Prediction of body fat by anthropometry in older Chinese people

Correlation between the arm-span and the standing height among males and females of the Khasi tribal population of Meghalaya state of North-Eastern India

Introduction:

The estimation of relationship between the arm span and the standing height has been an important tool in anthropometric measurements especially in cases where direct measurement of stature is not possible.

Objective:

To find the relationship between the arm-span and the standing height of both males and females in the population of Khasi tribal population of Meghalaya.

Materials and Methods:

The study involved 400 numbers (272 males and 128 females) of healthy human volunteer subjects belonging to Khasi tribe of Meghalaya. The standing height and arm-span were measured for each individual and analyzed.

Result:

Of the 400 healthy volunteers, 272 (68%) were males and 128 (32%) were females with age ranged from 25 to 45 years. Height and arm span in males (159.68 ± 4.12 cm and 166.30 ± 4.27 cm, respectively) werefound to be significantly (p < 0.001) higher than females (149.96 ± 3.04 cm and 155.77 ± 3.13 cm respectively). The Pearson correlation coefficient (r) between height (cm) and arm span (cm) showed significant positive correlation in both male (r = 0.988, P < 0.001) and female (r = 0.991, P < 0.001) study subjects. The regression equation was Height = 1.060 + 0.954 (Arm span); R2 = 0.976; SEE = 0.646 for male. For female subjects the regression equation was found as Height = 0.150 + 0.962 (Arm span); R2 = 0.983; SEE = 0.400.

Conclusion:

Arm-span can be used as one of the most reliable parameter in both males and females for obtaining the stature of an individual in alternative to the height.

Development and validation of body fat prediction models in American adults

INTRODUCTION

Commonly used statistical models to predict body fat percentage currently rely on skinfold measures, anthropometric measures, or some combination of the two but do not account for the wide ranges of age and body mass index (BMI) present in the American adult population. The objective of this study was to develop a statistical regression model to predict in vivo body fat percentage (dual energy X-ray) in men and women across significant age and obesity ranges.

METHODS

This study included 228 adults between the ages of 21 and 70, with BMI between 18.5 and 40.0 kg m-2. The study population was split into training (n = 163) and validation (n = 65) groups, which were used to develop and validate the prediction models. The models were developed on the training group using a backwards stepwise regression analysis, with the initial predictors including age, BMI, and several anthropometric and skinfold measurements.

RESULTS

The final statistical regression models included age, BMI, anthropometric measures, and skinfold measures with significant effects following the stepwise process. The models predicted body fat percentage in the testing group with average errors of less than 0.10% body fat in males and females, while the four previously existing methods (Durnin, Hodgdon, Jackson, and Woolcott) significantly underestimated or overestimated body fat in both genders, with errors ranging between 2% and 10%.

CONCLUSIONS

The final models included hand thickness, and the female model was dependent on waist circumference and two of the skinfold measures, while the male model used hip and thigh circumferences, along with three skinfold measures. By including the skinfold measurements separately, instead of only as sums like previous models have done, these models can account for the different relative contributions of each site to total body fat.

Development of an Easy-to-Use Visual Aid for the Prediction of Body Fat Based on Waist Circumference and Height in Asian Chinese Adults

BACKGROUND

Adiposity is a major risk factor for metabolic and cardiovascular diseases. Initial prediction equations to estimate adiposity are complex, requiring skinfold measurements that cannot be obtained conveniently by the general population.

OBJECTIVE

To develop simplified prediction equations to estimate body fat percentage (%BF) in Asian Chinese adults, evaluate the validity of the simplified %BF prediction equations, compare the simplified %BF prediction equations with an existing equation, and create visual charts to enable easy assessment of adiposity by the general public.

DESIGN

Simplified prediction equations were developed and evaluated for validity using anthropometric measurements obtained from a cross-sectional study.

PARTICIPANTS AND SETTING

Healthy participants with no major diseases and not taking long-term medications were recruited in a cross-sectional study conducted at Clinical Nutrition Research Centre, Singapore, between June 2014 and October 2017. A total of 439 participants were used for model building (269 women and 170 men) and another 107 participants were used for evaluating validity (62 women and 45 men).

MAIN OUTCOME MEASURES

Simplified but acceptable prediction models and generation of user-friendly charts.

STATISTICAL ANALYSES PERFORMED

Simplified sex-specific %BF prediction equations were developed using stepwise regression and the model-building dataset. The best models were selected using the Akaike information criterion. The models were further simplified and their performance was compared using the validation dataset before choosing the final prediction equations.

