Validity and reliability of a 4-compartment body composition model using dual energy x-ray absorptiometry-derived body volume

doi:10.1016/j.clnu.2016.05.006

Clinical Nutrition

Volume 36, Issue 3, June 2017, Pages 825-830

https://doi.org/10.1016/j.clnu.2016.05.006 Get rights and content

Summary

Background

Body volume (BV), one component of a four-compartment (4C) body composition model, is commonly assessed using air displacement plethysmography (BodPod). However, dual-energy x-ray absorptiometry (DEXA) has been proposed as an alternative method for calculating BV.

Aims

This investigation evaluated the validity and reliability of DEXA-derived BV measurement and a DEXA-derived 4C model (DEXA-4C) for percent body fat (%BF), fat mass (FM), and lean mass (LM).

Methods

A total sample of 127 men and women (Mean ± SD; Age: 35.8 ± 9.4 years; Body Mass: 98.1 ± 20.9 kg; Height: 176.3 ± 9.2 cm) completed a traditional 4C body composition reference assessment. A DEXA-4C model was created by linearly regressing BodPod BV with DEXA FM, LM, and bone mineral content as independent factors. The DEXA-4C model was validated in a random sub-sample of 27 subjects. Reliability was evaluated in a sample of 40 subjects that underwent a second session of identical testing.

Results

When BV derived from DEXA was applied to a 4C model, there were no significant differences in %BF (p = 0.404), FM (p = 0.295), or LM (p = 0.295) when compared to the traditional 4C model. The approach was also reliable; BV was not different between trials (p = 0.170). For BV, %BF, FM, and LM relative consistency values ranged from 0.995 to 0.998. Standard error of measurement for BV was 0.62 L, ranging from 0.831 to 0.960 kg. There were no significant differences between visits for %BF (p = 0.075), FM (p = 0.275), or LM (p = 0.542).

Conclusion

The DEXA-4C model appears to be a valid and reliable method of estimating %BF, FM, and LM. The prediction of BV from DEXA simplifies the acquisition of 4C body composition by eliminating the need for an additional BV assessment.

Introduction

The science of body composition measurement is expanding as it plays an important role in disease detection and prevention. Excess fat mass has been associated with orthopedic injury, cardiovascular disease, and other indices of metabolic dysfunction [1], [2], [3]. Conversely, inadequate lean mass and bone mineral content have been associated with increased musculoskeletal injury risk in aging and clinical populations, as well as compromised performance in athletes [4], [5]. Sophisticated anthropomorphic measures such as percent body fat (%BF), regional adiposity, and fat to lean mass ratio have been demonstrated as more suitable health predictors than the commonly used body mass index (BMI) [6], [7]. A variety of methods for assessing whole body composition have been developed to better evaluate each individual's health status, but technology is improving in order to better estimate body tissues.

Common body composition assessment techniques such as skinfold analysis and bioelectrical impedance are based on two-compartment (2C) models, which divide the body into fat mass (FM) and fat free mass (FFM). Such models assume uniform composition of FFM in making anthropomorphic predictions, despite the variation that exists in total body water (TBW), protein mass, and bone mineral content (BMC) [8], [9]. To compensate for such assumptions, multi-compartment models have been developed to individually assess the varying components of FFM [10]. A four-compartment (4C) model factoring in body mass (BM), body volume (BV), TBW, and BMC is considered by many as the gold standard in body composition [11].

The 4C model measurement, and associated body compartments, is accomplished using a variety of equipment, but also requires considerable time and cost. Dual energy x-ray absorptiometry (DEXA) is used to estimate total body BMC. The gold standard for TBW measurement is the use of deuterium oxide dilution; however, estimates have been shown to be valid when using multifrequency bioelectrical impedance spectroscopy (BIS) [12]. Historically, underwater weighing (UWW) has been the standard method of determining BV based on hydrodensitometry. In recent decades, air displacement plethsmyography (ADP) has replaced UWW as a less invasive and more reliable method of assessing BV [13], [14]. Though considered more convenient than earlier methods, ADP requires specialized equipment (BodPod^®), tight fitting clothing, and may be highly variable based on subject attire and body hair [15]. Additionally, both ADP and UWW must make assumptions regarding trapped air in the digestive tract or lungs that may compromise validity in certain individuals [16].

