Screening for gestational diabetes mellitus and itsprevalence in Bangladesh

Subrina Jesmin a,b,*, Shamima Akter a,c, Hidechika Akashi c,Abdullah Al-Mamun a,d, Md. Arifur Rahman a,d, Md. Majedul Islam a,b,Farzana Sohael a,b, Osamu Okazaki c, Masao Moroi c, Satoru Kawano b,Taro Mizutani b

aHealth & Disease Research Center for Rural Peoples (HDRCRP), Ena Arista, Flat # B-3, House # 802, Road # 3, Baitul

Aman Housing Society, Adabor, Shamoli, Dhaka 1207, BangladeshbGraduate School of Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki 305-8575, JapancNational Center for Global Health and Medicine (NCGM), 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, JapandShahid Ziaur Rahman Medical College, Bogra, Bangladesh

d i a b e t e s r e s e a r c h a n d c l i n i c a l p r a c t i c e 1 0 3 ( 2 0 1 4 ) 5 7 – 6 2

a r t i c l e i n f o

Article history:

Received 18 July 2013

Accepted 29 November 2013

Available online 7 December 2013

Keywords:

Gestational diabetes mellitus

Screening

Bangladesh

a b s t r a c t

Background: The prevalence of gestational diabetes mellitus (GDM) has important health

complications for both mother and child and is increasing all over the world. Although

prevalence estimates for GDM are not new in developed and many developing countries,

data are lacking for many low-income countries like Bangladesh.

Objective: To evaluate the prevalence of GDM in Bangladesh.

Research design and methods: This cross-sectional study included 3447 women who consec-

utively visited the antenatal clinics with an average gestation age of 26 weeks. GDM was

defined according to WHO criteria (fasting plasma glucose [FPG] �7.0 mmol/L or 2-h

�7.8 mmol/L) and the new ADA criteria (FPG �5.3 mmol/L or 2-h �8.6 mmol/L OGTT). We

also calculated overt diabetes as FPG �7.0 mmol/L.

Results: Prevalence of GDM was 9.7% according to the WHO criteria and 12.9% according to

the ADA criteria in this study population. Prevalence of overt diabetes was 1.8%. Women

with GDM were older, higher educated, had higher household income, higher parity,

parental history of diabetes, and more hypertensive, compared with non-GDM women.

Conclusion: This study demonstrates a high prevalence of GDM in Bangladesh. These

estimates for GDM may help to formulate new policies to prevent and manage diabetes.

# 2013 Elsevier Ireland Ltd. All rights reserved.

Contents available at ScienceDirect

Diabetes Researchand Clinical Practice

journal homepage: www.elsevier.com/locate/diabres

1. Introduction

Gestational diabetes mellitus (GDM) is one of the most

common medical complications of pregnancy, defined as

glucose intolerance with onset or first recognition during

* Corresponding author at: Health & Disease Research Center for Rural PAman Housing Society, Adabor, Shamoli, Dhaka 1207, Bangladesh. Te

E-mail addresses: jsubrina@gmail.com, jsubrina@hdrcrp.org (S. Jes

0168-8227/$ – see front matter # 2013 Elsevier Ireland Ltd. All rights

http://dx.doi.org/10.1016/j.diabres.2013.11.024

pregnancy [1]. GDM can adversely impact perinatal outcome,

increase the risk of obesity in offspring and the subsequent

development of diabetes in mothers [2–4]. Overall, GDM rates

have been on the rise in all ethnic groups, but most noticeable

in Asian countries, where the prevalence rate is around 17%

[5]. Further, among the Asians, South Asians are more prone to

eoples (HDRCRP), Ena Arista, Flat # B-3, House # 802, Road # 3, Baitull.: +88 01721 512282; fax: +81 29 853 3092.min).

reserved.

d i a b e t e s r e s e a r c h a n d c l i n i c a l p r a c t i c e 1 0 3 ( 2 0 1 4 ) 5 7 – 6 258

have diabetes at an earlier age [6] and thus more vulnerable to

GDM.

