Type 2 diabetes and impaired fasting blood glucose in rural Bangladesh: A population-based study

Abstract

Diabetes is a fast expanding global health problem but more so in the developing countries. Therefore, it is of particular interest to study the epidemiological transition of the state and to identify the risk factors in order to recognize the extent of the problem.
A random sample of 5000 rural individuals (age >/=20 years) were included in a cross-sectional study. Fasting capillary blood glucose levels were measured from 4757 individuals. Height, weight, waist, hips including blood pressure and demographic information was collected.
The study population was lean [mean body mass index (BMI) 19.4] with a low prevalence of type 2 diabetes but relatively high impaired fasting glucose. No relationship between type 2 diabetes and BMI in men, but an overall relationship was observed for women (P = 0.04) (data not shown). Age, sex, and waist/hip ratio appeared to be important risk factors for the occurrence of type 2 diabetes in this population.
Low prevalence of type 2 diabetes and relative high impaired fasting blood glucose was observed. The factors associated with the occurrence of diabetes in this population appeared to differ than its known relations with BMI. This may indicate that the risk factors for type 2 diabetes are likely to differ in different population. Our results are likely to be in line with the Indian data suggesting that a revised guideline for anthropometric measures in the South Asian population is called for, in order to classify people at risk.

Full text

European Journal of Public Health, Vol. 17, No. 3, 291–296
Ó The Author 2006. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
doi:10.1093/eurpub/ckl235 Advance Access published on September 28, 2006
............................................................................................................
Type 2 diabetes and impaired fasting blood
glucose in rural Bangladesh: a population-
based study
Akhtar Hussain
1
, Stein Vaaler
2
, M. A. Sayeed
3
, Hajera Mahtab
3
,
S. M. Keramat Ali
4
, A. K. Azad Khan
3
Background: Diabetes is a fast expanding global health problem but more so in the developing
countries. Therefore, it is of particular interest to study the epidemiological transition of the state and
to identify the risk factors in order to recognize the extent of the problem.
Methods: A random sample of 5000 rural individuals (age $20 years) were included in a cross-sectional
study. Fasting capillary blood glucose levels were measured from 4757 individuals. Height, weight,
waist, hips including blood pressure and demographic information was collected.
Results: The study population was lean [mean body mass index (BMI) 19.4] with a low prevalence of
type 2 diabetes but relatively high impaired fasting glucose. No relationship between type 2 diabetes
and BMI in men, but an overall relationship was observed for women (P ¼ 0.04) (data not shown). Age,
sex, and waist/hip ratio appeared to be important risk factors for the occurrence of type 2 diabetes in
this population.
Conclusions: Low prevalence of type 2 diabetes and relative high impaired fasting blood glucose was
observed. The factors associated with the occurrence of diabetes in this population appeared to differ
than its known relations with BMI. This may indicate that the risk factors for type 2 diabetes are likely to
differ in different population. Our results are likely to be in line with the Indian data suggesting that a
revised guideline for anthropometric measures in the South Asian population is called for, in order to
classify people at risk.
Keywords: Bangladesh, body mass index, obesity, type 2 diabetes, waist/hip ratio
............................................................................................................
T
he WHO report on diabetes prevalence alarmed
that diabetes has posed a serious threat to developing
countries with respect to their existing health care services.
1
Further, the prevalence of diabetes is predicted to increase
dramatically over the next 25 years, mostly as a result of type 2
diabetes.
2
Diabetes, insulin resistance hyperinsulinaemia, and other
coronary risk factors are more prevalent in Bangladeshis
compared with other South Asian migrants (Indian, Pakistani)
settled in United Kingdom.
3,4
It has also been reported that
Bangladeshis among all other South Asian immigrants had
highest morbidity and mortality from CHD in the United
Kingdom.
5
Higher prevalence of glucose intolerance and
hypertension were also shown in a number of small epidemio-
logical studies in Bangladesh.
6–10
Obesity is a known risk factor for the development of type 2
diabetes. However, obesity as measured by body mass index
(BMI) and its association with type 2 diabetes varied in
different ethnic groups, possibly as a consequence of different
body stature.