RESULTS

The final selected models for women and men included waist circumference and height with nonsignificant prediction bias in %BF of 0.84%±3.94% (P=0.098, Cohen's d_z=0.21) and -0.98%±3.65% (P=0.079, Cohen's d_z=0.27), respectively. The final equations were split into three height categories from which the sex-specific prediction charts were generated.

CONCLUSIONS

The sex-specific prediction charts provide a good visual guide for estimating %BF using height and waist circumference values that are easy to obtain by the general public.

An anthropometry-based equation of fat mass percentage as a valid discriminator of obesity

OBJECTIVE

To develop a new predictive equation for fat mass percentage (%FM) based on anthropometric measurements and to assess its ability to discriminate between obese and non-obese individuals.

DESIGN

Cross-sectional study.

SETTING

Mexican adults.ParticipantsAdults (n 275; 181 women) aged 20-63 years with BMI between 17·4 and 42·4 kg/m2.

RESULTS

Thirty-seven per cent of our sample was obese using %FM measured by air-displacement plethysmography (BOD POD®; Life Measurement Instruments). The fat mass was computed from the difference between weight and fat-free mass (FFM). FFM was estimated using an equation obtained previously in the study from weight, height and sex of the individuals. The %FM estimated from the obtained FFM showed a sensitivity of 90·3 (95 % CI 86·8, 93·8) % and a specificity of 58·0 (95 % CI 52·1, 63·8) % in the diagnosis of obesity. Ninety-three per cent of participants with obesity and 65 % of participants without obesity were correctly classified.

CONCLUSIONS

The anthropometry-based equation obtained in the present study could be used as a screening tool in clinical and epidemiological studies not only to estimate the %FM, but also to discriminate the obese condition in populations with similar characteristics to the participant sample.

New Equations to Predict Body Fat in Asian-Chinese Adults Using Age, Height, Skinfold Thickness, and Waist Circumference

BACKGROUND

Adiposity is an independent predictor of metabolic disease. However, highly accurate body fat assessment is not routinely done due to limited access to expensive and labor-intensive methods.

OBJECTIVE

The aim of the study was to develop body fat prediction equations for Asian-Chinese adults using easily attainable anthropometric measurements.

DESIGN

Prediction equations of body fat were developed using anthropometric and skinfold thickness measurements obtained from a cross-sectional study. These new equations were then validated using baseline data from an independent randomized controlled study.

PARTICIPANTS/SETTING

Healthy participants with no major diseases and not taking long-term medications were recruited in an ongoing cross-sectional study that began in June 2014 (n=439, 170 males, 269 females), as well as a randomized controlled trial (n=108, 58 males, 50 females) conducted from January 2013 to October 2014. Both the studies were conducted at Clinical Nutrition Research Center located in Singapore.

MAIN OUTCOME MEASURES

Data used to develop and validate equations were from two original studies that assessed body fat by dual-energy x-ray absorptiometry, age, waist circumference, height, and biceps and triceps skinfolds.

STATISTICAL ANALYSIS PERFORMED

Sex-specific percent body fat prediction equations were developed using stepwise regression with Akaike Information Criterion on the cross-sectional data. The equations were then validated using data from the randomized controlled study and also compared against Asian-specific Davidson equations.

RESULTS

The best body fat prediction model (R²=0.722, standard error of estimation=2.97 for females; R²=0.815, standard error of estimation=2.49 for males) for both sexes included biceps and triceps skinfolds, waist circumference, age, and height. The new equations developed resulted in modest discrepancies in body fat of 1.8%±2.7% in males (P<0.001) and 0.7%±3.1% in females (P=0.125; not significant) compared with the Asian-specific Davidson equations (-7.4%±3.2% [P<0.001] and -7.4%±2.7% [P<0.001], respectively).

CONCLUSIONS

Sex-specific equations to predict the percent body fat of Asian-Chinese adults with a higher degree of accuracy were developed. Ease of use in both field and clinical settings will be a major advantage.

