How Reliable is the Screening Method for Gestational Diabetes Mellitus in Predicting Fetal
Macrosomia?
Okunowo Bolanle,1* Odeniyi Ifedayo,2,3 Olopade Oluwarotimi,3 Okunowo Adeyemi,4,5
Adegbola Omololu 4,5, Fasanmade Olufemi, 2,3 Ohwovoriole Augustine2,3 .
1Endocrinology, Diabetes and Metabolism division, Department of Medicine. Lagos State University Teaching Hospital,
Lagos; Nigeria. 2Endocrinology, Diabetes and Metabolism division, Department of Medicine, College of Medicine,
University of Lagos (CMUL), Nigeria. 3Endocrinology, Diabetes and Metabolism division, Department of Medicine,
Lagos University Teaching Hospital, Lagos, Nigeria. 4Obstetrics and Gynecology Department, College of Medicine,
University of Lagos (CMUL), Nigeria. 5Obstetrics and Gynaecology Department,
Lagos University Teaching Hospital, Lagos, Nigeria.
ABSTRACT
a*Correspondence
a*Dr. Bolanle Okunowo
aboutbolanle@gmail.com.
+2347031188085. P.O BOX 21005,
Ikeja, Lagos; Nigeria
ORCID: 0000-0001-5471-6256
Background: Fetal macrosomia is a major complication of Gestational Diabetes
Mellitus (GDM). Universal screening of all pregnant women has been advocated by
WHO, however this remains a major challenge in low-income settings. The aim of
this study is to audit the reliability of the universal screening method used for GDM
on macrosomic delivery. Methods: This was a prospective study conducted over a
period of nine months. All the pregnant women were screened for GDM. They had
75g Oral Glucose Tolerance test (OGTT) and glycated hemoglobin (HBA1C) done
at 24 to 28 weeks’ gestation and they were followed up till delivery. Their newborns
had their weight measured at birth. Results: Ninety pregnant women were screened
for GDM. The overall prevalence of GDM was 23.3%. One-hour plasma glucose
correlated and independently predicted fetal macrosomia compared to fasting plasma,
two-hour post-glucose load and HBA1c. Also, one hour post glucose load had the
highest specificity (97.3%) and highest OR of 11 for predicting macrosomia delivery.
However, the highest diagnosis for GDM (16.67%) was using the two-hour post
glucose load. Conclusion: Our study demonstrated the importance of fasting glucose
and one-hour post glucose load in the prediction of fetal macrosomia. However, one-
hour post glucose load plasma glucose was the most significant parameter that
independently predicted the occurrence of macrosomic birth weight in this study
Keywords: Audit, Macrosomia, Gestational Diabetes Mellitus, Fasting Plasma, One-
Hour Post Glucose Load, Two-Hour Post Glucose Load
INTRODUCTION
Gestational Diabetes Mellitus (GDM) remains a major
cause of maternal and fetal mortality 1. It’s being linked
to both short and long term adverse fetal outcomes. 2-7
These short-term adverse outcomes include neonatal
hypoglycemia, birth trauma and macrosomic birth weight
amongst others.2-4 Meanwhile, the long-term adverse
outcomes include childhood obesity and risk of type 2
diabetes mellitus.5-7 One of the major complications of
GDM is the delivery of macrosomic baby with its
attendant metabolic implications in future years such as
childhood obesity and risk of type 2 diabetes mellitus. 5-7
Bolanle et al. Predicting the Risk of Macrosomic Birth Weight.
