Measuring obesity: results are poles apart obtained by BMI and bio-electrical impedance analysis

doi:10.4236/jbise.2011.411084

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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/

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: *[email protected]

Received 11 August 2011; revised 28 September 2011; accepted 8 October 2011.

ABSTRACT

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-

lysis

1. INTRODUCTION

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.

2. MATERIALS AND METHODS

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

678

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.

3. RESULTS

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.

4. DISCUSSION

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

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

Women

BMI < 18.5 (Underweight) 25 25 25

BMI ≥ 25 (Overweight) 35 35 36

BMI ≥ 30 (Obese) 40 41 41

Men

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.

R. Mittal et al. / J. Biomedical Science and Engineering 4 (2011) 677-683

680

9.8

26.0

23.5

21.3

64.7

52.0

58.8

56.3

19.6

14.0

13.7

16.7

5.9

8.0

3.9

5.7

Y oung M al eYoung Fem al eOl d M al eOld F em al e

% p opul ation

Underweight

Normal

Overweight

Obese

Figure 1. Categorization of subjects according to their body composition following

WHO guidelines based on BMI analysis.

3.9

20.0

21.6

18.5

45.1

34.0

44.1

45.1

25.5

24.0

16.7

14.5

25.5

22.0

17.6

22.0

Y oung Mal eY oung Fem al eOld M aleOld Female

% popul ation

Underweight

Norma l

Overwei g ht

Obese

Figure 2. Categorization of subjects according to their body composition evaluated by

BMI cutoff values recommended for Asians.

37.3

48.0

7.8

13.4

43.1

40.0

12.7

10.5

11.8

12.0

26.5

23.3

7.8

0.0

52.9

Y oung M aleY oung F em al eOld M al eOld Fem al e

% popul ation

Underweight

Normal

Overweight

Obese

Figure 3. Categorization of subjects according to their body composition as per PBFb

analysis.

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

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

equations.

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

recruited.

5. CONCLUSIONS

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

considered.

6. ACKNOWLEDGEMENTS

The financial support by Datta Meghe Institute of Medical Sciences

R. Mittal et al. / J. Biomedical Science and Engineering 4 (2011) 677-683

682

(Deemed University), Wardha (MS), INDIA, is thankfully acknowl-

edged.

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