Comparison between dual-energy X-ray absorptiometry and skinfold thickness in assessing body fat in overweigh/obese adult patients with type-2 diabetes

Percentage of body fat (%BF) is estimated in clinical practice using anthropometric equations, but little is known about their reliability in overweight/obese patients with type-2 diabetes. The aim of this study was to compare, in overweight/obese adults with type-2 diabetes, %BF estimated with several commonly used anthropometric equations and %BF measured with dual-energy X-ray absorptiometry (DXA, Hologic). The %BF was measured with DXA in 40 patients aged 40-68 years with type-2 diabetes (mean HbA1c, 7.3 ± 0.9%). Body density was estimated in the same patients by means of four anthropometric equations and converted to %BF using the Siri and Brozek equations. Paired-sample t-test and the mean signed difference procedure were used to compare anthropometric equation-derived %BF and DXA measurements. The coefficient of determination was computed. Bland-Altman analysis was used to test the agreement between methods. Among the four anthropometric equations, the Durnin-Womersley equation only showed close agreement with DXA in both female and male patients; the other equations significantly underestimated or overestimated %BF. Two new predictive equations were developed using DXA as the reference to predict total body and trunk %BF. Further comparative studies are required to confirm and refine the accuracy of practical, non-invasive methods for monitoring %BF in this population.

The development and validation of new equations for estimating body fat percentage among Chinese men and women

Equations based on simple anthropometric measurements to predict body fat percentage (BF%) are lacking in Chinese population with increasing prevalence of obesity and related abnormalities. We aimed to develop and validate BF% equations in two independent population-based samples of Chinese men and women. The equations were developed among 960 Chinese Hans living in Shanghai (age 46.2 (SD 5.3) years; 36.7% male) using a stepwise linear regression and were subsequently validated in 1150 Shanghai residents (58.7 (SD 6.0) years; 41.7% male; 99% Chinese Hans, 1% Chinese minorities). The associations of equation-derived BF% with changes of 6-year cardiometabolic outcomes and incident type 2 diabetes (T2D) were evaluated in a sub-cohort of 780 Chinese, compared with BF% measured by dual-energy X-ray absorptiometry (DXA; BF%-DXA). Sex-specific equations were established with age, BMI and waist circumference as independent variables. The BF% calculated using new sex-specific equations (BF%-CSS) were in reasonable agreement with BF%-DXA (mean difference: 0.08 (2 SD 6.64) %, P= 0.606 in men; 0.45 (2 SD 6.88) %, P< 0.001 in women). In multivariate-adjusted models, the BF%-CSS and BF%-DXA showed comparable associations with 6-year changes in TAG, HDL-cholesterol, diastolic blood pressure, C-reactive protein and uric acid (P for comparisons ≥ 0.05). Meanwhile, the BF%-CSS and BF%-DXA had comparable areas under the receiver operating characteristic curves for associations with incident T2D (men P= 0.327; women P= 0.159). The BF% equations might be used as surrogates for DXA to estimate BF% among adult Chinese. More studies are needed to evaluate the application of our equations in different populations.

Prediction of fasting plasma glucose status using anthropometric measures for diagnosing type 2 diabetes

It is well known that body fat distribution and obesity are important risk factors for type 2 diabetes. Prediction of type 2 diabetes using a combination of anthropometric measures remains a controversial issue. This study aims to predict the fasting plasma glucose (FPG) status that is used in the diagnosis of type 2 diabetes by a combination of various measures among Korean adults. A total of 4870 subjects (2955 females and 1915 males) participated in this study. Based on 37 anthropometric measures, we compared predictions of FPG status using individual versus combined measures using two machine-learning algorithms. The values of the area under the receiver operating characteristic curve in the predictions by logistic regression and naive Bayes classifier based on the combination of measures were 0.741 and 0.739 in females, respectively, and were 0.687 and 0.686 in males, respectively. Our results indicate that prediction of FPG status using a combination of anthropometric measures was superior to individual measures alone in both females and males. We show that using balanced data of normal and high FPG groups can improve the prediction and reduce the intrinsic bias of the model toward the majority class.

Percent body fat prediction equations for 8- to 17-year-old American children

BACKGROUND

Percent body fat equations are usually developed in specific populations and have low generalizability.

OBJECTIVES

To use a nationally representative sample of the American youth population (8-17 years old) from the 1999-2004 National Health and Nutrition Examination Survey data to develop gender-specific percent body fat equations.

METHODS

Percent body fat equations were developed for girls and boys using information on weight, height, waist circumference, triceps skin-folds, age, race/ethnicity and menses status compared to dual-emission X-ray absorptiometry. Terms were selected using forward and backward selection in regression models in a 2/3 development sample and were cross-validated in the remaining sample. Final coefficients were estimated in the full sample.

