body composition research paper

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Advanced body composition assessment: from body mass index to body composition profiling

Magnus borga.

1 Department of Biomedical Engineering, Linköping University, Linköping, Sweden

2 Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden

3 Advanced MR Analytics AB, Linköping, Sweden

4 Department of Medical and Health Sciences, Linköping University, Linköping, Sweden

Jimmy D Bell

5 Research Centre for Optimal Health, University of Westminster, London, UK

Nicholas C Harvey

6 MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK

7 NIHR Southampton Biomedical Research Centre, University of Southampton, University Hospital Southampton NHS Foundation Trust, Southampton, UK

Thobias Romu

Steven b heymsfield.

8 Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA

Olof Dahlqvist Leinhard

Associated data.

jim-2018-000722supp001.pdf

jim-2018-000722supp002.pdf

This paper gives a brief overview of common non-invasive techniques for body composition analysis and a more in-depth review of a body composition assessment method based on fat-referenced quantitative MRI. Earlier published studies of this method are summarized, and a previously unpublished validation study, based on 4753 subjects from the UK Biobank imaging cohort, comparing the quantitative MRI method with dual-energy X-ray absorptiometry (DXA) is presented. For whole-body measurements of adipose tissue (AT) or fat and lean tissue (LT), DXA and quantitative MRIs show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 and 4.6 per cent for fat (computed from AT) and LT, respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of >20 per cent. The additional ability of MRI to also measure muscle volumes, muscle AT infiltration and ectopic fat, in combination with rapid scanning protocols and efficient image analysis tools, makes quantitative MRI a powerful tool for advanced body composition assessment.

INTRODUCTION

The human body—as well as the body of every other animal—is mainly composed of four molecular-level components: water, fat, proteins and minerals, usually in that order of decreasing amounts. 1 The substance that has attracted the most attention, from laypeople to medical professionals, is fat. This is, of course, motivated by the well-established fact that an excessive amount of body fat is related to increased morbidity and mortality. But also because adipose tissue (AT) is, by far, the most varying compartment—between individuals, but also within an individual over time. The most widely used way to estimate body fat is the body mass index (BMI)—body weight normalized by height squared (kg/m 2 ). Being a very simple and inexpensive method, it is the basis for WHO’s definition of overweight (25≤ BMI <30) and obesity (BMI ≥30). However, for a given BMI, the body fat percentage changes with age, and the rate of this change is different depending on sex, ethnicity and individual differences. 2 And while BMI correlates with fat accumulation and metabolic health in large populations, it is insensitive to the actual distribution of body fat. 3

When comparing methods for body composition analysis, it is important to distinguish fat (triglyceride) from AT, 4 which contains approximately 80 per cent fat, the rest being water, protein and minerals. 5 While most of the body fat is stored in AT, fat is also present in organs such as liver and skeletal muscle. Today, it is well known that the metabolic risk related to fat accumulation is strongly dependent on its distribution. Central obesity and, in particular, ectopic fat accumulation are important metabolic risk factors. 6–8 Large amounts of visceral AT (VAT) are related to increased cardiac risk, 8 9 type 2 diabetes, 10 11 liver disease 12 and cancer. 13 14 High levels of liver fat increase the risk for liver disease and type 2 diabetes, 15 and increased muscle fat has been associated with increased risk for insulin resistance and type 2 diabetes 16 and reduced mobility. 17 While there are other anthropometric measures, such as waist circumference and waist-to-hip ratio, which more strongly correlate with metabolic risk, 18 19 it is now well recognized that BMI and other anthropometric surrogate measures are poor predictors for individual fat distribution and metabolic risk. 3 20 21

Besides fat, acting as the body’s long-term energy storage, skeletal muscles are of great interest to study, and the balance between the energy-consuming muscles and the energy-storing fat compartments is, of course, highly relevant in order to understand the metabolic balance of the body. Cachexia, involuntary loss of body weight, usually with disproportionate muscle wasting, is a life-threatening condition, often related to the progression of an underlying serious disease (eg, cancer 22 ). In cancer, cachexia is defined as weight loss of >5 per cent over 6 months, BMI <20 kg/m 2 or appendicular muscle mass normalized by body height squared of <7.26 kg/m 2 or 5.45 kg/m 2 for males and females, respectively. 23 Sarcopenia, which can be related to cachexia, but is also associated with aging, is often defined as reduced physical performance following loss of muscle mass, usually accompanied by increased fat infiltration of the muscles. 24 When diagnosing sarcopenia, muscle strength tests combined with muscle volume measurements are needed. 25 Furthermore, Willis et al showed that muscle pathology progression over 1 year could be detected by quantitative MRI but not by assessing muscle strength or function. 26 These examples illustrate the need for more sophisticated body composition analysis tools that go beyond simple anthropometric measures.

Since the early part of the last century, scientists have tried to determine the body composition in different ways, with a wide range of different physical principles and devices, and using different models and assumptions. Today, local in vivo measurements of different fat depots and fat infiltration in organs can be made using tomographic imaging techniques such as CT and MRI that were not even invented when the first scientific studies on body composition were published. These techniques are now recognized as golden standard for body composition analysis. 25 27

The purpose of this paper is to give a brief introduction to the most commonly used methods for body composition analysis and a review of an MRI-based body composition analysis technique, comparing its performance to other methods. This includes a previously unpublished validation study of the agreement between this method and dual-energy X-ray absorptiometry (DXA).

TECHNOLOGY OVERVIEW

A number of different techniques for body composition assessment have been developed, from very simple indirect measures such as waist-to-hip ratio and calipers to sophisticated direct volumetric measurements based on three-dimensional imaging techniques. There are also a range of invasive or in vitro methods for body composition analysis such as inhalation or injection of water-accumulating or fat-accumulating agents, or dissection and chemical analysis of cadavers. This overview will, however, focus solely on non-invasive in vivo measurement techniques.

Hydrostatic weighing (densitometry)

Hydrostatic weighing (underwater weighing), or densitometry, is based on Archimedes’ principle. The difference of the body weight in air and water is used to compute the body’s density. Assuming a two-component model with different densities for fat mass and fat-free mass and correcting for the air volume in the lungs, the total body fat percentage can be estimated. Obviously, this technique cannot give any measurements of the distribution of AT or lean tissue (LT).

Air displacement plethysmography (ADP)

ADP is perhaps better known under its commercial brand name BOD POD (Life Measurement, Concord, California, USA). Similar to hydrostatic weighing, ADP measures the overall body density and hence total body fat and LT but not their distributions. By putting the body in an enclosed chamber and changing the chamber’s volume, the volume of the displaced air (ie, the volume of the body) can be determined from the changes in air pressure. Since ADP is based on the same two-component model as hydrostatic weighing, it is also affected by the same confounders, mainly variations in bone mineral content (BMC) and hydration. Due to the limitations of the two-component model used in densitometry and ADP, a four-component (4C) model is often recommended. 28 29 In addition to fat and LT, the 4C model also takes BMC and total body water (TBW) into account. However, these two additional components have to be measured by other techniques (eg, DXA for the BMC and deuterium oxide dilution for TBW 30 ) The repeatability (coefficient of variation (CV)) of ADP for body fat has been reported to be between 1.7 and 4.5 per cent when measured within 1 day. 31 Obviously, ADP, as well as hydrostatic weighing, is limited to gross body composition analysis, not making any estimates of regional fat or muscles.

Bioelectrical impedance analysis (BIA)

BIA uses the electrical properties of the body to estimate the TBW and from that the body fat mass. 32 33 The body is modeled as five cylindrical LT compartments; the trunk and the four limbs, while fat is considered to be an insulator. The impedance is assumed to be proportional to the height and inversely proportional to the cross-sectional area of each compartment, and the electrical equivalent is a resistor (extracellular water) in parallel with a capacitor and a resistor in series (intracellular water). The model of uniform distribution of fat and water fits better to the extremities than the trunk, 34 and while there are BIA measurements that correlate well with total abdominal AT, BIA cannot be used for measuring VAT. 35 Potential error sources are variations in limb length (usually estimated from body height), recent physical activity, nutrition status, tissue temperature and hydration, blood chemistry, ovulation and electrode placement. 32 BIA requires different model parameters to be used depending on age, gender, level of physical activity, amount of body fat and ethnicity in order to be reliable. 36 37

Dual-energy X-ray absorptiometry

DXA is a two-dimensional imaging technique that uses X-rays with two different energies. The attenuation of an X-ray is dependent on the thickness of the tissue and the tissue’s attenuation coefficient, which is dependent on the X-ray energy. By using two different energy levels, the images can be separated into two components (eg, bone and soft tissue). DXA is mainly used for bone mineral density measurements, where it is considered as the gold standard, 38 but it can also be used to estimate total and regional body fat and LT mass. Pixels, where the ratio between attenuations of the two energies falls below a certain threshold, are classified as soft tissue (ie, without bone), and in those pixels, the attenuation is linearly dependent on the fat fraction of the soft tissue. Pixels above the threshold contain a mixture of bone and soft tissue, and there the soft tissue properties need to be interpolated from surrounding soft tissue pixels. 39 Approximately one-third of the pixels of the projected body contains bone. 40

DXA has been found to be more accurate than density-based methods for estimating total body fat. 41 A possible confounder is that the DXA analysis assumes a constant hydration of lean soft tissue, which is not always true as hydration varies with age, gender and disease. 42 Excellent repeatability (CV) in the range 1–2 per cent for body fat and 0.5–2 per cent for LT has been reported for DXA.