11
Epidemiological studies have suggested that
genetic factors and central obesity as measured by waist/hip
ratio (WHR) is a major contributing factor to insulin resistance
and is associated with diabetes, hypertension, dislipidaemia
[high triglycerides (TG) and low high-density lipoprotein-c
(HDL-c)].
12–16
The prevalence of type 2 diabetes has shown to vary in
different population probably as a consequence of food habits
and obesity. Fat deposition is shown to vary in different
population as a consequence of different body stature and
lifestyle. A more promising intermediate trait is abdominal
adiposity and its association with metabolic disturbances
including resistance to insulin. Large-scale population-based
studies to identify the extent of the problem and its associated
risk factors are scarce. The study was conducted to estimate the
prevalence of type 2 diabetes and impaired fasting glucose
(IFG) along with potential risk factors in a rural Bangladeshi
population.
Methods
Selection of study area
The study areas were selected from a rural community
35 miles north of Dhaka city between Dhaka and
Tangail. The areas may still be characterized as rural but
due to fast expanding urbanization the localities will transfer
in to semi-urban areas in a short while. Rationality for
choosing the areas is to observe the transition of the disease as
a consequence of changed lifestyle. Rural areas were included
as 80% of the total people in Bangladesh live in the
countryside.
17
Characteristics of rural life were defined to
reflect the livelihood primarily related to agriculture or related
to agrarian activities like ploughing, fishing, pottering, etc.
The dwellers of the area did not have access to the
municipal (urban) facilities like housing provided with
.............................................................
1 Institute of General Practice and Community Medicine,
Department of International Health, University of Oslo,
Norway
2 Centre for Clinical Epidemiology, National Hospital/University
of Oslo, Norway
3 Bangladesh Institute of Research and Rehabilitation in Diabetes,
Endocrine and Metabolic Disorders (BIRDEM), Dhaka,
Bangladesh
4 Institute of Nutrition and Food Sciences, University of Dhaka,
Bangladesh
Correspondence: Dr Akhtar Hussain, Department of International
Health, Faculty of Medicine, University of Oslo, P.O. Box 1130
Blindern, 0317 Oslo, Norway, tel.: þ47 22 850641, fax: þ47 22
850501, e-mail: akhtar.hussain@medisin.uio.no
by guest on June 8, 2013http://eurpub.oxfordjournals.org/Downloaded from

water, gas, and sanitation. Accordingly, 5 areas with 10 villages
were selected.
Selection of study subjects
There were 20 171 individuals and subjects $20 years of age,
identified by a census conducted prior to the commencement
of the survey. The census was conducted by the Bangladesh
Bureau of statistics to update the demographic characteristics
of the locality which is done every second year. All the
individuals were given an identification number including a
household number by the programme administration. Among
those 5000 individuals, subjects were selected following simple
random procedure with the presumption that the combined
prevalence of diabetes and IFG will be close to 15%. Of
the selected subjects 4757 individuals agreed to participate in
the study.
Ethics
Verbal consent was received from each of the individuals prior
to inclusion, as majority of the participants were illiterates.
They were informed of their rights to withdraw from the study
at any stage or to restrict their data from the analysis. Identified
cases for (type 2 diabetes mellitus) T2DM were referred to
the diabetic hospital (BIRDEM) for follow-up and treatment.
The protocol was approved by the Norwegian and Bangladeshi
ethical committee for medical research.
Survey procedures
The community leaders were invited for a meeting with the
project team. They were oriented of the purpose of the
study and requested for their opinion and/or comments. Their
co-operation was sought in a participatory manner. Each of
them were given specific tasks (organizing, collecting voter’s
list, co-ordination with the field team, and feedback to the
programme supervisors) based on their background and
interest.
Sixteen volunteers were recruited in four teams from
the local community and trained by the programme managers.
