Ndagire et al. BMC Public Health (2018) 18:687
https://doi.org/10.1186/s12889-018-5627-y
RESEARCH ARTICLE
Open Access
Assessing the reliability of FTIR
spectroscopy measurements and validity of
bioelectrical impedance analysis as a
surrogate measure of body composition
among children and adolescents aged 8–
19 years attending schools in Kampala,
Uganda
Catherine T. Ndagire1* , John H. Muyonga1, Dan Isabirye2, Benard Odur3, Serge M. A. Somda4, Richard Bukenya5,
Juan E. Andrade5,6 and Dorothy Nakimbugwe1
Abstract
Background: Accurate measurement of body composition in children and adolescents is important as the quantities
of fat and fat-free mass have implications for health risk. The objectives of the present study were: to determine the
reliability of Fourier Transform Infrared spectroscopy (FTIR) measurements and; compare the Fat Mass (FM), Fat Free
Mass (FFM) and body fat percentage (%BF) values determined by bioelectrical impedance analysis (BIA) to those
determined by deuterium dilution method (DDM) to identify correlations and agreement between the two methods.
Methods: A cross-sectional study was conducted among 203 children and adolescents aged 8–19 years attending
schools in Kampala city, Uganda. Pearson product-moment correlation at 5% significance level was considered for
assessing correlations. Bland Altman analysis was used to examine the agreement between of FTIR measurements and
between estimates by DDM and BIA.. Reliability of measurements was determined by Cronbach’s alpha.
Results: There was good agreement between the in vivo D2O saliva enrichment measurements at 3 and 4 h among
the studied age groups based on Bland-Altman plots. Cronbach’s alpha revealed that measurements of D2O saliva
enrichment had very good reliability. For children and young adolescents, DDM and BIA gave similar estimates of FFM,
FM, and %BF. Among older adolescents, BIA significantly over-estimated FFM and significantly under-estimated FM and
%BF compared to estimates by DDM. The correlation between FFM, FM and %BF estimates by DDM and BIA was high
and significant among young and older adolescents and for FFM among children.
Conclusions: Reliability of the FTIR spectroscopy measurements was very good among the studied population. BIA is
suitable for assessing body composition among children (8–9 years) and young adolescents (10–14 years) but not
among older adolescents (15–19 years) in Uganda. The body composition measurements of older adolescents
determined by DDM can be predicted using those provided by BIA using population-specific regression equations.
Keywords: Body composition, Bioelectric impedance analysis, Deuterium dilution method, Children, Adolescents,
agreement, reliability
* Correspondence: catherinendagire@gmail.com
1
School of Food Technology, Nutrition and Bio-engineering, Makerere
University, Kampala, Uganda
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Ndagire et al. BMC Public Health (2018) 18:687
Background
Nutrition-related non-communicable diseases (NCDs)
such as hypertension, high blood glucose and cholesterol
levels, diabetes and cardiovascular diseases, are increasing
and are predicted to be the major cause of morbidity and
mortality in most developing nations by 2020 [1]. In children and adolescents, the most common risk factors for
nutrition-related NCDs include overweight, obesity, physical inactivity and unhealthy diets [2]. Pediatric and adolescent overweight and obesity are the driving force
behind metabolic syndrome risk that has become a growing public health concern in low and middle-income
countries (LMICs) [3]. This calls for interventions to prevent and manage childhood and adolescent overweight
and obesity. These interventions’ design, monitoring and
evaluation rely on correct identification of overweight and
obese individuals. Therefore, there is need for accurate
body composition measures to correctly identify overweight and obese individuals.
Body mass index (BMI) is the commonly used technique
to determine nutrition status because it is inexpensive, fast
and non-invasive [4]. However, it is a poor index of fatness
and has poor sensitivity and inaccuracy for categorizing of
obesity and overweight [5]. These limitations make BMI a
poor outcome for research on the efficacy of nutrition programs. A number of reference methods are used to estimate
body composition, including underwater weighing (UWW)
technique, air displacement plethysmography (ADP),
dual-energy X-ray absorptiometry (DEXA) and Deuterium
Dilution Method (DDM) [6]. While DDM has widely been
used due to its simplicity and relatively low cost, no published study was found on FTIR spectroscopy measurements’ reliability among any population in Uganda.