Tropical Journal of Obstetrics and Gynaecology (TJOG) Vol. 43 No. 4 (2025)/Published by Journalgurus
256
There are various screening methods employed
for GDM. The method used in the screening for GDM is
very important. 8 The screening method could be
selective or universal screening. For selective screening
for GDM, the risk factors for GDM are used to determine
whether pregnant women should be screened for GDM
or not. 8 Those with risk factors are subjected to either a
75g OGTT or to an initial 50g glucose challenge test, if
positive they proceed to 100g OGTT. Meanwhile the
universal screening method screens all pregnant women
from 24 to 28 weeks gestations irrespective of their risk
factors for GDM. 8
The screening technique with 75g oral glucose
tolerance test involves fasting plasma glucose, 1 hour
post glucose load and 2-hour post glucose load. While
another screening technique involves an initial 50g
glucose challenge test, if positive, the patient proceeds to
100g OGTT using fasting plasma glucose, 1 hour post
glucose load, 2-hour post glucose load and 3-hour post
glucose load. Despite the various screening methods,
there are also different diagnostic criteria and glucose
thresholds for the diagnosis of GDM. 8
Glycated hemoglobin is yet to be used as a
screening tool for the diagnosis of GDM. Few studies
have shown good sensitivity and specificity of glycated
hemoglobin for the diagnosis of GDM and good
predictive value for birth weight.9,10 A similar study has
demonstrated the use of 75g OGTT screening method in
predicting macrosomia. 11 A similar study by N Chastang
et al showed ‘practical test’ which consisted of glucose
measurement in the fasting state and two hours after a
usual breakfast was superior to 100g OGTT in predicting
fetal macrosomia. 12 Yuting Z et al showed fasting blood
glucose was positively associated with newborn
birthweight compared to ultrasound Fetal Weight Gain
(FWG) and Amniotic Fluid Index (AFI). 13 Among the
major draw backs of OGTT are multiple prickings for
sample collection, fasting state etc.
Using the Hyperglycemia and Adverse Pregnancy
Outcome (HAPO) study which was conducted using the
two hour-75g OGTT. In this study the effect of plasma
glucose was measured on the both the maternal and fetal
outcomes of pregnancy.4 The findings in this study led to
the diagnostic criteria employed by the International
Association of Diabetes and Pregnancy Study Groups
(IADPSG).(4) This was similarly adopted by the
International Federation of Gynecology and Obstetrics
(FIGO) guidelines where universal screening of all
pregnant women for GDM using a 75g two-hour oral
glucose tolerance test (OGTT) was recommended.14
The glucose threshold employed for diagnosing
DM were the average glucose values at which the odds
for birth weight were >90th percentile, cord C-peptide >
90th percentile and neonatal per cent body fat >90th
percentile. 4 Due to these changes the prevalence of GDM
increased using FIGO guidelines which now varies
between 11.1% and 44.3%. 15,16 Wenlin B et al also
demonstrated the use of 75g OGTT using the IADPSG
criterion to demonstrate the risk of fetal macrosomia. 11
Hence, the huge demand on resources and cost of
screening all pregnant women for GDM are enormous
especially in resource challenged-settings. Do the
benefits of universal screening outweigh the risk of
adverse fetomaternal complications? Which screening
method is better associated with fetal macrosomia? These
are areas yet to be explored especially in resource-
challenged settings. The aim of this study was to
determine the component of 75g OGTT that predicts fetal
macrosomia.
Most studies reported in the literature were mainly
on the impact of the diagnosis of GDM and fetomaternal
outcomes. 2,17,18 Hence the results of this study will assist
the clinicians in predicting macrosomia in resource-
challenged settings.
MATERIALS AND METHODS
Ethical Approval
Ethical approval was obtained from Health Research and
Ethical Committee of the Lagos University Teaching
Hospital (LUTH) with ethical approval number
AM/DCST/HREC/APP/862. Written informed consent
was obtained from all the participants before the
commencement of the study. All the participants were
fully informed about the aim of the study and that their
personal and baby’s information would be treated with
strict confidentiality. They were also informed of their
freedom to voluntarily withdrawal from the study
without their care being compromised. No identifying
information was collected from the participants. The
study was conducted in accordance with the 1964
Declaration of Helsinki and its amendments.
Research Design: It was a prospective, cross-sectional
study.
Study Population and Duration: The population was
pregnant women who attended the antenatal clinics of
LUTH during the period of September 2016 to May
2017.
Inclusion/Exclusion Criteria
Pregnant women within 24 to 28 weeks of gestation at
the time of recruitment and those who gave written
informed consent to participate in the study were
included in the study. On the other hand, pregnant
women with chronic medical conditions such as chronic
kidney disease, hemoglobinopathies were excluded from
Bolanle et al. Predicting the Risk of Macrosomic Birth Weight.
Tropical Journal of Obstetrics and Gynaecology (TJOG) Vol. 43 No. 4 (2025)/Published by Journalgurus
257
the study. Women with preexisting Diabetes Mellitus
(DM), those on medications that could predispose to
dysglycemia for example steroids etc were also excluded.
Also, women who did not give informed consent to
participate in the study were excluded.
