Is Stratification of Low-Risk Women with Gestational Diabetes Mellitus ... · Women with...

Is Stratification of "Low-Risk"

Women with Gestational Diabetes

Mellitus to Usual Antenatal Care Safe?

YAN ZHANG

Department of Genome Science

John Curtin School of Medical Research

The Australian National University

June 2015

A thesis submitted for the degree of Master of Philosophy of The Australian National

University

Yan Zhang, Mphil Thesis, 2015

DECLARATION

This thesis is a presentation of my original research work. Wherever

contributions of others are involved, every effort is applied to indicate this clearly, with

due reference to the literature, and acknowledgement of people who offered help. This

work has not been submitted previously for the award of any other degree or diploma

at any university or other tertiary institution. Part of the results has been published as

an abstract in American Diabetes Association’ 74th Scientific Sessions.

I give my consent to this copy of my thesis being made available under open

access conditions in the ANU Digital Thesis Collection when deposited in The Australian

National University Library. This behaviour is subject to the provision of the Copyright

Act 1968.

YAN ZHANG Date


ACKNOWLEDGEMENT

This thesis could not be completed without the support from many people.

Foremost, I would like to express my sincere gratitude to my supervisors, Dr. Gilberto

Paz-Filho and Prof. Christopher Nolan for their great patience, enthusiasm, motivation,

and immense knowledge. I will always regard them as my role models and follow their

ways to develop my future career. I would like to thank my advisor Dr Peter Scott,

Associate Dean of CMBE Dr. Anna Cowan, and HDR Coordinator Ms. Wendy Riley for

their invaluable support and advise throughout my study period of time.

I am also grateful to all members from GDM multidisciplinary clinic in The

Canberra Hospital especially Lynelle Boisseau and Rosemary Young, who help me get

familiar with the GDM treatment system, all members in the endocrinology and research

unit, especially Viviane Delghingaro-Augusto who always give me precious advice as a

successful young researcher, van Dijk and Diana Wing from the Calvary hospital without

whom the data collection in the Calvary hospital could not be achieved, and Gloria

Spyropoulos from the Clinic Record Service in The Canberra Hospital who always reply

to me requirement very quickly even on her holidays.

Last but not the least, I would like to thank my parents: Wenhao Zhang and

Minhua Yan, for the endless supporting and enormous love through my whole life, and

my dear friends: Ran Li, Xinhua Zhong, Kaide Jin, Xili Huang, Tianqi Liu, Chuah Wooi who

company me just as family members when I am alone in a foreign country.


Abstract

Introduction: Gestational Diabetes Mellitus (GDM) patients are stratified into

low-risk and high-risk groups in Canberra, Australia, according to whether their

glycaemic control reaches the target levels with lifestyle measures only. High-risk

patients, in whom glycaemic control is unsatisfactory, are referred to a multidisciplinary

“diabetes in pregnancy” team, while low-risk patients continue regular antenatal care.

The aims of this study were to test the accuracy of the current stratification system of

GDM treatment in Canberra, and to access whether low-risk patients have satisfactory

perinatal outcomes compared to the high-risk patients, considering their less intensive

antenatal care.

Methods: A retrospective clinical audit of GDM patients treated between

01/01/2010 and 30/06/2014 was conducted. Maternal demographic data and

neonatal/maternal clinical outcomes data were analysed including, for key outcomes,

comparison with outcomes for the background population in the ACT.

Results: Low-risk (n=509) compared to high-risk (n=466) GDM mothers were

younger (31.7±4.8 vs 32.6±5.3 years-old, p=0.009), leaner [body mass index (BMI)

26.3±6.7 vs 29.3±7.5 kg/m2, p<0.001], and less parous (0.73±1.0 vs 0.98±1.2 times,

p<0.001), with less past GDM (13.2% vs 23.2%, p<0.001), less family history of diabetes

(55.4% vs 67.0%, p=0.001), and a lower fasting glucose level in the oral glucose tolerance

test (OGTT) (4.9±0.5 mmol/l vs 5.0±0.8 mmol/l, p<0.001). There were more South-East


Asian women in the low-risk group (19.4% vs 11.9%, p=0.002). Low-risk mothers had

lower rates of pregnancy-induced hypertension (PIH) (6.1% vs 11.8%, p=0.002; ACT

5.7%), induced labour (23.2% vs 50.6%, p<0.001) and elective Caesarean-section (CS)

(14.1% vs 20.4%, p=0.010). Rates of emergency CS were similar in the low- and high-risk

groups (16.7% vs 19.1%, p=0.328; ACT 14.9%). The rate of preterm delivery (delivery

before 37 weeks gestation) was higher in the low-risk group, (9.8% vs 6.0%, p=0.014;

ACT 8.3%), attributed to a higher rate of spontaneous preterm delivery (6.1% vs 2.6%,

p=0.010). After adjusting for maternal age, BMI, parity, smoking status and alcohol

consumption during pregnancy, premature delivery was still more likely in the low-risk

group (odds ratio 1.897, 95% Confidence Interval 1.137-3.164).

For neonatal outcomes, there were no differences in rates of babies with

birth weight >4000g (5.5% vs 7.1%, p=0.309; ACT 11.8%), shoulder dystocia (1.6% vs

1.5%, p=0.930), hypoglycaemia (6.1% vs 7.1%, p=0.532), respiratory disorder (6.3% vs

6.0%, p=0.857), and hyperbilirubinaemia (8.8% vs 10.7%, p=0.320). There was a trend

towards a lower rate of customized large for gestational age infants (cLGA) in the low-

risk group, compared to the high-risk group (6.1% vs. 9.4%, p= 0.050). The rate of

neonatal admission to the intensive care unit (NICU)/special care nursery (SCN) was

higher in the low-risk group (16.7% vs 10.9%, p=0.010; ACT 14.7%). However, this

difference might have been attributed to the different NICU/SCN admission criteria

adopted by the two evaluated hospitals.

Conclusion: The stratification system is efficient: low-risk compared to high

risk patients were younger, leaner, and had less past GDM, less family history of diabetes


and lower fasting glucose during the OGTT. Adverse pregnancy outcomes were either

less (PIH, delivery interventions, cLGA) or similar (emergency CS and some neonatal

complications) in the low compared to high risk group. One exception was a higher rate

of preterm delivery among low-risk women. Some adverse neonatal outcomes for low-

risk women were also higher than in the general ACT population. The treatment

pathway of low-risk GDM patients has considerable merit, but requires further

assessment and optimisation to ensure safety.


Abbreviations

ABE Acute bilirubin encephalopathy

ACHOIS Australian Carbohydrate Intolerance Study in Pregnant Women

ACOG American Congress of Obstetricians and Gynaecologists

ACT Australian Capital Territory

ACTH-DIPS ACT Health Diabetes in Pregnancy Service

ACTPAS ACT patients’ administration System

ADA American Diabetes Association

ADIPS Australasian diabetes in pregnancy society

AGA Appropriate for gestational age

BGL Blood glucose level

BOS Birthing Outcomes System

CHO Carbohydrate

CIS ACT Pathology Clinical Integration System

CIS Clinical Information System

CPAP Continuous positive airway pressure

CRIS Clinical Record Information System

CRP C-reactive protein


CS Caesarean section

CTG Cardiotocography

DIPS-MDC Multidisciplinary Diabetes in pregnancy Clinic

D-LGA Disproportionate LGA babies

ECFC Endothelial colony-forming cell

EMH Early onset neonatal hypocalcaemia

FHR Fetal heart rate

GAD65As Glutamic acid decarboxylase

GCT Glucose challenge test

GDM Gestational diabetes mellitus

GH Gestational hypertension

GI Glycaemic Index

GLUT4 Glucose transporter type 4

GPs General practitioners

HAPO Hyperglycaemia and Adverse Pregnancy Outcomes

HBGI High Blood Glucose Index

hct Haematocrit

HIE Hypoxic ischemic encephalopathy


HLA-G Human leukocyte antigen-G

IA-2As/IA-2Bs Insulinoma-associated antigens

IAAs Insulin autoantibodies

IADPSG International Association of the

Diabetes and Pregnancy Study Groups

ICAs Islet cell autoantibodies

IL-1 Interleukin 1

IL-6 Interleukin 6

IRS-1 Insulin receptor substrate 1

IUGR Intrauterine growth restriction

LADA Latent autoimmune diabetes of adulthood

LGA Large for gestational age

LNH Late onset neonatal hypocalcaemia

MAS Meconium aspiration syndrome

MiG Metformin in Gestational Diabetes

MNT Medical nutrition therapy

MODY Maturity-onset diabetes of the young

MOH Ministry of Health of the People's Republic of China


MRNs Medical Record Numbers

NDDG National Diabetes Data Group

NF-kB Nuclear factor kappa-light-chain-enhancer of activated B cells

NICE National Institute for Health and Care Excellence

NICU Neonatal intensive care unit

NIH National Institutes of Health

NPH Neutral Protamine Hagedorn

NST Fetal Non-stress Test

OCT Contraction stress test

OGTT Oral glucose tolerance test

OPG Osteoprotegerin

OVD Operative vaginal delivery

PCOS Polycystic ovary syndrome

PE Preeclampsia

PI Phosphatidylinositol

PI Ponderal index

PIH Pregnancy-induced hypertension

P-LGA Proportionate LGA babies


PPAR Peroxisome proliferator-activated receptor

PROM Prelabour rupture of membranes

PTH Parathyroid hormone

RANZCOG The Royal Australian and New Zealand College of

Obstetricians and Gynaecologists

RCT Randomised controlled trials

RD Respiratory distress

RDS Respiratory distress syndrome

Sca Serum calcium

SCN Special Care Nurseries

SGA Small for gestational age

sPTD Spontaneous preterm birth

TOFU Offspring Follow-Up

TTN Transient tachypnoea of the newborn

WHO World Health Organization


Table of Contents

CHAPTER ONE LITERATURE REVIEW OF GESTATIONAL DIABETES

MELLITUS (GDM) AND RESEARCH HYPOTHESIS ..................................... 18

1.1 History of GDM ................................................................................. 18

1.2 GDM pathology ................................................................................. 20

1.2.1. Auto-immune GDM.......................................................................... 20

1.2.2. Monogenetic diabetes ..................................................................... 21

1.2.3 Other pathological pathways ............................................................ 22

1.3 Diagnostic criteria of GDM ................................................................ 24

1.3.1 Historic diagnostic criteria of GDM................................................... 24

1.3.2 Hyperglycaemia and Adverse Pregnancy Outcomes (HAPO) study,

and new diagnostic criteria for GDM .......................................................................... 25

1.3.3 Diagnostic criteria for GDM in Australia ........................................... 32

1.4 Short term complications of GDM ..................................................... 33

1.4.1 Neonatal short term complications .................................................. 34

1.4.2 Maternal short term complications .................................................. 55

1.5 Long term complications of GDM ...................................................... 68

1.5.1 Neonatal long term complications ................................................... 69


1.5.2 Maternal long term complications ................................................... 73

1.6 GDM treatment ................................................................................ 77

1.6.1 Rationale for treatment .................................................................... 77

1.6.2 Self- glucose monitoring ................................................................... 78

1.6.3 Target blood glucose levels .............................................................. 80

1.6.4 Medical nutrition therapy (MNT) ..................................................... 81

1.6.5 Exercise (physical activity) ................................................................ 85

1.6.6 Insulin treatment .............................................................................. 87

1.6.7 Oral hypoglycaemic agents ............................................................... 90

1.6.8 Obstetric issues regarding GDM treatment ...................................... 93

1.7 Rationale for this research ................................................................ 95

1.7.1 High-risk group patients may have worse perinatal outcomes ........ 96

1.7.2 Low-risk group patients may not necessarily have better outcomes

..................................................................................................................................... 98

1.8 Research hypothesis and aims ......................................................... 101

1.8.1 Research hypothesis ....................................................................... 101

1.8.2 Research aims ................................................................................. 101

CHAPTER TWO RESEARCH SETTING AND METHODOLOGY .................. 103

2.1 General information about the Australian Capital Territory (ACT) area

............................................................................................................................. 103


2.2 Pregnancy related health care in Canberra ...................................... 104

2.3 Diagnosis of GDM in the ACT ........................................................... 104

2.4 Stratified care system for GDM in the ACT ....................................... 106

2.4.1 GDM education session (part one) ................................................. 106

2.4.2 GDM education session (part two) ................................................. 107

2.4.3 Patient review and stratification into low and high-risk pathways 108

2.5 Methodology .................................................................................. 115

2.5.1 Ethics approval ................................................................................ 115

2.5.2 Subjects ........................................................................................... 115

2.5.3 Data Collection ................................................................................ 117

2.5.4 Data management .......................................................................... 128

2.5.5 Statistical methods ......................................................................... 131

CHAPTER THREE RESULTS ................................................................... 133

3.1 General information ........................................................................ 133

3.2 Maternal demographic information ................................................ 135

3.2.1 Maternal age, BMI, gestational age at first appointment, gestational

age at GDM diagnosis, number of gestations and parity ......................................... 135

3.2.2 History of GDM, family history of diabetes .................................... 138

3.2.3 Smoking status and alcohol consumption ...................................... 140

3.2.4 Maternal ethnicity .......................................................................... 142


3.2.5 Maternal pre-existing complications .............................................. 144

3.2.6 GCT and OGTT results ..................................................................... 145

3.2.7 Summary of maternal information: ................................................ 147

3.3 Maternal outcomes ......................................................................... 148

3.3.1 Gestational hypertension (GH) and preeclampsia (PE) .................. 148

3.3.2 Gestational age, preterm birth and post-term birth ...................... 149

3.3.3 Onset of birth, mode of delivery and methods of birth ................. 151

3.3.4 Reasons for elective CS and emergency CS .................................... 154

3.3.5 Perineal status and suture .............................................................. 158

3.3.6 Delivery complications .................................................................... 161

3.3.7 Hospitalization and after delivery bleeding .................................... 162

3.3.8 Summary of maternal outcomes .................................................... 163

3.4 Neonatal Outcomes ........................................................................ 166

3.4.1 Birth status ...................................................................................... 166

3.4.2 Apgar Score ..................................................................................... 167

3.4.3 Birth weight..................................................................................... 169

3.4.4 Neonatal complications .................................................................. 173

3.4.5 Summary of the neonatal outcomes .............................................. 178

3.5 NICU admission information in The Canberra Hospital ..................... 180

3.5.1 General Information ....................................................................... 180

3.5.2 Neonatal Complications .................................................................. 181

3.5.3 Summary of NICU admission information ...................................... 189


3.6 Subgroup analysis of high-risk group patients who continued diet

treatment (HRD) ................................................................................................... 191

3.6.1 Maternal demographic information ............................................... 191

3.6.2 Results of HbA1c, TSH and Vitamin D ............................................. 197

3.6.3 Maternal outcomes of the three groups ........................................ 199

3.6.4 Neonatal outcomes of the three groups ........................................ 202

3.6.5 Summary of subgroup analysis of HRD group ................................ 205

CHAPTER FOUR DISCUSSION ............................................................... 207

4.1 DISCUSSION FOR AIM ONE ..................................................... 208

4.1.1 Baseline demographic information ................................................ 209

4.1.2 Maternal pre-existing conditions.................................................... 213

4.1.3 Conclusion for aim one ................................................................... 216

4.2 Discussion for aim two: ................................................................... 217

4.2.1 Maternal outcomes ........................................................................ 218

4.2.2 Neonatal outcomes ......................................................................... 225

4.2.4 Analysis of patients in the high-risk group who continued diet

treatment alone (HRD) .............................................................................................. 234

4.2.5 Conclusion for aim two ................................................................... 237

4. 3 Advantages and limitations ............................................................ 238

4.3.1 Advantages ..................................................................................... 238


4.3.2 Limitations and future studies ........................................................ 239

4.3.3 Suggestions for the improvement of the current treatment system

................................................................................................................................... 240

4.4 Final Conclusion: ............................................................................ 241

APPENDIX ........................................................................................... 243

Appendix 1 Ethical approval letter from The Canberra Hospital ............. 244

Appendix 2 Ethical approval letter from the Calvary Hospital ................ 246

Appendix 3 Indicators for Initiating Phototherapy ................................. 248

Appendix 4 The number of patients who presented as percentage in the

results (part one) .................................................................................................. 249

Appendix 5 The number of patients who presented as percentage in the

results (part two) .................................................................................................. 256

Appendix 6 List of flowchart, tables and charts ..................................... 258

REFERENCE ......................................................................................... 262


CHAPTER ONE LITERATURE REVIEW OF GESTATIONAL DIABETES

MELLITUS (GDM) AND RESEARCH HYPOTHESIS

1.1 History of GDM

Reports of Diabetes Mellitus, a disease originally described as “too great

emptying of urine”, were initially found in Egyptian manuscripts dated 1500 B.C.(1).

Aretaeus the Cappadocian coined the word ‘diabetes’ after the Greek word for siphon,

and described it as a condition in which “fluids do not remain in the body, but use the

body only as a channel through which they may flow out”, in the first century A.D (2). In

1769, William Cullen, a British clinician, added the adjective mellitus (Latin, ‘sweet like

honey’) to distinguish this variant of diabetes from others such as diabetes insipidus in

which the urine was tasteless (3). The modern understanding of diabetes mellitus is that

it is a group of metabolic diseases characterized by hyperglycaemia resulting from

defects in insulin secretion, insulin action, or both (4).

For more than a hundred years, doctors observed that women who developed

diabetes before pregnancy were more likely to have severe adverse effects on fetal and

neonatal outcomes (5). In 1824, Bennewitz first described diabetes in pregnancy in

Germany; in his case, the patient presented intensive thirst, recurrent glycosuria and

had a baby that weighed almost 5.5 kg (6).

However, during 1940-1950, it was recognized that patients who developed

diabetes years after pregnancy also suffered from higher rates of fetal and neonatal

mortality (7). The term gestational diabetes mellitus (GDM), coined by Elsie Reed in 1957


(8), became accepted at that time and was defined as the development of abnormalities

in carbohydrate metabolism in pregnancy (9-11). Dr O’Sullivan pioneered the diagnosis

of gestational diabetes by establishing a statistical upper limit for glycaemic normality in

pregnancy, through 100-g oral glucose tolerance testing (OGTT) in the 1960s (12). In

1979, the National Diabetes Data Group (NDDG) defined GDM as “glucose intolerance

that has its onset or recognition during pregnancy, regardless of the severity of the

disease or whether the condition persists after pregnancy.” “Women with diabetes who

become pregnant are not included (13). This definition was endorsed by the

International Workshop – Conference on Gestational Diabetes and became popularly

used all over the world (14-17). However, there are currently concerns that this

popularly used definition includes too wide a range of glucose abnormalities, especially

for those with undetected pre-gestational diabetes.

To address this issue, the International Association of Diabetes and Pregnancy

Study Groups (IADPSG) proposed a new definition of GDM, which is “the condition

associated with degrees of maternal hyperglycaemia less severe than those found in

overt diabetes but associated with an increased risk of adverse pregnancy outcomes”,

and divided patients who had hyperglycaemia first detected during pregnancy into two

subgroups, that is, overt diabetes and GDM (18). This new definition was endorsed in

the new World Health Organisation (WHO) and Endocrine Society guidelines (19, 20),

but was refused by the Australasian Diabetes in Pregnancy society (ADIPS). In the latest

ADIPS guidelines, authors insisted that a definitive diagnosis of non-gestational diabetes

cannot be made until the postpartum period (21).


1.2 GDM pathology

Pregnancy is characterized as a diabetogenic condition. To meet the energy

demands of the fetus and to prepare for delivery and lactation, the maternal metabolism

changes from carbohydrates to lipids (22). Insulin resistance, which starts from the

second trimester and goes on throughout the whole pregnancy, may contribute to this

change. The increased placental secretion of hormones, such as progesterone, cortisol,

placental lactogen, prolactin, and growth hormone, combined with increased maternal

adiposity, are responsible for the raised insulin resistance. To counteract the decrease

of insulin sensitivity, insulin secretion increases consistently from the first trimester and

peaks at the third trimester (23-25). It has been shown that insulin secretion increases

by 200 – 250%, to balance a 50% decrease in insulin-moderated glucose disposal during

pregnancy, to maintain normal glucose levels (26).

Gestational diabetes mellitus happens when impaired β-cell cannot produce

enough insulin to compensate for the increased insulin resistance of pregnant patients.

There are different pathways leading to GDM, including autoimmune and monogenetic

abnormalities in β-cell functions and most commonly, the same pathway as type 2

diabetes.

1.2.1. Auto-immune GDM:

Auto-immune GDM accounts for a small population of GDM patients (less

than 10%), and is correlated with the risk of type 1 diabetes in different ethnicities (27).

Islet antibodies include autoantibodies to islet cell cytoplasm (islet cell autoantibodies

[ICAs]), to native insulin (insulin autoantibodies [IAAs]), to glutamic acid decarboxylase


(GAD65As) and to tyrosine phosphatases (insulinoma-associated antigens [IA-2As and

IA-2Bs]) (28). The frequency of GAD65A and IA2 positivity is higher than other

autoantibodies in GDM patients but the antibody titres are lower compared to the type

1 diabetes patients (28).

GDM patients with autoantibodies are younger, leaner, have lower

prevalence of diabetes in first-degree relatives, have lower fasting plasma insulin and

experience lower weight gain during pregnancy, but need insulin treatment more

frequently compared to the autoantibody negative GDM patients (29). Auto-immune

GDM patients have higher risk of developing type 1 diabetes in the future (30) and share

some similar characteristics with latent autoimmune diabetes of adulthood (LADA)

patients (31).

1.2.2. Monogenetic diabetes:

Monogenetic diabetes is caused by autosomal dominant mutations, with an

early onset but a mild and relatively uncomplicated course (32). Previous monogenic

diabetes was termed “maturity-onset diabetes of the young (MODY)”, but currently, the

term “monogenetic diabetes” is found to be more suitable (33). The more common

mutations include: mutations in the glucokinase gene (MODY2), which presents as mild

fasting hyperglycaemia(34) and its prevalence ranges from 0% to 12% among all GDM

patients (35-37); mutations in the transcription factor HNF-1α (MODY3), which causes

slow progressive deterioration of insulin secretion (34), and accounts for up to 1% of

GDM (38, 39); mutations in the transcription factor HNF-4α (MODY 1), which may cause


macrosomia and neonatal hypoglycaemia (34), and has a rate of 1% in GDM patients (40).

There are other rare mutations, for example, mutations in the PDX1 gene (38) and

mutations in the mitochondrial genome that combine with neurosensory hearing loss

(41).

1.2.3 Other pathological pathways

However, most GDM cases share the similar pathological pathway as type 2

diabetes, due to β-cell secretion deficiency on the background of chronic insulin

resistance (42).

The cellular mechanisms for chronic insulin resistance are well described in

the literature. In skeletal muscle, decreased levels of phosphatidylinositol (PI) 3-kinase

activity is caused by increasing expression of the p85α subunit of PI 3-kinase, the

negative competitor to forming a PI 3-kinase heterodimer with the p110 subunit (43, 44).

In addition, lower levels of insulin receptor substrate 1 (IRS-1) tyrosine phosphorylation

and decreased concentrations of IRS-1 protein (45, 46) contribute to the reduced

translocation of glucose transporter type 4 (GLUT4) to the plasma membrane, and result

in decreased insulin-stimulated glucose uptake (47). In adipose tissue, insulin resistance

is induced by reduction of the transcription factor peroxisome proliferator-activated

receptor (PPAR)-γ1 gene and protein (48). The exact cause of chronic insulin resistance

is still unknown. Potential culprits could be obesity, inflammation in adipose tissue,

plasma and placenta, and hyperlipidaemia with increased levels of leptin, tumour

necrosis factor-alpha (TNF-α), interleukin 6(IL-6), interleukin 1 (IL-1), C-reactive protein


(CRP), and decreased levels of adiponectin (49-52).

Not all patients who have insulin resistance develop hyperglycaemia, due to

compensation via increased insulin secretion. Therefore, β-cell dysfunction plays a

critical role in the pathology of GDM. Insulin response to oral and intravenous glucose

are lower in GDM patients compared to normal pregnant women, even after

adjustments for insulin resistance (53). A mismatch between β-cell function and mass

(54), as well as failure of the cell to respond adequately to secretagogue stimulation,

could be a possible mechanisms of β-cell dysfunction (55). Genetic defects may

predispose patients to β-cell dysfunction.

Multiple genes interact with environment factors, which can lead to GDM.

Environmental factors include increased excessive energy intake and decreased physical

activity (56). Genes related to the development of GDM are mostly associated with the

regulation of insulin secretion, and include TCF7L2, GCK, KCNJ11, CDKAL1, IGF2BP2,

MTNR1B, Gly972Arg, and IRS1 (57).

Other than the well-recognized causes of GDM in the above discussion, other

hypotheses have also been suggested. For example, the development of GDM could be

triggered by an antigenic load that is the fetus itself, which is analogous to the

development of type 2 diabetes in some patients submitted to organ transplantation.

Human leukocyte antigen-G (HLA-G) expression that responds to the load of fetal

antigens and the complex interactions between this response and the increased nuclear

factor kappa-light-chain-enhancer of activated B cells (NF-kB) activity that cause

increased insulin resistance might also explain the development of GDM (58).


1.3 Diagnostic criteria of GDM

1.3.1 Historic diagnostic criteria of GDM

The diagnosis of GDM is a very complicated issue that has been intensely

debated for almost 40 years but still has not achieved worldwide consensus. The first

diagnostic criteria were made available in 1964. These criteria were based on identifying

women at high-risk for development of diabetes after pregnancy (59). GDM was

diagnosed by using a 100g OGTT test (12), and this method is still used today with some

modifications. NDDG started to test plasma or serum instead of whole venous blood.

This test method also changed from Somogyi-Nelson technology to the new approach

using glucose oxidase and hexokinase, which is more specific to glucose (60). Another

common diagnostic strategy, which has almost the same cut-off values of diabetes

mellitus outside pregnancy, was used by the World Health Organization (WHO). The

WHO diagnostic criteria were initially defined in 1980 as a fasting plasma glucose level

≥8 mmol/l or/and a 2-h glucose level (after 75g of glucose load) ≥11 mmol/l in the OGTT.

However, after lowering the threshold in 1985 and 1999, the criteria changed to a fasting

glucose of ≥ 6.1 mmol/l or/and a 2-h OGTT glucose of ≥ 7.8 mmol/l, which are widely

used in developing countries. The WHO criteria were based on the increased risk for

diabetic patients of developing microvascular complications, especially retinopathy, in

the future (61).


1.3.2 Hyperglycaemia and Adverse Pregnancy Outcomes (HAPO) study, and

new diagnostic criteria for GDM

None of the previous diagnostic criteria for GDM were based on the

prevalence of perinatal adverse outcomes. The Hyperglycaemia and Adverse Pregnancy

Outcomes (HAPO) is considered a landmark research in this area, and included 23,316

patients from different ethnic backgrounds. It showed that higher maternal glucose

levels during an OGTT are associated with an increased risk of complications for both

mothers and babies. The HAPO study found a strong and continuous association

between maternal glucose concentrations, even in mild hyperglycaemic levels, with

adverse birth outcomes, including birth weight > 90th percentile, primary caesarean

section, clinical neonatal hypoglycaemia, cord-blood serum C peptide > 90th percentile,

premature delivery, shoulder dystocia, intensive neonatal care admission, neonatal

hyperbilirubinaemia, and maternal preeclampsia (62) (Chart 1).


Chart 1 –Maternal glucose concentration associated with adverse perinatal

outcomes.

Fasting: category 1 = <4.2 mmol/l (75mg/dl), 2 = 4.3- 4.4 mmol/l (75-79 mg/dl), 3 = 4.5- 4.7 mmol/l (80-

84 mg/dl), 4 = 4.8-4.9 mmol/l (85-89 mg/dl), 5 = 5.0-5.2 mmol/l (90-94 mg/dl), 6 = 5.3- 5.5 mmol/l (95-99 mg/dl), 7 =

≥5.6 mmol/l (100 mg/dl).

One-hour oral glucose tolerance test (OGTT): category 1 = ≤5.8 mmol/l (105 mg/dl), 2 = 5.9- 7.3 mmol/l

(106-132 mg/dl), 3 = 7.4- 8.6 mmol/l (133-155 mg/dl), 4 = 8.7- 9.5 mmol/l (156-171 mg/dl), 5 = 9.6- 10.7 mmol/l (172-

193 mg/dl), 6 = 10.8- 11.7 mmol/l (194-211 mg/dl), 7 = ≥ 11.8 mmol/l (212 mg/dl).

Two-hour OGTT: category 1 = ≤ 5.0 mmol/l (90 mg/dl), 2 = 5.1- 6.0 mmol/l (91-108 mg/dl), 3 = 6.1-6.9

mmol/l (109-125 mg/dl), 4 = 7.0- 7.7 mmol/l (126-139 mg/dl), 5 = 7.8- 8.7 mmol/l (140-157 mg/dl), 6 = 8.8- 9.8 mmol/l

(158-177 mg/dl), 7 = ≥ 9.9 mmol/l (178 mg/dl).

Adopted from Coustan DR. The HAPO study: paving the way. Am J Obstet Gynecol 2010.


Based on the results of the HAPO study, the International Association of the

Diabetes and Pregnancy Study Groups (IADPSG) recommended new criteria for GDM

diagnosis, with compared to the previous ADIPS diagnostic criteria, a one-step 75-g

OGTT with a more stringent fasting cut-off value, the addition of a 1h cut-off value, and

a less stringent 2h cut-off value at 24–28 weeks of gestation in women not previously

diagnosed with overt diabetes. Moreover, only one abnormal value is sufficient for the

GDM diagnosis. There are two main issues addressed by the new criteria. Firstly, IADPSG

advised to screen for hyperglycaemia at the first prenatal clinic visit, in order to detect

overt diabetes as soon as possible. Secondly, the cut-off value for the diagnostic OGTT

test was based on reflecting a 75% (odds ratio 1.75) increased risk in three primary

adverse outcomes of GDM, including birth weight >90th percentile, baby’s C-

peptide >90th percentile and baby fat>90th percentile (63) (Flowchart 1).


Flowchart 1 –IADPSG diagnostic procedures of GDM.

GDM, gestational diabetes; IADPSG, International Association of Diabetes in Pregnancy Study Groups.

FPG, fasting plasma glucose; RPG, random plasma glucose. HbA1c, Hemoglobin A1c

OGTT, oral glucose tolerance test. Diagnosis of GDM if 1value ≥ threshold

There is hot debate on whether to implement these new criteria. The

advantages of the new criteria are quite obvious. Firstly, the IADPSG guideline is the only

one among all other guidelines in that it is based on pregnancy outcomes, and its

diagnostic thresholds are specifically selected by using the pathophysiologic condition

of hyperglycaemia in pregnancy (64). Secondly, it finally brings uniformity to GDM

diagnosis and makes the comparisons of outcomes between different studies possible.

Thirdly, the new guideline, as a one-step procedure, may be easier to implement by


health care providers. Finally, the data in the HAPO trial was collected from more than

23,000 patients with different ethnicities, age, and BMI. Having adjusted for these

potential confounders, the new guidelines are most likely to be suitable for the

worldwide GDM population (65).

There are also many concerns regarding the IADPSG recommendations. There

is a lack of evidence that patients diagnosed via the new GDM criteria would receive

significant clinical benefit, in both maternal and neonatal outcomes (66). Compared to

the ADA guidelines, using the IADPSG criteria will increase the number of GDM patients

by 3 times in the United Arab Emirates, which will increase the strain on health systems

(67). Similarly, the rate of GDM increased from 7.89% to 19.9%, almost twofold after

using the IADPSG diagnostic criteria instead of the ADA criteria in the Chinese population

(68). In Canada, there was also an increase in rates of GDM, from 7.9% to 9.4%, when

using the IADPSG criteria (69). There is a limited number of studies focusing on the cost-

effectiveness of the new screening consensus, and some have reported contradictory

results (70, 71). Some organizations, for example the WHO (72), Ministry of Health of

the People’s Republic of China (MOH) (73), and the Endocrine Society have decided to

follow the IADPSG recommendations while other institutions, such as the American

College of Obstetricians and Gynaecologists (ACOG) and the National Institutes of Health

(NIH), have refused to accept the new criteria (74, 75). Interestingly, the American

Diabetes Association (ADA) initially endorsed the IADPSG criteria in 2011, but in their

latest published guidelines (2014), it stated that there is no uniform approach for GDM


diagnosis, and both the one-step strategy (IADPSG criteria) and two-step strategy (ACOG

criteria) could be used (76, 77) (Table 1).

Table 1 –Recommendations for screening procedures and diagnostic criteria for GDM.

Organization Screen

population

Type of

test

Glucose

load

Cut-off

points

Number

of criteria

required

WHO 1999

Not

mentioned

One-

step

75g Fasting ≥ 6.1 mmol/l

2h ≥ 7.8 mmol/l

≥1

WHO 2013 Not

mentioned

One-

step

75g Fasting 5.1-6.9 mmol/l

1h ≥10.0 mmol/l

2h 8.5-11.0 mmol/l

≥1

ACOG 2013 Universal Two-

step

50g GCT

100g

OGTT

GCT ≥ 7.8 mmol/l or

7.5 mmol/l*

fasting ≥ 5.3 mmol/l

1h ≥ 10.0 mmol/l

2h ≥ 8.6 mmol/l

3h ≥ 7.6 mmol/l

≥ 2


ADA 2014 Universal Two- or

one-

step

50g GCT

100g

OGTT

Or 75g

OGTT

GCT ≥ 7.8 mmol/l or

7.2 mmol/l*

100g OGTT

fasting ≥ 5.3 mmol/l

1h ≥ 10.0 mmol/l

2h ≥ 8.6 mmol/l

3h ≥ 7.8 mmol/l

75g OGTT

Fasting ≥ 5.1 mmol/l

1h ≥ 10.0 mmol/l

2h ≥ 8.5 mmol/l

≥ 2

≥ 1

Endocrine

Society 2013

Universal One-

step


1h ≥ 10.0 mmol/l

2h ≥ 8.5 mmol/l

≥ 1

NICE 2015 Selective One-

step


2h ≥ 7.8 mmol/l

≥ 1

WHO, World Health Organization; ACOG, American College of Obstetrician and Gynaecologists; ADA,

American Diabetes Association; NICE, National Institute for Health and Care Excellence; GCT, Glucose challenge test;


OGTT, Oral glucose tolerance test; * ACOG recommended a lower cut-off point of GCT in high ethnic minorities with

higher prevalence of GDM.

1.3.3 Diagnostic criteria for GDM in Australia

In Australia, the first diagnostic criteria were developed in 1991 (78). They are

used nationwide with some modifications, even today. If a patient was suspected to be

at high risk for GDM, an OGTT test could be performed at any stage of pregnancy. If the

OGTT results were normal in early pregnancy, these patients would take another

diagnostic OGTT test between 26 to 30 weeks of gestational age. Besides high-risk

patients, all pregnant women were given a non-fasting glucose challenge test (GCT)

between 26 to 28 weeks of gestational age. If the results of the GCT were positive (50g

glucose load 1h>7.8mmol/l or 75g glucose load 1h> 8.0mmol/l), patients would have a

diagnostic 75g OGTT test before 30 weeks of gestational age. Patients would be

diagnosed with GDM if their fasting glucose level was higher than or equal to 5.5 mmol/l

or/and 2 hour glucose level higher than or equal to 8.0 mmol/l (79).

The Australasian Diabetes in Pregnancy Society (ADIPS) recently endorsed the

IADPSG’s cut-off points for the diagnostic OGTT test. Additionally, the new guidelines

recommended a 75g OGTT at first opportunity after conception in high-risk patients to

detect undiagnosed overt diabetes (80). The research in this thesis included patients

who were diagnosed with GDM from 2010 to 2014 when the previous ADIPS screening

guidelines were still used (Table 2).


Table 2 –Screening and diagnosis of GDM in this research.

