Protective effect of moderate overweight on bone density of the hip joint in elderly and old Austrians

The effect of weight, classified by body mass index (BMI), on bone mass (BMC) of the whole body and on bone mineral density BMD of the hip joint was analysed in a sample of 120 Austrians of Vienna and surroundings. The 68 females and 52 males of this cross sectional study ranged in age between 60 and 92 years (x = 71.7 +/- 7.7). Age distribution was not significantly different between sexes. The WHO (1997) classification of body mass index (BMI) was used for weight classification, i.e. normal weight (BMI 18.5-24.99) and moderate overweight (BMI 25.0-29.99). Obese subjects (BMI 30+) were not included in this study. Bone mass of the whole body as well as bone density of the hip joint were determined by Dual-energy-X-ray absorptiometry (DEXA) using a hologic 2000 scanner. As expected BMC and BMD values were significantly higher in males than in females. While in both females and males moderately overweight BMD of the hip was significantly higher than in those with normal BMI, statistically significant differences of BMC were restricted to females only. Such positive association between body weight and BMC and BMD is in agreement with previous studies on mature subjects, and menopausal and postmenopausal women in particular. In addition, this study demonstrates corresponding positive associations between moderate overweight and bone mass and -density in the elderly and old aged.     

Body mass and stature estimation based on the first metatarsal in humans

Archaeological assemblages often lack the complete long bones needed to estimate stature and body mass. The most accurate estimates of body mass and stature are produced using femoral head diameter and femur length. Foot bones including the first metatarsal preserve relatively well in a range of archaeological contexts. In this article we present regression equations using the first metatarsal to estimate femoral head diameter, femoral length, and body mass in a diverse human sample. The skeletal sample comprised 87 individuals (Andamanese, Australasians, Africans, Native Americans, and British). Results show that all first metatarsal measurements correlate moderately to highly (r = 0.62-0.91) with femoral head diameter and length. The proximal articular dorsoplantar diameter is the best single measurement to predict both femoral dimensions. Percent standard errors of the estimate are below 5%. Equations using two metatarsal measurements show a small increase in accuracy. Direct estimations of body mass (calculated from measured femoral head diameter using previously published equations) have an error of just over 7%. No direct stature estimation equations were derived due to the varied linear body proportions represented in the sample. The equations were tested on a sample of 35 individuals from Christ Church Spitalfields. Percentage differences in estimated and measured femoral head diameter and length were less than 1%. This study demonstrates that it is feasible to use the first metatarsal in the estimation of body mass and stature. The equations presented here are particularly useful for assemblages where the long bones are either missing or fragmented, and enable estimation of these fundamental population parameters in poorly preserved assemblages.     

Estimation of stature in Turkish adults using knee height

Body height is an important clinical indicator to derive body mass index (BMI), which is a useful screening tool for both excess adiposity and malnutrition. Height measurement in the elderly may impose some difficulties and the reliability is doubtful. Stature estimation from knee height is one of the commonly used methods; nevertheless no study has been carried out so far on the Turkish population. A cross sectional anthropometric study was conducted to develop body height estimation equations by using knee height measurement for Turkish people. Measurements of height and knee height were taken according to the International Biological Programme procedures from 1422 adults (610 males, 812 females) aged 18-90 years from Ankara, the capital city of Turkey. Samples were randomly split into two sub-samples, training and validation (control group) sub-samples. Height estimation equations were developed from the knee height measurements by linear regression analysis according to age groups and sexes. Males were significantly taller and have higher knee height values than females in all age groups. Height and the knee height variables showed a gradual decrease (P 50) with aging in females and males. Evaluated knee height equations for stature estimation were tested through the validation sample and the results showed high accuracy. The study presents sex and age specific regression equations for height estimation by using the knee height measurement for Turkish adults and suggests facilitating the accurate usage of knee height.     

