Validity of formulas used in bioelectrical impedance
analysis: which is most accurate in predicting
changing amounts of fat mass during weight loss?
Annemarie Nieberg
Scriptie: 2010222
Juni 2010
Opdrachtgever:
Mevr. A. Zuur,
Kenniskring Lectoraat Gewichtsmanagement
Hogeschool van Amsterdam
Opleiding Voeding en Diëtetiek
Docent:
Mevr. A. Verreijen
Hogeschool van Amsterdam
Opleiding Voeding en Diëtetiek
Bachelor opleiding Voeding en Diëtetiek
Annemarie Nieberg
2010222
ABSTRACT
Background
The aim of this study is to validate predictive equations for bioelectrical impedance using air
displacement plethysmography (ADP) as reference method to assess changes in fat mass
(FM) during weight loss. The study of body composition in overweight populations during
weight loss by techniques such as bioelectrical impedance analysis (BIA) and bioelectrical
impedance spectroscopy (BIS) requires validation based on standard reference methods.
Methods
This study included 81 male and 192 female subjects, 16–76 years of age, with a BMI
between 25 kg/m2 and 35 kg/m2. Body composition was measured by BodPod, Tanita BC418 and BodyScout. Body weight (WT, kg) and height (HTM, m) were obtained by standard
anthropometric techniques. Impedance (Imp, Ω), Resistance, (R, Ω) and reactance, (Xc, Ω)
were also measured. PubMed was used for a systematic search for BIA formulas. By using
the % correct predictions (within 20% of reference value), RMSE, Concordance Correlation
Coefficient, and Bland and Altman approach the formulas were tested on accuracy and
precision.
Results en Discussion
In total 20 formulas were found in literature and tested for validity. Also the measured FM
according to the Tanita BC-418 and BodyScout were tested on validity. Almost all formulas
predicted more than 50% of the change in FM lower than 20% within the measured value
from the BodPod. The formulas of Kyle et al. 2003, Gray et al. 1985 and Segal et al. A and B
1985 predicted, within 20% of the measured value, respectively 17.3%, 21.1%, 21.0% and
23.0% correct.
Values from the Tanita BC-418 and BodyScout were also compared to the BodPod. The
BodyScout had better results than the Tanita BC-418 with a Bias of 0.1±5.1, a RMSE of
0.004 and 11.8% accurate measurements within 20% of the measured value from the
BodPod.
Conclusion
The examined formulas are not valid when used to predict changes in fat mass when into
weight loss. Within the examined formulas the predictions of Kyle et al. 2003, Gray et al. A
1989 and Segal et al. A and B 1985 performed best in predicting changes in FM. A new
equation for subjects into weight loss, which compensates for the loss of water bound
glycogen, should be developed to accurately predict changes in fat mass.