Indication Timing for

testing

Population Glucose load Cut-off points

Clinical

suspicion of

GDM

Any stage Selective 75g Fasting ≥ 5.5 mmol/l or

2h ≥ 8.0 mmol/l

Screening

(GCT)

26-28 weeks Universal 50g

75g

1h ≥ 7.8 mmol/l (50g)

1h ≥ 8.0 mmol/l (75g)

Confirmative

test

(OGTT)

26-30 weeks Women

who had

positive

GCT results


2h ≥ 8.0 mmol/l

GCT, Glucose challenge test; OGTT, Oral glucose tolerance test

1.4 Short term complications of GDM

It is known that pre-existing diabetes is associated with an array of maternal

and neonatal adverse outcomes including gestational hypertension, preterm delivery,

stillbirth, malformation, macrosomia, neonatal hypoglycaemia, neonatal

hyperbilirubinaemia, neonatal respiratory distress, neonatal polycythaemia, neonatal

hypocalcaemia and neonatal cardiomyopathy during pregnancy (81, 82). GDM is defined

as any degree of glucose intolerance with onset of first recognition during pregnancy,

including a wide spectrum of diseases, from undetected type 1 and type 2 diabetes to


mild glucose intolerance that disappears after birth. GDM patients might suffer from the

same adverse perinatal outcomes as patients with pre-existing diabetes, but the

incidence and the severity of these complications are significantly lower among GDM

patients.

As previously described, the researchers of the HAPO study demonstrated

that maternal glucose intolerance less severe than overt diabetes was associated with

increased risk of neonatal birth weight above 90th percentile and cord-blood serum C-

peptide level above the 90th percentile, primary caesarean delivery (CS), clinical

neonatal hypoglycaemia, premature delivery, shoulder dystocia or birth injury,

hyperbilirubinaemia, preeclampsia and neonatal intensive care unit (NICU) admission

(83).

These adverse perinatal complications of GDM can be divided into short term

neonatal complications, short-term maternal complications, long-term neonatal

complications, and long term maternal complications.

1.4.1 Neonatal short term complications

1.4.1.1 Stillbirth and malformation

1) Stillbirth

The HAPO study did not find any association between the risk of stillbirth and

maternal glucose intolerance less severe than overt diabetes (83). Fadl and collaborators

also demonstrated similar results in a large cohort study that included 1,260,297


patients (10,525 had GDM) and aimed to analyse the maternal and neonatal outcomes

among GDM patients. They found there was no difference in terms of the risk of stillbirth

between GDM patients and non-GDM women (Adjusted OR 0.85, 95% CI 0.59-1.23, p=

0.284) (84). However, the presence of fasting hyperglycaemia (>5.8 mmol/l) might have

been associated with increased risk of stillbirth during the last 4-8 weeks of gestation,

which might have been attributed to undetected overt diabetes among GDM patients

(85).

2) Malformation

Pre-existing diabetes increases the risk of neonatal malformations due to

poor maternal glycaemic control in the periconceptual period. Janssen and colleagues

conducted a population based retrospective study involving 1511 patients with pre-

existing diabetes and 8869 patients with GDM to examine the relationship between

diabetes in pregnancy and the development of congenital malformations in the United

States. The authors indicated that there was a slightly higher prevalence of

malformation among babies of GDM mothers (OR 1.3, 95%CI 1.0-1.6), however the risk

was significantly higher among infants of mothers with overt diabetes (OR 4.0, 95% CI

3.1-5.1) (86). Fadl and colleagues in Sweden also found a mild increase in the risk of

neonatal malformation among GDM patients compared to non-GDM sufferers (Adjusted

OR 1.19, 95% CI 1.02-1.39) (84).

Researchers demonstrated that there was an increased risk of a particular

type of malformation among babies of GDM mothers, as well as babies of patients with

pre-existing diabetes; these malformations include cardiac malformation, oesophageal


atresia and spinal anomalies (87). Interestingly, when analysing the relationship

between major malformation and maternal blood glucose concentration at entry into

prenatal care, the authors found that the rate of major malformation was 2.1% when

the initial fasting plasma glucose (FPG) was < 6.6 mmol/l; 5.9% when FPG was between

6.6-11.0 mmol/l, and 12.9% when FPG exceeded 11 mmol/l (p< 0.0001) (88). Based on

the available information, researchers concluded in a large literature review that the risk

of malformation was slightly higher in pregnancies of women with GDM compared to

the general population. However, this increased risk was probably associated with the

presence of undiagnosed pre-existing diabetes among women with GDM (89).

1.4.1.2 Fetal overgrowth

Macrosomia, a common term for fetal overgrowth, is defined as birth weight

higher than 4000g (National Institute for Health and Care Excellence) or 4500g

(American College of Obstetrician and Gynaecologists). Larger for gestational age (LGA)

is another term to describe fetal overgrowth, which corresponds to a birth weight ≥ 90th

percentile for gestational age. This criterion can also identify excessive fetal growth in

premature infants. Recently, more advanced percentile calculations have been made

available for populations in specific countries, to enable adjustments for maternal height,

maternal weight, parity, maternal ethnicity, gestational age and gender, and to provide

more precise results for LGA (the customized LGA) rate (90, 91). The reasons for

macrosomia could be divided into two categories: non-modifiable factors and modifiable

factors. Non-modifiable factors include genes, epigenetic regulation, maternal age,


maternal height and fetal gender, while modifiable determinants include pre-gestational

maternal BMI, gestational weight gain, maternal nutritional status, level of physical

activity, smoking and metabolic parameters (92).

GDM has been known to be associated with an increased risk of fetal

overgrowth for some time. According to the Pedersen Hypothesis, fetal hyperglycaemia

caused by maternal hyperglycaemia, could induce hypertrophy of fetal β islet tissue and

cause hyper-secretion of insulin. This extra insulin acts as a growth hormone and

contributes to fetal overgrowth (93). In the HAPO study, the researchers demonstrated

a strong continuous relationship between maternal hyperglycaemia, even below

diagnostic glucose level of GDM, with increased rate of birth weight > 90th percentile and

neonatal cord-blood serum C peptide > 90th percentile. This result supports the Pedersen

hypothesis (62). Recently, maternal dyslipidaemia, another common metabolic disorder

found in GDM, is suspected to be related to fetal overgrowth as well (94).

Fetal overgrowth has both short-term and long-term consequences. The

babies’ short-term complications include birth trauma (shoulder dystocia, plexus injuries,

and bone fractures), metabolic disorders (hypoglycaemia, hyperbilirubinaemia), fetal

hypoxia and NICU admission (95-97). For those mothers who gave birth to overgrown

babies, the adverse outcomes include prolonged labour, operative deliveries, perineal

lacerations, uterine atonia, abnormal haemorrhage and a necessity for caesarean

section deliveries (98, 99). Long-term complications for overgrown babies include

increased susceptibility to diabetes, overweight, metabolic syndrome, asthma,

persistent plexus injuries and cancer (especially breast cancer and childhood leukaemia)


(100, 101).

Interestingly, infants of diabetic mothers are more likely to have

disproportionate overgrowth that refers to excessive weight characterized by a high

weight/length ratio. Anthropometric and skinfold measurements in newborns of

mothers with gestational diabetes suggested a disproportionate pattern of growth in

fetuses of diabetic mothers with increased tendency for deposition of subcutaneous fat

(102). Compared to non-diabetic mothers, macrosomic babies born to diabetic mothers

have a significantly higher rate of shoulder dystocia and Caesarean deliveries (103, 104).

Previous studies also showed that disproportionate macrosomic babies have a higher

rate of neonatal hypoglycaemia, hyperbilirubinaemia and acidosis compared to

proportionate macrosomic babies and babies without macrosomia (105).

1.4.1.3 Shoulder Dystocia

Shoulder dystocia is objectively defined as “a prolonged head to body delivery

time [i.e., more than 60 seconds], and/or the necessitated use of ancillary obstetric

manoeuvres.” (106), and can be further categorized as mild and severe, according to the

kind of manoeuvre applied (107). It results from a size discrepancy between the fetal

shoulders and the pelvic outlet. The classic maternal risk factors include obesity,

diabetes, excessive weight gain, high parity and a prior birth complicated by shoulder

dystocia (108-110). Labour risk factors include prolonged first or second stages of labour

and instrumental delivery (111-113). Vacuum extractions increase the risk of shoulder

dystocia more than forceps deliveries (114). The risk of shoulder dystocia also increases


along with the increment of birth weight: risk is 5.2% for infants between 4000 to 4250g,

9.1% for those between 4250 to 4500g, 14.3% for those between 4500 to 4750g and

21.1% for those between 4750 to 5000g (115). Mansor and collaborators demonstrated

a similar positive relationship between infants’ birth weight and the rate of shoulder

dystocia (116).

Landon and associates found that the shoulder dystocia rate was higher

among women with mild gestational diabetes compared to women with a normal GCT

(approximately 4% vs. less than 1%) (117). Large observational studies also indicated

that the incidence of shoulder dystocia appeared to be doubled across all birth weight

categories, for babies born to diabetic mothers, both with overt diabetes and GDM.

These rates are 12.2% for infants 4000-4250g, 16.7% for those 4250-4500g, 27.3% for

those 4500-4750g, and 34.8% for those 4750-5000g, whereas the incidence rate of

shoulder dystocia for non-diabetic mothers is 5.2% for infants 4000-4250g, 9.1% for

those 4250-4500g, 14.3% for those 4500 to 4750, and 21.1% for those 4750-5000g (115).

This increased rate could be attributed to the higher rate of disproportionate

overgrowth among babies of GDM patients. Macrosomic infants born to diabetic

mothers have large shoulders and extremity circumferences, decreased head-to-

shoulder ratio, higher body fat and thicker upper-extremity skin folds compared with

babies born to non-diabetic mothers (118). Furthermore, among GDM patients,

maternal fasting hyperglycaemia was associated with increased risk of shoulder dystocia,

with 1 mmol/l increases in fasting OGTT results leading to a two-fold increase in the rate

of shoulder dystocia (Relative Risk 2.09, 95% CI 1.03-4.25) (119).


Shoulder dystocia increases morbidity and mortality in both mothers and

babies. Common maternal complications are postpartum haemorrhage and

unintentional extension of the episiotomy or laceration into the rectum (fourth-degree

laceration) (120). Other complications include vaginal lacerations, cervical tears, bladder

atony, and uterine rupture (121). For babies, the most common complication is

temporary or prominent brachial plexus injuries, including Erbs palsy (C5, C6), Klumpke

palsy (C8, T1), total brachial plexus palsy (C5-T1) and Horner’s syndrome (C5-T1 and

facial nerve) (122, 123). Other rare complications include bone fracture (commonly

clavicle or humerus), and hypoxic-ischemic encephalopathy and even death (123, 124).

It is still controversial whether GDM patients who had shoulder dystocia will have worse

outcomes compared to the glucose tolerant women who also suffered from shoulder

dystocia (125, 126).

The manoeuvres for shoulder dystocia alleviation include McRoberts’

manoeuvre, suprapubic pressure, fetal rotational manoeuvres or posterior arm

extraction (Woods’ corkscrew manoeuvre and Rubin’s manoeuvre), and “all-four”

techniques. There are also other more aggressive techniques, for examples, the

Zavanelli manoeuvre, symphysiotomy, hysterotomy and deliberate clavicular fracture

(127, 128).

1.4.1.4 Neonatal hypoglycaemia

Neonatal hypoglycaemia was first described by Hartmann and Jaudon in 1937

(129) and has been associated with prominent brain damage for more than 40 years


(130). The “numerical” definition of neonatal hypoglycaemia, meaning the blood

glucose concentration cut-off point below which would create risk of long-term

neurological and developmental consequences, has been continually debated all over

the world (131). The most widely accepted cut-off value of hypoglycaemia is a glucose

level less than 2.6mmol/l, and it was based on an observational research done by Lucas

et al in 1988 (132). Other published definitions range from a blood glucose

concentration of less than 1.1 mmol/l in preterm birth and less than 1.7 mmol/l in term

infants, to a plasma concentration of less than 2.5 mmol/l (133). The reasons why it is

so difficult to achieve a uniform definition, are that healthy newborn infants also

experience low glucose levels during the early postnatal period, and that newborn

infants have the ability to compensate by producing other metabolic fuels and provide

energy to maintain brain function, for example ketone bodies and lactate. There is also

a lack of evidence regarding the correlation between the level of glucose concentration,

and adverse neurologic and developmental outcomes (134).

Recently, the American Academy of Paediatrics stated that a specific

concentration of glucose that can discriminate euglycaemia from hypoglycaemia, or

predict acute or chronic irreversible neurologic damage could not be supported due to

the lack of sufficient evidence (135). Instead, Hawdon proposed a new diagnostic term

such that neonatal hypoglycaemia should be accurately defined as a persistently low

blood glucose level, measured with an accurate device, in a baby at risk of impaired

metabolic adaptation but with no abnormal clinical signs; or a single low blood glucose

level in a baby presenting with abnormal clinical signs (136). To make the definition more


practicable, a group of experts suggested a new concept of “operational thresholds”-

the blood glucose thresholds for taking action (137).

Fetuses do not produce glucose under normal conditions and their energy

needs are entirely dependent on maternal supply and placental transfer of glucose,

amino acid, and free fatty acids, ketones and glycerol (138, 139). After birth and

clamping of the umbilical cord, neonatal glucose levels drop quickly and reach a nadir at

1-2 hours of age, then rise to levels that are similar to late gestation fetal concentrations

(about two-thirds of normal maternal values) by 2-4 hours, mostly through

glycogenolysis and gluconeogenesis. Following that period of rapid change, the neonatal

glucose levels rise slowly and reach adult levels in 3-4 days of age (140-142). These

glucose changes are vital for promoting glucose production, stimulation of appetite,

adaptation to fast/feed cycles and enhancement of oxidative fat metabolism (143).

These crucial changes in neonatal glucose levels are modified by several factors

including umbilical concentrations of glucose, plasma insulin concentrations, and the

onset time and capability of neonatal glucose production (144).

With the occurrence of GDM, fetuses are exposed to hyperglycaemia and

develop fetal pancreatic hyperplasia and fetal hyperinsulinaemia, which contribute to

neonatal hypoglycaemia after birth (62, 145). Furthermore, the liver is stimulated by

glucagon to generate glucose, and this glucagon response appears to be blunted in

babies of diabetic mothers (146). In the HAPO study, the authors demonstrated that

maternal hyperglycaemia, even under the diagnostic criteria of GDM, was associated

with increased risk of clinical neonatal hypoglycaemia (83). Observational studies also


confirmed the relationship between GDM with the increased risk of neonatal

hypoglycaemia. Shand and colleagues conducted a population-based study in Sydney,

Australia, and they found that the rate of neonatal hypoglycaemia was increased in

babies of GDM patients (19.1%) compared to their non-diabetic counterparts (1.6%) (OR

15.07, 95%CI 14.38- 15.80) (147). In the United States, Langer and collaborators

presented similar results, and showed that infants of GDM patients had increased risk

of neonatal hypoglycaemia (6% vs. 2%, OR 2.98, 95%CI 1.84-4.84) (148). However, the

rate of neonatal hypoglycaemia is hard to examine in the general population and might

be underestimated.

Another cause of neonatal hypoglycaemia is maternal hyperglycaemia during

labour, which results in persistent excessive secretion of fetal insulin 1 to 2 hours after

birth. Balsells and colleagues found that the capillary blood glucose level of GDM

patients during labour was associated with the development of neonatal hypoglycaemia

(149). Moreover, previous studies demonstrated that higher maternal pre-gestational

BMI, previous GDM history, higher fasting glucose level at diagnosis, and insulin

treatment were all associated with increased risk of neonatal hypoglycaemia among

babies of GDM mothers (149, 150).

The clinical signs of neonatal hypoglycaemia are not specific; these signs

include jitteriness, cyanosis, apnoeic episodes, tachypnoea, a weak and high pitched cry,

hypotonia, poor feeding, eye rolling, pallor, hypothermia, sweating, temperature

instability, and tachycardia. If the hypoglycaemia is prolonged, more severe signs could

be noticed, including changes in level of consciousness (irritability, lethargy, stupor, and


coma), and seizures (151, 152). The literature confirmed that persistent low blood

glucose level is associated with acute neurological dysfunction and presents a great risk

for cerebral injury (153). The spectrum of cerebral injury includes: white matter injury

(parenchymal haemorrhage and ischemic stroke), cortical neuronal injury, and

sometimes changes in the basal ganglia (mostly the globus pallidus) and thalami.

Posterior parietal and occipital lobes are affected more severely than other regions of

the brain (153-155).

Long term neurodevelopmental adverse sequelae following neonatal

hypoglycaemia is still controversial (156); the other possible outcomes include milder

motor,visual, learning and behavioural problems (157-161). Research also showed that

neonatal hypoglycaemia in diabetic pregnancy was associated with worse neurological

dysfunction: minimal brain deficits in attention, motor control, and perception,

compared to children born to diabetic mothers, but without hypoglycaemia (162).

The treatment strategies include prudent observation of a fetus’s clinical

signs, regular blood glucose checking using a proper method, and feeding and/or

intravenous glucose infusions (151, 152).

1.4.1.5 Neonatal hyperbilirubinaemia

Bilirubin is a metabolite resulting from the break-down of senescent or

haemolysed red blood cells through the reticuloendothelial system. Biliverdin, released

from degraded haem, is reduced to bilirubin by biliverdin reductase. This type of

bilirubin (unconjugated bilirubin) is water-insoluble and needs to attach to albumin for


transportation via the blood to the liver. Then, in the liver, it is conjugated to glucuronic

acid to become more water-soluble. The conjugated bilirubin enters the small intestine

and colon and is converted into different forms; most forms will be excreted outside the

body through urine and faeces, while others enter the enterohepatic recirculation by

reabsorption. Unconjugated bilirubin is highly soluble in lipid, which means it can cross

the cell membranes including blood-brain barrier and is toxic to the developing neonatal

brain (163, 164).

High levels of bilirubin in the plasma causes yellow discolouration in elastin-

rich tissues, including sclera and skin. This phenomenon is known as jaundice. Neonatal

jaundice is present in about 60% of term babies and 80% of preterm babies. It could be

further divided into two categories, physiological jaundice and the more severe case of

pathological jaundice (165). Increased production of bilirubin, deficiency of hepatic

uptake, impaired conjugation of bilirubin, and increased enterohepatic circulation of

bilirubin are the most common causes of neonatal pathologic jaundice (166). In the most

serious cases of hyperbilirubinaemia, neonates could develop acute bilirubin

encephalopathy (ABE) and kernicterus, due to neurotoxicity. Symptoms of ABE include

lethargy, hypotonia and poor feeding. If this condition is not treated in this stage, it could

lead to coma, seizures and, sometimes even death. Kernicterus is the chronic form of

bilirubin encephalopathy, characterized by athetoid cerebral palsy, auditory dysfunction,

dental-enamel dysplasia, paralysis of upward gaze and intellectual and other handicaps

(167-169).


In the HAPO study, the researchers demonstrated that maternal

hyperglycaemia was associated with increased risk of neonatal hyperbilirubinaemia,

requiring phototherapy (83). Sayin and colleagues found that the rate of neonatal

jaundice requiring treatment was higher in the GDM group compared to non-GDM

group (17.7% vs. 6.3%, p< 0.001) (170). Similarly, Landon and colleagues indicated the

rate of neonatal hyperbilirubinaemia (plasma values greater than 12 mg/dl) was higher

in the untreated GDM group compared to the non-diabetic group (14% vs. 2%, OR 3.87,

95%CI 2.64-5.67) (148). The underlying causes of this increased risk among infants of

GDM patients include newborn polycythaemia due to fetal hypoxia, the resultant

misbalance between increased fetal oxygen consumption that is caused by

hyperglycaemia and hyperinsulinaemia, and reduced oxygen supply from the maternal

side, that is due to increased maternal haemoglobin affinity of oxygen and reduced

placental function, and also the relative immaturity of hepatic bilirubin conjugation and

excretion in infants. The higher numbers of broken-down red cells contribute to delivery

of extra bilirubin to the immature glucuronosyltransferase enzyme system and results

in increased serum unconjugated bilirubin concentration, with a rapid rate of rise

followed by a later peak (171). Preterm birth and superficial head bruising due to birth

trauma could also exacerbate the situation (172).

The therapeutic options for hyperbilirubinaemia include phototherapy,

immunoglobulins for isoimmune haemolytic disease (Rh and ABO haemolytic disease)

and exchange transfusion (173).


1.4.1.6 Neonatal respiratory distress

Respiratory distress (RD) is one of the most common neonatal complications

after delivery, and its overall incidence rate is around 7%. Among these affected babies,

preterm birth has the highest incidence, followed by post-term deliveries (174). RD is

defined as any sign of breathing difficulties in the neonates, including tachypnoea

(RR >60/min) and tachycardia (HR>160/min), cyanosis, nasal flaring, grunting, apnoea/

dyspnoea, and chest wall recession (suprasternal, intercostal and subcostal). There are

different pathogeneses of neonatal respiratory distress. The common causes of RD are

transient tachypnoea of the newborn (TTN), respiratory distress syndrome (RDS),

pneumonia, meconium aspiration syndrome (MAS) and primary or secondary

pulmonary arterial hypertension (175, 176).

TTN is now recognized as the main cause of RD in newborns. It is caused by

inadequate lung fluid clearance at birth, resulting in excess lung liquid (177). It is

generally a self-limiting disorder, and will present with grunting and mild signs of

respiratory distress. Usually the symptoms will disappear within 48 hours. However,

some of the affected infants will develop an oxygen requirement and will necessitate

admittance into the Neonatal unit for a few days (178). The risk factors of TTN include

elective and emergency caesarean section, gestational age, birth weight (both low birth

weight and high birth weight), maternal age and male sex (179, 180).

A higher rate of TTN among infants of GDM patients has been reported.

Persson and colleagues demonstrated that the rate of TTN was doubled among infants

of GDM mothers (181, 182). Pinter and collaborators also indicated that fetuses of


diabetic rats had decreased fluid clearance in lung and a lack of thinning of the lung’s

connective tissue, which might be the cause of TTN (183). Moreover, increased birth

weight, increased rate of caesarean section and increased incidence of preterm birth

are all potential explanations for elevated rates of TTN (83). However, other studies

found that the rate of TTN was not different between infants of GDM mothers and

infants of non-diabetic mothers (170, 184). This inconsistency might be due to the low

prevalence of TTN among infants of GDM patients.

RDS, also called hyaline membrane disease, is caused by a deficiency of

surfactant (185). The type 2 pneumocytes start to produce surfactant around 24-25

gestational weeks and reach adequate amount to support breathing after birth by 36-

37 weeks. The lack of surfactant causes widespread alveolar collapse and results in

poorly compliant lungs (186). Normally, infants with RDS present respiratory distress

within the first minutes or hours after birth, and most will require respiratory support

with oxygen or mechanical ventilation (185).

Babies of diabetic mothers are more likely to suffer from RDS due to two

reasons, prematurity and abnormal surfactant production (181, 187, 188). GDM patients

have higher incidence rates of preterm birth compared to the general population, which

means their babies are at increased risk of developing straightforward surfactant-

deficient RDS (189, 190). Furthermore, high blood glucose is involved in several

mechanisms that contribute to abnormal production of surfactant, including inhibited

surfactant synthesis and secretion by type 2 cells, fewer type 2 pneumocytes/alveolar

lining cells and fewer lamellar bodies/alveolar lining cells, blocked trafficking of lipids


from fibroblasts to type 2 cells, and decreased expression of mRNA for surfactant

proteins B and C (191, 192). Thomas and collaborators found that infants of GDM

patients had delayed onset of surfactant (phosphatidyl glycerol) compared to babies

born to non-GDM women (37.3 ± 0.9 weeks vs. 35.9 ± 1.1 weeks, p<0.001) (193).

Researchers have also indicated that maternal glucose control during pregnancy plays

an important role in the rate of neonatal RDS; for patients who had well-controlled

diabetes, their babies experienced similar lung maturation compared to their non-

diabetic counterparts. Babies born to mothers with poorly controlled diabetes showed

a significant delay in the appearance of phosphatidyl glycerol (194, 195).

Cardiomyopathy is another cause of RD in infants delivered by diabetic

patients. The rate of cardiomyopathy is increased among babies of diabetic mothers

with overt diabetes or GDM (196, 197). Fetal hyperinsulinaemia, triggered by maternal

hyperglycaemia, increases the synthesis and deposition of fat and glycogen in the

myocardial cells and leads to an asymmetric septal enlargement in heart, characterized

by increased left ventricular mass and contractility leading to obstruction in the left

ventricular outflow tract (198, 199). The infants with cardiomyopathy are usually

asymptomatic, but 5-10% of cases have respiratory distress or signs of cardiac

dysfunction. Normally, after two to three weeks of supportive care, symptomatic

infants recover, and echocardiographic findings resolve within 6 to 12 months (200).


1.4.1.7 Other neonatal short term complications

The increased risk of other neonatal complications, for example,

polycythaemia, hypocalcaemia and hypomagnesaemia among infants of GDM patients

has been documented, but the prevalence of these complications is relatively low.

1) Fetal hypoxaemia, neonatal polycythaemia, and hyperviscosity

Accumulated evidence indicates that diabetes increases the risk of fetal

hypoxaemia during pregnancies (201). It happens more frequently when glucose levels

are not well-controlled (202). The exact cause of fetal hypoxaemia is not fully

understood. However, there are several potential explanations. Firstly, thickening of the

basement membrane of the chorionic villi is more common in diabetic pregnancies,

which increases the diffusion distance of oxygen between the mother side and the baby

side. Secondly, uncontrolled diabetes could cause decreased uterine blood flow in the

placental bed and result in decreased oxygen transfer to the foetus. Thirdly, in GDM

patients who have vascular complications (mostly women with unknown pre-existing

type 1 or type 2 diabetes prior to pregnancy), pathological changes can be found in the

spiral arteries of the placental bed, which narrow the blood flow from the mother to the

baby (203-205). Fetal hyperglycaemia and hyperinsulinaemia both contribute to the

development of fetal hypoxaemia. Since glucose and insulin are anabolic resources and

hormones respectively, they increase oxygen consumption with increased

erythropoietin levels in fetal plasma (206, 207). Research has also confirmed the


relationship between the severity of fetal macrosomia and the risk of fetal chronic

hypoxia (208).

Chronic fetal hypoxaemia can lead to cord blood acidosis, low Apgar scores,

reticulocytosis and, in the worst case, perinatal death (209). The best method to avoid

fetal hypoxaemia is still under discussion. However, one commonly used method is

intensive fetal monitors, including biophysical profile, doppler blood flow

measurements and non-stress testing (210, 211). There are also guidelines that

recommend ending pregnancy before term to avoid unpredictable perinatal death (212).

Neonatal polycythaemia is defined by a venous haematocrit (hct) that

exceeds normal values for gestational and postnatal age by two standard deviations

(213). For a term infant, polycythaemia is diagnosed if the hct from a peripheral venous

sample is greater than 65% or the haemoglobin in greater than 22 g/dl (214).

The incidence of polycythaemia is 1.5-4% in the general population and the

risk factors include fetal transfusions, intrauterine hypoxia and other rare fetal causes

such as Beckwith Weidemann syndrome (214, 215). Infants of diabetic mother have

higher risk, about 10% to 30%, of developing polycythaemia compared to babies of non-

diabetic mothers (216-218). The underlying pathogenesis of this higher incidence might

be the increased erythropoietin concentration caused by chronic fetal hypoxemia (82).

Hyperviscosity, normally caused by polycythaemia, is defined as a viscosity

greater than 14.6 centipoise, at a shear rate of 11.5 reciprocal seconds. It can lead to

different symptoms including intravascular aggregation, ischaemia, and infarction of

vital organs (219).


2) Neonatal Hypocalcaemia

In the human body, calcium exists in two places: skeleton (99%) and

extracellular fluid (1%). There are three different forms of calcium in the extracellular

fluid: calcium bound to albumin (40%), calcium bound to anions like phosphorus, citrate,

sulphate and lactate (10%) and free ionized calcium. Ionized calcium is a crucial element

for many important biochemical processes, such as neuromuscular excitability,

coagulation, cell membrane integrity and function, as well as cellular enzymatic and

secretory activity; it is tightly regulated by parathyroid hormones and vitamin D levels.

Elevated extracellular pH in cases such as acute respiratory alkalosis, could increase the

binding of calcium to albumin, which decreases the concentration of ionized calcium

without affecting the total serum calcium level (217, 218).

In the third trimester, fetal serum calcium (SCa) is higher than the mother’s

SCa, and is actively transferred from the maternal side to the fetal side. After birth,

neonatal SCa starts to decrease and reaches a nadir of 7.5-8.5 mg/dl (1.9-2.1 mmol/l) in

healthy term babies in two days (220). However, PTH levels increase gradually in the

first 48 hours of life and determine similar levels of SCa are seen in older children and

adults by two weeks of age (221).

Neonatal hypocalcaemia is defined as a total serum calcium level below 1.75

mmol/l or ionized calcium less than 1 mmol/l in preterm babies. In term infants, the

diagnostic criteria are total serum calcium below 2 mmol/l or ionized calcium less than

1.2 mmol/l. It can be divided into two categories: early onset neonatal hypocalcaemia


(ENH), that is relatively common and occurs within the first 3-4 days of life, and late

onset neonatal hypocalcaemia (LNH), that is rare and usually presents at the end of the

first week of life. Prematurity, infants of diabetic mother, perinatal asphyxia, maternal

hyperparathyroidism and intrauterine growth restriction are the common causes of

EMH. The reasons for developing LNH include hypomagnesaemia, high phosphate load,

hypoparathyroidism, vitamin D deficiency states, and iatrogenic cause.

The clinical presentations of neonatal hypocalcaemia vary widely. Some cases

are asymptomatic, others may present non-specific symptoms (apnoea, cyanosis,

tachypnoea and vomiting). In some cases, neuromuscular symptoms (myoclonic jerks,

jitteriness, exaggerated startle, tetany and seizures) and cardiac abnormalities

(tachycardia, prolonged QT interval, decreased contractibility and heart failure) are also

observed. Calcium supplementation for at least 72 hours is the regular treatment for

EMH, and usually will resolve hypocalcaemia in 48-72 hours without any significant

sequelae. The therapy for LNH is longer and more complicated, based on the aetiology;

in the most severe cases, babies with LNH may need lifelong treatment (221, 222).

Diabetes increases the risk of hypocalcaemia in newborns; hypocalcaemia

occurs in 10%-20% of babies of mothers with diabetes, including overt diabetes and

GDM (223, 224). This may be related to the increased calcium demands of a macrosomic

baby, as well as maternal hypomagnesaemia caused by magnesium depletion in diabetic

mothers, combined with functional hypoparathyroidism in infants (225). The higher risk

of birth asphyxia and prematurity might also contribute to the increased risk of

hypocalcaemia in babies of diabetic mothers. Recent research demonstrated that


infants of GDM patients have specific calcium sensing receptor genotype that is

associated with decreased calcium levels both at birth and on the 2nd day of life (226).

Strict management of diabetes in pregnancy might reduce the risk of neonatal

hypocalcaemia (227).

3) Neonatal Hypomagnesaemia

During pregnancy, maternal serum magnesium (Mg) levels decrease,

combined with a 25% increase of renal Mg excretion. Diabetes will further exacerbate

urinary loss of Mg, particularly the intracellular free magnesium level, which may cause

neonatal hypomagnesaemia (228, 229). Little is known about fetal magnesium

homeostasis. Animal studies show that the materno-fetal flux of magnesium is reduced

in the presence of maternal diabetes mellitus (230).

Neonatal hypomagnesaemia, defined as less than 0.02 mmol/l might have an

increased rate among infants of diabetic mothers within the first three days after birth.

Prematurity and pregnancy complicated with preeclampsia will increase the risk of

developing hypomagnesaemia (231, 232). Neonatal hypomagnesaemia is usually

transient and asymptomatic; however, there are also severe cases with different

manifestations, including neuromuscular hyperexcitablility (e.g. tetany, convulsions),

cardiovascular symptoms (e.g. changing in ECG, atrial and ventricular arrhythmias), and

hypokalaemia (233, 234). It can also reduce both parathyroid hormone (PTH) secretion

and PTH responsiveness. Thus, for neonates that have both hypomagnesaemia and


hypocalcaemia, the treatment for hypocalcaemia might not work until the

hypomagnesaemia is corrected (85, 235).

1.4.2 Maternal short term complications

1.4.2.1 Hypertension disease induced by pregnancy

Hypertension is a common maternal complication during pregnancy and is

associated with increased maternal and neonatal mortalities and morbidities. They can

generally be divided into four categories: 1) Chronic hypertension: hypertension that

was present at <20 weeks gestation that does not progress to preeclampsia. 2)

Gestational hypertension: a systolic blood pressure ≥ 140 mm Hg and/ or a diastolic

blood pressure ≥ 90 mmHg on two occasions at least 4 hours apart after 20 weeks of

gestation in a woman with previously normal blood pressure. 3) Preeclampsia:

hypertension after 20 weeks of gestation associated with proteinuria of ≥ 300 mg per 24

hours or ≥1+ dipstick. Preeclampsia is also diagnosed as hypertension in association with

thrombocytopenia, impaired liver function, new development of renal insufficiency,

pulmonary oedema or new-onset cerebral or visual disturbances, even in the absence

of proteinuria. 4) Chronic hypertension with superimposed preeclampsia: chronic

hypertension combined with the symptoms of preeclampsia that develop after 20

gestational weeks (236-238). There are also more severe types of pregnancy-induced

hypertension such as eclampsia (hypertensive patients with convulsions during

pregnancy or in the first 10 days postpartum) and HELLP syndrome (a syndrome

characterized by Haemolysis, Elevated Liver enzymes and a Low Platelet count).


However, their prevalence is quite low, around 0.27% and 0.8% among all the pregnant

women, respectively (239, 240).

Approximately 10% of women have gestational hypertension during

pregnancy. For preeclampsia, the prevalence is around 2-8% depending on the studied

population (241). Maternal adverse outcomes associated with hypertensive disorders

include increased caesarean deliveries, abruptio placentae, renal failure, stroke, cardiac

arrest, adult respiratory distress syndrome, coagulopathy and liver failure. It also

increases the risk of developing perinatal morbidities, including fetal growth restriction,

prematurity, and respiratory distress syndrome after birth (242, 243).

Gestational diabetes is associated with increased risk of pregnancy-induced

hypertension including gestational hypertension and pre-eclampsia, even after

adjustment for body mass index, age, ethnicity, parity and prenatal care (244-247). In

the HAPO study, authors indicated that maternal hyperglycaemia was associated with

increased risk of preeclampsia (83). In a large Australian population-based study, Shand

and colleagues demonstrated that GDM patients had increased risk of gestational

hypertension (6.9% vs. 4.2%, OR 1.74, 95%CI 1.64-1.85) and preeclampsia (6.7% vs. 4.4%,

OR 1.63, 95%CI 1.53-1.74) (147). Accumulated evidence indicates that this association,

at least in part, could be due to insulin resistance (248-250). A secondary analysis of the

HAPO study proved that higher fasting C-peptide, an acceptable measure of insulin

sensitivity in pregnancy if patients have normal insulin secretory capacity, is an

independent risk factor of preeclampsia, after being adjusted for BMI and fasting

glucose (251).


The underlying mechanisms between insulin resistance and gestational

hypertension are not fully understood. However, it is believed that insulin resistance

could cause hypertension at cellular, circulatory and neurologic levels. In the

sympathetic nervous system, insulin resistance stimulates the release of plasma

norepinephrine and reduces the capacity of vasodilation. At the circulatory level,

hyperinsulinaemia can cause hypertrophy of the vascular smooth muscle cells and

vasoconstriction. Hyperinsulinaemia also has an effect on the cellular membrane pump,

and can increase intercellular calcium and vascular tone (252).

Independent of insulin resistance, hypertension could also be induced by

functional abnormalities of the vascular endothelium that results from maternal

hyperglycaemia. Hyperglycaemia increases the concentration of free radicals and

advanced glycosylation endproducts, which reduce the availability of NO for

vasodilation (253, 254). Placental abnormalities, such as plethora, chorangiosis, and a

relatively immature villous structure, happen more frequently in diabetic mothers, and

could lead to the release of different vasoactive and pro-inflammatory substances (α-

TNF, leptin, etc.), that are related to the development of gestational hypertension (255-

257). Finally, gestational diabetes could cause dyslipidaemia, characterized by increased

oxidized low density lipoprotein (LDL) that can inactivate endothelial nitric oxide (NO)

and cause vascular dysfunction (254, 258, 259).

Several randomized controlled trials have found that strict treatment of GDM

could reduce the risk of developing pregnancy-induced hypertension during pregnancy.

The Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) found a


lower incidence of preeclampsia in the treated group compared to the routine-care

group (12% vs. 18%, p=0.02)(260). Similarly, Landon and colleagues indicated that

treatment reduced the risk of pregnancy-induced hypertension, both for gestational

hypertension and preeclampsia, in GDM patients (8.6% vs. 13.6%, p=0.01) (261).