Dual x-ray absorptiometry may serve as an alternative method of estimating BV [11], [16]. Unlike other displacement techniques, the x-ray attenuations utilized by DEXA exclude internal air voids when analyzing soft tissues, and therefore may provide more accurate volume estimations. One previous investigation from Wilson et al. [11] has suggested that DEXA may be used to determine BV, but the population utilized was small (n = 11) and the authors suggested that further validation with a larger sample is necessary. The ability to use a DEXA-derived method for BV estimation may eliminate the need for ADP and/or UWW, reducing the time and cost required to obtain BV and use in a multi-compartment model. Greater testing efficiency would make use of a 4C body composition model more practical in both clinical and laboratory settings. Therefore, the aims of the current investigation were three-fold: 1) to develop a method of deriving BV from standard DEXA tissue measurements; 2) evaluate the validity of using DEXA-derived BV in a 4C body composition model; and 3) determine the reliability of DEXA-derived BV and 4C composition variables, including %BF, FM, and LM.

Section snippets

Participants

A sample of 127 men and women (Mean ± SD; Age: 35.8 ± 9.4 years; Body Mass: 98.1 ± 20.9 kg; Height: 176.3 ± 9.2 cm, BMI: 31.4 ± 5.5 kg m⁻²) volunteered to participate in body composition assessments for two separate approved studies (IRB#12-1026, 14-1045). Participant BMIs ranged from normal to obese (BMI:19.9–45.6 kg m⁻²); with 104 Caucasians, 19 African Americans, and 3 Hispanics. A sample of 100 people was used to develop the coefficients reported in Equation (2); a subsample of 27 was used

Validity

The inverse of the density coefficients of FM, LM, and BMC determined to predict BV were 0.84 (P < 0.01), 1.03 (P < 0.01), and 11.63 (P = 0.853) respectively, with a residual volume of −3.12 L. $DEXA Volume (L) = \frac{F M}{0.84} + \frac{L M}{1.03} + \frac{B M C}{11.63} + (- 3.12)$

Compared to the sub-sample, BV derived from DEXA (94.55 ± 17.74 L) was not significantly different than BodPod BV (94.33 ± 17.58 L; P = 0.295, CI: [−1.05–0.01]). No significant differences were seen between any of the traditional predictions and the body

Discussion

Traditionally, the measurement of body composition using a 4C-model has required a BV measurement using either hydrostatic weighing or ADP and measurements of TBW from deuterium oxide. This multi-compartment measurement requires about 4 h to complete for one participant, allowing for deuterium oxide to equilibrate, and is considered a gold standard [26]. The results of the current study demonstrate that a single DEXA scan may be used as an accurate method for determining BV and subsequent

Conclusions

Valid and reliable body composition techniques are essential for detecting clinical conditions associated with both over- and under-fatness, overall health, injury prevention, and tracking changes from diet and exercise [2], [4], [30], [32], [33], [34]. Despite the known improvement in precision when using a multi-compartment model for body composition, many researchers, clinicians, and coaches still use single 2C techniques or DEXA (3C) as methods for determining body composition. The choice

Conflict of interest

The authors have no conflicts to declare.

