J. Biomedical Science and Engineering, 2011, 4, 677-683
doi:10.4236/jbise.2011.411084 Published Online November 2011 (http://www.SciRP.org/journal/jbise/
Published Online November 2011 in SciRes. http://www.scirp.org/journal/JBiSE
Measuring obesity: results are poles apart obtained by BMI
and bio-electrical impedance analysis
Rashee Mittal1, Madhur M. Goyal2, Raju C. Dasude2, Syed Zahiruddin Quazi3, Anjan Basak2*
1Department of Anatomy, Jawaharlal Nehru Medical College, DMIMS (DU), Wardha, India;
2Department of Biochemistry, Jawaharlal Nehru Medical College, DMIMS (DU), Wardha, India;
3Department of Community Medicine, Jawaharlal Nehru Medical College, DMIMS (DU), Wardha, India.
Email: *drabasak1@yahoo.com
Received 11 August 2011; revised 28 September 2011; accepted 8 October 2011.
Objective: To analyse the use of BMI and bioelectri-
cal impedance analysis (BIA) in assessment of adi-
posity among young and elderly population. Materi-
als and methods: Age, height, weight and percent
body fat (PBF) of 101 young and 276 elder subjects
were recorded. PBF was measured directly by BIA
instrument (PBFb) and also calculated from BMI
(PBFf). The classification of subjects into underweight,
normal, overweight and obese was based on the age-
and sex-specific BMI cutoff values and PBFb follow-
ing standard guidelines. Results: The ca lculated mean
BMI values of young and old age groups were statis-
tically same. PBF was significantly high in elder sub-
jects. There was no statistical difference in mean
PBFb and PBFf in young subjects but the difference
was significant in elder subjects. The PBFf values
were highly correlated (r: 0.92 to 0.96) with PBFb
values in young age groups unlike elder groups of
both males and females. PBFb based categorization of
subjects’ presented totally different scenario com-
pared to results obtained by BMI analysis to assess
adiposity. Conclusion: The cases such as increasing
fatness with aging even when BMI remains constant,
the causes of country or ethnic differences in BMI
analysis, poor correlation in PBFb and PBFf values in
elder age group emphasize on the limitations of BMI
based analysis. PBFb within limitations seems to be
an improved phenotypic char a cteristi c over BMI.
Keywords: Obesity; BMI; Bioelectrical Impedance Ana-
Obesity is a complex condition having serious social and
psychological dimensions. It has reached epidemic pro-
portions globally, with more than one billion adults over-
weight—at least 300 million of them clinically obese [1].
Its prevalence in developing countries is increasing at
alarming rate with incomes rising and populations’ be-
coming more urban [1-3]. The health consequences as-
sociated with obesity like osteoarthritis, Type 2 diabetes
and hypertension reduce the overall quality of life and
induce disability in adults [2,4,5]. Thus it contributes in
increasing health related burden (2% - 7% of total health
care costs) on society [1,6].
To date, the choice of weight-loss medications or sur-
gical interventions is very limited [6]. Therefore the
health care officials are more concentrating on effective
weight management for individuals and groups who are
overweight and at risk of developing obesity [7,8]. This
makes it indispensable to evaluate prevalence of over-
weight and obesity in different populations.
In present study, with results observed, a rational ana-
lysis is imparted on the use of BMI and BIA in assess-
ment of obesity.
The data was collected from 101 young (undergraduate
medical students; age 18 - 22 years) and 276 elder sub-
jects (age 55 - 70 years) reported for routine medical
checkup at AVB Rural Hospital, Wardha (M.S.), India.
Subjects were included in the study with their verbal
consent. They were excluded if they had a history of
recent acute illness (e.g. pneumonia or myocardial in-
farction), had a chronic condition (e.g. cancer, uncon-
trolled high blood pressure, dialysis or with symptoms of
edema or osteoporosis), or taking any drug therapy like
vascodialating or vascoconstricting medications.
Data were collected in the morning after an overnight
fast and the first urine void. Percentage body fat (PBFb)
was measured using the stand-on Beurer’s body fat ana-
lyser (BS 60). All instructions were followed as pre-
scribed by manufacturer. With this process, an imper-
ceptible and completely safe current allow to pass from
R. Mittal et al. / J. Biomedical Science and Engineering 4 (2011) 677-683
the body tissue for few seconds. The measurement of
electrical resistance (impedance) used to determine the
percentage of fat. Muscle tissue and water have good
electrical conductivity, and therefore a lower resistance.
On the other hand, fatty tissue has a low conductivity, as
the fat cells hardly conduct the current due to their very
high resistance [9].