Sample Size
The sample size was determined using a correlational
formula for the objective and assuming a significance
level of 0.05 in a two-tailed analysis, a moderate effect
size (rho=3 according to Cohen) 19 and allowing for 20%
attrition rate in view of the prospective nature of the
study. A total of 90 pregnant women were recruited for
the study.
N = {(Za + ZB)/C}2 +3
Where type 1 error = a=Za=0.05
B = ZB=0.20
r or rho = 0.3 (using moderate r)19
C = 0.5aIn ({1+0.3}/{1-0.3}
N = {(0.05a +0.20)/C}2+3
N = minimum sample size
Sampling Technique
A simple random sampling method was used to recruit
the study participants over a nine months period from the
antenatal clinics in LUTH. These pregnant women who
met the inclusion criteria were randomly selected and
were invited to participate in the study. The women who
gave informed consent were enrolled in the study until
the desired sample size was achieved.
Data Collection
The study data form was used to collect all the
information of the study participants. This information
included the sociodemographic, medical and obstetrics
history. Women with risk factors for GDM such as
previous delivery of macrosomic babies, previous GDM,
family history of DM, previous Intrauterine Fetal Death
(IUFD) amongst others were recorded. All the pregnant
women had 75g OGTT and glycated hemoglobin
(HBA1c) done between gestational age of 24 and 28
weeks. They were subsequently classified into the GDM
group and Non GDM group based on the outcomes of the
result of 75g OGTT. The diagnosis of GDM was based
on the International Association of Diabetes in
Pregnancy Study Group (IADPSG) using FBS ≥
5.1mmol/L, one Hour post glucose load ≥ 10.0mmol/L,
and two-Hour post glucose load ≥ 8.5mmol/L.
Subsequently all the pregnant women were followed up
till delivery and the weight of their newborns measured
at birth.
Birth weight of the new borns were measured
using an electronic baby weighing scale MBC 15K2DM
after removal of the diaper with the newborns naked.
Two measurements were taken and the average of the two
measurement was used as the birth weight of the
newborn. The electronic weighing scale was set at zero.
The measurement was taken to the nearest grams. Two
measurements were taken. However, ,if the measurement
differ by at least 10g, a third measurement was done. The
average of the two measurements was used unless a third
was taken.
Data Analysis
The data generated was entered into Excel work sheet
cleaned and analysis was done using Statistical Package
for Social Sciences (SPSS) for window version 26. A p
value < 0.05 was considered to be statistically significant.
The descriptive statistics were presented as mean and
standard deviation for normally distributed data. Chi
square analysis was used to compare proportions
between the groups of pregnant women diagnosed with
GDM and those diagnosed without GDM. The 95%
confidence interval and p values were used to determine
clinical and statistical significances. Multiple linear
regression was used to determine independent predictors
of birth weight. The sensitivity and specificity of
maternal plasma glucose and macrosomia were
calculated.
RESULTS
Table 1 shows the sociodemographic characteristics of
study participants. The mean age of the pregnant women
was 32.6 ± 5.0 years. Women with GDM were 24 while
those without GDM were 66 in a study
Table I: Socio-demographic Characteristics of Study
Participants
Variable
Number (%)
Age (years)
N=90
20-29
19 (21.1)
30-39
66 (73.3)
40-49
5 (5.6)
Total
90 (100.0)
Socioeconomic class
Upper class
32 (35.6)
Middle class
32 (35.6)
Lower class
26 (28.8)
Total
90 (100.0)
Bolanle et al. Predicting the Risk of Macrosomic Birth Weight.