Since DXA only gives a two-dimensional (coronal) projection, it is not possible to obtain direct compartmental volumetric measurements, so regional volume estimates are obtained indirectly using anatomical models. For example, VAT and parts of the subcutaneous adipose tissue (SAT) are mixed and cannot be separated in the DXA image. The distribution between VAT and SAT then needs to be estimated from an anatomical model predicting the SAT thickness. Furthermore, the physical properties of the technology do not allow for measurements of ectopic fat in organs such as liver fat or muscle fat infiltration. However, due to its ability to estimate regional fat and measure LT, in combination with relatively high availability, DXA has been used for body composition analysis in a wide range of clinical applications. 43

CT gives a three-dimensional high-resolution image volume of the complete or selected parts of the body, computed from a large number of X-ray projections of the body from different angles. The known differences in attenuations of X-rays between lean soft tissue and AT can then be used to separate these tissues, as well as to determine mixtures between them. As opposed to the previously described techniques, CT can accurately determine fat in skeletal muscle tissue 16 and in the liver. 44 It is, however, significantly less accurate for liver fat <5 per cent which limits its use to diagnose low-grade steatosis. 44 Being a three-dimensional imaging technique, CT has the potential of giving direct volumetric measurements of organs and different AT depots. In practice, however, CT-based body composition analysis is in most cases limited to two-dimensional analysis of one or a limited number of axial slices of the body, leading to the utilization of the area measured as a proxy for the volume. There are two reasons for this limitation: first, it is important to keep the part of the body being scanned to a minimum in order to minimize the ionizing radiation dose. 45 This is particularly important in the ethical considerations of research studies on healthy subjects. Second, manual segmentation of different compartments in the images is a very labor intensive task, which can be reduced by limiting the analysis to a few slices rather than a complete three-dimensional volume. This approach, however, limits its precision since the exact locations of slices, in relation to internal organs, cannot be determined a priory and will therefore vary between scans. Nevertheless, CT, together with MRI, is today considered the gold standard for body composition analysis, in particular regional.

MRI uses the different magnetic properties of the nuclei of certain chemical elements (normally hydrogen in water and fat) in the cells to produce images of soft tissue in the body. A number of MRI-based methods for quantification of AT (eg, see the review by Hu et al 46 ) and muscles 47–52 have been developed and implemented in the past.

By using so-called ‘quantitative fat water imaging’, precise measurements of regional AT and LT, as well as diffuse fat infiltration in other organs, can be obtained. The basis for quantitative fat water imaging is fat water separated, or Dixon, imaging, 53 where the different magnetic resonance frequencies of protons in fat and water are used for separating the two signals into a fat image and a water image. Due to a number of undeterminable factors affecting the MR signal, an MR image is not calibrated on an absolute scale and therefore not quantitative in itself. But by using different postprocessing techniques, the image can be calibrated to quantitatively measure fat or AT. Examples of such methods are proton density fat fraction (PDFF) 54 measuring the fraction of fat in MR-visible soft tissue and fat-referenced MRI 55–57 measuring the amount of AT in each voxel.

As opposed to CT and DXA, MRI does not use ionizing radiation, which enables true volumetric three-dimensional imaging even in healthy volunteers and infants. Still, many studies using MRI for body composition analysis have used one or a limited set of two-dimensional slices, mostly due to the lack of efficient image analysis tools for handling three-dimensional image segmentation. However, since there is no ionizing radiation limiting the image acquisition, the slices can be selected from a complete image volume, thereby reducing the uncertainty in their locations. Still, using a sparse set of slices as a proxy for the complete volume will inevitably negatively affect accuracy and precision as only a fraction of the data is used. It has, for example, been shown that single-slice MRI is poor at predicting VAT and SAT changes during weight loss. 58 59

BODY COMPOSITION PROFILING USING FAT-REFERENCED MRI

Body composition profiling implies the simultaneous collection and analysis of a number of body composition parameters, including subcutaneous and visceral AT, ectopic fat such as liver and skeletal muscle fat and muscle volumes. Fat-referenced MRI is a methodology that enables all such measurements in one single rapid examination. This section gives a brief introduction to body composition profiling using fat-referenced MRI, together with a review of published validation results of the method. Finally, a previously unpublished validation study of the agreement between this method and DXA for measurements of body fat/AT, body LT and VAT is presented.

The body composition profiling methodology combines fat-referenced MRI with automated image segmentation of different compartments and was first described by Dahlqvist Leinhard et al. 55 Different aspects of the method have been further described in other publications. 47 60–62 The two key features of this method are that it produces quantitative fat-referenced images and that it uses a supervised automated segmentation tool.

In a quantitative fat-referenced image, the value in each image volume element (voxel) represents the amount of fat in that voxel in relation to the amount of fat in pure AT. Hence, a voxel in pure AT has a value of one and a voxel without any fat has the value zero. This means that the following can be measured: the total amount of AT in any given region by summation of the voxel values in that region, AT-free volume by removal of amount of AT from volume measurements of regional LT (eg, muscles) and fractions of fat in specific internal organs, such as the liver.

The supervised automated segmentation tool enables an efficient way of segmenting different AT compartments, as well as different muscle groups, reducing the manual work to a few minutes, rather than hours, for analyzing a whole-body data set. Anatomical compartments, such as the visceral compartment and different muscle groups, are automatically segmented using predefined anatomical atlases and the operator can then adjust the segmentations if needed. An example of such segmentations is illustrated in figure 1 .

Example of segmentation of abdominal subcutaneous AT (ASAT), visceral AT (VAT) and 10 muscle groups from fat water separated MRI using fat-referenced MRI and multi-atlas image segmentation. To the left is the fat image with ASAT (blue) and VAT (red), and to the right is the water image with the different muscle groups colored. Reproduced with permission from AMRA Medical AB.

See online  supplementary appendix 1 for a summary of how fat-referenced MRI is implemented in AMRA Profiler (AMRA Medical AB, Linköping, Sweden), which is the tool for body composition profiling that was used in the validation studies of fat-referenced MRI.

Supplementary data

Precision and accuracy.

In a previous study, 61 the accuracy of body composition profiling using fat-referenced MRI, in terms of agreement with manual quantification of T1-weighted MR images, was evaluated on 23 (11 females, 12 males) subjects with an average BMI of 31.7±5.1 kg/m 2 (range 22–46 kg/m 2 ); age 36–66 years. There was no significant difference in the measured amount of VAT (4.73±1.99 vs 4.73±1.75 L, P=0.97). Furthermore, the agreement between the methods was excellent for both VAT (95 per cent limits of agreement (LoA) −1.06 to 1.07 L) and abdominal subcutaneous AT (ASAT) (−0.36 to 1.60 L). However, a very small yet statistically significant difference in ASAT was observed (10.39±5.38 vs 9.78±5.36 L, P<0.001). Clearly this small difference has no clinical significance.

Test–retest repeatability and agreement with manual quantification for VAT was evaluated by Newman et al . 63 The study included 30 subjects with five subjects from each gender for each of the following categories of BMI: 18–25 kg/m 2 , 25–30 kg/m 2 and >30 kg/m 2 . Each subject was scanned twice with at least 20 min interval, during which the subject left the scanner room. There was no significant difference between the evaluated method and manual quantification of VAT (P=0.73). Bland-Altman analysis of the test–retest repeatability showed a bias of −0.04 L (95 per cent LoA −0.12 to 0.13 L) for VAT and 0.05 L (95 per cent LoA −0.55 to 0.64 L) for ASAT. The CV was 1.80 per cent for VAT and 2.98 per cent for ASAT using the method above. The CV for manual quantification of VAT was 6.33 per cent as a comparison.