In addition, four physicians were employed to supervise and
to measure blood pressure (BP). Each team investigated
around 30 subjects a day. Residents were informed of the
objectives of the study including their approval by the locally
recruited volunteers. Further, every one was made aware of
the fasting state of a minimum of 8 h prior to the test. The
investigating team moved from village to village. The identified
people were reminded of the importance of the fasting
state prior to the day of investigation and verbal confirmation
was made prior to blood test. Three days of training
(both theoretical and field) for the project workers were
conducted prior to the commencement of the programme.
The census and the population were defined during the
months of September–October 1999. Fasting blood glucose
(FBG and biophysical examination was conducted for the
selected 5000 people during the months of November 1999 to
January 2000).
Anthropometry and measurement of BP. Measurements for
height, weight, waist, and hip are taken with light clothes
without shoes. The weighing tools (Salter 918 Electronic) were
calibrated daily by known standard weight (10 kg). For height,
the subject stood in erect posture vertically touching occiput,
back, buttocks, and heels on the wall gazing horizontally in
front keeping tragus and lateral orbital margin in the same
horizontal plane. Waist girth was measured by placing a plastic
tape horizontally midway between 12th rib and iliac crest on
the mid-axillary line. Hip was measured horizontally on the
greater trochanters.
Measurement of BP needs special precaution. Variation in BP
was minimized by (i) ensuring 10 min rest before BP record,
(ii) using standard cuffs for adults fitted with standard mercury
sphygmomanometer, and (iii) placing the stethoscope bell
lightly over the pulsatile brachial artery on the right hand.
The physicians were particularly trained to record BP. All the
four physicians had 10 years of experience in working with
diabetic patients at the hospital.
Blood glucose estimation. FBG from capillary whole blood was
performed from 4757 individuals following the newly proposed
diagnostic criteria.
18
FBG > 6.1 mml/l (>180 mg/100 ml)
and were used to classify diabetic cases and >5.6–6.0 mml/l
(>100 mg/ml) to identify IFG. The estimation was performed
by the HEMOCUE glucose analyser in the field. The machine
was calibred everyday with the calibration cuvette prior to
estimations. The microcuvettes were stored in a refrigerator in
the field and ice bags were used during transport of the
cuvettes. Open packs were used within 3 weeks. The sensitivity
and specificity of the HemoCue glucose analyser was reported
in previous studies.
19
Data analysis and statistical methods
The data was registered using Microsoft Access data entry.
Control of data entry was secured through both programme
appliance and manually. The prevalence rates of DM were
determined by simple percentages. These rates were further
standardized for the ‘New World Population’ following WHO
suggestions in order to make a uniform prevalence data.
20
The odds ratio (OR) with 95% confidence interval (CI) for
risk factor(s) were calculated taking the least prevalence of
complication or clinically relevant criteria as a reference
value. All P-values presented are two-tailed. Multiple logistic
regression was executed to adjust for potential confounding
factors. All statistical analyses were performed using SPSS
9.0 software.
Results
Diabetes prevalence increased with increasing age both for
males and females (table 1). The standardized rate was also
provided in parenthesis for the total number and prevalence
following the ‘‘New World Population’’ proposed by the
WHO. Though non-significant, females had higher prevalence
of diabetes in all age groups compared with males. The
difference in prevalence by sex widened in the older age group
(>50 years). The same was true for IFG but the difference by
sex was less prominent. Only six people were reported to have
known diabetes but none of them were under medication.
Characteristics of the participants were presented in (table 2)
with 10 years age interval. Male subjects were older compared
to the female participants but with almost no differences
in BMI. However, the total population was lean with a mean
BMI of 19.3 and 19.4 for the male and female population,
respectively. This picture is also reflected in the assessment
of BP.
Elevated levels of FBG were observed with growing age both
for males and females (figure 1). The difference in glucose
values was somewhat similar for both sexes until the age of
50 after which the glucose values increase notably for women.
The differences were not statistically significant.