Tropical Journal of Obstetrics and Gynaecology (TJOG) Vol. 43 No. 4 (2025)/Published by Journalgurus
258
Table 2: Clinical and Biochemical Characteristics of GDM and Non GDM Group
Characteristics
Non GDM
Total
Ჯ
P value
Number (%)
Age group
(years)
20-29
16(17.8)
19 (21.1)
1.709
0.425
30-39
46(51.1)
66(73.3)
40-49
4(4.4)
5(5.6)
BMI (kg/m2)
<30
60(66.7)
76(84.4)
7.874
0.005
>30
6(6.7)
14(15.6)
Mean (± SD) F CI
P value
FBS
4.2(0.78)
0.03
0.20-0.90
0.003
1 Hour post
glucose load
6.31(1.46)
0.12
1.91-3.35
0.001
2 Hour post
glucose load
5.86(1.0)
1.98
2.54-3.46
0.001
BMI; body mass index, GDM: Gestational Diabetes Mellitus, DM : diabetes mellitus GDM : Gestational diabetes mellitus,
IUFD; intrauterine fetal death
Table 3: Correlation between birth weight and the results of Oral Glucose Tolerance Test versus Glycated
Hemoglobin
Birth weight versus
Maternal variables
N=90
Pearson’s correlation
(r)
P value
95 % C.I
FPG (mmol/L)
0.278
0.007
0.761-0.771
1HPG(mmol/L)
0.424
0.001
0.748- 0.765
2HPG(mmol/L)
0.398
0.001
0.931- 0.941
HBA1C (%)
0.383
0.001
0.012- 0.016
Multiple linear regression between birth weight and the results of oral glucose tolerance versus glycated
haemoglobin
BW versus MG
N=90
t
P value
95% C.I
FPG
-0.032
0.974
-0.003- 0.564
1HPG
3.262
0.002
0.067-0.174
2HPG
0.192
0.316
0.136- 0.446
HBA1C
2.580
0.011
0.080- 0.618
C.I; confidence interval, FPG; fasting plasma glucose, HBA1C; glycated hemoglobin, 1HPG; one hour
plasma glucose; 2HPG; two-hour plasma glucose, MG; maternal glucose, t; t test
Bolanle et al. Predicting the Risk of Macrosomic Birth Weight.
Tropical Journal of Obstetrics and Gynaecology (TJOG) Vol. 43 No. 4 (2025)/Published by Journalgurus
259
Table 4: Maternal Glycaemic Predictors of Macrosomia
Macro
Normal
BW
SN
(%)
SP
ACC
PPV
NPV
LR+
LR-
OR
FPG
≥5.1¥
< 5.1
N=9
4
5
N=75
6
69
44.4
92.0
86.9
40.-0
93.2
-0.49
-0.47
6.64
1HPG
≥10¥
<10
4
5
2
73
44.4
97.3
91.7
66.7
93.6
-0.46
-0.45
11.0
2HPG
≥8.5¥
< 8.5
5
4
7
68
45.5
90.7
87.0
41.7
94.4
-0.51
-0.49
6.55
ACC: accuracy, FPG: Fasting plasma glucose, LR: Likelihood ratio, OR: odds ratio, NPV: negative predictive
value, PPV: positive predictive value, SN: sensitivity, SP: specificity, 1HPG: one hour plasma glucose, 2HPG:
two-hour plasma glucose.
¥ADPSG criteria
Figure 1 showing the prevalence of Gestational Diabetes Mellitus (GDM) using two-hour post glucose load.
Bolanle et al. Predicting the Risk of Macrosomic Birth Weight.
Tropical Journal of Obstetrics and Gynaecology (TJOG) Vol. 43 No. 4 (2025)/Published by Journalgurus
260
population of 90 pregnant women. The study showed that
the prevalence of GDM was 26.7%. The women with
GDM had higher fasting plasma glucose, one hour post
glucose load and two-hour post glucose values which
were statistically significant (table 2).
Using the multiple linear regression, analysis the
one-hour plasma glucose and glycated hemoglobin
independently predicted birth weight. (table 3). However,
one hour plasma glucose was a better predictor of fetal
birth weight than glycated hemoglobin. The fasting
plasma glucose and two-hour plasma glucose values
were not predictive of birth weight.
Table 4 shows the maternal glycaemic predictors
of macrosomia. The use of one hour post glucose load
values ≥ 10.0mmol/L has the highest specificity (97.3%)
and is associated with highest occurrence of macrosomia
(OR= 11.0).
Figure 1 shows the incidence of GDM using two-
hour post glucose load alone which was 16.67%. In this
study using fasting plasma glucose alone 13.33% of the
participants were diagnosed with GDM, while one hour
post glucose load was 7.78%- and 2-hour post glucose
load was 16.67%. Four women (8.7%) had GDM among
those without traditional risk factors for GDM.