Although all the subjects were healthy, the scale was
not used on persons with medical implants (e.g. pace-
makers) or having substantial anatomical deviations in
the legs relative to their total height (leg length consi-
derably shorter or longer than usual), having severe obe-
sity [9] or were actively engaged in a vigorous (>6 h/wk)
physical activity training program. Anthropometric meas-
urements including height and body weight were taken
according to protocols recommended for prediction of
BMI. It was calculated as body weight divided by squared
height (kg/m2). Five standard equations [10-15] were
employed separately (Table 1) for the prediction of per-
cent body fat (PBFf) using BMI.
Subjects were divided into four groups: young male,
young female, old male and old female. They according
to their body compositions were further classified into
four categories: underweight, normal, overweight and
obese. Body compositions were assessed by two differ-
ent methods. First method was based on BMI analysis
(Table 2) following WHO’s published guidelines. A
lower BMI cutoff values specific for Asians has also
been suggested [16-18]. In second method, subjects were
classified according to PBFb using published guidelines
(Table 3) [19]. According to Table 3, PBF equal to 25
or less is corresponding to “BMI < 18.5” hence consid-
ered underweight and PBF equal to 35 corresponds
to ”BMI 25” so overweight. That means if a woman
subject aged between 20 - 39 years having PBF in-be-
tween 25 to 35 will be classified under normal category.
Mean and standard deviation (SD) values of each pa-
rameter were calculated for each group. The analysis of
variance (ANOVA) was performed to determine whether
any statistically significant difference exist among PBFb
and PBFf. Polynomial regression analysis were applied
to calculate correlation coefficient between values of
PBFb and PBFf.
The data obtained are summarized in Table 4. Age,
height, weight, PBFb were recorded directly whereas
BMI and PBFf(1-5) were calculated. Data has been repre-
sented in form of calculated mean ± SD values. In young
male group, PBF values predicted by five different for-
mulas (PBFf(1-5)) were almost same and vary from 14.23
± 8.19 to 16.30 ± 6.12. This variation was much lesser
(24.84 ± 7.35 to 25.95 ± 5.50) in young female group.
In old age groups, the PBFf values showed greater
variation and were poorly correlated with PBFb of the
same group. Figures 1-3 show percent populations of
different groups classified according to their body com-
position. Subjects were divided in young and old age
groups of male and female separately.
For the prevention of obesity WHO expert consultation
identified research needs of prospective studies on body
composition and risk factors mainly in younger popula-
tions and adolescents [4]. With increasing age and num-
ber, prevalence of overweight and obesity is also in-
creasing in elderly people who, as expected, are more
prone to diseases even at lower BMI [20].
In this study, two different but most common ap-
proaches, BMI as an indirect and BIA as direct measure
of body fat, were used to define overweight and obesity.
The obtained data was specifically examined to answer
whether the use of BMI and BIA analysis show same
values of adiposity.
Obesity has been identified as a condition of excessive
fat accumulation to the extent that health and well-being
are affected. This fat in term of percent body fat can di-
rectly be measured by a number of methods including
underwater weighing, deuterium dilution, dual energy
X-ray absorptiometry (DEXA), and skinfold thickness
measurements; however, the applications of these direct
methods are limited to laboratory settings or small sam-
ples. For a clinician in routine practice or for a re-
searcher when conducting epidemiological studies, these
Table 1. Five standard equations for the prediction of percent body fat (PBFf) using BMI.
Reference Proposed formula for calculation of Percent Body Fat (PBFf)
1 Deurenberg formula [10] PBFf1 = (1.20 × BMI) + (0.23 × Age) – (10.8 × gender) – 5.4
2 Deurenberg formula [11] PBFf2 = (1.29 × BMI) + (0.20 × Age) – (11.4 × gender) – 8.0
3 Gallagher formula [12] PBFf3 = (1.46 × BMI) + (0.14 × Age) – (11.6 × gender) – 10
4 Jackson-Pollock formula
[13,14] PBFf4= (1.61 × BMI) + (0.13 × Age) – (12.1 × gender) – 13.9
5 Jackson AS formula [15] PBFf5 = (1.39 × BMI) + (0.16 × Age) – (10.34 × gender) – 9
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R. Mittal et al. / J. Biomedical Science and Engineering 4 (2011) 677-683 679
Tab le 2. BMI cutoff values (in kg/m2) to classify subjects into
different categories according to their body compositions.
Category Recommended by WHO for Asians
Underweight <18.5 <18.0
Normal 18.5 - 24.9 18.0 - 22.9
Overweight 25.0 - 29.9 23.0 - 24.9
Obese >30 >25
Ta ble 3. Age and sex specific percent body fat values to clas-
sify subjects into different categories.