New equations for estimating body cell mass from bioimpedance parallel models in healthy older Germans

The objectives of this study were to assess for elderly Germans the validity of existing equations for predicting body cell mass (BCM) and to develop from single- and multifrequency bioimpedance (SFBIA, MFBIA) models new prediction equations. In a data-splitting approach, validation and cross-validation were performed in 160 healthy elderly (60- to 90-yr) subjects. BCM was determined using a tetrapolar bioimpedance analyzer (800 microA; 4 fixed frequencies: 1, 5, 50, and 100 kHz; electrodes placed to hand, wrist, ankle, and foot) and whole body (40)K counting as a reference method. New prediction equations were derived by multiple stepwise regression analysis. The Bland-Altman procedure was used for methods comparison. Relative to whole body counting, the manufacturer's equation overestimated BCM by 9% in men (P < 0.0001, paired t-test) and 4% in women (P = 0.002). Compared with the manufacturer's equation, the newly derived equations (r = 0.92, RMSE = 6-9%) improved accuracy (pure error = 13 vs. 7-8%) and reduced bias and limits of agreement. SFBIA and MFBIA equations did not differ in precision or accuracy. We conclude that the newly derived equations improved BCM estimates in the elderly compared with existing equations. There was no advantage of MFBIA over SFBIA equations.     

Validation of BIA in Obese Children and Adolescents and Re‐evaluation in a Longitudinal Study

Decrease in fat mass (FM) is a one of the aims of pediatric obesity treatment; however, measurement techniques suitable for routine clinical assessment are lacking. The objective of this study was to validate whole‐body bioelectrical impedance analysis (BIA; TANITA BC‐418MA) against the three‐component (3C) model of body composition in obese children and adolescents, and to test the accuracy of our new equations in an independent sample studied longitudinally. A total of 77 white obese subjects (30 males) aged 5–22 years, BMI‐standard deviation score (SDS) 1.6–3.9, had measurements of weight, height (HT), body volume, total body water (TBW), and impedance (Z). FM and fat‐free mass (FFM) were calculated using the 3C model or predicted from TANITA. FFM was predicted from HT²/Z. This equation was then evaluated in 17 other obese children (5 males) aged 9–13 years. Compared to the 3C model, TANITA manufacturer's equations overestimated FFM by 2.7 kg (P < 0.001). We derived a new equation: FFM = ?2.211 + 1.115 (HT²/Z), with r² of 0.96, standard error of the estimate 2.3 kg. Use of this equation in the independent sample showed no significant bias in FM or FFM (mean bias 0.5 ± 2.4 kg; P = 0.4), and no significant bias in change in FM or FFM (mean bias 0.2 ± 1.8 kg; P = 0.7), accounting for 58% (P < 0.001) and 55% (P = 0.001) of the change in FM and FFM, respectively. Our derived BIA equation, shown to be reliable for longitudinal assessment in white obese children, will aid routine clinical monitoring of body composition in this population.     

New percentage body fat prediction equations for Japanese males

Anthropometry is simple, cheap, portable and non-invasive method for the assessment of body composition. While the Nagamine and Suzuki body density prediction equation has been frequently used to estimate %BF of Japanese, the equation was developed more than 40 years ago and its applicability to the current Japanese population has not been studied. This study aimed to compare %BF results estimated from anthropometry and dual energy X-ray absorptiometry (DXA) in order to examine applicability of the Nagamine and Suzuki equation. Body composition of 45 Japanese males (age: 24.3+/-5.5 years, stature: 171.6+/-5.8 cm, body mass: 62.6+/-7.1 kg, %BF: 15.7+/-5.6%) were assessed using whole-body DXA (Hologic QDR-2000) scan and anthropometry using the protocol of the International Society for the Advancement of Kinanthropometry (ISAK). From anthropometric measurements %BF was calculated using the Nagamine and Suzuki equation. The results showed that the Nagamine and Suzuki equation significantly (p<0.05) underestimated %BF of Japanese males compared to the DXA results. There was a trend towards greater underestimation as the estimated %BF values using DXA increased. New %BF prediction equations were proposed from the DXA and anthropometry results. Application of the proposed equations may assist in more accurate assessment of body fatness in Japanese males living today.     

A revision of body surface area estimations

The results from the present study indicate that the equation of Dubois and Dubois (1916), now in common use for estimating body surface area, is not the most accurate of those available. Our new equation is based on the measurement of body weight and upper calf circumference only. It accounts for a large percentage of the total variance (88.9%), and has a low standard error of estimate (0.05 m2), which suggests that this equation may be more useful in the estimation of human body surface area than many of the equations previously produced. Direct comparisons of the accuracy of other workers' equations is difficult to assess due to the different methods by which the equations have been produced and expressed. We do not advocate the use of different equations for males and females because the data indicate that the present equation probably applies equally well to both sexes.     