Keywords
Bio-electrical impedance; Body Composition; Equation; Validity; Obese;
Annemarie Nieberg
2010222
TABLE OF CONTENTS
INTRODUCTION ................................................................................................................... 2
SUBJECTS AND METHODS ................................................................................................ 3
Subjects ............................................................................................................................. 3
Measurements of body composition................................................................................... 4
Air Displacement Plethysmograph .................................................................................. 4
Bioelectrical Impedance Analysis and Bioelectrical Impedance Spectroscopy ................ 4
Anthropometry ................................................................................................................ 4
Predictive equations........................................................................................................... 5
Statistics ............................................................................................................................ 5
RESULTS.............................................................................................................................. 6
Pubmed search.................................................................................................................. 6
Subject Characteristics ...................................................................................................... 6
Validity of equations ........................................................................................................... 6
Mean and bias as mean difference ................................................................................. 6
Percentage of Accurate Predictions................................................................................ 7
RMSE and Concordance Correlation Coefficient ............................................................ 7
Bland and Altman analysis ............................................................................................. 8
Summary table of used bioelectrical impedance formulas .................................................11
Summary table of results ..................................................................................................13
DISCUSSION .......................................................................................................................14
CONCLUSION .....................................................................................................................15
REFERENCES .....................................................................................................................16
APPENDIX ...........................................................................................................................18
A. Used abbreviations ....................................................................................................18
B. Bland and Altman plots ..............................................................................................19
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INTRODUCTION
In the Netherlands 35.4% of the adults are overweight (BMI 25-30 kg/m2) and 11.8% of the
adults are severely overweight (BMI >30 kg/m2). [6] For a dietician it is important to know the
body composition of a client. The body fat percentage in the human body is strongly
associated with the risk of chronic diseases such as hypertension, dyslipidemia, diabetes
mellitus, and coronary heart disease. [1-5] To know whether or not the client is losing fat
mass or fat free mass can be important information for the success of the diet, reducing the
associated risk factors. [7] A weight reduction of 5-15% and a reduction of the waist
circumference of 10% will strongly decrease obesity related health risks. [7,8]
Several techniques have been used to assess body composition in controlled laboratory
conditions. These include underwater weighing densitometry (UW), dual energy x-ray
absorptiometry (DEXA), air displacement plethysmography (ADP), magnetic resonance
imaging (MRI), bioelectrical impedance analysis (BIA) and bioelectrical impedance
spectroscopy (BIS) [when referring to BIA both bioelectrical impedance -analysis and spectroscopy are meant]. However, UW, DEXA, ADP and MRI are expensive, inconvenient
for the participant, and not feasible to conduct in the field because they require large
specialized equipment. For these reasons, their use in practice is limited. BIA, by contrast, is
relatively simple, quick (takes only a few minutes), safe, reproducible when used by different
researchers and non-invasive [9-11]. Segal et al. [12] demonstrated that bioelectrical
impedance spectroscopy is reproducible with <1% error on repeated measurements on the
same day. This technique became commercially available in the 1980s, and requires
inexpensive, portable equipment, making it an appealing alternative to assess body
composition in practice [13,14].
The disadvantage of using BIA are the formulas used to predict fat mass (FM) and fat free
mass (FFM). These formulas use factors like gender, height and weight combined with the
resistance and/or reactance measurements to predict FM and FFM. However it’s becoming
clear that other factors like race and age can adversely affect the validity [14-16]. As a result
some formulas may become inaccurate when it comes to exact individual predictions,
especially when applied to other populations than the population for which the formula was
developed. Taking this in consideration, it might not be the best way to measure exact body
composition in individuals. However, since the reproducibility of BIA is high, monitoring
changes in body composition might be possible. Therefore the aim of this study is:
“Which formula used in bio-impedance analysis (BIA) and –spectroscopy (BIS) is most
accurate in predicting the changing amount of fat mass in the overweight human body when
losing weight compared to air displacement plethysmography (ADP)?”
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SUBJECTS AND METHODS
Subjects
................................................................................................................................................
The included subjects were participants in several previously executed weight loss studies
performed by students from the Department of Nutrition and Dietetics, Applied University of
Amsterdam (Hogeschool van Amsterdam) from September 2006 until March 2010. The aim
of these studies was to evaluate effectiveness of several hypocaloric diets using control and
intervention groups. Both the intervention and control groups used hypocaloric diets but of
different macro nutritional composition. Body composition was assed at baseline and
depending on the length of the study after eight to twelve weeks.
Subjects used for this study were adults (age ≥18 years) with a Body Mass Index (BMI)
between 25 and 35 kg/m2 that are healthy. The data of 273 subjects (192 women and 81
men) from both intervention and control groups were used in the sample. Subjects who did
not fulfill these criteria or with incomplete measurement of BIA and ADP data were excluded.