Middleton et al evaluated the accuracy and repeatability of VAT, ASAT and thigh muscle quantification by comparing with manual segmentation on 20 subjects. 64 Due to the laborious work with manual segmentation, 15 two-dimensional axial slices were manually segmented in the abdominal region for VAT and ASAT and 5 slices over the thigh muscles. For repeatability assessment, the subjects were scanned three times, with the subject remaining in the same position on the scan table between scans 1 and 2 and with the subject removed from the table between scans 2 and 3. The intraexamination (scans 1–2) repeatability test obtained a CV of 3.3 per cent for VAT, 2.2 per cent for ASAT and 1.5 per cent for total thigh muscle volume. For the inter-examination test (scans 2–3), the CVs were 3.6, 2. 6 and 1.5 per cent for VAT, ASAT and thigh muscle volume, respectively. Good agreement with the manual measurements in the 20 slices was observed for all measurements. Neither the slopes nor the intercepts of the regression lines were significantly different from those of the identity lines.

Test–retest repeatability of muscle quantification of left and right abdominal muscles, left and right, anterior and posterior thigh muscles and left and right lower limb muscles, as well as accuracy of lower leg muscle quantification, were evaluated by Thomas et al 65 comparing the method above with manual segmentation. The study included 15 subjects of each gender, ranging from normal weight to obese. Each subject was scanned twice with at least 20 min interval, during which the subject left the scanner room. The intraclass correlation (ICC) between the first and second scan was almost perfect (between 0.99 and 1.0) for all muscle groups. The 95 per cent LoA ranged from −0.04 to 0.02 L for the posterior thigh muscles to −0.15 to 0.08 L for the left lower limb. The lowest accuracy for the lower limbs was a bias of −0.08 L with 95 per cent LoA of −0.25 to 0.09 L.

Test–retest repeatability of measurements of VAT and ASAT volumes and volumes and fat infiltration of left and right posterior and anterior thigh muscles, lower leg muscles and abdominal muscles were evaluated by West et al on 36 sedentary postmenopausal women. 66 Each subject was scanned twice, and the subjects were removed from the scanner room between the acquisitions. The intraexamination CV was 1.54 per cent for VAT, 1.06 per cent for ASAT, 0.8–1.9 per cent for volumes of muscle groups (thigh, lower leg and abdomen) and 2.3–7.0 per cent for individual muscle volumes. The 95 per cent LoA was −0.13 to 0.10 L for VAT, −0.38 to 0.29 L for ASAT. The LoA for liver PDFF were within ±1.9 per cent, and for muscle fat infiltration, they were within ±2.06 per cent for muscle groups and within ±5.13 per cent for individual muscles.

The method’s reproducibility of fat-free muscle volume quantification between 1.5 T and 3 T MR scanners, as well as the agreement with manual segmentation, was investigated on 11 different muscle groups. 47 The ICC between the automated method and manual measurements was at least 0.97 for all muscle groups except in the arms. Except for the arms, the ICC between 1.5 T and 3 T data ranged from 0.97 (left lower leg) to 1.00 (left posterior thigh) with a mean difference volume ranging from 0.39 L (95 per cent LoA 0.01 to 0.77 L) (left abdomen) to 0.0 L (95 per cent LoA −0.10 to 0.09 L) (right lower leg). The muscles of the arms had worse accuracy and reproducibility due to difficulties to include the arms in the field of view.

Agreement with ADP

A previous study 67 compared AT measured using fat-referenced MRI with total body fat measured by ADP. The ICC was 0.984. After converting the ADP body fat measures to AT volume (assuming that most of the fat resided in AT and a density of 0.9 kg/L for AT), a Bland-Altman analysis showed that ADP underestimated AT by 0.78 L on average, but the bias was strongly dependent on the level of adiposity with significant underestimation for lean subjects and significant overestimation for subjects with higher amounts of AT. Similar bias dependence has been observed when ADP has been compared with DXA 31 and MRI. 68

Agreement with BIA

Ulbrich et al 69 investigated the agreement between fat-referenced MRI and BIA on 80 subjects between 20 and 62 years with a BMI range from 17.5 to 26.2 kg/m 2 . The linear correlation between body fat mass measured by BIA and AT volume measured by MRI was 0.75 and 0.81 for females and males, respectively. The total AT measured by MRI was converted to total fat mass (again assuming that most of the fat resided in AT and using a constant density of 0.94 kg/L). Compared with MRI, the BIA underestimated the total fat with approximately 5 kg (±7 kg LoA) on average, this despite the fact that the MRI-based measurements of total body fat excluded the arms and lower legs. The highest linear correlation found between BIA and MRI-derived measures was 0.75 and 0.81 for females and males, respectively. These correlations were found between BIA-derived body mass percentage and the MRI-derived ‘total AT index’ (total AT divided by body height squared).

Agreement with DXA

Methods and materials.

The agreement between DXA and the fat-referenced MRI technique was assessed using data from the UK Biobank study, 70 approved by the North West Multicenter Research Ethics Committee, UK, and with written informed consent obtained from all subjects prior to study entry. The age range for inclusion was 40–69 years of age. For the present analysis, participants were selected, out of the first 6214 scanned, who had both DXA and MRI scans. One subject with obviously erroneous DXA values (2.7 kg total fat and 6.8 kg LT) was excluded, yielding a total 4753 subjects (2502 females and 2251 males). All included MRI images were analyzable for VAT, ASAT and both thigh muscles according the predefined quality criteria. 62 The BMI range was 16.4–54.3 with a mean of 26.2 kg/m 2 .

The MR images were acquired using a Siemens Aera 1.5 T scanner (Syngo MR D13) (Siemens, Erlangen, Germany) with the dual-echo Dixon Vibe protocol, covering neck to knees as previously described. 62 The MR images were analyzed using AMRA Profiler. The body AT and LT were measured from the bottom of the thigh muscles to level of the top of vertebrae T9 ( figure 2 ). The LT was defined as the volume of soft tissue subtracted by the volume of AT. 47

(A) The definition of lean and adipose tissue measured by MRI from the bottom of the thigh muscles to top of vertebrae T9 marked in blue color in the water (left) and fat (right) image. (B) An example of a dual-energy X-ray absorptiometry (DXA) image from the study cohort. DXA image copyright UK Biobank. Reprinted with permission.

Whole-body DXA data were acquired using a GE-Lunar iDXA (GE Healthcare, Madison, Wisconsin, USA) with the subjects in supine position. 71 The images were analyzed using the GE enCORE software by the radiographer at, or soon after, the scan. The GE iDXA estimates VAT within an automatically segmented region with the lower border at the top of the iliac crest and its height is set to 20 per cent of the distance from the top of the iliac crest to the base of the skull. 72

Since the DXA and MRI analyses measure different entities (fat and LT mass vs AT and LT volume, respectively) and they do not cover the same part of the body, a linear model was estimated by linear regression between the MRI and DXA measurements using a training data set of 2376 randomly selected subjects. The remaining 2377 subjects were then used for estimating the agreement between the techniques after linear transformation using the linear model (ie, validating the linear model). The MRI-based measurements (L) were transformed to predict the DXA measurements (kg) using the linear regression coefficients from the training data, and a Bland-Altman analysis was performed to investigate the agreement between MRI-derived and DXA-derived measurements in the validation data. To investigate the agreement between DXA and MRI-derived VAT measurements, a linear model was estimated between the DXA and MRI measurements. Of the 4669 subjects with available DXA VAT measurements, 2334 cases were used to estimate the model and the remaining 2335 subjects were used to validate the agreement between VAT measured by MRI and the transformed DXA measurements using Bland-Altman analysis.

The linear regression between MRI and DXA was 1.23 x – 0.12 (kg/L) for body fat/AT and 1.88 x + 1.82 (kg/L) for body LT. The linear correlation coefficient, r, between DXA and the transformed MRI measurements was 0.99 for body fat and 0.97 for LT. The 95 per cent LoA from the Bland-Altman analysis were −2.25 to 2.31 kg for fat and −4.33 to 4.31 kg for LT ( figure 3 ). The prediction error SD relative to the mean (CV) was 4.5 per cent for body fat and 4.6 per cent for LT. The correlation between VAT measured by MRI and VAT as predicted by DXA was 0.97 and the LoA were −1.02 to 1.05 L, with CV=21 per cent ( figure 4 ).

Correlation plots (upper row) between dual-energy X-ray absorptiometry (DXA) and corresponding measurement predicted from MRI using a linear transformation for body fat (left) and body lean tissue (right). The bottom row shows Bland-Altman plots of the agreement between DXA and corresponding measures predicted from MRI.

Correlation between visceral adipose tissue (VAT) predicted by dual-energy X-ray absorptiometry (DXA) and VAT measured by MRI (left) and Bland-Altman plot showing the agreement (liters) between the methods (right).

