Skip to main content Accessibility help


  • Access
  • Cited by 2



      • Send article to Kindle

        To send this article to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

        Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

        Find out more about the Kindle Personal Document Service.

        Development of bioelectrical impedance analysis-based equations for estimation of body composition in postpartum rural Bangladeshi women
        Available formats

        Send article to Dropbox

        To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

        Development of bioelectrical impedance analysis-based equations for estimation of body composition in postpartum rural Bangladeshi women
        Available formats

        Send article to Google Drive

        To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

        Development of bioelectrical impedance analysis-based equations for estimation of body composition in postpartum rural Bangladeshi women
        Available formats
Export citation


Equations for predicting body composition from bioelectrical impedance analysis (BIA) parameters are age-, sex- and population-specific. Currently there are no equations applicable to women of reproductive age in rural South Asia. Hence, we developed equations for estimating total body water (TBW), fat-free mass (FFM) and fat mass in rural Bangladeshi women using BIA, with 2H2O dilution as the criterion method. Women of reproductive age, participating in a community-based placebo-controlled trial of vitamin A or β-carotene supplementation, were enrolled at 19·7 (sd 9·3) weeks postpartum in a study to measure body composition by 2H2O dilution and impedance at 50 kHz using multi-frequency BIA (n 147), and resistance at 50 kHz using single-frequency BIA (n 82). TBW (kg) by 2H2O dilution was used to derive prediction equations for body composition from BIA measures. The prediction equation was applied to resistance measures obtained at 13 weeks postpartum in a larger population of postpartum women (n 1020). TBW, FFM and fat were 22·6 (sd 2·7), 30·9 (sd 3·7) and 10·2 (sd 3·8) kg by 2H2O dilution. Height2/impedance or height2/resistance and weight provided the best estimate of TBW, with adjusted R2 0·78 and 0·76, and with paired absolute differences in TBW of 0·02 (sd 1·33) and 0·00 (sd 1·28) kg, respectively, between BIA and 2H2O. In the larger sample, values for TBW, FFM and fat were 23·8, 32·5 and 10·3 kg, respectively. BIA can be an important tool for assessing body composition in women of reproductive age in rural South Asia where poor maternal nutrition is common.

Poor maternal nutritional status may be one reason for high rates of infant low birth weight in South Asian countries(1), where women of reproductive age are likely to suffer from macro- and micronutrient deficiencies(2, 3). Low BMI at the onset of pregnancy has been associated with adverse birth outcomes in a variety of settings(4), but more specifically birth weight has been positively associated with maternal fat-free mass (FFM) content measured following pregnancy in Indian(5) and Mexican(6) mothers. Conversely, excess body fat has been linked to increasing rates of metabolic syndrome and cardiovascular risk, now observed even in rural populations of South Asia(7). Because specific components of body composition appear to be linked to two of the major public health problems of South Asia, adequately characterising body composition in populations in this region of the world is critical.

Body composition in field settings is often assessed using weight, height, circumference, or skinfolds as proxies for direct measures of fat and lean mass components. Using weight, height or BMI (kg/m2) alone will not distinguish fat and FFM compartments, which play different metabolic roles. Moreover, there is evidence that individuals of Asian descent have more body fat for a given BMI than Caucasian populations(810). Skinfolds may provide regional information on fat depots and can be used to estimate whole-body composition, and waist circumference reflects central fat deposition. However, more sophisticated body composition methods are not readily applicable in field settings on a large scale (e.g. 2H2O dilution) or are completely laboratory or clinic-based (dual energy X-ray absorptiometry, air displacement plethysmography, etc).

Bioelectrical impedance analysis (BIA), in contrast, provides a potentially field-applicable means for assessing body composition in large, community-based epidemiological studies. It is safe, easy to use, non-invasive, portable and inexpensive. In single-frequency BIA (SF-BIA), a single current, typically 50 kHz, is applied to the body. The resistance of the body to the current is inversely proportional to the amount of body water and, thus, FFM content. Additionally, with multi-frequency BIA (MF-BIA), a current at several frequencies is applied that allows for the distinction between inter- and intra-cellular fluid(11).

Numerous studies have reported that BIA reliably predicts total body water (TBW) or FFM, and a variety of equations to estimate these outcomes from BIA have been established, as summarised elsewhere(11). However, to ensure the validity of such prediction equations, they should be derived under population-specific (e.g. sex, age and ethnicity) conditions by comparing bioelectrical impedance measures to more direct measures of TBW or FFM. Relatively few studies(1216) have been conducted to establish body composition equations from BIA in Asian populations. Furthermore, these equations are probably not applicable to rural South Asian women with poor nutritional status, whose body size is considerably different from that of women reported elsewhere(17).