Reliability is defined as the degree of consistency and the
lack of error in a measurement [7]. Despite the scarcity of
studies on the reliability of FTIR measurements, it is a prerequisite for investigators aiming to validate a device or technique to evaluate the reliability of the reference method, as
lack of reliability often masks the actual effects and leads to
misinterpretation [8]. Internal reliability which measures repeatability of a tool is determined by Cronbach’s alpha [7]
and by Bland-Altman analysis [9].
BIA is a rapid, cheap, safe and simple technique for
measuring body composition both in the field and in
clinical settings [10], based on population-specific predictive equations [11]. Since the validity of BIA measurements varies with age and ethnicity [12], a number of
studies have assessed the validity of BIA devices in various populations of children and adolescents commonly
using DEXA and DDM as reference techniques [13–16].
However, BIA’s validity for assessment of body composition and agreement with reference techniques like
DDM has not been assessed among any population in
Uganda, including children and adolescents. Therefore,
Page 2 of 9
the objectives of the present study were to: i) assess the
reliability of FTIR spectroscopy measurements of saliva
D2O enrichment for determination of body composition
and; ii) compare the body composition variables determined by BIA and DDM and, identify possible correlations and agreement between the two methods.
Methods
Subjects
In a cross-sectional study, 203 apparently healthy (based
on self-proclamation) participants attending primary and
secondary schools in Kampala city, Uganda were selected through a two-stage cluster sample design. The
Ministry of Education and Sports provided an up to date
list of all the primary and secondary schools in Kampala
from which schools to participate in the study were randomly selected. Due to homogeneity between schools
and between students in divisions of Kampala, schools
were treated as clusters. Sampling of students from
schools followed probability proportion to size procedure and a sample of 203 participants aged 8–19 years
was randomly selected using random numbers.
Since sample size determination for validation studies
is rarely ever justified a priori [17], for this study validation sample size was based on recommendations of researchers in the field of validity studies and from sample
sizes used in previous validity studies as stated below.
For a study of agreement between two methods of measurement, a sample size of 100 subjects is sufficient, giving a 95% CI of about +/− 0.34 s, where s is the standard
deviation of the differences between measurements by
the two methods. A sample of 200 subjects is better
since it gives a 95% CI of about +/− 0.24 s [18]. A sample
size of 100 to 200 subjects is a reasonable size for validation studies as it’s adequate for a range of likely degrees
of validity and allows for appropriate deletion of some
subjects [19]. Furthermore, a minimum of 80 subjects
for validity studies provides highly representative estimates of the main study samples [20]. For most studies,
sample sizes used have often been small, ranging from
15 to 189 subjects [21–25]. Against this background, for
this study, 203 participants were selected. At least four
subjects were targeted for each age. The subject to item
ratio (n = 4) is the frequently recommended approach
when performing an exploratory factor analysis [17]. In
a similar study to assess body composition in Mexican
school children of different geographical regions and
ethnicity, two children per age and ethnic group were
regarded as sufficient [26].
The selected subjects’ nutritional status was evaluated
by anthropometric measurements: BMI, waist circumference, waist to hip ratio and weight to height ratio and
their body composition was assessed by BIA and DDM.
Immediately after the anthropometric and BIA
Ndagire et al. BMC Public Health (2018) 18:687
measurements were taken, saliva samples were collected
from the subjects and D2O doses were given to them.
This permitted the assessments to be performed at the
same time and under the same conditions, with a consequent constant state of hydration during all methods of
body composition assessment used in the study.
Assessing height and weight
Height and weight were taken by trained researchers
using standard equipment. Body weight was measured
to the nearest 0.1 kg using a weighing scale, (Seca 899;
Seca Weighing and Measuring Systems, Model No.
8691321004, SECA Gmbh & Co. Germany made in
China) with minimal clothing and no shoes. Height was
measured to the nearest 0.1 cm using a height board
(Shorr-board, height board, Weight and Measure LLC,
Irwin J. Shorr, MPH, MPS. Olney, Maryland USA) without shoes. BMI (kg/m2) was calculated as weight in kilogram divided by the square of height in meters.