Densitometry, including ADP, shows relatively good precision and high correlation with MRI-based measurements of whole-body AT, but with a significant volume-dependent bias. Since these methods only measure the volume or density of the body, they cannot be used for regional measurements and body composition profiling.

BIA is highly available and its relatively low cost is an advantage, which also makes it useful for consumer products. Furthermore, it can differentiate intracellular water from extracellular water, which is a unique capability of BIA. BIA can also, in principle, be used for regional measurements, but it is severely limited when it comes to measuring VAT or ectopic fat in internal organs.

DXA techniques have shown good accuracy when evaluated against MRI for whole-body measurements and very good repeatability. The prediction of whole-body fat and LT from MRI agrees well with DXA after a linear transformation, but less so for VAT. While the correlation between DXA and MRI-derived VAT was high (r=0.97), the agreement after a linear transformation was, however, much lower than for total body fat and body LT, with a CV >20 per cent. The high linear correlation, despite a modest agreement, can be explained by the very wide range of measured VAT volumes, ranging from almost 0 to >14 L. The CV for VAT is in line with the results by Kaul et al with a CV of 15.6 per cent for females and 25.9 per cent for males when comparing the same DXA model with CT. 72 Park et al found a linear correlation of 0.85 between VAT measured by DXA and MRI in a study including 90 non-obese men. 73 However, Kamel et al found that the correlation was much lower (r=0.46) for obese men. 74 The fact that the agreement is lower for obese subjects can also be observed in figure 4 where the prediction error increases with increased VAT volume. Silver et al found an excellent correlation without significant bias between fat water MRI and DXA for ‘gross body adipose tissue’ but with a significant negative bias (MRI – DXA) for ‘total trunk adipose tissue’ as well as total and trunk LT. 75 Interestingly, for DXA, the lowest precision is for fat in the arms, with reported CV up to 11 per cent. 76 This is the same compartment that is difficult to measure with MRI due to signal loss in the outer parts of the field of view. A strength with DXA, compared with MRI, is the simultaneous assessment of bone mineral density and mass.

When comparing different technologies, both accuracy and precision are important. Accuracy, however, can be rather difficult to compare between technologies for several reasons. First, there is no ground truth available. Even though there is a growing consensus that tomographic methods are the gold standard that can be used to assess accuracy for other methods, they differ between themselves and are difficult to compare in terms of accuracy. Using physical phantoms is one way to assess accuracy, but they miss the difficulties caused by anatomical variations that we know can lead to different measurement errors. Automated tomographic imaging methods can be evaluated against manual methods, but this addresses only one of several important components in the measurement system—the segmentation of different compartments. Second, not all methods measure the same thing, so even if two technologies correlate strongly, there may be a significant bias if they measure different physical entities. For example, AT is not equivalent to fat—besides fat AT also contains water, protein and minerals. When comparing a method that measures AT in volume units, such as MRI, to a method that measures fat in weight units (eg, DXA), we have to convert one unit to the other using a density that is assumed to be constant, which again may not be always accurate.

Although this review has not focused on measurements of ectopic fat, this is an important component in body composition profiling, especially for understanding metabolic status and assessing risk. Among the techniques discussed here, CT and quantitative MRI are the only methods that can quantify local diffuse infiltration of AT and ectopic fat. (Non-invasive measurements of ectopic fat, in particular liver fat, are commonly done by MR spectroscopy (MRS), but since MRS only measures local substance concentrations and not absolute amounts of fat, AT or LT, this technology was not included in this study.) While it is possible—and sometimes necessary—to use different equipment for different measurements in a study, it is often desirable to keep the number of different examinations and modalities to a minimum in order to optimize the work flow. By using quantitative MRI, or CT if the radiation dose is not a concern, a large number of metabolically relevant body composition parameters can be measured with high accuracy and precision in a single examination.

A comparison of the capabilities of different measurements of the techniques discussed above is summarized in table 1 .

Comparison of the capabilities of different techniques for body composition analysis

ADP, air displacement plethysmography; BIA, bioelectrical impedance analysis; DXA, dual-energy X-ray absorptiometry; VAT, visceral adipose tissue.

There are several methods available that can measure whole-body AT or fat and LT. In terms of precision and accuracy, DXA and MRI are comparable as they show excellent agreement after a linear transformation. However, the agreement is much lower for compartmental measurements such as VAT. Moreover, MRI gives access to accurate and direct measurements of diffuse infiltration of AT in muscles and ectopic fat (eg, liver fat). Rapid MRI scanning protocols, in combination with efficient image analysis methods, have promoted MRI to a competitive option for advanced body composition assessment, thus enabling a more complete description of a person’s body composition profile from a single examination.

Acknowledgments

This research has been conducted using the UK Biobank Resource under Data Access Application 6569. For full acknowledgements, see online supplementary appendix 2 .

Contributors: MB, JW and ODL planned the work. MB, TR and ODL developed and applied the MR analysis methods used. JDB was responsible for the UK Biobank body MRI and NCH for the UK Biobank DXA scans. MB and JW performed the statistical analyses. SBH contributed with expertise on body composition. MB drafted the manuscript. All authors contributed to editing the text.

Funding: Funding support for analysis of UK Biobank data was provided by Pfizer.

Competing interests: MB, JW, TR and ODL are employees and stockholders of AMRA Medical AB.

Patient consent: Obtained.

Ethics approval: North West Multicenter Research Ethics Committee (MREC), UK.

Provenance and peer review: Commissioned; externally peer reviewed.

Data sharing statement: The underlying data used in this study are available though the UK Biobank resource. All bonafide researchers can apply to use the UK Biobank resource for health-related research that is of public interest by applying in the Access Management System (AMS). For details, see: www.ukbiobank.ac.uk/

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• Published: 02 August 2023

Nutrition and Health (including climate and ecological aspects)

The bioelectrical impedance analysis (BIA) international database: aims, scope, and call for data

• Analiza M. Silva   ORCID: orcid.org/0000-0002-8984-8600 1 ,
• Francesco Campa   ORCID: orcid.org/0000-0002-3028-7802 2 ,
• Silvia Stagi   ORCID: orcid.org/0000-0002-6469-4334 3 ,
• Luís A. Gobbo 4 ,
• Roberto Buffa 3 ,
• Stefania Toselli 5 ,
• Diego Augusto Santos Silva 6 ,
• Ezequiel M. Gonçalves 7 ,
• Raquel D. Langer   ORCID: orcid.org/0000-0002-9098-1863 7 ,
• Gil Guerra-Júnior 7 ,
• Dalmo R. L. Machado 8 ,
• Emi Kondo   ORCID: orcid.org/0000-0001-9145-0549 9 ,
• Hiroyuki Sagayama   ORCID: orcid.org/0000-0002-9040-7650 9 ,
• Naomi Omi 9 ,
• Yosuke Yamada   ORCID: orcid.org/0000-0002-4284-6317 10 ,
• Tsukasa Yoshida 10 ,
• Wataru Fukuda 11 ,
• Maria Cristina Gonzalez   ORCID: orcid.org/0000-0002-3901-8182 12 ,
• Silvana P. Orlandi 13 ,
• Josely C. Koury   ORCID: orcid.org/0000-0002-3189-9261 14 ,
• Tatiana Moro 2 ,
• Antonio Paoli   ORCID: orcid.org/0000-0003-0474-4229 2 ,
• Salome Kruger 15 ,
• Aletta E. Schutte   ORCID: orcid.org/0000-0001-9217-4937 16 ,
• Angela Andreolli 17 ,
• Carrie P. Earthman   ORCID: orcid.org/0000-0002-7783-7437 18 ,
• Vanessa Fuchs-Tarlovsky 19 ,
• Alfredo Irurtia   ORCID: orcid.org/0000-0002-3463-6643 20 ,
• Jorge Castizo-Olier 21 ,
• Gabriele Mascherini   ORCID: orcid.org/0000-0002-8842-0354 22 ,
• Cristian Petri 23 ,
• Laura K. Busert 24 ,
• Mario Cortina-Borja   ORCID: orcid.org/0000-0003-0627-2624 24 ,
• Jeanette Bailey 25 ,
• Zachary Tausanovitch 25 ,
• Natasha Lelijveld 26 ,
• Hadeel Ali Ghazzawi 27 ,
• Adam Tawfiq Amawi   ORCID: orcid.org/0000-0001-7810-748X 28 ,
• Grant Tinsley   ORCID: orcid.org/0000-0002-0230-6586 29 ,
• Suvi T. Kangas 25 ,
• Cécile Salpéteur 30 ,
• Adriana Vázquez-Vázquez 24 ,
• Mary Fewtrell   ORCID: orcid.org/0000-0001-9783-3444 24 ,
• Chiara Ceolin 31 ,
• Giuseppe Sergi 31 ,
• Leigh C. Ward   ORCID: orcid.org/0000-0003-2378-279X 32 ,
• Berit L. Heitmann   ORCID: orcid.org/0000-0002-6809-4504 33   nAff34 ,
• Roberto Fernandes da Costa   ORCID: orcid.org/0000-0002-8789-1744 35 ,
• German Vicente-Rodriguez 36 ,
• Margherita Micheletti Cremasco   ORCID: orcid.org/0000-0002-5948-7584 37 ,
• Alessia Moroni   ORCID: orcid.org/0000-0003-3780-2931 37 ,
• John Shepherd 38 ,
• Jordan Moon 39 ,
• Tzachi Knaan   ORCID: orcid.org/0000-0002-6697-9894 40 ,
• Manfred J. Müller   ORCID: orcid.org/0000-0002-7280-2411 41 ,
• Wiebke Braun 41 ,
• José M. García‐Almeida 42 ,
• António L. Palmeira 43 ,
• Inês Santos 44 ,
• Sofus C. Larsen 45 , 46 ,
• Xueying Zhang   ORCID: orcid.org/0000-0001-5746-2191 47 ,
• John R. Speakman   ORCID: orcid.org/0000-0002-2457-1823 47 , 48 ,
• Lindsay D. Plank   ORCID: orcid.org/0000-0003-2737-0151 49 ,
• Boyd A. Swinburn 50 ,
• Jude Thaddeus Ssensamba 51 , 52 ,
• Keisuke Shiose 53 ,
• Edilson S. Cyrino   ORCID: orcid.org/0000-0001-9016-8779 54 ,
• Anja Bosy-Westphal 41 ,
• Steven B. Heymsfield   ORCID: orcid.org/0000-0003-1127-9425 55 ,
• Henry Lukaski   ORCID: orcid.org/0000-0002-5418-5851 56 ,
• Luís B. Sardinha   ORCID: orcid.org/0000-0002-6230-6027 1 ,
• Jonathan C. Wells   ORCID: orcid.org/0000-0003-0411-8025 24 &
• Elisabetta Marini   ORCID: orcid.org/0000-0001-8779-8745 3  