Acknowledgments

Sources of Support: This study was supported by the Nutrition Obesity Research Center (P30DK56350), North Carolina Occupational Safety and Health Education and Research Center Pilot Grant (T42OH008673), and the University of North Carolina Junior Faculty Development Award. The project described was also supported by the National Center for Advancing Translational Sciences, National Institutes of Health (1KL2TR001109). The content is solely the responsibility of the authors and does not

References (34)

M. Zamboni et al.
Sarcopenic obesity: a new category of obesity in the elderly
Nutr Metab Cardiovasc Dis
(2008)
A.H. Forslund
Evaluation of modified multicompartment models to calculate body composition in healthy males
Appl Radiat Isot Incl Data Instrum Methods Use Agric Ind Med
(1998)
S.B. Heymsfield et al.
Body composition of humans: comparison of two improved four-compartment models that differ in expense, technical complexity, and radiation exposure
Am J Clin Nutr
(1990)
J.P. Wilson et al.
Dual-energy X-ray absorptiometry-based body volume measurement for 4-compartment body composition
Am J Clin Nutr
(2012)
J.R. Moon et al.
Total body water changes after an exercise intervention tracked using bioimpedance spectroscopy: a deuterium oxide comparison
Clin Nutr
(2009)
J.P. Wilson et al.
Total and regional body volumes derived from dual-energy X-ray absorptiometry output
J Clin Densitom Off J Int Soc Clin Densitom
(2013)
Z. Wang et al.
Multicomponent methods: evaluation of new and traditional soft tissue mineral models by in vivo neutron activation analysis
Am J Clin Nutr
(2002)
E.W. Demerath et al.
Visceral adiposity and its anatomical distribution as predictors of the metabolic syndrome and cardiometabolic risk factor levels
Am J Clin Nutr
(2008)
D.A. Schoeller et al.
QDR 4500A dual-energy X-ray absorptiometer underestimates fat mass in comparison with criterion methods in adults
Am J Clin Nutr
(2005)
D.A. Fields et al.
Body-composition assessment via air-displacement plethysmography in adults and children: a review
Am J Clin Nutr
(2002)

M.D. Jensen et al.

Assessment of body composition with use of dual-energy x-ray absorptiometry: evaluation and comparison with other methods

Mayo Clin Proc

(1993)

K.E. Friedl et al.

Reliability of body-fat estimations from a four-compartment model by using density, body water, and bone mineral measurements

Am J Clin Nutr

(1992)

A. Booth et al.

Detrimental and protective fat: body fat distribution and its relation to metabolic disease

Horm Mol Biol Clin Investig

(2014)

K. Dhana et al.

Body shape index in comparison with other anthropometric measures in prediction of total and cause-specific mortality

J Epidemiol Community Health

(2015)

M.D. Jensen

Role of body fat distribution and the metabolic complications of obesity

J Clin Endocrinol Metab

(2008)

E.J. Roelofs et al.

Muscle size, quality, and body composition: characteristics of division I cross-country runners

J Strength Cond Res/Natl Strength Cond Assoc

(2015)

J.W. Bea et al.

Risk of mortality according to body mass index and body composition among postmenopausal women

Am J Epidemiol

(2015)

Cited by (65)