RESULTS

Final equations included ten terms in girls and eight terms in boys including interactions with age and race/ethnicity. In the cross-validation sample, the adjusted R2 was 0.818 and the root mean squared error was 2.758 in girls. Comparable estimates in boys were 0.893 and 2.525. Systematic bias was not detected in the estimates by race/ethnicity or by body mass index categories.

CONCLUSION

Gender-specific percent body fat equations were developed in youth with a strong potential for generalizability and utilization by other investigators studying adiposity-related issues in youth.

A comparison of prediction equations for the estimation of body fat percentage in non-obese and obese older Caucasian adults in the United States

OBJECTIVES

The predictive capabilities of skinfold regression equations are limited across populations and current equations may not be well suited for the prediction of body fat in older adults or obese Americans. The goal of this study was to compare percent body fat (%BF) predicted by several skinfold regression equations to %BF determined by Dual-Energy X-ray Absorptiometry (DXA) in obese and non-obese Caucasian men and women in the United States over the age of 65 years.

DESIGN

A block design was used with two blocks: obesity (non-obese/obese) and gender (male/female). All subjects underwent the same testing procedures in one visit.

SETTING

University of Pittsburgh Clinical and Translation Research Center.

PARTICIPANTS

Seventy-eight older healthy adults were recruited for participation.

MEASUREMENTS

Actual percent body fat was determined from a whole body DXA scan. Estimated percent body fat (%BF) was calculated using skinfold measurements and established regression equations. The predictive accuracy of the regression equations was evaluated by comparing the estimated %BF to the actual %BF measured with DXA using a within subject ANOVA (α=0.05). This was done within subgroups: obese males, obese females, non-obese males and non-obese females.

RESULTS

Durnin and Womersly and Jackson and Pollock had reasonably good agreement with DXA in older Caucasian American females and males, respectively. The remaining equations significantly overestimated %BF in older Caucasian American males. Mixed results were found in females with Gause-Nilsson and Jackson and Pollock significantly underestimating %BF, while Visser and Kwok overestimated %BF.

CONCLUSION

Numerous factors of a population including age, race, ethnicity, gender and obesity should be considered when selecting a skinfold regression equation to estimate %BF. While Durnin and Womersly and Jackson and Pollock are recommended for predicting %BF in older Caucasian American females and males, respectively, there exists a need to develop accurate regression models that consider obesity, gender, race or ethnicity when predicting %BF in a diverse geriatric American population.

Prediction of Percentage Body Fat in Rural Thai Population Using Simple Anthropometric Measurements

OBJECTIVE

To develop and validate sex-specific equations for predicting percentage body fat (%BF) in rural Thai population, based on BMI and anthropometric measurements.

RESEARCH METHODS AND PROCEDURES

%BF (DXA; GE Lunar Corp., Madison, WI) was measured in 181 men and 255 women who were healthy and between 20 and 84 years old. Anthropometric measures such as weight (kilograms), height (centimeters), BMI (kilograms per meter squared), waist circumference (centimeters), hip circumference (centimeters), thickness at triceps skinfold (millimeters), biceps skinfold (millimeters), subscapular skinfold (millimeters), and suprailiac skinfold (millimeters) were also measured. The sample was randomly divided into a development group (98 men and 125 women) and a validation group (83 men and 130 women). Regression equations of %BF derived from the development group were then evaluated for accuracy in the validation group.

RESULTS

The equation for estimating %BF in men was: %BF(men) = 0.42 x subscapular skinfold + 0.62 x BMI - 0.28 x biceps skinfold + 0.17 x waist circumference - 18.47, and in women: %BF(women) = 0.42 x hip circumference + 0.17 x suprailiac skinfold + 0.46 x BMI - 23.75. The coefficient of determination (R2) for both equations was 0.68. Without anthropometric variables, the predictive equation using BMI, age, and sex was: %BF = 1.65 x BMI + 0.06 x age - 15.3 x sex - 10.67 (where sex = 1 for men and sex = 0 for women), with R2 = 0.83. When these equations were applied to the validation sample, the difference between measured and predicted %BF ranged between +/-9%, and the positive predictive values were above 0.9.

DISCUSSION

These results suggest that simple, noninvasive, and inexpensive anthropometric variables may provide an accurate estimate of %BF and could potentially aid the diagnosis of obesity in rural Thais.

Improved Prediction of Body Fat by Measuring Skinfold Thickness, Circumferences, and Bone Breadths

OBJECTIVE

To develop improved predictive regression equations for body fat content derived from common anthropometric measurements.

