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Relative accuracy of anthropometric-based body fat equations in males and females with varying BMI classifications
Clinical Nutrition ESPEN xxx (xxxx) xxx
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Original article
Relative accuracy of anthropometric-based body fat equations in males and females with varying BMI classifications Brett S. Nickerson a, *, Cherilyn N. McLester b, John R. McLester b, Brian M. Kliszczewicz b a b
College of Nursing and Health Sciences, Texas A&M International University, Laredo, TX, USA Department of Exercise Science and Sport Management, Kennesaw State University, Kennesaw, GA, USA
a r t i c l e i n f o
s u m m a r y
Article history: Received 6 August 2019 Accepted 19 October 2019
Background: BMI based body fat equations developed from Womersley and Durnin (BMIWO), Jackson et al. (BMIJA), Deurenberg et al. (BMIDE), and Gallagher et al. (BMIGA) are commonly used to quantify body fat percentage (BF%). However, relative fat mass (RFM) is a new anthropometric-based method that has been proposed as an alternative. Aims: The purpose of this study was to examine the independent and interactive effects of sex and BMI classification on the relative accuracy of BMI-based body fat equations and RFM. Methods: Males (n ¼ 75) and females (n ¼ 75) were stratified and classified into three different groups; 1) normal weight (n ¼ 50 [NW: 50% males]; BMI<25.0 kg/m2); 2) overweight (n ¼ 50 [OW: 50% males]; BMI25.0e29.9 kg/m2); 3) obese (n ¼ 50 [OB: 50% males]; BMI30.0 kg/m2). A criterion threecompartment model (3C model) was determined with air displacement plethysmography for body volume and multi-frequency bioimpedance analysis for total body water. Data were stratified by sex and BMI classification. Difference scores were created by subtracting estimated BF% from 3C model BF%. Results: A significant SEX BMI interaction was detected for all comparisons (all p < 0.05). Post hoc analysis indicated the differences in BF% were statistically significant between OW females and males for all equations (BMIWO:-2.99 ± 4.79% vs. 4.71 ± 5.86%, p ¼ 0.003; BMIJA:-1.77 ± 4.83% vs. 5.77 ± 5.85%, p < 0.001; BMIDE:-3.09 ± 4.80% vs. 4.97 ± 5.98%, p < 0.001; BMIGA:0.36 ± 4.51% vs. 4.56 ± 5.55%, p ¼ 0.018; RFM:-2.17 ± 4.84% vs. 3.01 ± 5.34%, p ¼ 0.004, respectively). In addition, there were significant differences between females and males classified as NW (BMIJA:-2.11 ± 4.15% vs. 2.61 ± 5.98%, p ¼ 0.008) and OB (BMIGA:2.40 ± 3.36% vs. 1.09 ± 6.40%, p ¼ 0.006). Conclusions: The current findings highlight that RFM does not appear to overcome error commonly associated with BMI-based body fat equations when stratifying by sex and BMI classification. Nonetheless, practitioners can use BMIWO, BMIDE, and RFM in males and females classified as NW or OB, but should employ caution prior to use in OW persons. © 2019 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.
Keywords: Adiposity Body fat Fat mass Multi-compartment
1. Introduction Body composition has increasingly become a characteristic of interest in health related industries. This is primarily due to the relationship of the distribution and prevalence of adipose tissue to chronic diseases ranging from cardio-metabolic disorders to cardiovascular disease to certain cancers [1e3]. It is because of this relationship that the accuracy of body composition assessment has
* Corresponding author. College of Nursing and Health Sciences, Texas A&M International University, 5201 University Boulevard, Laredo, TX, 78041, USA. E-mail address: [email protected] (B.S. Nickerson).
led to the development of more advanced techniques such as multicompartment models (e.g., three-compartment [3C] model). Traditional assessments (i.e., two-compartment models) make body composition estimates with the assumption that the hydration of fat-free mass (FFM) is approximately 73% [4,5]. However, this value has been found to range from 68 to 81% [6e8], which is problematic since aqueous content is the largest constituent of FFM. Due to these factors, a 3C model that accounts for total body water (TBW) is considered a leading technique because it accounts for the inter-individual variations in FFM hydration, which subsequently reduces measurement error [6,7].
https://doi.org/10.1016/j.clnesp.2019.10.014 2405-4577/© 2019 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: Nickerson BS et al., Relative accuracy of anthropometric-based body fat equations in males and females with varying BMI classifications, Clinical Nutrition ESPEN, https://doi.org/10.1016/j.clnesp.2019.10.014
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In order to obtain the components of a 3C model, TBW must be obtained via bioelectrical impedance analysis (BIA) or dilution techniques and body volume (BV) through air displacement plethysmography (BOD POD®) or hydrostatic weighing (HW). The necessity of BV often limits the widespread use of a 3C model in epidemiological and clinical settings since densitometry methods (i.e., BOD POD® and HW) are often restricted to research settings. As a result, there is growing interest in the development of practical and pragmatic anthropometric methods for determining body composition. Body mass index (BMI), which classifies individuals into categories based on a weight to height relationship, is perhaps one of the most widely used and reported metrics of body composition [9]. Although it is commonly utilized, BMI has been widely criticized for its inability to distinguish fat and lean mass [10,11]. Due to these factors, BMI may not be appropriate for individuals with large muscle mass. Nonetheless, regression equations have been derived to predict body fat percentage (BF%) based upon BMI values [12e16]. Despite these attempts, researchers have consistently sought to overcome the limitations of BMI by developing more accurate anthropometric-based BF% equations. A new anthropometric-based BF% equation, known as the relative fat mass (RFM), has been developed as an alternative to BMI-based BF% equations. The RFM was developed from a large sample of adults via the National Health and Nutrition Examination Survey data set [17]. The validation sample of Woolcott and Bergman [17] revealed that RFM predicted BF% more accurately than BMI-based BF% equations previously developed from Gallagher et al. (BMIGA) [12] and Deurenberg et al. (BMIDE) [18]. Fedewa et al. [19] recently advanced these findings by demonstrating that RFM was more accurate than the body adiposity index (BAI), which is an anthropometric equation that accounts for hip circumference. These findings highlight that RFM may reduce measurement error commonly observed when using previous anthropometric-based BF% equations. Although RFM has emerged as a surrogate of BMI-based BF% equations, additional questions remain. For instance, it is unknown whether RFM has better relative accuracy than the BMI-based body fat equations of Womersley and Durnin (BMIWO) [16] and Jackson et al. (BMIJA) [14]. In addition, previous research has utilized dual energy X-ray absorptiometry (DXA) as a reference technique, which has been shown to produce large error when compared to more advanced multi-compartment models (i.e., five-compartment [5C] model) for the estimation of BF% [6,7]. Lastly, the impact of stratifying estimated BF% (i.e., BMI-based body fat equations and RFM)