Predicting body mass of bonobos (Pan paniscus) with human-based morphometric equations

A primate's body mass covaries with numerous ecological, physiological, and behavioral characteristics. This versatility and potential to provide insight into an animal's life has made body mass prediction a frequent and important objective in paleoanthropology. In hominin paleontology, the most commonly employed body mass prediction equations (BMPEs) are “mechanical” and “morphometric”: uni- or multivariate linear regressions incorporating dimensions of load-bearing skeletal elements and stature and living bi-iliac breadth as predictor variables, respectively. The precision and accuracy of BMPEs are contingent on multiple factors, however, one of the most notable and pervasive potential sources of error is extrapolation beyond the limits of the reference sample. In this study, we use a test sample requiring extrapolation—56 bonobos (Pan paniscus) from the Lola ya Bonobo sanctuary in Kinshasa, Democratic Republic of the Congo—to evaluate the predictive accuracy of human-based morphometric BMPEs. We first assess systemic differences in stature and bi-iliac breadth between humans and bonobos. Due to significant differences in the scaling relationships of body mass and stature between bonobos and humans, we use panel regression to generate a novel BMPE based on living bi-iliac breadth. We then compare the predictive accuracy of two previously published morphometric equations with the novel equation and find that the novel equation predicts bonobo body mass most accurately overall (41 of 56 bonobos predicted within 20% of their observed body mass). The novel BMPE is particularly accurate between 25 and 45 kg. Given differences in limb proportions, pelvic morphology, and body tissue composition between the human reference and bonobo test samples, we find these results promising and evaluate the novel BMPE's potential application to fossil hominins.     

Prediction of Body Fat by Anthropometry in Older Chinese People

Objective: To derive regression equations for fat percentage by using simple anthropometric measurements applicable in normal and immobile (cannot stand or walk) older people. Research Methods and Procedures: The study population comprised 352 females and 261 males, apparently well and community‐dwelling, aged 69 to 82 years. Fifty‐one females and 27 males were recruited for external validation. Body weight, standing height, arm span, triceps and biceps skinfold thicknesses (SFTs), and midarm circumference were measured. The reference method of total body fat percentage was dual‐energy X‐ray densitometry. Predictive equations for fat percentages were derived by stepwise multiple linear regression on anthropometric indices and gender. Results: Upper‐limb SFTs, body mass index, and gender yielded the more predictive equation. The SEE was 4.1% weight. There was a significant trend of underestimation in overweight subjects, especially in females. The equation using SFTs and midarm circumference was less reliable but more applicable to older immobile people and those with significant kyphoscoliosis. Conclusions: The combination of body mass index and upper‐limb SFTs gives reliable prediction of fat percentages in older Chinese people, except in the obese.     

Development and Validation of Computed Tomography Derived Anthropometric Regression Equations for Estimating Abdominal Adipose Tissue Distribution

The purpose of this study was twofold: (1) to develop multiple regression equations for predicting computed tomography (CT) derived intra-abdominal (IAF), subcutaneous (SCF), and total (TOTF= IAF+SCF) abdominal adipose tissue areas from anthropometric measures in adult white males with a large range of age (18–71 years) and percent body fat (2.0–40.6); and (2) to validate the new and existing equations that used similar Hounsfield Units (HU) for determining IAF for estimating these fat depots. One hundred fifty-one white male subjects had IAF, SCF, and TOTF determined by a single CT scan, skinfold and circumference measures taken and body density determined. Linear intra-correlations and factor analysis procedures were used to identify variables for inclusion in stepwise multiple regression solutions. IAF was estimated from age, waist circumference, the sum of mid-thigh and lower thigh circumferences, and vertical abdominal skinfold. SCF was estimated from age, umbilicus circumference, chest and suprailiac skinfolds. TOTF was estimated from age, body mass index (BMI), chest skinfold, and umbilicus circumference. R² for IAF, SCF, and TOTF was .73, .77, and .86 respectively. The existing and the new equations were validated on an independent sub-sample of 51 subjects. The only existing equation that met validation criteria had a validation R² = .67 for IAF. All three new equations met validation criteria with R ² validations of .75, .79, and .85 for IAF, SCF, and TOTF respectively. It is concluded that the new equations might be used as an inexpensive estimation of IAF, SCF, and TOTF in adult white males varying greatly in age and percent body fat.     