Assessing waist and hip circumferences
Waist circumference (WC) was measured to the nearest
0.1 cm in standing position at the midpoint between the
lowest rib and the iliac crest and at the end of normal
expiration, using a measuring tape. Hip circumference
(HC) was measured to the nearest 0.1 cm in standing
position at the widest point of the hips using a measuring tape (Lufkin Executive Diameter Steel Tape, 2 m
Thinline Model W606 PM, Apex Tool Group, LLC NC
27502, USA).
Body composition assessment by bioelectrical impedance
analysis
Body composition by BIA was measured using a BIA
(Tanita SC-331S Body Composition Analyzer; Tanita
Inc., Arlington Heights, IL) instrument, which provides
a measure of fat mass and fat-free mass using in-built
manufacturers’ equations. Impedance was measured
with the subject standing barefoot on the metal
foot-plates of the machine for approximately 1 min. The
subject’s age, gender, and height were entered into the
machine, and a standard 0.5 kg was entered as an adjustment for clothing weight for all participants.
Body composition assessment using deuterium dilution
technique
A baseline saliva sample was collected from participants
2 hours after their last meal. Each participant then received an oral dose of D2O (0.5 g/kg body weight). Two
endpoint saliva samples were collected at 3 and 4 h after
D2O dose ingestion. Samples were stored in plastic saliva
vials at − 20 °C until they were analyzed for D2O using
FTIR spectroscopy instrument (FTIR-8400S, Shimadzu
Corporation, Japan) according to manufacturer’s
Page 3 of 9
instructions. The instrument was housed in the Department of Biochemistry, Makerere University Kampala,
Uganda. The instrument settings were: measurement
mode: absorbance; apodization: square triangle; number
of scans: 32; resolution: 2.0 and; range (cm− 1): minimum
2300 - maximum 2900.
A ‘background’ scan was performed using the unenriched drinking water that was used to make the calibration standard (zero standard) and the instrument was
calibrated using a prepared D2O standard (1000 mg/kg).
Total body water (TBW) was calculated from the saliva
sample by plateau method, based on the assumption that
this plateau was reached at 3 or 4 h. FM and %BF were
estimated from TBW while FFM was calculated from
FM.
Statistical analysis
Descriptive statistics (means and confidence intervals)
were used for presentation of measurements data for
D2O enrichment, participants’ characteristics and body
composition (FFM, FM, and %BF) by DDM and BIA.
Normality of variables was inspected visually using normal histogram plots. Box plots were used to inspect for
data outliers 8 of which were removed. To show the relationship between saliva D2O enrichment at 3 and 4 h
after ingestion of the D2O dose when equilibration is
achieved, Pearson product-moment correlation was
used. Reliability of the two FTIR measurements was
verified using the Bland-Altman analysis by plotting the
differences between the two measurements of each subject against the mean value of the two measurements.
Mean differences and limits of agreement were determined according to Bland Altman procedures. Limits of
agreement were considered as the mean of differences
between the measurements at 3 and 4 h ± 1.96 × their
standard deviation. Cronbach’s alpha was used to assess
the level of reliability of the FTIR spectroscopy measurements at 3 and 4 h after D2O dose ingestion. Cronbach’s
α values between 0.7–0.9 were considered representative
of good reliability, while values above 0.9 were considered representative of very good reliability [27].
Paired t-tests were used to compare mean measures of
FFM, FM, and %BF by BIA and DDM. To show the relationship
between
DDM
and
BIA,
Pearson
product-moment correlation was considered. The Bland
Altman plots examined the agreement between DDM
and BIA for measuring FFM, FM, and %BF. Mean differences and limits of agreement were calculated according
to Bland Altman procedures. Limits of agreement were
considered as the mean of differences between measurements by DDM and BIA ± 1.96 × their standard deviation. The analyses were done using with STATA
version 13 software and the level of significance was set
at P < 0.05.
Ndagire et al. BMC Public Health (2018) 18:687
Page 4 of 9
Results
There were wide ranges for body weight, height, BMI,
waist circumference and hip circumference across the
different age groups (Table 1) In the current study, 16
children aged 8–9 years, 112 young adolescents aged
10–14 years and 67 older adolescents aged 15–19 years;
84 males and 111 females with mean (95% confidence
interval) age 13.44 (12.98 to 13.90) years, weight 44.61
(42.92 to 46.31) kg, height 1.51 (CI: 1.50, 1.53) m, waist
circumference 65.87 (CI: 65.02, 66.71) cm and hip circumference 82.48 (CI: 80.97, 83.99) cm participated.