Figure 1: Exclusion results
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Measurements of body composition
The following measurements were done: Air Displacement Plethysmography (ADP),
Bioelectrical impedance Analysis (BIA) and Spectroscopy (BIS). The measurements were
taken according to the following protocol:
All subjects were measured after an overnight fast, or being sober for at least three to four
hours. The subjects drank a glass of water one hour before the measurements and had
emptied their bladder just before measurements started. Subjects were asked not to do any
kind of heavy exercise on the day of and the day before the measurements.
Air Displacement Plethysmograph
ADP measurements were performed using a BodPod (Life Measurement, Inc.). Subjects
were wearing tight underwear/bathing clothes and a swim cap. The subject was measured
twice. When the two measurements of volume were too deviant (more than 2%) then a third
measurement was performed. The fat mass (FM) derived form the ADP measurement was
considered as reference method in this study. The FM was calculated by subtracting FFM,
calculated by the Siri formula [17], from the total body weight.
Bioelectrical Impedance Analysis and Bioelectrical Impedance Spectroscopy
BIA and BIS measurements were performed at the non-dominant side of the subject, using a
Tanita (Tanita BC-418MA) Bioelectrical Impedance Analyzer and a BodyScout (FresenuisKabi) Bioelectrical Impedance Spectroscope. When using the BodyScout, the subjects were
lying in a supine position for at least two minutes before starting the measuring procedure.
Two current and two detector electrodes were placed on the dorsal surfaces of the hand and
foot: on the distal portion of the second metacarpal, between the styloid processes of the
radius and ulna, the distal portion of the metatarsal and at the anterior ankle between the
tibia and fibula, according to the procedure described in the BodyScout manual. The current
electrodes were placed farthest of the heart. The impedance, measured at a frequency of
50kHz using the Tanita and the resistance and reactance, measured at a frequency of 50kHz
using the BodyScout, were used in the analysis.
Anthropometry
Height was measured with a wall mounted tape to the nearest 0.1 cm. The bodyweight was
measured to the nearest 0.001 kg by the scale from the BodPod before the ADP
measurements.
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Predictive equations
PubMed was used for a systematic search for publications on formulas predicting fat mass
(FM) and fat free mass (FFM) using Impedance, Resistance and/ or Reactance. The
following Mesh-derived search terms were used: ‘Bioelectrical Impedance’ and ‘Body
Composition’, and additional terms: ‘formula’, ‘validity’, ‘equation’, and ‘resistance’ in every
possible combination. Applied limitations were ‘English language’ and ‘humans’. More
references were obtained by screening cited publications. BIA equations were included when
developed based on the following methods: MRI, DEXA, UW, ADP or another scientifically
accepted reference method to measure body composition. Formulas constructed for the
following populations were excluded: age <16 y, critically ill patients, athletes or when
variables needed which were not available in our dataset, such as race. Also the predicted
FM calculated using the manufacturer prediction imbedded into the Tanita and BodyScout
was used.
Statistics
Data were analyzed by using SPSS 17.0 (SPSS Inc, Chicago, IL), except for Concordance
Correlation Coefficient (CCC) and Bland-Altman which were analyzed with MedCalc.
software (MedCalc. Software BV, Mariakerke, Belgium). A negative or positive difference
between the change in FM according to ADP (the reference value) and the predicting
formulas was indicated respectively as an underestimated (<-20% from reference value), an
accurate (>-20% and <20% from reference value) or an overestimated (>20% from reference
value) prediction. The root mean squared prediction error (RMSE) was used to indicate how
well the formulas predicted FM individually. The CCC was also used to show the precision
and accuracy of the formulas. The CCC was calculated using Pearson correlation coefficient
multiplied by a correction factor for deviation from line of identity (x=y). P values <0.05 were
considered significant. Bias was calculated as the mean of the difference. Limits of
agreement were calculated as ±1.96*SD by using the Bland and Altman approach and was
considered a measure of validity on a group level. The Paired samples T-test was used to
calculate the significant difference in body fat percentage between the ADP values and BIA
formulas.