Age, sex, systolic BP, and WHR for men showed to
be important risk factors for the occurrence of type 2
diabetes (table 3). The findings were also evident in the
multivariate model adjusted for age, sex, BMI, diastolic,
and systolic BP. The risk for diabetes was almost 2-fold higher
in subjects aged >40 compared with the age group 20–30,
systolic BP >140 mm Hg, and WHR for men. An over all
292 European Journal of Public Health
by guest on June 8, 2013http://eurpub.oxfordjournals.org/Downloaded from

significant association of BMI and type 2 diabetes (P ¼ 0.04)
was also observed for females (data not shown). BMI >30
showed to be exceedingly risky state for the occurrence of
diabetes but with only 11 subjects in the group the apparent
statistical significance was not observed in the multivariate
model. WHR (>0.9) were found to be significantly associated
with the diabetic state in men both in the univariate and
multivariate model adjusted for age, systolic, and diastolic BP
and BMI.
Discussions
We have observed a low (2.8%) prevalence of type 2
diabetes with a relatively high IFG in the population aged
20 years and above. This may indicate a possibility for
exponential increase of type 2 diabetes in this population
unless necessary measures are taken for the specified
group. Type 2 diabetes were found in 7% of the adult
population (>25 years of age) and 10–50% in the minority
communities including Asians in the United States.
21
The rate
was found to be 14.8 in a population aged >20 years in
Kuwait.
22
In a random survey of 1195 subjects aged >40 years
in Shimla, India, the prevalence of type 2 diabetes were shown
to be 4.9%.
23
Among other rural population in India
the prevalence of type 2 diabetes was found to be 2.8%.
24
The prevalence of type 2 diabetes was found to be 3.8% in rural
Table 1 Prevalence of diabetes and impaired fasting glucose by age and sex, Tangail, Dhaka, Bangladesh, 1999
Male Female Total
Age group
in years
Diabetic
cases
n Prevalence
per 1000
Diabetic
cases
n Prevalence
per 1000
Diabetic
cases
n Prevalence
per 1000
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
20–30 8 700 11.4 24 1380 17.4 32 2080 15.4
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
>30–40 10 589 17.0 17 663 25.6 27 1252 21.6
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
>40–50 9 337 26.7 10 318 31.4 19 655 29.0
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
>50 12 411 29.2 18 359 50.1 30 770 39.0
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
Total 39 2037 19.1 69 2720 25.4 108 4757 (4760) 22.7 (27.3)
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
IFG
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
Age group
in years
IFG
cases
n Prevalence
per 1000
IFG
cases
n Prevalence
per 1000
IFG
cases
n Prevalence
per 1000
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
20–30 25 700 35.7 47 1380 34.1 72 2080 34.6
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
>30–40 26 589 44.1 32 663 48.3 58 1252 46.3
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
>40–50 15 337 44.5 21 318 66.0 36 655 55.0
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
>50 30 411 73.0 27 359 75.2 57 770 74.0
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
Total 96 2037 47.1 127 2720 46.7 223 4757 (4760) 46.9 (54.4)
Numbers in the parentheses is standardized for the World population—WHO
Table 2 Distribution of participants for BMI, waist/hip ratio, systolic, and diastolic blood pressure by age and sex,
Bangladesh 1999
Variables Age 20–30 Age >30–40 Age >40–50 Age >50
Male mean
(700)
Female mean
(1380)
Male mean
(589)
Female mean
(663)
Male mean
(337)
Female mean
(318)
Male mean
(411)
Female mean
(359)
BMI 19.22 19.61 19.74 19.52 19.62 19.45 18.81 18.60
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
Waist/hip ratio 0.86 0.81 0.88 0.82 0.91 0.87 0.90 0.84
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
SBP (mm Hg) 116.8 116.3 116.8 118.5 118.6 124.6 127.7 132.2
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
DBP (mm Hg) 75.2 75.1 76.9 77.4 77.3 80.9 80.7 81.6
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
Height (cm) 161.7 150.5 161.2 150.7 160.6 149.5 157.8 145.9
.......... ........... ........ ........... ........... ......... .......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... .......... ......... .
Weight (kg) 50.3 44.5 51.3 44.4 50.7 43.5 46.9 39.6
Age category (in years)
>50>40-50>30-4020-30
Mean
FBG
mmol/L
5,1
5,0
4,9
4,8
4,7
4,6
4,5
Sex of the Participants
Male
Female
Figure 1 Fasting blood glucose by age and sex.