Development and validation of a rapid multicompartment body composition model using 3-dimensional optical imaging and bioelectrical impedance analysis
2024, Clinical Nutrition
The multicompartment approach to body composition modeling provides a more precise quantification of body compartments in healthy and clinical populations. We sought to develop and validate a simplified and accessible multicompartment body composition model using 3-dimensional optical (3DO) imaging and bioelectrical impedance analysis (BIA).
Samples of adults and collegiate-aged student-athletes were recruited for model calibration. For the criterion multicompartment model (Wang-5C), participants received measures of scale weight, body volume (BV) via air displacement, total body water (TBW) via deuterium dilution, and bone mineral content (BMC) via dual energy x-ray absorptiometry. The candidate model (3DO-5C) used stepwise linear regression to derive surrogate measures of BV using 3DO, TBW using BIA, and BMC using demographics. Test-retest precision of the candidate model was assessed via root mean square error (RMSE). The 3DO-5C model was compared to criterion via mean difference, concordance correlation coefficient (CCC), and Bland-Altman analysis. This model was then validated using a separate dataset of 20 adults.
67 (31 female) participants were used to build the 3DO-5C model. Fat-free mass (FFM) estimates from Wang-5C (60.1 ± 13.4 kg) and 3DO-5C (60.3 ± 13.4 kg) showed no significant mean difference (−0.2 ± 2.0 kg; 95 % limits of agreement [LOA] −4.3 to +3.8) and the CCC was 0.99 with a similar effect in fat mass that reflected the difference in FFM measures. In the validation dataset, the 3DO-5C model showed no significant mean difference (0.0 ± 2.5 kg; 95 % LOA -3.6 to +3.7) for FFM with almost perfect equivalence (CCC = 0.99) compared to the criterion Wang-5C. Test-retest precision (RMSE = 0.73 kg FFM) supports the use of this model for more frequent testing in order to monitor body composition change over time.
Body composition estimates provided by the 3DO-5C model are precise and accurate to criterion methods when correcting for field calibrations. The 3DO-5C approach offers a rapid, cost-effective, and accessible method of body composition assessment that can be used broadly to guide nutrition and exercise recommendations in athletic settings and clinical practice.
Evaluation of a Rapid Four-Compartment Model and Stand-Alone Methods in Hispanic Adults
2023, Journal of Nutrition
A rapid 4-compartment (4C) model integrates dual-energy x-ray absorptiometry (DXA) and multi-frequency bioimpedance analysis (MFBIA), which may be useful for clinical and research settings seeking to employ a multi-compartment model.
This study aimed to determine the added benefit of a rapid 4C model over stand-alone DXA and MFBIA when estimating body composition.
One hundred and thirty participants (n = 60 male; n = 70 female) of Hispanic descent were included in the present analysis. A criterion 4C model that employed air displacement plethysmography (body volume), deuterium oxide (total body water), and DXA (bone mineral) was used to measure fat mass (FM), fat-free mass (FFM), and body fat percent (%BF). A rapid 4C model (DXA-derived body volume and bone mineral; MFBIA-derived total body water) and stand-alone DXA (GE Lunar Prodigy) and MFBIA (InBody 570) assessments were compared against the criterion 4C model.
Lin’s concordance correlation coefficient values were >0.90 for all comparisons. The standard error of the estimates ranged from 1.3 to 2.0 kg, 1.6 to 2.2 kg, and 2.1 to 2.7% for FM, FFM, and %BF, respectively. The 95% limits of agreement ranged from ±3.0 to 4.2 kg, ±3.1 to 4.2 kg, and ±4.9 to 5.2% for FM, FFM, and %BF, respectively.
Results revealed that all 3 methods provided acceptable body composition results. The MFBIA device used in the current study may be a more economically friendly option than DXA or when there is a need to minimize radiation exposure. Nonetheless, clinics and laboratories that already have a DXA device in place or that value having the lowest individual error when conducting a test may consider continuing to use the machine. Lastly, a rapid 4C model may be useful for assessing body composition measures observed in the current study and those provided by a multi-compartment model (e.g., protein).
Total and regional dual X-ray absorptiometry derived four-compartment model
2023, Clinical Nutrition ESPEN
Dual X-ray absorptiometry (DXA) software allows for total and regional (i.e., arms and legs) assessment of body composition, with recent advancements allowing for DXA derived volume. The use of DXA derived volume allows for the development of a convenient four-compartment model to accurately measure body composition. The purpose of the current study is to evaluate the validity of a regional DXA derived four-compartment model.
A total of 30 males and females underwent one whole body DXA scan, underwater weighing, total and regional bioelectrical impedance spectroscopy, and regional measures of water displacement. Manually created region of interest boxes assessed regional DXA body composition. Linear regression models with fat mass from the DXA as the dependent variable and body volume from water displacement, total body water from bioelectrical impedance spectroscopy, and DXA bone mineral and body mass as independent variables created regional four-compartment models. Measures of fat-free mass and percent fat were calculated using the four-compartment derived fat mass. T-tests assessed DXA derived four-compartment model to the traditional four-compartment model with volume assessed by water displacement. Regression models were cross-validated using the Repeated k-fold Cross Validation method.
Arm and leg regional DXA derived four-compartment model for fat mass (p = 0.999, both arm and leg), fat-free mass (p = 0.999, both arm and leg), and percent fat (arm: p = 0.766; leg: p = 0.938) were not significantly different from the regional four-compartment model with regional volume measured via water displacement. Cross-validation of each model produced R² values of 0.669 for the arm and 0.783 for the leg.
The DXA can be used to create four-compartment model for estimating total and regional fat mass, fat-free mass, and percent fat. Therefore, these results allow for a convenient regional four-compartment model with DXA derived regional volume.
Dual X-ray absorptiometry-derived total and regional body volume