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RESULTS
Pubmed search
The Pubmed search produced 20 suitable formulas, found in 16 different articles as shown in
table 2. Of these 20 formulas, 3 used impedance as a variable, whereas the other 17
formulas used ether resistance, reactance or a combination of both.
Subject Characteristics
Data of 273 overweight subjects (192 women and 81 men) were used in this study. The
subject characteristics are shown in table 1, grouped by gender. The subjects’ ages ranged
from 18 to 76 year.
Table 1: Subject characteristics
Total
Group
N=
273
Age (years)
43.2±12.1
Height (cm)
170.8±9.5
Body Weight (kg)
87.3±12.9
2
BMI (kg/m )
29.8±2.6
Weight change
-2.5±3.1
BodPod (kg)
FM change
-2.5±3.5
BodPod (kg)
Woman
Men
192
42.9±12.1
166.6±6.6
82.7±10.2
29.8±2.6
-2.3±2.7
81
43.8±12.2
180.0±7.9
98.1±12.1
29.9±2.6
-2.9±3.8
-2.4±3.0
-2.9±4.5
FM change is shown as mean, the percentage bias, the RMSE (in kg), the concordance
correlation coefficient (CCC), the percentage of accurate predictions, the percentage of
under predictions, and the percentage of over predictions (table 3).
Validity of equations
Mean and bias as mean difference
The mean and bias as mean difference was calculated for all formulas. The BodyScout had
the lowest mean difference compared to ADP with -0.02 kg, but the highest standard
deviation (5.1). Kyle et al. 2003 had the second lowest mean difference compared to ADP
with 0.03 and a standard deviation of 3.1.
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Percentage of Accurate Predictions
A negative or positive difference between the change in FM measured by ADP and the
predicting formulas is indicated respectively as underestimated, accurate or overestimated
predictions. The percentage of accurate predictions as well as the over- and underestimated
predictions is shown in table 3 and figure 2. Remarkably, almost all formulas predict more
than 50% of the change in FM lower than the actual change in FM according to ADP. The
highest score of accurate predictions is 23.0% for the Segal et al B 1985 formula, whilst the
lowest scoring formula (Wang 1995) only predicted 4.9% of the predictions accurately.
Figure 2: Accuracy of formulas predicting changing FM compared to ADP in percentages.
RMSE and Concordance Correlation Coefficient
The RMSE and Concordance Correlation Coefficient (CCC) were calculated for the total
group. A prediction equation with relative low RMSE and high CCC was considered to be a
good predictor of lost FM. The RMSE ranged from 0.002kg to 0.150kg (figure 3) and the
CCC ranged from 0.2220 to 0.775 (figure 4). Kyle et al 2003 had a RMSE of 0.002 kg and a
CCC of 0.651. Gray et al. A 1985 had a RMSE of 0.016 kg and a CCC of 0.678. Segal et al.
A and B had respectively a RMSE of 0.013 and 0.022 kg and a CCC of 0.710 and 0.775.
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Figure 3: Root Mean Squared Error (RMSE) of the mean difference in changing FM predicted by the
formulas compared to ADP.
Figure 4: Concordance Correlation Coefficient of the predicted change FM compared to ADP.
The measured change in FM from the ADP and BIA equations were compared with use of
the Paired Samples T-tests. Of all the compared equations seven formulas were not
significantly different from the ADP results (table 3).
Bland and Altman analysis
The predictive equations were also tested for relative bias and 95% limit of agreement with a
Bland and Altman analysis. The results are summarized in table 3 and illustrated in the
appendix as the Bland and Altman plots. Kyle et al. 2003 (figure 5) had a Bias of 0.03±3.1
kg, Gray et al. A 1989 (figure 6) had a Bias of 0.30±2.8 kg and Segal et al. A (figure 7) and B
(figure 8) 1985 had respectively a Bias of 0.21±2.8 and 0.35±2.8. These formulas also had
the lowest limits of agreement (as shown in figures 5-8) ranging from -4.0 to 4.7 in Segal et
al. B 1985 to -5.5 to 5.5 in Kyle et al. 2003.