Cronbach’s alpha values for the two measurements of
saliva D2O enrichment were high (0.999, 0.997 and
0.996 for children, young and older adolescents, respectively) (Table 2).
The correlation coefficients for deuterium enrichment
at 3 and 4 h were high and positive among children,
young and older adolescents at r = 0.998, 0.995, and
0.993 respectively (Fig. 1a). The Bland-Altman plots
showed random nature of spread with no detectable proportional bias for saliva D2O enrichment at 3 and 4 h
among the different age groups (Fig. 1b). For children
and young adolescents, FFM, FM and %BF estimates by
DDM were not statistically significantly different from
those measured by BIA (Table 3). Among older adolescents, DDM significantly underestimated FFM (P <
0.0001) and significantly overestimated FM and %BF at
P < 0.0001 and P < 0.0001 respectively compared to BIA.
Among young and older adolescents, the correlations
between FFM, FM and % BF estimates by DDM and BIA
were high and significant at r > 0.7 and P < 0.0001
(Figs. 3a and 4a). The Bland-Altman plots for FFM, FM,
and %BF showed a random nature of spread with no detectable significant negative bias for FFM, FM and % BF
values estimated by DDM and BIA among the different
age groups (Figs. 2b, 3b and 4b).
DDM and BIA exhibited generally narrower limits of
agreement for FFM, FM or % BF among children and
young adolescents than among older adolescents (Fig. 2b,
3b, and 4b). Older adolescents (15–19-years) exhibited
the largest mean differences for FFM (− 2.84 kg), FM
(2.84 kg), and %BF (5.01) while young adolescents (10–
Table 1 Participants’ characteristics
Mean (95% Confidence Interval)
Characteristic
Children (8–9 years)
Young adolescents (10–
14 years)
Older adolescents (15–
19 years)
Overall
N
16
112
67
195
Male
7
51
26
84
Female
Age
9
8.34 (8.11 to 8.64)
61
11.80 (11.57 to 12.04)
41
17.39 (17.09 to 17.69)
111
13.44 (12.98 to 13.90)
Weight (kg)
28.31 (25.88 to 30.75)
40.40 (38.74 to 42.05)
55.55 (53.48 to 57.62)
44.61 (42.92 to 46.31)
Height (m)
1.31 (1.28 to 1.35)
1.48 (1.47 to 1.50)
1.62 (1.60 to 1.64)
1.51 (1.50 to 1.53)
BMI (kg/m2)
16.30 (15.65 to 16.95)
18.16 (17.69 to 18.63)
21.23 (20.53 to 21.93)
19.07 (18.64 to 19.50)
Waist circumference (cm)
58.56 (56.83 to 60.28)
65.07 (64.06 to 66.08)
68.94 (67.62 to 70.26)
65.87 (65.02 to 66.71)
Hip circumference (cm)
68.43 (66.25 to 70 .60)
79.20 (77.70 to 80.71)
91.32 (89.17 to 93.47)
82.48 (80.97 to 84.26)
Waist height ratio
0.44 (0.44 to 0.46)
0.44 (0.43 to 0.44)
0.43 (0.42 to 0.44)
0.44 (0.43 to 0.44)
Waist hip ratio
0.86 (0.84 to 0.88)
0.82 (0.82 to 0.83)
0.76 (0.74 to 0.78)
0.80 (0.80 to 0.81)
3 h deuterium enrichment
(ppm)
722.86 (626.83 to
818.88)
796.77 (772.52 to 821.03)
790.85 (744.72 to 836.99)
788.67 (766.49 to
810.86)
4 h deuterium enrichment
(ppm)
720.06 (622.18 to
817.94)
799.78 (775.74 to 823.83)
798.69 (753.26 to 844.11)
792.87 (770.84 to
814.89)
DDM Total body water (litres)
17.18 (15.90 to 18.47)
24.97 (24.07 to 25. 87)
32.05 (30.75 to 33.35)
26.76 (25.84 to 27.68)
DDM Total body water (%)