In the present study, we developed an equation to predict TBW from BIA measures in postpartum women in rural Bangladesh, using 2H2O dilution as the criterion method. We then applied this equation to a larger population of postpartum women in the same community to demonstrate its utility for determining body composition in this population.

Experimental methods

Study subjects

The present study was conducted within a large, randomised, community-based trial to evaluate the impact of vitamin A and β-carotene supplementation on all-cause, pregnancy-related maternal and infant mortality(18). The trial was conducted in ninety unions in the District of Gaibandha in northwestern Bangladesh from August 2001 to February 2007. Details of the main trial and its more intensive substudy have been provided elsewhere(18, 19). Briefly, of 596 individual ‘sectors’, small geographical regions that functioned as randomisation units, thirty-two contiguous sectors were designated for enhanced, home-based biochemical and anthropometric assessments that occurred shortly following pregnancy ascertainment (early pregnancy), in late pregnancy, and at approximately 3 months following the end of pregnancy, i.e. after a birth or after pregnancy termination due to abortion, miscarriage or stillbirth. These assessments were carried out by five specially trained teams and included anthropometry and SF-BIA. Postpartum data were collected on women from the main study from June 2003 until March 2007. Weight of the women was measured with a scale accurate to 0·2 kg (SECA Electronic Scale 890; UNICEF). Height was measured using a stadiometer to the nearest 0·1 cm, and skinfolds and mid-upper arm circumference were measured using standardised techniques. A single-frequency, portable bioelectrical body composition analyser (Quantum II; RJL Systems) at 50 kHz was used to obtain resistance and reactance measurements from women as they lay supine on a non-conducting surface, as described previously(17).

Beginning in the autumn of 2005, the postpartum visit also served as an opportunity to recruit women for a more intensive body composition study, which took place from spring of 2006 to early 2007. A subset of lactating women at the 3-month postpartum visit, selected in part based on their proximity to main roads to facilitate bringing them to a central location, was invited to participate in a clinic-based study to develop body composition equations for resistance (using SF-BIA; RJL Systems) or impedance (using MF-BIA, Quadscan 2000; BodyStat Limited). The purpose of this study was to generate equations to predict TBW (kg), using 2H2O dilution as the referent method, as will be described next. Further, the prediction equation for TBW was applied to the larger population of postpartum women in the community with successful pregnancy outcomes (i.e. living infant) and valid resistance measures at 3 months postpartum to examine body composition in the larger population.

The study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board in the USA and the Bangladesh Medical Research Council in Bangladesh. Verbal consent was obtained from all participants. Consent was witnessed and formally recorded.

Body composition study procedures

Women who consented to participate in the body composition study were brought in groups of five to eight, with their infants and a family member, to a central facility by vehicle in the morning after an overnight fast. A uniform breakfast was served upon arrival. A pre-weighed sari (median weight 300 g) was provided to each woman in order to measure body weight with uniform clothing. Before the initiation of any procedures, women were asked to breastfeed their infants and to void urine. Weight was measured to the nearest 0·2 kg (SECA Electronic Scale 890; UNICEF), and height was measured to the nearest 0·1 cm using a locally made fixed stadiometer modified with a level affixed to the cross-bar to help position subjects along the Frankfort plane. Baseline saliva samples were collected using sterile cotton cylinders (Salivette tubes; Sarstedt AG and Company) 2 h following breakfast to determine the natural 2H enrichment for each participating woman. Doses of enriched 2H2O (99·8 % reported 2H enrichment; Cambridge Isotope Laboratories, Inc.) had been prepared the night before the study for each woman based on her body weight obtained during the home visit. A 0·15 g/kg body weight dose(20) was sealed in an individually labelled plastic cup.

Individually labelled doses of 2H2O were carefully administered to each woman, with the exact dose consumed calculated by difference in the weight of the dose cup and its cap before and after dosing. Following dosing, 75 ml of water was provided as a rinse to ensure that the total dose was swallowed. Follow-up saliva samples were collected 3 h after dose administration. During the 3 h equilibration period, women were not allowed to consume food or liquid and were requested to allow us to take their infants' weight before and after breast-feeding and measure the volume of any urine voided to account for any loss of body fluids.

During the isotope equilibration period, MF-BIA was measured in all women, while SF-BIA was also measured in a subset of women at the same visit to allow us to apply prediction equations to resistance data already collected in the field. When both MF- and SF-measurements were taken, they were done so one after the other so that the study conditions were identical. Using the MF-BIA instrument and 2 × 7 cm electrodes recommended by the manufacturer, impedance (Z) was recorded at 5, 50, 100 and 200 kHz. Resistance and reactance in Ohms (Ω) at 50 kHz were assessed for SF-BIA using 1 × 4 cm electrodes provided by the manufacturer. Analysers were tested in the morning on each day of use with standard resistors at 500 Ω supplied by the manufacturers, with acceptable limits of 496–503 Ω for the MF-BIA unit and 495–505 Ω for the SF-BIA unit. Only resistance (R 50) from SF-BIA and impedance at 50 kHz (Z 50) from MF-BIA were used for developing body composition prediction equations.