1.4.2.2 Preterm Birth

Preterm birth is defined as birth occurring before 37 weeks of completed

gestation. An estimated 10% of all births worldwide are preterm, which means around

41,000 infants are born before term each day (262). In a comprehensive systematic

review, authors indicated that most countries have shown an increase in preterm birth

rates over the last 20 years (263). In Australia, the risk of preterm birth increased from

5.5% in 1991 to 7.5% in 2011: 0.8% pregnant women gave birth at 20-27 weeks, 0.7% at

28-31 weeks, and 6.0% delivered at 32-36 weeks (264, 265).

Preterm birth can be divided into two categories depending on the clinical

presentation: spontaneous preterm birth (which contemplates either spontaneous

onset of labour with intact membranes, or after preterm premature rupture of

membranes - PROM), and medically-indicated preterm birth for maternal or fetal

indications. This includes maternal preeclampsia, fetal distress, SGA, and placental

abruption. It is estimated that around 2/3 of all preterm births are spontaneous (266).

Preterm birth is associated with increased perinatal mortality, as well as

short- and long-term morbidities. It is known that the risk of adverse consequences

decline with increasing gestational age (267). In Australia, the rate of neonatal death


was 410 per 1000 births for infants born <28 weeks, but decreased exponentially to 30

per 1000 birth for infants born at 28-31 weeks, and 4.7 per 1000 for infants born at 32-

36 weeks. However, even the lowest mortality rate of babies born preterm at 32-36

weeks is still five times higher than mortality for infants born after 37 weeks (268). Other

countries show similar trends (269). Compared to term-born babies, preterm born

infants had increased risk of respiratory distress, infection, hypothermia, hypoglycaemia,

hyperbilirubinaemia, hypoxic-ischaemic encephalopathy (HIE), intraventricular

haemorrhage and feeding problems. Necrotizing enterocolitis and retinopathy have also

been reported, although they are more common in the infants born extremely preterm

(< 28 weeks) (267, 270, 271). Infants born preterm experience long-term complications,

such as an increased risk of cerebral palsy, developmental coordination disorder, visual

impairment, hearing impairment, cognitive impairment, and neurobehavioral disorders

(272, 273).

The HAPO study also confirmed that an increased risk of preterm delivery is

associated with elevated 1h OGTT results (OR 1.18 95% CI 1.12-1.25), and 2h OGTT

results (OR 1.16 CI 1.10-1.23) but not for the fasting OGTT result (OR 1.05 95%CI 0.99-

1.11) after being adjusted for confounders (83). Yang and colleagues found that patients

who had impaired glucose tolerance during pregnancy with conventional obstetric care,

had increased risk of preterm birth (OR 6.42 95%CI 1.46-28.34) (274). In a large

population-based cohort study including 1,260,297 women with singleton pregnancies

in Sweden, researchers demonstrated that GDM patients had a higher risk of preterm

birth (8.6% vs. 5.0%, adjusted OR 1.71, 95% CI 1.58-1.86) (84).


It is important to note that these studies did not separate spontaneous

preterm birth from medically-indicated preterm birth. The increased rate of preterm

birth may be attributed to the higher risk of indicated preterm birth, caused by increased

risk of preeclampsia in GDM patients. There were inconsistent results regarding the

relationship between GDM and spontaneous preterm birth (sPTD). Jensen and co-

workers found that despite treatment, an increased risk of sPTD is associated with

higher 2h OGTT results, either between 9.0-11.0 mmol/l (OR 2.0 95% CI 1.0-3.6) or ≥11.0

(OR 5.1 95%CI 2.4-11.0) (275). Similar results were confirmed by Lao and colleagues

(275). They demonstrated that the incidence of sPTD correlated significantly with

increasing glucose intolerance in diet-treated GDM patients; the rate of sPTD increased

from 5.5%, in patients whose 2h OGTT results were 5.9 mmol/l or less, to 10.3%, in

patients whose 2h OGTT results were 11.0 mmol/l or greater (276). Conversely, Yogev

and collaborators conducted a retrospective study, and concluded that the rate of sPTD

was similar between GDM and non-GDM patients (10.7% vs. 11.7%, p=0.20). However,

they indicated that sPTD patients in their research had a higher mean blood glucose

result in the OGTT (114± 16 mml/l vs. 106± 14 mml/l, p= 0.001), and a lower proportion

of patients with well-controlled glucose levels (35% vs. 54%, p= 0.004), when compared

to GDM patients who had term birth. GDM patients who had sPTD were likely to have

fewer prenatal visits, although the result was not statistically robust (6.2± 1.3 times vs.

7.7 ± 2.2 times, p= 0.08). Moreover, the rate of insulin use was comparable between

GDM patients who had preterm deliveries and GDM patients who had term birth (47%

vs. 50%, p= 0.22) (277). Bar-Hava and colleagues also demonstrated that the incidence

of sPTD among GDM patients was similar to the non-diabetic population (6.2% vs. 6.5%,


p= 0.82). They also stated that there was no statistical difference in the proportion of

patients who had poor glycaemic control, defined as a mean blood glucose level higher

than 6.1mmol/l, between GDM patients who had sPTD and GDM patients who had term

birth through the entire treatment period (26.6% vs. 31.2%. p= 0.83), as well as the week

preceding delivery (40% vs.17.9%, p=0.28). However, patients in this study generally had

relatively good glucose control, based on their low mean blood glucose, which might

explain the inconsistent results. Moreover, the sample size in this study was also

relatively small (34 sPTD patients) (278).

In conclusion, the overall rate of preterm birth is increased among GDM

patients. It is still controversial whether GDM patients have an increased risk of

spontaneous preterm birth. However, the rate of sPTD might be associated with the

severity of glucose intolerance and glucose control during pregnancy.

1.4.2.3 Rate of caesarean section (CS)

Caesarean section is defined as the delivery of a baby through surgical incision

in the abdomen and uterus. It is also known as an intervention used to reduce the risk

of obstructed labour and the adverse complications associated with it, such as shoulder

dystocia (279). Just like any surgical procedure, CS has its own side effects. For mothers,

it is directly associated with several unpleasant outcomes, including wound infection and

dehiscence, postpartum infection, postpartum haemorrhage, urinary and

gastrointestinal injuries and deep venous thrombosis (280, 281). Furthermore, CS for a

current pregnancy, increases the likelihood that CS will become necessary in subsequent


pregnancies, and increases the risk of malpresentation, placenta praevia, antepartum

haemorrhage, placenta accreta, prolonged labour, uterine rupture, small for gestational

age babies, and in the worst case scenario, unexplained stillbirth (282). There are also

fetal risks related to CS. The incidence of transient tachypnoea of the newborn born was

about three times higher among babies born to CS compared to those who went through

vaginal birth (283). The increased risk of respiratory distress syndrome was also reported

among neonates after CS; however, having labour before CS lowers the risk of respiratory

distress syndrome. The OR was 2.6 (95% CI 1.3 – 2.8) without labour and 1.9 (95%CI 2.2-

2.9) with labour (284, 285). Children born to CS may have a higher risk of developing

allergies, asthma, type 1 diabetes and malignancies in the future (286).

Evidence from different studies indicate that untreated GDM increases the

risk of CS. Langer and colleagues demonstrated that the overall risk of CS increased 2-

fold for untreated GDM patients, compared to non-diabetic mothers, and the risk was

further increased to 4-fold for GDM patients with LGA babies (148). Similar results were

demonstrated by Naylor et al in the Tri-Toronto Hospital Gestational Diabetes Project.

They found the rate of CS in the untreated GDM group was higher than in the

normoglycaemic controls (29.6% vs. 20.2%, p=0.02) and this increase could be attributed

to the increased rate of macrosomia (28.7% vs. 13.7%, p<0.001) (287). The most decisive

evidence comes from the HAPO study; the authors found that after adjustments for

maternal BMI and other confounders, elevated fasting glucose level (OR 1.11 95% CI

1.06-1.15), 1-hour glucose level (OR 1.10 95%CI 1.06-1.15), 2-hour glucose level (OR 1.08

95%CI 1.03- 1.12) were all related to increased risk of primary CS. Importantly, in the


HAPO study, the results of OGTT were blinded to both the care providers and the patients,

which excluded the influence of subjective bias on decision making, as well as the

patients themselves (81).

However, the relationship between treated GDM and the rate of CS is still

controversial. Landon and collaborators conducted a large randomized study to examine

the effects of GDM treatments. They demonstrated that GDM treatment could reduce

the risk of CS among GDM patients (26.9% vs. 33.8%, p=0.02) (261). However, in another

study assessing the efficacy of GDM treatment, Crowther and colleagues found that the

risk of elective CS was comparable between GDM patients who received intervention

and GDM patients who had routine care (15% vs.12%, p= 0.33), as well as the rate of

emergency CS (16% vs.20%, p= 0.31) (288). Moreover, Naylor et al demonstrated that

although treatment of GDM normalized the babies’ birth weights, the rate of CS was still

increased after adjustments for potential confounders (OR 2.1 95% CI 1.3-3.6) when

compared to the normoglycaemic controls, and the risk was comparable to the

untreated group (33% among treated GDM patients vs. 29.6% among un-treated GDM

patients). The author concluded that the awareness of GDM may lower the threshold for

surgical delivery (287). Similar results were found in a recent study; Gorgal et al

demonstrated that patients treated for GDM have an increased risk of having non-

elective CS compared to glucose tolerant women after adjustments for maternal age,

pre-pregnancy BMI, gestational weight gain, previous caesarean section, gestational age

at delivery and birth weight (adjusted RR 1.52 CI 1.06- 2.16). Given the similar rates of

fetal distress and failure to progress in both GDM patients and the control group,


researchers suggested that GDM might modify obstetrical practice and increase the risk

of CS (289).

In conclusion, the rate of CS is increased among untreated GDM patients.

Whether the treatment of GDM could reduce the risk of CS was controversial. However,

awareness of GDM might lower the threshold for CS.

1.4.2.4 Other maternal complications during pregnancy

There is increased risk of other maternal complications during pregnancy, for

example, operative vaginal delivery and psychological problems. However, these

complications have not been fully analysed and are less frequently reported in the

literature.

1) Operative vaginal delivery

Operative vaginal delivery was defined as the application of forceps or

vacuum extractors during the second stage of labour (290). The association between

operative vaginal delivery (OVD) and GDM has not been comprehensively analysed.

Kristina et al found a comparative risk of OVD between untreated GDM patients and

non-diabetic women (19% v.s.19%, p> 0.05) (291). In another study that compared the

outcomes of glucose-tolerant women with patients who had gestational impaired

glucose tolerance (a fasting glucose level <6.7 mmol/l and a 2-h glucose level of the

OGTT in the 9.0-11.0 mmol range) but were not treated, the risk of OVD was also similar

between the two groups (7.5% vs. 7.9%, p>0.05) (184). Similar results were reported in


terms of the risk of OVD among GDM patients with treatment. Svare et al demonstrated

a comparable rate of OVD between treated GDM patients with non-diabetic women (7%

vs. 10%, p>0.05) (292). However, there are also contradictory results in the literature. In

a recent large population-based cohort study conducted in Sweden, authors found that

the rate of OVD was lower in the treated GDM group, compared to non-diabetic women

(5.5% vs.6.6%, p<0.001) (84). Conversely, in an Australian population-based study,

authors found that the risk of OVD was higher in the treated GDM group compared to

non-diabetic women after adjustment for the high prevalence of macrosomia (28.7% vs.

41.0%, OR 1.4, 95% CI 1.4-1.7) (293).

Furthermore, GDM was associated with an increased risk of failure in OVD.

Sameer et al conducted a population-based case control study to determine the risk

factors of failed OVD. They demonstrated that, after controlling for macrosomia, GDM

was still associated with an increased risk of failed OVD (OR 1.54 CI 1.13- 2.10) (294).

2) Perineal injuries

Perineal injuries are classified into degrees, depending on the anatomical

structures involved, from first degree (injury to perineal skin only) to fourth degree

(injury to perineum involving the anal sphincter complex and anal epithelium) (295). The

relationship between GDM and perineal injuries is controversial. In a population study

in Australia, the authors found that the risk of third and fourth degree perineal tears

were higher among GDM patients compared to non-diabetic women (1.9% vs. 1.3%, OR

1.43, 95%CI 1.24-1.65) (147). Similarly, in Saudi Arabia, researchers found an increased


risk of perineal lacerations in GDM patients compared to non-GDM women (18% vs.

10.7%, p<0.001) (296).

However, this association was not confirmed in other studies. Adams

demonstrated comparable risk of rectal injury between women with unrecognized GDM

and non-diabetic women (291). In the Toronto Tri-hospital Gestational Diabetes Project,

researchers also demonstrated that there was no statistical difference in terms of the

risk of perineal injuries between treated GDM patients and non-diabetic controls (287).

3) Post-partum Haemorrhage

Post-partum Haemorrhage is normally defined as maternal blood loss more

than 500 ml within the 24 hours following childbirth (297). The relationship between

GDM and post-partum haemorrhage has not been fully evaluated. According to a recent

large population-based cohort study in Denmark, post-partum haemorrhage was similar

between the GDM group and the non-GDM control population (298). In Australia,

researchers demonstrated that the risk of post-partum haemorrhage was comparable

between GDM patients and non-GDM women (6.3% vs. 6.0%, OR 1.06, 95%CI 0.99-1.13)

(147).

Similarly, the treatment of GDM did not reduce the risk of post-partum

haemorrhage. Crowther el at found that the rate of post-partum haemorrhage (> 600

ml) was comparable between the intervention GDM group and the routine-care GDM

group (6% vs. 6%, p=0.86) (288). There is no difference in the risk of post-partum


haemorrhage between GDM patients who were treated with metformin and patients

who were treated with insulin (299).

4) Psychological problems

A diagnosis of GDM has a significant influence on women’s perception of

health, pregnancy experience and may create psychological problems. Nolan et al

interviewed women who had type 2 diabetes and GDM during pregnancy, and

demonstrated three primary themes regarding their health concerns, including concern

for the infants, concern for self and sensing a loss of personal control over their health

(300). Rumbold and co-workers conducted a survey to evaluate the experience of

pregnant women after being screened for GDM. They found that patients who had

positive screen results had lower health perceptions (p< 0.005), were less likely to rate

their health as “much better than one year ago” (p<0.005) and were more likely to only

rate their health as “fair” compared to pregnant women who had negative results.

However, no differences were found in levels of anxiety or depression between the two

groups (301). An American study indicated that GDM diagnosis was associated with an

increased level of anxiety and depression throughout pregnancy and six weeks

postpartum (302). Interestingly, in a longitudinal prospective study, researchers found

that the GDM group had significantly higher anxiety scores at the beginning of the

treatment for GDM, but the scores became comparable to the non-diabetic group at 36

weeks or 6 weeks postpartum (303). Researchers from the Australian Carbohydrate

Intolerance Study in Pregnant Women (ACHOIS) also revealed that glucose intolerant


patients who underwent intensive intervention had lower rates of depression and

improved health-related quality of life, compared to those who had routine obstetric

care (304).

The results regarding the patients’ experiences of treatment were

inconsistent. Carolan and colleagues found that most GDM patients spoke of the

challenge of implementing a complex regimen of blood testing and dietary manipulation

within a very short time frame, while they were still in shock from the GDM diagnosis.

Instead, GDM patients found treatment via the use of insulin an easier option, compared

to dietary control alone. They were happy to start insulin treatment as they felt that it

made their task more achievable (305). Conversely, Melinda and collaborators indicated

that GDM patients treated with insulin were more likely to experience shock, fear or

anxiety (306).

1.5 Long term complications of GDM

There is an increased risk of developing metabolic abnormalities,

cardiovascular problems and other diseases among GDM patients and their offspring.


1.5.1 Neonatal long term complications

1.5.1.1 Metabolic abnormalities: obesity, type 2 diabetes, and metabolic

syndrome

The association between maternal diabetes during pregnancy and increased

risk of metabolic abnormalities in offspring has been demonstrated by a number of

studies, using various methodologies. A study focused on the Pima Indians of the Arizona

population, who had very high levels of obesity, type 2 diabetes and gestational diabetes,

provided convincing evidence of the aforementioned link between maternal diabetes

and metabolic abnormalities in offspring. Diabetic diseases during pregnancy include

pre-existing type 1 and type 2 diabetes and gestational diabetes, and gestational

diabetes accounts for the majority (about 88% gestational diabetes, 7% type 1 diabetes

and 5% type 2 diabetes) (307). In this population, babies of diabetic mothers had a

higher prevalence of obesity at 15 to 19 years of age, as compared to babies of pre-

diabetic and non-diabetic mothers, even for the babies who had normal birth weight

(307, 308).

In order to exclude genetic and postnatal maternal lifestyles influence, sibling

studies were performed. Offspring of diabetic mothers showed higher mean BMI and

risk of type 2 diabetes compared to their siblings born before their mothers were

diagnosed with diabetes. Further, there was no association between paternal diabetes

around the time of their partner’s pregnancy and the offspring’s BMI or the risk of

diabetes (308). These results showed that in the Pima Indian population, the increased


risk of obesity and diabetes in the offspring of diabetic mothers was at least partially due

to intrauterine exposure mechanisms.

This association has also been confirmed by studies in other populations. In a

large epidemiologic study including more than 280,000 Swedish males, the authors

found an average 0.94 kg/m2 BMI higher in subjects whose mother had either GDM or

overt diabetes during their pregnancy, compared to their brothers born before their

mothers were diagnosed with diabetes (309). A recent study analysed the relationship

between maternal GDM and female offspring adiposity. The results indicated that

maternal GDM increased the risk of childhood adiposity in female offspring. Moreover,

the risk of adiposity was also increased if the mother was overweight (310).

Metabolic syndrome is defined as central obesity, plus any two of four

additional factors: elevated triglyceride level, reduced HDL-cholesterol,

hypertension/elevated blood pressure, or elevated fasting plasma glucose (311). In Pima

Indians, a diagnosis of diabetes in the mother was strongly associated with elevated

systolic blood pressure in the offspring (312). In another study in Denmark, researchers

compared adult offspring of women with diet-treated GDM, women with type 1

diabetes and offspring from the normal population, and found that the risk of metabolic

syndrome increased 4-fold in the offspring of GDM mothers and 2.5-fold in the offspring

of mothers who had type 1 diabetes. The offspring’s risk of the metabolic syndrome

increased in parallel with increasing maternal fasting blood glucose and 2-h blood

glucose (313). However, in an American study, researchers demonstrated that only the

LGA offspring of diabetic mothers had an increased risk of metabolic syndrome in


childhood; this increased risk was not found in LGA offspring of non-GDM mothers,

appropriate–for–gestational age (AGA) offspring of GDM mothers and AGA offspring of

non-GDM mothers (314).

Epigenetic modifications may explain the association between maternal

diabetes during pregnancy, and increased risk of metabolic disease of the offspring in

their later lives, since such modifications are generally assumed to moderate gene-

environment interactions (315). Among these modifications, DNA methylation of

specific sequences is best understood. The affected genes include the leptin gene

promoter (316), the adiponectin gene promoter (317), mesoderm-specific transcript

(MEST) (318) and others. Research also showed that reduced circulating cord blood

endothelial colony-forming cell (ECFC) numbers and premature ECFC senescence may

predispose infants born to diabetic mothers to develop endothelial dysfunction and

hypertension (319).

1.5.1.2 Cognitive ability

Whether maternal pregnancy diabetes will adversely affect offspring

cognitive development is still controversial. The offspring of diabetic mothers, not

specific the type of maternal diabetes, were found to have higher rate of memory

deficits (age 1) (320), higher rate of attention deficits, lower cognitive scores, lower gross

and fine motor achievements at younger age (321), higher army rejection rates (322),

higher risk of not completing compulsory schooling (age 16) (323), and lower IQ score

(age 9)(324). Aberrant lipid metabolisms, such as third-trimester plasma β-


hydroxybutyrate and free fatty acid in GDM mothers were found to be associated with

poorer intellectual performance and psychomotor development in offspring (325).

However, researchers in India failed to prove the association between

maternal GDM and poorer cognitive ability in their offspring, when assessed for learning,

reasoning, verbal ability, attention, and concentration (326). Interestingly, in a Swedish

sibling study, subjects whose mother had pregnancy diabetes had, an IQ that was lower

on average than subjects whose mothers did not had diabetes in pregnancy (OR -1.36,95%

CI -2.12, -0.60), in non-siblings. But in siblings, offspring exposed to maternal diabetes

in pregnancy did not show a significant difference in IQ score compared to their non-

exposed siblings. This finding suggested that exposure to diabetic milieu could not fully

explain the observed decrease in offspring cognitive abilities (327).

1.5.1.3 Other complications

Researchers found an increased risk of malignant neoplasm in children

prenatally exposed to maternal type 2 diabetes, but found no relation to paternal

diabetes. The authors suggested the risk of malignant neoplasm later in life may to some

extent be programmed by a suboptimal intrauterine environment associated with

maternal diabetes (328). Similarly, studies showed an increased risk of hospitalization

for neoplasms in children (up to 10 years old) born to the mothers who had pre-existing

diabetes, but no increased risk was observed for children of GDM mothers (329).


Researchers also indicated that children born to GDM mothers (OR 1.20 95%

CI 1.11-1.28), and children born to the mothers who had pre-existing diabetes (OR 1.56

95% CI 1.43-1.70) had an increased risk of being hospitalized for infections (329).

In Pima Indians with type 2 diabetes, their offspring had a higher prevalence

of elevated urinary albumin excretion (UAE) (58%), compared to the offspring of

prediabetic mothers (i.e. not diabetic at the time of the pregnancy but whom developed

diabetes after pregnancy) (43%), and also the offspring of non-diabetic mothers (40%).

This result suggested that the diabetic intrauterine environment might be independent

of other susceptibility factors that may lead to nephropathy (330).

1.5.2 Maternal long term complications

1.5.2.1 GDM recurrence

The recurrence rate of GDM is high, and previous studies have found rates

that ranged from 35% to 69%. In a retrospective Canadian study, authors found a 35.6%

recurrence rate of GDM between 1980 and 1996. Infant birth weight in the index

pregnancy and maternal pre-pregnancy weight before the subsequent pregnancy were

predictive factors in multivariate regression analysis (331). An American study indicated

that the risk of GDM in the second pregnancy among women with previous GDM was

higher, compared to those without previous GDM (adjusted OR 13.2 95% CI 12.0 – 14.6).

For the third pregnancy, the highest rate of recurrence was found for patients whose

first and second pregnancies were all complicated with GDM (adjusted OR 25.9 95% CI

17.4- 38.4), which means the magnitude of recurrence risk increased with the number


of prior episodes of GDM. Researchers also found that Hispanics and Asian/Pacific

Islanders had a higher risk of GDM recurrence, compared to other ethnicities (332). In

Australia, a recent study presented a similar rate of GDM recurrence (41.2%). Authors

concluded that a maternal age over 35 years old, particular ethnicities (Middle

East/North Africa and Asia), pregnancy hypertension, LGA in infants, preterm birth in

the first pregnancy, longer inter-pregnancy period, and multiple pregnancies in the

second pregnancy were all independent predictors of GDM recurrence (333). Higher

rates of GDM recurrence was reported by Spong et al and Major et al, 68% and 69%

respectively (334, 335).

1.5.2.2 Type 2 diabetes

GDM history can increase the risk of developing type 2 diabetes in the future.

A comprehensive meta-analysis including 20 studies and 675 455 women (10 859 with

type 2 diabetes) concluded that women with previous gestational diabetes had at least

a seven-fold increased risk of developing type 2 diabetes in the future, compared to

women without GDM history. The risk ratio was generally consistent after controlling

for ethnicity, follow-up time, study design, BMI at index pregnancy, and follow-up BMI

(336). The elevated pre-pregnancy BMI, later GDM diagnosis and insulin requirement

during pregnancy were all risk factors for the development of type 2 diabetes in the

future (337). Further, Kim et al concluded in another systemic review that the

cumulative incidence of type 2 diabetes increased markedly in the first 5 years after

delivery and appeared to plateau after 10 years. Higher fasting glucose level during


pregnancy was the most common risk factor for the development of type 2 diabetes

(338).

All these risk factors potentially represent the magnitude of the insulin

resistance, which is a hallmark of developing future diabetes (339). The association

between GDM and type 2 diabetes has also been supported by genetic analysis. Some

of the type 2 diabetes-associated genetic variants were also associated with GDM (340,

341).

1.5.2.3 Cardiometabolic abnormalities

Women with previous GDM history had a higher risk of developing metabolic

abnormalities, as well as cardiovascular problems. A recent large meta-analysis,

including more than 5000 participants (2520 cases and 3312 control) confirmed that

women with GDM history had almost 4-fold higher risk of developing metabolic

syndrome (OR 3.96 95% CI 2.98 – 5.26). Interestingly, Caucasian women demonstrated

a significantly higher chance of having metabolic syndrome after pregnancy complicated

with GDM than Asian patients (Caucasian OR 4.54 95% CI 3.78 – 5.46; Asian OR 1.28 95%

CI 0.64 – 2.56). Further, the author found that GDM patients with higher BMI were more

likely to develop metabolic syndrome compared to the BMI matched group (higher BMI

OR 5.39 95%CI 4.47 – 6.50; matched BMI OR 2.53 95% CI 1.88 – 3.41) (342).

To examine the association between GDM history and future cardiovascular

disease, a scoping review was performed, which included 11 studies until mid-2012. In

the review, the author stated that previous GDM history is associated with an increase


in the risk of cardiovascular disease; however, this association disappeared after

adjustments for subsequent diagnoses of diabetes (343). Recently, Fadl et al and

colleagues conducted a large population-based case-control study in Sweden. They

found that previous GDM history increased the risk of cardiovascular disease 1.5-fold

(95% CI 1.07- 2.14) after being adjusted for common risk factors for cardiovascular

disease, including chronic hypertension, smoking, BMI, education level, parity and

ethnicity. However, a diabetes diagnosis after pregnancy presented an even higher risk

for cardiovascular disease (OR 4.8 95% CI 3.23- 7.13). The association between GDM

history and cardiovascular disease lost statistical significance after adjustments for

postpartum diabetes, excepted for women who were overweight or obese (344).

Inflammation that is associated with insulin resistance may be a potential link

between GDM history and subsequent cardiometabolic disease (345). Research

indicates that high levels of C-reactive protein (CRP) is the mark of cardiometabolic

diseases (346, 347). Recently, osteoprotegerin (OPG), a soluble member of the tumour

necrosis factor receptor superfamily that increases inflammation has been linked with

both cardiovascular disease(348) and metabolic syndrome (349) and further confirmed

the relationship between inflammation and cardiometabolic abnormalities. Further,

research found that small artery function was impaired at 2 years postpartum, in women

with abnormal glucose levels during pregnancy (350).


1.6 GDM treatment

1.6.1 Rationale for treatment

As indicated in the HAPO study, higher maternal plasma glucose levels are

associated with adverse maternal and neonatal complications such as pregnancy-

induced hypertension, macrosomia, and neonatal hypoglycaemia (83). In spite of an

accumulation of evidence indicating that abnormal lipid profiles of GDM women may

also play a role in causing these complications, there is still a lack of specific treatments

for this pathological pathway. The current treatment strategies mainly tackle maternal

hyperglycaemia through self-monitoring of glucose, diet and exercise, and, if needed,

pharmaceutical treatment. Several large randomized clinical trials have confirmed the

effectiveness of GDM treatment. The Australian Carbohydrate Intolerance Study in

Pregnant Women (ACHOIS) trial was published in 2005. It included 1000 patients with

mild glucose intolerance who were randomly assigned to receive dietary advice, blood

glucose monitoring and insulin therapy if needed (the intervention group) or routine

care in which mothers and their treating clinicians were blinded to the abnormal glucose

tolerance. The researchers showed that patients in the intervention group had less

serious perinatal complications with a significantly reduced occurrence of the composite

outcome of perinatal death, shoulder dystocia, bone fracture, or nerve palsy (1% v.s. 4%,

p= 0.01). However, patients in the intervention group also had higher rates of induced

labour (39% v.s. 29%, p<0.001) and similar rates of CS (31% v.s. 32%, p= 0.73), and their

infants were also more likely to be admitted into NICU (71% v.s. 61%, p=0.01) (260).

Later, Landon and colleagues conducted another randomized clinical trial to compare

the outcomes of patients with mild GDM who received routine prenatal care unaware


of the diagnosis (control group) with women who had dietary intervention, self-

monitoring of blood glucose, and insulin therapy (treatment group). They demonstrated

that patients in the treatment group had reduced rates of pregnancy-induced

hypertension (8.6% v.s. 13.6%, p= 0.01) and a lower rate of CS (26.9% v.s. 33.8%, p=

0.02). The babies of mothers in the treatment group had lower mean birth weight

(3302± 502g v.s. 3408± 589g, p<0.001), reduced fat mass (427± 198g v.s. 464±222g,

p=0.003), lower risk of being LGA (7.1% v.s. 14.3%, p<0.001) and lower risk of being born

macrosomic (5.9% v.s. 14.3%, p<0.001). However, the frequency of the composite

adverse outcome that included stillbirth, neonatal death, hyperbilirubinaemia,

hypoglycaemia, hyperinsulinaemia and birth trauma was comparable between the two

groups (32.4% v.s. 37.0%,p=0.14) (351). In a recent systematic review, Hartling and

colleagues concluded that treating gestational diabetes results in a lower incidence of

preeclampsia, shoulder dystocia and macrosomia. However, there was still insufficient

evidence to prove that treating GDM would reduce the risk of neonatal hypoglycaemia

or future adverse metabolic outcomes. The only short-term harm regarding GDM

treatment that was found was an increased demand for service (352).

1.6.2 Self- glucose monitoring

GDM patients can be made aware of whether the target glucose levels are

being achieved or not, through self-measurement of blood glucose. Hawkins and

collaborators compared the outcomes in diet-treated GDM patients who used personal

glucose monitors, with those who underwent intermittent fasting glucose evaluation


during semi-weekly obstetrical visits. They found that patients in the self-BGL

monitoring group had significantly fewer macrosomic and LGA infants, and also gained

less weight (353, 354). These findings support the practice of self-monitoring of BGL for

women with diet-treated gestational diabetes.

DeVeciana and colleagues performed a randomized clinical trial to compare

the outcomes between insulin-treated GDM patients who checked for pre-prandial BGL

with those who were doing 1-hour post-prandial BGL monitoring. They showed that

patients using post-prandial BGL measurements had greater HbA1c change (-3.0 ±2.2

percent v.s. -0.6±1.6 percent, p<0.001); their infants had lower birth weight (3469±668g

v.s. 3848±434 g, p=0.01), a lower rate of neonatal hypoglycaemia (3% v.s. 21%, p=0.05),

less risk of being LGA (12% v.s. 36%,p=0.04) and were less frequently delivered by CS

due to cephalopelvic disproportion (12% v.s. 36%,p=0.04) (355). This study confirmed

that postprandial glucose levels are more important in terms of predicting neonatal

adverse outcomes. Based on these findings, a self-monitored BGL regimen including four

daily glucose checks, performed after fasting and either 1 or 2 hours after each meal was

recommended (356). Weisz and collaborators conducted a prospective observational

study and found that GDM patients using one hour postprandial glucose measurements

had a lower need for insulin therapy (28% v.s. 40%, p<0.05) compared to patients using

two hour postprandial glucose measurements. Furthermore, even though it was not

statistically different, patients using one hour glucose measurements had fewer

macrosomic infants (7.5% v.s. 10.6%), fewer LGA infants (7.4% v.s. 15.2%) and a lower

rate of CS (24% v.s. 30%) (357).


Studies have shown that continuous glucose monitoring could even more

accurately detect high postprandial blood glucose levels and nocturnal hypoglycaemic

events, compared to self-monitoring of blood glucose in women with GDM (358).

However, there is no established evidence regarding the cost-effectiveness of

continuous glucose monitoring in GDM treatment.

1.6.3 Target blood glucose levels

Glucose control during diabetic pregnancy is a balancing act between

hyperglycaemia and hypoglycaemia. Hyperglycaemia leads to LGA babies, but overly

strict control of maternal glucose levels may initiate frequent maternal hypoglycaemic

episodes and/or cause SGA babies. The available evidence suggests that mean plasma

glucose levels around 5.8 mmol should avoid both of these adverse pregnancy outcomes

(359).

The optimal therapeutic target levels remain uncertain, due to the lack of

randomized trials. There is substantial inconsistency regarding the target glucose levels

in different international guidelines. The most popular target glucose levels are those

endorsed by ADA, ACOG, Endocrine Society and ADIPS, which stipulates: fasting glucose

level ≤ 5.3 mmol/l, 1-h postprandial glucose level ≤ 7.8 mmol/l, and 2-h postprandial

glucose level ≤ 6.7 mmol/l (80, 356, 360, 361). These postprandial glucose levels were

supported by Veciana and colleagues; they found that achieving these target

postprandial glucose levels could reduce the risk of neonatal hypoglycaemia,

macrosomia, and CS (355). Similarly, the fasting glucose target is supported by Landon


and collaborators. They found that by using this fasting glucose level as the target

glucose level, patients had fewer adverse perinatal outcomes (261).

However, a recent review that included 255 pregnant women with normal

weight and glucose tolerance from 12 studies, spread over half a century, reported that

the weighted average glucose values (± 1 SD) were as follows: a fasting glucose level of

3.9± 0.4 mmol/l, 1-h postprandial equal to 6.0± 0.7 mmol/l, and 2-h postprandial equal

to 5.5± 0.6 mmol/l. Based on these results, authors proposed that the target glucose

level for GDM patients should be 4.5 mmol/l for the fasting level, 6.8 mmol/l for 1-h

postprandial, and 6.1 mmol/l for 2-h postprandial, which is considerably lower than the

current target glucose levels (362). Similarly, another comprehensive meta-analysis that

aimed to evaluate the ideal glucose targets for GDM patients recommended a lower

fasting glucose level for GDM treatment. This meta-analysis included 34 studies,

enrolled 9433 women, and demonstrated that a fasting glucose target of < 5.0 mmol/l

was most strongly associated with reduced risk of macrosomia (OR 0.53 95% 0.31-0.90)

for GDM patients during the third trimester. However, the overall evidence is still

relatively sparse, and is inconclusive in terms of selecting pre-prandial and postprandial

targets for GDM treatment (363). A large, prospective randomized trial is needed to

decide the optimal glucose targets for GDM patients.

1.6.4 Medical nutrition therapy (MNT)

MNT is the corner stone of GDM management. The ultimate goals of MNT are

to allow appropriate weight gain based on the mother’s pre-pregnancy and prenatal

weight, along with normoglycaemia, adequate fetal growth and absence of urine


ketones. An individualized nutritional counselling based on the pre-gestational BMI was

recommended by the ADA (364).

There is no definitive evidence that the optimal weight gain for women with

gestational diabetes is different from that of healthy women; the same

recommendations for weight gain during normal pregnancy from the Institute of

Medicine have been endorsed for GDM patients by a number of guidelines. Additionally,

the ADA discourages weight reduction during pregnancy, in order to avoid ketosis (365,

366) (See Table 3).

Table 3 -2009 Institute of Medicine Recommendations for Total Weight Gain and Rate

of Weight Gain during Pregnancy, by Pre-pregnancy BMI.

Pre-pregnancy BMI Total Weight Gain (kg) Rates of Weight Gain in

Second and Third

Trimester (kg/week)

Underweight (<18.5 kg/m2) 12.5-18 0.51 (0.44-0.58)

Normal Weight

(18.5-24.9 kg/m2)

11.5-16 0.42 (0.35-0.50)

Overweight

(25.0-29.9 kg/m2)

7-11.5 0.28 (0.23-0.33)

Obese

(≥ 30.0 kg/m2)

5-9 0.22 (0.17-0.27)

BMI, Body mass index


Nutritional recommendations generally include a carbohydrate-controlled

diet that is sufficient to maintain normoglycaemia and avoid ketosis. Calorie restriction

is not warranted for underweight or normal-weight GDM patients, as long as fetal

growth and weight gain targets are met. Overly strict calorie restriction might increase

ketosis (367) and limited studies have indicated that ketonuria may be associated with

impaired psychomotor development in the offspring (368, 369). However, the ADA

suggested that obese women (BMI > 30kg/m2) might benefit from a 30 percent caloric

restriction (approximately 25 kcal/kg/d), and still achieve the appropriate weight gain

without ketosis and intrauterine growth retardation (370).

The composition of the calories to be consumed is also a controversial topic.