60.92 (59.38 to 62.47)
62.29 (61.44 to 63.14)
58.25 (56.21 to 60.28)
60.79 (59.90 to 61.68)
DDM Fat free mass (kg)
23.48 (21.72 to 25.23)
34.11 (32.88 to 35.34)
43.78 (42.00 to 45.56)
36.56 (35.30 to 37.82)
BIA Fat free mass (kg)
24.13 (22.10 to 26.16)
33.89 (32.72 to 35.06)
46.62 (45.05 to 48.19)
37.46 (36.13 to 38.79)
DDM Fat mass (kg)
4.84 (3.90 to 5.77)
6.29 (5.61 to 6.96)
11.77 (9.97 to 13.58)
8.05 (7.23 to 8.87)
BIA Fat mass (kg)
4.18 (3.48 to 4.88)
6.51 (5.80 to 7.22)
8.93 (7.54 to 10.32)
7.15 (6.50 to 7.81)
DDM Fat (%)
16.77 (15.65 to 16.95)
14. 90 (13.74 to 16.06)
20.43 (17.64 to 23.71)
16.95 (15.74 to 18.17)
BIA Fat (%)
14.61 (12.83 to 16.39)
15.34 (14.21 to 16.45)
15.42 (13.32 to 17.52)
15.30 (14.34 to 16.27)
Impedance
660.00 (626.16 to
693.84)
596.63 (581.35 to 611.90)
515.04 (500.91 to 529.16)
573.79 (561.67 to
585.92)
Ndagire et al. BMC Public Health (2018) 18:687
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Table 2 Cronbach’s alpha values of the two readings for the
different age groups
Age category
Cronbach’s Alpha
8–9 years
0.999
10–14 years
0.997
15–19 years
0.996
14-years) exhibited lowest mean differences for FFM
(0.22 kg), FM (− 0.22 kg) and %BF (− 0.44) (Table 3).
Furthermore, the mean differences between DDM and
BIA for measures of FFM, FM and %BF for older
adolescents exhibited largest 95% confidence intervals
compared to those for children and young adolescents.
The Bland Altman plots for FFM, FM, and %BF for older
adolescents exhibited largest limits of agreement
compared those of children and young adolescents
(Fig. 2b, 3b and 4b).
Discussion
Prior to this work, no published studies were found on
the reliability of FTIR saliva D2O enrichment measurements among populations in Uganda. In this study, the
reliability of FTIR spectroscopy measurements among
children and adolescents in Uganda was assessed. The
high and positive correlation between 3 and 4-h FTIR
spectroscopy measurements is indicative of similarity
and reproducibility of the two sets of measurements.
The Bland-Altman plots that showed no apparent trend
in error differences between the measurements taken
after 3 and those taken after 4 h imply that saliva D2O
enrichment measurements were reproducible among the
study population. The high Cronbach’s alpha value (>
0.9) among all studied age groups indicates very good repeatability of the FTIR spectroscopy saliva D2O enrichment measurements among children, young and older
adolescents in Uganda. The FTIR spectroscopy instrument can, therefore, provide reliable measures for D2O
saliva enrichment and thus suitable for validation of
other body composition assessment techniques for more
accurate assessment of body composition among children and adolescents in Uganda. Furthermore, the FTIR
spectroscopy technique has several advantages in assessing body composition including simplicity to carry out,
minimal subject cooperation requirements, acceptability
in all age groups [28], non-invasiveness, relatively low
cost, easy administration of tracers, and easy collection
of samples [29].