Among the developing countries several studies has been

conducted to estimate the prevalence of GDM, including

India [7–9], China [10,11], Sri Lanka [12], Iran [13,14], and

Malaysia [15]. However, to date no study has been conducted

in Bangladesh. Like many developing countries, Bangladesh

is also experiencing a high prevalence of diabetes [16]. In

order to effectively manage this condition in a cost effective

manner in a low-income country like Bangladesh, it is

imperative to identify mothers with GDM early on in their

pregnancy. In this manner, lifestyle interventions and

treatment may prevent the development of diabetes and

other health complications both for mother and offspring,

and to avoid high treatment costs. The aim of this study was

to determine the prevalence of GDM for the first time among

women in Bangladesh by using the World Health Organiza-

tion (WHO) and the new American Diabetes Association

(ADA) criteria.

2. Data and methodology

2.1. Study design

A base-line survey was done in 12 Upzillas of 6 districts under 3

divisions during 2012–2013 in Bangladesh. Twelve GDM

corners were established in antenatal clinics, where antenatal

care was offered to all pregnant women. A total of 4890

pregnant women, with an average gestation age of 26 weeks,

participated in this study. We used the WHO STEPS approach

(modified), which entails a stepwise collection of risk factor

data based on standardized questionnaires covering the

following parameters: demographic characteristics, somatic

illnesses, somatic and mental symptoms, medications, life

style, and health-related behavior (step 1), basic physical

measures (step 2) and basic biochemical investigations, such

as blood glucose and cholesterol (step 3). The study was

approved by the Ethical Committee of the Health and Disease

Research Center of Rural Peoples (HDRCRP), Dhaka,

Bangladesh, and conforms to the principles outlined in the

Helsinki Declaration. All subjects gave their written informed

consent prior to participation.

2.2. Study subjects

Of the 4890 subjects, we excluded 1410 subjects who were not

fasting. Among the 3480 subjects, who had an oral glucose

challenge test (OGCT), 624 women were found to have an

abnormal OGCT (�7.8 mmol/l). Of the 624 subjects who were

advised to have an oral glucose tolerance test (OGTT),

591participated and 33 dropped out. Ultimately a total of

3447subjects were included in the present study.

2.3. Anthropometric and other variables

Well-trained examiners conducted anthropometric measure-

ments on individuals wearing light clothing and without

shoes. Height was measured to the nearest 0.1 cm using the

portable stadiometer. Weight was measured in an upright

position, to the nearest 0.1 kg, using a calibrated balance beam

scale. Body mass index (BMI) was calculated as the body

weight (kg) divided by the square of the body height (m2). Blood

pressure was measured twice in the right arm in a sitting

position using a standard mercury manometer and cuff, to the

nearest 2 mmHg, with the initial reading taken at least

5 minutes after the subject was made comfortable, and again

after an interval of 15 min. The average systolic blood pressure

(SBP) and diastolic blood pressure (DBP) were then estimated.

Hypertension was defined as SBP �140 or DBP �90 or taking

antihypertensive medication. Number of parity, history of still

birth or abortion, parental history of diabetes, parental history

of hypertension, respondent’s education, and household

income were self-reported.

2.4. Assessment for GDM

All pregnant women were first screened for GDM using a 1-h

50 g OGCT, performed in the morning after an overnight fast.

As we performed a fasting GCT we also measured fasting

plasma glucose (FPG) using a glucometer. Subjects with

abnormal 1-h blood glucose level (�7.8 mmol/l) proceeded to

an OGTT within one week of the abnormal screening test.

Women with abnormal OGCT had a standard 2-h OGTT with a

75-g glucose load administered after a 12–14-h fast with blood

collected fasting and 1-h and 2-h.