2022, Clinical Nutrition ESPEN
Citation Excerpt :
These coefficients correspond with densities of 0.83, 1.06, and 0.53 g/mL for fat, lean, and BMC, respectively. Studies conducted by Smith-Ryan et al. and Wilson et al. have reported fat, lean, and BMC densities as 0.84 vs. 0.88, 1.03 vs. 1.05, and 11.63 vs. 4.85 kg/L [6,7]. While the densities for fat and lean are similar between studies, the density for BMC are not only different between these studies, but also the present study.
Although dual X-ray absorptiometry (DXA) has been used to determine total body volume, using DXA to determine regional (i.e., arm and leg) volumes needs further assessment. Thus, the aim of the present study is to evaluate the validity of total and regional DXA-derived body volume compared to a traditional method for measuring body volume.
A total of 30 males and females (Age: 25.9 ± 4.0 yrs; Height: 1.75 ± 0.10 m; Weight: 70.98 ± 14.02 kg) underwent one whole body DXA scan, underwater weighing, and regional measures of volume via water displacement. Manually created DXA region of interest boxes were used to determine regional DXA body composition. Total body volume was calculated by taking the participant’s dry weight and dividing it by the average density from underwater weighing. Linear regression models with body volume from underwater weighing for total body volume and water displacement for regional volume as the dependent variable and DXA lean mass, fat mass, and bone mass as independent variables created total and regional DXA-derived body volume. T-tests assessed DXA-derived body volume to the traditional method of body volume assessment. Regression models were cross-validated using the Repeated k-fold Cross Validation method.
DXA-derived total body volume was not significantly (p = 0.999) different from total body volume measured via total body water displacement. In addition, both arm and leg regional DXA-derived volume was not significantly different (p = 0.999) compared to regional volume measured by regional water displacement. Cross-validation of each model produced R² values of 0.992, 0.923, and 0.932 for total body, arm, and leg, respectively.
The DXA may be used as valid method for estimating total and regional body volume. Thus, these results expand the DXA's capabilities and potentially allow for a convenient regional four-compartment model with DXA-derived regional volume.
Visual body composition assessment methods: A 4-compartment model comparison of smartphone-based artificial intelligence for body composition estimation in healthy adults
2022, Clinical Nutrition
Visual body composition (VBC) estimates produced from smartphone-based artificial intelligence represent a user-friendly and convenient way to automate body composition remotely and without the inherent geographical and monetary restrictions of other body composition methods. However, there are limited studies that have assessed the reliability and agreement of this method and thus, the aim of this study was to evaluate VBC estimates compared to a 4-compartment (4C) criterion model.
A variety of body composition assessments were conducted across 184 healthy adult participants (114 F, 70 M) including dual-energy X-ray absorptiometry and bioimpedance spectroscopy for utilization in the 4C model and automated assessments produced from two smartphone applications (Amazon Halo®, HALO; and myBVI®) using either Apple® or Samsung® phones. Body composition components were compared to a 4C model using equivalence testing, root mean square error (RMSE), and Bland–Altman analysis. Separate analyses by sex and racial/ethnic groups were conducted. Precision metrics were conducted for 183 participants using intraclass correlation coefficients (ICC), root mean squared coefficients of variation (RMS-%CV) and precision error (PE).
Only %fat produced from HALO devices demonstrated equivalence with the 4C model although mean differences for HALO were <±1.0 kg for FM and FFM. RMSEs ranged from 3.9% to 6.2% for %fat and 3.1–5.2 kg for FM and FFM. Proportional bias was apparent for %fat across all VBC applications but varied for FM and FFM. Validity metrics by sex and specific racial/ethnic groups varied across applications. All VBC applications were reliable for %fat, fat mass (FM), and fat-free mass (FFM) with ICCs ≥0.99, RMS-%CV between 0.7% and 4.3%, and PEs between 0.3% and 0.6% for %fat and 0.2–0.5 kg for FM and FFM including assessments between smartphone types.
Smartphone-based VBC estimates produce reliable body composition estimates but their equivalence with a 4C model varies by the body composition component being estimated and the VBC being employed. VBC estimates produced by HALO appear to have the lowest error, but proportional bias and estimates by sex and race vary across applications.
Influence of the amount of skeletal muscle mass on rocuronium-induced neuromuscular block
2022, Anaesthesia Critical Care and Pain Medicine
To evaluate the effects of skeletal muscle mass on the rocuronium-induced neuromuscular block.
A prospective, double-blinded, observational study.
A tertiary care university hospital.
One hundred one patients aged 18–65 years who were scheduled to undergo major surgery lasting more than 1 h under general anaesthesia.
All participants underwent body composition analysis before anaesthesia and were allocated into two groups; the muscular and non-muscular group, according to skeletal muscle mass. During anaesthesia induction, rocuronium 1.0 mg kg⁻¹ of total body weight was injected followed by neuromuscular monitoring using train-of-four stimulation every 15 s.
The onset time of rocuronium included the elapsed time from the rocuronium injection until 95% depression of first twitch (T1) and the time to no response to TOF stimulation. The duration was evaluated as the elapsed time from the rocuronium injection to 25% recovery of the final T1 (T_DUR25), and the time to the reappearance of T1 (T_TOF1) and T4 (T_TOF4). These pharmacologic data were compared between two groups.
There was no significant difference in the onset time of rocuronium between the two groups. However, T_DUR25 (min) was significantly shorter in the muscular group than in the non-muscular group (p = 0.035 and p = 0.014 in males and females, respectively). T_TOF1 and T_TOF4 were also shorter in the muscular group than in the non-muscular group.
Total body weight-based dosing of rocuronium might prolong the neuromuscular relaxation effect in patients with a small amount of skeletal muscle.