Diabetes in rural Bangladesh
293
by guest on June 8, 2013http://eurpub.oxfordjournals.org/Downloaded from

Bangladesh among subjects in the age category 30–64 years
10
and the same in a recent study in Bangladesh among 30–70
years.
25
Our results are in agreement with the Indian rural
population but show somewhat lower prevalence compared to
other Bangladeshi studies
10
probably because of the selection of
the study population in the other study were from an adjacent
location of Dhaka city. Our population was also leaner.
However, other published data from Bangladesh
25
may also
indicate varied prevalence of diabetes within the country.
Besides, the results may also have been influenced by the
diagnostic tool used for FBG estimation. The other studies in
Bangladesh have employed autoanalyser where as our estima-
tion was performed by HemoCue glucose analyser. The
sensitivity and specificity of HemocCue glucose analyser was
mentioned earlier.
19
Further, the most recent HemoCue
glucose meter measures B-glucose and convert the result to a
plasma equivalent glucose concentration and was found to be
satisfactory for diagnostic determination.
26
The non-
responders were only 4.9% (243) is not likely to explain the
lower prevalence observed.
We have observed a higher, though non-significant, occur-
rence of the disease among females in all age categories
compared with males especially in the highest age strata. We
have observed higher mean FBG among women aged more
than 50 years compared to men. Finding of the pre-dominancy
of the disease by sex was not consistent in the previous studies.
European investigations have shown a higher prevalence of
type 2 diabetes in males compared to females.
27
Non-
significant higher prevalence of type 2 diabetes was also
observed in women (2.2% compared with 1.6% in men) among
Mexican Indians.
28
The exact mechanism for this finding is
not well understood but a selection process in the phases of
female survival in a society where female mortality is higher
may have influenced our findings.
Table 3 Prevalence and odds ratio (OR) and 95% CI of diabetes by the following risk factors, Bangladesh, 1999
Variables Cases n Prevalence
per 1000
OR
a
(95% CI) OR
b
(95% CI)
Age
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
20–30 32 2080 15.4 1.0 1.0
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
>30–40 27 1252 21.6 1.4 (0.8–2.4) 1.4 (0.9–2.2)
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
>40–50 19 655 29.0 1.9 (1.0–3.5) 2.0 (1.1–3.6)
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
>50 30 770 39.0 2.6 (1.5–4.4) 2.6 (1.5–4.5)
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
Sex
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
Male 39 2037 19.1 1.0 1.0
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
Female 69 2720 25.4 1.3 (0.9–2.0) 1.4 (1.0–2.2)
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
Systolic BP
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
0–140 (mmHg) 91 4416 20.7 1.0 1.0
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
>140 high 17 341 47.7 2.4 (1.3–4.1) 2.0 (1.0–3.9)
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
Diastolic BP
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
0–90 (mmHg) 97 4416 22.0 1.0 1.0
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
>90 high 11 341 32.3 1.5 (0.7–2.9) 0.7 (0.3–1.5)
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
BMI
c
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
18.5–<25 normal 60 2534 23.7 1.0 1.0
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
<16 severe PEM
d
10 392 25.5 1.1 (0.5–2.2) 0.8 (0.4–1.7)
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
16.0–18.4 Mod
d
. 31 1662 18.6 0.8 (0.5–1.2) 0.7 (0.5–1.2)
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
25.0–<30 overweight 6 158 38.0 1.6 (0.5–4.0) 1.6 (0.7–3.7)
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
$30 Obese 1 11 90.9 4.6 (1.8–36.1) 4.1 (0.5–33.3)
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
Waist/hip ratio
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
Male
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
0–0.9 normal 21 1533 13.7 1.0 1.0
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
>0.9 high 18 504 35.7 2.7 (1.3–5.1) 1.7 (1.1–2.7)
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
Female
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
0–0.8 normal 22 1112 19.8 1.0 1.0
......... ........... ......... .......... ........... ......... ........... .......... ......... ........... .......... ......... ........... ........... ........ ........... ........... ........ ........... ........... ........ ..