by sex and BMI classification (i.e., normal weight [NW], overweight [OW], and obese [OB]) has yet to be evaluated. This is problematic since BMI has been found to be sex dependent. Specifically, for an equivalent BMI, women have significantly greater amounts of BF% than men [20]. In addition, the observed error of BMI-based body fat has been found to vary at different BMI levels [14]. As a result, partitioning subjects based upon sex and BMI classification may help further explain discrepancies of BMI-based body fat equations and whether RFM overcomes these aforementioned issues. Therefore, the purpose of this study was to examine the independent and interactive effects of sex and BMI classification on the relative accuracy of BMI-based body fat equations and RFM. 2. Materials/subjects and methods A total of 150 subjects (75 males and 75 females) participated in this study. Participants ranged in age from 18 to 65 years. Exclusion criteria included pregnant women, or individuals missing limbs or with pacemaker implants. Males and females were stratified and classified into three different groups; 1) NW (n ¼ 50; BMI < 25.0 kg/m2; 50% males); 2) OW (n ¼ 50; BMI 25.0e29.9 kg/m2; 50% males); 3) OB (n ¼ 50; BMI 30.0 kg/m2; 50% males). Table 1 displays participant characteristics. 2.1. Procedures Prior to data collection, the university's Institutional Review Board approved this study. A single visit lasting approximately 45e60 min was required for each participant with all assessments completed within the university's exercise physiology laboratory. Participants were instructed to abstain from any exercise and alcohol consumption for 24 h and from eating or drinking, with the exception of water, for 4 h prior to testing. They were also instructed to wear minimal, tight fitting, lycra clothing that was free from metal. If the participant did not have the proper attire, tight fitting lycra shorts and sports bra were provided as needed. Informed written consent was obtained followed by a brief demographic questionnaire to gather information required for the assessments such as age, sex and race. Participants were then asked to void their bladder and remove extra clothing, shoes, metal, watches, wristbands, and jewelry. Height and weight were measured with a Tanita WB-3000 (Tanita, Arlington Heights, IL, USA) digital physician's scale and BMI was calculated from height and weight measurements (kg/m2). Each participant then completed two body composition assessments
Table 1 Descriptive characteristics of the study sample (n ¼ 150). Normal Weight
Age (yrs) Height (cm) Weight (kg) BMI (kg/m2) WC (cm) BMIWO BF% BMIJA BF% BMIDE BF% BMIGA BF% RFM BF% BOD POD® BF% 3C Model FFM (kg) 3C Model FM (kg) 3C Model BF%
Overweight
Obese
Females (n ¼ 25)
Males (n ¼ 25)
Females (n ¼ 25)
Males (n ¼ 25)
Females (n ¼ 25)
Males (n ¼ 25)
26.68 ± 10.62 163.64 ± 5.80 57.27 ± 6.98 21.32 ± 1.90 67.60 ± 4.20 25.73 ± 2.61 23.59 ± 4.15 26.32 ± 3.74 25.53 ± 4.68 27.44 ± 2.93 26.14 ± 7.19 42.26 ± 3.86 15.01 ± 5.54 25.70 ± 6.97
28.48 ± 10.50 177.54 ± 5.66 73.56 ± 7.42 23.11 ± 1.57 78.23 ± 4.76 18.26 ± 2.11 17.63 ± 3.08 18.08 ± 3.59 16.84 ± 3.40 18.48 ± 2.57 16.30 ± 6.89 62.42 ± 6.87 11.14 ± 4.75 15.02 ± 5.75
28.16 ± 10.58 164.49 ± 6.67 74.06 ± 7.62 27.34 ± 1.54 80.11 ± 5.16 33.99 ± 2.11 35.20 ± 2.51 33.88 ± 3.36 37.33 ± 2.37 34.81 ± 2.59 37.41 ± 6.13 46.66 ± 5.94 27.40 ± 4.83 36.97 ± 5.10
32.88 ± 11.04 180.16 ± 9.45 88.75 ± 11.68 27.23 ± 1.56 86.36 ± 6.34 23.79 ± 2.09 24.84 ± 2.45 24.04 ± 3.22 23.63 ± 2.37 22.08 ± 3.61 19.11 ± 6.64 71.65 ± 9.62 17.10 ± 6.85 19.07 ± 6.34
35.32 ± 13.55 163.85 ± 5.93 93.53 ± 12.48 34.94 ± 4.56 95.62 ± 11.68 44.20 ± 5.79 43.72 ± 3.01 44.47 ± 6.45 45.91 ± 3.76 41.31 ± 3.82 43.80 ± 4.96 52.57 ± 6.40 40.96 ± 9.13 43.50 ± 5.20
33.56 ± 9.81 175.08 ± 8.97 107.57 ± 20.93 34.79 ± 4.23 103.81 ± 11.98 34.53 ± 5.99 34.36 ± 3.72 33.88 ± 5.75 31.47 ± 3.82 30.44 ± 3.74 33.08 ± 7.65 71.56 ± 10.51 36.01 ± 15.24 32.56 ± 8.42
BMI ¼ body mass index; WC ¼ waist circumference; BMIWO ¼ BMI-based body fat using Womersley and Durnin equation; BMIJA ¼ BMI-based body fat using Jackson et al. equation; BMIDE ¼ BMI-based body fat using Deurenberg et al. equation; BMIGA; BMI-based body fat using Gallagher et al. equation BF% ¼ body fat percentage; RFM ¼ relative fat mass; 3C Model ¼ three-compartment model; FFM ¼ fat-free mass; FM ¼ fat mass.
Please cite this article as: Nickerson BS et al., Relative accuracy of anthropometric-based body fat equations in males and females with varying BMI classifications, Clinical Nutrition ESPEN, https://doi.org/10.1016/j.clnesp.2019.10.014
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BF% (females) ¼ (1.37 BMI) - 3.47
which included air-displacement plethysmography with the BOD POD® (COSMED USA Inc, Concord, CA, USA) and bioelectrical impedance analysis (BIA) with the InBody 720 (InBodyUSA, Cerritos, CA, USA). Following these assessments, WC was obtained from each participant.
BF% (men) ¼ (3.76 BMI) e (0.04 BMI2) e 47.80
2.2. Air displacement plethysmography
BF% (women) ¼ (4.35 BMI) e (0.05 BMI2) e 46.24
The BOD POD®, which uses air displacement plethysmography, was used to estimate criterion BV. Prior to testing each day, the BOD POD® was calibrated according to manufacturer specifications. Participants were required to wear minimal, lycra compression clothing and lycra swim-caps were provided and required for testing. Body mass (BM) was measured using the manufacturer's scale that interfaces with the BOD POD ®. In order to assess BV, participants were instructed to sit in the BOD POD ® chamber for 2 trials of roughly 50 s for each trial. A third trial was necessary if the first two trials did not agree within 150 ml of each other. Thoracic gas volume was estimated for all assessments. Body density (i.e., BV and BM) obtained from the BOD POD® was used to calculate BF% via the 3C model.