Jovanovic-Peterson and colleagues indicated that a low carbohydrate diet prescription,

including 40% carbohydrate, 20% protein, and 40% fat that was calculated based on

body weight was associated with a reduction in macrosomia incidence (371). It was

reported that a low carbohydrate diet could decrease the need for insulin therapy, the

incidence of having LGA infants and the rate of CS due to cephalopelvic disproportion

(372). However, Moreno-Castilla and collaborators performed a randomized trial that

assigned 152 GDM patients to either a 40 percent or 50 percent daily carbohydrate diet

and found no difference in pregnancy outcomes (373). Moreover, Roman and colleagues

found that a higher carbohydrate intake was associated with a decreased incidence of

newborn macrosomia in GDM patients (374). Despite the inconsistencies in the

literature, some authorities still recommend a low carbohydrate diet; carbohydrate


intake is limited to 35% -45%, and distributed in three small to moderately-sized meals,

and two to four snacks (20, 375).

Carbohydrate-rich foods with a low glycaemic index (GI) appear to be

healthier. In a crossover study, researchers recruited 14 non-pregnant women without

diabetes, to compare the post-prandial blood glucose levels after a 540 kcal meal

containing foods from a low GI group and a high GI group. Each meal contained 17%

protein, 28% fat and 55% carbohydrate, but were different in the GI value (54 v.s. 92).

They indicated that women in the low GI group had a significantly lower average

increase in blood sugar (p<0.001) and lower insulin response (p<0.001) (376). Similarly,

a small crossover study including 5 GDM patients, which aimed to compare the

outcomes of a low-fat, high- carbohydrate (unrefined) diet (70% carbohydrate, 10% fat,

20% protein, and 70g fibre each day) with a low-carbohydrate and high-fat diet (35%

carbohydrate, 45% fat, 20% protein, 70g fibre each day). The authors showed that

patients who had the low-fat, high unrefined carbohydrate diet, had significant lower

urinary glucose output (1.3± 1.1 mmol/l v.s. 2.6±3.0 mmol/l, p<0.05), lower fasting

plasma cholesterol concentration (5.9±1.1 mmol/l v.s. 6.3 ± 1.1 mmol/l, p<0.01) and

lower fasting free fatty acid level (590±270 µmol/l v.s. 690± 270 µmol/l, p<0.02) (377).

Moreover, Moses and collaborators demonstrated that GDM patients who consumed a

low GI diet required less insulin compared to those who had a high GI diet. They also

found that 9 patients in the high GI group who needed insulin treatment could cease

insulin use after switching to a low GI diet (378).


Maternal lipids including serum triglycerides (TGs), cholesterol, free fatty

acids (FFAs) were shown to be strong predictors for fetal lipids and fetal growth in GDM

patients, independent of glycaemic control (379). However, no organization currently

recommends specific amounts and sources of fat consumption for GDM patients.

Research indicated that mono-unsaturated fatty acids may be protective for patients

with impaired glucose tolerance, whereas saturated fatty acid can increase glucose and

insulin levels in women with GDM (380). A recent systematic review regarding strategies

in the nutritional management of GDM patients demonstrated that a diet rich in ad

libitum complex carbohydrates (using higher fibre and lower glycaemic index

carbohydrates) and limited saturated fats might be optimal in normalizing glycaemia,

preventing further insulin resistance and reducing excess fetal fat accretion. However,

the evidence is far from conclusive; larger prospective and randomized trials are

undoubtedly in need to identify optimal diets for GDM women (381). However, research

has shown that insulin was necessary to reduce excess birth weight in the offspring of

obese women with gestational diabetes, regardless of whether the appropriate glucose

control was achieved (382).

1.6.5 Exercise (physical activity)

Up to 39% of women with GDM could not meet the target BGL only by diet

treatment (383). Thus, physical activity, as a supplement to diet treatment, has been

advocated since it can improve insulin sensitivity through increased muscle glucose

uptake and glycogen synthesis (385, 386).


There is insufficient evidence to determine the most appropriate exercise

regimen for GDM patients. Jovanovic-Peterson and colleagues indicated that a program

that combined diet treatment and exercise was more likely to achieve normoglycaemia,

and was also safe for the mother and her baby (384). Dye et al conducted a population-

based study in America and demonstrated that exercise was associated with reduced

rates of GDM, specifically among women with a BMI greater than 33 kg/m2 (OR 1.9,

95%CI 1.2-3.1) (385). Bung and colleagues performed a prospective randomized trial to

evaluate the effectiveness and safety of exercise treatment among GDM women who

require insulin treatment. Patients in the exercise group had a compliance rate higher

than 90%. They found that in the absence of ominous fetal heart rate (FHR) changes or

significant changes in uterine activity following the exercise sessions, regular physical

activity seemed to be a safe therapeutic option for GDM patients (386). Brankston et al

demonstrated that compared to a diet-alone group, overweight GDM patients (BMI >25

kg/m2 ) in a diet-plus-exercise treatment group had lower rates of insulin use and a

longer delay from GDM diagnosis to the initiation of insulin therapy (387). Based on

these findings, in 2003, ACOG recommended 30 or more minutes of moderate exercise

a day on most, if not all days of the week, for GDM patients without medical or obstetric

complications (375, 388).

However, there were also studies that demonstrated contradictory results.

Stafne and colleagues indicated that there were no differences in insulin resistance and

GDM prevalence, between pregnant women who followed a 12-week standard exercise

program, and pregnant women who received standard antenatal care. Furthermore,


only 55% of women in the exercise group managed to follow the recommended exercise

protocol. However, these participants had a relatively low average BMI, equal to

24.8±3.2 kg/m2, which might explain the inconsistency between different studies (389).

1.6.6 Insulin treatment

Insulin treatment is considered standard therapy in women with GDM, when

target glucose levels cannot be consistently achieved through nutrition treatment and

exercise. GDM patients produce insulin endogenously, but cannot meet the increased

insulin requirement to counter the diabetogenic placental hormones, to maintain

euglycaemia. A variety of rapid and longer acting exogenous insulin preparations are

available to supplement the mothers endogenous insulin, so as to reproduce the

physiological insulin requirements as close as possible in order to accomplish tight

glycaemic control (390).

There is no consensus on the glucose levels that signals a necessity for insulin

treatment. ACOG recommended that insulin should be administrated when fasting

glucose levels are at 5.3 mmol/l or more, or if 1-h postprandial glucose levels exceed

7.2- 7.8 mmol/l , or if 2-hour postprandial glucose levels reach or exceed 6.7 mmol/l

(391). In Australia, prior to recent changes in guidelines (80), insulin replacement was

indicated if blood glucose targets (a fasting glucose level < 5.5 mmol/l and 2-h

postprandial glucose level < 7.0 mmol/l) were exceeded on two or more occasions

within a 1 to 2 week interval, particularly in association with suspicion of macrosomia

(392). In England, NICE recommends initiating insulin treatment after 1-2 weeks, if


lifestyle treatments are insufficient, or if fetal abdominal circumference measurements

are above the 70th centile at the time of GDM diagnosis (393).

Insulin treatment could be a combination of intermediate- or long-acting and

short-and rapid-acting insulin and dose adjustments are based on glucose levels at

particular times of the day. Regular human insulin and neutral protamine Hagedorn

(NPH) were the commonly used insulin types. Regular human insulin has been used to

control the postprandial glucose level. It firstly self-collaborates to form hexamers, and

then dis-collaborates into the monomeric form that can be absorbed through the

capillary wall after subcutaneous injection. The slow diffusion into circulation leads to a

delayed peak action as well as a longer duration of action compared to endogenous

insulin, which may result in increased risk of post-meal hyperglycaemia and pre-prandial

hypoglycaemia. NPH is an intermediate-acting insulin, which is used to mimic basal

insulin secretion. There are also some limitations regarding NPH. The effective duration

of NPH is about 16-18 h, and thus a single dose is unable to provide basal insulin for a

full day. Furthermore, night-time injections of NPH may result an un-physiological rise

in insulin levels in the early morning and increases the risk of hypoglycaemia (394).

Rapid-acting insulin analogues such as insulin Aspart and insulin Lispro, with less time to

reach peak action, have been approved by the Food and Drug Administration for use

during pregnancy. Studies demonstrated comparable if not better treatment capacity

and safety between rapid-acting insulin analogues and regular human insulin (395, 396).

Long-acting insulin analogues including Glargine and Detemir, are also promising (397,

398). However, a large prospective clinical trial for Glargine has not been performed.


A combination of intermediate- and short-acting insulin analogues is mostly

used to achieve ideal glucose control. Insulin could be administered according to the

patient’s pattern of glucose concentration during the day; if the fasting glucose is

elevated, evening NPH insulin injections can be used. If postprandial glucose levels are

elevated, regular human insulin or rapid-acting insulin analogues can be prescribed. If

both pre and postprandial glucose levels are elevated, a regimen of four injections,

including 3 pre-meal short-acting insulin injections and one night NPH insulin injection

could be implemented (399). Although the basal-bolus regimen more closely mimics

physiological insulin secretion, the regimen itself is complicated and difficult to follow.

Research has shown that there is an inverse relationship between patient compliance

with treatment and regimen complexity (400). Premixed insulin formulations are

produced by including a rapid-acting insulin analogue for prandial coverage and its

protaminated counterpart for basal coverage, in formulations that are available in

different ratios (30/70, 25/75 and 50/50). These premixed formulations can reduce the

number of insulin injections to twice a day. However, an open label, randomized trial

which compared the level of glycaemic control and perinatal outcomes between

diabetic patients (the majority of which are GDM patients) who received insulin four

times daily (3 injections after meals and 1 injection before bed) with those who received

insulin twice daily (1 injection before breakfast and 1 injection before dinner), shows

that GDM patients who received the four-times-daily regimen had better glycaemic

control and lower rates of overall neonatal morbidity than patients who received the

twice-daily regimen (401).


1.6.7 Oral hypoglycaemic agents

Traditionally, insulin therapy has been the gold standard for GDM treatment

if lifestyle treatments were not enough, because a tight level of glucose control could be

achieved without pharmacological treatment crossing the placental barrier. However,

insulin therapy is also expensive and invasive. Oral hypoglycaemic agents are less

invasive, and usually are a cheaper alternative. Oral hypoglycaemic agents also enhance

the patients’ compliance to GDM treatment, while achieving similar perinatal outcomes

(353).

Metformin has its major effect on lowering blood glucose by reducing hepatic

glucose production. It is also able to slow intestinal glucose absorption and may have

some additional effect to decrease peripheral insulin resistance (402, 403). However,

Metformin crosses the placental barrier such that the fetal concentration is at least half

the maternal level. However, in animal studies, using rats and rabbits, metformin is

shown to not be teratogenic at doses of up to 600 mg/kg a day, which is equivalent to

2-6 times higher than the maximum recommended human dose (404). The Metformin

in Gestational Diabetes (MiG) study was designed to evaluate the effectiveness and

safety of metformin in the treatment of gestational diabetes. It randomly allocated 751

GDM patients to open-label treatment with metformin (1000-2000 mg daily) and insulin

treatment. There was no significant difference in the primary outcomes, which is a

composite of neonatal complications including hypoglycaemia, respiratory distress,

hyperbilirubinaemia needing phototherapy, birth trauma, low Apgar score and


prematurity. When these adverse outcomes were compared separately, the metformin-

treated group had a lower rate of hypoglycaemia but a higher number of preterm births.

In the secondary outcomes, only maternal weight gain and treatment satisfaction were

in favour of metformin. In the metformin group, 168 (46.3%) patients required insulin

to achieve the ideal glucose level, but the median insulin dose required was lower than

patients who were treated with insulin therapy only (405). Because of concerns that

metformin crosses the placental barrier and may affect the babies’ metabolic function,

the Offspring Follow-Up (TOFU) study was performed. This study aimed to compare the

fat distribution of children born to women who participated in the MiG trial, at an age

of 2 years old. The results demonstrated that the body fat percentage is comparable

between the children of metformin-treated women and the children of insulin-treated

women. Interestingly, children born to metformin-treated mothers had more fat being

stored in subcutaneous sites. The authors suggested that a further follow-up is required

to determine whether children exposed to metformin in the uterus will develop less

visceral fat, or show any changes in insulin sensitivity (406).

Glyburide (glibeclamide in Australia) is a second-generation sulfonylurea drug

that lowers glucose levels by increasing insulin secretion. In vitro placental perfusion

studies indicated that glyburide does not cross the placenta in significant amounts (407).

In 2005, Langer and colleagues reported that glyburide concentrations in neonatal

umbilical cord serum at the time of delivery were below the limit of assay detection

(10ng/ml) (408). However, in 2009, Hebert and collaborators found that glyburide


concentrations in umbilical cord plasma were approximately 70% of the maternal

plasma concentration, by using a more sensitive assay (409).

Glyburide treatment might have comparable effects on maternal glucose

control compared to insulin treatment. In a large clinical study, 404 GDM patients were

randomly assigned to receive either insulin or glyburide treatment. The results indicated

that there was no difference in the mean blood glucose measurement and HbA1c level.

However, 18% of glyburide treated patients could not achieve the target glucose control,

compared to 12% in the insulin-treated group, but patients in the glyburide group also

had a significantly lower number of hypoglycaemia episodes (4% v.s. 20%, p= 0.03). The

rates of preeclampsia and CS were comparable between the two groups (408). In

addition, researchers demonstrated that glyburide treated GDM patients had less post-

diagnosis weight gain compared to the insulin treated women (410)

Whether glyburide-treated GDM patients have comparable neonatal

outcomes compared to insulin-treated patients is still controversial. London et al

reported that babies born to glyburide-treated women had similar rates of LGA,

macrosomia, hypoglycaemia, lung complications and NICU admission (408). Conversely,

other studies reported an increased risk of neonatal hypoglycaemia, macrosomia, and

NICU admission in infants of GDM patients who were treated with glyburide (411-413).

A recent meta-analysis was performed to evaluate glyburide management in

comparison with insulin. The authors indicated that glyburide is as effective and as well-

tolerated as insulin. However, the risk of neonatal hypoglycaemia, high fetal birth weight


and macrosomia was higher in the babies born to glyburide treated women. They also

suggested that a large, long term follow-up study is needed (414).

Like metformin, not all patients who received glyburide treatment achieved

target glucose levels. Chmait and colleagues conducted a prospective observational

study and reported a 19% failure rate in glyburide treated patients. The researchers also

demonstrated that gestational age at the time of dietary treatment failure, and the

mean fasting blood glucose level prior to initiating glyburide were the two most

significant indicators of glyburide success (415). Based on these findings, the Endocrine

Society recommended that glyburide could be used as an alternative to insulin therapy,

in GDM patients who failed to achieve the target glucose level after one week of diet

and exercise, except for those women who were diagnosed with GDM before 25 weeks

gestation and had fasting plasma glucose levels > 6.1mmol/l (20).

1.6.8 Obstetric issues regarding GDM treatment

Obstetric surveillance is a key part in the treatment of GDM patients, and

includes regular cardiotocography (CTG), ultrasound (the measurements of amniotic

fluid volume, biophysical profile, and umbilical artery Doppler) and fetal movement

counts. If the results of these tests are not reassuring, more aggressive options may be

taken, such as a contraction stress test (OCT) or membrane rupture. There is no

consensus regarding the value or timing of antepartum fetal testing. The ACOG

recommends fetal surveillance in women with GDM, who display poor glycaemic control

(375).


In spite of monitoring fetal vitality, ultrasound is useful for assessing fetal

growth, and to provide information vital to the treatment plan. Kjos and collaborators

have shown that adjusting the insulin level based on a combination of glucose testing

results and fetal growth ultrasound, could identify pregnancies at low risk, and reduce

the rate of CS, compared to glucose testing alone (416). Furthermore, ultrasound is also

used for detection of fetal anomalies.

The timing and method of deliveries for GDM patients is an important

obstetric decision. Whether elective induction reduces the risk of shoulder dystocia

compared to spontaneous labour remains controversial. Kjos and colleagues randomly

grouped 200 diabetic pregnant women requiring insulin (187 of whom had GDM) at 38

week of gestation into two groups, a group which had active induction of labour within

5 days, and a group which had expectant management. They found that expectant

management increased the gestational age at delivery by 1 week. Patients in the

expectant management group had similar rates of CS (31% v.s. 25%), but had an

increased risk of LGA (23% v.s. 10%) and shoulder dystocia (3% v.s. 0%). Based on these

results, the authors recommended that induced delivery should be considered at 38

weeks for women with insulin-requiring diabetes during pregnancy. However, this is the

only randomized controlled trial that compared perinatal outcomes between elective

induction and expectant management of labour. In a systematic review, including other

retrospective information, Witkop and collaborators demonstrated that GDM patients

who went through elective labour induction at term had a reduced rate of having

macrosomic babies and related complications. However, they also concluded that there


was insufficient evidence to recommend induction of labour in GDM patients at 38

weeks (417).

It is also not conclusive whether elective CS should be performed on GDM

patients to avoid obstructed delivery. Garabedian and co-workers conducted a

systematic review and indicated that as many as 588 CS deliveries in GDM patients with

an estimated fetal weight ≥ 4250g would be necessary to avoid one case of permanent

brachial plexus palsy (418). In a retrospective study including more than 16000 pregnant

women, Ron and colleagues found that elective CS for GDM patients who had an

estimated fetal weight higher than 4500g had no significant effect on the incidence of

brachial plexus injury (419). However, the ACOG still suggests that CS should be

considered in women with GDM whose fetuses have a sonographically estimated weight

≥ 4500g (375).

1.7 Rationale for this research

As discussed before, the perfect treatment strategy for GDM is still unknown

and different institutions have their own treatment systems. In the ACT, Australia, GDM

patients are categorized into two groups based on whether they can achieve the target

glucose levels by diet and exercise within one week. Patients who can achieve target

BGLs with lifestyle treatment only are stratified into a low-risk group and they are

referred back for usual obstetric care provided by GPs and midwives without further

appointment with the diabetes team. Alternatively, patients who cannot control their

glucose level only by lifestyle treatment are stratified into a high-risk group and they


attend the Diabetes in Pregnancy Service Multidisciplinary Clinic (DIPS-MDC). During the

appointment in the DIPS-MDC, high-risk patients are cared for by a team including

endocrinologists, diabetes educators, dietitians, midwives and obstetricians; the

majority of these patients require insulin treatment. More detailed information on this

stratified treatment strategy for GDM patients in the ACT is provided in the research

setting (Chapter two).

1.7.1 High-risk group patients may have worse perinatal outcomes

Patients in the high-risk group who are unable to maintain the target BGLs

through lifestyle changes in one week are assumed to have worse glucose tolerance and,

therefore, a more severe form of GDM associated with higher rates of other risk factors

such as obesity and hypertension. Some will also have previously undiagnosed type 1

and type 2 diabetes.

This assumption is supported by a previous study that used a similar

stratification criterion. Wong and collaborators performed a study to examine the

factors of insulin initiation in GDM patients. Notably, they used the same diagnostic

criteria of GDM as in this current study, and initiated insulin treatment based on whether

patients could maintain the target BGL (fasting BGL < 5.3 mmol/l, and 2h BGL < 6.8

mmol/l) after applying lifestyle changes for one week. Furthermore, this study was

conducted in NSW, Australia, which means that the ethnic composition of their

population is more likely to be similar to this study compared to the studies that have

been conducted in other countries. Overall, the characteristics of the patients in their


insulin treated group are quite similar to the high-risk group patients seen in the ACT.

The author indicated that, compared to the non-insulin treated patients, their insulin-

treated patients had a higher BMI (29.9 ± 7.3 kg/m2 vs. 26.5 ± 6.3 kg/m2, p<0.001), a

higher rate of having GDM history (25.9% vs. 16.7%, p=0.006), and a higher fasting

glucose level in the diagnostic OGTT test (5.57 ± 1.19 mmol/l vs. 5.05 ± 0.67 mmol/l,

p<0.001). Patients in the insulin-treated group were most likely to be Anglo–European

(30.8% vs. 23.8%) and least likely to be South-east Asian (17.8% vs. 28.4%) (420).

Moreover, the majority of the high-risk group patients needed insulin

treatment to achieve the targeted level of glucose control. And, previous studies also

demonstrated that GDM patients who needed insulin treatment had worse OGTT results

and higher pre-gestational BMI, and they were more likely to be Anglo-Celtic and less

likely to be Asian (421, 422).

Poor OGTT results, higher pre-gestational BMI and particular ethnicity have

been shown to be associated with adverse perinatal outcomes. The HAPO study

confirmed that increased levels of fasting glucose, 1-hour postprandial and 2-hour

postprandial results in the OGTT test were associated with birth weight above the 90th

percentile, cord-blood serum C-peptide level above the 90th percentile, primary CS and

clinical neonatal hypoglycaemia (83). For pre-gestational BMI, Jensen and colleagues

found that after excluding glucose intolerant patients, overweight (BMI 25.0-29.9) and

obese (BMI > 30) pregnant women were associated with an increased risk of having

hypertensive complications, CS, induction of labour and macrosomic babies when

compared to pregnant women with normal weight (BMI 18.5-24.9) (423). This


relationship has also been confirmed by other studies (426, 427, 428). Ethnicity is also a

factor that contributes to the differences in perinatal outcomes. In Australia, Wong and

colleagues demonstrated that compared to South-East Asians, Anglo-European GDM

patients had higher rates of CS (28.8% vs. 17.9%, p<0.01), and the babies of Anglo-

European mothers also had higher birth weight (3.38 ± 0.57 kg vs. 3.18± 0.50 kg,

p<0.001), higher risk of macrosomia (10.8% vs. 3.9%, p<0.01), and higher risk of being

LGA (12.2% vs. 3.9%, p<0.001) when treated with similar management pathways (424).

Other studies also demonstrated that compared to Caucasian GDM patients, Asian

women had lower rates of primary CS (aOR = 0.86, 95% CI 0.77-0.96) and lower rates of

having macrosomic infants (aOR = 0.58, 95% CI 0.48-0.70) (425).

In conclusion, patients in the high-risk group might have more risk factors that

lead to worse perinatal outcomes, such as poor OGTT results and higher maternal BMI.

1.7.2 Low-risk group patients may not necessarily have better outcomes

However, there may be another side to the story. Firstly, the low-risk group

of patients receive a lower level of antenatal care compared to the high-risk group; they

do not have follow-up sessions with the diabetes team and they do not meet

obstetricians as regularly. Previous studies have indicated that lower levels of antenatal

care are associated with an increased risk of adverse outcomes. Cao and collaborators

conducted a study and randomly categorised GDM patients into a group that received

intensive treatment (including individualized diabetes education, dietary and exercise

advice, intensive BGL monitoring, frequent clinic visits) and a group that received the


standard therapeutic regimen (including group education of diet and exercise, but no

individualized appointment with a dietitian and diabetes educator and no special clinic

visit). The diagnostic criteria of GDM and the criteria of insulin treatment initiation was

the same between both groups. The authors found that patients in the standard

treatment group had higher risk of preterm birth (8.3% vs. 2.4%, p=0.033), higher

neonatal birth weight (3.45 ± 0.55 kg vs. 3.26 ± 0.53 kg, p<0.001) and higher NICU

admission rates (33.3% vs. 21.3%, p= 0.036). Because the rate of insulin use between

the two groups was comparable, the authors concluded that these suboptimal

outcomes in the standard treatment groups could be solely attributed to the lower level

of obstetric care (426).

Secondly, insulin therapy has been shown to improve some perinatal

outcomes. Coustan and colleagues conducted a small randomized trial with 61 GDM

patients, to compare two treatment regimens (insulin plus diet treatment vs. diet

treatment alone) in 1978. They found that patients treated with insulin had a lower risk

of having heavy infants (birth weight higher than 3856g) (427). Similar results were

found in later studies. Thompson and collaborators randomized 108 GDM patients into

an insulin plus diet treatment group, or a group treated through diet alone, and

compared the perinatal outcomes between the two groups. The authors demonstrated

that the infants of insulin-treated mothers had significantly reduced mean birth weight

(3170 ± 522g vs. 3584 ± 543g, p=0.002), macrosomia rate ( 5.9% vs. 26.5%, p= 0.048),

and lower ponderal index (2.51 ± 0.39 g/cm3 vs. 2.69 ± 0.28 g/cm3, p= 0.03) (428).

Buchanan and colleagues also found that the mean birth weight, the percentile of LGA


infants and neonatal skin-fold measurements were reduced in insulin-treated GDM

patients when compared to the GDM patients who were treated with diet alone (429).

In a systematic review, researchers concluded that a diet plus insulin treatment could

reduce the risk of having macrosomic babies in GDM patients when compared to

treatment with diet alone; the risk difference was -0.098 (95% CI -0.168 to -0.028) (430).

Finally, the treatment efficacy of the low-risk group patients is largely

dependent on their compliance with self-management of their lifestyle. Previous studies

have shown that the compliance level to lifestyle treatments is generally not satisfactory.

Cypryk and collaborators analysed the compliance of GDM patients to the

recommended diets, and found that the compliance rate was only about 50% (430).

Ruggiero and colleagues analysed the self-reported compliance with diabetes self-

management in pre-existing diabetic patients during pregnancy. They found that

achieving compliance with dietary management was more difficult than achieving

compliance with other management methods including insulin administration and

glucose testing (431). Similarly, in GDM patients, Carolan et al reported that they

considered insulin treatment as “an easy option” compared to diet treatment (305). Hui

and collaborators conducted a case study to find the obstacles facing women with GDM

to follow dietary advice. They found that 1) personal food preferences conflicted with

dietary advice; 2) eating in different social environments where food intake is difficult

to control; 3) lack of knowledge and skills in dietary management and lack of tailored

dietary planning; and 4) limited time for dietary changes, were all barriers for adherence

to diet treatment (432). Moreover, research demonstrated that patients treated with


diet alone may obsessively reduce carbohydrate intake to avoid the initiation of insulin

treatment. The fear of insulin outweighed the concern of eating unbalanced meals (432).

In conclusion, patients in the low-risk group may not necessarily have better

perinatal outcomes than the high-risk group due to the lower level of antenatal care,

differences in treatment, and potentially lower compliance with lifestyle self-

management.

1.8 Research hypothesis and aims

1.8.1 Research hypothesis

Based on the arguments mentioned above, the hypothesis of this research is

that compared to patients in the high-risk group, who might have worse glucose

tolerance and other risk factors causing adverse perinatal outcomes, patients in the low-

risk group might not necessarily have better outcomes due to a lower level of antenatal

care provided, different treatments and uncertainties in compliance with lifestyle self-

management.

1.8.2 Research aims

Aim one: To test the accuracy of the current stratification system of GDM

treatment in the ACT, Australia. By comparing demographic data, medical history, OGTT

results, and other information from low-risk and high-risk patients, the accuracy of the

stratification system will be determined by whether it successfully allocates patients

with better OGTT results and fewer risk factors for adverse obstetric outcomes into the

low-risk group.


Aim two: To access the outcome of the current management of GDM patients

in the ACT, Australia, especially for low-risk group patients. The treatment strategy for

the low-risk group will be considered satisfactory if (i) patients in the low-risk group have

at least as good, if not better, perinatal outcomes compared to patients in the high-risk

group, and (ii) the rate of perinatal adverse outcomes among both GDM treatment

groups are not higher when compared to the background population in the ACT and

other related studies.


CHAPTER TWO RESEARCH SETTING AND METHODOLOGY

2.1 General information about the Australian Capital Territory (ACT) area

This research includes patients who were diagnosed with gestational

diabetes mellitus in the ACT and were referred to the ACT Health Diabetes Service for

education and treatment from 1st January 2010 to 30th June 2014. The ACT Health

Diabetes Service is primarily located in The Canberra Hospital with three outreach

community centres, and is responsible for the care of patients with diabetes across the

ACT. The total area of the ACT is 2358 km², including Canberra, nearby small towns,

agricultural land and national parks. The ACT has a population that has increased from

about 361,900 (2010) to 384,100 (2013). Its annual population growth rate is 1.8% (433).

Within the ACT population in 2013, 28.6% were born in countries other than

Australia. The largest immigrant populations were from North-West Europe (22.4%)

followed by South-East Asia (11.9%) and Southern and Central Asia (10.8%). In the ACT,

the majority of employed people work in professional (29.7%), clerical/administration

(19.2%) and management (15.8%) occupations (434).

The number of births was 5152 in 2010, 5121 in 2011, 5461 in 2012 and 5545

in 2013. The mean age of mothers and fathers in 2013 was 31.6 years and 33.8 years,

respectively, being the highest of all states and territories in Australia. From the

Australian Health Survey 2011-2012, almost 63% of people living in the ACT were

overweight or obese (BMI 25 kg/m2 or more), with the female rate being 54.7% (435).


2.2 Pregnancy related health care in ACT

There are four hospitals that provide maternity services: two are public

hospitals, The Canberra Hospital and The Calvary Hospital; while the other two are

private hospitals, the Calvary Private Hospital and the Calvary John James Hospital. For

women who choose the public health care system during pregnancy, their maternity

health care is provided by their general practitioners (GPs) and midwives, unless they

are identified as high-risk patients, for example if they have a poor obstetric history, a

twin pregnancy or develop complications such as preeclampsia. The high-risk pregnant

women are referred to the special antenatal clinics and are seen by obstetricians and

other specialists as required. For women who choose the private health care system,

their care is provided primarily by private obstetricians.

Currently, there are three pathology companies in Canberra: ACT Pathology,

Capital Pathology and Laverty Pathology. Patients are free to choose one company to

complete their blood and other required tests. If the tests are performed at ACT

Pathology, ACT health employees can assess their patients’ results through the Clinical

Information System (CIS) on the ACT Government Intranet. Capital Pathology and

Laverty Pathology have not incorporated their results into the CIS yet; instead, their

results are posted and/or faxed to the treating clinicians.

2.3 Diagnosis of GDM in the ACT

Within the study period of this research (01/01/2010-30/06/2014), the

diagnosis of GDM was done through a two-step procedure for the majority of women

according to the old ADIPS guideline published in 1998 (79). Usually, for the first step,


all pregnant women without previously diagnosed diabetes, would take a screening test

for GDM, called the glucose challenge test (GCT), which was performed between 26-28

weeks of gestation. The GCT in the ACT involved the measurement of the maternal

plasma glucose level one hour after the consumption of a 50g glucose load by the

woman in the non-fasting status. If the result was greater or equal to 7.8 mmol/l, the

patient would then undergo the second step of screening which was a 75g oral glucose

tolerance test (OGTT) performed after overnight fasting. The diagnostic criteria for GDM

based on the OGTT results were a fasting glucose level greater or equal to 5.5 mmol/l

and/or a 2h glucose level greater or equal to 8.0 mmol/l. However, an early OGTT test,

without prior GCT, was advocated at any stage during pregnancy if the clinical suspicion

of GDM was high, such as obesity, previous GDM history and having a previous

macrosomic baby. In this scenario, if the OGTT results were negative at an early

gestational stage, this test would be repeated between 26 and 30 weeks of gestation

(79). By applying this diagnostic strategy, the prevalence of GDM in the ACT increased

from 49.9 per 1000 people in 2010 to 59.3 per 1000 people in 2011 (263, 441).

GDM screening could be ordered by GPs, midwives or obstetricians. The

pathology laboratories released the GCT and OGTT on the same day of testing. For public

patients, midwives checked the results and informed the patients of their results within

the week of testing. The midwives (and private clinicians) refer patients with diagnosed

GDM to the ACT Health Diabetes Service for education regarding GDM to be provided

by diabetes educators and dietitians.


2.4 Stratified care system for GDM in the ACT

2.4.1 GDM education session (part one):

After being referred to the ACT Health Diabetes Service, GDM patients

receive a comprehensive two-part education session provided by a registered diabetes

educator and a dietitian. This education session is performed in groups of 4-8 patients.

In the first part, presented by the registered diabetes educator, they are provided with

information about GDM including causes, potential complications and treatment

options. Additionally, they are taught how to monitor their own blood glucose levels at

home. They are asked to monitor blood glucose four times a day: fasting and 2h after

each of the three main meals. They are also taught to carefully record all the blood

glucose values in a glucose diary provided by the educators. During this session, the

women with GDM also complete two forms, the first providing key baseline

demographic and pregnancy data to the clinical team (GDM Pathway Form), the other

is a form that registers the GDM diagnosis with the National Diabetes Services Scheme

(NDSS), which allows women to access subsidised glucose monitoring strips. The most

recommended glucose meter for GDM pregnancy in the ACT is the ACCU-CHEK Performa

and ACCU-CHEK Performa Nano produced by Roche (Basel, Switzerland).

Before 2013, the target blood glucose levels for GDM patients were fasting

<5.5 mmol/l and 2-h < 7.0 mmol/l. After June 2013, the target blood glucose levels used

by the ACT Health Diabetes in Pregnancy Service (ACTH-DIPS) were changed to fasting

<5.3mmol/l and 2h < 6.8 mmol/l after consideration of the suggested tighter targets of

the Australasian Diabetes In Pregnancy Society (ADIPS) and the results of two


randomised controlled trials (RCT) conducted by Crowther and colleagues (ACHOIS)

(288), and Landon and collaborators (261). Those two trials showed benefits of treating

GDM using target levels close to these values chosen by the ACTH-DIPS. ADIPS has

suggested tighter targets for the fasting blood glucose level of <5.1 mmol/l and the 2h

blood glucose level of <6.7 mmol/l, but in the consensus guideline statement

commented that further research was required before these targets could be fully

endorsed (80).

2.4.2 GDM education session (part two)

After a 10-minute break, the dietitians provide the second part of the

education session. During this session, information and advice regarding medical

nutrition therapy (MNT) is provided. A wide range of topics are covered, for example the

appropriate pregnancy rate of weight gain, food safety, adequate intake of macro- and

micronutrients, and carbohydrate (CHO) counting. The recommended weight gain in

pregnancy is based on the Institute of Medicine’s Guideline and is dependent on pre-

pregnancy BMI. A weight gain of 12-18 kg is recommended if the patients' pre-

pregnancy BMI is less than 18 kg/m2, gradually reducing to a 5-9 kg weight gain if the

pre-pregnancy BMI is above 30 kg/m2 (436). Based on the Healthy Eating

Recommendation of the National Health and Medical Research Council (NHMRC) (437),

patients are encouraged to take appropriate serves from five core food groups:

vegetables, fruit, grain foods, lean meat and milk. Patients are taught how to count CHO

in exchanges (one CHO exchange is equal to 15 grams of carbohydrate) and to spread


out their CHO intake within 3 meals and 3 snacks, about 2-4 exchanges per main meal

and 1-2 exchanges per snack. Further, low Glycaemic Index (GI) foods and low fat intake

are recommended for their diets. GDM patients are also advised to take enough calcium,

iron, folate and iodine, from normal food or from supplementation. The consumption of

caffeine should be minimal and alcohol should be avoided. Food safety (e.g. food

preparation and the avoidance of raw food) is re-emphasized during the education

session. Additionally, GDM patients are asked to keep a food record for reviewing

purposes.

During the course of this study, an exercise physiologist started to attend

some second part education sessions along with the dietician to provide advice

regarding optimal exercise for achievement of glucose control. A 30-minute walk or

equivalent moderate exercise every day was recommended.

2.4.3 Patient review and stratification into low and high-risk pathways:

Following the group education sessions (part 1 and 2), the GDM patients are

requested to follow the lifestyle advice given as best they can and to start self-blood

glucose monitoring at home to achieve the target blood glucose levels. All women with

GDM (private and public health systems) are reviewed by a dietitian of the ACTH-DIPS

within one week of the education sessions


2.4.3.1 GDM patients managed within the private health system

Patients who choose the private health system are reviewed by an ACTH-DIPS

dietitian within one week of following the education sessions with the blood glucose

record book to determine if lifestyle change alone is adequate to control their blood

glucose. The women also receive individualised dietary advice in this session. The review

results are communicated to the diabetes educators. If the patients can achieve ideal

BGL control with diet and exercise changes only, their latest information regarding GDM

treatment is sent to their obstetrician by diabetes educators and further reviews will be

booked. If patients, however, are not maintaining target BGL levels, the diabetes

educators will inform their private obstetricians and the obstetrician will refer these

patients to a private endocrinologist for consideration for insulin treatment. They will

then continue the antenatal care with the private obstetrician and endocrinologist, with

assistance by the ACTH-DIPS diabetes nurse educators and dietitians, but they do not

come to the ACTH-DIPS-MDC. However, these patients did not give birth in either The

Canberra Hospital or The Calvary Hospital and their delivery information was not

available. Thus, they were not included in this study.