Corresponding BMI
(Category) 20 - 39 y 40 - 59 y 60 - 79 y
BMI < 18.5 (Underweight) 25 25 25
BMI 25 (Overweight) 35 35 36
BMI 30 (Obese) 40 41 41
BMI < 18.5 (Underweight) 13 13 14
BMI 25 (Overweight) 23 24 24
BMI 30 (Obese) 28 29 29
direct approaches are time consuming, expensive, or
unavailable [4].
BMI and BIA are relatively simple, quick and nonin-
vasive techniques, have good acceptability among clini-
cians and been widely used in epidemiological studies
[21-26]. The National Institutes of Health (NIH) and the
World Health Organization (WHO) has adopted similar
body weight guidelines (Table 2) for overweight and
obesity [5]. The lower BMI cut-off points were con-
firmed for observed risk in Asian populations by many
studies [16-18,27,28]. However, the WHO BMI cut-off
points were retained as international classifications [4].
4.1. Significance of PBF over BMI
BMI is a surrogate of body fat. The consequences lead to
mortality and morbidity are due to access accumulation
of fat. As shown in Table 4, the BMI of young males
(23.47 ± 5.08) and females (22.67 ± 4.60) were almost
same that of old males (21.63 ± 4.42) and females (22.12
± 4.23). Unexpectedly, the PBF values of young and old
age group were significantly different, either measured
by BIA instrument or calculated by different formulas.
Studies indicate that relative fatness in adults in-
creases with age. Although the mechanisms behind this
observation are not fully understood, an important and as
yet unanswered question is whether the greater fatness
with older age, even after BMI is same as of young
population, poses additional health risks [19]. Experts
has recommend to measure adiposity in combination of
BIA and with other risk factors of morbidity and mortal-
ity; rather than relying only on BMI cut-points [29,30].
However, our results shows that increased PBF and its
consequences cannot be predicted by BMI analysis in
elder group of both, males and females.
4.2. PBFb Is Different from PBFf in Aged Group
In young age group of either of males or females, there
Table 4. Summery of the data obtained in present study.
Male Female
Young (n = 51) (mean ± SD) Old (n = 102) (mean ± SD) Young (n = 50) (mean ± SD) Old (n = 174) (mean ± SD)
Age (years) 18.90 ± 1.57 63.78 ± 7.01 18.08 ± 0.94 59.81 ± 6.88
Height (cm) 173.67 ± 6.24 161.51 ± 6.37 160.11 ± 5.59 148.47 ± 5.78
Weight (kg) 70.79 ± 15.69 56.52 ± 12.42 58.29 ± 12.93 48.76 ± 9.65
BMI (kg/m2) 23.47 ± 5.08 21.63 ± 4.42 22.67 ± 4.60 22.12 ± 4.23
PBFb 15.38 ± 8.17 28.13 ± 9.06 24.8 ± 7.28 38.06 ± 10.99
PBFf1 16.30 ± 6.12NS (r: 0.92) 24.43 ± 5.30* (r: 0.60) 25.95 ± 5.50NS (r: 0.96) 34.67 ± 5.95* (r: 0.41)
PBFf2 15.20 ± 6.57NS (r: 0.92) 21.26 ± 5.65* (r: 0.59) 25.39 ± 5.91NS (r: 0.96) 32.27 ± 6.25* (r: 0.40)
PBFf3 15.30 ± 7.43NS (r: 0.93) 18.92 ± 6.37* (r: 0.57) 25.61 ± 6.70NS (r: 0.96) 30.44 ± 6.85* (r: 0.38)
PBFf4 14.23 ± 8.19NS (r: 0.93) 17.12 ± 7.03* (r: 0.57) 24.84 ± 7.35NS (r: 0.92) 29.25 ± 7.50* (r: 0.38)
PBFf5 16.30 ± 7.07NS (r: 0.93) 20.94 ± 6.07* (r: 0.58) 25.40 ± 6.36NS (r: 0.93) 31.09 ± 6.58* (r: 0.39)
NS: Non significant and *: Significant difference (p < 0.001) between PBFf and PBFb values of same group. r: Correlation coefficient obtained by polynomial
regression analysis form/with PBFb in the same column.
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R. Mittal et al. / J. Biomedical Science and Engineering 4 (2011) 677-683
Y oung M al eYoung Fem al eOl d M al eOld F em al e
% p opul ation
Figure 1. Categorization of subjects according to their body composition following
WHO guidelines based on BMI analysis.
Y oung Mal eY oung Fem al eOld M aleOld Female
% popul ation
Norma l
Overwei g ht
Figure 2. Categorization of subjects according to their body composition evaluated by
BMI cutoff values recommended for Asians.