Percentage Fat in Overweight and Obese Children: Comparison of DXA and Air Displacement Plethysmography**

Objective: To compare percentage body fat (percentage fat) estimates from DXA and air displacement plethysmography (ADP) in overweight and obese children. Research Methods and Procedures: Sixty‐nine children (49 boys and 20 girls) 14.0 ± 1.65 years of age, with a BMI of 31.3 ± 5.6 kg/m² and a percentage fat (DXA) of 42.5 ± 8.4%, participated in the study. ADP body fat content was estimated from body density (D_b) using equations devised by Siri (ADP_Siri) and Lohman (ADP_Loh). Results: ADP estimates of percentage fat were highly correlated with those of DXA in both male and female subjects (r = 0.90 to 0.93, all p < 0.001; standard error of estimate = 2.50% to 3.39%). Compared with DXA estimates, ADP_Siri and ADP_Loh produced significantly (p < 0.01) lower estimates of mean body fat content in boys (?2.85% and ?4.64%, respectively) and girls (?2.95% and ?5.15%, respectively). Agreement between ADP and DXA methods was further examined using the total error and methods of Bland and Altman. Total error ranged from 4.46% to 6.38% in both male and female subjects. The 95% limits of agreement were relatively similar for all percentage fat estimates, ranging from ±6.73% to ±7.94%. Discussion: In this study, conversion of D_b using the Siri equation led to mean percentage fat estimates that agreed better with those determined by DXA compared with the Lohman equations. However, relatively high limits of agreement using either equation resulted in percentage fat estimates that were not interchangeable with percentage fat determined by DXA.     

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 × subscapular skinfold + 0.62 × BMI ? 0.28 × biceps skinfold + 0.17 × waist circumference ? 18.47, and in women: %BF(women) = 0.42 × hip circumference + 0.17 × suprailiac skinfold + 0.46 × 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 × BMI + 0.06 × age ? 15.3 × 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.     

Peak vertical jump power estimations in youths and young adults

The purpose of this study was to develop and validate a regression equation to estimate peak power (PP) using a large sample of athletic youths and young adults. Anthropometric and vertical jump ground reaction forces were collected from 460 male volunteers (age: 12-24 years). Of these 460 volunteers, a stratified random sample of 45 subjects representing 3 different age groups (12-15 years [n = 15], 16-18 years [n = 15], and 19-24 years [n = 15]) was selected as a validation sample. Data from the remaining 415 subjects were used to develop a new equation ("Novel") to estimate PP using age, body mass (BM), and vertical jump height (VJH) via backward stepwise regression. Independently, age (r = 0.57), BM (r = 0.83), and VJ (r = 0.65) were significantly (p < 0.05) correlated with PP. However, age did not significantly (p = 0.53) contribute to the final prediction equation (Novel): PP (watts) = 63.6 × VJH (centimeters) + 42.7 × BM (kilograms) - 1,846.5 (r = 0.96; standard error of the estimate = 250.7 W). For each age group, there were no differences between actual PP (overall group mean ± SD: 3,244 ± 991 W) and PP estimated using Novel (3,253 ± 1,037 W). Conversely, other previously published equations produced PP estimates that were significantly different than actual PP. The large sample size used in this study (n = 415) likely explains the greater accuracy of the reported Novel equation compared with previously developed equations (n = 17-161). Although this Novel equation can accurately estimate PP values for a group of subjects, between-subject comparisons estimating PP using Novel or any other previously published equations should be interpreted with caution because of large intersubject error (± >600 W) associated with predictions.     