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Figure 5: Bland and Altman plot Kyle et al. 2003
Figure 6: Bland and Altman plot Gray et al. A 1989
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Figure 7: Bland and Altman plot Segal et al. A 1985
Figure 8: Bland and Altman plot Segal et al. B 1985
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Table 2: Summary table of used bioelectrical impedance formulas
Publication
Boulier et al.
[19]
Year
1990
N=
202
Equation
FFM= 6.37+0.64WT+0.40(HTCM2/IMP)-0.16AGE-2.71SEX
(SEX: 1=male 2= female)
Reference
UW
13
FFM = 0.698*l04*(HTM2/R)+3.55+9.4
UW
1990
Subjects
Healthy
adolescents
and adults
Healthy,
adults
Young adults
Deurenberg
et al [20]
Deurenberg
et al. [21]
1989
246
FFM= 0.258*104*(HTM2/IMP)+0.375WT+6.3SEX+10.5HTM-0.164AGE-6.5
(SEX: 1=male 0=female)
UW
Deurenberg
et al. [22]
1991
Adults
827
FFM= 0.340*104*(HTM2/IMP)+15.34HTM-0.127AGE+4.56SEX-12.44
(SEX: 1=male 0=female)
UW
Gray et al.
[23]
1989
Healthy and
obese adults
87
A) FFM = 0.00139 HTCM2-0.0801(R)+0.l87(WT)+39.830
FFM = 0.00151 HTCM2-0.0344(R)+0.140(WT)-0.158(AGE)+20.387
B) FFM = 0.000985 HTCM2-0.0387(R)+0.158(WT)-0.124(AGE)+29.612
UW
Kushner et
al. [24]
1992
Adults
41
FFM= (0.59ZI+0.065WT+0.04)/0.732
Kyle et al.
[25]
2001
Healthy
adults
343
FFM= -4.104+0.518ZI+0.231WT+0.130 R+4.229SEX
(SEX: 1=male 0=female)
Isotope
dilution
(H218O)
DXA
Kyle et al.
[26]
2003
Healthy
adults
5635
FFM= -4.211+0.267ZI+0.095WT+1.909SEX)+-0.012AGE+0.058Xc
(SEX: 1=male 0=female)
DXA
Lohman [27]
1992
Black and
white adults
306
Male: FFM=0.485ZI+0.338WT+5.32
Female: FFM=0.475ZI+0.295WT+5.49
Lukaski et
al. [28]
1986
Healthy
adults
114
FFM=0.756ZI+0.110WT+0.107Xc-5.463
40-K spectrometry and
densitometry
UW
Page 11 of 31
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Segal et al.
[29]
1985
Adults
75
A) LBM= -8.98751+0.36273ZI+0.21411HT +0.13290WT-5.61911SEX
B) LBM=-10.54635+0.48271HT-0.04426R +0.12869WT5.56699SEX
FFM=LBM*0.97(male) FFM=LBM*0.92(female) (SEX : 1=female 0=male)
Total body
electrical
conductivity
Segal et al.
[30]
1988
Adults, obese
adults
1567
A) Male LBM = 0.00132HTCM2-0.04394R+0.30520WT-0.16760AGE+22.66827
Female LBM = 0.00108HTCM2-0.02090(R +0.23199WT0.06777AGE+14.59453
B) Male LBM = 0.00066360HTCM2-0.02117R+0.62854WT0.12380AGE+9.33285
Female LBM = 0.00064602HTCM2-0.01397R+0.42087WT+10.43485
C) Male LBM = 0.00088580HTCM2-0.02999R+0.42688WT0.07002AGE+14.52435
Female LBM = 0.00091186HTCM2-0.01466R+0.29990WT0.07012AGE+9.37938
FFM=LBM*0.97(male) FFM=LBM*0.92(female)
UW
Sun et al.