RESEARCH METHODS AND PROCEDURES

117 healthy German subjects, 46 men and 71 women, 26 to 67 years of age, from two different studies were assigned to a validation and a cross-validation group. Common anthropometric measurements and body composition by DXA were obtained. Equations using anthropometric measurements predicting body fat mass (BFM) with DXA as a reference method were developed using regression models.

RESULTS

The final best predictive sex-specific equations combining skinfold thicknesses (SF), circumferences, and bone breadth measurements were as follows: BFM(New) (kg) for men = -40.750 + {(0.397 x waist circumference) + [6.568 x (log triceps SF + log subscapular SF + log abdominal SF)]} and BFM(New) (kg) for women = -75.231 + {(0.512 x hip circumference) + [8.889 x (log chin SF + log triceps SF + log subscapular SF)] + (1.905 x knee breadth)}. The estimates of BFM from both validation and cross-validation had an excellent correlation, showed excellent correspondence to the DXA estimates, and showed a negligible tendency to underestimate percent body fat in subjects with higher BFM compared with equations using a two-compartment (Durnin and Womersley) or a four-compartment (Peterson) model as the reference method.

DISCUSSION

Combining skinfold thicknesses with circumference and/or bone breadth measures provide a more precise prediction of percent body fat in comparison with established SF equations. Our equations are recommended for use in clinical or epidemiological settings in populations with similar ethnic background.

Accuracy of Siri and Brozek equations in the percent body fat estimation in older adults

OBJECTIVE

To identify which equation, Siri or Brozek, based on the two compartment model, provides a more accurate conversion of body density (BD) in percent body fat (%BF) in a group of older adults.

STUDY DESIGN

Cross-sectional study.

SETTING

Research Centre in Physical Activity, Health and Leisure, Faculty of Sport, University of Porto.

PARTICIPANTS

60 older adults, aged 60-92 years.

MEASUREMENTS

Skinfold thickness was used to estimate BD through Visser et al. prediction equation. The conversion of BD to %BF was done with Siri (%BF-Siri) and Brozek (%BF-Brozek) formulas and these determined values were both compared to Dual-Energy X-ray Absorptiometry (%BF-DXA) evaluations.

RESULTS

A strong correlation between the %BF-DXA value and %BF-Siri (r=0.91, p < 0.001) and %BF-Brozek (r=0.91, p < 0.001) was found, although %BF-Siri and %BF-Brozek overestimated %BF-DXA (p < 0.001). The comparison of the %BF-Siri and %BF-Brozek mean values also revealed significant differences (p < 0.001). The %BF-Brozek reflects a better agreement than the %BF-Siri with %BF-DXA with respectively a mean difference of -4.0 %BF (limits of agreement = -10.9 to 2.9%) and -5.7 %BF (-12.6 to 1.2). The Bland and Altman plots confirmed that %BF-Brozek reflects a better agreement with %BF-DXA.

CONCLUSION

The results of the present study show that the use of Brozek equation may correspond to a more accurate alternative than Siri equation for the conversion of BD in %BF in older adults.

ACE inhibitor use was associated with lower serum dehydroepiandrosterone concentrations in older men

CONTEXT

Angiotensin converting enzyme (ACE) activity may influence the production of adrenal androgen precursors and testosterone. Use of ACE inhibitors may therefore have an influence on serum sex hormone concentrations in older men.

DESIGN AND METHODS

1486 out of 2,000 community-dwelling Chinese men aged 65years who participated in a cohort study were randomly selected to have archived fasting morning serum analyzed for androgen precursors and sex hormones. DNA was extracted from whole blood and analyzed for ACE gene I/D polymorphism.

RESULTS

Subjects with the ACE gene D allele (higher ACE activity) had higher serum dehydroepiandrosterone (DHEA) sulphate and DHEA than those with I/I genotype (P=0.014 and 0.018 respectively, Mann Whitney test). These differences were not significant after Bonferroni correction. Among those with history of hypertension, but without diabetes mellitus or cardiac failure, users of ACE inhibitors had significantly lower serum DHEA (median 1.78 versus 1.49ng/ml in non-users, P=0.0074, Mann Whitney test) and also tended to have lower serum androstenedione and androst-5-ene-3beta,17beta-diol (0.68 versus 0.72ng/ml in non-users; 552.4 versus 624.1pg/ml respectively, both P values <0.05). Serum testosterone and estradiol were not significantly changed.

CONCLUSIONS

ACE inhibitor use was associated with lower serum DHEA in older men.