In this study, the ability of the inbuilt equations from
the Tanita SC-331S BIA instrument to assess body composition of children and adolescents in Uganda by using
DDM as a reference method also was investigated. Prior
to this work, no published studies were found comparing the body composition estimates obtained by BIA to
those obtained by DDM among children and adolescents
in Uganda. Since estimates for body composition had
varying agreement across the studied age groups, DDM
and BIA are generally not interchangeable across children and adolescents in Uganda. The none-statistically
significantly different (P > 0.05) FFM, FM and %BF measures by DDM and BIA among children and young adolescents imply possibility for agreement between the two
Fig. 1 Regression and Bland-Altman plots for saliva D2O enrichment among children (left), young adolescents (middle) and older
adolescents (right)
Ndagire et al. BMC Public Health (2018) 18:687
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Table 3 Body composition mean values (CI), mean difference (CI) and P-values between DDM and BIA among different age groups
Body composition means
DDM
BIA
Mean difference
P-value
FFM (kg)
23.48 (21.72–25.23)
FM (kg)
4.84 (3.90–5.77)
24.13 (22.10–26.16)
−0.657 (−1.474–0.160)
0.1071
4.18 (3.48–4.88)
0.657 (− 0.160–1.474)
0.1071
%BF
16.77 (14.66–18.88)
14.61 (12.83–16.39)
2.157 (−0.397–4.711)
0.0920
Children (8–9 years)
Young adolescents (10–14 years)
FFM (kg)
34.11 (32.88–35.34)
33.89 (32.72–35.06)
0.224 (− 0.111–0.559)
0.1876
FM (kg)
6.29 (5.61–6.96)
6.51 (5.80–7.22)
−0.224 (− 0.559–0.111)
0.1876
%BF
14. 90 (13.74–16.06)
15.34 (14.21–16.45)
−0.436 (− 1.239–0.367)
0.2846
Older adolescents (15–19 years)
FFM (kg)
43.78 (42.00–45.56)
46.62 (45.05–48.19)
−2.841 (− 3.983 - -1.699)
< 0.0001
FM (kg)
11.77 (9.97–13.58)
8.93 (7.54–10.32)
2.841 (1.699–3.983)
< 0.0001
%BF
20.43 (17.64–23.71)
15.42 (13.32–17.52)
5.006 (3.068–6.944)
< 0.0001
methods in these age categories. For children and young
adolescents, the generally narrow limits of agreement,
the small mean discrepancies (biases) for the FFM, FM
and %BF estimates and their narrow 95% confidence intervals of means imply that DDM and BIA estimates for
FFM, FM, and %BF agree and can be used interchangeably for either FM, FFM, or %BF for these age categories
in Uganda. These findings are similar to those by Mehta
and others who found agreement between BIA and
DDM for FFM, FM and %BF among children 14 years of
age or younger with Intestinal Failure [23]. In a study to
validate 2 portable BIA devices; the Inbody 230 and the
Tanita BC-418 for body composition assessment in
healthy Taiwanese school-age children, Bland-Altman
analysis showed clinically acceptable agreement between
the Inbody 230 device and DEXA for FFM measurements [15].
On the other hand, the statistically significantly different mean values (P < 0.05) for FFM, FM and %BF among
older adolescents imply no possibility for agreement between the two methods. The wide limits of agreement
for FFM, FM, or %BF exhibited by Bland Altman plots
Fig. 2 Regression and Bland-Altman plots for FFM (left), FM (middle) and % body fat (right) determined by DDM and BIA among children
Ndagire et al. BMC Public Health (2018) 18:687
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Fig. 3 Regression and Bland-Altman plots for FFM (left), FM (middle) and % body fat (right) determined by DDM and BIA among
young adolescents
for older adolescents, the big mean discrepancies
(biases) for the FFM, FM and %BF estimates and their
wide 95% confidence intervals in this age group imply
limited agreement between the two methods. This reveals that DDM and BIA are not directly
interchangeable for either FM, FFM, or %BF among
older adolescents (15–19 years) in Uganda. Similar to
this study’s findings where BIA overestimated FFM
among older adolescent and underestimated their FM
and %BF are those by Resende and others who reported
Fig. 4 Regression and Bland-Altman plots for FFM (left), FM (middle) and % body fat (right) determined by DDM and BIA among old adolescents
Ndagire et al. BMC Public Health (2018) 18:687
that BIA overestimated the measures of FFM and underestimated the measures of FM compared to those provided by DDM among obese adolescents in Brazil [5].
Resende and others reported a high, positive and significant correlation between FFM and FM values determined by DDM and BIA but there was no agreement
between the two methods among obese adolescents [5]
as was the case for older adolescents in this study. In a
study to validate predictive equations of BIA to FFM estimation in army cadets aged 17–24 years, Langer and
others observed significant differences between FFM
values from 8 predictive BIA equations and no good
agreement with DXA [11] Also, among healthy Indian
children and adolescents aged 5–18 years, there was no
agreement between BIA and DXA in assessment of body
composition [13]. A possible explanation for the discrepancy of body composition among older adolescents by
the BIA system’s inbuilt prediction equations is that they
are normally based on Western European or North
American populations, which may differ in body composition and proportion when compared to the population under study [30]. Growth involves the deposition of
both fat mass (FM) and fat-free mass (FFM) components
and human body composition is ethnicity dependent
[31]. No literature was found regarding the age- and
sex-related pattern of changes in body composition for
populations in Uganda.