2.4.3.2 GDM patients managed within the public hospital system

Patients who choose public hospital care are similarly reviewed one-to-one

by an ACTH-DIPS dietitian within one week of the education sessions. They have their

BGL record book reviewed and receive individualised dietary advice. Patients achieving

their target BGL are referred back to their previously determined (usual) antenatal care


pathway. Patients who have more than three abnormal glucose readings that cannot

easily be explained by diet are referred on to the diabetes educators. Then, the diabetes

educators inform the endocrinologist about the patient’s situation and an appointment

is arranged for them to attend the Diabetes in Pregnancy Clinic in The Canberra Hospital.

2.4.3.3 Low-risk group

The low-risk group in this research is comprised of patients who can control

their glucose level by diet and exercise alone. For these patients, dietitians send a letter

to their GPs, midwives and obstetricians describing their GDM status and the

management that has been initiated. These low-risk group patients continue within their

usual obstetric care pathway from this time, which mean they will not meet the diabetes

team again unless they have trouble in maintaining target BGLs and are referred back.

The usual obstetric care pathway in The Canberra Hospital is shared care

between GPs, midwives and obstetricians. After midwifery preadmission at 12-14 weeks,

patients will meet their care providers about 8 times during their pregnancies. They will

have two ultrasounds, one at 11-13 weeks for nuchal translucency checking and another

morphology ultrasound at 18-20 weeks. They do not meet the obstetrician unless they

have other risk factors, for example, gestational hypertension, placenta praevia or poor

obstetric history. They are generally allowed to continue the pregnancies to 10 days

beyond term if fetal monitoring is reassuring, and they are more likely to have

spontaneous labour (79).


Some of the low-risk patients choose to deliver in the Calvary Hospital. The

usual obstetric care pathway in The Calvary Hospital is almost the same as in The

Canberra Hospital, except they have one scheduled appointment with the obstetrician

at 36 weeks. Based on the BGL level, the obstetrician might arrange a growth scan

ultrasound. However, the BGL levels are mostly self-reported. Like in The Canberra

Hospital, care providers in the Calvary Hospital recommend that these patients continue

their pregnancies up to 10 days beyond term at the latest, if no other risk factors are

present.

All patients in this group are asked to contact the ACTH-DIPS if they later have

more than 3 elevated BSL readings in any 5-day period. Some of these patients initially

allocated to be low-risk will then be re-stratified into the high-risk category.

2.4.3.4 High-risk group

The high-risk group in this research are patients who cannot maintain their

target BGLs by lifestyle treatment only on review by the ACTH-DIPS one week after the

education session. These patients are referred to Multidisciplinary Diabetes in

pregnancy Clinic (DIPS-MDC), which is attended by endocrinologists, dietitians, diabetes

educators, midwives, obstetricians and social workers. Therefore, the patients be seen

by several members of a multidisciplinary team at one single visit. Most of high-risk

patients who attend the DIPS-MDC start insulin treatment; however, a small number of

them continue diet treatment. This treatment decision is based on the judgement of the

endocrinologist after evaluating the whole information available to them on the


individual GDM patient. This includes information on the patient’s overall medical and

obstetric history, BGL record, diet and exercise compliance, stress, and the patient’s

attitude toward treatment options. Patients who continue diet treatment attend the

MDC regularly. Some do achieve the target BGL with extra dietary advice and are then

referred back to usual antenatal care. Others eventually start insulin treatment if their

glucose levels cannot be adequately controlled even after several extra weeks of lifestyle

change. For patients who initiate insulin therapy, they attend this clinic every two to

four weeks. During these visits, endocrinologists check the patients’ glucose levels and

adjust their insulin dosages accordingly. The oral glucose lowering agent metformin is

rarely used, and if so, is most often added as an adjunct to insulin therapy. However,

metformin is occasionally used alone in patients in whom insulin therapy is not an option.

HbA1c, thyroid function, liver function, kidney function and vitamin D tests are also

ordered by the endocrinologist. In terms of obstetric care, all these patients are able to

meet with both obstetricians and midwives, instead of meeting with midwives alone.

Further, patients who attend DIPS-MDC in the third trimester receive at least one

growth scan ultrasound, most often at 36 weeks gestation. All the patients are required

to count fetal movements at home. Fetal Non-stress Test (NST) is not routinely

prescribed unless abnormal fetal movements or other risk factors are encountered.

Patients who use insulin treatment, have suboptimal glucose control, or are suspected

of having LGA babies, are more likely to end their pregnancies earlier at 38-39 weeks by

induction of labour or elective CS in order to avoid macrosomia and obstructed labour.


2.4.3.5 Determination of the two different risk groups

These are the main characteristics of each risk group:

1) The decision to allocate patients to the low-risk or the high-risk group is

made based on whether they attended the DIPS-MDC or not. Accordingly, patients who

went to the DIPS-MDC one or more times and were then referred to the usual antenatal

care pathway, remained in the high-risk group for the purposes of this study. Patients

initially designated low-risk and managed in usual antenatal care, but were later

referred to the DIPS-MDC for elevated BGLs, were also determined to be in the high-risk

group

2) The majority of the patients in the high-risk group start insulin treatment;

however, a small proportion of them continue lifestyle treatment only. Conversely,

patients in the low-risk group are all treated with diet and exercise.

3) Compared to the patients in the high-risk group, patients in the low-risk

group have a lower level of antenatal surveillance. They have less antenatal clinic visits,

less ultrasounds and blood tests, and are less likely to meet specialists individually

(obstetricians and endocrinologists, dietitians and diabetes educators) regardless of the

final treatment (Flow Chart 2).

Flow chart 2 -The public stratification system for GDM management in ACT Health

Diabetes service.


Patients diagnosed with GDM (ADIPS, 1998):

1. 50g GCT 1h≥ 7.8 mmol/l

2. 75g OGTT FPG≥ 5.5 mmol/l or/and 2h ≥8.0 mmol/l

Diabetes education sessions:

1. Provided by diabetes educators and dietitians

2. Provides information about GDM, advice regarding diet and exercise, and teaches self-monitoring of blood glucose levels

Home blood glucose monitoring:

1. During 1 week

2. Target glucose levels: Fasting < 5.5 mmol/l

2h < 7.0 mmol/l *

Review by dietitian after 1 week

Divide patients into two groups according to whether they can achieve the target blood glucose level by diet and exercise alone.

High Risk Group: **

Attend the Diabetes in Pregnancy Multidisciplinary Clinic in The Canberra Hospital

Most of them start insulin treatment

Higher level of antenatal care

Low Risk Group:

Usual antenatal care, by GPs and midwives

Continue self-monitoring of blood glucose levels

Healthy diet and exercise reinforced

*Target BGL changed from mid-2013 to: Fasting < 5.3 mmol/l, 2h <

6.8 mmol/l.

** Patients who were managed within both pathways (e.g. referred from

the DIPS-MDC to complete pregnancy in the usual antenatal care

pathway, or referred from usual antenatal care to the DIP-MDC) were

determined to be in the high-risk group for the purpose of this study.


2.5 Methodology

This research is a retrospective clinical review of maternal characteristics and

the pregnancy outcomes of women diagnosed with GDM who have attended the ACTH-

DIPS. Maternal demographic and antenatal clinical information, as well as maternal and

neonatal pregnancy outcome data were collected from the patient medical records.

Then, descriptive and inferential statistical analyses were performed.

2.5.1 Ethics approval:

This research was a low-risk research project, as there was no direct contact

with patients and de-identified data only has been used and reported. No consent form

was needed. This research was conducted at The Canberra Hospital and the Calvary

Hospital. Ethics approvals from the ethics committees of the two hospitals were granted

before the start of this research. The Ethics Reference Numbers for this research are

ETHLR.12.302 for The Canberra Hospital and 28-2014 for the Calvary Hospital. The

official approval letters are shown in Appendix 1 and Appendix 2.

2.5.2 Subjects:

2.5.2.1 Inclusion and exclusion criteria:

The subjects of this research included patients who were diagnosed with

GDM and referred to the ACT Health Diabetes Service for a GDM education session and

risk stratification from 01/01/2010 to 30/06/2014. This research recruited patients who

gave birth at The Canberra Hospital or the Calvary Hospital. If a patient had more than


one pregnancy during the research period, each pregnancy was counted as a different

case.

Patients who were diagnosed with diabetes before pregnancy were excluded

because they did not come to the GDM education session. Similarly, GDM patients who

were referred to the ACTH-DIPS-MDC directly without attending the education session

and did not go through the stratification process were not involved in this study.

Unfortunately, the number of patients with pre-existing diabetes and patients who had

GDM but did not go through the stratification procedure was not available due to the

data collection strategy: only patients who attended the education session were

identified.

Patients who delivered in private hospitals or hospitals outside of Canberra

were excluded due to the lack of outcome information and heterogeneity of the

treatment regimen. Multiple pregnancies were excluded from this research due to the

added risk of complications compared to singleton pregnancies; Pregnancies that ended

before the full 28 gestational weeks were also removed because GDM is usually

diagnosed between 26-28 weeks. These patients would have had insufficient time to be

categorized into either the low-risk or high-risk groups.

2.5.2.2 Medical record searching

GDM patients in the ACT who were referred to the ACTH-DIPS attended the

GDM education session either in The Canberra Hospital or at the Gungahlin Community

Health Centre and their attending records were stored in the ACT Patient Administration


System (ACTPAS) and Clinical Record Information System (CRIS) data bases, which were

assessed for eligibility to the study. The GDM education session was ACTPAS coded for

The Canberra Hospital as TGDMIG before Jan 2014, which was changed to CHGDGE after

Jan 2014. The Gungahlin Community Health Centre sessions had the ACTPAS code

GGDMIG. The records of all women who attended these sessions in the period of

interest were reviewed and they were included in the study if they met the inclusion

criteria and none of the exclusion criteria. Allocation to the low-risk or high-risk

treatment groups for the purpose of this study was also determined by use of ACTPAS

codes. The low-risk group patients were those who attended TGDMIG, CHGDGE or

GGDMIG sessions alone. The DIPS-MDC at The Canberra Hospital was coded ANEND and

ANTEND. So patients who attended one of the education sessions and were also coded

as attending an ANEND or ANTEND clinics, were categorized to be in the high-risk group.

2.5.3 Data Collection

2.5.3.1 Electronic medical record systems:

Four electronic medical record systems have been used in this study.

1) Clinical Record Information System (CRIS):

CRIS contains the scanned copies of the patients’ original medical files,

including the entire outpatient clinic and hospital admission records to The Canberra

Hospital. It is maintained by the staff in the medical records department. The currently

used version is 20.1.10.


2) Birthing Outcomes System (BOS)

BOS has the antenatal, perinatal and postnatal information of patients who

delivered in The Canberra hospital, as well as the newborns. The midwives are

responsible for updating it every time patients come to the hospital and receive

obstetric care. The current used version is BOS 6.02.01.

3) ACT Pathology Clinical Integration System (CIS)

CIS provides pathology results of patients who have their tests performed by

ACT Pathology to authorized clinicians. It can be accessed through the ACT Health

intranet https:// actpath/cis/cis.dll.

4) BirthPac

BirthPac is an obstetrics-reporting package used in the Calvary Hospital. Just

like the BOS at The Canberra Hospital, it covers information of mothers, births and

infants and is maintained by the midwives. The currently used version is 1.2.608.

2.5.3.2 Pathological data collection:

All the pathology data of patients who had their blood tests performed by

ACT Pathology were collected from CIS by searching their Medical Record Numbers

(MRNs). This information included GCT and OGTT results. For the high-risk group,

outcomes of HbA1c, TSH and Vitamin D tests were also extracted. The GCT and OGTT

results performed by pathology laboratories other than ACT Pathology were collected


from the medical records if they were noted by clinicians during clinic visits.

Unfortunately, some GCT and OGTT results could not be found by all means available.

2.5.3.3 Maternal demographic data collection:

Maternal demographic information was collected from the ACT Health

Gestational Diabetes Mellitus Clinical Pathway Form. Each newly diagnosed GDM

patient is asked to complete this form, regardless of whether they planned to deliver in

the ACT or not. The demographic information is based on self-report, including age, pre-

pregnancy weight, height, gestation, parity, previous history of GDM, family history of

diabetes, smoking status, consumption of alcohol and other medical illnesses including

hypertension, hypothyroidism, hyperthyroidism, asthma, polycystic ovary syndrome

(PCOS). The gestational week when a patient completed this form was used as the GDM

diagnosed time. The gestational week was calculated based on the date of the last

normal menstrual period often confirmed by an early week ultrasound, or by a dating

ultrasound if the menstrual history was unreliable.

Gestational week at first appointment and country of birth were collected

from either the BOS Antenatal Assessment section if delivered at The Canberra Hospital

or the Event Summary Form from the BirthPac system if delivered at the Calvary Hospital.

Due to the nature of self-reporting, this part of the data is less reliable.


2.5.3.4 Outcome information collection at The Canberra Hospital

1) Maternal complications:

The diagnoses of maternal complications that related to GDM were collected

from the Maternal Admission Summary Form in CRIS and the Antenatal Summary in BOS.

In order to achieve the greatest accuracy, all the notes of ANEND and ANTEND clinics

were reviewed. Maternal complications were also recorded if noted by the doctors

during the clinic visits.

These complications included gestational hypertension (GH), preeclampsia

(PE). The diagnostic criteria of these complications at The Canberra Hospital were the

same as outlined in the latest guideline from The Royal Australian and New Zealand

College of Obstetricians and Gynaecologists (RANZCOG). GH was defined as new onset

of hypertension (≥ 140 mmHg systolic or ≥ 90 mmHg diastolic) after 20 weeks gestation.

PE was classified as newly developed hypertension after 20 weeks gestations combined

with existence of at least one of the following new-onset conditions, including

proteinuria (spot urine protein/creatinine ≥30 mg/mmol or ≥ 300 mg urinary

protein/day or at least 1 g/L on urine dipstick testing), other maternal organ dysfunction

and uteroplacental dysfunction (438).

2) Delivery information:

The intrapartum information was collected from the neonatal sections of BOS,

as well as Birth Summary Forms, ACT Midwives Data Collection Forms and Discharge


Summary Forms from CRIS. This information included onset of delivery (spontaneous

onset, induction and no labour), mode of delivery (spontaneous labour, induced labour,

elective CS and emergency CS), method of birth (normal birth, forceps, vacuum and CS),

reasons for elective CS and emergency CS, perineal status including sutured status,

gestational week of delivery, after delivery bleeding, length of hospitalization (after

delivery), presence of the meconium and shoulder dystocia. Shoulder dystocia was

defined as a vaginal cephalic delivery that requires additional obstetric manoeuvres to

deliver the fetus after the head has delivered and gentle traction has failed (439).

3) Neonatal information:

Neonatal information was collected from the same documents as delivery

information. It included birth status (live and stillbirth), Apgar score, gender, birth

weight, birth length and admission to Neonatal Intensive Care Unit (NICU) and/or Special

Care Nursery (SCN). The diagnoses of neonatal hypoglycaemia, neonatal jaundice and

neonatal respiratory distress were recorded if mentioned.

Neonatal hypoglycaemia was defined as blood glucose level less than 2.6

mmol/l. The diagnoses of neonatal jaundice were based on yellow colouration of the

skin and the sclera caused by a raised level of bilirubin in the circulation. Respiratory

distress was defined as having grunting, flaring or retractions and/or respiratory rate

outside the normal range (40-60 times per minute) (440).


4) NICU and SCN admission information

The Medical Record Numbers (MRNs) of infants who were admitted into NICU

and SCN were extracted from the mothers’ medical records. By searching these MRNs,

a new research group, infants in NICU (SCN) was generated. The information regarding

diagnoses and treatments of these neonates were collected from the Department of

Neonatology Centenary Hospital Discharge Summary Forms in CRIS. Also, the reason for

admission, the length of hospitalization, main diagnoses and treatments were recorded

from these records.

Based on the local policy, infants with BGLs less than 1.5 mmol/l were

admitted into the NICU immediately for intravenous infusion of 10% dextrose. If BGLs

were in the range of 1.5-2.0 mmol/l, NICU or SCN admissions were considered for

initiation of 2 hourly supplementary feeds. Jaundiced infants requiring peripheral IV

fluids and closer monitoring were also admitted in to NICU/SCN. The decision between

treatments with phototherapy or exchange transfusion was made based on the

guideline for Serum Bilirubin (SRB) levels and outcomes of phototherapy (Appendix 3).

For infants with respiratory distress, those requiring crib oxygen, up to 25% or low flow

oxygen were admitted into SCN, while those who needed continuous positive airway

pressure (CPAP) or high flow oxygen were nursed in the NICU. Mechanical ventilation

was indicated for infants managed with CPAP treatment if one of the following situations

occurred: significant respiratory distress, respiratory failure (pCO2 > 55mmHg) or

progressive hypoxaemia [despite fraction of inspired oxygen (FiO2) up to 0.6].


2.5.3.5 Outcome information collection at the Calvary Hospital

1) Maternal Complications:

The diagnoses of maternal complications related to GDM were collected from

the Antenatal Notes sections in the BirthPac system. These complications include GH

and PE.

Based on the local policy of the Calvary hospital, GH was diagnosed as

hypertension arising in pregnancy after 20 weeks gestation without any other feature of

a multi-system disorder. The diagnostic criteria for preeclampsia were: hypertension

arising after 20 weeks gestation and the new onset of one or more of the following

situations: proteinuria, renal insufficiency, liver disease, neurological disturbances or

haematological disturbances.

2) Delivery information

The Event Summary Form and the Discharge Summary Form in the BirthPac

system provided the delivery details. This information included onset of delivery

(spontaneous onset, induction and no labour), mode of delivery (spontaneous labour,

induced labour, elective CS and emergency CS), method of birth (normal birth, forceps,

vacuum and CS), reasons for elective CS and emergency CS, perineal status including

sutured status, gestational week of delivery, after delivery bleeding, length of

hospitalization (after delivery), presence of meconium and shoulder dystocia.


3) Neonatal information

The Mother’s Infant section in the BirthPac system provides general

information about the newborn. Data regarding birth status (live and stillbirth), Apgar

score, gender, birth weight, birth length, admission to NICU and/or SCN, neonatal

morbidities and treatments were collected. However, the detailed medical records of

neonates admitted into the NICU or SCN were not available in the BirthPac system.

Based on the local policies of the Calvary hospital, neonatal hypoglycaemia

was diagnosed as a blood glucose level of 2.5 mmol/l or below. The neonates were

admitted to SCN if their BGL was less than 2 mmol/l. Intravenous treatment with 10%

Dextrose was indicated for BGLs of <1.6 mmol/l. Neonatal jaundice was diagnosed based

on yellow discoloration of infants’ skin and sclera, caused by increased bilirubin level.

Jaundiced infants requiring peripheral IV fluid therapy and closer monitoring were sent

to the SCN. Treatment for jaundice was based on the SRB and was recorded in Neonatal

Phototherapy Record charts. Respiratory distress was defined as having grunting, flaring

or retractions and/or respiratory rate outside the normal range (40-60 times per minute).

The infants requiring oxygen therapy were admitted to the SCN. Severe cases were

transferred to the NICU at The Canberra Hospital (See table 4).


Table 4 -Data collection from the medical record system

For patients in the

Canberra hospital

For patients in the

Calvary Hospital

Maternal demographic data

Age GDM Clinical Pathway

Form in CRIS

GDM Clinical Pathway

Form in CRIS

weight GDM Clinical Pathway

Form in CRIS


Form in CRIS

height GDM Clinical Pathway

Form in CRIS


Form in CRIS

gestation GDM Clinical Pathway

Form in CRIS


Form in CRIS

parity GDM Clinical Pathway

Form in CRIS


Form in CRIS

GDM history GDM Clinical Pathway

Form in CRIS


Form in CRIS

Family history

of diabetes


Form in CRIS


Form in CRIS

smoking GDM Clinical Pathway

Form in CRIS


Form in CRIS

alcohol GDM Clinical Pathway

Form in CRIS


Form in CRIS

medical issues GDM Clinical Pathway

Form in CRIS


Form in CRIS

gestational

week when diagnosed

with GDM


Form in CRIS


Form in CRIS

Gestational

week at first appointment

Antenatal Assessment in

BOS

The Canberra Hospital

Event Summary Form in

BirthPac

the Calvary Hospital

Country of birth Antenatal Assessment in

BOS

The Canberra Hospital

Event Summary Form in

BirthPac

the Calvary Hospital

Maternal Pathology Data

HbA1c CIS/Clinic-visit notes CIS/Clinic-visit notes

TSH CIS/Clinic-visit notes CIS/Clinic-visit notes

Vitamin D CIS/Clinic-visit notes CIS/Clinic-visit notes

GCT CIS/Clinic-visit notes CIS/Clinic-visit notes

OGTT CIS/Clinic-visit notes CIS/Clinic-visit notes

Maternal Outcomes Information

GH Maternal Admission

Summary Form

Antenatal Notes in

BirthPac


ANEND and ANTEND

Clinic Notes in CRIS/

Antenatal Summary in

BOS

PE Maternal Admission

Summary Form

ANEND and ANTEND

Clinic Notes in CRIS/

Antenatal Summary in

BOS

Antenatal Notes in

BirthPac

onset of delivery Birth Summary Form/

ACT Midwives Data

Collection Form

Discharge Form in CRIS/

Neonatal section in BOS

Event Summary Form/

Discharge Summary Form

in BirthPac

mode of delivery Birth Summary Form

ACT Midwives Data

Collection Form



Event Summary Form/


in BirthPac

method of birth Birth Summary Form

ACT Midwives Data

Collection Form



Event Summary Form/


in BirthPac

reason for emergency CS

and elective CS

Birth Summary Form

ACT Midwives Data

Collection Form



Event Summary Form/


in BirthPac

perineal status including

sutured status

Birth Summary Form

ACT Midwives Data

Collection Form



Event Summary Form/


in BirthPac

gestational week of

delivery

Birth Summary Form

ACT Midwives Data

Collection Form



Event Summary Form/


in BirthPac

after delivery bleeding Birth Summary Form

ACT Midwives Data

Collection Form



Event Summary Form/


in BirthPac

length of hospitalization Birth Summary Form Event Summary Form/



ACT Midwives Data

Collection Form



in BirthPac

meconium stained liquor Birth Summary Form

ACT Midwives Data

Collection Form



Event Summary Form/


in BirthPac

shoulder dystocia Birth Summary Form

ACT Midwives Data

Collection Form



Event Summary Form/


in BirthPac

Neonatal Outcome Information

Birth status Birth Summary Form

ACT Midwives Data

Collection Form



Mother’s infants

in BirthPac

Apgar score Birth Summary Form

ACT Midwives Data

Collection Form



Mother’s infants

in BirthPac

gender Birth Summary Form

ACT Midwives Data

Collection Form



Mother’s infants

in BirthPac

birth weight Birth Summary Form

ACT Midwives Data

Collection Form



Mother’s infants

in BirthPac

birth length Birth Summary Form

ACT Midwives Data

Collection Form



Mother’s infants

in BirthPac

admission to NICU/SCN Birth Summary Form

ACT Midwives Data

Collection Form



Mother’s infants

in BirthPac

Diagnosis neonatal

complications

Birth Summary Form Mother’s infants

in BirthPac


ACT Midwives Data

Collection Form



NICU/SCN Admission Information

reasons for admission Department of

Neonatology Centenary

Hospital Discharge

Summary Form in CRIS

Nil

length of stay in

NICU/SCN

Department of


Hospital Discharge


Nil

main diagnosis and

treatment

Department of


Hospital Discharge


Nil

GDM, gestational diabetes mellitus; GCT, glucose challenge test OGTT oral

glucose tolerance test;

GH, gestational hypertension; PE, preeclampsia; NICU, neonatal intensive

care unit; SCN, special care nurseries;

CRIS, clinical record information system; BOS, birthing outcomes system;

CIS, ACT pathology clinical integration system

2.5.4 Data management

2.5.4.1 Data security:

Microsoft Office Excel 2007 was used to store and process data in this study.

All data was stored in a password protected Excel file. Patients’ MRNs and the assigned

case numbers were recorded in a lab notebook manually and locked in an assigned

bookcase located in the office of the Department of Endocrinology and the ACT Diabetes

Service in The Canberra Hospital (Building 6, Level 3).


2.5.4.2 Data settlement:

For analysis purposes, the original data was managed as follows. BMI was

calculated using the formula BMI=bodyweight (kg)/height2 (m2). Based on the countries

of birth, patients were divided into the following ethnic groups: Anglo-European

(Australian, New Zealander, American and European), South Asian (Indian, Pakistani, Sri

Lankan, Nepalese, Bangladeshi and Bhutanese), South-East Asian (Chinese including

from Hong Kong and Taiwan, Vietnamese, Thai, Cambodian, Filipino, Malaysian,

Indonesian, Lao people, Burmese, Korean and Japanese), Pacific Islander (Samoan,

Tongan, Papua New Guinean and Fijian), Middle-Eastern (Syrian, Iranian, Iraqi, Jordanian

and Afghanistan), and others (424).

The outcome information also needed to be redefined. Preterm birth was

defined as patients delivering before 37 weeks and patients who gave birth after 42

weeks were identified as post-term birth (441). Apgar scores at 5 min of age were

classified into three ordinal groups: low (Apgar 0-3), intermediate (Apgar 4-6), and

normal (Apgar 7-10). Low Apgar score is closely related to the occurrence of neonatal

adverse outcomes (442). Macrosomia was defined as birth weight greater than or equal

to 4000g (443), while low birth weight was defined when birth weight was less than

2500g, both irrespective of the gestational age (444). Small for gestational age (SGA)

refers to a birth weight below the 10th percentile of the general population after

adjustment for gestational age and gender (445). Alternatively, large for gestational age

(LGA) was defined as birth weight above the 90th percentile after adjustment for

gestational age and gender. A centile calculator was used to calculate the customized


birth weight percentage, which was specifically designed for the Australian population.

Except for gestational age and neonates’ gender, customized birth weight centile also

adjusted for maternal height, weight, ethnicity and parity (90, 446). Ponderal index (PI),

another measurement of body size, was also calculated for the neonates. PI yields valid

results even for very short and very tall persons and that is the reason why it is

commonly used in paediatrics. It is a ratio of body weight to length expressed as PI=

weight (g)*100/length3 (cm3). According to some authorities, normal PI value is from

2.32-2.85 g/cm3. Neonates with a PI greater than 2.85 are classified as obese, while

those with a PI less than 2.32 are classified as thin (447).

While the majority of the patients categorized into the high-risk group

received insulin, some did not. For some data analyses, the high-risk group was

separated into subgroups, those receiving insulin [high-risk insulin treated (HRI)] and

those treated with diet only [high-risk diet treated (HRD)]. Patients in the HRD subgroup

were mostly those referred by their midwives and GPs to the DIPS-MDC due to abnormal

BGL. However, rather than starting insulin, the endocrinologists considered that there

was a reasonable chance that extra attention to diet or approaches to manage stress

could improve their glycaemic control. A few patients attended as HRD patients due to

psychosocial issues and/or language difficulties. The HRD patients attending the DIPS-

MDC were reviewed by the full team, including endocrinologist and obstetrician. Those

HRD patients who achieved target BGL following one or more visits to the DIPS-MDC

were referred back to the usual antenatal care pathway, unless the treating clinicians

had other antenatal concerns. These patients kept stayed within the category of HRD


for the purposes of this study. Those patients initially managed with diet in the DIPS-

MDC, but subsequently treated with insulin, were categorized in the HRI subgroup for

the purposes of this study.

Data reliability was evaluated based on two factors: accuracy of data

recording and collection, and number of missing data. Information that was clearly

recorded in the electronic medical record system and percentage of missing data is

lower than 10% were deemed as data with good reliability. Otherwise, the data

reliability is intermediate if there is inconsistency in results from different medical

record systems, or the percentage of the missing data is higher than 10%. The data

reliability is classified as low when the percentage of the missing data is higher than 50%.

2.5.5 Statistical methods

2.5.5.1 Sample size calculation:

The hypothesis of this research was that outcomes in the low-risk group may

not be better than in the high-risk group due to a lower level of antenatal surveillance

and different treatment composition. Since birth weight is one of the primary outcomes

of GDM patients, a difference of 100 g in the mean birth weight, if adjusted for

gestational age, between the two research groups, either heavier or lighter, was

considered clinically significant. The variation of the study population was determined

from using the results of the HAPO study, which is the largest clinical data set in GDM

research, with almost 25,000 participants. The standard deviation of birth weight in the

HAPO study was 529g (83). By applying the equation of sample size calculation, n=


(Zα/2+Zβ)2*2*σ2/d2 (α=confidence level, 1-β= power, σ= measurement variation, d=

smallest meaningful difference to be detected), it is determined that at least 439

patients are required in each group to detect a 100g difference in birth weight with a

95% confidence level and a power of 80%.

2.5.5.2 Statistical analysis

Tabulation of data and statistical analysis were performed using SPSS-version

21.0. Kolmogorov-Smirnov test was used to exam whether the continuous data follow a

normal distribution. The means of the parametric variables were compared using

Student’s t-test whereas non-parametric data were compared using Mann Whitney U

Tests. Categorical data were analysed using Chi square (x2) tests and Fisher s exact tests

-- for contingency tables with minimum expected frequencies of less than 5. Stepwise

logistic regression was performed for variables that were significant in univariate

analysis to control for confounders. Odds ratios (ORs) with a 95% confidence interval

(CIs) were calculated for variables of interest. Confidence intervals excluding 1.0 were

considered significant. All tests were two-tailed with p< 0.05 considered significant.


CHAPTER THREE RESULTS

3.1 General information:

From 01/01/2010 to 30/06/2014, 1428 patients were diagnosed with GDM and

were referred to the ACT Health Diabetes Service for education sessions. Among these

patients, 414 were excluded from this study because their deliveries occurred in

hospitals other than The Canberra Hospital and the Calvary Hospital. Further, patients

who had twin pregnancies (36 cases), triple pregnancy (1 case) and delivery before the

28th gestational week (2 cases) were also removed from the study population.

Consequently, 975 patients were included in this study, with 509 in the low-risk group

and 466 in the high-risk group (Flowchart 3). This number fulfilled the sample size

requirement to detect a 100 g difference in birth weight, with 95% confidence level and

power of 80%.


Flowchart 3 -Study Population.

1428 patients were diagnosed with GDM and

attended the education session *

414 patients had no delivery

information

1014 patients gave birth in either The Canberra

Hospital or The Calvary Hospital

36 twins

1 triplet

2 patients delivered before 28 weeks

975 patients in the research group

509 patients in the

low-risk group

466 patients in the

high-risk group

* This number does not include patients who received care from the

DIP pregnancy service but did not attend the GDM education session

and stratification procedures (e.g. women with pre-existing diabetes

or late transfers from other hospitals).


3.2 Maternal demographic information

3.2.1 Maternal age, BMI, gestational age at first appointment, gestational

age at GDM diagnosis, number of gestations and parity

GDM patients in the low-risk group were younger (31.7 ± 4.8 years-old

compared to 32.6 ± 5.3 years-old, p= 0.009), and had a lower BMI (26.3 ± 6.7 kg/m2 v.s.

29.3 ± 7.5 kg/m2, p< 0.001). Low-risk group patients also had later first appointments

with their care providers (15.1 ± 6.6 gestational weeks v.s. 13.2 ± 7.3 weeks, p< 0.001),

later diagnosis of GDM (28.3 ± 2.8 gestational weeks v.s. 26.6 ± 4.5 weeks, p< 0.001),

and a lower percentage of patients diagnosed with GDM before 24 weeks of gestation

(6.0% v.s.18.9%, p<0.001). Interestingly, of the 975 women making up this study cohort,

only 47 had diabetes in pregnancy according to WHO criteria. Moreover, of these 26

were stratified to the high risk group and 21 were stratified to the low risk groups (non-

significant; p=0.28). Patients within the low-risk group had fewer pregnancies (2.1 ± 1.3

times v.s. 2.5 ± 1.8 times, p<0.001), as well as fewer births (0.73 ± 1.0 times v.s. 0.98 ±

1.2 times, p< 0.001) (Table 5).


Table 5 -Maternal age, BMI, gestational age at first appointment, gestational age at GDM

diagnosis, number of gestations and parity.

Low-risk group

(n=509)

High-risk group

(n=466)

p- value*

Age (years-old) 31.7 ± 4.8 32.6 ± 5.3 0.009

BMI (kg/m2) 26.3 ± 6.7 29.3 ± 7.5 <0.001

Gestational age at first

appointment (weeks)

15.1 ± 6.6 13.2 ± 7.3 <0.001

Gestational age at GDM

diagnosis (weeks)

28.3 ± 2.8 26.6 ± 4.5 <0.001

Diagnosed with GDM before

24 gestational weeks (%)**

6.0% 18.9% <0.001

Gravida (times) 2.1 ± 1.3 2.5 ± 1.8 <0.001

Parity (times) 0.73 ± 1.0 0.98 ± 1.2 <0.001

* Student’s t-test was used.

** Chi-square test was used

Regarding data reliability, less than 10 % of the cases had missing data (Table 6).

However, these data are self-reported, and the reliability is intermediate.


Table 6 –Prevalence of cases with missing data on maternal age, BMI, gestational age at

first appointment, gestational at GDM diagnosis, number of gestations and parity.

Number of cases with missing data (%)

Low-risk High-risk

Age 0 0

BMI 0.8% 0

Gestational age at first

appointment

0.8% 0.6%

Gestational age at GDM

diagnosis

1.2% 0.2%

Diagnosed with GDM before

24 gestational weeks (%)

1.2% 0.2%

Gravida 0 0

Parity 0 0


3.2.2 History of GDM, family history of diabetes

The prevalence of previous history of GDM and of family history of diabetes

were lower in the low-risk group, compared to the high-risk group patients, 13.2% v.s.

23.2%, p< 0.001, and 55.4% v.s. 67.0%, p=0.001, respectively (Chart 2).

Chart 2 -History of GDM and family history of diabetes.

* 2*2 Chi-square (χ2) test was used.

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

Previous GDM Family history of Diabetes

Low-risk Group High-risk Group

p<0.001

p=0.001


Data reliability: less than 10 % of the cases had missing data regarding history

of GDM and family history of diabetes (Table 7). Based on the nature of self-report

information, the reliability of these data is intermediate.

Table 7 –Prevalence of cases with missing data regarding GDM history and family history

of diabetes.


Low-risk group High-risk group

GDM History 0 0

Family History of Diabetes 0.2% 0.2%


3.2.3 Smoking status and alcohol consumption

The two groups had comparable number of patients who continued smoking

(10.1% v.s. 10.1%, p= 0.996), and consumed alcohol (5.4% v.s. 4.7%, p= 0.653) during

pregnancy (Chart 3).

Chart 3 -Smoking status and alcohol consumption during pregnancy.

* 2*2 Chi-square (χ2) test was used.

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

smoking Alcohol consumption


p=0.653

p=0.996


Data reliability: The prevalence of cases with missing data regarding smoking

status and alcohol consumption was less than 10% (Table 8). Based on the nature of self-

reporting information, the reliability of these data are intermediate.

Table 8 -Missing data on smoking status and alcohol consumption.



Smoking 4.7% 8.6%

Alcohol Consumption 4.7% 8.6%


3.2.4 Maternal ethnicity

Based on the country of birth, mothers in the low-risk group were mostly

Anglo-European (56.3%) , followed by South-East Asian (19.4%), South Asian (18.0%),

Pacific Islander (1.6%), Middle-Eastern (1.2%) and other ethnicities (3.6%). In the high-

risk group, the majority of patients were Anglo-European (58.7%), followed by South

Asian (21.4%), South-East Asian (11.9%%), Middle-Eastern (1.7%) Pacific Islander (1.1%),

and other ethnicities (5.2%). Only the proportion of South-East Asian patients was

statistically different between two groups with more South-East Asians in the low-risk

group (19.4% v.s. 11.9%, p=0.002) (Chart 4).

Chart 4 -Maternal Ethnicity

* 2*2 Chi-square (x2) test was used

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

Anglo-European South-East Asian South Asian Pacific Islander Middle-Eastern


p=0.457

p=0.002 p=0.176

p=0.489p=0.489


Data reliability: There are 12 missing data in this section, 1.6% in the low-risk

group and 0.9% in the high-risk group (Table 9). This ethnicity information is based on

self-reporting. The patients’ ethnicity is defined by the country of birth. There might be

patients who born in Australia or other countries, but from ethnic backgrounds with

higher GDM risk than the general population (eg: Asian, Maori). So, the reliability is

intermediate.

Table 9 -Missing data on maternal ethnicity.



