aloia JF, Nelson ME, Heymsfield SB, Sardinha LB, Heshka S: Three-compartment model: critical evaluation based on neutron activation analysis. Am J Physiol Endocrinol Metab 2004,287(5):E962-9. 10.1152/ajpendo.00104.2004View ArticleGoogle Scholar
- Das SK, Roberts SB, Kehayias JJ, Wang J, Hsu LK, Shikora SA, Saltzman E, McCrory MA: Body composition assessment in extreme obesity and after massive weight loss induced by gastric bypass surgery. Am J Physiol Endocrinol Metab 2003,284(6):E1080-8.View ArticleGoogle Scholar
- Evans EM, Saunders MJ, Spano MA, Arngrimsson SA, Lewis RD, Cureton KJ: Body-composition changes with diet and exercise in obese women: a comparison of estimates from clinical methods and a 4-component model. Am J Clin Nutr 1999,70(1):5-12.Google Scholar
- Strauss BJ, Marks SJ, Growcott JP, Stroud DB, Lo CS, Dixon JB, O'Brien PE: Body composition changes following laparoscopic gastric banding for morbid obesity. Acta Diabetol 2003, 40 Suppl 1: S266-9. 10.1007/s00592-003-0083-1View ArticleGoogle Scholar
- Siri WE: Body composition from fluid spaces and density: analysis of methods. 1961. Nutrition 1993,9(5):480-91; discussion 480, 492.Google Scholar
- Brozek J, Grande F, Anderson JT, Keys A: Densitometric Analysis of Body Composition: Revision of Some Quantitative Assumptions. Ann N Y Acad Sci 1963, 110: 113-140. 10.1111/j.1749-6632.1963.tb17079.xView ArticleGoogle Scholar
- Wang Z, Shen W, Withers RT, Heymsfield SB: Multicomponent molecular level models of body composition analysis. In Human Body Composition. 2nd edition. Edited by: Heymsfield, S. B., Lohman TG, Wang Z, Going SB. Champaign, IL , Human Kinetics; 2005:163-176.Google Scholar
- Ortiz O, Russell M, Daley TL, Baumgartner RN, Waki M, Lichtman S, Wang J, Pierson RN Jr., Heymsfield SB: Differences in skeletal muscle and bone mineral mass between black and white females and their relevance to estimates of body composition. Am J Clin Nutr 1992,55(1):8-13.Google Scholar
- Schutte JE, Townsend EJ, Hugg J, Shoup RF, Malina RM, Blomqvist CG: Density of lean body mass is greater in blacks than in whites. J Appl Physiol 1984,56(6):1647-1649. 10.1063/1.334152View ArticleGoogle Scholar
- Deurenberg P, Yap M, van Staveren WA: Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes Relat Metab Disord 1998,22(12):1164-1171. 10.1038/sj/ijo/0800741View ArticleGoogle Scholar
- Oppenheimer BW, Berger KI, Rennert DA, Pierson RN, Norman RG, Rapoport DM, Kral JG, Goldring RM: Effect of circulatory congestion on the components of pulmonary diffusing capacity in morbid obesity. Obesity (Silver Spring, Md 2006,14(7):1172-1180.View ArticleGoogle Scholar
- Entenman C, Goldwater WH, Ayres NS, Behnke AR Jr.: Analysis of adipose tissue in relation to body weight loss in man. J Appl Physiol 1958,13(1):129-134.Google Scholar
- Bjorntorp P, Hood B, Martinsson A: The sucrose space of human subcutaneous adipose tissue in obesity. Acta Med Scand 1966,180(1):123-127.View ArticleGoogle Scholar
- Thomas LW: The chemical composition of adipose tissue of man and mice. Q J Exp Physiol Cogn Med Sci 1962, 47: 179-188.Google Scholar
- Levitt DG: The pharmacokinetics of the interstitial space in humans. BMC Clin Pharmacol 2003,3(1):3. 10.1186/1472-6904-3-3View ArticleGoogle Scholar
- Kral JG, Mazariegos M, McKeon EW, Pierson RN, Wang J: Body composition studies in severe obesity: 1993. Edited by: Kral JG, VanItallie TB. Smith-Gordon; 137-146.Google Scholar
- Waki M, Kral JG, Mazariegos M, Wang J, Pierson RN Jr., Heymsfield SB: Relative expansion of extracellular fluid in obese vs. nonobese women. Am J Physiol 1991,261(2 Pt 1):E199-203.Google Scholar
- Weight-height relationships and body mass index: some observations from the Diverse Populations Collaboration Am J Phys Anthropol 2005,128(1):220-229. 10.1002/ajpa.20107Google Scholar
- Jackson AS, Stanforth PR, Gagnon J, Rankinen T, Leon AS, Rao DC, Skinner JS, Bouchard C, Wilmore JH: The effect of sex, age and race on estimating percentage body fat from body mass index: The Heritage Family Study. Int J Obes Relat Metab Disord 2002,26(6):789-796. 10.1038/sj.ijo.0802006View ArticleGoogle Scholar
- Snyder WS: Report of the task group on referance man. In International commission on radiological protection. Volume 23. Oxford , Pergamon Press; 1975.Google Scholar
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