Jackson et al. (BMIJA) [14]:
Deurenberg et al. (BMIDE) [18]: BF% ¼ (1.20 BMI) þ (0.23 age) e (10.8 sex) e 5.4 sex ¼ 1 for men and 0 for women. Gallagher et al. (BMIGA) [12]: BF% ¼ 76.0 e (1097.8 [1/BMI]) e (20.6 sex) þ (0.053 age) þ (95.0 Asian [1/BMI]) e (0.044 Asian age) þ (154 sex [1/BMI]) þ (0.034 sex age) sex ¼ 1 for male and 0 for female; Asian ¼ 1 for Asians and 0 for the other races. Siri Three-Compartment Model [21]:
2.3. Bioelectrical impedance analysis
FM (kg) ¼ (2.118 BV) e (0.78 TBW) e (1.351 BM)
The InBody 720 was used to calculate TBW for the 3C model. This analyzer utilizes a tetrapolar 8-point tactile electrode system and takes 30 impedance measurements with six frequencies (1 kHz, 5 kHz, 50 kHz, 250 kHz, 500 kHz, and 1000 kHz) over a test duration of approximately 60 s. Per manufacturer specifications, a participant's age, sex, and height were entered into the InBody 720. Participants were then instructed to remove their socks and cleanse their hands and feet with an antibacterial tissue purchased from the manufacturer. The testing protocol requires individuals to stand upright with their feet centered on the electrodes, and to hold the hand electrodes while placing their arms wide enough to ensure no contact between their arms and torso. Further instruction to maintain good posture and abstain from speaking was given before each assessment.
BF% ¼ (FM ÷ BM) 100
2.4. Waist circumference Waist circumference (WC) was obtained per American College of Sports Medicine guidelines [9]. A spring-loaded Gulick tape measure (Fabrication Enterprises, White Plains, NY, USA) was used for all measurements. A horizontal measurement was taken at the narrowest portion of the torso above the umbilicus and below the xiphoid process while participants stood with feet together and arms at their sides. Two measurements were taken and a third was obtained if the first two were not within 5 mm of each other. 2.5. Body composition equations The following equations were used for estimating BF% based upon RFM, BMI, and 3C model. Relative Fat Mass [17]: BF% ¼ [64 - (20 height ÷ WC) þ (12 sex)] where male is 0, and female is 1, and height and WC are expressed in meters. Womersley and Durnin (BMIWO) [16]: BF% (males) ¼ (1.34 BMI) - 12.47
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2.6. Statistical analyses Statistical analyses were performed using Statistical Package for the Social Sciences (SPSS) for Windows (SPSS 25.0. Chicago IL). Data were stratified by sex (i.e., males and females) and BMI classification (i.e., NW, OW, and OB). Difference scores for each participant were created by subtracting an individual's estimated BF% (e.g., BMIWO) from their 3C model BF%. A series of 2 (SEX) 3 (BMI) ANOVAs were then used to assess the observed error of estimated BF% and the 3C model. Statistical significance was indicated using an a level of p < 0.05. All data are expressed as mean ± standard deviation (M ± SD) unless otherwise indicated. 3. Results A significant SEX BMI interaction was detected when examining the observed error between all of the BMI-based body fat equations and 3C model BF% (all p < 0.05). Post hoc analysis indicated the differences in BF% were statistically significant between OW females and males for all equations (BMIWO: 2.99 ± 4.79% vs. 4.71 ± 5.86%, p ¼ 0.003; BMIJA: 1.77 ± 4.83% vs. 5.77 ± 5.85%, p < 0.001; BMIDE: 3.09 ± 4.80% vs. 4.97 ± 5.98%, p < 0.001; BMIGA: 0.36 ± 4.51% vs. 4.56 ± 5.55%, p ¼ 0.018, respectively). In addition, there were significant differences between females and males classified as NW (BMIJA: 2.11 ± 4.15% vs. 2.61 ± 5.98%, p ¼ 0.008) and OB (BMIGA: 2.40 ± 3.36% vs. 1.09 ± 6.40%, p ¼ 0.006). There was a significant SEX BMI interaction when examining the observed error between RFM and 3C model BF% (p ¼ 0.004). Post hoc analysis revealed the differences in BF% were statistically significant between males and females classified as OW (2.17 ± 4.84% vs. 3.01 ± 5.34%, respectively; p ¼ 0.004) whereas no significant differences were observed between males and females classified as NW and OB (p ¼ 0.28 and 0.93, respectively). Observed error between the anthropometric equations and 3C model, stratified by sex and BMI classification are displayed in Table 2.
Please cite this article as: Nickerson BS et al., Relative accuracy of anthropometric-based body fat equations in males and females with varying BMI classifications, Clinical Nutrition ESPEN, https://doi.org/10.1016/j.clnesp.2019.10.014
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Table 2 Observed error between anthropometric equations and 3C model, stratified by sex and BMI classification (n ¼ 150). Normal Weight Females (n ¼ 25) BMIWO BF% BMIJA BF% BMIDE BF% BMIGA BF% RFM BF%
0.03 2.11a 0.61 0.18 1.74
Overweight Males (n ¼ 25) 3.24 2.61 3.06 1.82 3.46
Obese
Females (n ¼ 25) b
2.99 1.77b 3.09b 0.36b 2.17b
Males (n ¼ 25)
Females (n ¼ 25)
Males (n ¼ 25)
4.72 5.77 4.97 4.56 3.01
0.69 0.22 0.97 2.40c 2.20
1.98 1.80 1.33 1.09 2.11
3C Model ¼ three-compartment model; BMI ¼ body mass index; BF% ¼ body fat percentage; BMIWO ¼ BMI-based body fat using Womersley and Durnin equation; BMIJA ¼ BMI-based body fat using Jackson et al. equation; BMIDE ¼ BMI-based body fat using Deurenberg et al. equation; BMIGA; BMI-based body fat using Gallagher et al. equation; RFM ¼ relative fat mass. a Significantly different than normal weight males. b Significantly different than overweight males. c Significantly different than obese males.
4. Discussion The purpose of this study was to examine the independent and interactive effects of sex and BMI classification on the relative accuracy of BMI-based body fat equations and RFM. The current study findings indicate that practitioners should be aware of the impact that sex and BMI classification can have on the observed error of BMI-based body fat equations and RFM. For instance, the observed error between all of the prediction methods (i.e., BMI-based body fat equations and RFM) and 3C model BF% were significant for OW females and males. Specifically, the estimated BF% of OW females were generally underestimated whereas OW males tended to be overestimated. In addition, the differences in observed error was also applicable for females and males classified as NW and OB when using BMIJA and BMIGA, respectively. These findings indicate that RFM does not appear to add benefit over BMIWO and BMIDE when stratifying males and females by BMI classification. Woolcott and Bergman [17] evaluated the accuracy of RFM in a separate validation sample, but used DXA as a reference technique. Similarly, Fedewa et al. [19] recently found that RFM yields greater relative accuracy than the BAI when compared against DXA. More research is available on the relative accuracy of BMI-based body fat equations. For instance, Esco et al. [10] demonstrated that BMIJA was not statistically significant when compared to DXA (0.85%) in adults with Down syndrome. However, BMIWO, BMIDE, and BMIGA all significantly underestimated DXA-derived BF% (4.89 to e 5.55%) in the special population [10]. The discrepancies between the current study and Esco et al. [10] could be attributed to different criterion methods used (i.e., 3C model and DXA, respectively). To support this postulation, Nickerson et al. [11] revealed that the observed error of BMIWO, BMIJA, BMIDE, and BMIGA ranged from 0.60 to 1.00% for females and varied from 3.30 to 5.50% for males, which is similar to findings of the current study. Nickerson et al. [11] utilized a multicompartment model (i.e., four-compartment [4C] model) as a criterion, which produces nearly identical validity statistics as a 3C model and is more acceptable than DXA [6,7]. The observed error of all the BMI-based body fat equations in the current study were significant when comparing females and males with a BMI classified as OW. The reason for the differences could be related to the inability of BMI to distinguish between fat and lean mass, which is problematic for individuals with large muscle mass. For instance, the males classified as OW in the present study had the largest 3C model FFM. This implies that some of the males classified as OW in the current study might have been physically active with high quantities of muscle mass. Theoretically, this might have introduced differences if the group of OW females did not have similar physical activity patterns of behavior as the OW males. Therefore, future studies evaluating the relative accuracy of BMI-based BF% equations should consider accounting for physical activity.