GDM was defined according to the 1999 WHO criteria – FPG

�7.0 mmol/L or 2-h �7.8 mmol/L [17]. It was additionally

defined according to the new ADA criteria of FPG �5.3 mmol/L

or 2-h �8.6 mmol/L after a 2-h OGTT [18]. We also calculated

overt diabetes according to the new ADA criteria as, FPG

�7.0 mmol/L [18].

2.5. Statistical analysis

Differences in anthropometric and socio-demographic char-

acteristics between subjects with GDM and non-GDM were

assessed by t-test and Chi-square test for continuous and

categorical variables, respectively. Mean � S.D. and percent-

age were presented, where appropriate. Two-sided P values of

less than 0.05 were considered statistically significant. All

analyses were performed using Stata version 12.0 (StataCorp,

College Station, Texas, USA).

3. Results

The mean age of our study population was 22 � 4 years (mean

�SD), with a median schooling of 7 years. Among the pregnant

women only 7.7% had a basic knowledge about GDM. Only

51.6% women were receiving antenatal care during their

pregnancy.

Table 1 shows the characteristics of the study population

who completed the OGCT. The majority (38.4%) were in the 20–

24 year age group, education below 5 years (43.8%), and

household income more than 15,000 Tk (31.1%). More than half

of the pregnant women were zero parity women (51.5%) and

had normal BMI (18.5–23.0) (55.3%).

Table 2 shows total and age-specific prevalence of GDM and

overt diabetes. The total prevalence of GDM was 9.7%

Table 1 – Characteristics of study population completing a 1-h OGCT test.

Subjects screened, n (%) Subjects with abnormal OGCT, n (%)

Age (years)

<20 1029 (29.8) 192 (32.5)

20–24 1323 (38.4) 186 (31.5)

25–29 855 (24.8) 159 (26.9)

�30 240 (7.0) 54 (9.1)

Education (years of schooling, %)

�5 1509 (43.8) 273 (46.2)

5–10 1368 (40.2) 192 (32.5)

�10 552 (16.0) 126 (21.3)

Monthly household income (Tk)

<8000 945 (27.4) 96 (16.2)

8000–10,000 702 (20.4) 111 (18.8)

10,001–15,000 729 (21.1) 153 (25.9)

>15,000 1071 (31.1) 231 (39.1)

Number of parity

0 1776 (51.5) 252 (42.6)

1 1101 (31.9) 208 (34.5)

2 408 (11.8) 75 (12.7)

�3 162 (4.7) 60 (10.2)

Pregnancy weight status (BMI, kg/m2)

Under weight (<18.5) 498 (14.4) 171 (28.9)

Normal (18.5–23.0) 1905 (55.3) 297 (50.2)

Overweight (23.0–25.0) 474 (13.8) 69 (11.7)

Obese (�25.0) 570 (16.5) 54 (9.1)

Table 2 – Age-specific prevalence of gestational diabetes mellitus (GDM) and overt diabetes.

Age group (years) Prevalence of GDM ADA criteria, n (%) WHO criteria, n (%) Overt diabetes, n (%)

<20 75 (7.3) 102 (9.9) 6 (0.58)

20–24 306 (11.6) 90 (6.8) 18 (1.4)

25–29 159 (18.6) 102 (11.9) 21 (2.5)

�30 60 (25.0) 42 (17.5) 18 (7.5)

Total 447 (12.9) 336 (9.7) 63 (1.8)

d i a b e t e s r e s e a r c h a n d c l i n i c a l p r a c t i c e 1 0 3 ( 2 0 1 4 ) 5 7 – 6 2 59

according to WHO criteria and 12.9% according to the ADA

criteria. The prevalence of overt diabetes was 1.8% using the

ADA criterion. The prevalence of GDM increased significantly

with increased age for both ADA and WHO criteria. Fig. 1

WHO onlyn=201

Bothn=135

ADA onlyn=312

WHO, n=336 ADA, n=447

Neither ADA nor WHO, n=2799

Fig. 1 – Overlap of cases of GDM as diagnosed by the ADA

and WHO criteria for a 2–h 75 g OGTT.

shows the overlap of GDM cases diagnosed by the ADA and

WHO criteria.