Using simple anthropometric measures to predict body fat in South Asians

Previously determined predictive equations for body fat mass (BFM) are primarily derived from populations of European origin, which may not be appropriate for all ethnic groups. The objective of this study was to develop an improved predictive equation for BFM specific to South Asians and derived from common anthropometric measurements that include measures of central adiposity. A total of 208 apparently healthy South Asian men and women, aged 30–65 years, were recruited. Anthropometric measurements and BFM by dual energy X-ray absorbitometry (DEXA) were obtained. Sex-specific equations predicting BFM were developed using regression models on a reference subset (68 men, 70 women) and tested on a validation group. New predictive equations (BFMNEW) were tested for agreement with Durin and Wormersley and Siri equations and with the reference method, DEXA. The best predictive sex-specific equation involved a combination of skinfolds, waist circumference, hip circumference, humerus breadth, height, mass, and age. Models significantly correlated with BFM determined by DEXA (r = 0.946 for men; r = 0.974 for women; p < 0.001). The estimates of BFM from reference and validation groups had excellent correlations and displayed excellent agreement to DEXA measures. We demonstrated new predictive equations for BFM that are specific to South Asians and incorporate measures of central adiposity. This may help resolve issues surrounding inaccurate determination of adiposity in South Asians, and consequently provide better estimations of disease risk.

Two-dimensional predictive equation to classify visceral obesity in clinical practice

OBJECTIVE

Visceral obesity assessment is not easy, and although computed tomography (CT) is an accurate tool, this technique is expensive and sometimes not suitable in clinical practice. We developed a new two-dimensional elliptical anthropometric equation to classify visceral obesity and evaluated the validity and the reliability of the new equation compared with CT.

RESEARCH METHODS AND PROCEDURES

We collected anthropometric and CT data from overweight/obese subjects (n = 61, BMI = 32.4 +/- 3.7 kg/m2). A validation group of 32 subjects was also selected. An equation for the assessment of visceral obesity was developed using multiple regression analysis. Once validated, the equation was compared with previous models. Tests for accuracy included mean differences, analysis of diagnostic, R2, Snedecor's F-test, and Bland-Altman plot.

RESULTS

Multiple regression analysis revealed that the sagittal and coronal diameters and the triceps skinfold were significant contributors to the model. The final equation was: visceral area (VA)/subcutaneous area (SA)predicted = 0.868 + 0.064 x sagittal diameter - 0.036 x coronal diameter - 0.022 x triceps skinfold. Patients with visceral-subcutaneous area ratio (VA/SA) > 0.42 were classified as having visceral obesity. The predictive equation was valid, showing a significant association with VA/SA assessed by CT (VA/SA(CT); r = 0.68; p < 0.0001). Paired Student's t test showed no significant differences with VA/SACT (p = 0.541). The reliability was high [F(24/60) = 2.12; p = 0.01].

DISCUSSION

The new two-dimensional and elliptical predictive equation is valid to assess visceral obesity and is more precise than previous models.

Method for estimating body weight in persons with lower-limb amputation and its implication for their nutritional assessment

BACKGROUND

Body weight is a good indicator of a person's size and is widely used in clinical assessment. However, health-status assessment based on observed body weight (W(O)) is incorrect for persons with limb amputation.

OBJECTIVES

The objectives were 1) to develop a more accurate and generalized method for estimating body weight in persons with limb amputation, 2) to determine whether corrected body weight can be used to assess nutritional status in persons with limb amputation, and 3) to test the validity of the estimation by using empirical data.

DESIGN

Anthropometric data were collected from men from Calcutta and adjoining areas with unilateral lower-extremity amputation (n = 102). Mathematic formulas were developed for determining estimated body weight (W(E)) and body mass index (BMI) calculated from both W(O) and W(E) (ie, BMI(O) and BMI(E), respectively). We assessed nutritional status by using BMI(O) and BMI(E) and tested the validity of each by considering the result of nutritional assessment from midupper arm circumference as the gold standard. We also compared the nutritional status results for the subjects with limb amputation with those for a similar sample size of healthy control subjects.

RESULTS

BMI(E) had a stronger association with midupper arm circumference and a higher efficiency (ie, proportion of correct results given by any test method) than did BMI(O). Moreover, the results obtained with BMI(E) were similar to those obtained with BMI in healthy control subjects. However, the nutritional assessments made with BMI(O) and BMI(E) did not differ significantly from one another.

CONCLUSION

For persons with limb amputation, W(E) provides a better basis for appropriate nutritional evaluation than does W(O).