While the study was the first of its kind among
populations in Uganda, it was not without limitations.
DDM was used as the reference method which,
although widely validated as a reliable estimate, is not
a gold standard for body composition. Ideally, a
four-component model would have been used as the reference method, but this was not possible in our study
setting. While DDM has the advantage that it is relatively easy to perform, it is not without limitations: one
assumption is the hydration of FFM, which may vary
among persons by age, sex, maturation and ethnicity
and to estimate FFM from TBW, age, and sex-specific
hydration fractions were used [6]. But the hydration of
FFM values used for computation of TBW to estimate
FFM, FM, and %BF were not Uganda specific. Higher
hydration factors have been observed among African
American adults compared to whites using a
four-component model [32]. However, there is no information on the hydration factors of FFM for Ugandan
populations.
Conclusions
The reliability of the FTIR spectroscopy saliva D2O enrichment measurements was very good among the studied population. This technique can be used as a
reference technique in the validation of field techniques
like BIA for more accurate estimation of body
Page 8 of 9
composition in resource-poor countries that cannot afford four-compartment (gold standard) techniques.
The other results of the study showed that DDM and
BIA can be used interchangeably for FFM, FM, and %BF
for children and young adolescents aged 8–14 years in
Uganda but not interchangeable for the assessment of
body composition in older adolescents aged 15–19 years
in Uganda. For that reason, among older adolescents in
Uganda, BIA is not a valid measure for body composition, so deriving population-specific BIA equations may
be a suitable approach for assessing body composition.
The study, therefore, revealed BIA’s limitations in assessing body composition among children and adolescents
in Uganda.
Abbreviations
%BF: Body fat percentage; 2H: Deuterium; ADP: Air displacement
plethysmography; BIA: Bioelectrical impedance analysis; BMI : Body mass
index; D2O: Deuterium oxide; DDM: Deuterium dilution method; DEXA: Dualenergy x-ray absorptiometry; FFM: Fat-free mass; FM: Fat mass; FTIR: Fourier
transform infrared; IAEA: International atomic energy agency; LMICs: Low and
middleiIncome countries; NCDs : Non-communicable diseases; TBW: Total
body water; UWW : Underwater weighing; VD: Dilution space; WHO: World
Health Organization
Acknowledgements
The authors are grateful to the children and adolescents who participated in
this study, their parents/guardians, schools’ administration and teachers and
the entire research team.
Funding
The International Atomic Energy Agency (IAEA) offered the study support
under project number UGA6017.
Availability of data and materials
The datasets used and/or analyzed during the current study are available
from the corresponding author on reasonable request.
Authors’ contributions
CTN, DI, JHM, JEA, and DN conceived, designed, and revised the manuscript.
CTN did the literature search. SMAS, RB, and BO did the statistical analysis. All
authors read and approved the final manuscript.
Ethics approval and consent to participate
The purpose and objectives of the study were carefully explained to each
participant and their parents. Informed consent to the study was obtained
from participants’ parent/guardian to affirm their willingness or not. The
parents or guardians of participants provided consent to allow their children
to take part in the study while participants signed assent accepting to
participate in the study. Ethical clearance to engage human subjects was
obtained from Makerere University School of Biomedical Sciences Higher
Degrees, Research and Ethics Committee and Uganda National Council for
Science and Technology under reference numbers: SBS 291 and HS 1950
respectively.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
School of Food Technology, Nutrition and Bio-engineering, Makerere
University, Kampala, Uganda. 2Department of Biochemistry and Sports
Science, Makerere University, Kampala, Uganda. 3Department of Statistical
1
Ndagire et al. BMC Public Health (2018) 18:687
Methods and Actuarial Science, School of Statistics, Makerere University,
Kampala, Uganda. 4Centre MURAZ, Bobo-Dioulasso, Burkina Faso. 5Division of
Nutritional Sciences, University of Illinois, Urbana-Champaign, USA.
6
Department of Food Science and Human Nutrition, University of Illinois,
Urbana-Champaign, USA.
Received: 3 November 2017 Accepted: 29 May 2018
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