Laboratory analysis of saliva and dose 2H2 enrichment

Cotton cylinders saturated with saliva were centrifuged for 5 min at 6000 rpm to precipitate saliva, which was kept at − 20°C until analysis. Baseline and post-equilibration saliva samples, along with an aliquot of 2H2O from each of two bottles of 2H2O stock from which the doses were prepared, were transported to Saint John's Research Institute, Bangalore, India for the analysis of 2H enrichment by Zn reduction followed by dual-inlet MS (Europa Scientific)(21). Each sample was analysed in duplicate and the mean was used for analysis. Repeated analysis for natural background samples gave a CV of 0·02 %.

Body composition calculations

TBW (kg) was calculated using data on dose enrichment and weight and the change in the enrichment of 2H in maternal saliva over the equilibration period, using the molar weight of 2H2O of 20·0274 g/mol and of pure water of 18·0153 g/mol(22), and accounting for 4 % hydrogen exchange(21). After determining TBW either via 2H2O dilution or from BIA prediction equations, FFM (kg) was determined assuming hydration of FFM was 0·732(23, 24). Fat mass (kg) was determined as the difference between total body weight (kg) and FFM (kg). All these body composition components were also expressed as a percentage of total body weight.

Statistical analysis

Differences in subject characteristics between the women selected for body composition assessment and those of the larger population of women with a living infant from a singleton birth, complete data for weight, height and resistance, and a valid resistance measure of >400 Ω were examined by t test or χ2 analysis, with differences considered significant at P< 0·05. Within the body composition group, differences in women who had only impedance measured v. those with both impedance and resistance were also examined by t test. Comparability of impedance and resistance among women with both measurements was examined by correlation analysis and paired t test.

We used TBW (kg) derived from 2H2O dilution as the dependent variable to develop regression equations to predict TBW from resistance or impedance using forward stepwise regression, with height2/Z 50 or height2/R 50, weight and age as predictor variables. Other anthropometric variables were also initially examined. For comparing the models, we sought the highest adjusted R 2 values, and lowest root mean square error (RMSE), a measure of precision(25). The precision and robustness of the prediction equations were also assessed by calculating the PRESS statistic as part of an internal cross-validation strategy(25, 26). The PRESS statistic is calculated as the square root of the sum of the squared prediction residuals divided by the total number of observations, with a lower PRESS statistic indicative of better fit of the prediction equation. The prediction residuals were obtained by fitting the regression equations with actual TBW as the dependent variable and height2/Z 50 (or R 50) and weight as independent variables for all but one observation sequentially, obtaining the predicted value of the excluded observation, and calculating the prediction residual as the observed − predicted value for TBW(25, 26). Scatterplots of predicted v. actual measures of TBW and the predicted residuals against predicted TBW were generated.

The equation for TBW from resistance measures was applied to the larger population from whom the body composition study women were derived, and body composition data were explored in relation to age and BMI category by ANOVA.

Statistical analyses were performed using STATA version 10 (Stata Corporation), although the PRESS residuals and statistic were obtained using the PROC REG procedure with PRESS Statistics in SAS version 9.2 (SAS Institute, Inc.).


A total of 147 women successfully completed the body composition assessments, of which eighty-two had both resistance and impedance measures taken. Data for one woman had been dropped due to implausibly high TBW assessed by 2H2O dilution. The larger sample of women in the main trial substudy (n 1020) was similar to the women in the body composition study in that they also had a living infant and complete and valid data for weight, height and resistance at the time of the 3-month postpartum home visit. Data from the 3-month postpartum home visit comparing the women in the 2H2O dilution study and the larger sample are shown in Table 1. Women participating in the body composition study were significantly younger, weighed less, had lower skinfold and mid-upper arm circumference measures and had higher resistance values at 3 months postpartum. Additionally, body composition study participants were more likely to be primiparous (66 v. 45 %; P< 0·0001). Despite these differences, both groups revealed women of reproductive age with high rates of underweight and short stature.

Table 1 Characteristics of postpartum women participating in the body composition study at the time of enrolment in early pregnancy and their comparability to the larger group of women (Mean values and standard deviations)

MUAC, mid-upper arm circumference; R 50, resistance at 50 kHz.