Table 3 shows age-adjusted prevalence of GDM for WHO

criteria according to the characteristics of the study popula-

tion. GDM was higher among women with higher education,

higher monthly household income, and those who had higher

parity. Prevalence was also higher among women with

hypertension, currently seeking antenatal care, no previous

history of still birth/abortion, and parental history of hyper-

tension and diabetes.

Table 4 shows differences in anthropometric and socio-

demographic characteristics of the study population accord-

ing to GDM. There were significant differences in age,

education, monthly household income, parity, presence of

hypertension, parental history of diabetes, and those who

were seeking antenatal care during pregnancy (P < 0.05 for all).

4. Discussion

This is the first study to estimate the prevalence of GDM in

Bangladesh. We also compared prevalence according to WHO

and ADA criteria. We found that the prevalence of GDM was

9.7% according to WHO criteria and 12.9% according to the

ADA criteria and the prevalence of overt diabetes was 1.8%

according to ADA criterion.

Table 3 – Age-adjusted prevalence of GDM according toWHO criteria.

Characteristics Prevalence (95% Cl)

Education (years of schooling)

�5 10.9 (8.5–14.0)

5–9 6.2 (4.3–8.8)

�10 13.0 (8.9–18.7)

Monthly income of household (Tk)

<8000 4.5 (2.7–7.3)

8000–10,000 9.7 (6.5–14.2)

10,001–15,000 9.7 (6.6–14.1)

>15,000 13.3 (10.1–17.3)

Number of parity

0 7.4 (5.4–10.1)

1 9.8 (7.1–13.4)

2 12.9 (7.9–20.3)

�3 22.7 (12.0–38.6)

Antenatal care

Yes 12.6 (10.2–15.4)

No 5.1 (3.5–7.4)

Still birth/abortion

Yes 5.6 (2.7–11.1)

No 9.8 (8.1–11.8)

Hypertension

Yes 22.9 (13.9–35.1)

No 8.7 (7.1–10.5)

Parental history of hypertension

Yes 13.7 (7.6–23.3)

No 9.1 (7.5–10.9)

Parental history of diabetes

Yes 9.9 (8.3–11.9)

No 4.0 (1.7–9.4)

d i a b e t e s r e s e a r c h a n d c l i n i c a l p r a c t i c e 1 0 3 ( 2 0 1 4 ) 5 7 – 6 260

The prevalence of GDM observed and reported here (9.6%

and/or 12.9%) is comparable with other studies published from

South Asian and South East Asian countries, including India, Sri

Lanka, and Malaysia [8,12,15]. By contrast, this prevalence is

Table 4 – Differences in anthropometric and socio-demograph(GDM) status (WHO criteria).

GDM (n = 112

Age (years) 23.7 � 5.5b

Education (years of schooling, %)

�5 52.7

5–10 25.9

�10 21.4

Monthly household income (Tk)

<8000 14.3

8000–10,000 20.5

10,001–15,000 22.3

>15,000 42.9

Parental history of hypertension (yes, %) 9.8

Parental history of diabetes (yes, %) 10.5

Number of parity

0 44.4

1 29.6

2 14.1

�3 11.9

Antenatal care (yes, %) 75.0

Still birth/abortion (yes, %) 7.1

Hypertension (yes, %) 13.4

a Based on chi-square test for categorical variables, t-test for continuousb Mean � S.D. (all such values).

lower than a Middle Eastern study from Qatar (16.3%) [13] and

the United Arab Emirates (20.6%) [19], but higher than Iran (4.8%)

[14] and Turkmenistan (6.3%) [20]. Overall prevalence of GDM

varies from 4% to 6% in USA [21,22] and 2–6% in European

countries [23]. Thus, prevalence of GDM seems greater in

developing countries from Asians. However, it is important to

note that the prevalence of GDM varies widely according to the

specific cut-off points used in the various studies. The variation

may be also due to time lag, specific study subject, environmen-

tal diversity, dietary habits, and other national or sub-national

socio-behavioral factors. It is also difficult to compare disease

prevalence, particularly for diabetes, with results from older

literature because of the rapid epidemiologic and demographic

transitions occurring in most developing countries.