Zanovec et al. [13] developed a BMI-based body fat equation (BMIZ) that employs physical activity as a predictor variable of BF% via the International Physical Activity Questionnaire [22]. It is no surprise that Nickerson et al. [11] found BMIZ produced the lowest observed error when compared to other BMI-based body fat equations (i.e., BMIWO, BMIDE, BMIJA, and BMIGA) in a group of physically active men and women. As a result, future research in a group of individuals with varying physical activity levels (e.g., sedentary, moderately active, physically active, etc.) is warranted. The observed error of RFM was similar as the BMI-based body fat equations when compared against the 3C model. Specifically, there were significant differences in the observed error between OW males and females. The reason for these dissimilarities could be related to the absolute differences in WC for each BMI group. For example, the absolute difference (i.e., mean differences) in WC was smaller between males and females classified as OW (6.25 cm) than those categorized as NW and OB (10.63 and 8.19 cm, respectively). It is possible that the relative accuracy of RFM is enhanced when WC varies to a larger extent between sexes. Another potential explanation is the characteristics of the validation sample utilized to develop the RFM equation. Particularly, the mean BMI of the development sample of males and females for RFM were classified as OW (28.2 and 27.9 kg/m2, respectively) [17]. However, the mean WC values for the development sample of males and females were 93.1 and 99.5 cm, respectively, which are similar to measurements obtained in the OB individuals in the present study. This indicates there are differences between samples, which might partially explain some of the current study findings. Due to these inconsistencies, future research might seek to stratify subjects based upon WC. The differences in observed error between NW and OB persons when using BMIJA and BMIGA, respectively, are also worth discussion. Jackson et al. [14] reported that mean differences between BMIJA and HW were larger in women when BMI values were below 25.0 kg/m2, but that the differences became smaller as women's BMI approached 30 kg/m2. These findings might be an indication of why the observed error for NW females was significantly different than the NW males in the present study. Finally, the differences in observed error between OB males and females when using BMIGA in the current study could be related the body composition methods used to derive the prediction equation. For instance, BMIGA was developed from three different universities, which used a combination of 4C models and DXA [12]. Researchers noted that there was a statistically significant bias between DXA and 4C model BF% and that ideally all testing sites would have used the same method for estimating adiposity [12]. The bias of DXA reported by Gallagher et al. [12] is similar to previous findings by Nickerson and Tinsley [6] and Moon et al. [7] whom both found significant bias (i.e., overestimation) when comparing DXA to advanced multicompartment models (i.e., 5C model). Consequently, the use of
Please cite this article as: Nickerson BS et al., Relative accuracy of anthropometric-based body fat equations in males and females with varying BMI classifications, Clinical Nutrition ESPEN, https://doi.org/10.1016/j.clnesp.2019.10.014
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DXA as a reference method might have embedded error in the development of BMIGA. Although the current study has many strengths, it is not without limitations. For example, one limitation of the current study is the inability to evaluate BF% with other anthropometric-based measurements such as the BAI. For instance, the estimation of BF% via the BAI is determined with height and hip circumference measurements. The present study did not measure hip circumference. Therefore, the BAI and other anthropometric-based body fat methods that account for hip circumference could not be evaluated. Nevertheless, Fedewa et al. [19] previously revealed that RFM is more accurate than the BAI when compared to DXA in a group of males and females with- and without DS. Therefore, the present study sought to advance these findings by examining whether RFM adds any benefit over BMI-based body fat equations. 5. Conclusions The purpose of this study was to examine the independent and interactive effects of sex and BMI classification on the relative accuracy of BMI-based body fat equations and RFM. The observed error between males and females classified as OW were significant for all comparisons. Further, significant differences between males and females were observed in the NW and OB individuals when using BMIJA and BMIGA, respectively. Collectively, these findings highlight that RFM does not appear to overcome error commonly associated with BMI-based body fat equations when stratifying by sex and BMI classification. Nonetheless, practitioners can use BMIWO, BMIDE, and RFM in males and females classified as NW or OB, but should employ caution prior to use in OW persons. Financial disclosure Brett S. Nickerson has no financial disclosures. Cherilyn N. McLester has no financial disclosures. John R. McLester has no financial disclosures. Brian M. Kliszczewicz has no financial disclosures. Declaration of Competing Interest The authors have no potential, perceived, or real conflicts of interest to disclose. References [1] Brown JC, Meyerhardt JA. Obesity and energy balance in GI cancer. J Clin Oncol 2016;34:4217e24.