European Journal of Clinical Nutrition volume  77 ,  pages 1143–1150 ( 2023 ) Cite this article

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Bioelectrical impedance analysis (BIA) is a technique widely used for estimating body composition and health-related parameters. The technology is relatively simple, quick, and non-invasive, and is currently used globally in diverse settings, including private clinicians’ offices, sports and health clubs, and hospitals, and across a spectrum of age, body weight, and disease states. BIA parameters can be used to estimate body composition (fat, fat-free mass, total-body water and its compartments). Moreover, raw measurements including resistance, reactance, phase angle, and impedance vector length can also be used to track health-related markers, including hydration and malnutrition, and disease-prognostic, athletic and general health status. Body composition shows profound variability in association with age, sex, race and ethnicity, geographic ancestry, lifestyle, and health status. To advance understanding of this variability, we propose to develop a large and diverse multi-country dataset of BIA raw measures and derived body components. The aim of this paper is to describe the ‘BIA International Database’ project and encourage researchers to join the consortium.

The Exercise and Health Laboratory of the Faculty of Human Kinetics, University of Lisbon has agreed to host the database using an online portal. At present, the database contains 277,922 measures from individuals ranging from 11 months to 102 years, along with additional data on these participants.

The BIA International Database represents a key resource for research on body composition.

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Data availability

The data sets generated and/or analyzed during the current project are not publicly available due to the data confidentiality requirements of the ethics committee for each study but are available from the corresponding author on reasonable request and approval from the ethics committee.

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Acknowledgements

Faculdade Motricidade Humana-Universidade de Lisboa kindly hosted the BIA database in the website for which we are thankful. Management group of the BIA International Database: AMS, LCW, ESC, AB-W, SBH, HL, LBS, JCW, EM.

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Berit L. Heitmann

Present address: Section for general Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark

Authors and Affiliations

Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, 1499-002, Lisbon, Portugal

Analiza M. Silva & Luís B. Sardinha

Department of Biomedical Science, University of Padova, 35100, Padova, Italy

Francesco Campa, Tatiana Moro & Antonio Paoli

Department of Life and Environmental Sciences, University of Cagliari, Cittadella Universitaria, Monserrato, 09042, Cagliari, Italy

Silvia Stagi, Roberto Buffa & Elisabetta Marini

Skeletal Muscle Assessment Laboratory, Physical Education Department, School of Technology and Science, São Paulo State University, Presidente Prudente, 19060-900, Brazil

Luís A. Gobbo

Department for Life Quality Studies, University of Bologna, 47921, Rimini, Italy

Stefania Toselli

Research Center of Kinanthropometry and Human Performance, Sports Center, Universidade Federal de Santa Catarina, Florianópolis, Brazil

Diego Augusto Santos Silva

Growth and Development Laboratory, Center for Investigation in Pediatrics (CIPED), School of Medical Sciences, University of Campinas (UNICAMP), Campinas, 13083-887, Brazil

Ezequiel M. Gonçalves, Raquel D. Langer & Gil Guerra-Júnior

Laboratory of Kinanthropometry and Human Performance, School of Physical Education and Sport of Ribeirão Preto, University of São Paulo, 05508-030, São Paulo, Brazil

Dalmo R. L. Machado

Faculty of Health and Sport Sciences, University of Tsukuba, Ibaraki, 305-8574, Japan

Emi Kondo, Hiroyuki Sagayama & Naomi Omi

National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, 566-0002, Japan

Yosuke Yamada & Tsukasa Yoshida

Yokohama Sports Medical Center, Yokohama Sport Association, Kanagawa, 222-0036, Japan

Wataru Fukuda

Postgraduate Program in Nutrition and Food, Federal University of Pelotas, 96010-610 Pelotas, Brazil

Maria Cristina Gonzalez

Nutrition Department, Federal University of Pelotas, 96010-610, Pelotas, Brazil

Silvana P. Orlandi

Nutrition Institute, State University of Rio de Janeiro, 20550-013, Rio de Janeiro, Brazil

Josely C. Koury

Centre of Excellence for Nutrition, North-West University, Potchefstroom, 2520, South Africa

Salome Kruger

School of Population Health, University of New South Wales, The George Institute for Global Health, Sydney, NSW, Australia

Aletta E. Schutte

University of Rome Tor Vergata, Rome, Italy

Angela Andreolli

University of Delaware, Newark, DE, USA

Carrie P. Earthman

Hospital General de México, Dr. Eduardo Liceaga, Ciudad de México, Mexico

Vanessa Fuchs-Tarlovsky

National Institute of Physical Education of Catalonia (INEFC), University of Barcelona (UB), Barcelona, Spain

Alfredo Irurtia

School of Health Sciences, TecnoCampus, Pompeu Fabra University, Barcelona, Spain

Jorge Castizo-Olier

Department of Experimental and Clinical Medicine, University of Florence, Firenze, Italy

Gabriele Mascherini

Department of Sports and Computer Science, Section of Physical Education and Sports, Universidad Pablo de Olavide, Seville, Spain

Cristian Petri

Population, Policy & Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK

Laura K. Busert, Mario Cortina-Borja, Adriana Vázquez-Vázquez, Mary Fewtrell & Jonathan C. Wells

International Rescue Committee, New York, NY, 10168, USA

Jeanette Bailey, Zachary Tausanovitch & Suvi T. Kangas

Emergency Nutrition Network (ENN), OX5 2DN, Kiddlington, UK

Natasha Lelijveld

Department of Nutrition and Food Technology, School of Agriculture, The University of Jordan, Amman, Jordan

Hadeel Ali Ghazzawi

Department of Physical and Health Education, Faculty of Educational Sciences, Al-Ahliyya Amman University, Al-Salt, Jordan

Adam Tawfiq Amawi

Energy Balance & Body Composition Laboratory, Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, 79409, USA

Grant Tinsley

Department of Expertise and Advocacy, Action contre la Faim, 93358, Montreuil, France

Cécile Salpéteur

Department of Medicine (DIMED), Geriatrics Division, University of Padova, Padova, 35128, Italy

Chiara Ceolin & Giuseppe Sergi

School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, 4072, Australia

Leigh C. Ward

Research Unit for Dietary Studies, The Parker Institute, Frederiksberg and Bispebjerg Hospital, Copenhagen, Denmark

Department of Physical Education, Research Group in Physical Activity and Health, Federal University of Rio Grande do Norte, Natal, Brazil

Roberto Fernandes da Costa

Faculty of Health and Sport Science FCSD, Department of Physiatry and Nursing, University of Zaragoza, 50009, Zaragoza, Spain