0.8 high 47 1608 29.2 1.5 (0.9–2.6) 1.0 (0.6–1.6)
a
Crude odds ratio
b
Adjusted odds ratio for age, sex, diastolic and systolic blood pressure, and body mass index (BMI)
c
Body mass index (BMI) refers to the ratio body weight/height
2
(BMI, kg/m
2
)
d
Protein energy malnutrition
294 European Journal of Public Health
by guest on June 8, 2013http://eurpub.oxfordjournals.org/Downloaded from

Systolic hyper tension (sHTN > 140 mm Hg) was associated
with the occurrence of diabetes in our population both in
crude and adjusted analysis. Another study in Bangladesh
found that the rate for sHTN and dHTN as 23.2 and 13.6%,
respectively, among the newly diagnosed type 2 diabetic
patients.
8
Observed higher hypertension in the mentioned
study compared with ours may have been due to selection of
samples. The participants were selected among the newly
diagnosed diabetic type 2 cases not a population-based study.
Systolic hyper tension was found to be associated with the
prevalence of diabetes in southern Taiwan,
29
Nigeria,
30
China,
31
and Australia.
32
Although generalized obesity appeared to ensue type 2
diabetes, we have not observed any association of overweight
(as defined by the standard BMI criterions)
33
and diabetes in
our material. Our samples appeared to exemplify a lean
population with only 3.6% people defined as overweight, while
25% of the men and 59% of the women had higher WHR. The
association with BMI and type 2 diabetes appeared to differ
in different ethnic groups. Epidemiological data from Asian
Indians (AI), and Mexican Americans (MA), and non-Hispanic
Whites (NHW) from San Antonio heart study showed that MA
had the highest rate of obesity and highest prevalence of
diabetes (men 19.6%; women 11.8%). NHW had also high
rates of obesity but a low prevalence of diabetes (men 4.4%;
women 5.7%). Although AI had lower BMI than MA, the risk
conferred by BMI was similarly high in AI and MA than
NHW.
11
WHR ratio appeared to be significantly associated with the
occurrence of diabetes in men but not in women. In a review of
59 references it was found that average waist/hip circumference
ratios are higher in South Asians than in Europeans of similar
BMI.
34
Previous study in Bangladesh also showed that the
prevalence of diabetes was related to WHR.
35
We have observed
central obesity even among people with normal BMI in our
population (data not shown).
Measures applied to define obesity in relation to the occur-
rence of type 2 diabetes in different population appeared to
provide inconsistent results. Individuals with a predominance
of abdominal fat exhibit numerous metabolic disturbances,
including insulin resistance compared with those having fat
primarily distributed subcutaneously over the lower extremi-
ties.
36
Our data showed low BMI and relatively high central
obesity with low prevalence of type 2 diabetes but rather high
fasting impaired blood glucose level may indicate genetically
predetermined fat deposition. Further this may also indicate
that so-called ‘lean diabetes’ may be explained by higher genetic
risk factor and a low calorie intake in this population. This may
call for a new guide line for anthropometric measures in South
Asian population in line with the Indian data
37
suggesting
different cut-off values in order to classify people at risk. More
studies are needed on metabolic, hormonal, and biophysical
profiles in order to understand the transitional epidemiology of
lean diabetes.
Acknowledgements
We acknowledge the contribution of our field medical officer
Dr Shamim Talukder, the village leaders, and the volunteers
for their sincere and enduring contribution to the collection
and quality of data. We express our gratitude to the Division
of Medical Statistics including Mr Ishtiaq Khushi (Computer
Manager), Centre for Clinical Epidemiology, National
Hospital, Norway, for their input in the statistical analysis.
We also thank all the participants in the study for their active
co-operation. Finally, our gratitude to Prof. Emiratus Jak
Jervell for his continued support.
Key points
 More than 80% of the population in South Asia reside
in the countryside. Often prevalence data are presented
from co-incidental urban population involving small
samples.