[31]
2003
White and
black adults
1829
Male FFM= -10.68+0.65ZI+0.26WT+0.02R
Female FFM= -9.53+0.69ZI+0.17WT+0.02R
Multi
component
model
Suprasongsi
n et al. [32]
1995
Young adults
56
FFM=0.524ZI+0.415WT-0.32
Isotope
dilution
(H2180)
Wang et al.
[33]
1995
808
FFM = 0.427ZI+0.132WT+0.206HTM-19.71
DXA
Wattanapen
paiboon et
al. [34]
1998
Healthy
adults black
white and
asian
Adults
196
Male FFM= 0.4936ZI+0.332WT+6.493
Female FFM= 0.6483ZI+0.1699WT+5.091
DXA
FFM = fat free mass; LBM= lean body mass; HTM= height in meters; HTCM= height in centimeters; WT= weight; AGE= age; SEX= gender; R= Resistance;
2
Xc= Reactance; ZI = HTCM /R;
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Table 3: Summary table of results
Predictive equations
N=
BIA*
BIS
Boulier et al 1990 [19]*
Deurenberg et al 1989 [20]
Deurenberg et al 1990 [21]*
Deurenberg et al 1991 [22]*
Gray et al A 1989 [23]
Gray et al B 1989 [24]
Kushner et al 1992 [25]
Kyle et al 2001 [26]
Kyle et al 2003 [27]
Lohman 1992 [28]
Lukaski et al 1986 [29]
Segal et al A 1985 [30]
Segal et al B 1985 [31]
Segal et al A 1988 [32]
Segal et al B 1988 [30]
Segal et al C 1988 [30]
Sun et al 2003 [31]
Suprasongsin et al 1995 [32]
Wang 1995 [33]
Wattanapenpaiboon 1998 [34]
266
252
263
248
263
263
248
173
248
248
248
248
248
248
248
248
248
248
248
248
248
248
Mean
equations1
(kg)
-2.28
-2.55
-4.77
-2.81
-1.59
-1.87
-2.24
-2.11
-2.64
-2.15
-2.51
-1.93
-2.57
-2.33
-2.19
-1.88
-1.34
-1.72
-2.23
-1.70
-0.18
-2.18
Mean
Bodpod2
(kg)
-2.55
-2.53
-2.56
-2.54
-2.56
-2.56
-2.54
-2.36
-2.54
-2.54
-2.54
-2.54
-2.54
-2.54
-2.54
-2.54
-2.54
-2.54
-2.54
-2.54
-2.54
-2.54
Bias±SD3
(kg)
Pvalue4
Accurate
predictions5
Under
predictions6
Over
Predictions7
RMSE8
(kg)
CCC9
(p)
0.27±3.1
-0.02±5.1
-2.21±2.8
-0.27±3.7
0.97±2.0
0.78±2.3
0.30±2.8
0.25±2.5
-0.10±3.8
0.39±2.8
0.03±3.1
0.61±2.6
-0.03±3.6
0.21±2.8
0.35±2.8
0.66±2.7
1.20±1.9
0.82±2.2
0.31±2.9
0.84±2.5
2.36±1.8
0.36±3.0
0.137
0.860
<0.001
0.197
<0.001
<0.001
0.074
0.129
0.676
0.040
0.850
0.001
0.906
0.187
0.014
<0.001
<0.001
<0.001
0.091
<0.001
<0.001
0.071
8.1%
11.8%
9.5%
15.7%
10.7%
14.9%
21.1%
20.4%
10.9%
16.1%
17.3%
12.9%
13.7%
21.0%
23.0%
16.2%
7.8%
13.3%
16.1%
10.9%
4.9%
12.9%
65.8%
57.7%
42.6%
47.6%
76.4%
68.6%
55.9%
54.1%
53.2%
59.3%
51.4%
62.5%
53.8%
53.6%
58.5%
64.4%
78.4%
70.6%
56.5%
66.4%
83.8%
58.9%
26.2%
30.5%
47.9%
36.7%
12.9%
16.5%
23.1%
25.6%
35.9%
24.6%
31.3%
24.6%
32.5%
25.4%
18.6%
19.4%
13.9%
16.1%
27.4%
22.7%
11.3%
28.2%
0.017
0.004
0.136
0.017
0.060
0.043
0.019
0.016
0.008
0.025
0.002
0.039
0.002
0.013
0.022
0.042
0.076
0.053
0,020
0.054
0.150
0.023
0.616
0.355
0.220
0.597
0.738
0.734
0.678
0.711
0.518
0.580
0.651
0.585
0.525
0.710
0.775
0.626
0.584
0.678
0.595
0.515
0.262
0.544
*Formulas using impedance measured by the Tantia BC-418
1
2
3
Mean predicted amount of lost FM. Mean amount of lost fat mass calculated using BodPod (in the same population used by the formula). The relative bias of the
4
5
equations. P-value showing significant difference at P=<0.05 Accurate predictions: The percentage of subjects predicted by this predictive equation within 20% of the
6
7
measured value. Under predictions: The percentage of subjects predicted by this predictive equation <20% of the measured value. Over predictions: The percentage of