The subgroup of eighty-two women in the body composition study who had the resistance measure was similar in most characteristics to the larger group of 147 (Table 2), although they had somewhat higher impedance values than the sixty-five women who had data for impedance values alone (P= 0·01). Median length of time between the home visit and the body composition assessment was 1·9 weeks among all women.

Table 2 Anthropometric and bioelectrical impedance characteristics of all women in the body composition study, and in the subgroup in whom resistance was measured (Mean values and standard deviations)

Z 50, impedance at 50 kHz; R 50, resistance at 50 kHz.

Prediction equation

TBW (kg) by 2H2O dilution was 22·6 (sd 2·7) kg among the 147 body composition study women, and 22·6 (sd 2·8) kg among the subset of eighty-two women. Although 141 (96 %) women breastfed their infants one to three times during the 2H2O equilibration period, the contribution of the loss of fluid as breast milk to calculations of TBW was negligible (median weight of breast milk 70 g). Only six (4 %) women voided urine during the equilibration period, also in amounts negligible to the calculation of TBW (median weight of urine, 180 g).

Impedance measures using the MF-BIA instrument and resistance measures using the SF-BIA instrument in the same eighty-two women were highly correlated with r 0·985, (P< 0·0001) for raw values, and r 0·993 (P< 0·0001) for height2/Z 50 and height2/R 50. The within-woman difference in these measures, while statistically significant (P< 0·0001), was minimal ( − 14·3 (sd 12·7) for impedance-resistance; 0·57 (sd 0·49) when expressed as the denominator of height2) relative to a range of 580–998 Ω for Z 50 and R 50 and 20–44 cm2/Ω for height2/Z 50 and height2/R 50, respectively, observed in these women. Thus, coefficients for the prediction equation for TBW were similar whether using impedance or resistance. Regression parameters for the stepwise prediction equation development process are shown in Table 3 for impedance and resistance measures, as variables were added into the prediction equations. Age did not contribute significantly in the prediction of TBW and was therefore ultimately excluded from both models. The final equations were

$$Impedance\,based: \,TBW = \,4\cdot 573 + 0\cdot 177\times W + 0\cdot 351\times Ht^{2}/ Z _{50}, $$<label>(1)</label>
$$Resistance\,based:\,TBW = \,4\cdot 297 + 0\cdot 190\times W + 0\cdot 349\times Ht^{2}/ R _{50}, $$<label>(2)</label>

where TBW is in kg; Ht, height in cm; W, weight in kg. The total variance in TBW that was explained by the prediction models was 76 and 78 % using impedance and resistance, respectively. Body weight explained 65 and 68 % of the variance in TBW when it alone was in the models (not shown) and weight was highly correlated with Ht2/Z 50 (r 0·76) and Ht2/R 50 (r 0·77), both P< 0·0001. The RMSE for TBW for the final equations were 1·34 and 1·30 kg, respectively (Table 3).

Table 3 Equation development for predicting total body water from height2/impedance at 50 kHz (Z 50, n 147) or height2/resistance at 50 kHz (R 50, n 82): regression parameters (β Coefficients and 95 % confidence intervals)

Adj, adjusted; RMSE, root mean square error.

Application of bioelectrical impedance analysis equations

Mean TBW, FFM and fat mass obtained using the final developed equations and those obtained by 2H2O dilution are shown in Table 4. Predicted TBW using the equation for Z 50 was 0·02 (sd 1·33) kg higher than actual TBW, and predicted TBW using R 50 differed by 0·00 (sd 1·28) kg from actual TBW, both near zero and neither different by paired t test. This gives a 95 % limit of agreement (mean ± 2SD) of − 2·64 to 2·68 kg for predicted relative to actual TBW by impedance and − 2·56 to 2·56 kg for TBW for resistance; that is, for 95 % of women with a resistance measure, predicted measures were within 2·56 kg of actual measures of TBW. Among just the eighty-two women for whom both resistance and impedance were measured, predicted TBW using the equation for Z 50 was 0·01 (sd 1·28) kg lower than actual TBW, with a 95 % limit of agreement of − 2·52 to 2·50 kg between predicted and actual TBW, and TBW predicted from Z 50 and R 50 were similar (mean difference of − 0·01 (sd 0·17) kg, P= 0·71).

Table 4 Comparability of body composition measures using 2H2 dilution and newly derived prediction equations based on impedance or resistance (Mean values and standard deviations)

Z 50, impedance at 50 kHz; R 50, resistance at 50 kHz.

The correlation coefficient in TBW determined via 2H2O dilution and BIA using Z 50 was 0·87 (P< 0·001) and using R 50 was 0·89 (P< 0·001). There was some over-prediction of TBW at the low end and under-prediction at the upper end of the distribution of actual TBW using both impedance and resistance measures, as reflected in the plots of actual v. predicted TBW (Figs. 1 and 2).