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[2] Alexopoulos N, Katritsis D, Raggi P. Visceral adipose tissue as a source of inflammation and promoter of atherosclerosis. Atherosclerosis 2014;233: 104e12. [3] Zheng Y, Manson JE, Yuan C, Liang MH, Grodstein F, Stampfer MJ, et al. Associations of weight gain from early to middle adulthood with major health outcomes later in life. J Am Med Assoc 2017;318:255e69. [4] Pace N, Rathbun EN. Studies on body composition. 3. The body water and chemically combined nitrogen content in relation to fat content. J Biol Chem 1945;158:685e91. [5] Moon JR. Body composition in athletes and sports nutrition: an examination of the bioimpedance analysis technique. Eur J Clin Nutr 2013;67:S54e9. [6] Nickerson B, Tinsley G. Utilization of BIA-derived bone mineral estimates exerts minimal impact on body fat estimates via multicompartment models in physically active adults. J Clin Densitom 2018;21:541e9. [7] Moon JR, Eckerson JM, Tobkin SE, Smith AE, Lockwood CM, Walter AA, et al. Estimating body fat in NCAA Division I female athletes: a five-compartment model validation of laboratory methods. Eur J Appl Physiol 2009;105:119e30. [8] Wang ZM, Deurenberg P, Wang W, Pietrobelli A, Baumgartner RN, Heymsfield SB. Hydration of fat-free body mass: review and critique of a classic body-composition constant. Am J Clin Nutr 1999;69:833e41. [9] American College of Sports Medicine. ACSM's guidelines for exercise testing and prescription. 10th ed. Lippincott Williams & Wilkins; 2017. [10] Esco MR, Nickerson BS, Bicard SC, Russell AR, Bishop PA. Agreement of BMIbased equations and DXA in determining body-fat percentage in adults with down syndrome. Adapt Phys Act Q (APAQ) 2016;33:89e96. [11] Nickerson BS, Esco MR, Bishop PA, Fedewa MV, Snarr RL, Kliszczewicz BM, et al. Validity of BMI-based body fat equations in men and women: a 4compartment model comparison. J Strength Cond Res 2018;32:121e9. [12] Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 2000;72:694e701. [13] Zanovec M, Johnson LG, Marx BD, Keenan MJ, Tuuri G. Self-reported physical activity improves prediction of body fatness in young adults. Med Sci Sport Exerc 2009;41:328e35. [14] Jackson AS, Stanforth PR, Gagnon J, Rankinen T, Leon AS, Rao DC, et al. The effect of sex, age and race on estimating percentage body fat from body mass index: the Heritage Family Study. Int J Obes Relat Metab Disord 2002;26: 789e96. [15] Deurenberg P, Andreoli A, Borg P, Kukkonen-Harjula K, de Lorenzo A, van Marken Lichtenbelt WD, et al. The validity of predicted body fat percentage from body mass index and from impedance in samples of five European populations. Eur J Clin Nutr 2001;55:973e9. [16] Womersley J, Durnin J. Comparison of the skinfold method with extent of “overweight” and various weight-height relationships in the assessment of obesity. Br J Nutr 1977;38:271e84. [17] Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentagee A cross-sectional study in American adult individuals. Sci Rep 2018;8:10980. [18] Deurenberg P, Weststrate JA, Seidell JC. Body mass index as a measure of body fatness: age- and sex-specific prediction formulas. Br J Nutr 1991;65:105e14. [19] Fedewa MV, Russell AR, Nickerson BS, Fedewa MP, Myrick JW, Esco MR. Relative accuracy of body adiposity index and relative fat mass in participants with and without down syndrome. Eur J Clin Nutr 2018;73(8):1117e21. https://doi.org/10.1038/s41430-018-0351-3. [20] Gallagher D, Visser M, Sepúlveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 1996;143:228e39. [21] Siri WE. Body composition from fluid spaces and density: analysis of methods. Techniques for measuring body composition 1961;61:223e44. [22] Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sport Exerc 2003;35:1381e95.
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Original article
Relative accuracy of anthropometric-based body fat equations in males and females with varying BMI classifications Brett S. Nickerson a, *, Cherilyn N. McLester b, John R. McLester b, Brian M. Kliszczewicz b a b
College of Nursing and Health Sciences, Texas A&M International University, Laredo, TX, USA Department of Exercise Science and Sport Management, Kennesaw State University, Kennesaw, GA, USA
a r t i c l e i n f o
s u m m a r y
Article history: Received 6 August 2019 Accepted 19 October 2019
Background: BMI based body fat equations developed from Womersley and Durnin (BMIWO), Jackson et al. (BMIJA), Deurenberg et al. (BMIDE), and Gallagher et al. (BMIGA) are commonly used to quantify body fat percentage (BF%). However, relative fat mass (RFM) is a new anthropometric-based method that has been proposed as an alternative. Aims: The purpose of this study was to examine the independent and interactive effects of sex and BMI classification on the relative accuracy of BMI-based body fat equations and RFM. Methods: Males (n ¼ 75) and females (n ¼ 75) were stratified and classified into three different groups; 1) normal weight (n ¼ 50 [NW: 50% males]; BMI<25.0 kg/m2); 2) overweight (n ¼ 50 [OW: 50% males]; BMI25.0e29.9 kg/m2); 3) obese (n ¼ 50 [OB: 50% males]; BMI30.0 kg/m2). A criterion threecompartment model (3C model) was determined with air displacement plethysmography for body volume and multi-frequency bioimpedance analysis for total body water. Data were stratified by sex and BMI classification. Difference scores were created by subtracting estimated BF% from 3C model BF%. Results: A significant SEX BMI interaction was detected for all comparisons (all p < 0.05). Post hoc analysis indicated the differences in BF% were statistically significant between OW females and males for all equations (BMIWO:-2.99 ± 4.79% vs. 4.71 ± 5.86%, p ¼ 0.003; BMIJA:-1.77 ± 4.83% vs. 5.77 ± 5.85%, p < 0.001; BMIDE:-3.09 ± 4.80% vs. 4.97 ± 5.98%, p < 0.001; BMIGA:0.36 ± 4.51% vs. 4.56 ± 5.55%, p ¼ 0.018; RFM:-2.17 ± 4.84% vs. 3.01 ± 5.34%, p ¼ 0.004, respectively). In addition, there were significant differences between females and males classified as NW (BMIJA:-2.11 ± 4.15% vs. 2.61 ± 5.98%, p ¼ 0.008) and OB (BMIGA:2.40 ± 3.36% vs. 1.09 ± 6.40%, p ¼ 0.006). Conclusions: The current findings highlight that RFM does not appear to overcome error commonly associated with BMI-based body fat equations when stratifying by sex and BMI classification. Nonetheless, practitioners can use BMIWO, BMIDE, and RFM in males and females classified as NW or OB, but should employ caution prior to use in OW persons. © 2019 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.
Keywords: Adiposity Body fat Fat mass Multi-compartment
1. Introduction Body composition has increasingly become a characteristic of interest in health related industries. This is primarily due to the relationship of the distribution and prevalence of adipose tissue to chronic diseases ranging from cardio-metabolic disorders to cardiovascular disease to certain cancers [1e3]. It is because of this relationship that the accuracy of body composition assessment has
* Corresponding author. College of Nursing and Health Sciences, Texas A&M International University, 5201 University Boulevard, Laredo, TX, 78041, USA. E-mail address: [email protected] (B.S. Nickerson).
led to the development of more advanced techniques such as multicompartment models (e.g., three-compartment [3C] model). Traditional assessments (i.e., two-compartment models) make body composition estimates with the assumption that the hydration of fat-free mass (FFM) is approximately 73% [4,5]. However, this value has been found to range from 68 to 81% [6e8], which is problematic since aqueous content is the largest constituent of FFM. Due to these factors, a 3C model that accounts for total body water (TBW) is considered a leading technique because it accounts for the inter-individual variations in FFM hydration, which subsequently reduces measurement error [6,7].