 Data on the large-scale population-based study are
needed especially from ethnic groups representing the
highest increase in the incidence of type 2 diabetes in
order to understand the differential risk factors
implicated in different population necessary for both
prevention and case management.
 Prevalence and differential risk factors.
 Representativeness.
 Large-scale rural population.
References
1 King H, Rewers M. Global estimates for prevalence of diabetes mellitus and
impaired glucose tolerance in adults. Diabetes Care 1993;16:157–77.
2 King H, Aubert RE, Herman WH. Global burden of diabetes 1995–2025.
Prevalence, numerical estimates and projections. Diabetes care
1998;21:1414–31.
3 McKeigue PM, Miller GJ, Marmot MG. Coronary heart disease in South
Asians overseas—a review. J Clin Epidemiol 1989;42:597–609.
4 McKeigue PM, Bela Shah, Marmot MG. Relation of central obesity and
insulin resistance with high diabetes prevalence and cardiovascular risk in
South Asians. Lancet 1991;337:382–6.
5 McKeigue PM, Pierpoint T, Ferrie JE, Marmot MG. Relationship of
glucose intolerance and hyperinsulinemia to body fat pattern in South
Asians and Europeans. Diabetologia 1992;35:785–91.
6 West KM, Kalbfleisch JM. Glucose tolerance, nutrition and diabetes in
Uruguay, Venezuela, Malaya and East Pakistan. Diabetes 1966;15:9–18.
7 Mahtab H, Ibrahim M, Banik NG, et al. Diabetes detection survey in a rural
and semiurban community in Bangladesh. Tohoku J Exp Med
1983;141:211–7.
8 Sayeed MA, Khan AR, Banu A, Hussain MZ. Prevalence of diabetes and
hypertension in a rural population of Bangladesh. Diabetes Care
1995;18:555–8.
9 Sayeed MA, Hussain MZ, Banu A, et al. Prevalence of diabetes in a
suburban population of Bangladesh. Diabetes Res Clin Pract
1997;34:149–55.
10 Sayeed MA, Hussain MZ, Banu A, et al. Effect of socio-economic risk factor
on difference between rural and urban in the prevalence of diabetes in
Bangladesh. Diabetes Care 1997;20:551–5.
11 Ramachandran A, Snehalatha C, Viswanathan V, et al. Risk of noninsulin
dependent diabetes mellitus conferred by obesity and central adiposity in
different ethnic groups: a comparative analysis between Asian Indians,
Mexican Americans and Whites. Diabetes Res Clin Pract 1997;36:121–5.
12 Zavaroni I, Dall’Aglio E, Alpi O, et al. Evidence for an independent
relationship between plasma insulin and concentration of high density
lipoprotein cholesterol and triglyceride. Atherosclerosis 1985;55:259–66.
13 Orchard TJ, Becker DJ, Bates M, et al. Plasma insulin and lipoprotein
concentrations: an atherogenic association? Am J Epidemiol
1983;118:326–37.
14 Cambien F, Warnet M, Eschwe
`
ge E, et al. Body mass, blood pressure,
glucose and lipids. Does plasma insulin explain their relationship?
Arteriosclerosis 1987;7:197–202.
15 Modan M, Halkin H, Almog S, et al. Hyperinsulinemia: a link between
hypertension, obesity and glucose intolerance. J Clin Invest 1985;75:809–17.
16 Reaven GM, Greenfield MS. Diabetes hyperglyceridemia. Evidence for
three clinical syndromes. Diabetes 1981;30:66–75.
17 Islam W. Bangladesh Bureau of Statistics. Statistical Pocket Book of
Bangladesh 1997 Ed. Bangladesh: Statistical Division, Ministry of planning,
Government of the People’s Republic of Bangladesh.
18 Alberti KGMM, Zimmet PZ. Definition, diagnosis and classification of
diabetes mellitus and its complications. Part 1: diagnosis and classification
Diabetes in rural Bangladesh 295
by guest on June 8, 2013http://eurpub.oxfordjournals.org/Downloaded from

of diabetes mellitus. Provisional report of a WHO consultation. Diabet Med
1998;15:539–53.