Development of guidelines for assessing obesity in captive chimpanzees (Pan troglodytes)

Many captive chimpanzees (Pan troglodytes) are subjectively considered to be overweight or obese. However, discussions of obesity in chimpanzees are rare in the literature, despite the acknowledged problem. No study to date has systematically examined obesity in captive chimpanzees. This project develops guidelines for defining obesity in captive chimpanzees through the examination of morphometric and physiologic characteristics in 37 adult female and 22 adult male chimpanzees. During each animal's biannual physical exam, morphometric data was collected including seven skinfolds (mm), body mass index (BMI), waist‐to‐hip ratio (WHR), and total body weight (kg). The morphometric characteristics were correlated with triglycerides and serum glucose concentration, to test the utility of morphometrics in predicting relative obesity in captive chimpanzees. Abdominal skinfold (triglyceride: F=3.83, P=0.05; glucose: F=3.83, P=0.05) and BMI (triglyceride: F=10.42, p=0.003; glucose: F=6.20, P=0.02) were predictive of increased triglycerides and serum glucose in females; however no morphometric characteristics were predictive of relative obesity in males. Results suggest that no males in this population are overweight or obese. For females, there were additional significant differences in morphometric (skinfolds, BMI, WHR, total body weight) and physiologic measurements (systolic and diastolic blood pressure, red blood cells) between individuals classified overweight and those classified non‐overweight. Skinfold measurements, particularly abdominal, seem to be an accurate measure of obesity and thus potential cardiovascular risk in female chimpanzees, but not males. By establishing a baseline for estimated body fat composition in female captive chimpanzees, institutions can track individuals empirically determined to be obese, as well as obesity‐related health problems. Zoo Biol 0:1–12, 2007. © 2007 Wiley‐Liss, Inc.     

Validation of tetrapolar bioelectrical impedance method to assess human body composition

This study was conducted to validate the relationship between bioelectrical conductance (ht2/R) and densitometrically determined fat-free mass, and to compare the prediction errors of body fatness derived from the tetrapolar impedance method and skinfold thicknesses, relative to hydrodensitometry. One-hundred and fourteen male and female subjects, aged 18-50 yr, with a wide range of fat-free mass (34-96 kg) and percent body fat (4-41%), participated. For males, densitometrically determined fat-free mass was correlated highly (r = 0.979), with fat-free mass predicted from tetrapolar conductance measures using an equation developed for males in a previous study. For females, the correlation between measured fat-free mass and values predicted from the combined (previous and present male data) equation for men also was strong (r = 0.954). The regression coefficients in the male and female regression equations were not significantly different. Relative to hydrodensitometry, the impedance method had a lower predictive error or standard error of the estimates of estimating body fatness than did a standard anthropometric technique (2.7 vs. 3.9%). Therefore this study establishes the validity and reliability of the tetrapolar impedance method for use in assessment of body composition in healthy humans.     

A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults

Background

Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation.

Methods

Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis.

Results

The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE).

Conclusions

There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.     

Validity of convenient indicators of obesity

A validation study of convenient indicators of obesity was undertaken in 540 male and female subjects, aged 7-14 yr. Four adiposity measures that have commonly accepted obesity classification points [relative weight, relative body mass index (BMI), sum of five skinfolds, and triceps skinfold] were derived from measures of height, weight, and five skinfold thickness measurements. Body density measures were converted to percentage of body fat using Lohman's (1986) age- and gender-specific regression equations. Using greater than or equal to 20% body fat for males and greater than or equal to 25% for females as the standard for obesity, the diagnostic utilities (sensitivity, specificity, overall accuracy, and positive and negative predictive values) of the four obesity indicators at their commonly used obesity cutoff points were determined. Preliminary analyses demonstrate that use of these indicators should not be considered independent of the gender of the subject or without reference to the purpose for classifying subjects as obese. Secondary analyses, in which the obesity cutoff point in each indicator was altered to obtain a minimum specificity level of 95%, demonstrated that a sum of skinfolds was better at identifying true obesity than all other indicators in both males and females. There is potential for inappropriate labeling with all convenient indicators of obesity, and thus they should be used with caution.     