https://doi.org/10.1016/j.clnesp.2019.10.014 2405-4577/© 2019 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: Nickerson BS et al., Relative accuracy of anthropometric-based body fat equations in males and females with varying BMI classifications, Clinical Nutrition ESPEN, https://doi.org/10.1016/j.clnesp.2019.10.014
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In order to obtain the components of a 3C model, TBW must be obtained via bioelectrical impedance analysis (BIA) or dilution techniques and body volume (BV) through air displacement plethysmography (BOD POD®) or hydrostatic weighing (HW). The necessity of BV often limits the widespread use of a 3C model in epidemiological and clinical settings since densitometry methods (i.e., BOD POD® and HW) are often restricted to research settings. As a result, there is growing interest in the development of practical and pragmatic anthropometric methods for determining body composition. Body mass index (BMI), which classifies individuals into categories based on a weight to height relationship, is perhaps one of the most widely used and reported metrics of body composition [9]. Although it is commonly utilized, BMI has been widely criticized for its inability to distinguish fat and lean mass [10,11]. Due to these factors, BMI may not be appropriate for individuals with large muscle mass. Nonetheless, regression equations have been derived to predict body fat percentage (BF%) based upon BMI values [12e16]. Despite these attempts, researchers have consistently sought to overcome the limitations of BMI by developing more accurate anthropometric-based BF% equations. A new anthropometric-based BF% equation, known as the relative fat mass (RFM), has been developed as an alternative to BMI-based BF% equations. The RFM was developed from a large sample of adults via the National Health and Nutrition Examination Survey data set [17]. The validation sample of Woolcott and Bergman [17] revealed that RFM predicted BF% more accurately than BMI-based BF% equations previously developed from Gallagher et al. (BMIGA) [12] and Deurenberg et al. (BMIDE) [18]. Fedewa et al. [19] recently advanced these findings by demonstrating that RFM was more accurate than the body adiposity index (BAI), which is an anthropometric equation that accounts for hip circumference. These findings highlight that RFM may reduce measurement error commonly observed when using previous anthropometric-based BF% equations. Although RFM has emerged as a surrogate of BMI-based BF% equations, additional questions remain. For instance, it is unknown whether RFM has better relative accuracy than the BMI-based body fat equations of Womersley and Durnin (BMIWO) [16] and Jackson et al. (BMIJA) [14]. In addition, previous research has utilized dual energy X-ray absorptiometry (DXA) as a reference technique, which has been shown to produce large error when compared to more advanced multi-compartment models (i.e., five-compartment [5C] model) for the estimation of BF% [6,7]. Lastly, the impact of stratifying estimated BF% (i.e., BMI-based body fat equations and RFM)
by sex and BMI classification (i.e., normal weight [NW], overweight [OW], and obese [OB]) has yet to be evaluated. This is problematic since BMI has been found to be sex dependent. Specifically, for an equivalent BMI, women have significantly greater amounts of BF% than men [20]. In addition, the observed error of BMI-based body fat has been found to vary at different BMI levels [14]. As a result, partitioning subjects based upon sex and BMI classification may help further explain discrepancies of BMI-based body fat equations and whether RFM overcomes these aforementioned issues. Therefore, the purpose of this study was to examine the independent and interactive effects of sex and BMI classification on the relative accuracy of BMI-based body fat equations and RFM. 2. Materials/subjects and methods A total of 150 subjects (75 males and 75 females) participated in this study. Participants ranged in age from 18 to 65 years. Exclusion criteria included pregnant women, or individuals missing limbs or with pacemaker implants. Males and females were stratified and classified into three different groups; 1) NW (n ¼ 50; BMI < 25.0 kg/m2; 50% males); 2) OW (n ¼ 50; BMI 25.0e29.9 kg/m2; 50% males); 3) OB (n ¼ 50; BMI 30.0 kg/m2; 50% males). Table 1 displays participant characteristics. 2.1. Procedures Prior to data collection, the university's Institutional Review Board approved this study. A single visit lasting approximately 45e60 min was required for each participant with all assessments completed within the university's exercise physiology laboratory. Participants were instructed to abstain from any exercise and alcohol consumption for 24 h and from eating or drinking, with the exception of water, for 4 h prior to testing. They were also instructed to wear minimal, tight fitting, lycra clothing that was free from metal. If the participant did not have the proper attire, tight fitting lycra shorts and sports bra were provided as needed. Informed written consent was obtained followed by a brief demographic questionnaire to gather information required for the assessments such as age, sex and race. Participants were then asked to void their bladder and remove extra clothing, shoes, metal, watches, wristbands, and jewelry. Height and weight were measured with a Tanita WB-3000 (Tanita, Arlington Heights, IL, USA) digital physician's scale and BMI was calculated from height and weight measurements (kg/m2). Each participant then completed two body composition assessments
Table 1 Descriptive characteristics of the study sample (n ¼ 150). Normal Weight
Age (yrs) Height (cm) Weight (kg) BMI (kg/m2) WC (cm) BMIWO BF% BMIJA BF% BMIDE BF% BMIGA BF% RFM BF% BOD POD® BF% 3C Model FFM (kg) 3C Model FM (kg) 3C Model BF%
Overweight
Obese
Females (n ¼ 25)
Males (n ¼ 25)
Females (n ¼ 25)
Males (n ¼ 25)
Females (n ¼ 25)
Males (n ¼ 25)
26.68 ± 10.62 163.64 ± 5.80 57.27 ± 6.98 21.32 ± 1.90 67.60 ± 4.20 25.73 ± 2.61 23.59 ± 4.15 26.32 ± 3.74 25.53 ± 4.68 27.44 ± 2.93 26.14 ± 7.19 42.26 ± 3.86 15.01 ± 5.54 25.70 ± 6.97
28.48 ± 10.50 177.54 ± 5.66 73.56 ± 7.42 23.11 ± 1.57 78.23 ± 4.76 18.26 ± 2.11 17.63 ± 3.08 18.08 ± 3.59 16.84 ± 3.40 18.48 ± 2.57 16.30 ± 6.89 62.42 ± 6.87 11.14 ± 4.75 15.02 ± 5.75
28.16 ± 10.58 164.49 ± 6.67 74.06 ± 7.62 27.34 ± 1.54 80.11 ± 5.16 33.99 ± 2.11 35.20 ± 2.51 33.88 ± 3.36 37.33 ± 2.37 34.81 ± 2.59 37.41 ± 6.13 46.66 ± 5.94 27.40 ± 4.83 36.97 ± 5.10
32.88 ± 11.04 180.16 ± 9.45 88.75 ± 11.68 27.23 ± 1.56 86.36 ± 6.34 23.79 ± 2.09 24.84 ± 2.45 24.04 ± 3.22 23.63 ± 2.37 22.08 ± 3.61 19.11 ± 6.64 71.65 ± 9.62 17.10 ± 6.85 19.07 ± 6.34
35.32 ± 13.55 163.85 ± 5.93 93.53 ± 12.48 34.94 ± 4.56 95.62 ± 11.68 44.20 ± 5.79 43.72 ± 3.01 44.47 ± 6.45 45.91 ± 3.76 41.31 ± 3.82 43.80 ± 4.96 52.57 ± 6.40 40.96 ± 9.13 43.50 ± 5.20
33.56 ± 9.81 175.08 ± 8.97 107.57 ± 20.93 34.79 ± 4.23 103.81 ± 11.98 34.53 ± 5.99 34.36 ± 3.72 33.88 ± 5.75 31.47 ± 3.82 30.44 ± 3.74 33.08 ± 7.65 71.56 ± 10.51 36.01 ± 15.24 32.56 ± 8.42
BMI ¼ body mass index; WC ¼ waist circumference; BMIWO ¼ BMI-based body fat using Womersley and Durnin equation; BMIJA ¼ BMI-based body fat using Jackson et al. equation; BMIDE ¼ BMI-based body fat using Deurenberg et al. equation; BMIGA; BMI-based body fat using Gallagher et al. equation BF% ¼ body fat percentage; RFM ¼ relative fat mass; 3C Model ¼ three-compartment model; FFM ¼ fat-free mass; FM ¼ fat mass.