8
9
subjects predicted by this predictive equation >20% of the measured value. Root Mean Squared Error. Concordance Correlation Coefficient.
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DISCUSSION
In total 20 formulas were found in literature and tested for validity. Also the measured FM according to
the BIA and BIS were tested on validity. This study shows that the examined formulas were not valid.
Almost all formulas predicted more than 50% of the change in FM lower than 20% within the measured
value from the BodPod. Within the existing formulas the predictions of Kyle et al. 2003, Gray et al. A
1989 and Segal et al. A and B 1985 performed best in predicting changes in FM. Kyle et al. 2003, Gray
et al. 1985 and Segal et al. A and B 1985 predicted, within 20% of the measured value, respectively
17.3%, 21.1%, 21.0% and 23.0% correct.
The BodyScout was more accurate in predicting changing fat mass compared to the Tanita BC-418.
Scoring higher in almost all categories: Accurate predictions, RMSE, Bias and Bland and Altman,
compared to the Tanita BC-418. However the use of a separate formula might be most accurate.
Measuring FFM, FM and TBW is heavily discussed in literature. Many ways of measuring the body
composition have been developed over the years [35-38], BIA being one of them.
Several articles show that absolute measurements on an individual level, predicting body composition
using BIA, are not accurate. [9,23,38-43] BIA has been shown to give inaccurate predictions because,
the weight changes that occur over longer time periods, such as during weight loss or weight change in
pregnancy, affect the TBW. [39-43] Deurenberg et al. 1989 [40] suggested that because of changes in
TBW as a result of depleted water bound glycogen stores after weight loss, the BIA measurements
might be disrupted, causing an overestimation of FFM and therefore an underestimation of FM.
To our knowledge, this is the first study which compared the changes in FM and not the absolute
measurements. The validity and accuracy of the absolute measurements is not under discussion in this
article but could provide insights on the reasons why the used equations did not provide accurate
predictions on the change of FM.
Fields et al. [44] published in a review an analysis of the validity of ADP relative to UW in male and
female adults, aged between 20–56 years. In 5 of the studies used in the review no significant
difference between the two compared methods was found. In the other studies that did show a
significant mean difference, the direction of the differences were inconsistent. Fields et al. [44] also
states that even though many studies consider ADP as a reliable and valid technique, it should also be
noted that more validation studies are required using a four compartment model as a reference
standard, rather than UW (two compartment model), when validating ADP (two compartment model).
Fortunately there have been studies since, which validate ADP against 4 Compartment Models. [45] It
could be recommended based on this information to conduct studies on the validity of ADP compared
to a 4 Compartment Model during weight loss.