Y oung M aleY oung F em al eOld M al eOld Fem al e
% popul ation
Figure 3. Categorization of subjects according to their body composition as per PBFb
was no statistical difference between PBFb and PBFf;
and the PBFf values are highly correlated with PBFb
values as correlation coefficient (r) varies from 0.92 to
0.96 (Table 4). We do not know whether it can be taken
as a standard for reliability of both methods. The differ-
ence between PBFb and PBFf of each elder group was
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R. Mittal et al. / J. Biomedical Science and Engineering 4 (2011) 677-683 681
highly significant and the formula values (PBFf) did not
or poorly correlated with PBFb. Perhaps, the formulas
were derived on young populations; and elder popula-
tions were ignored during the development of standard
The BMI scale cannot be generalized for all popula-
tions [4]. It was recommended to modify BMI cutoff
value for different ethnic groups in relation of risk fac-
tors and other consequences associated with obesity
[16,31]. It is interesting to see, as present work shows
that even in same population BMI could not predict PBF
(PBFf) accurately in different (elder) age groups.
4.3. Classification Gives Opposite Results
The subjects were classified into four groups as per dif-
ferent recommendations (Figures 1-3). The bar dia-
grams illustrate that all three approaches give different
results. According to Figure 1, about 52% - 65% popu-
lation of different groups had normal body composition,
less than 20% were overweight and less than 10% were
obese. 9.8% young males, 26% young females, 23.5%
old males and 21.3% old females were underweight.
As per BMI cut off values recommended for Asians
[17], percent population having normal body composi-
tion was reduced to range of 34% - 45% in all four
groups (Figure 2). The number of overweight and obese
had increased significantly.
It was quite unexpected that PBFb based categoriza-
tion present totally different scenario. As shown in Fig-
ure 3, among young subjects maximum population was
almost equally distributed in underweight and normal
category. Less than 12% subjects were overweight and
less than 8% were obese. In case of elder subjects, maxi-
mum (about 53%) were obese, 23% - 27% were over-
weight, upto 14% were underweight and about 13% were
under normal category. These results were poles apart
with those, were obtained from BMI based analysis.
4.4. Limitations of BIA
Studies indicate that PBF measured by BIA is highly
correlated with visceral and subcutaneous adipose tissue
in both genders, being reasonably effective in discrimi-
nating the presence or absence of excess visceral fat
alone or associated with overweight/obesity [22]. Still, it
is not the gold standard method for estimation of body
fat. There are 2 or 3 subtypes also available in BIA
analysis whose relative accuracy is again matter of de-
bate [31,32]. The optimum situation (having gold stan-
dard method) was not possible in the present study and it
is likely that in routine practice or any large-scale study
clinicians would face similar methodological issues.
However, exclusion criteria were designed to cover all
possible limitations of BIA analysis [33].
It was recently identified the need of a feasible tool
which can differentiate individual with benign obesity
from non-benign obese individual [34].
4.5. Strength of the Study
Though our findings are not very much new but evoke
an important issue on the use of BMI based analysis in
routine clinical practice and in many research reports
[35,36]. BMI based analysis is in use without consider-
ing age, sex and ethnic variations among subjects. Few
reports which declare good correlation of BMI analysis
with other methods of body fat evaluation were con-
ducted on children or young population [37,38].
It was recently confirmed that the use of BMI as a
measure of obesity can introduce misclassification prob-
lems [39,40]. Our results make further addition that BMI
cutoff values developed for an ethnic group can misclas-
sify elder subjects even in the same population.
The increase body fat does not drive clinician towards
treatment of obesity until and unless presence of any
symptomatic disease that may improve with weight loss.
However, increased values of body fat may inspire older
as well as in younger adults for voluntary weight loss
which may help to prevent the adverse health conse-
quences of obesity. Accurate body fat measurement is
also required in studies on the pharmacokinetics of drugs
in humans. We acknowledge the fact that our subjects,
by necessity, were a convenient sample and may not be
representative of the population from which they were
The cases such as increasing fatness with aging even
when BMI remains constant, the causes of country or
ethnic differences in BMI analysis, poor correlation in
PBFb and PBFf values in elder age group emphasize on
the limitations of BMI based analysis. The healthy range
of PBF needs to be validated in different populations and
other methodological problems are yet to be overcome.
There is a requirement of a method with nominal sensi-
tivity and specificity which can be opted for routine
clinical practice and for population study to evaluate
body composition according to adiposity without getting
affected with factors like age, sex and ethnicity. Being
associated with Clinical Biochemistry, authors expect
that it would be a biochemical parameter which can
identify and quantify healthy range of adiposity. Till that
PBFb can be taken as an improved phenotypic character-
istic over BMI when functionality and mortality risk are
The financial support by Datta Meghe Institute of Medical Sciences
opyright © 2011 SciRes. JBiSE
R. Mittal et al. / J. Biomedical Science and Engineering 4 (2011) 677-683
(Deemed University), Wardha (MS), INDIA, is thankfully acknowl-
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