German Vicente-Rodriguez

Laboratory of Anthropology, Anthropometry and Ergonomics, Department of Life Sciences and Systems Biology, University of Torino, 10123, Torino, Italy

Margherita Micheletti Cremasco & Alessia Moroni

University of Hawaii Cancer Center, Honolulu, HI, USA

John Shepherd

United States Sports Academy, Daphne, AL, 36526, USA

Jordan Moon

Weight Management, Metabolism & Sports Nutrition Clinic, Metabolic Lab, Tel-Aviv, Tel Aviv-Yafo, Israel

Tzachi Knaan

Department of Human Nutrition, Institute of Human Nutrition and Food Sciences, Christian-Albrechts University, 24105, Kiel, Germany

Manfred J. Müller, Wiebke Braun & Anja Bosy-Westphal

Department of Endocrinology and Nutrition, Virgen de la Victoria Hospital, Malaga University, 29010, Malaga, Spain

José M. García‐Almeida

CIDEFES, Universidade Lusófona, Lisboa, Portugal

António L. Palmeira

Laboratório de Nutrição, Faculdade de Medicina, Centro Académico de Medicina de Lisboa, Universidade de Lisboa, Lisboa, Portugal

Inês Santos

Research Unit for Dietary Studies at the Parker Institute, Bispebjerg and Frederiksberg Hospital, The Capital Region, Frederiksberg, Denmark

Sofus C. Larsen

The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark

Shenzhen Key Laboratory of Metabolic Health, Center for Energy Metabolism and Reproduction, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Xueying Zhang & John R. Speakman

School of Biological Sciences, University of Aberdeen, Aberdeen, UK

John R. Speakman

Department of Surgery, University of Auckland, Auckland, New Zealand

Lindsay D. Plank

School of Population Health, University of Auckland, Auckland, New Zealand

Boyd A. Swinburn

Center for Innovations in Health Africa (CIHA Uganda), Kampala, Uganda

Jude Thaddeus Ssensamba

Makerere University Walter Reed Project, Kampala, Uganda

Faculty of Education, University of Miyazaki, Miyazaki, Japan

Keisuke Shiose

Metabolism, Nutrition, and Exercise Laboratory. Physical Education and Sport Center, State University of Londrina, Rod. Celso Garcia Cid, Km 380, 86057-970, Londrina-PR, Brazil

Edilson S. Cyrino

Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA

Steven B. Heymsfield

Department of Kinesiology and Public Health Education, Hyslop Sports Center, University of North Dakota Grand Forks, Grand Forks, ND, 58202, USA

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Silva, A.M., Campa, F., Stagi, S. et al. The bioelectrical impedance analysis (BIA) international database: aims, scope, and call for data. Eur J Clin Nutr 77 , 1143–1150 (2023). https://doi.org/10.1038/s41430-023-01310-x

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• Volume 6, Issue 1
• Assessment of adult body composition using bioelectrical impedance: comparison of researcher calculated to machine outputted values
• Article Text
• Article info
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• Maria Franco-Villoria 1 ,
• Charlotte M Wright 2 ,
• John H McColl 1 ,
• Andrea Sherriff 2 ,
• Mark S Pearce 3 ,
• and the Gateshead Millennium Study core team
• 1 School of Mathematics and Statistics, University of Glasgow , Glasgow , UK
• 2 PEACH Unit , School of Medicine, University of Glasgow , Glasgow , UK
• 3 Institute of Health and Society, Newcastle University , Newcastle upon Tyne , UK
• Correspondence to Professor Charlotte M Wright; charlotte.wright{at}glasgow.ac.uk

Objectives To explore the usefulness of Bioelectrical Impedance Analysis (BIA) for general use by identifying best-evidenced formulae to calculate lean and fat mass, comparing these to historical gold standard data and comparing these results with machine-generated output. In addition, we explored how to best to adjust lean and fat estimates for height and how these overlapped with body mass index (BMI).

Design Cross-sectional observational study within population representative cohort study.

Setting Urban community, North East England

Participants Sample of 506 mothers of children aged 7–8 years, mean age 36.3 years.

Methods Participants were measured at a home visit using a portable height measure and leg-to-leg BIA machine (Tanita TBF-300MA).

Measures Height, weight, bioelectrical impedance (BIA).

Outcome measures Lean and fat mass calculated using best-evidenced published formulae as well as machine-calculated lean and fat mass data.

Results Estimates of lean mass were similar to historical results using gold standard methods. When compared with the machine-generated values, there were wide limits of agreement for fat mass and a large relative bias for lean that varied with size. Lean and fat residuals adjusted for height differed little from indices of lean (or fat)/height 2 . Of 112 women with BMI >30 kg/m 2 , 100 (91%) also had high fat, but of the 16 with low BMI (<19 kg/m 2 ) only 5 (31%) also had low fat.

Conclusions Lean and fat mass calculated from BIA using published formulae produces plausible values and demonstrate good concordance between high BMI and high fat, but these differ substantially from the machine-generated values. Bioelectrical impedance can supply a robust and useful field measure of body composition, so long as the machine-generated output is not used.

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Strengths and limitations of this study

Population-based cohort, but female only and restricted to parents.

Explicit, well-evidenced computational approach.

Validated against published gold standard data.

Compared with widely used commercial methods.

Introduction

The WHO defines obesity as “the disease in which excess body fat has accumulated to such an extent that health may be adversely affected”. 1 Although prevention is the first step, being able to reliably identify people with excess fat is essential if the problem is to be recognised and appropriate measures taken. Body mass index (BMI) (weight/height 2 ) is only an indirect measure of fatness, so reliable methods of assessing body composition are also needed. Hydrodensitometry is usually regarded as the nearest to a gold standard, 2 but is impractical for most studies. For this reason, alternative less direct techniques have been developed. These include stable isotope methods and X-ray densitometry (DXA), but isotope methods require costly materials and processing while DXA equipment is non-portable. Thus, for ambulatory assessment, a cheaper and portable method such as Bioelectrical Impedance Analysis (BIA) is valuable. The equipment necessary is portable, relatively inexpensive and the procedure simple and painless, making it a suitable method for studying large groups of participants. 3 , 4 Measurements are taken by using four surface electrodes at different sites which send an imperceptible electrical current through the body (50 kHz alternating current of 800 μA between electrodes). Although there are also whole body machines, the most commonly used field method has been the four electrode leg-to-leg (eg, Tanita), where the participant stands with bare feet on the analyser’s footpads. The impedance value (Z) reflects the resistance and reactance that the electrical signal encounters when passing through the body; the ionised fluid in lean tissue acts as a conductor, and the current passes only through these fluids. 4 The objective physical reading of impedance cannot be interpreted without further statistical manipulation, but assuming that LM∝TBW∝height 2 /Z, lean mass (LM) and total body water (TBW) can then be estimated, from which fat mass (FM) can be calculated. 3 , 4

Although BIA is already widely used in practice and some body composition research, there remain doubts about its accuracy and precision. 5 In fact, the measurement of impedance itself is reasonably precise and repeatable as long as it is performed in healthy individuals using the same method. 6 However, the problems begin with the transformation of the impedance data. As described above, impedance has to be mathematically transformed to create meaningful estimates of TBW and thus LM. However, the prediction equations used to convert impedance measurements into measures of body fatness seem to vary between BIA machine manufacturers and incorporate elements other than the key components of height, impedance and the resistivity and hydration constants. Most manufacturers do not publish their formulae for commercial reasons, but the formulae used for a Tanita leg-to-leg machine have been published and these reveal that they incorporate weight as well as height 2 /Z. 7 It is not clear what impact this would have on the results.

A further problem is that lean and FM values are difficult to interpret in isolation, as they differ systematically depending on the participant’s height, 6 , 7 so in estimates of adiposity, FM is usually adjusted for body size by expressing it as a percentage of total mass. However, this then renders LM invisible, which is inappropriate, in individuals where LM varies markedly, since this will create differences in percentage fat (%fat), despite identical FM. 6 This thus risks misclassifying individuals with low LM as having excess body fat and underestimating FM in very muscular individuals. It has been proposed as an alternative that lean and fat should simply be expressed as indices by dividing each by height 2 , 6 but we have shown in children that this still leaves considerable unadjusted confounding by height. 6 We have previously described an alternative approach in children which produces lean and fat residuals that fully adjust both lean and FM for height and compares them to a large population reference. 6 , 8 We have now further applied these to children from the Gateshead Millennium cohort. 9 As part of the same study, we wished to compare these children to similar measures collected in their parents, but there is no generally recognised method of doing this for adults.