Fig. 1 Scatterplot of the association of total body water (TBW, kg) predicted from impedance (Z 50) using equation 1 with actual TBW (kg) obtained from 2H2O dilution. The dark line in the centre represents the predicted fit, with the dashed lines indicating the CI for the forecast values at the 95 % confidence level. The thin grey line indicates the line of identity. Correlation between calculated and actual TBW is 0·87 (P< 0·0001).

Fig. 2 Scatterplot of the association of total body water (TBW, kg), predicted from resistance (R 50) using equation 2 with actual TBW (kg) obtained from 2H2O dilution. The dark line in the centre represents the predicted fit, with the dashed lines indicating the CI for the forecast values at the 95 % confidence level. The thin grey line indicates the line of identity. Correlation between calculated and actual TBW is 0·89 (P< 0·0001).

Using the internal cross-validation approach, prediction residuals were within 4·0 kg of predicted TBW for both equations, demonstrating individual variability in the ability of the equations to accurately predict TBW (Figs. 3 and 4). The extent of this variability did not differ across the range of predicted TBW. The largest predicted residuals were negative, consistent with an overestimation of TBW using the derived prediction equation in some women compared to their actual TBW. The PRESS statistic (square root of the sum of squared prediction residuals divided by n) for impedance was 1·35 kg, while that for resistance was 1·33 kg.

Fig. 3 Plot of prediction residuals (observed total body water (TBW) − predicted TBW, where predicted TBW is derived from a regression equation that excludes each observation once) v. corresponding predicted TBW (kg) for the prediction equation using impedance at 50 kHz (Z 50).

Fig. 4 Plot of prediction residuals (observed total body water (TBW) − predicted TBW, where predicted TBW is derived from a regression equation that excludes each observation once) v. corresponding predicted TBW (kg) for the prediction equation using resistance at 50 kHz (R 50).

Body composition parameters in the wider population using the prediction equation are shown in Table 5. Values across the entire population were similar to those observed among the body composition study participants. One woman whose resistance value was 418 Ω, near the cutoff for exclusion as an outlier, had a negative value for fat mass, consistent with a slight over-prediction of TBW, and thus FFM, and subsequent under-prediction of fat mass at the low extreme of the available data.

Table 5 Application of the height2/resistance at 50 kHz (R 50) prediction equation for body composition to the postpartum women not selected for the body composition study (Mean values and standard deviations)

TBW, total body water; FFM, fat-free mass.

* Age group differences all significant by ANOVA, P< 0·0001, except TBW (%) and FFM (%), P= 0·008; fat (kg), P= 0·0006; for multiple comparison tests of height2/R 50, TBW (kg), FFM (kg), women < 20 differ from women 20–30 and >30 years; for TBW (%), FFM (%), fat (%), women 20–30 differ from women >30 years; for fat (kg), women 20–30 differ from women < 20 and >30 years, P< 0·05.

BMI group differences all significant by ANOVA (P< 0·0001).

Associations of body composition measures with age and BMI are also explored in Table 5. Measures of height2/R 50, TBW (kg) and FFM (kg) were lowest in the youngest women (P< 0·01 for ANOVA). Expressed as percentage of total body weight, TBW (%) and FFM (%) were lower in women 20–30 years of age compared to those over 30 years (P= 0·008 for ANOVA), while body fat was highest in 20–30-year-old women whether expressed as mass or percentage of total body weight. While TBW (kg), FFM (kg) and fat mass (kg) increased with increasing BMI, the percentage of body fat relative to total body weight increased with increasing BMI while the percentage of body FFM declined.


The purpose of the present study was to develop prediction equations for body composition in rural South Asian women using bioelectrical properties of impedance and resistance to predict TBW assessed using 2H2O dilution. We selected postpartum women from a large, community-based trial designed to examine pregnancy outcomes in whom to derive these equations. Body composition measures in these women will allow us to more fully explore the facets of nutritional status associated with pregnancy outcomes. Moreover, women in this study were typical of women of reproductive age in the area, who are generally undernourished as determined by weight, height, skinfolds and arm circumference. Thus, we expect these prediction equations to be broadly applicable for describing distributions of body composition parameters among women of reproductive age in the region.