19 Hannestad U, Lunsblad A. Accurate and precise isotope dilution mass
spectrometry method for determining glucose in whole blood. Clin Chem
1997;43:794–800.
20 Ahmad OB, Boschi-Pinto C, Lopez AD, et al. Age standardization of rates: a
new WHO standard. EPI/GPE/EBD. Series No.31. Geneva: WHO.
21 Florez H. Steps toward the primary prevention of type II diabetes mellitus.
Various epidemiological considerations. Invest Clin 1997;38:39–52.
22 Abdella N, Al Arouj M, Al Nakhi A, et al. Non-insulin dependent diabetes
in Kuwait: prevalence rates and associated risk factors. Diabetes Res Clin
Pract 1998;42:187–96.
23 Dhadwal D, Ahluwalia SK, Das Gupta DJ, et al. Prevalence of NIDDM in the
general population (<40 years) in Shimla. Indian J Med Sci 1997;51:459–64.
24 Sing RB, Bajaj S, Niaz MA, et al. Prevalence of type 2 diabetes mellitus and
risk of hypertension and coron ary artery disease in rural and urban
population with low rates of obesity. Int J Cardiol 1998;66:65–72.
25 Sayeed MA, Mahtab H, Khanam PA, et al. Diabetes and impaired fasting
glycemia in a rural population of Bangladesh. Diabetes care 2003;26:1034–9.
26 Niels Fogh. Evaluation of HemoCue glucose meter (201þ) converting
B-glucose to P-glucose. Point of Care. 2004;3:172–5.
27 Gatling W, Budd S, Walters D, et al. Evidence of an increasing prevalence
of diagnosed diabetes mellitus in the Poole area from 1983 to 1996. Diabet
Med 1998;15:1015–21.
28 Castro-Sanchez H, Escobedo-de la Pena J. Prevalence of non
insulin dependent diabetes mellitus and associated risk factors in the
Mazatec population of the State of Oaxaca, Mexico. Gac Med Mex
1997;133:527–34.
29 Lu FH, Yang YC, Wu JS, et al. A population-based study of the prevalence
and associated factors of diabetes mellitus in southern Taiwan. Diabet Med
1998;15:564–72.
30 Olatunbosun ST, Ojo PO, Fineberg NS, Bella AF. Prevalence of diabetes
mellitus and impaired glucose tolerance in a group of urban adults in
Nigeria. J Natl Med Assoc 1998;90:293–301.
31 Pan XR, Yang WY, Li GW, Liu J. Prevalence of diabetes and its risk factors
in China, 1994. National Diabetes Prevention and Control Cooperative
Group. Diabetes Care 1997;20:1664–9.
32 Donnelly R, Molyneaux L, McGill M, Yue DK. Detection and treatment of
hyper tension with non-insulin-dependent diabetes mellitus: does the ‘‘rule
of halves’’ apply to a diabetic population? Diabetes Res Clin Pract
1997;37:35–40.
33 Shils ME, Olson JA, Shike M, editors. Modern nutrition in health and
disease. Vol 1, 8th edition. Philadelphia: Lea & Febiger, 1994.
34 McKeigue PM. Metabolic consequences of obesity and body fat pattern:
lessons from migrant studies. Ciba Found Symp 1996;201:54–64.
35 Sayeed MA, Banu A, Malek MA, Khan AK. Blood pressure and
coronary heart disease in NIDDM subjects at diagnosis: prevalence
and risks in Bangladeshi population. Diabetes Res Clin Pract
1998;39:147–55.
36 Mohan V, Vijayaprabha R, Rema M, et al. Clinical profile of lean NIDDM
of south India. Diabetes Res Clin Pract 1997;38:149–50.
37 Snehalatha C, Viswanathan V, Ramachandran A. Cutoff values for normal
anthropometric variables in Asian Indian adults. Diabetes Care
2003;26:1380–84.
Received 5 July 2005, accepted 28 August 2006
296 European Journal of Public Health
by guest on June 8, 2013http://eurpub.oxfordjournals.org/Downloaded from