Maternal ethnicity 1.6% 0.9%


3.2.5 Maternal pre-existing complications

GDM patients in the low-risk group had lower prevalence of hypothyroidism

(4.5% v.s. 7.7%, p=0.036) and of essential hypertension (0.6% v.s. 3.4 %, p= 0.001). There

were no differences between low- v.s. high-risk groups regarding the prevalence of

hyperthyroidism (0.6% v.s. 0.9%, p=0.619), asthma (3.7% v.s. 4.7%, p=0.443), and PCOS

(2.9% v.s. 3.6%, p=0.539) (Chart 5).

Chart 5 -Maternal pre-existing complications.

* 2*2 Chi-square (x2) test was used in comparing hypothyroidism, asthma,

PCOS. Fisher’s exact test was used to compare essential hypertension and

hyperthyroidism.

Data reliablity: There is no missing data regarding this part of information.

Due to the nature of self-reporting data, the reliability is intermediate.

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

10.0%

Hypothyroidism EssentialHypertension

Hyperthyroidism Asthma PCOS


p=0.001

p=0.036

p=0.619

p=0.443

p=0.539


3.2.6 GCT and OGTT results

Patients in the low-risk group had lower 1-h glucose levels in the GCT test (8.9

± 1.1 mmol/l v.s. 9.2 ± 1.5 mmol/l, p= 0.003) and lower glucose levels while fasting,

during the OGTT (4.9 ± 0.5mmol/l v.s. 5.0 ± 0.8 mmol/l, p< 0.001). Interestingly, both

groups had similar glucose levels, both 1-h (9.6 ± 1.4 mmol/l v.s. 9.8 ± 1.4 mmol/l,

p=0.137) and 2-h (9.0 ± 1.0 mmol/l v.s. 9.0 ± 1.0, p=0.388) at the OGTT (Table 10).

Table 10 -GCT and OGTT results.

Low-risk Group High-risk Group P Value

GCT (mmol/l) 8.9 ± 1.1 9.2 ± 1.5 0.003

OGTT

Fasting(mmol/l)

4.9 ± 0.5 5.0 ± 0.8 < 0.001

OGTT 1H(mmol/l) 9.6 ± 1.4 9.8 ± 1.4 0.137

OGTT 2H(mmol/l) 9.0 ± 1.0 9.0 ± 1.0 0.388


** GCT, Glucose Challenge Test; OGTT, Oral Glucose Tolerance Test

Data reliability: more than 10% of the cases have missing GCT and OGTT data

(Table 11). These data were collected from the CIS. By considering the relatively large

number of missing data, the reliability is intermediate.


Table 11 -Missing data of GCT and OGTT results.



GCT 29.1% 45.1%

OGTT Fasting 10.4% 13.7%

OGTT 1H 12.6% 29.2%

OGTT 2H 12.6% 29.2%

* GCT, Glucose Challenge Test; OGTT, Oral Glucose Tolerance Test


3.2.7 Summary of maternal information:

Patients in the low-risk group were younger and leaner. They had lower

gravida, lower parity, and less personal history of GDM and family history of diabetes.

There were more South-East Asian women in the low-risk group. Both groups had similar

prevalence of smoking and alcohol consumption during pregnancy. In terms of pre-

existing complications, patients in the low-risk group had lower prevalence of

hypertension and hypothyroidism. Additionally, patients in the low-risk group had lower

GCT and fasting glucose results of the OGTT. However, the 1-h and 2-h results of the

OGTT were not statistically different between the two groups.

In conclusion, low-risk group patients had less risk factors for adverse

perinatal outcomes, as well as lower GCT results and fasting glucose level of OGTT, which

indicated that the stratification system for GDM in the ACT is effective at differentiating

risk.


3.3 Maternal outcomes

3.3.1 Gestational hypertension (GH) and preeclampsia (PE)

Patients in the low-risk group had lower prevalence of GH (3.9% v.s. 7.7%,

p=0.011). They also have lower prevalence of PE, but it was not statistically different

(2.2% v.s. 4.1%, p=0.084). However, the low-risk group patients had lower overall risk of

developing pregnancy-induced hypertension, which includes GH and PE (6.1% v.s. 11.8%,

p= 0.002) (Chart 6).

Chart 6 -Pregnancy-induced hypertension.

* 2*2 Chi-square (x2) test was used.

** GH, gestational hypertension; PE, preeclampsia

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

GH PE Pregnancy-induced hypertension


p=0.084

p=0.011

p=0.002


3.3.2 Gestational age, preterm birth and post-term birth

Patients in the low-risk group delivered later than the high-risk group patients

(39.0 weeks v.s. 38.7 weeks, p< 0.001). More patients delivered after 40 weeks of

gestation in the low-risk group compared to the high-risk group (33.4% v.s. 8.6%,

p<0.001). Of note, one patients in the high-risk group delivered at 42 weeks, which could

be diagnosed as post-term birth.

Interestingly, the prevalence of having preterm births was significantly higher

in the low-risk group patients (9.8% v.s. 6.0%, p= 0.028). In the multi-variant regression

model, the risk of preterm birth was still higher in the low-risk group (OR 1.897, 95%CI

1.137 – 3.164, p= 0.014), even after controlling for age (OR 1.050, 95%CI 0.998-1.106,

p= 0.062), parity (OR 0.713, 0.523-0.972, p= 0.032), smoking status (OR 0.994, 95%CI

0.429-2.304, p= 0.988) and alcohol consumption (OR 1.664, 95%CI 0.661-4.187, p=

0.279).

Among the low-risk patients, 6.1% had preterm spontaneous onset of labour,

while 1.0% had preterm induction of labour and 2.8% had preterm birth without labour.

Alternatively, in the high-risk group patients, 2.6% had preterm spontaneous labour, 1.3%

had preterm induction and 2.1% had preterm birth without labour. Low-risk patients

had higher rates of spontaneous preterm labour compared to the high-risk group (6.1%

v.s. 2.1%, p= 0.010), however, the rates of having induced preterm birth and preterm

birth without labour were not statistically different, p value equal to 0.656 and 0.370,

respectively (Chart 7).


Chart 7 -The onset of preterm birth.


Data reliability: There is no missing data in this part of information. The

gestational age is precisely recorded in the medical forms. The reliability of this

information is good.

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

Spontaneous preterm birth Induced preterm birth No labour preterm birth


p=0.656

p=0.370

p=0.010


3.3.3 Onset of birth, mode of delivery and methods of birth

1) Onset of birth

Patients in the low-risk group had a higher rate of spontaneous-onset labour

(59.9% v.s. 27.0%, p< 0.001). The rate of induced onset of labour was lower in the low-

risk group (23.2% v.s. 50.6%, p< 0.001). Similarly, the rate of patients who did not

undergo labour was lower in the low-risk group compared to the high-risk group (16.9%

v.s. 22.3%, p= 0.033) (Chart 8).

Chart 8 -Onset of labour.


0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Spontaneous Induced No labour


p<0.001

p=0.033

p<0.001


2) Mode of delivery

Patients in the low-risk group had a higher rate of spontaneous deliveries

(spontaneous onset of labour and vaginal delivery) (52.1% v.s. 21.9%, p<0.001), but a

lower rate of induced deliveries (induced onset of labour and vaginal delivery) (17.1%

v.s. 38.6%, p<0.001) and a lower rate of elective CS (14.1% v.s. 20.4%, p=0.010).

Interestingly, patients in the low-risk group had a similar rate of emergency CS (16.7%

v.s. 19.1%, p=0.328) (Chart 9).

Chart 9 -Mode of delivery.


0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Spontaneous Induced Elective CS Emergency CS


p<0.001

p<0.001

p=0.010p=0.328


3) Methods of birth

In the low-risk group, patients had a higher rate of normal vaginal delivery

(53.8% v.s. 45.9%, p= 0.014), and a lower rate of CS (30.8% v.s. 39.7%, p= 0.004).

However, the prevalence of instrumental delivery was comparable between the two

groups. In the low-risk group, 8.6% of the patients needed forceps during labour,

compared to 8.4% of the patients in the high-risk group (p= 0.878). Vacuum extractors

were used in 6.7% of the deliveries in the low-risk group, compared to 6.0% in the high-

risk group (p= 0.668) (Chart 10).

Chart 10 -Methods of birth.


Data reliability: There is no missing data in this section. All data were

accurately recorded in the medical form. The reliability is good.

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Normal vaginal delivery Forceps Vacuum extractor CS


p=0.014

p=0.878 p=0.668

p=0.004


3.3.4 Reasons for elective CS and emergency CS

1) Reasons for elective CS

Patients in the low-risk group compared to the high-risk group delivered by

elective CS had comparable indications of repeated CS (54.2% vs. 60.0%, p= 0.450),

malpresentation (15.3% vs. 14.7%, p= 0.922), intrauterine growth restriction (IUGR) (1.4%

vs. 1.1%, p= 0.843), and unspecified reason (19.4% vs. 16.8%, p= 0.664). However, the

low-risk group patients had more elective CSs because of placenta praevia (8.3% v.s.

3.2%, p< 0.001) while there were more high-risk group patients who had elective CS

because of LGA (4.2% v.s. 1.4%, p= 0.006) (Chart 11).

Chart 11 -Reasons for elective CS.

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%


p=0.450

p=0.922p<0.001

p=0.006p=0.843

p=0.664


* 2*2 Chi-square (x2) test was used to compare the indications of repeated

CS, malpresentation, and other reasons. Fisher’s exact test was used to compare

indications of placenta praevia, LGA, and IUGR.

** LGA, large for gestational age; IUGR, intrauterine growth restriction


2) Reasons for emergency CS

Among the reasons for performing emergency CS, the low-risk group had

comparable rates of obstructed labour (27.4% vs. 38.2%, p= 0.147), fetal distress (38.1%

vs. 37.1%, p= 1.000) and non-significant trends for reduced obstructed labour combined

with fetal distress (7.1% vs.15.7%, p= 0.097), increased malpresentation (7.1% vs. 3.4%,

p= 0.319), increased placenta abruption (7.1% vs. 1.1%, p= 0.058) and increased

placental praevia (4.8% vs. 0.0%, p= 0.054) compared to the high-risk group (Chart 12).

Chart 12 -Reasons for emergency CS.

`

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%


p=0.147 p=1.000

p=0.097

p=0.319 p=0.058

p=0.054

p=0.361


* 2*2 Chi-square (x2) test was used to compare obstrcted labour and fetal

distress, obstructed labour combined with fetal distress, and unknown reasons. Fisher’s

exact test was used to compare placental abruption, and placenta praevia.

Data reliability: There is no missing data in this section. Some inconsistencies

were found in the medical records regarding this part of information. The reliability is

intermediate.


3.3.5 Perineal status and suture

1) Perineal status

Of the low-risk group patients who underwent vaginal delivery, 21.1% had

intact perineum, 14.5% had first degree perineal laceration, 42.5% had second degree

perineal laceration, 6.0% had third degree perineal laceration, 0% had forth degree

perineal laceration, and 16% had lateral episiotomy. Among the high-risk group patients

who did not have CS, 23.0% had intact perineum, 18.4% had first degree perineal

laceration, 38.3% had second degree perineal laceration, 7.4% had third degree perineal

laceration, 0.4% had forth degree perineal laceration and 12.4% had lateral episiotomy.

There was no statistical difference regarding perineal status between the two groups

(p=0.340) (Chart 13).

Chart 13 -Perineal status.

* Fisher’s exact test was used.

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

intact First degreelaceration

Second degreelaceration

Third degreelaceration

Forth degreelaceration

Episiotomy


p=0.340


2) Perineum suture

After vaginal delivery, perineum suturing was performed in 71.9% of patients

in the low-risk group and 68.1% of patients in the high-risk group. There is no statistical

difference regarding the suture status between the two groups (p=0.300).


Data reliability: Information about perineal status was unavailable for 0.2% of

patient in the low-risk group. Information in this section was well recorded in the

medical forms. The reliability is good (Table 12).

Table 12 -Missing data of perineal status and sutured status.



Perineal status 0.2% 0

Sutured status 0 0


3.3.6 Delivery complications

Patients in the low-risk group had rates of shoulder dystocia comparable to

the patients in the high-risk group (1.6% v.s. 1.5%, p=0.930), as well as of meconium

liquor (4.9% v.s. 3.0%, p=0.129) (Chart 14).

Chart 14 -Delivery complications including shoulder dystocia and meconium

liquor.


Data reliability: There is no missing data in this section. The information

regarding delivery complications were precisely recorded in the medical forms. The

reliability is good.

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

Shoulder dystocia Meconium liquor

Low-Risk Group High-risk Group

p=0.129

p=0.930


3.3.7 Hospitalization and after delivery bleeding

After delivery, patients in the low-risk group tended to leave the hospital

earlier than the patients in the high-risk group, but this number did not achieve

statistical significance (2.7 ± 1.9 days v.s. 3.0 ± 2.07 days, p= 0.052). These two groups

had similar amounts of after-birth bleeding (422.8 ± 336.7 ml v.s. 444.4 ± 393.4 ml, p=

0.358) (Table 13).

Table 13 -Information regarding after delivery hospital stay and after delivery

bleeding.

Low-risk Group High-risk Group P value

Hospital stay after

delivery

(days)

2.7 ± 1.9 3.0 ± 2.07 0.052

Bleeding after

delivery

(ml)

422.8 ± 336.7 444.4 ± 393.4 0.358



Data reliability: Missing data in the section is less than 10%. Data regarding after

delivery information is well documented in the medical forms. The reliability is good

(Table 13).

Table 13 -Missing data of after delivery information.



Hospital stay after delivery

0.4% 0

Bleeding after delivery

0.2% 0.4%


3.3.8 Summary of maternal outcomes

The most interesting finding of this audit was the significant higher rate of

spontaneous preterm birth among low-risk group patients. However, the low-risk group

patients had a lower rate of developing pregnancy induced-hypertension compared to

the high-risk group.

Due to the different delivery strategy between the two groups, the low-risk

group patients were more likely to start their labour spontaneously, while the high-risk

group patients had more induced labours. Regarding the rate of having CS, patients in

the low-risk group had a lower rate of elective CS. The main reasons of elective CS for

both groups were repeated CS, followed by malpresentation. However, patients in the

high-risk group were more likely to experience elective CS due to the suspicion of having

a LGA baby. The emergency CS rate was comparable between the two groups. The

reasons for emergency CS were similar between both groups, including rates of

obstructed labour and fetal distress. There was also a strong trend for shorter hospital

stays in patients of the low-risk group, which might be attributed to the lower CS rate.

In terms of delivery complications, the rates of shoulder dystocia were low in

both groups and also were comparable with each other. Similarly, there was no

difference regarding perineal status and the requirement for perineal suturing between

the two groups, as well as blood loss after deliver.

In conclusion, patients in the low-risk group had significantly increased risk of

preterm birth even after adjusting for multiple confounders. Patients in the low-risk

group had lower prevalence of pregnancy induced hypertension, lower rate of induced


labour and a lower rate of elective CS. However, the rate of emergency CS was

comparable between the two groups, as was the rate of shoulder dystocia.


3.4 Neonatal Outcomes

3.4.1 Birth status

There were two cases of stillbirth among all 975 patients (0.2%), one in each

group. In the high-risk group, the mother was obese (BMI = 35.4 kg/m2) with a high GCT

results (12.1 mmol/l). This baby was delivered at 37.6 weeks of gestation, with birth

weight equal to 3,295 g. In the low-risk group, the mother had a normal BMI (19.5 kg/m2),

but abnormal OGTT results (fasting = 4.3 mmol/l, 1h = 12.5 mmol/l and 2h = 13 mmol/l).

The baby was delivered at 38.5 weeks of gestation, weighing 3,190 g. Based on their GCT

and OGTT results, it is possible that both mothers who had stillbirth might have had

diabetes prior to their pregnancies.


3.4.2 Apgar Score

Patients in the low-risk group had Apgar scores at 1 minute (8.2 ± 1.5 v.s. 8.3

± 1.6, p= 0.717) and at 5 minutes (8.9 ± 0.9 v.s. 8.8 ± 1.2, p= 0.158), comparable or similar

to patients in the high-risk group.

1) Apgar scores at 1 minute

Apgar scores at 1 minute were divided into three groups: low Apgar score (0-3),

intermediate Apgar score (4-6) and normal Apgar score (7-10). In the low-risk group, 3.1%

of patients had a low score, 14.2% had an intermediate score and 82.7% had a normal

Apgar score. In the high-risk group, 3.3% had a low score, 13.7% had an intermediate

score and 83.0% had a normal Apgar score. There was no statistical difference between

the two groups regarding low Apgar score (p=0.947), intermediate Apgar score

(p=0.853), and normal Apgar score (p=0.889) (Chart 15).

Chart 15 -proportion of three groups of Apgar score at 1 minute.

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

Low Apgar score Intermediate Apgar score Normal Apgar score


p=0.947

p=0.853

p=0.889


* R*C Chi-square (x2) test was used.

2) Apgar score at 5 minutes

Compared to the babies of high-risk mothers, those of low-risk women had

similar rates of a low score at 5 minute (0.8% vs. 1.5%, p= 0.289), an intermediate score

at 5 minute (1.4% vs. 2.8%, p= 0.174), and a normal Apgar score at 5 minute (97.8% vs.

95.7%, p= 0.068) (Chart 16).



Data reliability: There is no missing data in this section. The information of

Apgar score was well documented in the medical forms. The reliability is good.

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

Low Apgar score Intermediate Apgar score Normal Apgar score


p= 0.289 p= 0.174

p= 0.068


3.4.3 Birth weight

1) Birth weight, length, and ponderal index

Compared to babies of high-risk mothers, babies born to mothers in the low-

risk group had similar birth weight (3,257.7 ± 498.3 g v.s. 3,299.9 ± 503.6 g, p=0.189),

birth length (49.0 ± 2.4 cm v.s. 49.0 ± 2.4 cm, p= 0.776), and ponderal Index (2.8 ± 0.3

g/cm3 v.s. 2.8 ± 0.3 g/cm3, p=0.234) (Table 15).

Table 15 -Information regarding birth weight, birth length and Ponderal Index.


Birth weight (g) 3,257.7 ± 498.3 3,299.9 ± 503.6 0.189

Birth length (cm) 49.0 ± 2.4 49.0 ± 2.4 0.776

Ponderal index

(g/cm3)

2.8 ± 0.3 2.8 ± 0.3 0.234



2) Macrosomia, low birth weight, neonatal obese, neonatal underweight

Mothers in the low-risk group had comparable prevalence of macrosomic

babies (5.5% v.s. 7.1%, p=0.309) and low-birth-weight babies (6.7% v.s. 5.2%, p=0.313),

compared to the mothers in the high-risk group. Babies of low-risk group mothers also

had similar rate of being obese (33.5% v.s. 37.7%, p=0.179) and being underweight (3.6%

v.s. 4.3%, p=0.533) based on the PI value, compared to the babies born from mothers in

the high-risk group (Chart 17).

Chart 17 -Information regarding macrosomia, low birth weight, neonatal

obese, and neonatal underweight.


0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

Macrosomia Low birth weight Neonatal obese Neonatal underweight


p=0.309 p=0.313

p=0.179

p=0.533


3) Customized LGA and customized SGA

The customized LGA (cLGA) and SGA (cSGA) rates were calculated by using

the customised centile calculator for the Australian population, which controlled for

maternal height, maternal weight, maternal ethnicity, parity, neonatal sex, and

gestational age. Compared to the babies of the low-risk group patients, more infants of

the high-risk group women tended to be cLGA (9.4% vs. 6.1% in the low-risk group, p=

0.050), but had a comparable rate of being cSGA (13.1% v.s. 12.4% in the low-risk group,

p=0.739) (Chart 18).

Chart 18 -Prevalence of cLGA and cSGA babies.

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Customized LGA Customized SGA


p=0.050

p=0.739


Data reliability: Birth weight had no missing data. Information in this section

is precisely recorded in the medical forms. The reliability is good (Table 16).

Table 16 -Missing data for birth weight, length, ponderal index, macrosomia,

low birth weight, obesity, leanness, cLGA, cSGA.



Birth weight 0 0

Birth length 0.4% 0.9%

Ponderal Index 0.4% 0.9%

Macrosomia 0 0

Low birth weight 0 0

Obesity 0.4% 0.9%

Leanness 0.4% 0.9%

cLGA 0 0

cSGA 0 0

cLGA customized large for gestational age

cSGA customized small for gestational age


3.4.4 Neonatal complications

1) Bone fracture and nerve palsy

There were two cases of bone fractures, all in babies born to high-risk

patients. One case had a right humerus fracture and other case had a left humerus

fracture.

Two cases of nerve palsies occurred during the study period of time: a case

of facial nerve palsy in the low-risk group, and a case of Erb’s palsy in the high-risk group.


2) Hypoglycaemia, jaundice, respiratory distress and NICU/SCN admission

Babies of patients in the low-risk group had a similar prevalence of

hypoglycaemia (6.1% v.s. 7.1%, p=0.532), jaundice (8.8% v.s. 10.7%, p=0.320), and

respiratory distress (6.3% v.s. 6.0%, p=0.857) compared to babies of mothers in the high-

risk group. Interestingly, the rate of admission into NICU/SCN was significantly higher in

babies of low-risk group patients (16.7% v.s. 10.9%, p=0.010) (Chart 19).

Chart 19 -Information regarding hypoglycaemia, jaundice, respiratory

distress, NICU/SCN admission.


Data reliability: There are no missing data in this section. The neonatal

complications were well recorded in the medical forms. The reliability is good.

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

Hypoglycaemia Jaundice Respiratory distress NICU/SCN admission


p=0.320

p=0.857

p=0.010

p=0.532


3) Stay in the NICU/SCN and reasons for NICU/SCN admission

Babies of low-risk mothers stayed in the NICU/SCN for the same amount of

time as babies of high-risk women (7.12 ± 6.67 days v.s. 8.57 ± 7.34 days, p=0.258).

Among babies of low-risk group mothers, 35.3% were admitted for

prematurity, 20.0% for respiratory distress, 10.6% for hypoglycaemia, 5.9% for low

Apgar score, 4.7% for prematurity combined with respiratory distress, 3.5% for

congenital abnormality, 1.2% for jaundice, and 18.8% for unspecific reasons.

Alternatively, among babies of high-risk group mothers, 27.5% were admitted due to

prematurity, 19.6% due to respiratory distress, 17.6% due to low Apgar score, 5.9% due

to hypoglycaemia, 3.9% due to prematurity combined with respiratory distress, 3.9%

due to congenital abnormality, 2.0% due to jaundice, and 19.6% due to unknown

reasons. Only low Apgar score was statistically different between two groups among all

these reasons (5.9% v.s. 17.6%, p= 0.028) (Chart 20).


Chart 20 –Reasons for NICU/SCN admissions.


NICU, neonatal intensive care unit; SCN, special care nursery

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

Prematurity Respiratorydistress

Hypoglycaemia Low Apgarscore

Bothprematurity

and respiratorydistress

Congenitalabnormality

Jaundice


p=0.312

p=0.955

p=0.349

p=0.028

p=0.829 p=0.713p=0.906


Data reliability: There were no missing data regarding the NICU/SCN

admission reasons. There were 5 cases with missing information in the low-risk group

about the time that babies spent in the NICU/SCN. Information in this section is well

recorded in the medical forms. The reliability is good (Table 17).

Table 17 -Missing data of NICU/SCN staying time and admission reasons.



Time stay in the NICU/SCN 1.0% 0

Reasons for NICU/SCN

admission

0 0


3.4.5 Summary of the neonatal outcomes

The rate of serious neonatal complications was low: only two cases of

stillbirth, two cases of bone fractures and two cases of nerve palsy were recorded among

975 babies born to GDM patients.

Birth weight is an important indicator of the effect of GDM treatment. Mean

birth weights were comparable between the two groups. However, after adjusting for

confounding factors such as gestational age, gender, maternal weight, maternal height,

parity and ethnicity, high-risk group mothers tended to have an increased prevalence of

LGA babies (9.4% vs. 6.1%, p=0.05). Interestingly, when measuring the ponderal index

of babies born to mothers with GDM, obesity rates were similarly high in both groups.

Infants of these two groups had comparable rates of macrosomia, low birth weight, SGA,

and leanness.

In terms of neonatal complications, babies in these two groups had

comparable rates of hypoglycaemia, jaundice, and respiratory distress. Babies of

mothers in the low-risk group had a higher rate of NICU/SCN admission. The main reason

for NICU/SCN admission was prematurity, followed by respiratory distress. However,

babies of low-risk group patients were less likely to be admitted into NICU/SCN because

of low Apgar score. The duration of the NICU/SCN stay was comparable between both

groups.

In conclusion, the rate of severe neonatal complications, including stillbirth,

bone fracture and nerve palsy was low in both groups. The rate of neonatal

hypoglycaemia, jaundice and respiratory distress was comparable between the two


groups. Interestingly, more babies in the high-risk group tended to be LGA, and neonatal

obesity, assessed by the ponderal index, was observed in babies from both groups.


3.5 NICU admission information in The Canberra Hospital

3.5.1 General Information

The rate of NICU/SCN admission was significantly lower at The Canberra

Hospital compared to the Calvary Hospital (12.5% vs. 20.1%, p= 0.010). This is most likely

due to differences in NICU admission policy between The Canberra hospital and the

Calvary Hospital. For this reason, only an analysis of babies admitted to NICU at The

Canberra Hospital was performed.

Among 786 babies born to mothers with GDM in The Canberra Hospital, 47

(14.7%) babies born to low-risk group mothers were admitted into NICU, while 51

(10.9%) babies born to high-risk group were admitted. Interestingly, the admission rates

were not statistically different between the two groups (p=0.119). The length of stay in

NICU was also not significantly different between the two groups (8.5 ± 6.7 days v.s. 8.6

± 8.3 days, p= 0.941) (Table 18).

Table 18 -NICU admissions of neonates born in The Canberra Hospital.


NICU Admission 14.7% 10.9% 0.119

Stay in the NICU

(days)

8.5 ± 6.7 8.6 ± 8.3 0.941

* 2*2 Chi-square (x2) test was used to compare the rate of NICU admission. Student’s t-

test was used to compare the number of days these babies spent in the NICU.


3.5.2 Neonatal Complications

1) Neonatal Death:

In The Canberra Hospital NICU, there was one neonatal death (1%) of a baby

born to a mother in the low-risk group. This infant died on the first day after being

admitted into NICU, and the reason of death was a severe diaphragmatic hernia.


2) Hypoglycaemia:

In babies admitted to NICU, those born to low-risk mothers had similar

prevalence of having hypoglycaemia compared to babies of high-risk group patients

(36.2% vs. 51.0%, p= 0.140) (Chart 20). Furthermore, the degree of hypoglycaemia was

comparable between the two groups as well (low-risk group 1.42 ± 0.64 mmol/l v.s. high-

risk group 1.65 ± 0.52 mmol/l, p= 0.296). In terms of treatment options for neonatal

hypoglycaemia, there was no statistical difference in requirement for intravenous

glucose infusion between the two groups (low-risk group 84.6% vs. high-risk group

73.9%, p= 0.682) (Chart 21).

Chart 21 -Information regarding neonatal hypoglycaemia and its treatment

in The Canberra Hospital NICU.


0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

Hypoglycaemia Infusion treatment required


p=0.140

p=0.682


3) Jaundice:

In babies admitted to NICU, those born to low-risk mothers with GDM had

similar prevalence of developing jaundice compared to babies of high-risk group

mothers ( 48.9% v.s. 58.8%, p= 0.417). Similarly, of the NICU babies diagnosed with

jaundice, those born from mothers of the low-risk GDM group had comparable rates of

needing phototherapy to those babies of the high-risk GDM group (39.1% v.s. 43.3%,p=

0.786) (Chart 22).

Chart 22 -Information regarding neonatal jaundice and its treatment in The

Canberra Hospital NICU.


0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Jaundice Phototherapy required


p=0.417

p=0.786


4) Respiratory distress:

(1) Rate and main causes of respiratory distress

In babies admitted to NICU, those born to the low-risk GDM mothers had

similar prevalence of having respiratory distress compared to babies of the high-risk

GDM mothers (59.6% v.s. 51.0%, p=0.422). The three main causes of neonatal

respiratory distress among babies of the GDM mothers who were admitted into NICU

were transient tachypnoea of the newborn (TTN) (31.6%), hyaline membrane disease

( HMD) (12.2%) and pneumothorax (3.1%). Babies of the NICU admitted babies of the

low-risk group patients had comparable rates of TTN ( 36.2% v.s. 27.5%, p= 0.391), HMD

( 14.9% v.s. 9.8%, p= 0.543), and pneumothorax (2.1% v.s. 3.9%, p=1.000) compared to

the babies of the NICU admitted babies of the high-risk group mother (Chart 23).


Chart 23 -Respiratory distress and causes in The Canberra Hospital NICU


** TTN, Transient Tachypnoes of Newborn (TTN); HMD, Hyaline Membrane Disease.

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

Respiratory Distress TTN HMD Pneumothorax


p=0.422

p=0.391

p=0.543

p= 1.000


(2) Treatment of respiratory distress

For those NICU babies who had respiratory distress, those born to low-risk

group women were more likely to be treated with oxygen alone, compared to babies

born to high-risk group patients ( 17.9% v.s. 0.0%, p= 0.038). However, the uses of

continuous positive airway pressure (CPAP) (60.7% v.s. 65.4%, p= 0.723), mechanical

ventilation (10.7% v.s. 15.4%, p= 0.610), and expectant (observational only) treatment

(10.7% vs. 19.2%, p= 0.378) were comparable between the two groups (Chart 24).

Chart 24 -The treatment options of respiratory distress in The Canberra

Hospital NICU.


** CPAP, Continuous Positive Airway Pressure

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

Oxygen CPAP Mechanical Expectant treatment


p=0.038

p=0.723

p=0.610

p=0.378


5) Other main complications

In babies admitted to NICU, those of the low-risk group GDM mothers had

comparable prevalence of developing other metabolic abnormalities including

hyponatremia, hypernatremia, hypocalcaemia, hypophosphataemia and metabolic

acidosis, compared to the babies born of the high-risk group GDM mothers (14.9% v.s.

15.7%, p= 1.000).

Babies admitted to NICU born to the low-risk compared to the high-risk group

of GDM patients had similar rates of hypoxic ischemic encephalopathy (HIE) (4.3% v.s.

7.8%, p= 0.679) and coagulopathy (4.3% v.s. 7.8%, p= 0.679) (Chart 25).

Chart 25 -Other main complications in The Canberra Hospital NICU


** HIE,Hypoxic Ischemic Encephalopathy

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

Metabolic abnormality HIE Coagulopathy


p=0.679 p=0.679

p= 1.000


*** Metabolic abnormalities include hyponatremia, hypernatremia,

hypocalcaemia, hypophosphataemia and metabolic acidsis, but exclude hypoglyaemia

and jaundice.

Data reliability: There is no missing data of this section. This kind of

information was precisedly recorded in the medical forms. The reliability is good.


3.5.3 Summary of NICU admission information

After excluding babies who were delivered in the Calvary Hospital, the rate of

NICU/SCN admission in The Canberra Hospital was comparable between the two groups.

This suggests that the increased rate of NICU/SCN admission in the low-risk group overall

(see section 3.4.4) might be attributed to the different strategy of NICU/SCN admission

between The Canberra Hospital and the Calvary Hospital. Additionally, the length of

NICU/SCN stay was comparable between the two groups.

There was only one case of neonatal death due to serious malformation. The

rates of hypoglycaemia were comparable between babies of mothers in both groups, as

well as the degree of hypoglycaemia and the number of babies who needed intravenous

glucose infusion for treatment. Similarly, the rate of jaundice and the need of

phototherapy was comparable between the two groups. The three main causes of

respiratory distress among babies who were admitted into NICU/SCN in The Canberra

Hospital were TTN, HMD and pneumothoraces. There was no difference regarding the

rates of TTN, HMD and pneumothoras between infants of the low-risk and the high-risk

group mothers. However, more babies of the low-risk group mothers who had

respiratory distress were treated with oxygen alone, which might indicate that these

babies had less severe forms of resipartory distress. The babies of both groups also had

similar prevalence of other complications including HIE, coagulopathy and other

metabolic abnormalities.

In conclusion, the infants of mothers who delivered in The Canberra Hospital

requiring NICU admission from the low- and high-risk GDM groups had comparable rates


of several neonatal complications including hypoglycaemia, jaundice, and respiratory

distress. However, respiratory distress in the infants of the low-risk group mothers might

be less severe.


3.6 Subgroup analysis of high-risk group patients who continued diet treatment

(HRD)

High-risk patients were subdivided into two groups: patients who attended

the diabetes clinics and started insulin treatment (HRI), and patients who attended the

multidisciplinary clinic, did not start insulin treatment, and were advised to undergo

dietary treatment (HRD). The level of antenatal obstetric care of the HRD patients was

higher than the low-risk group patients, but lower than the HRI patients. There were 509

patients in the low-risk group, 75 patients in the HRD group, and 391 patients in the HRI

group.

3.6.1 Maternal demographic information

Patients in the HRI group were the oldest (32.8 ± 5.4 years-old), and had the

highest BMI (29.7 ± 7.7 kg/m2), the most parity (1.0 ± 1.2 times), the highest rates of

GDM history (25.1%) and family history of diabetes (68.7%), and the earliest diagnosis

of GDM (26.5 ± 4.5 weeks) among the three groups. The HRI group had the highest

percentage of patients who were Anglo-European (61.0%), but the lowest percentage

of patients who were South-East Asian (8.8%). Regarding the level of glucose intolerance,

the HRI group also had the highest GCT results (9.3 ± 1.6 mmol) and the highest fasting

glucose levels in the OGTT (5.1±0.8 mmol/l).

Maternal demographic data of patients in the HRD group were quite similar

to those of patients in the low-risk group. These two groups had comparable age, BMI,

parity, GDM history, family history of diabetes, and results of GCT and OGTT. However,


patients in the HRD group met care providers earlier (11.4 ± 5.6 weeks vs. 15.1 ± 6.6

weeks, p< 0.001) and were diagnosed with GDM earlier (27.1 ± 4.0 weeks vs. 28.3 ± 2.8

weeks, p=0.007), compared to the low-risk group patients. Moreover, more HRD

patients were of South-East Asian ethnic background (28.0% vs. 19.4%).

All three groups had comparable rates of smoking and alcohol consumption,

as well as 1-h and 2-h OGTT results (Table 19 and Table 20).

Table 19 -Maternal demographic information for the three groups part I.

Low-

risk

group

HRD

group *

HRI

group *

P value

**

P value ***

Low-

risk

vs

HRD

Low-

risk

vs

HRI

HRD

vs

HRI

Age

(years-old)

31.7 ±

4.8

31.6 ±

5.1

32.8 ±

5.4

0.006 0.821 0.002 0.065

BMI

(kg/m2)

26.3 ±

6.7

27.0 ±

5.8

29.7 ±

7.7

<0.001 0.392 <0.001 0.003

Parity

(times)

0.7 ±

1.0

0.7 ±

1.0

1.0 ±

1.2

<0.001 0.844 <0.001 0.003

Gestational

age of first

antenatal

15.1 ±

6.6

11.4 ±

6.0

13.6 ±

7.5

<0.001 <0.001 0.002 0.014


visit

(weeks)

Gestational

age when

GDM was

diagnosed

(weeks)

28.3 ±

2.8

27.1 ±

4.0

26.5 ±

4.5

<0.001 0.007 0.002 0.234

GCT results

(mmol/l)

8.9 ±

1.1

8.5 ±

0.7

9.3 ±

1.6

<0.001 0.104 <0.001 <0.001

Fasting

OGTT

results

(mmol/l)

4.5 ±

0.5

4.5 ±

0.5

5.1 ±

0.8

<0.001 0.864 <0.001 <0.001

1-h OGTT

results

(mmol/l)

9.6 ±

1.4

9.6 ±

1.3

9.8 ±

1.4

0.264 0.780 0.103 0.502

2-h OGTT

results

(mmol/l)

9.0 ±

1.0

8.9 ±

0.7

9.0 ±

1.1

0.394 0.174 0.674 0.290

* HDR, High-risk group on diet treatment; HRI, High-risk group on insulin treatment; BMI,

Body Mass Index; OGTT, Oral glucose tolerance test

** One-way ANOVA *** Post Hoc analysis


Table 20 -Maternal demographic information for the three groups Part II.