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BF% (females) ¼ (1.37 BMI) - 3.47
which included air-displacement plethysmography with the BOD POD® (COSMED USA Inc, Concord, CA, USA) and bioelectrical impedance analysis (BIA) with the InBody 720 (InBodyUSA, Cerritos, CA, USA). Following these assessments, WC was obtained from each participant.
BF% (men) ¼ (3.76 BMI) e (0.04 BMI2) e 47.80
2.2. Air displacement plethysmography
BF% (women) ¼ (4.35 BMI) e (0.05 BMI2) e 46.24
The BOD POD®, which uses air displacement plethysmography, was used to estimate criterion BV. Prior to testing each day, the BOD POD® was calibrated according to manufacturer specifications. Participants were required to wear minimal, lycra compression clothing and lycra swim-caps were provided and required for testing. Body mass (BM) was measured using the manufacturer's scale that interfaces with the BOD POD ®. In order to assess BV, participants were instructed to sit in the BOD POD ® chamber for 2 trials of roughly 50 s for each trial. A third trial was necessary if the first two trials did not agree within 150 ml of each other. Thoracic gas volume was estimated for all assessments. Body density (i.e., BV and BM) obtained from the BOD POD® was used to calculate BF% via the 3C model.
Jackson et al. (BMIJA) [14]:
Deurenberg et al. (BMIDE) [18]: BF% ¼ (1.20 BMI) þ (0.23 age) e (10.8 sex) e 5.4 sex ¼ 1 for men and 0 for women. Gallagher et al. (BMIGA) [12]: BF% ¼ 76.0 e (1097.8 [1/BMI]) e (20.6 sex) þ (0.053 age) þ (95.0 Asian [1/BMI]) e (0.044 Asian age) þ (154 sex [1/BMI]) þ (0.034 sex age) sex ¼ 1 for male and 0 for female; Asian ¼ 1 for Asians and 0 for the other races. Siri Three-Compartment Model [21]:
2.3. Bioelectrical impedance analysis
FM (kg) ¼ (2.118 BV) e (0.78 TBW) e (1.351 BM)
The InBody 720 was used to calculate TBW for the 3C model. This analyzer utilizes a tetrapolar 8-point tactile electrode system and takes 30 impedance measurements with six frequencies (1 kHz, 5 kHz, 50 kHz, 250 kHz, 500 kHz, and 1000 kHz) over a test duration of approximately 60 s. Per manufacturer specifications, a participant's age, sex, and height were entered into the InBody 720. Participants were then instructed to remove their socks and cleanse their hands and feet with an antibacterial tissue purchased from the manufacturer. The testing protocol requires individuals to stand upright with their feet centered on the electrodes, and to hold the hand electrodes while placing their arms wide enough to ensure no contact between their arms and torso. Further instruction to maintain good posture and abstain from speaking was given before each assessment.
BF% ¼ (FM ÷ BM) 100
2.4. Waist circumference Waist circumference (WC) was obtained per American College of Sports Medicine guidelines [9]. A spring-loaded Gulick tape measure (Fabrication Enterprises, White Plains, NY, USA) was used for all measurements. A horizontal measurement was taken at the narrowest portion of the torso above the umbilicus and below the xiphoid process while participants stood with feet together and arms at their sides. Two measurements were taken and a third was obtained if the first two were not within 5 mm of each other. 2.5. Body composition equations The following equations were used for estimating BF% based upon RFM, BMI, and 3C model. Relative Fat Mass [17]: BF% ¼ [64 - (20 height ÷ WC) þ (12 sex)] where male is 0, and female is 1, and height and WC are expressed in meters. Womersley and Durnin (BMIWO) [16]: BF% (males) ¼ (1.34 BMI) - 12.47
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2.6. Statistical analyses Statistical analyses were performed using Statistical Package for the Social Sciences (SPSS) for Windows (SPSS 25.0. Chicago IL). Data were stratified by sex (i.e., males and females) and BMI classification (i.e., NW, OW, and OB). Difference scores for each participant were created by subtracting an individual's estimated BF% (e.g., BMIWO) from their 3C model BF%. A series of 2 (SEX) 3 (BMI) ANOVAs were then used to assess the observed error of estimated BF% and the 3C model. Statistical significance was indicated using an a level of p < 0.05. All data are expressed as mean ± standard deviation (M ± SD) unless otherwise indicated. 3. Results A significant SEX BMI interaction was detected when examining the observed error between all of the BMI-based body fat equations and 3C model BF% (all p < 0.05). Post hoc analysis indicated the differences in BF% were statistically significant between OW females and males for all equations (BMIWO: 2.99 ± 4.79% vs. 4.71 ± 5.86%, p ¼ 0.003; BMIJA: 1.77 ± 4.83% vs. 5.77 ± 5.85%, p < 0.001; BMIDE: 3.09 ± 4.80% vs. 4.97 ± 5.98%, p < 0.001; BMIGA: 0.36 ± 4.51% vs. 4.56 ± 5.55%, p ¼ 0.018, respectively). In addition, there were significant differences between females and males classified as NW (BMIJA: 2.11 ± 4.15% vs. 2.61 ± 5.98%, p ¼ 0.008) and OB (BMIGA: 2.40 ± 3.36% vs. 1.09 ± 6.40%, p ¼ 0.006). There was a significant SEX BMI interaction when examining the observed error between RFM and 3C model BF% (p ¼ 0.004). Post hoc analysis revealed the differences in BF% were statistically significant between males and females classified as OW (2.17 ± 4.84% vs. 3.01 ± 5.34%, respectively; p ¼ 0.004) whereas no significant differences were observed between males and females classified as NW and OB (p ¼ 0.28 and 0.93, respectively). Observed error between the anthropometric equations and 3C model, stratified by sex and BMI classification are displayed in Table 2.
Please cite this article as: Nickerson BS et al., Relative accuracy of anthropometric-based body fat equations in males and females with varying BMI classifications, Clinical Nutrition ESPEN, https://doi.org/10.1016/j.clnesp.2019.10.014
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Table 2 Observed error between anthropometric equations and 3C model, stratified by sex and BMI classification (n ¼ 150). Normal Weight Females (n ¼ 25) BMIWO BF% BMIJA BF% BMIDE BF% BMIGA BF% RFM BF%
0.03 2.11a 0.61 0.18 1.74
Overweight Males (n ¼ 25) 3.24 2.61 3.06 1.82 3.46
Obese
Females (n ¼ 25) b
2.99 1.77b 3.09b 0.36b 2.17b
Males (n ¼ 25)
Females (n ¼ 25)
Males (n ¼ 25)
4.72 5.77 4.97 4.56 3.01
0.69 0.22 0.97 2.40c 2.20
1.98 1.80 1.33 1.09 2.11
3C Model ¼ three-compartment model; BMI ¼ body mass index; BF% ¼ body fat percentage; BMIWO ¼ BMI-based body fat using Womersley and Durnin equation; BMIJA ¼ BMI-based body fat using Jackson et al. equation; BMIDE ¼ BMI-based body fat using Deurenberg et al. equation; BMIGA; BMI-based body fat using Gallagher et al. equation; RFM ¼ relative fat mass. a Significantly different than normal weight males. b Significantly different than overweight males. c Significantly different than obese males.