Percentage of total body fat as estimated by three automatic bioelectrical impedance analyzers

The present study aimed to compare the accuracy of estimating the percentage of total body fat (%TBF) among three bioelectrical impedance analysis (BIA) devices: a single-frequency BIA with four tactile electrodes (SF-BIA4), a single-frequency BIA with eight tactile electrodes (SF-BIA8) and a multi-frequency BIA with eight tactile electrodes (MF-BIA8). Dual-energy x-ray absorptiometry (DXA) and hydrostatic weighing (HW) were used as references for the measured values. Forty-five healthy college student volunteers (21 males: 172.9 +/- 5.5 cm and 65.8 +/- 9.1 kg and 24 females: 160.7 +/- 6.6 cm, 52.6 +/- 6.2 kg) were the subjects. Correlation coefficients between the BIA measurements and the references were calculated. The standard error of estimation (SEE) was calculated by regression analysis when estimating the reference measures (DXA and HW) from the predictor (SF-BIA4, SF-BIA8 and MF-BIA8). The differences in %TBF between the reference and the predictor, calculated by the reference minus the predictor, were plotted against the %TBF measured by the references. The MF-BIA 8 here showed the highest correspondence to the reference and the least estimation error compared with the other BIA methods. It is considered that there is a limit to directly estimate FFM from a regression equation using impedance, weight, height and age as independent variables, and that %TBF can be more accurately estimated by measuring segmental impedances using eight electrodes and multi-frequency electric currents and then estimating total body water from these impedances.     

Air Displacement Plethysmography: Validation in Overweight and Obese Subjects

Objective: Patients with moderate and severe obesity, because of their physical size, often cannot be evaluated with conventional body composition measurement systems. The BOD POD air displacement plethysmography (ADP) system can accommodate a large body volume and may provide an opportunity for measuring body density (D_b) in obese subjects. D_b can be used in two‐ or three‐compartment body composition models for estimating total body fat in patients with severe obesity. The purpose of this study was to compare D_b measured by ADP to D_b measured by underwater weighing (UWW) in subjects ranging from normal weight to severely obese. Research Methods and Procedures: D_b was measured with UWW and BOD POD in 123 subjects (89 men and 34 women; age, 46.5 ± 16.9 years; BMI, 31.5 ± 7.3 kg/m²); 15, 70, and 10 subjects were overweight (25 ≤ BMI < 30 kg/m²), obese (30 ≤ BMI < 40 kg/m²), and severely obese (BMI ≥ 40 kg/m²), respectively. Results: There was a strong correlation between D_b(kilograms per liter) measured by UWW and ADP (r = 0.94, standard error of the estimate = 0.0073 kg/L, p < 0.001). Similarly, percent fat estimates from UWW and ADP using the two‐compartment Siri equation were highly correlated (r = 0.94, standard error of the estimate = 3.58%, p < 0.001). Bland‐Altman analysis showed no significant bias between D_b measured by UWW and ADP. After controlling for D_b measured by ADP, no additional between‐subject variation in D_b by UWW was accounted for by subject age, sex, or BMI. Discussion: Body density, an important physical property used in human body composition models, can be accurately measured by ADP in overweight and obese subjects.     

Prediction Equations Do Not Eliminate Systematic Error in Self-Reported Body Mass Index

PLANKEY MICHAEL W, JUNE STEVENS, KATHERINE M FLEGAL, PHILIP F RUST. Prediction equations do not eliminate systematic error in self-reported body mass index. Epidemiological studies of the risks of obesity often use body mass index (BMI) calculated from self-reported height and weight. The purpose of this study was to examine the pattern of reporting error associated with self-reported values of BMI and to evaluate the extent to which linear regression models predict measured BMI from self-reported data and whether these models could compensate for this reporting error. We examined measured and self-reported weight and height on 5079 adults aged 30 years to 64 years from the second National Health and Nutrition Examination Survey. Measured and self-reported BMI (kg/m2) was calculated, and multiple linear regression techniques were used to predict measured BMI from self-reported BMI. The error in self-reported BMI (self-reported BMI minus measured BMI) was not constant but varied systematically with BMI. The correlation between measured BMI and the error in self-reported BMI was ?0.37 for men and ?0.38 for women. The pattern of reporting error was only weakly associated with self-reported BMI, with the correlation being 0.05 for men and ?0.001 for women. Error in predicted BMI (predicted BMI minus measured BMI) also varied systematically with measured BMI, but less consistently with self-reported BMI. More complex models only slightly improved the ability to predict measured BMI compared with self-reported BMI alone. None of the equations were able to eliminate the systematic reporting error in determining measured BMI values from self-reported data. The characteristic pattern of error associated with self-reported BMI is difficult or impossible to correct by the use of linear regression models.