Low-risk group

HRD group

*

The HRI group

*

P value

**

GDM history 13.2% 13.3% 25.1% <0.001

Family history

of diabetes

55.5% 58.7% 68.7% <0.001

Smoking 10.1% 7.1% 10.7% 0.669

Alcohol

consumption

5.4% 4.3% 4.8% 0.890

Anglo-

European

56.3% 46.7% 61.0% 0.055

South-East

Asian

19.4% 28.0% 8.8% <0.001

South Asian 18.0% 20.0% 21.7% 0.378

* HDR, High-risk group on diet treatment; HRI, High-risk group on insulin treatment

** Pearson Chi-square Test


Data reliability: The data reliability regarding maternal demographic data is good.

However, for the GCT and OGTT results, there were a few missing data, so the reliability

is intermediate (Table 21).

Table 21 -Missing data in demographic data.

Low-risk Group HRD Group HRI Group

Age 0 0 0

BMI 0 0 0

Parities 0 0 0

GDM history 0.2% 0 0

Family history of

diabetes

0 0 0.3%

Anglo-European 0 0 0

South- East Asian 0 0 0

Gestational Age at

first appointment

0.8% 0 0.8%

Gestational Age

when diagnosed

with GDM

1.2% 0 0.3%

GCT 29% 42.7% 45.5%

Fasting OGTT 10.4% 5.3% 15.3%

1-h OGTT 12.6% 8.0% 33.0%


2-h OGTT 12.6% 8.0% 33.2%


3.6.2 Results of HbA1c, TSH and Vitamin D

Compared to the HRI group patients, patients in the HRD group had similar

results regarding HbA1c (5.3 ± 0.4 % v.s. 5.4 ± 0.5 %, p= 0.196), TSH (1.3 ± 0.9 mU/L

v.s.1.2 ± 0.8 mU/L, p= 0.447), and Vitamin D (57.9 ± 21.2 ng/ml v.s. 57.3 ± 21.0 ng/ml,

p= 0.887) (Table 22).

Table 22 -Results of HbA1c, TSH and Vitamin D.

HRD Group HRI Group P Value

*

HbA1c (%) 5.3 ± 0.4 5.4 ± 0.5 0.196

TSH (mU/L) 1.3 ± 0.9 1.2 ± 0.8 0.447

Vitamin D(ng/ml) 57.9 ± 21.2 57.3 ± 21.0 0.887

* Student’s t-test was used

** HDR, High-risk group on diet treatment; HRI, High-risk group on insulin

treatment


Data reliability: Not all patients who attended the multidisciplinary clinic were

required to be submitted to these tests, and some patients might have choosen to

complete these test at a pathology laboratory other than ACT pathology, which caused

a few missing data in this section (Table 23). This information was extracted from CIS

and clinic notes. Due to the number of missing data, the reliability of this information is

low.

Table 23 -Missing data of HbA1c tests, TSH tests, and Vitamin D levels.

HRD Group HRI Group

HbA1c 53.3% 9.7%

TSH 48.0% 14.8%

Vitamin D 58.7% 29.7%


3.6.3 Maternal outcomes of the three groups

Patients in the HRI group had the highest prevalence of PIH (7.9%) followed

by the patients in the HRD group (6.7%); patients in the low-risk group patients had the

lowest prevalence (3.9%). For preeclampsia, although not statistically different, there

was a similar trend among the three groups.

In terms of preterm delivery, patients in the low-risk group had the highest

rate (10.6%) compared to the HRD patients (6.7%) and HRI patients (5.9%), and the

difference is statistically significant (p= 0.034).

For the onset of labour, the HRD group patients were most likely to deliver

spontaneously (64.0%) compared to the low-risk group patients (59.9%) and the HRI

patients (20.0%). Patients in the HRI group were most likely to be induced (55.9%).

Patients in the HRI group had higher rates of having an elective CS (22.3 %) compared to

the HRD patients (10.7%) and the low-risk group patients (14.1%). However, these three

groups of patients had comparable rates of having emergency CS. Surprisingly, in terms

of the method of birth, although not statistically different, patients in the HRD group

had the highest rate of instrumental vaginal deliveries compared to the low-risk group

patients and the HRI group patients (HRD 22.7% vs. low-risk group 12.8% and HRI 15.3%,

p= 0.081).

Shoulder dystocia was a rare event in all three groups, 8 (1.6%) cases in the

low-risk group, 2 (2.7%) in the HRD group, and 5 (1.3%) in HRI group and the risk was

comparable between the three groups (Table 24).


Table 24 -Maternal outcomes for the three groups.

Low-risk group HRD group HRI group P value

**

Gestational

Hypertension

3.9% 6.7% 7.9% 0.036

Preeclampsia 2.2% 2.7% 4.3% 0.166

Preterm

Delivery

10.6% 6.7% 5.9% 0.034

Spontaneous

onset of

labour

59.9% 64.0% 20.0% <0.001

Induced onset

of labour

23.2% 22.7% 55.9% <0.001

Elective CS 14.1% 10.7% 22.3% 0.002

Emergency CS 16.7% 21.3% 18.7% 0.533

Instrumental

delivery

12.8% 22.7% 15.3% 0.081

Shoulder

dystocia

1.6% 2.7% 1.3% 0.668

* HRD, High-risk group on diet treatment; HRI, High-risk group on insulin treatment

** 2*3 Chi-square Test


Data reliability: There was no missing data in this section. However, some of

the results were not consistent between different medical record systems. Thus, the

reliability is intermediate.


3.6.4 Neonatal outcomes of the three groups

Babies in these three groups had comparable mean birth weight. HRI patients

had the highest rate of customized LGA babies (10.5%) and, although not statistically

different, the lowest rate of customized SGA babies (11.5%) among the three groups. In

contrast, patients in the HRD group had the lowest rate of customized LGA babies (4.0%)

and they tended to have the highest rate of customized SGA babies (21.3%).

In considering ponderal index (PI), a measure of the degree of leanness or

obesity of the infants, the rates of obese and thin babies were comparable among the

three groups. Obesity as determined by PI, was observed in 33.5% of the low-risk group

babies, 39.2% in the HDR babies, and 37.4% in HRI babies (p=0.377). Excessively lean

babies were observed in 3.5% in low-risk babies, 2.7% in HRD babies, and 4.6% in HRI

babies.

The rates of GDM-related neonatal complications were comparable among

infants born to the three groups of patients; these complications included

hypoglycaemia, jaundice, and respiratory distress. However, the rate of NICU/SCN

admission was highest in infants born to the low-risk group patients (16.7%) compared

to the HRD group (10.7%) and HRI group (11.0%) (Table 25).


Table 25 -Neonatal outcomes of the three groups.

Low-risk group HRD

group *

HRI

group *

P value

**

Mean birth

weight (g)

3257.7± 498.3 3270.3± 481.8 3305.6± 508.0 0.362

***

Customized

LGA

6.1% 4.0% 10.5% 0.023

Customized

SGA

12.4% 21.3% 11.5% 0.061

Obesity rate

(determined

by high PI)

33.5% 39.2% 37.4% 0.377

Leanness rate

(determined

by low PI)

3.5% 2.7% 4.6% 0.602

Hypoglycaemia 6.1% 4.0% 7.7% 0.664

Jaundice 8.8% 10.7% 10.7% 0.610

Respiratory

distress

6.3% 6.7% 5.9% 0.951

NICU/SCN

admission

16.7% 10.7% 11.0% 0.035

* HRD, High-risk group on diet treatment; HRI, High-risk group patients on insulin treatment; PI, ponderal index


** 2*3 Chi-square test was used

*** One-way ANOVA was used and Post Hoc test showed no statistical difference

Data reliability: There is no missing data in this section. The reliability is the same as

mentioned above (good).


3.6.5 Summary of subgroup analysis of HRD group

Patients in the HRD group who went to the multidisciplinary clinic but

continued diet treatment only had outcomes more similar to those of the low-risk

patients, in terms of maternal demographic and antenatal information (excluding time

of appointment with care provider, time of diagnosis of GDM, and ethnicity). They were

clearly different to the high-risk group patients with respect to these characteristics.

Similarly, patients in the HRD group had similar prevalence of pregnancy-

related complications (pregnancy-induced hypertension, preterm delivery, and shoulder

dystocia) compared to the low-risk group patients. The method of delivery was also

comparable between the HRD group patients and the low-risk group patients.

Interestingly, the rate of instrumental delivery, although not statistically different, was

higher in the HRD group compared to both the low-risk group and the HRI group.

Regarding neonatal outcomes, patients in the HRD group had the lowest rate

of LGA babies and a strong trend towards having the highest rate of SGA babies among

the three groups. However, the mean birth weight of infants and the rate of neonatal

complications were comparable among the three groups. The difference of NICU/SCN

admission could be attributed to the different NICU/SCN admission criteria of the two

hospitals.

In conclusion, patients in the HRD group were more similar to the low-risk

group patients in terms of maternal information, except the early appointment with care

providers, early diagnosis and ethnic background. Patients in the HRD group had

potentially highest rate of instrumental delivery among the three groups. Interestingly,


patients in the HRD group had the lowest prevalence of LGA babies and possibly the

highest prevalence of SGA babies among three groups.


CHAPTER FOUR DISCUSSION

This research is a clinical audit to evaluate the current stratified treatment

system for GDM in the ACT. According to this system, patients who are diagnosed with

GDM are instructed to optimise their lifestyle and monitor their blood glucose level at

home. After a one-week adjustment period, patients who are able to achieve the target

glucose level only through diet and exercise are categorised into the low-risk group and

receive usual antenatal care from midwives and GPs. Patients who have more than three

BGL higher than the target level (which cannot be attributed to diet) are categorized into

the high-risk group and are asked to attend the multidisciplinary clinic. These women

are cared for by endocrinologists, obstetricians, diabetes educators, dietitians and

midwives. Most patients in this category require insulin treatment to achieve the target

BGL.

Compared to patients in the high-risk group, patients in the low-risk group

have lower levels of antenatal surveillance and most require fewer medical resources;

they do not receive specialist clinical services, nor additional ultrasound scans and blood

tests. However, the glucose control of the low-risk group patient is unknown due to the

lack of information on compliance of this group to lifestyle modification and self-

monitoring of glucose levels.

It has been shown that, after the new IADPSG diagnostic criteria were

endorsed, the prevalence of GDM in different populations did not change or increased

by 3-fold (448-450). In Australia, researchers demonstrated that GDM prevalence

increased from 9.6% to 13.0% if the IADPSG criteria were used instead of the ADIPS


criteria used for the period of this study (451). This high prevalence of GDM will most

likely increase the burden on the already limited medical resources in Australia. The

appropriate distribution of medical resources will become a major issue in the near

future. It could be argued that the change in diagnostic criteria could increase the

number of mild cases of GDM that could be managed within low-risk pathways.

However, this may not be the case, as Duran and associates reported that GDM patients

diagnosed by IADPSG criteria had comparable rates of insulin treatment compared to

those diagnosed by Carpenter-Coustan (CC) criteria (450). Nevertheless, a safe

mechanism to stratify women with lower risk GDM into less intensive management

pathways, no matter what diagnostic criteria are used, should help with allocation of

limited medical resources. Thus, it is crucial to conduct this study at this time.

This research compared the demographic features and perinatal (fetal and

maternal) outcomes between the low-risk group and the high-risk group patients. The

purpose of this study was to test the effectiveness of the stratification system and to

access and compare the perinatal outcomes in two differently treated groups, with

comparison also to the background pregnancy outcome data of the ACT. Particular focus

was on safety, and therefore, the outcomes of the low-risk GDM group.

4.1 Discussion for aim one:

The first aim of this study was to test whether the stratification system

actually allocated patients with fewer risk factors for adverse outcomes to the low-risk

group. The results indicate that the stratification system of GDM treatment in the ACT

is effective.


4.1.1 Baseline demographic information

Patients in the low-risk group had fewer risk factors associated with adverse

pregnancy outcomes, including younger age, lower BMI, less parity, later gestational age

of GDM diagnosis, less previous history of GDM, less family history of diabetes, and

lower fasting OGTT results. There were more South-East Asian patients in the low-risk

group than patients of other races.

The severity of glucose intolerance is associated with adverse outcomes. In

the HAPO study, elevated fasting glucose level in OGTT tests was proven to be associated

with increased risk of birth weight in the >90th percentile, primary CS, cord-blood serum

C peptide >90th percentile, shoulder dystocia, and preeclampsia (83). As confirmed in

previous studies, an elevated fasting BGL was also a strong predictor of insulin use.

McFarland et al. even suggested that GDM patients should start insulin treatment earlier

if fasting glucose levels were above 95 mg/ml (5.3 mmol/l) (452).

Similarly, pre-pregnancy obesity is a crucial risk factor for perinatal adverse

outcomes. Pregnant women who are obese have increased risk of developing

preeclampsia, and they are more likely to deliver a baby by induction and CS (453, 454).

Moreover, they have increased risk of having a macrosomic baby (455). Researchers

analysed the independent effects of maternal obesity and gestational diabetes on

pregnancy outcomes, and found that obesity alone increased the risk of CS delivery (OR

2.16 95%CI 1.74-2.67), and increased the rate of macrosomic babies (OR 1.46 95%CI

0.94-2.27). Furthermore, it was found that the combination of GDM and obesity further


increased the risk of CS delivery (OR 2.26 95%CI 1.65-3.11) and delivering macrosomic

babies (OR 3.45 95%CI 2.05-5.81) compared to GDM and obesity alone (456).

The ethnicity of the patient also influences perinatal outcomes. In an

Australian study, authors found that GDM patients of South-East Asian ethnicity had

lower average birth weight (3.08 ± 0.58kg vs.3.38 ± 0.58 kg) and lower rates of CS (17.9%

vs. 28.8%) compared to women of Anglo-European ethnicity (424). Similar results were

found in the USA. Sridhar and colleagues indicated that Asian women with GDM were

less likely to have LGA babies compared to Caucasian patients (457). Esakoff and

colleagues also demonstrated that among GDM patients, Asians had lower odds ratios

for primary CS (adjusted OR 0.86 95%CI 0.77-0.96) and lower odds ratios for birth

weight >4000g (adjusted OR 0.58 95%CI 0.48-0.70) compared to other ethnicities (425).

The high risk group had later diagnosis of GDM (28.3 ± 2.8 weeks v.s. 26.6 ±

4.5 weeks, p< 0.001), as well as a lower percentage of patients diagnosed with GDM

before 24 weeks of gestation (6.0% v.s.18.9%, p<0.001). This difference might be caused

by the local diagnostic policy. In the ACT, during the time of this study, patients who had

a higher BMI, a previous GDM history, and/or a previous macrosomic baby did not

require a GCT prior to an early OGTT. Considering that the low risk group women had

less of these risk factors for GDM, they were less likely to be screened early. The wider

range of timing of screening in the high risk group is the reason for the higher SD for the

gestational age at diagnosis parameter for this group.

Unexpected, despite greater risk factors for GDM and earlier diagnosis of

GDM in the high risk group, the number of women meeting diagnostic criteria for WHO

diabetes in pregnancy was not higher in the high risk group. Of note, previous


researchers have shown higher rates of adverse perinatal outcomes for women

diagnosed with early GDM (458). Similarly, advanced maternal age, family history of

diabetes, higher parity, and previous GDM history have all been shown to be associated

with multiple adverse perinatal outcomes, including increased risks for gestational

hypertension, CS deliveries and LGA babies (459).

Maternal smoking and alcohol consumption are associated with increased

risk of adverse perinatal outcomes, including low birth weight, preterm birth, placental

abruption, and stillbirth (460-462). In this study, the rates of maternal smoking and

alcohol consumption were comparable between the low-risk and high-risk groups,

which is expected as the stratification was only based on whether patients could achieve

target glucose levels solely through diet and exercise, within one week, rather than any

criteria based on smoking or alcohol consumption.

These results are consistent with other similarly designed studies. There is no

previous study that tested the efficacy of the stratification system used for GDM patients

in the ACT. However, Wong and colleagues did conduct a study in New South Wales

Australia, and found several risk factors for GDM patients who needed insulin treatment

during pregnancy. As mentioned in section 1.7.1, there are several similarities between

that study and this one, such as the diagnostic criteria, ethnic background of patients,

dietary recommendations, GDM education, and more importantly, the stratification

criteria (patients who exceeded target BGL on two or more occasions within 1 week

were started on insulin treatment). In other words, the characteristics of insulin treated

patients in the study by Wong et al. were comparable to the women in the high-risk

group of this study; they also demonstrated that patients in the insulin-treated group


had higher BMI, higher fasting glucose level on OGTT, diagnosis of GDM at earlier age

and were more likely to have previous GDM history (420).

The majority of the patients in the high-risk group needed insulin treatment

(84%). Although these results are not directly comparable, our results were consistent

with previous studies that analyzed the risk factors of insulin initiation among GDM

patients. Advanced maternal age, higher BMI, a higher rate of family history of diabetes,

higher rate of previous GDM history, and worse OGTT results all contributed to the

increased risk of insulin use. Similarly, other studies also indicated that GDM patients

who needed insulin treatment were less likely to be South–East Asian (421, 463, 464).

Interestingly, patients in the low-risk group had comparable values of the 1-

h and 2-h OGTT results compared to the high-risk group patients, which might be due to

the large number of 1-h and 2-h results unavailable in this study, especially in the high-

risk group (12.6% in the low-risk group and 29.2% in the high-risk group). The potential

explanations include: 1) patients who had previous GDM (more in the high-risk group)

or other risk factors do not need to undergo a GCT, and are submitted to OGTT directly.

2) Patients who have abnormal fasting glucose levels are diagnosed with GDM prior to

receiving a glucose load, which is then not given, such that the 1h or 2h tests, likely to

be higher in these patients, are not performed. High-risk group patients had higher

fasting glucose levels compared to the low-risk patients. And it is assumed that more

patients in the high-risk group were diagnosed with GDM on elevated fasting glucose

alone. 3) Some patients complete their GCT and OGTT in a pathology lab other than ACT

Pathology and their results were not always recorded by their doctors in the clinic notes.


In the HAPO study, higher 1-h and 2-h glucose levels of the OGTT were related

to unfavourable outcomes (83). However, in terms of the requirement of insulin

treatment, some researchers found that there was no statistical difference in the 1-h

and 2-h OGTT results between patients who needed insulin treatment and who did not

need it (420, 463), while others showed different findings; Pertot and colleagues found

that higher 1-h OGTT results were associated with increased risk of insulin treatment

(421). González-Quintero and colleagues suggested that, compared to 1-h and 2-h OGTT

results, 3-h BGLs of the OGTT test might have greater predictive value for the initiation

of insulin treatment (463).

4.1.2 Maternal pre-existing conditions

Patients in the low-risk group had lower rates of pre-existing hypertension

and hypothyroidism, but comparable rates of asthma, hyperthyroidism and PCOS.

4.1.2.1 Pre-existing hypertension

Patients in the high-risk compared to low-risk group had higher rates of pre-

existing hypertension (3.4% vs. 0.6%), a complication that is associated with adverse

perinatal outcomes. A recent meta-analysis encompassing 55 eligible studies and

795,221 patients demonstrated that pregnant women with chronic hypertension have

an increased frequency of adverse perinatal outcomes, including superimposed

preeclampsia (RR 7.7, 95%CI 5.7-10.1), CS (RR 1.3, 95%CI 1.1-1.5), pre-term birth (RR 2.7,

95%CI 1.9-3.6), low birth weight (RR 2.7, 95%CI 1.9-2.8), NICU admission (RR 3.2, 95%CI

2.2-4.4), and perinatal death (RR 4.2, 95%CI 2.7-6.5) (465). Anyaegbunan and colleagues


performed a study to compare pregnancy outcomes between GDM patients with

chronic hypertension and GDM patients without hypertension. They found that infants

of hypertensive GDM patients had higher birth weight, higher frequency of being LGA,

and higher rates of induced delivery. There was no difference regarding the average

blood glucose and frequency of SGA deliveries between the two groups (466).

Patients who have essential hypertension are also more likely to be obese

and have advanced age, which were consistent with the characteristics of high-risk

group patients (467). Moreover, essential hypertension is associated with increased

insulin resistance that is independent of age, BMI, sex and waist-hip ratio, which may

indicate patients in the high-risk group had higher levels of insulin resistance compared

to the low-risk group patients (468).

4.1.2.2 Hypothyroidism

Patients in the high-risk group had a greater rate of hypothyroidism

compared to the patients in the low-risk group. Hypothyroidism is known to be a risk

factor for several adverse perinatal outcomes, including pregnancy-induced

hypertension, intrauterine growth restriction, pre-term delivery and increased risk of CS

(469-471). However, researchers demonstrated that instead of the severity of the

disease itself, the treatment of hypothyroidism during pregnancy was the main factor

that influenced perinatal outcomes. By receiving adequate treatment, patients with

hypothyroidism were not at any increased risk for perinatal morbidity (472, 473).

The higher rate of hypothyroidism in the high-risk group patients might be

attributed to undiagnosed type 1 diabetes, a higher rate of GDM history and a higher


rate of family history of diabetes among high-risk group patients. Firstly, the association

between hypothyroidism and type 1 diabetes has been confirmed in the literature. Up

to 30% of female patients with type 1 diabetes develop hypothyroidism (474, 475),

particularly those with positive thyroid peroxidase antibodies (TPOAb) (476). It is

possible that there could be undetected early type 1 diabetes patients who need insulin

treatment in the high-risk group. Secondly, Vitacolonna and colleagues conducted a

prospective study to analyse the association between hypothyroidism and GDM. They

found that thyroid function and prevalence of thyroid disorders during pregnancy was

not associated with the rate of GDM. However, they indicated a significant increase in

thyroid autoimmunity in women with GDM history and hypothesized that

hyperglycaemia in previous pregnancy could trigger thyroid autoimmune disorders

through upregulating major histocompatibility complex (MHC) class I expression and

increasing thyroid antigen presentation (477). Thirdly, researchers also found that

having a family history of diabetes mellitus was associated with the increased risk of

being TPOAb positive (478). All these factors could contribute to the higher rate of

hypothyroidism among the high-risk group patients.

4.1.2.3 Other pre-existing maternal conditions

Maternal hyperthyroidism is associated with increased risk of preeclampsia,

preterm birth, induced labour and ICU admission (479). Similarly, the pregnancies of

asthmatic women are also associated with increased risk of fetal death, preterm labour,

pregnancy induced hypertension, and CS (480). However, it is not surprising that the

rates of maternal hyperthyroidism and asthma were comparable between the low-risk


group and the high-risk group, because the stratification was based on the patients’

glucose control within one week by diet and exercise, which is not influenced by

maternal hyperthyroidism and asthma.

PCOS is associated with elevated insulin resistance and worse glucose

intolerance (481, 482). Moreover, recent meta-analysis concluded that women with

PCOS had a greater risk of being overweight and obese (483). Patients in the high-risk

group had higher fasting glucose level and higher maternal BMI, which lead to the

assumption that high-risk patients might have an increased rate of PCOS. However, the

rate of PCOS was comparable between two groups (3.6% vs. 2.9%, p= 0.539). This result

might be attributed to the small number of patients who had PCOS in this study or due

to poor recording of this condition in the clinic notes.

4.1.3 Conclusion for aim one

The stratification system for the GDM patients that is currently applied in the

ACT is effective. It allocated patients with fewer risk factors for adverse pregnancy

outcomes to the low-risk group. The low-risk group patients had less advanced age,

lower BMI, lower parity, later gestational age at diagnosis of GDM, lower rate of GDM

history, lower rate of family diabetes, lower rate of pre-existing maternal conditions

(essential hypertension and hypothyroidism), and a lower fasting glucose level in the

OGTT. Additionally, more South-East Asian patients were allocated to the low-risk group,

which is also related to the lower rate of adverse perinatal outcomes.


4.2 Discussion for aim two:

The second aim of this research was to compare the maternal and neonatal

outcomes between the two groups, and to test the effectiveness of the stratified

treatment pathways of GDM in the ACT, especially with regards to the safety of the

treatment pathway of the low-risk group.

The results indicated that patients in the low-risk group had a lower rate of

pregnancy-induced hypertension and were less likely to be submitted to delivery

interventions. The rate of shoulder dystocia was low and comparable between the two

groups. However, the low-risk group patients had significantly higher risk of

spontaneous preterm delivery, which is of concern. In terms of neonatal outcomes,

babies of the low-risk group patients had comparable rates of being macrosomic and

having low birth weight. Although not statistically significant, there was a strong trend

for low-risk patients to have fewer LGA babies, while the rates of SGA were similar

among the patients of both groups. The PI, which is a better measure of leanness or

obesity than birth weight, was comparable between the two groups; however, the PI

results were relatively high in both groups. There was no difference between the two

groups in terms of neonatal complications. The rate of NICU/SCN admission was higher

in the low-risk group, which might be due to the difference in admission policy between

the two hospitals, The Canberra Hospital and the Calvary Hospital.


4.2.1 Maternal outcomes:

4.2.1.1 Pregnancy induced hypertension (PIH)

Patients in this study, both in the low-risk and high-risk groups, had higher

rates of PIH compared to the general population in the ACT (low-risk 6.1%, high-risk

11.8%, general population 5.7%) (484). The association between PIH and GDM has long

been confirmed. A secondary analysis of the Calcium for Preeclampsia Prevention

multicentre trial demonstrated that the adjusted relative risk of developing PIH among

GDM patients compared with glucose tolerant women was 1.54 (95% CI 1.28-2.11) (485).

In the HAPO study, maternal hyperglycaemia was significantly associated with increased

risk of preeclampsia. However, the rate of gestational hypertension was not collected in

the HAPO study (83).

The low-risk group patients had a lower rate of gestational hypertension and

potentially a lower rate of preeclampsia compared to the high-risk patients (6.1% vs.

11.8%), which could be attributed to lower fasting glucose levels, lower pre-gestational

BMI, and lower maternal age compared to the high-risk patients. As seen in the HAPO

study, an increased fasting glucose level was associated with a higher risk of

preeclampsia. Thus, the higher fasting glucose level of the high-risk group patients might

partially explain the increased rate of PIH compared to low-risk group women. Higher

BMI and advanced maternal age are also associated with increased risk of PIH among

both diabetic and non-diabetic women, independent of glucose levels (486, 487). These

results are consistent with the observations of this study, that patients in the high-risk

group had higher pre-gestational BMI and advanced maternal age. Insulin resistance is

believed to be the underlying connection between GDM and PIH (488, 489).


However, the lower rate of PIH in the low-risk group could also be explained

by the lower level of medical surveillance. The blood pressure of the high-risk group

patients was checked by the care providers every time when they came to the diabetes

clinic, which may have increased the detection rate of PIH. In a subgroup analysis, HRD

patients who presented comparable pre-gestational BMI, maternal age and OGTT

results as the low-risk group patients but received a higher level of perinatal care and

had an increased rate of PIH (9.4% vs. 6.1%), which appears to support this inference.

However, the rate of PIH in the HRI group was higher than the HRD group (12.2% vs.

9.4%) suggesting more a real increase in the high-risk group.

4.2.1.2 Preterm delivery

The most interesting finding in this research is that patients in the low-risk

group had a significantly higher rate of preterm delivery (9.8% vs. 6.0%, p= 0.028),

particularly for the rate of spontaneous preterm delivery (5.9% vs. 2.6%, p=0.011)

compared to the high-risk group. The rate of preterm delivery among the general

population in the ACT was 8.4% in 2011 and 8.3% in 2012 (264, 484), which is lower than

the rate in low-risk group by comparison.

The exact reason for the increased risk of preterm delivery in the low-risk

group is unknown, although there are several potential explanations. Firstly, compared

to the high-risk group, the low-risk group patients had a lower level of perinatal care.

Cao and colleagues performed a randomized study to analyse the outcomes of intensive

treatment compared to standard treatment, among GDM patients. Both the intensive

and the standard treatment group used the same target glucose levels and criteria of


insulin initiation. However, patients in the intensive treatment group received

individualized diabetes education, lifestyle intervention and special clinic follow-ups

instead of group-based diabetes education, which was used in the standard group. The

difference in the level of antenatal care between the two groups in their research was

similar to the difference in this study. The authors demonstrated that intensive

treatment significantly reduced the risk of preterm birth (2.4% vs. 8.3%, p= 0.033). It is

noteworthy that because of the comparable rate of insulin treatment, the authors

concluded that the increased rate of preterm delivery was purely attributed to the lower

level of antenatal care (426). The underlying mechanism that links intensive treatment

and reduced risk of preterm birth could be better glucose control. Although the patients’

glucose levels during pregnancy were not available in this study, the high-risk group

patients may have had better glucose control due to the more intensive treatment (490).

Glucose control has been reported to be closely associated with the rate of spontaneous

preterm birth in GDM patients. Yogev and colleagues demonstrated that higher mean

blood glucose (p=0.001) and lower rates of diabetes being well controlled (p= 0.004)

were all significantly related to an increased risk of spontaneous preterm birth (277).

Secondly, the extra ultrasound and blood tests for high-risk patients could

reduce the risk of preterm birth. The extra ultrasound tests during the third trimester

might detect cervical incompetence and polyhydramnios, both of which are common

risk factors of preterm delivery. Timely treatment of patients in the high-risk group may

have lowered the incidence of preterm birth (491). The high-risk group patients also had

checks of their full blood counts, thyroid function and Vitamin D level, which may have

led to the diagnosis of thyroid dysfunction, anaemia, and vitamin D deficiency. Previous


studies indicated that overt and subclinical hypothyroidism is associated with increased

risk of preterm birth, and found that the risk could be reduced by providing adequate

treatment (492). Although the patients in the high-risk group had a higher rate of pre-

existing hypothyroidism, this complication might be better controlled due to the more

frequent clinic visits compared to the low-risk group patients. Similarly, maternal

anaemia during pregnancy also increased the risk of preterm birth (493). The

relationship between vitamin D deficiency and preterm birth is complicated. Vitamin D

deficiency may increase a specific type of preterm birth (inflammation associated

spontaneous preterm delivery) in a selective population (non-white population) (494).

If patients in the high-risk group had undiagnosed hypothyroidism, anaemia or vitamin

D deficiency, they would likely be detected and treated accordingly, while these

complications would remain undetected in low-risk group patients and consequently,

lead to the increased risk of preterm birth. However, this is only speculation at this point,

as future studies would be required to prove these assertions.

Thirdly, lower BMI in the low-risk group compared to the high-risk group

might contribute to the increased rate of preterm delivery. In a sub-analysis of the HAPO

study, authors found that preterm delivery was less frequent with higher maternal BMI

after being adjusted for several confounders and glucose levels. However, the type of

preterm birth is not clearly distinguished in that study (495). Parker and colleagues

conducted a cohort study and demonstrated that pre-pregnancy obesity is associated

with higher risk of medically-induced preterm birth, but not spontaneous preterm

delivery (496). Another meta-analysis concluded that the relationship between maternal

BMI and preterm birth is complicated. The researchers found that overweight (BMI 25-


29.9 kg/m2) and obese I (BMI 30-34.9 kg/m2) women had a reduced risk for spontaneous

preterm birth, although obese II (BMI 35-40 kg/m2) and obese III (BMI >40 kg/m2)

women had increased risk of preterm birth (497). Overall, the relationship between

maternal BMI and preterm delivery is not conclusive.

Finally, gestational weight gain (GWG) may also have played a role in

increasing the risk of preterm birth in the low-risk group. In a large meta-analysis, the

authors indicated that mothers with higher total GWG had lower risk of all types of

preterm delivery among pregnant women (498). This study is unable to collect

information regarding GWG, because it was not often recorded in the medical files.

However, a majority of the patients (84%) in the high-risk group needed insulin

treatment to achieve the target BGL, and previous studies in the literature suggested

that insulin treatment was associated with increased GWG (499). Therefore, the low-

risk group patients might have had lower GWG compared to the high-risk patients and

consequently, had a higher risk of spontaneous preterm delivery.

There may be other relevant factors that contribute to the increased risk of

preterm birth in the low-risk group patients, other than those mentioned above. Future

studies are needed to discover the exact reason, and to develop a suitable strategy to

reduce the preterm birth rate in the low-risk group.

4.2.1.3 Induction rate and elective CS rate

High-risk patients had higher rates of induced labours and elective CS

compared to low-risk patients, which might be due to the elective delivery strategy for

high-risk patients who need insulin treatment. In the ACT, high-risk patients who are


treated with insulin are likely to be induced between the 38th to 39th weeks of gestation.

Additionally, if there is serious suspicion that the baby is LGA, these patients are offered

an elective CS to deliver the babies. Diet-treated GDM patients are normally allowed to

continue pregnancy until the spontaneous onset of labour. However, the rate of

emergency CS was comparable between the two groups.

As mentioned in the introduction part, there is no consensus in terms of

delivery time and method for GDM patients due to a lack of conclusive evidence. The

ADA (20), the ACOG (500) in America, the NICE 2008 (501), the NICE 2013 (502) in the

United Kingdom, and ADIPS (392) in Australia all have different recommendations. The

advised timing of delivery for GDM patients without other complications varies from 38

completed weeks to 10 days beyond term.

Regarding the criteria for offering elective CS, ACOG suggested that it should

be an option for GDM patients who have an estimated fetal weight ≥ 4500g (375).

However, the ADA concluded that the role of fetal weight estimation in determining the

route of delivery is unknown due to the lack of sufficient data (500).

The main benefit of elective delivery is to avoid stillbirth in later pregnancy

and to reduce the risk of fetal macrosomia and shoulder dystocia. However, elective

delivery may also be associated with both maternal and neonatal complications, such as

the increased risk of emergency CS due to the failure of induction and increased

incidence of neonatal respiratory distress and NICU admission (502). There was only one

randomized controlled trial that compared the outcomes between active induction and

expectant management in diabetic pregnant women who needed insulin treatment

(mostly GDM patients). The authors found that the rate of CS was comparable between


the two groups, and the babies of the patients in the active induction group had lower

mean birth weight and a decreased rate of being LGA. There was also no difference

regarding the rate of neonatal respiratory distress including TTN and RDS between the

active-induced group and the expectant-treated group (503). Similarly, in this study,

high-risk patients had comparable rates of emergency CS compared to low-risk patients,

and also had similar rates of neonatal respiratory distress (both TTN and RDS) and

NICU/SCN admission when using the same admission criteria.

In this study, high-risk group patients had higher BMI and higher fasting

glucose levels during the OGTT compared to the low-risk group patients, which may have

led to an increased risk of having macrosomic babies and, consequently, shoulder

dystocia. By applying the current elective delivery strategy, that is, early induction and

elective CS for LGA babies, patients in the high-risk group had an acceptable rate of

having macrosomic babies (7.1% vs. 5.5%, p= 0.309), as well as a low and comparable

rate of shoulder dystocia, compared to the low-risk patients (1.5% vs. 1.6%, p=0.930). It

is assumed that if the expectant delivery strategy had not been used in the high-risk

group, more cases of macrosomia and shoulder dystocia would have occurred.

The currently elective delivery management employed in the ACT, including

earlier induction and elective CS, appears to be satisfactory. It did not increase the rate

of related adverse perinatal outcomes (the increased rate of emergency CS and neonatal

respiratory distress), while the rates of shoulder dystocia and LGA babies were

acceptable in the high-risk group patients. However, conclusive evidence of the delivery

timing and method for GDM patients in still lacking.


4.2.2 Neonatal outcomes

4.2.2.1 Stillbirth

The number of stillbirths is quite low in this study. Only two cases occurred,

with one in each group, among 975 deliveries (0.2%), which is even lower than the

stillbirth rate in the general population (0.7% in Australia, 2011) (264). GDM is suggested

to not be an independent risk factor for stillbirth (504). In both stillbirth cases, the two

mothers might have had pre-existing diabetes, based on their GCT and OGTT results.

This is consistent with the previous literature showing that pre-existing diabetes is

associated with a higher risk of stillbirth (504, 505). The IADPSG consensus statement

on the diagnosis of hyperglycaemic disorders in pregnancy advocates universal testing

of pre-existing diabetes in populations with a high prevalence of type 2 diabetes during

the early stage of pregnancy (506). This result also emphasized the importance of

identifying overt diabetes in the early stage of pregnancy, and a recommendation from

this research could be that any women diagnosed with WHO diabetes in pregnancy

should always be managed within high-risk multidisciplinary teams.