4. Discussion The purpose of this study was to examine the independent and interactive effects of sex and BMI classification on the relative accuracy of BMI-based body fat equations and RFM. The current study findings indicate that practitioners should be aware of the impact that sex and BMI classification can have on the observed error of BMI-based body fat equations and RFM. For instance, the observed error between all of the prediction methods (i.e., BMI-based body fat equations and RFM) and 3C model BF% were significant for OW females and males. Specifically, the estimated BF% of OW females were generally underestimated whereas OW males tended to be overestimated. In addition, the differences in observed error was also applicable for females and males classified as NW and OB when using BMIJA and BMIGA, respectively. These findings indicate that RFM does not appear to add benefit over BMIWO and BMIDE when stratifying males and females by BMI classification. Woolcott and Bergman [17] evaluated the accuracy of RFM in a separate validation sample, but used DXA as a reference technique. Similarly, Fedewa et al. [19] recently found that RFM yields greater relative accuracy than the BAI when compared against DXA. More research is available on the relative accuracy of BMI-based body fat equations. For instance, Esco et al. [10] demonstrated that BMIJA was not statistically significant when compared to DXA (0.85%) in adults with Down syndrome. However, BMIWO, BMIDE, and BMIGA all significantly underestimated DXA-derived BF% (4.89 to e 5.55%) in the special population [10]. The discrepancies between the current study and Esco et al. [10] could be attributed to different criterion methods used (i.e., 3C model and DXA, respectively). To support this postulation, Nickerson et al. [11] revealed that the observed error of BMIWO, BMIJA, BMIDE, and BMIGA ranged from 0.60 to 1.00% for females and varied from 3.30 to 5.50% for males, which is similar to findings of the current study. Nickerson et al. [11] utilized a multicompartment model (i.e., four-compartment [4C] model) as a criterion, which produces nearly identical validity statistics as a 3C model and is more acceptable than DXA [6,7]. The observed error of all the BMI-based body fat equations in the current study were significant when comparing females and males with a BMI classified as OW. The reason for the differences could be related to the inability of BMI to distinguish between fat and lean mass, which is problematic for individuals with large muscle mass. For instance, the males classified as OW in the present study had the largest 3C model FFM. This implies that some of the males classified as OW in the current study might have been physically active with high quantities of muscle mass. Theoretically, this might have introduced differences if the group of OW females did not have similar physical activity patterns of behavior as the OW males. Therefore, future studies evaluating the relative accuracy of BMI-based BF% equations should consider accounting for physical activity.
Zanovec et al. [13] developed a BMI-based body fat equation (BMIZ) that employs physical activity as a predictor variable of BF% via the International Physical Activity Questionnaire [22]. It is no surprise that Nickerson et al. [11] found BMIZ produced the lowest observed error when compared to other BMI-based body fat equations (i.e., BMIWO, BMIDE, BMIJA, and BMIGA) in a group of physically active men and women. As a result, future research in a group of individuals with varying physical activity levels (e.g., sedentary, moderately active, physically active, etc.) is warranted. The observed error of RFM was similar as the BMI-based body fat equations when compared against the 3C model. Specifically, there were significant differences in the observed error between OW males and females. The reason for these dissimilarities could be related to the absolute differences in WC for each BMI group. For example, the absolute difference (i.e., mean differences) in WC was smaller between males and females classified as OW (6.25 cm) than those categorized as NW and OB (10.63 and 8.19 cm, respectively). It is possible that the relative accuracy of RFM is enhanced when WC varies to a larger extent between sexes. Another potential explanation is the characteristics of the validation sample utilized to develop the RFM equation. Particularly, the mean BMI of the development sample of males and females for RFM were classified as OW (28.2 and 27.9 kg/m2, respectively) [17]. However, the mean WC values for the development sample of males and females were 93.1 and 99.5 cm, respectively, which are similar to measurements obtained in the OB individuals in the present study. This indicates there are differences between samples, which might partially explain some of the current study findings. Due to these inconsistencies, future research might seek to stratify subjects based upon WC. The differences in observed error between NW and OB persons when using BMIJA and BMIGA, respectively, are also worth discussion. Jackson et al. [14] reported that mean differences between BMIJA and HW were larger in women when BMI values were below 25.0 kg/m2, but that the differences became smaller as women's BMI approached 30 kg/m2. These findings might be an indication of why the observed error for NW females was significantly different than the NW males in the present study. Finally, the differences in observed error between OB males and females when using BMIGA in the current study could be related the body composition methods used to derive the prediction equation. For instance, BMIGA was developed from three different universities, which used a combination of 4C models and DXA [12]. Researchers noted that there was a statistically significant bias between DXA and 4C model BF% and that ideally all testing sites would have used the same method for estimating adiposity [12]. The bias of DXA reported by Gallagher et al. [12] is similar to previous findings by Nickerson and Tinsley [6] and Moon et al. [7] whom both found significant bias (i.e., overestimation) when comparing DXA to advanced multicompartment models (i.e., 5C model). Consequently, the use of
Please cite this article as: Nickerson BS et al., Relative accuracy of anthropometric-based body fat equations in males and females with varying BMI classifications, Clinical Nutrition ESPEN, https://doi.org/10.1016/j.clnesp.2019.10.014
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DXA as a reference method might have embedded error in the development of BMIGA. Although the current study has many strengths, it is not without limitations. For example, one limitation of the current study is the inability to evaluate BF% with other anthropometric-based measurements such as the BAI. For instance, the estimation of BF% via the BAI is determined with height and hip circumference measurements. The present study did not measure hip circumference. Therefore, the BAI and other anthropometric-based body fat methods that account for hip circumference could not be evaluated. Nevertheless, Fedewa et al. [19] previously revealed that RFM is more accurate than the BAI when compared to DXA in a group of males and females with- and without DS. Therefore, the present study sought to advance these findings by examining whether RFM adds any benefit over BMI-based body fat equations. 5. Conclusions The purpose of this study was to examine the independent and interactive effects of sex and BMI classification on the relative accuracy of BMI-based body fat equations and RFM. The observed error between males and females classified as OW were significant for all comparisons. Further, significant differences between males and females were observed in the NW and OB individuals when using BMIJA and BMIGA, respectively. Collectively, these findings highlight that RFM does not appear to overcome error commonly associated with BMI-based body fat equations when stratifying by sex and BMI classification. Nonetheless, practitioners can use BMIWO, BMIDE, and RFM in males and females classified as NW or OB, but should employ caution prior to use in OW persons. Financial disclosure Brett S. Nickerson has no financial disclosures. Cherilyn N. McLester has no financial disclosures. John R. McLester has no financial disclosures. Brian M. Kliszczewicz has no financial disclosures. Declaration of Competing Interest The authors have no potential, perceived, or real conflicts of interest to disclose. References [1] Brown JC, Meyerhardt JA. Obesity and energy balance in GI cancer. J Clin Oncol 2016;34:4217e24.
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Please cite this article as: Nickerson BS et al., Relative accuracy of anthropometric-based body fat equations in males and females with varying BMI classifications, Clinical Nutrition ESPEN, https://doi.org/10.1016/j.clnesp.2019.10.014