We previously demonstrated the unique bioelectrical characteristics of this population, with the distribution of resistance shifted to higher values than those of women reported elsewhere, and we speculated that this shift was explained by lower body volumes of these undernourished women, estimated from their low heights and weights(17). Among women in the body composition study, impedance and resistance were strongly correlated and had a similar range of values, but on average somewhat lower (14 Ω) values were observed for impedance. The close association of these measures was expected, as impedance is related to resistance and reactance by phase angle(11) but comprised primarily (approximately 95 %) of resistance(27). The somewhat lower mean values for impedance compared to resistance observed in this study were probably attributable to the differences in equipment and electrodes used. Nonetheless, while distinct prediction equations were generated for resistance and impedance, the 95 % CI around the β coefficients for each variable were overlapping, in agreement with the overall similarity of resistance and impedance across this group of women.

Both prediction equations provided excellent agreement with direct measures of TBW, and estimated TBW and its variance were nearly identical when compared between the eighty-two women in whom equations for impedance and resistance could both be applied. Moreover, both equations demonstrated a high R 2 and low RMSE as measures of fit and relatively low PRESS statistics in the cross-validation approach. Although there are no specific criteria against which to evaluate these statistics(25), Sun et al. (26) reported an R 2 of 0·79, but a RMSE and PRESS statistic of 2·6 litres, and range of PRESS residuals of about ± 9 litres in women in the US population in whom prediction equations were generated, although greater homogeneity in the US than Bangladeshi population may have contributed to higher RMSE and PRESS statistics in the US compared to Bangladeshi setting. Individual variability in the ability of the new equations to predict TBW was consistent across the TBW distribution of this population. Although bias in the measures for TBW was low using the new prediction equations, BIA somewhat overestimated actual TBW at the low end and underestimated TBW at the high end of its distribution, explaining lower standard deviations for distributions of body composition measures when assessed by BIA compared to 2H2O dilution. Thus, despite favourable performance characteristics of these equations overall, users must be mindful of the degree of error surrounding the use of the equations to predict TBW of individuals.

Nonetheless, we demonstrated the utility of these equations for women in this population-based study, in whom body composition information is of great importance to enrich our knowledge of nutritional status. When the prediction equation using resistance was applied to the larger group of postpartum women, FFM comprised approximately 76 % and fat mass 24 % of body weight, similar to the body composition group despite some differences in age and anthropometry between the women selected and those not selected for the body composition study. Among the larger group of women, the highest fat mass and percentage body fat occurred among 20–30-year-old women. A tendency towards the highest fat mass in the 20–30-year-old age group is consistent with baseline characteristics that showed greater mean age and higher weight, skinfolds and arm circumference among the women who did not participate in the body composition study compared to typically younger women who did. The lowest total FFM was observed among the youngest study participants, consistent with a hypothesis that these youngest women may have still been acquiring lean body mass at the time they became pregnant(17). A previous study of married adolescents in this population using triceps and subscapular skinfolds to derive percentage body fat showed that body fat was approximately 19 % of body weight, declining by 1·4 % at 6 months postpartum among young women who became pregnant(28). Other studies of body composition in women of reproductive age have shown percentage body fat to average over 30 % among women of Asian descent(12, 15, 29, 30), although mean BMI in those studies typically averaged over 22 kg/m2. One study in lactating women from India showed persistently low body fat across lactation (6-month intervals to 18 months postpartum) of approximately 28 % body fat(31). It is clear that women in the area of Bangladesh under study here have considerably lower fat mass than women of reproductive age elsewhere worldwide.

Regardless of age, data relating body composition components to BMI show that TBW, FFM and fat mass all increase with increasing BMI. However, when expressed as a percentage of total body weight, only percentage body fat increased with increasing BMI. These findings demonstrate that BMI reflects body fat across the range of BMI observed in this study, extending findings in other studies that relate body fat to BMI to the low end of the BMI distribution(3234).

For our calculations, we assumed a hydration constant for FFM of 0·732 among all women. The accuracy of estimates of FFM and fat mass depend on the accuracy of that constant, which in reality may vary from 0·70 to 0·76, and may differ by individual and in relation to body fat(24, 30), a limitation of the two-compartment approach for assessing body composition(35). The hydration of FFM increases during pregnancy, to an estimated 0·76 in the third trimester, before declining in the postpartum period. At 2 weeks postpartum, Hopkinson et al. (36) reported a hydration constant of 0·75, and Butte et al. (37) reported that hydration of FFM had returned to prepregnancy values by 3 months postpartum in a relatively small group of American women. It is reasonable to assume that hydration of FFM had returned to pre-pregnancy values by 3 months postpartum in our study participants, particularly given that resistance measures across an entire reproductive cycle returned to early pregnancy (assumed to be close to non-pregnancy) values by 3 months postpartum in this population(17). If, however, the true hydration of FFM was higher among women assessed at the 3-month postpartum home visit compared to women of the body composition study who were assessed somewhat later postpartum, estimates of TBW would still be valid, but FFM would be somewhat overestimated and fat mass underestimated in the larger population relative to the body composition study group. Nonetheless, because the equations are valid for predicting TBW, we believe the equations derived here can be generally applied for population studies of body composition among women of reproductive age in this region, with the caveat that ideally the potential variability in hydration of the FFM compartment should be better understood.