In this study, presence of GDM was significantly higher

among older, higher educated, higher household income, higher

parity and hypertensive women. Consistent with our study,

previous studies had been shown higher age was associated

with GDM [7–9,24] indicating that older age is an independent

risk factor for GDM irrespective of race and ethnicity. In a

previous study, Bener et al. did not find a significant difference

between education, household income, and GDM status of

women [19]. In our study higher education and higher income

groups were more likely to engage in sedentary work that may

relate to obesity and GDM. Obesity and associated type 2

diabetes or cardiovascular disease are a growing challenge in

developing world [25]. Overweight or obesity is associated with

GDM in all racial and ethnic groups [26].

Regarding parity and hypertension, the results of our study

were consistent with previous studies [7,13,14], wherein higher

parity and gestational hypertension were significantly associ-

ated with GDM. Also prevalence of GDM was higher among

women with parental history of hypertension and as with other

studies, parental history of diabetes was associated with GDM

ic characteristics according to gestational diabetes mellitus

) Non-GDM (n = 1037) P valuea

22.2 � 3.9 <0.001

0.004

42.8

41.8

15.4

0.004

28.8

20.4

21.0

29.8

6.2 0.14

4.5 0.04

56.9 <0.001

29.2

10.4

3.4

55.2 <0.001

9.2 0.48

4.3 <0.001

variables.

d i a b e t e s r e s e a r c h a n d c l i n i c a l p r a c t i c e 1 0 3 ( 2 0 1 4 ) 5 7 – 6 2 61

[8,10,27]. Thus, women with higher parity, presence of

hypertension, and parental history diabetes should be consid-

ered a high-risk group and compulsory screening should be

considered in these specific groups of pregnant women.

There are some limitations to our study. We only performed

OGTT in women who had abnormal OGCT and there will be

some women with a normal OGCT who would have GDM if they

had an OGTT. On the other hand, a greater proportion of women

with an abnormal OGCT is likely to have an abnormal OGTT

which would tend to exaggerate the prevalence of GDM. The

combined effect of these on the overall prevalence of GDM is not

known. Another limitation relates to cross-sectional design of

our study which could have resulted in selection bias during

case recruitment because we only examined pregnant women

who had an antenatal check-up during a limited time interval

and in selected clinics, and thus the results may not be

generalizable to all Bangladeshi women.

In conclusion, this study shows a relatively high preva-

lence of GDM in Bangladeshi women and suggests screening

for glucose intolerance in pregnancy should be considered as

part of routine antenatal care. This information is also

important in order to develop effective and targeted

preventive approaches to complications associated with

GDM in both the mothers and their offspring and to

formulate new policies or strategies to increase awareness,

prevention, and management of diabetes among pregnant

women in Bangladesh.

Conflict of interest

The authors declare that they had no conflict of interest.

Author contributions

SJ wrote the report and overall conducted this research. SA

analyzed the data and contributed to potential scientific

discussion. MMI contributed to the epidemiological survey.

HA, OO, MM, SK and TM contributed to scientific supervision

of this work and contributed to discussion. All authors read

and approve the final version.

Acknowledgments

This work was supported by Grant-in-Aid for Scientific

Research (overseas academic) from the Ministry of Education,

Culture, Sports, Science and Technology of Japan (23406037,

23406016, 23406029, 24406026, 25305034), and Japan Society for

the promotion of Science. Current project (WDF11-610) on

gestational diabetes from World Diabetes Foundation (WDF),

Denmark to HDRCRP has also supported a part of this work.

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