View all citing articles on Scopus

View full text

Original articleValidity and reliability of a 4-compartment body composition model using dual energy x-ray absorptiometry-derived body volume

Summary

Background

Aims

Methods

Results

Conclusion

Introduction

Section snippets

Participants

Validity

Discussion

Conclusions

Conflict of interest

Acknowledgments

Nutr Metab Cardiovasc Dis

Appl Radiat Isot Incl Data Instrum Methods Use Agric Ind Med

Am J Clin Nutr

Am J Clin Nutr

Clin Nutr

J Clin Densitom Off J Int Soc Clin Densitom

Am J Clin Nutr

Am J Clin Nutr

Am J Clin Nutr

Am J Clin Nutr

Mayo Clin Proc

Am J Clin Nutr

Detrimental and protective fat: body fat distribution and its relation to metabolic disease

Horm Mol Biol Clin Investig

Body shape index in comparison with other anthropometric measures in prediction of total and cause-specific mortality

J Epidemiol Community Health

Role of body fat distribution and the metabolic complications of obesity

J Clin Endocrinol Metab

Muscle size, quality, and body composition: characteristics of division I cross-country runners

J Strength Cond Res/Natl Strength Cond Assoc

Risk of mortality according to body mass index and body composition among postmenopausal women

Am J Epidemiol

Original article
Validity and reliability of a 4-compartment body composition model using dual energy x-ray absorptiometry-derived body volume