In conclusion, the present study provided new equations for measuring TBW based on resistance or impedance, height and weight. BIA is simple, rapid and accurate for South Asian rural women and can be a useful tool for determining body composition in nutritional surveys in field studies. Future studies will allow us to examine associations of body composition with birth outcomes for mother and child in this population. Moreover, because this population remains under surveillance, future work may also allow us to link body composition to other health outcomes among women in this setting.


Support for the present work was provided by the International Atomic Energy Agency, Vienna, Austria; Bill and Melinda Gates Foundation, Seattle, WA, USA (grant no. 614 Global Control of Micronutrient Deficiency); US Agency for International Development (Washington, DC, USA; Global Research Activity GHS-A-00-03-00019-00); Sight and Life Research Institute, Baltimore, MD, USA. This study was conducted under an agreement with the National Integrated Population and Health Program of the Ministry of Health and Family Welfare of the Government of the People's Republic of Bangladesh. The authors' contributions to the study were as follows: S. S. provided scientific oversight, operationalised the study, analysed the data and drafted the manuscript; K. J. S. engaged in study design, analysis and manuscript preparation; A. K. completed the 2H2O assessments; H. A. oversaw field operations, subject recruitment and participation; A. A. S. and M. R. provided scientific and administrative oversight of the main trial and its intersection with this study; S. M. assisted with manuscript preparation and study organisation and management; L. S.-F. W. managed all trial data and supported the data analysis; A. B. L., as project scientist for the main trial, coordinated activities with IAEA and within Bangladesh and oversaw the study design; P. C. and K. P. W. were the principal investigators for the main trial and provided guidance throughout the research process. None of the authors has conflicts of interest to declare. The contributions of the JiVitA field and data management teams, Johns Hopkins collaborators, and the staff at Saint Johns Research Institute are gratefully acknowledged.