Finally, it is widely believed in the lay population that BMI is a poor predictor of actually fatness. Published information on this suggests generally that BMI has high specificity, but low sensitivity to identify high %fat, 10 but as described above, %fat may not be the best way to identify excess FM. We have already explored the concordance between BMI and fat residuals in children and found good concordance in the upper ranges of both, with very weak concordance for low BMI. 11

We thus set out to:

Identify best-evidenced formulae to calculate lean and FM and compare these to historical gold standard data;

Compare these results with machine-generated output;

Explore how to best adjust estimates for height and how these overlap with BMI.

Participants and methods

Participants.

The impedance data were obtained from mothers of participants in the Gateshead Millennium Study (GMS). 12 This study set out to recruit all babies born to Gateshead residents between 1 June 1999 and 31 May 2000 in prespecified recruiting weeks. A wide range of information relating to feeding, growth and latterly obesity were collected on both children and parents and they have now been followed up to beyond age 9 years. 12 The work presented here is based on data collected on the children's mothers in 2007, when the children were aged around 7 years. Written informed consent was obtained from all participants.

The data were collected on the children's parents at a home visit. While it was possible to study mothers at most of these visits, participation by fathers was minimal, so the paternal data were not used further. Impedance was measured using a single frequency (50 kHz) leg-to-leg BIA machine (Tanita TBF-300MA, Tokyo, Japan). The participants were measured wearing light clothing and bare feet after being asked to empty their bladders. The raw impedance and the machine calculated values for LM, FM and %fat were recorded. Height was measured without shoes and socks using a portable scale (Leicester height measure) to 0.1 cm with the head in the Frankfort plane. Weight was measured to 0.1 kg using the Tanita TBF-300MA. BMI was calculated as weight (kg)/height (m) 2 .

Analytical methods

The analysis was carried out using the software package R (V.2.2.0). We used the measured impedance to arrive at our own estimates of TBW and thus lean and FM using best published estimates of various constants. We assumed the hydration constant to be equal to 0.732 in adults, supported by previous studies 13–15 which gives the equation LM=TBW/0.732. Values for the resistivity constant from various papers differ, but we used those of Bell, 15 the only study where impedance was measured using leg-to-leg techniques. This gives a resistivity constant for adults ρ=0.66, that is, TBW=0.66 (height 2 /Z). Combining these two formulas, we obtained the following, simple prediction equation for adult women: LM=0.66/0.732 (height 2 /Z) or LM=0.898 (height 2 /Z). FM was then obtained as weight minus LM. To check whether the values we obtained for TBW, LM and FM using this approach were reasonable, we compared them to reference values from the two previous studies which had used gold standard measurement methods and published separate values for women. 16 , 17 The first 16 estimated TBW using 2 H 2 O dilution, body density using underwater weighting and a three-component model to estimate %fat and LM. The second 17 estimated TBW using either 2 H 2 O or 3 H 2 O dilution and FM and LM using dual-energy X-ray absorptiometry (DXA).

LM residual and FM residual

In order to produce estimates of FM and LM adjusted for height, a regression method to obtain lean and fat residuals for children 8 was adapted to produce lean and fat residuals for their mothers, using their height as a covariate. A range of transformations of raw LM and raw FM were explored in order to achieve approximate normality and constant variance of residuals when regressed on height. The residuals from regression were then standardised (subtracting the mean and dividing by the SD) to get the so-called lean and fat standardised residuals. In addition, lean and fat indices were calculated (LM or FM divided by height 2 ).

The Bland-Altman method 18 was used to compare the Tanita-generated values of FM and LM and the ones produced following the equations presented in this paper. The Bland-Altman plot is widely used in the literature to evaluate the agreement between two methods that are measuring the same thing. This involves calculating the mean difference (bias) between the two measures for each individual and the limits of agreement. In addition, the difference is then plotted against the mean of the two measures, which supplies a visual presentation of how the spread and pattern of the points varies with the reading (variable bias). Linear regression was then used to test for a significant degree of variable bias.

When the cohort was formed in 1999–2000, 1009 (81%) eligible mothers agreed to join the study and impedance and growth data were collected on 498 mothers in 2007, with mean (SD) age 36.3 (5.6) years (age range 23.6–53.1 years). Sixteen (3.2%) women were underweight (BMI <19), 141 women (28%) were overweight (BMI 25–30) and 112 (22%) were obese (BMI >30).

Total body water, lean mass and fat mass

Descriptive statistics for the anthropometric measurements are summarised in table 1 . Values for TBW, LM, FM and %fat were produced according to the predictive equations discussed in the previous section. Summary statistics were then calculated using both this method and that of Tanita ( table 1 ).

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Descriptive statistics for the anthropometric measurements

Our results are compared with the results from the two historical papers in table 2 , 16 , 17 with the GMS mothers stratified by age to allow direct comparability. Compared with the historical papers, the GMS values for weight BMI and %fat were much higher, with differences of the order of 1 SD in the youngest women. In contrast, the LM differed by no more than ¼ SD and were actually lower in the youngest GMS group.

Values (only females) reported by Hewitt 1993 and Chumlea 2001 (means±SD) compared with GMS values

Using our method, 22 (4%) women had fat <20%, and 180 (36%) had fat >40%. A majority of women with BMI >30 kg/m 2 (88, 79%) also had greater than 40% fat, but only a minority of women with BMI <19 kg/m 2 (4, 25%) had less than 20% fat.

How well do the manufactures algorithms describe body composition?

The machine-calculated values were also available for all but eight mothers. The sample mean of the Tanita LM values was lower than our calculated values (mean (SD) difference −1.19 (3.33) kg, 95% CI −1.49 to −0.90) while they were higher for FM and %fat (1.19 (3.33) kg, 95% CI 0.90 to 1.49 and 1.97 (4.86) %, 95% CI 1.54% to 2.40%, respectively). The two sets of results were compared using the Bland-Altman method, 18 and major discrepancies were found between the two methods. The relative bias in LM calculated by Tanita varied from a mean of −4.68 for all participants in the lowest quintile for LM to +2.55 for the highest quintile ( figure 1 A). Using regression, this revealed a statistically significant slope (B=0.387 p<0.001). No equivalent relationship was seen for FM or %fat ( figure 1 B, C).

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Bland-Altman plots for (A) lean mass (LM), (B) fat mass (FM) and (C) percentage fat (%fat) comparing our own calculated values to the machine output values (Tanita).

Calculation of lean residual and fat residual

These residuals were normally distributed with mean 0 and variance 1. Fourteen women (2.4%) had fat residuals <−2 SD (roughly the 2.5th centile for the normal distribution) and 124 (25%) had fat residuals >0.68 SD (roughly the 75th centile) as expected.

Relationship of the FM and LM residuals to other measurements

As would be expected, there was no association between height and the lean and fat residuals (Spearman correlation (95% CI) of height with lean residual −0.02 (−0.11 to 0.07); with fat residual 0.01 (−0.07 to 0.10)), but nor was there any significant correlation of height with the lean index (LM/height 2 : −0.05 (−0.14 to 0.04)) or the fat index (FM/height 2 : −0.03 (−0.12 to 0.06)). Of the 112 women with BMI >30 kg/m 2 , 100 (91%) also had fat residuals >75th centile, while a BMI of >30 kg/m 2 identified 81% of all with high fat residual. In contrast, of the 16 with BMI <19 kg/m 2 only 5 (31%) also had fat residual <2nd centile ( figure 2 ).

Scatter plot of lean mass adjusted for height (lean mass residual) against fat mass adjusted for height (fat mass residual) per body mass index (BMI) category (underweight (<19) and obese (≥30). The vertical lines denote the cut-off for low (<2nd centile) and high (>75th centile) fat residual.

In this analysis, we set out first to identify the best published constants to use for estimating lean and FM from BIA. The use of different devices and methods, under different conditions and on different populations, can make it difficult to extrapolate formulas from one study to another, but when we compared our estimated values for FM, LM and TBW to historical data, these revealed that results for LM were similar, while in contrast there were striking increases in average fat for the youngest, though not in the oldest category, who were already relatively more adipose even in the earlier cohorts. 16 , 17 Overall, the participants had a high median %fat (34.5) and nearly a quarter had BMI in the obese range. The results thus vividly reflect the well-recognised secular trend to increased fatness in the population of young to middle-aged women. They also illustrate good concordance between high BMI and high adiposity.

Although based on a simplified mathematical model of the human body’s shape and composition, BIA has been shown to be a reliable method in population studies, though likely to have less accuracy in individuals. 3 , 18 The large number of different published equations reflects differences in the reference methods, instrument used or the characteristics of the sample, but the constants used here seem to be the best ones currently in the public domain. The resulting prediction equation is strikingly simple in comparison to many others proposed in the literature, since it expresses LM as directly proportional to height 2 /Z with no involvement of other variables.