When looking to BIA and BIS, the BIS predictions stand out. Scoring high in almost all categories
(RMSE, BIAS, SD, Bland and Altman) the BIS might be a usable instrument to measure changes in FM
without the use of a separate formula. BIS (Fresenuis- Kabi – Bioelectrical Impedance Spectroscope)
uses Resistance (Ω) and Reactance (Ω) in the formulas to predict body composition whereas BIA
(Tanita - Bioelectrical Impedance Analyzer) uses Impedance (Ω). The three formulas derived from
Boulier et al. 1990, Deurenberg et al. 1990 en Deurenberg et al. 1991 used Impedance as variable
whereas the other formulas used Resistance, Reactance or both. These different instruments measure
at different frequencies: BIA at 50 kHz and BIS from 5 kHz to 1 MHz. To compare the outcomes all the
equations used the measured values at 50 kHz. However, because of the different devices used to
measure impedance, resistance and reactance, it might be possible that the measurements taken differ
from each other. It could be recommended to not only validate the formulas, but also to validate the
used devices.
It should be noted that ethnicity can influence the validity of predictions [14]. However, in the used
database, ethnicity was not recorded properly. Also obesity influences the validity of the predictions; a
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high BMI (kg/m2) can adversely affect the accuracy. [16,23,36,41] For future research it is
recommended to include ethnicity and create subgroups based on BMI(kg/m2).
CONCLUSION
The examined formulas are not valid when used to predict changes in fat mass when into weight loss.
Within the examined formulas the predictions of Kyle et al. 2003, Gray et al. A 1989 and Segal et al. A
and B 1985 performed best in predicting changes in FM. A new equation for subjects into weight loss,
which compensates for the loss of water bound glycogen, should be developed to accurately predict
changes in fat mass.
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APPENDIX
A. Used abbreviations
Text:
BMI
UW
DEXA
ADP
MRI
BIA
BIS
FM
FFM
LBM
RMSE
CCC
SD
Body Mass Index (kg/m2)
Underwater Weighing densitometry
Dual Energy X-ray Absorptiometry
Air Displacement Plethysmography
Magnetic Resonance Imaging
Bioelectrical Impedance Analysis
Bioelectrical Impedance Spectroscopy
Fat Mass
Fat Free Mass
Lean Body Mass
Root Mean Squared Error
Concordance Correlation Coefficient
Standard Deviation
Equations:
WT
HTM
HTCM
IMP
R
Xc
AGE
SEX
ZI
Weight (Kg)
Height (meters)
Height (centimeters)
Impedance (Ω)
Resistance (Ω)
Reactance (Ω)
Age (years)
Gender
2
HTCM /R
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B. Bland and Altman plots
Figure 9: Bland and Altman plot BIA
Figure 10: Bland and Altman plot BIS
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Figure 11: Bland and Altman plot Boulier 1990
Figure 12: Bland and Altman plot Deurenberg 1989
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Figure 13: Bland and Altman plot Deurenberg 1990
Figure 14: Bland and Altman plot Deurenberg 1991
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Figure 15: Bland and Altman plot Gray A 1989
Figure 16: Bland and Altman plot Gray B 1989
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Figure 17: Bland and Altman plot Kushner 1992
Figure 18: Bland and Altman plot Kyle 2001
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Figure 19: Bland and Altman plot Kyle 2003
Figure 20: Bland and Altman plot Lohman 1992
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Figure 21: Bland and Altman plot Lukaski 1986
Figure 22: Bland and Altman plot Segal A 1985
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Figure 23: Bland and Altman plot Segal B 1985
Figure 24: Bland and Altman plot Segal A 1988
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Figure 25: Bland and Altman plot Segal B 1988
Figure 26: Bland and Altman plot Segal C 1988
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Figure 27: Bland and Altman plot Sun 2003
Figure 28: Bland and Altman plot Suprasongsin 1995
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Figure 29: Bland and Altman plot Wang 1995
Figure 30: Bland and Altman plot Wattanapenpaiboon 1998
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