1Lawn, JE, Cousens, S & Zupan, J (2005) 4 Million neonatal deaths: when? where? why? Lancet 365, 891900.
2Christian, P, West, KP, Khatry, SK, et al. (2003) Effects of maternal micronutrient supplementation on fetal loss and infant mortality: a cluster-randomized trial in Nepal. Am J Clin Nutr 78, 11941202.
3Jiang, T, Christian, P, Khatry, SK, et al. (2005) Micronutrient deficiencies in early pregnancy are common, concurrent, and vary by season among rural Nepali pregnant women. J Nutr 135, 11061112.
4Ronnenberg, AG, Wang, X, Xing, H, et al. (2003) Low preconception body mass index is associated with birth outcome in a prospective cohort of Chinese women. J Nutr 133, 34493455.
5Kulkarni, B, Shatrugna, V & Balakrishna, N (2006) Maternal lean body mass may be the major determinant of birth weight: a study from India. Eur J Clin Nutr 60, 13411344.
6Sanin Aguirre, LH, Reza-Lopez, S & Levario-Carrillo, M (2004) Relation between maternal body composition and birth weight. Biol Neonate 86, 5562.
7Misra, A, Pandey, RM, Devi, JR, et al. (2001) High prevalence of diabetes, obesity and dyslipidaemia in urban slum population in northern India. Int J Obes Relat Metab Disord 25, 17221729.
8Wang, J, Thornton, JC, Russell, M, et al. (1994) Asians have lower body mass index (BMI) but higher percent body fat than do Whites: comparisons of anthropometric measurements. Am J Clin Nutr 60, 2328.
9WHO Expert Consultation (2004) Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363, 157163.
10Deurenberg, P, Deurenberg-Yap, M & Guricci, S (2002) Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes Rev 3, 141146.
11Kyle, UG, Bosaeus, I, De Lorenzo, AD, et al. (2004) Bioelectrical impedance analysis – part I: review of principles and methods. Clin Nutr 23, 12261243.
12Deurenberg, P, Deurenberg-Yap, M & Schouten, FJ (2002) Validity of total and segmental impedance measurements for prediction of body composition across ethnic population groups. Eur J Clin Nutr 56, 214220.
13Deurenberg, P & Deurenberg-Yap, M (2002) Validation of skinfold thickness and hand-held impedance measurements for estimation of body fat percentage among Singaporean Chinese, Malay and Indian subjects. Asia Pac J Clin Nutr 11, 17.
14Deurenberg, P, van der Kooy, K, Leenen, R, et al. (1991) Sex and age specific prediction formulas for estimating body composition from bioelectrical impedance: a cross-validation study. Int J Obes 15, 1725.
15Rush, EC, Chandu, V & Plank, LD (2006) Prediction of fat-free mass by bioimpedance analysis in migrant Asian Indian men and women: a cross validation study. Int J Obes (Lond) 30, 11251131.
16Kuriyan, R, Petracchi, C, Ferro-Luzzi, A, et al. (1998) Validation of expedient methods for measuring body composition in Indian adults. Indian J Med Res 107, 3745.
17Shaikh, S, Schulze, KJ, Ali, H, et al. (2011) Bioelectrical impedance among rural Bangladeshi women during pregnancy and in the postpartum period. J Health Popul Nutr 29, 236244.
18West, KP Jr, Christian, P, Labrique, AB, et al. (2011) Effects of vitamin A or beta-carotene supplementation on pregnancy-related mortality and infant mortality in rural Bangladesh: a cluster randomized trial. JAMA 305, 19861995.
19Labrique, AB, Christian, P, Klemm, RD, et al. (2011) A cluster-randomized, placebo-controlled, maternal vitamin A or beta-carotene supplementation trial in Bangladesh: design and methods. Trials 12, 102.
20Powers, ME, Arnold, BL, Weltman, AL, et al. (2003) Creatine supplementation increases total body water without altering fluid distribution. J Athl Train 38, 4450.
21International Atomic Energy Agency (1990) The Doubly Labelled Water Method for Measuring Energy Expenditure: Technical Recommendation for Use in Humans. A Consensus Report by the IDECG Working Group [Prentice, AM, editor]. Vienna: International Atomic Energy Agency.
22Schoeller, DA & Jones, PJH (1987) Measurement of total body water by isotope dilution: a unified approach to calculations. In In Vivo Body Composition Studies, pp. 131137 [Ellis, K, Yasamura, S and Morgan, WD, editors]. London: Institute of Physical Sciences in Medicine.
23Wang, Z, Deurenberg, P, Wang, W, et al. (1999) Hydration of fat-free body mass: new physiological modeling approach. Am J Physiol 276, E995E1003.
24Wang, Z, Deurenberg, P, Wang, W, et al. (1999) Hydration of fat-free body mass: review and critique of a classic body-composition constant. Am J Clin Nutr 69, 833841.
25Guo, SS, Chumlea, WC & Cockram, DB (1996) Use of statistical methods to estimate body composition. Am J Clin Nutr 64, 428S435S.
26Sun, SS, Chumlea, WC, Heymsfield, SB, et al. (2003) Development of bioelectrical impedance analysis prediction equations for body composition with the use of a multicomponent model for use in epidemiologic surveys. Am J Clin Nutr 77, 331340.
27Buffa, R, Floris, G & Marini, E (2002) Bioelectrical impedance vector in pre- and postmenarcheal females. Nutrition 18, 474478.
28Rah, JH, Christian, P, Shamim, AA, et al. (2008) Pregnancy and lactation hinder growth and nutritional status of adolescent girls in rural Bangladesh. J Nutr 138, 15051511.
29Kulkarni, B, Shatrugna, V, Nagalla, B, et al. (2010) Regional body composition of Indian women from a low-income group and its association with anthropometric indices and reproductive events. Ann Nutr Metab 56, 182189.
30Deurenberg-Yap, M, Schmidt, G, van Staveren, WA, et al. (2001) Body fat measurement among Singaporean Chinese, Malays and Indians: a comparative study using a four-compartment model and different two-compartment models. Br J Nutr 85, 491498.
31Kulkarni, B, Shatrugna, V, Nagalla, B, et al. (2011) Regional body composition changes during lactation in Indian women from the low-income group and their relationship to the growth of their infants. J Am Coll Nutr 30, 5762.
32Gallagher, D, Visser, M, Sepulveda, D, et al. (1996) How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 143, 228239.
33Gallagher, D, Heymsfield, SB, Heo, M, et al. (2000) Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 72, 694701.
34Meeuwsen, S, Horgan, GW & Elia, M (2010) The relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex. Clin Nutr 29, 560566.
35Wang, ZM, Pierson, RN Jr & Heymsfield, SB (1992) The five-level model: a new approach to organizing body-composition research. Am J Clin Nutr 56, 1928.
36Hopkinson, JM, Butte, NF, Ellis, KJ, et al. (1997) Body fat estimation in late pregnancy and early postpartum: comparison of two-, three-, and four-component models. Am J Clin Nutr 65, 432438.
37Butte, NF, Hopkinson, JM, Ellis, KJ, et al. (1997) Changes in fat-free mass and fat mass in postpartum women: a comparison of body composition models. Int J Obes Relat Metab Disord 21, 874880.