4.2.2.2 Rate of customized LGA

The mean birth weight as well as the rate of macrosomia (5.5% vs. 7.1%, p=

0.309) was comparable for both the high-risk and low-risk groups. The rate of

macrosomic babies in both groups was low compared to the general population in the

ACT 2012 (11.8%). However, it might not represent the real fetal growth due to

differences in gestational age and maternal demographic information. In this study, a

customized birth weight percentile calculator that controls for maternal age, maternal


height, maternal weight, parity, and ethnicity, which are all important factors that might

influence the rate of LGA and SGA in babies, was used. This calculator, obtained from

the www.gestation.net website, uses the method of Gardosi and Francis, which was

specifically designed for the Australian population (507). By using the customised

calculator, babies defined as LGA and SGA were more closely associated with perinatal

complications than LGA and SGA babies categorized by the standard population-based

chart (508). In the current study, when the customised calculator was used, we observed

a strong trend towards high-risk group patients having more LGA babies, when

compared to the rate of LGA babies in the low-risk group (9.4% vs. 6.1%, p= 0.050).

The LGA rate in this study was reasonable compared to other studies. The

rate of LGA was 9.5% in the HAPO study, which might represent the rate of LGA in the

general population, due to the large number of participants and the exclusion of the

mothers with significant glucose intolerance (83). There were two large randomized

controlled trials aimed to demonstrate the influence of GDM treatment. Landon and

colleagues found that the rate of LGA in their treated group was 7.1 %, while the rate of

LGA was 13.0% among the treated group in another study conducted by Crowther and

colleagues (260, 261). Although the LGA rates in this study could not be directly

compared to previous studies, due to the differences in demographic characteristics of

the patients, study design, diagnostic criteria and treatment strategy, we found

comparable rates of LGA to those studies.

The potentially higher rate of LGA in the high-risk group could be explained

by several factors. Firstly, the high-risk group patients had higher fasting glucose levels

compared to patients in the low-risk group, which might contribute to the greater rate

http://www.gestation.net/


of LGA babies. As confirmed in the HAPO study, the fasting, 1-h and 2-h OGTT results

were all significantly related to the increased rate of LGA (83).

Secondly, this difference in the rate of LGA between groups could also be

explained by the higher BMI of patients in the high-risk group. In a sub-analysis of the

HAPO study, researchers demonstrated that higher BMI was associated with an

increased risk of LGA (adjusted OR 3.31, 95%CI 2.68-4.10), independent of the patients’

glucose levels in the OGTT (509). This association was also supported by other studies

(510, 511). Although the customized LGA calculation already controlled for maternal

height and weight, these two factors were adjusted separately, unlike BMI, which

combines both factors. Sjaarda and colleagues indicated that after excluding the

adjustment of maternal weight from the customized calculator, more LGA babies were

diagnosed by the modified percentile calculator, which was associated with greater

rates of shoulder dystocia, NICU admission and neonatal respiratory complications. The

authors suggested that the mathematic method for maternal weight adjustment in the

customized calculator could be further improved (512). Therefore, the influence of

elevated maternal BMI in the high-risk group may still be a potential cause of higher

rates of LGA babies in this group.

Thirdly, the other possible explanation for the increased rate of LGA babies in

the high-risk group is higher gestational weight gain. Lee and colleagues conducted a

study to analyse the relationship between gestational weight gain, pre-pregnancy BMI

and GDM with the risk of LGA. They found that compared to higher BMI and GDM status,

excessive gestational weight gain (≥ 15 kg) was a more important risk factor (513).

Similarly, Kim and colleagues performed a study to assess this association in a multi-


ethnic population. They indicated that for all ethnic groups, GDM contributed the least

(2.0-8.0%), whereas excessive gestational weight gain contributed the most (33.3%-

37.7%) to LGA rates (514). Insulin treatment was suggested to be associated with

increased weight gain (499, 515). The majority of high-risk patients needed insulin

treatment, which could have resulted in higher gestational weight gain, which on the

one hand could contribute to the increased rate of LGA, but on the other hand by

lowering BGL should reduce rates of LGA.

The results of this study are consistent with the previous literature. Barnes

and colleagues conducted an epidemiologic study to evaluate the customised predictors

of LGA and SGA babies in GDM patients. This study used the same diagnostic criteria of

GDM, had similar target glucose levels and used the same LGA and SGA calculator.

Moreover, it took place in New South Wales Australia, which has a similar ethnic

background compared to the population in this study. The authors found that higher

gestational weight gain, higher pre-gestational BMI and insulin treatment were all risk

factors of having LGA babies diagnosed under the customised calculation (515).

4.2.2.3 Neonatal obesity rate assessed by ponderal index in both groups

In addition to birth weight, birth length is another important anthropometric

measurement in neonates. In the ponderal index (PI), the neonatal weight and length

are inter-related [PI (g/cm3)= weight (g)*100/length (cm) 3], and PI is an alternative

measurement in newborns, because the measurement more accurately accounts for

variances in height (short or tall) (516). Fetal development could be asymmetrical or

symmetrical according to PI. In symmetrical neonates, an appropriate relationship


between fetal weight and length is presented by a normal PI. However, asymmetrical

neonates with a high PI have relatively greater weight than length, which is taken as a

measure of neonatal obesity (517). Previously, PI values higher than 2.85 g/cm3 were

used to define neonatal obesity, while PI values less than 2.32 was used to defined

neonatal thinness (148, 447). In this research, the mean PI value, the rate of neonatal

obesity and the rate of neonatal thinness were comparable between the low-risk group

and high-risk group. However, the rate of obese babies was relatively high in both groups

(33.5% in the low-risk group and 37.7% in the high-risk group) when compared to

previous studies that showed that the neonatal obesity rate among non-diabetic women

ranged from 13% to 16.5% (148, 518). However, it is possible that the assessment of

neonatal obesity for Australian babies is overestimated by a PI of >2.85 g/cm3, as is

indicated in the study of Roje et al, who showed the 90th centile for PI at birth for

neonates born in Croatia in the 39th week was higher at 3.03 g/cm3 (517).

Previous research demonstrated that an increased PI value is associated with

higher mean glucose value and increased High Blood Glucose Index (HBGI) (a measure

of the frequency and extent of high blood glucose in continuous glucose monitoring).

The parameters account for the frequency and amplitude of hyperglycaemic events

during the second trimester, but not the third trimester (519). This relationship between

glucose levels and glycaemic variability in the second trimester might explain why babies

of GDM patients in this research had high obesity rates. Since GDM treatment is typically

administered after GDM diagnosis, which occurs around 28 weeks, the treatment may

be too late to reduce the PI value. Also, the current parameters used to predict fetal


growth, such as fasting glucose and postprandial glucose levels may not be sufficiently

accurate as they do not dictate glucose excursions.

Maternal obesity also is a potential contributor to the elevated rate of

neonatal obesity. Hill and colleagues indicated that even babies born to patients with

well-controlled GDM had increased PI values, compared to babies born to non-diabetic

mothers. However, after adjustments for maternal age, parity and fat mass, this

difference disappeared (520). Similarly, Black and colleagues conducted a study aimed

to analyse the relationship between maternal pre-pregnancy obesity, gestational weight

gain, GDM status and fetal overgrowth. They demonstrated that maternal obesity,

regardless of GDM status, was associated with increased neonatal PI values (455). The

GDM patients in this research, both the low-risk and high-risk groups, had higher BMI

than the general population in Australia in 2011 (low-risk group 26.3 kg/m2 and high-risk

group 29.3 kg/m2 vs. 25.9 kg/m2 general pregnancy women) (264), which might explain

the higher rate of neonatal obesity. Similarly, although not reaching statistical difference,

the rate of obesity is lower among infants born to the low-risk mothers who had lower

BMI compared to the infants of high-risk mothers (33.5% vs 37.7%).

Interestingly, researchers found that there was no difference between

disproportionate LGA babies (D-LGA) (PI > 90th percentile) and proportionate LGA babies

(P-LGA) (PI ≤90th percentile) among GDM patients, in terms of severe neonatal

complications including low Apgar score (<4 in 5 minutes), birth trauma (Erb’s palsy and

fractured clavicle), hypoglycaemia, acute respiratory distress (RDS, TTN, and meconium

aspiration), and hyperbilirubinaemia requiring treatment with phototherapy or

exchange transfusion. However, the risk of CS was highest among D-LGA babies of


diabetic mothers (41.3%) compared to P-LGA babies of diabetic mothers (31.8%), D-LGA

babies of non-diabetic mothers (24.2%), and P-LGA babies of non-diabetic mothers

(18.1%) (521). Those findings are consistent with the outcomes of this study. There was

no difference between the infants born to high-risk patients and infants born to low-risk

mothers in terms of neonatal complications (discussed below). However, the increased

rate of neonatal obesity might have contributed to the higher emergency CS rate, both

the low-risk patients and the high-risk patients, compared to the general population in

the ACT (low-risk group 16.7% and high-risk groups 19.1% vs. ACT general population

14.9%) (264).

4.2.2.4 Neonatal complications

The babies of low-risk patients had comparable rates of hypoglycaemia,

hyperbilirubinaemia, and respiratory distress compared to the babies of high-risk

patients. It is not possible to perform direct comparison regarding rates of neonatal

complications between the current study and others, due to the different study

populations, small numbers of cases of these complications, and varied diagnostic

criteria for both GDM and neonatal complications.

The HAPO study demonstrated that the rate of clinical neonatal

hypoglycaemia and the rate of hyperbilirubinaemia (treatment required) was 2.1% and

8.3%, respectively, in their study population. Compared to the HAPO study population,

patients in our study had a higher rate of neonatal hypoglycaemia (low-risk group 6.3%

and high-risk group 7.9%) and a higher rate of neonatal hyperbilirubinaemia (low-risk

group 9.4% and high-risk group 10.9%). However, patients in this study were all GDM


patients and had worse glucose intolerance compared to participants in the HAPO study.

The higher rate of maternal hyperglycaemia was associated with the increased risk of

neonatal hypoglycaemia and hyperbilirubinaemia. Additionally, the diagnostic criteria of

hypoglycaemia and hyperbilirubinaemia were stricter in the HAPO study, which might

be why the risks of hypoglycaemia and hyperbilirubinaemia in this study were

apparently higher (83).

Two milestone studies aimed at examining the effectiveness of GDM

treatment have both indicated that GDM treatment might not reduce the rate of

neonatal complications significantly. The Australian Carbohydrate Intolerance Study in

Pregnant Women (ACHOIS) demonstrated lack of difference between the intervention

group (under glucose monitoring, diet and insulin treatment) and the standard-care

group, regarding risk of jaundice requiring phototherapy (9% vs. 9%, p=0.72),

hypoglycaemia requiring IV therapy (7% vs. 5%, p= 0.16) and respiratory distress

syndrome (5% vs.4%, p= 0.15) (260). Landon and colleagues also showed comparable

rates of hypoglycaemia (16.3% vs. 15.4%, p= 0.75), hyperbilirubinaemia (9.6% vs. 12.9%,

p= 0.12), and respiratory distress (1.9% vs. 2.9%, p=0.33) between their treated group

and routine-care group (351).

Moreover, randomized controlled studies have also suggested that the type

of GDM treatment and the level of antenatal care (which are the two major differences

between the low-risk group and high-risk group patients in this study) did not affect the

rate of neonatal complications significantly. Buchanan and colleagues conducted a RCT

that aimed to compare the outcomes between diet treatment and insulin treatment in

GDM patients, and they demonstrated that the rate of hypoglycaemia was similar


between the two differently treated groups (14% vs, 18%, p>0.05) (429). Similarly,

another study has also indicated that the rate of hypoglycaemia (12.6% vs 14.9%,

p=0.556), hyperbilirubinaemia (42.1% vs. 32.9%, p=0.118) and respiratory distress (8.7%

vs. 10.9%, p=0.528) was comparable between patients who received intensive antenatal

care and patients who had standard levels of antenatal care (426).

Overall, GDM treatment did not significantly reduce the rate of neonatal

complications, and there was also no difference between different treatment strategies

in terms of the rate of neonatal complications. The comparable rates of neonatal

hypoglycaemia, hyperbilirubinaemia and respiratory distress between the low-risk and

high-risk group patients are consistent with these previous findings.

The higher rate of NICU/SCN admission in the low-risk group could be

attributed to the different NICU/SCN admission strategies in the two hospitals. The rate

of NICU/SCN admission rate was 16.7% in the low-risk group and 10.9% in the high-risk

group (p= 0.010). However, if patients delivered at the Calvary Hospital were excluded,

the admission rate was comparable between the high-risk and low-risk groups (14.7%

vs. 10.9%, p=0.119); babies born in the Calvary Hospital were more likely to be admitted

into NICU/SCN compared to babies born in The Canberra Hospital. The rate of NICU/SCN

admission rate in this study appears to be acceptable when compared to the admission

rate of the general ACT population (14.7%) (484).

A smaller proportion of the babies from the low-risk group admitted into

NICU/SCN were due to low Apgar scores, compared to the high-risk group (5.9% vs.

17.6%, p=0.028). Patients in the high-risk group had a higher rate of CS and pregnancy-

induced hypertension; both conditions have been shown to be associated with


increased risk of low neonatal Apgar scores (522). The rate of preterm birth, which is

also an important risk factor, did not have a remarkable influence on the rate of low

neonatal Apgar scores due to the relatively small number of preterm births (523).

The infants who were admitted into NICU had comparable rates of

hypoglycaemia, hyperbilirubinaemia and respiratory distress between the two groups,

as well as neonatal complications such as hypoxic ischemic encephalopathy and

coagulopathy. Furthermore, babies of both groups had similar rates of needing

intravenous glucose infusion treatment for hypoglycaemia and phototherapy for

hyperbilirubinaemia. Interestingly, in spite of the comparable rates of needing CPAP and

mechanical ventilation for respiratory distress, the low-risk group patients were more

likely to be treated with oxygen alone. The difference in the treatment of respiratory

distress might have been due to the lower rate of low Apgar scores in the babies of the

low-risk group patients. It might also indicate that babies in the low-risk group had a

lower incidence of severe respiratory distress.

4.2.4 Analysis of patients in the high-risk group who continued diet

treatment alone (HRD)

This research also identified a subgroup of patients (HRD group) who

belonged to the high-risk group (entering the multidisciplinary clinic) but continued diet

treatment to the end of the pregnancy. Patients in the HRD group had characteristics

that were generally more similar to those of low-risk patients in terms of maternal age,

BMI, gestation, parity, GDM history, family history of diabetes, and GCT and OGTT

results (both fasting and 2-h results). However, HRD patients had earlier appointments

with care providers during pregnancy, and were diagnosed with GDM at earlier stages,


when compared to low-risk women. Additionally, the percentage of patients with South-

East Asian backgrounds was highest in the HRD group (HRD group 28% vs. low-risk group

19.1% and HRI group 8.7%).

The earlier appointment with care providers and the earlier diagnosis of GDM

among HRD patients could indicate that they have more risk factors related to the

development of GDM and other complications, or have higher GCT results that could

lead to the omission of OGTT. However, the similar maternal demographic information

and comparable GCT results between the HRD and low-risk group patients did not

support this assumption. Another explanation for this phenomenon is that patients in

the HRD group might be more compliant with medical advice during pregnancy, which

also indicates that these patients might pay more attention to their health and take

greater care of themselves. The earlier diagnosis of GDM might have influenced the

treatment decision made by the endocrinologist. The specialists might give patients

more time to adapt to the lifestyle treatment if patients had earlier GDM diagnosis,

instead of starting insulin treatment immediately because of the limited treatment time.

The ethnic difference was also consistent with the previous literature. Wong and

colleagues indicated that South-East Asian patients had the lowest rate of insulin

treatment compared to GDM patients from other ethnicities (South-East Asian 37.2%,

South Asian 55%, and Anglo-European 56.7%, p<0.001) (424).

In terms of perinatal outcomes, the patients in the HRD group had similar

rates of spontaneous delivery, induced labours, and CS as low-risk patients. They had

middling rates of pregnancy-induced hypertension and preterm delivery amongst the


three groups. Further, the rate of neonatal complications was similar among the three

groups.

The patients in the HRD group had the lowest rate of LGA babies among the

three groups (HRD 4.0% vs. low-risk group 6.1% and HRI 10.5%, p= 0.023). Paradoxically,

their rate of assisted vaginal delivery was the highest and approached statistical

significance (HRD 22.7% vs. low-risk group 15.3% and HRI 12.8%, p=0.081). A possible

explanation for this phenomenon could be that there were more South-East Asian

patients in the HRD group, whom are more likely to experience instrumental delivery

compared to other ethnicities (524, 525). The exact cause of the increased risk of

instrumental delivery among South-East Asian patients who had lower rate of having

LGA babies compared to other ethnicities (424) is unknown. However, researchers

found that there was a strong trend towards shorter mean perineal length in normal

Asian pregnant women when measured in the first stage of labour, compared to

Caucasian women (3.6±0.09 cm vs. 3.7±0.09, p=0.06), which might be the cause of the

increased rate of instrumental delivery in the Asian population (526).

Even after adjustments for maternal weight, maternal height and ethnicity,

the HRD group still had the highest rate of having SGA babies, approaching statistical

significance (HRD 22.7% vs. low-risk group 15.3% and HRI 12.8%, p=0.061). One possible

reason for the increased rate of SGA babies in the HRD group could be insufficient

gestational weight gain. The maternal weight gain during pregnancy has been shown to

be inversely associated with increased risk of having SGA babies in GDM patients (515).

It is likely that some of the HRD patients were advised that insulin treatment would be

necessary if their glucose levels did not improve quickly, which might have led to HRD


patients excessively restricting their food intake to avoid the use of insulin. On the other

hand, some of the HRD patients may have been asked to attend the multidisciplinary

clinic due to concerns of the health professionals that they were too restrictive on food

intake. Furthermore, as discussed before, patients in the HRD group are more likely to

stick to the medical advice and pay more attentions to their health, which might also

result in unnecessary food restriction. Insufficient food intake could have led to reduced

maternal weigh gain during pregnancy, and subsequently caused the highest rate of SGA

babies.

4.2.5 Conclusion for aim two:

Patients in the low-risk group had comparable rates of stillbirth, macrosomia,

shoulder dystocia, as well as a lower rate of pregnancy induced hypertension, LGA, and

CS, which indicates that the treatment pathway of the low-risk group is promising.

However, the higher rate of preterm delivery in the low-risk group is concerning, such

that further assessment and optimisation of the low risk pathway is necessary.

As discussed before, it is also reasonable to continue elective delivery in the

high-risk group patients who need insulin treatment until more conclusive evidence is

available. That approach most likely leads to more acceptable rates of macrosomic

babies and shoulder dystocia. Furthermore, the elevated rate of neonatal obesity

observed as assessed by ponderal index in both groups needs further analysis, including

analysis of cut-points for the Austalian population. Finally, the high rate of SGA babies

among HRD patients also needs to be addressed.


4. 3 Advantages and limitations

4.3.1 Advantages:

1) This is the first clinical audit that evaluates the effectiveness of the current

GDM care system used in the ACT, Australia, which has collected data from the two

largest public hospitals in the region, i.e. The Canberra Hospital and the Calvary Hospital.

2) To the best of our knowledge, this is the first study that compares the

perinatal outcomes in the low-risk group and the high-risk group, categorized according

to whether the patients attended the multidisciplinary diabetes clinic. This information

could further complete the knowledge map regarding GDM treatment.

3) This is a relatively large study. It included 975 patients diagnosed with GDM

from 01/01/2010 to 30/06/2014. The number of patients fulfilled the requirements of

sample size to detect a 100g difference in neonatal birth weight between the two groups.

4) A special group of patients who needed to attend the multidisciplinary

clinic but managed to control their glucose level only through lifestyle treatment was

identified in this research. These patients had the lowest risk of having LGA babies, but

the highest risk of having SGA babies, which may indicate that these patients may have

over-restricted their food intake to avoid insulin treatment.

5) The original medical records and electronic information systems were

accessed. All important information was double checked, which increased the accuracy

of the results.

6) This research was able to provide detailed information regarding reasons

for elective CS and emergency CS, NICU admission information, and treatments for

neonatal complications.


4.3.2 Limitations and future studies

1) This is a retrospective study, which can only identify the association

between treatment and outcomes, but not the causal relationship. Because of the

retrospective design, the information regarding the control of the glucose level,

gestational weight gain, and the compliance of lifestyle management that are all crucial

contributors to perinatal outcomes was not available. A future prospective study is

needed, especially for identifying the cause of increased pre-term delivery in low-risk

patients.

2) Due to the retrospective nature of the study, it was not possible to identify

clearly the women that were initially designated as low risk, but were later referred to

the DIP-MDC. For this reason, all of these women were assessed as being in the high-

risk group. This did not allow an “intention to treat” analysis of the initial designation of

risk.

3) This study did not include the information of GDM patients who delivered

in private hospitals because of the limited study time and resources. However, it will be

interesting to compare the demographic data and perinatal outcomes of private hospital

patients with patients treated in public hospitals. This would be expected to provide a

more comprehensive perspective in terms of the GDM treatment system in the ACT.

4) There is no control group of untreated GDM patients in this study. However,

the main aim of this study was to compare the outcomes between the low-risk and the

high-risk group patients. The maternal and neonatal information of the background


population in Australian and from other relevant researches was included in this study

to create a more complete picture.

4.3.3 Suggestions for the improvement of the current treatment system

1) The exact reason for the increased risk of preterm birth in the low-risk

group patients is unknown. However, previous research indicated that increasing the

level of antenatal care could reduce the risk of preterm delivery (426). Thus, an

additional appointment with a diabetes educator or dietitian could be beneficial to

further document glycaemic control, adequacy of nutrition and potential need for

insulin therapy. The patients’ glucose control and gestational weight gain, which are

both factors that are related to the risk of preterm birth need to be closely monitored

during an additional appointment (277, 498). Regarding obstetric surveillance, the meta-

analysis did not demonstrate sufficient evidence to endorse a specialised antenatal clinic

for the prevention of preterm birth (527), however, there is no harm in providing

improved education for low-risk group patients, especially about the signs of preterm

labour. Whether it is necessary to have extra ultrasound for cervical assessment and

blood tests for detection of maternal anaemia, thyroid dysfunction and vitamin D

deficiency is still unknown. Further research and cost-effectiveness analyses are

required.

2) Pre-gestational obesity is associated with several perinatal adverse

outcomes, such as increased rates of pregnancy induced hypertension and greater risk

of having neonatal overgrowth (528). In a recent systematic review of the risks

associated with obesity in pregnancy, the authors suggested that women with obesity

need support to lose weight before pregnancy, to reduce the rate of adverse perinatal


outcomes (529). A special clinic for overweight and obese women who plan to have a

baby in the near future might be advisable. Information about the influence of obesity

on perinatal outcomes and individualized plans for weight loss should be provided.

3) Patients in the HRD group had the highest risk of having SGA babies, which

might be due to the excessive food restriction. Instead of being referred back to normal

antenatal care, they might need follow-up sessions with a dietitian. The importance of

adequate food intake should be re-emphasized and weight gain should be assessed

during these sessions. Further attention should be paid to determining the timing and

delivery method of the HRD group patients to avoid unnecessary instrumental delivery.

4) The patients’ ethnicities in this study were broadly classified according to

the self-reported country of birth that does not always accurately reflect ethnicity. It

would be helpful if future data collection included a field for self-reported ethnicity, as

well as country of birth.

4.4 Final Conclusion:

The stratification system of GDM patients in ACT is effective, as it allocates

patients who have fewer risk factors and lower fasting glucose levels in the OGTT that

are related to the adverse pregnancy outcomes into the low-risk group. The treatment

pathway of low-risk group GDM patients, although associated with some less adverse

outcomes such as pregnancy-induced hypertension, delivery interventions and cLGA

neonates, several neonatal outcomes were not less. Furthermore, some neonatal

outcomes were higher than in the ACT background population. For these reasons, the

low risk pathway has merit, but further assessment and optimisation are required. One


optimisation could be the exclusion of women with WHO diabetes in pregnancy from

the low risk group, as one women in the low risk group with WHO diabetes in pregnancy

had a stillbirth. In particular, the increased rate of spontaneous pre-term birth warrants

attention. The earlier delivery strategy for the high-risk patients should be continued.


Appendix


Appendix 1 Ethical approval letter from The Canberra Hospital


Appendix 2 Ethical approval letter from the Calvary Hospital


Appendix 3 Indicators for Initiating Phototherapy


Appendix 4 The number of patients who presented as percentage in the results (part

one)

Conditions


Maternal

baseline

demographic

information

(low-risk group

n= 509,

high-risk group

n= 466

History of GDM 67 (13.2%) 108 (23.2%)

Family history of

diabetes

282 (55.4%) 312 (67.0%)

Smoking 49 (10.1%) 43 (10.1%)

Alcohol consumption 26 (5.4%) 20 (4.7%)

Anglo-European 282 (56.3%) 271 (58.7%)

South-East Asian 97 (19.4%) 55 (11.9%)

South Asian 90 (18%) 99 (21.4%)

Pacific Islander 8 (1.6%) 5 (1.1%)

Middle-Eastern 6 (1.2%) 8 (1.7%)

Other ethnicity 18 (3.6%) 24 (5.2%)

Essential hypertension 3 (0.6%) 16 (3.4%)

Hypothyroidism 23 (4.5%) 36 (7.7)

Hyperthyroidism 3 (0.6%) 4 (0.9%)

Asthma 19 (3.7%) 22 (4.7%)

PCOS 15 (2.9%) 17 (3.6%)

Maternal

outcomes

Gestational

hypertension

36 (3.9%) 20 (7.7%)


(low-risk group

n= 509,

high-risk group

n= 466)

Preeclampsia 11 (2.2%) 19 (4.1%)

Pregnancy induced

hypertension

47 (6.1%) 39 (11.8%)

Preterm birth 50 (9.8%) 28 (6.0%)

Spontaneous preterm

birth

31 (60.2%) 12 (43.3%)

Induced preterm birth 5 (10.2%) 6 (21.7%)

Preterm birth without

labour

14 (29.6%) 10 (35%)

Spontaneous onset of

labour

305 (59.9%) 126 (27%)

Induced onset of

labour

118 (23.2%) 236 (23.2%)

Onset of no labour 86 (19.6%) 104 (22.3%)

Spontaneous mode of

delivery

265 (52.1%) 102 (21.9%)

Induced mode of

delivery

87 (17.1%) 180 (38.6%)

Maternal

outcomes

(low-risk group

n= 509,

Elective CS mode of

delivery

72 (14.1%) 95 (20.4%)

Emergency CS mode of

delivery

85 (16.7%) 89 (19.1%)


high-risk group

n= 466)

Normal vaginal

delivery

274 (53.8%) 214 (45.9%)

Forceps 44 (8.6%) 39 (8.4%)

Vacuum extractors 34 (6.7%) 28 (6.0%)

Delivered by CS 157 (45.9%) 185 (54.1%)

Reasons for the elective CS

Repeated CS 39 (54.2%) 57 (60.0%)

Malpresentation 11 (15.3%) 14 (14.7%)

IUGR 1 (1.4%) 1 (1.1%)

Placenta praevia 6 (8.3%) 3 (3.2%)

LGA 1 (1.4%) 4 (4.2%)

Un-specific reasons 14 (19.4%) 16 (16.8%)

Reasons for the emergency CS

Obstructed labour 23 (27.4%) 34 (38.2%)

Fetal distress 32 (38.1%) 33 (37.1%)

Obstructed labour

combined with fetal

distress

6 (7.1%) 14 (15.7%)

Malpresentation 6 (7.1%) 3 (3.4%)

Placental abruption 6 (7.1%) 1 (1.1%)

Placental praevia 4 (4.8%) 0 (0.0%)

Un-specific reasons 8 (8.4%) 4 (4.5%)


Perineal status

Intact 74 (21.1%) 64 (23%)

First degree laceration 51 (14.5%) 55 (18.4%)

Second degree

laceration

149 (42.5%) 107 (38.2%)

Third degree

laceration

21 (6%) 21 (7.4%)

Forth degree

laceration

0 (0%) 1 (0.4%)

Lateral episiotomy 56 (16%) 35 (12.4%)

Perineum suture 253 (71.9%) 190 (68.1)

Shoulder dystocia 8 (1.6%) 7 (1.5%)

Meconium liquor 25 (4.9%) 14 (3.0%)

Neonatal

outcomes

(low-risk group

n= 509,

high-risk group

n= 466)

Apgar score for 1 minute

Low Apgar score (0-3) 16 (3.1%) 15 (3.3%)

Intermediate Apgar

score (4-6)

72 (14.2%) 64 (13.7%)

Normal Apgar score (7-

10)

421 (82.7%) 387 (83.0%)

Apgar score for 5 minutes

Low Apgar score (0-3) 4 (0.8%) 7 (1.5%)


Intermediate Apgar

score

(4-6)

7 (1.4%) 13 (2.8%)

Normal Apgar score (7-

10)

498 (97.8%) 446 (95.7%)

Macrosomia 28 (5.5%) 33 (7.1%)

Low birth weight 34 (6.7%) 24 (5.2%)

Being obesity 170 (33.5%) 174 (37.7%)

Being underweight 18 (3.6%) 20 (4.3%)

Customized LGA 31 (6.1%) 44 (9.4%)

Customized SGA 63 (12.4%) 61 (13.1%)

Hypoglycaemia 31 (6.1%) 33 (7.1%)

Jaundice 45 (8.8%) 50 (10.7%)

Respiratory distress 32 (6.3%) 28 (6.0%)

NICU/SCN admission 85 (16.7%) 51 (10.9%)

NICU/SCN admission reasons

Prematurity 30 (35.3%) 14 (27.5%)


Hypoglycaemia 9 (10.6%) 3 (5.9%)

Low Apgar score 5 (5.9%) 9 (17.6%)


Prematurity combined

with respiratory

distress

4 (4.7%) 2 (3.9%)

Congenital

abnormality

3 (3.5%) 2 (3.9%)

Jaundice 1 (1.2%) 1 (2.0%)

Un-specific reason 16 (18.8%) 0 (19.6%)

Information for

infants in

NICU/SCN in The

Canberra

Hospital

(low-risk

n= 47,

high-risk

n= 51)

NICU/SCN admission

rate

47 (14.7%) 51 (10.9%)

Hypoglycaemia 17 (36.2%) 26 (51.0%)

Hypoglycaemia need

infusion

14 (84.6%) 19 (73.9%)

Jaundice 23 (48.9%) 30 (58.8%)

Jaundice need

phototherapy

9 (39.1%) 13 (43.3%)


Transient tachypnoes 17 (36.2%) 14 (27.5%)

Hyaline membrane

disease

7 (14.9%) 5 (9.8%)

Pneumothorax 1 (2.1%) 2 (3.9%)

Treatment for neonatal respiratory distress

Oxygen 5 (17.9%) 0 (0.0%)


Continuous positive

airway pressure

17 (60.7%) 17 (65.4%)

Mechanical ventilation 3 (10.7%) 4 (15.4%)

Un-specific treatment 3 (10.7%) 5 (19.2%)

Other metabolic

abnormality

7 (14.9%) 8 (15.7%)

Hypoxic ischemic

encephalopathy

2 (4.3%) 4 (7.8%)

Coagulopathy 2 (4.3%) 4 (7.8%)


Appendix 5 The number of patients who presented as percentage in the results (part

two)

Conditions Low-risk group

(n= 509)

HRD group

(n= 75)

HRI group

(n= 391)

Maternal

demographic

information

GDM history 67 (13.2%) 10 (13.3%) 98 (25.1%)

Family history of

diabetes

282 (55.5%) 44 (58.7%) 268 (68.7%)

Smoking 49 (10.1%) 5 (7.1%) 38 (10.7%)

Alcohol

consumption

26 (5.4%) 3 (4.3%) 17 (4.8%)

Anglo-European 282 (56.3%) 35 (46.7%) 236 (61.0%)

South-East Asian 97 (19.4%) 21 (28.0%) 34 (8.8%)

South Asian 90 (18.0%) 15 (20.0%) 84 (21.7%)

Maternal

outcomes

information

Gestational

hypertension

20 (3.9%) 5 (6.7%) 31 (7.9%)

Preeclampsia 11 (2.2%) 2 (2.7%) 17 (4.3%)

Preterm delivery 54 (10.6%) 5 (6.7%) 23 (5.9%)

Spontaneous

onset of labour

305 (59.9%) 48 (64.0%) 78 (20.0%)


Induced onset of

labour

118 (23.2%) 17 (22.7%) 218 (55.9%)

Elective CS 72 (14.1%) 8 (10.7%) 87 (22.3%)

Emergency CS 85 (16.7%) 16 (21.3%) 73 (18.7%)

Instrumental

delivery

78 (12.8%) 17 (22.7%) 50 (15.3%)

Shoulder dystocia 8 (1.6%) 2 (2.7%) 5 (1.3%)

Neonatal

outcome

information

Customized LGA 31 (6.1%) 3 (4.0%) 41 (10.5%)

Customized SGA 63 (12.4%) 16 (21.3%) 45 (11.5%)

Obesity 170 (33.5%) 29 (39.2%) 145 (37.4%)

Leanness 18 (3.5%) 2 (2.7%) 18 (4.6%)

Hypoglycaemia 31 (6.1%) 3 (4.0%) 30 (7.7%)

Jaundice 45 (8.8%) 8 (10.7%) 42 (10.7%)

Respiratory

distress

32 (6.3%) 5 (6.7%) 23 (5.9%)

NICU/SCN

admission

85 (16.7%) 8 (10.7%) 43 (11.0%)


Appendix 6 List of flowchart, tables and charts

1. Flowchart

Flowchart 1 -IADPSG diagnostic procedures of GDM

Flowchart 2 –The public stratification system for GDM management in ACT health

Diabetes service

Flowchart 3 - Study Population

2. Table

Table 1 -Recommendations for screening procedures and diagnostic criteria for GDM

Table 2 -Screening and diagnosis of GDM in this research

Table 3 -Institute of Medicine Recommendations for total weight gain and rate of

weight gain during pregnancy, by pre-pregnancy BMI

Table 4 -Data collection from the medical record system

Table 5 -Maternal age, BMI, gestational age at first appointment, gestational age when

GDM was diagnosed, number of gestations and parity


Table 6 -Prevalence of cases with missing data on maternal age, BMI, gestational age

at first appointment, gestational at when GDM was diagnosed, number of gestations

and parity.

Table 7 -Prevalence of cases with missing data regarding GDM history and Family

History of Diabetes

Table 8 -Missing data on smoking status and alcohol consumption

Table 9 -Missing data on maternal ethnicity.

Table 10 -GCT and OGTT results.

Table 11 -Missing data of GCT and OGTT results.

Table 12 -Missing data of perineal status and sutured status.

Table 13 -Information regarding after delivery hospital staying and after delivery

bleeding

Table 14 -Missing data of after delivery information

Table 15 -Information regarding birth weight, birth length and Ponderal Index.

Table 16 -Missing data for birth weight, length, PI, macrosomia, low birth weight,

obesity, leanness, LGA, SGA).

Table 17 -Missing data of NICU/SCN staying time and admission reasons.

Table 18 -NICU admissions of neonates born in The Canberra Hospital.

Table 19 -Maternal demographic information for three groups part I.


Table 20 -Maternal demographic information for three groups Part II.

Table 21 -Missing data in demographic data.

Table 22 -Results of HbA1c, TSH and Vitamin D.

Table 23 -Missing data of HbA1c tests, TSH tests, and Vitamin D levels.

Table 24 -Maternal outcomes for three groups.

Table 25 -Neonatal outcomes of three groups.

3. Chart

Chart 1 -maternal glucose concentration associated with adverse perinatal outcomes

Chart 2 -History of GDM and family history of diabetes

Chart 3 -Smoking status and alcohol consumption during pregnancy.

Chart 4 -Maternal Ethnicity

Chart 5 -Maternal pre-existing complications.

Chart 6 -Pregnancy-induced hypertension

Chart 7 -The onset of preterm birth

Chart 8 -0nset of labour.

Chart 9 -Mode of delivery

Chart 10 -Methods of birth.


Chart 11 -Reasons for elective CS

Chart 12 -Reasons for emergency CS.

Chart 13 -Perineal statuses.

Chart 14 -Delivery complications including shoulder dystocia and meconium liquor

Chart 15 -proportions of three groups of Apgar score at 1 minute


Chart 17 -Information regarding macrosomia, low birth weight, neonatal obese, and

neonatal underweight.

Chart 18 -Prevalence of cLGA and cSGA babies.

Chart 19 -Information regarding hypoglycaemia, jaundice, respiratory distress,

NICU/SCN admission

Chart 20 -Reasons for NICU/SCN admissions.

Chart 21 -Information regarding neonatal hypoglycaemia and treatment.

Chart 22 -Information regarding neonatal jaundice and treatment.

Chart 23 -Respiratory distress and causes.

Chart 24 -The treatment options of respiratory distress.

Chart 25 -Other main complications.


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