There are limitations to the study. We were not able to directly compare the results to a gold standard method and had to rely instead on published data. However, we were able to show how similar our results were to these, when using this simple parsimonious computational approach. The age range of the women was relatively narrow, but while there are major changes in body composition, hydration and body proportions during infancy and again in old age, 16 in young adults body composition is fairly settled, making the model reported here valid for most adult women. We have data only on women as there were insufficient data on fathers in the GMS for useful analysis. The equations published here may well also be valid for use in men, but ideally this approach should also be extended in future, using a data set of adult men. While the hydration constant (relating TBW to LM) is fairly well established in adults, the resistivity constant (which relates impedance to TBW) was particularly difficult to find. A range of different values were found in the literature, but they were based on unusual samples, 19 only males, 13 , 20 or samples covering a wide age range. 21 Only one study 15 used the now more common leg-to-leg method and this is the value we used.

The results we obtained were very different from those automatically produced by the Tanita machine. It is important to understand the distinction between these essential mathematical transformation and factors that then correlate with or influence LM and FM, such as weight, sex and age. Manufacturers may seek to include these other variables in their output to contextualise their final estimates of adiposity. However, this then is no longer the true estimate of actual LM for that individual, derived solely from the impedance reading.

The equations used by different manufacturers and for different models are not made generally available, but have been published for a machine similar to the one used in our study. 7 These show that the prediction equation used by Tanita for LM in adult women relies on weight as well as height 2 /Z and that the relative contribution of impedance to the final value is tiny relative to that of weight. For example, within our study, a decrease in impedance by 1SD (75 Ω) changes the Tanita-generated FFM estimate by <1 g while an increase in weight by 1SD (16 kg) increases it by 10% (2.8 kg). Thus, the machine estimate of FFM at least is actually largely based on weight rather than impedance.

Our results are in substantial agreement with the findings of Jebb et al , 7 that Tanita underestimates LM in adult women by between 1 and 2 kg on average, and adds the new conclusion that the relative bias in the Tanita estimate of LM varies with size. The size of the positive biases in FM and %fat obtained using Tanita, relative to our method, are very similar to the bias relative to the four-compartment model reported in Jebb et al 23 and our results also confirm poor agreement in individual cases.

Meanwhile, BIA technology has been moving on and there are now multifrequency devices and eight electrode techniques which aim to estimate different body segments and intracellular and extracellular fluids separately. However, the underpinning assumption and prediction formulae for these machines are likely to be even more complex and difficult to assess objectively.

We also considered the most robust way to adjust measures of fat and lean for height. A method that expresses lean and FM separately adjusted for height is much more informative than raw LM and FM estimates. We have shown previously in children that lean and fat residuals are effective in fully adjusting for height as well as allowing the data to be expressed as SD scores compared with a reference population. 8 , 9 However, with adult women, simply dividing LM and FM by height 2 also fully adjusted for height, suggesting that this would be equally valid and simpler. This adds further weight to Well's proposal 22 that 1/Z could be used as a simple height-adjusted lean index, since lean index=LM/Ht 2 =(H 2 /Z)/Ht 2 =1/Z. Ideally, any reference should be validated against a more direct measure of body composition, but such studies seem only to have been done in children.

We have also shown that, as in children, 11 the correspondence between high fat index and BMI is strong, with BMI >30 kg/m 2 showing 90% specific and 80% sensitivity for fat index above the internal 75th centile. This is generally a much better correspondence than was found in a systematic review of the use of various BMI thresholds to detect high %fat measured, using different methods. 10 However, most reviewed studies used much less stringent thresholds for both BMI and %fat, making comparison difficult.

In conclusion, these data demonstrate that using BIA in models with published constants produces estimates of LM that are, on average, very similar to earlier studies using more direct methods, while the larger FM values are entirely plausible given the secular trends in obesity. These suggest that the physical measurement of impedance can produce useful estimates when appropriately transformed. However, the machine-generated estimates are likely to vary between machines and manufacturers and usually do not only reflect the physical measurement of impedance. They cannot therefore be used to validate or verify other measures of adiposity such as BMI. We would recommend that researchers using BIA in future should not rely on machine-generated estimates and should instead express lean and fat indices, divided by height 2 in order to adjust for height.

Acknowledgments

The authors are grateful for the participation and advice of all those involved with The Gateshead Millennium Study—the research team, the families and children who took part, the External Reference Group, Gateshead Health NHS Foundation Trust, Gateshead Education Authority and local schools.

• ↵ [No authors listed ]. Obesity: preventing and managing the global epidemic. Report of a WHO consultation . World Health Organ Tech Rep Ser 2000 ; 894 : i – xii , 1–253 . OpenUrl PubMed
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Collaborators Gateshead Millennium Study core team: Ashley Adamson, Anne Dale, Robert Drewett, Ann Le Couteur, Paul McArdle, Kathryn Parkinson, John J Reilly.

Contributors MSP and the Gateshead Millennium Study core team designed the research and supervised the data collection and data entry. MF-V analysed the data, performed the statistical analysis and initially drafted the paper. JHM and AS supervised the analysis and commented on successive drafts of the paper. CMW designed the research study, supervised the analysis, edited the paper and has primary responsibility for final content. All authors read and approved the final manuscript.

Funding Gateshead Millennium Study was first established with funding from the Henry Smith Charity and Sport Aiding Research in Kids (SPARKS) and followed up with grants from Gateshead NHS Trust R&D, Northern and Yorkshire NHS R&D, and Northumberland, Tyne and Wear NHS Trust. This study wave was supported by a grant from the National Prevention Research Initiative (incorporating funding from British Heart Foundation; Cancer Research UK; Department of Health; Diabetes UK; Economic and Social Research Council; Food Standards Agency; Medical Research Council; Research and Development Office for the Northern Ireland Health and Social Services; Chief Scientist Office, Scottish Government Health Directorates; Welsh Assembly Government and World Cancer Research Fund).

Competing interests None declared.

Ethics approval Gateshead Local Research Ethics Committee (LREC).

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement No additional data are available.

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Body Composition

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The five-level model: a new approach to organizing body-composition research

Affiliation.

• 1 Obesity Research Center, St Luke's-Roosevelt Hospital, Columbia University, College of Physicians and Surgeons, New York, NY.
• PMID: 1609756
• DOI: 10.1093/ajcn/56.1.19

Body-composition research is a branch of human biology that has three interconnecting areas: body-composition levels and their organizational rules, measurement techniques, and biological factors that influence body composition. In the first area, which is inadequately formulated at present, five levels of increasing complexity are proposed: I, atomic; II, molecular; III, cellular; IV, tissue-system; and V, whole body. Although each level and its multiple compartments are distinct, biochemical and physiological connections exist such that the model is consistent and functions as a whole. The model also provides the opportunity to clearly define the concept of a body composition steady state in which quantitative associations exist over a specified time interval between compartments at the same or different levels. Finally, the five-level model provides a matrix for creating explicit body-composition equations, reveals gaps in the study of human body composition, and suggests important new research areas.

• Adipose Tissue / anatomy & histology
• Body Composition*
• Bone and Bones / anatomy & histology
• Extracellular Matrix
• Extracellular Space
• Glycogen / analysis
• Lipids / analysis
• Minerals / analysis
• Models, Biological*
• Muscles / anatomy & histology
• Proteins / analysis

Body Composition and Wages

This paper examines the effect of body composition on wages. We develop measures of body composition - body fat (BF) and fat-free mass (FFM) - using data on bioelectrical impedance analysis (BIA) that are available in the National Health and Nutrition Examination Survey III and estimate wage models for white respondents in the National Longitudinal Survey of Youth 1979. Previous research used body size or BMI for measuring obesity despite the growing concern in the medical literature that BMI-based measures do not distinguish between body fat and fat-free body mass and that BMI does not adequately control for non-homogeneity inside human body. Therefore, measures used in this paper represent a useful alternative to BMI-based proxies of obesity. This paper also contributes to the growing literature on the role of non-cognitive skills on wage determination. Our results indicate that calculated BF is unambiguously associated with decreased wages for both males and females among whites We also present evidence indicating that FFM is consistently associated with increased wages. We show that these results are not the artifacts of unobserved heterogeneity. Finally, our findings are robust to numerous specification checks and to a large number of alternative BIA prediction equations from which the body composition measures are derived.

Roy Wada is an AHRQ postdoctoral fellow in Health Service Research at UCLA and RAND. Alexander Brumlik and Jason Delaney provided excellent research assistance. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

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• November 9, 2007

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