DISCUSSION
Using 75g OGTT as our screening method, two-hour post
glucose load, one hour post glucose load and Fasting
plasma glucose showed similar sensitive in the diagnosis
of GDM but one hour post glucose load had highest
specificity. This is similar to the study by Holt et al where
they advocated for one hour post glucose load which was
more specific in diagnosis of GDM. 20,21 A similar study
by Chukwunyere et al compared fasting plasma glucose
and random plasma glucose and demonstrated that FPG
has a higher sensitivity and specificity. However, in the
same study, the random plasma glucose used was not
based on one hour or two post glucose loads.22
It is interesting to note that the fasting plasma glucose
and post glucose load showed a significant correlation
with weight. This is similar to the findings in some
studies where fasting glucose correlated with occurrence
of the macrosomia .21,23 However, one hour post glucose
load plasma glucose best independently predicted the
occurrence of macrosomic birth weight in our study with
the highest specificity. This is similar to the findings by
Kim et al where one-hour plasma glucose best predicted
birth weight. 25 Krstevska et al also demonstrated that
fasting plasma glucose and one hour post load glucose
from 75g OGTT were the strongest predictors of
macrosomia.26 Meanwhile, Seabra et al showed that
women who had macrosomic babies had a higher second
and third trimester fasting plasma glucose.27
Many of the studies have demonstrated the role of
one hour plasma glucose predicting macrosomia
compared to fasting plasma glucose as many factors can
influence fasting plasma glucose including diet, maternal
prepregnancy body mass index, gestational weight gain
among others. 27,28
Findings in our study showed that almost 20% of
women with GDM were diagnosed using two-hour post
glucose load. Considering the short and long term feto-
maternal effects of undiagnosed and poorly managed
GDM, universal screening using 75g OGTT is
advocated. The American Diabetes Associated, World
Health Organization and International Association of
Diabetes in Pregnancy Study group guidelines
recommend universal one step screening for all pregnant
women between 24-28 weeks with or without risk factors
for GDM. 4,29
The role and timing of glycated hemoglobin in the
diagnosis of GDM still remains unclear as most
guidelines employs 75g OGTT as the gold standard for
screening.30,31 The increased hemoglobin turn over in
pregnancy could affect the use of glycated hemoglobin as
a monitoring tool for glycemic control in pregnancy.32,33
Glycated hemoglobin is an irreversible, non-enzymatic
reaction between plasma glucose and hemoglobulin in
the last two to three months.30,31 This is affected by many
factors ranging from any cause of anemia to
hemoglobinopathies in accurately diagnosing diabetes
mellitus.32,33 Hence the proposed use of glycated albumin
which is a non-enzymatic reaction between glucose and
albumin appears more stable and measures glycemia
within a shorter period of one to three weeks compared
to glycated hemoglobin of two to three months.(34) Some
studies have advocated the use of glycated hemoglobin
early in pregnancy to predict, monitor and possibly
diagnose hyperglycemia first detected in pregnancy
while other studies demonstrated its role in predicting
birth weight.35-42 In our study the glycated hemoglobin
showed a good correlation with birth weight and this may
be an independent tool in screening for the risk of
macrosomic delivery. This is similar to the study by
Jianing et al which showed glycated hemoglobin is an
independent risk of macrosomic delivery, preterm
delivery and large for gestation age.43
About 9% of women with GDM would have been
missed if universal screening was not employed in
screening for GDM in this study. Should all pregnant
women be subjected to sampling plasma glucose for one
hour post glucose load, two-hour post glucose load when
75 g OGTT method is employed? Considering the cost
implication of universal screening especially in resource-
constraint settings, we advocate from the findings in our
study that women should have universal screening using
the 75g OGTT screening method.
Bolanle et al. Predicting the Risk of Macrosomic Birth Weight.
Tropical Journal of Obstetrics and Gynaecology (TJOG) Vol. 43 No. 4 (2025)/Published by Journalgurus
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Strengths and Limitations
The study demonstrates the impact of the screening
method used for the diagnosis of GDM on birth weight.
Our study provides useful information to clinicians on the
preferred screening method for Gestational Diabetes
Mellitus in resource challenged settings. The prospective
design of the study also represents the main strengths of
this study as most studies done in our environment were
retrospective in nature. A long-term follow-up study of
the macrosomic babies to determine the impact of
macrosomia on the newborn’s well-being was not done
which is a limitation of our study.
CONCLUSION
Our study showed the importance of fasting glucose and
one hour post glucose load in the prediction of fetal
macrosomia. However, one hour post glucose load
plasma glucose was the most significant parameter that
independently predicted the occurrence of macrosomic
birth weight in this study.
Recommendations
There is a need for a large multi-center-based study to
further evaluate the role of the screening method
employed on delivery of macrosomic babies and
diagnosis of GDM. This can be useful in designing a
model for use in low resource settings.
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