A Clinical Study on Gestational Diabetes Mellitus and

Pregnancy Induced Hypertension

THESIS

Submitted to

The Tamilnadu Dr. M.G.R Medical University, Chennai

For the award of degree of

DOCTOR OF PHILOSOPHY In the

Faculty of Pharmacy Submitted by

Mr. V. Sivakumar, M.Pharm.,

Under the guidance of

Dr. A. RAJASEKARAN, M.Pharm., Ph.D.,

March 2014

KMCH COLLEGE OF PHARMACY KOVAI ESTATE, KALAPATTI ROAD

COIMBATORE-641048

Contents Contents Page

List of Abbreviation List of Tables List of Graphs 1. Introduction …………………………………………………………………………………………………….. 1 2. Aims and Objectives…………………………………………………………………………………………. 5

2.1. Aims ……………………………………………………………………………………………………………. 5 2.2. Objectives ………………………………………………………………………………………………...... 5

2.2.1. Gestational diabetes mellitus…………………………………………………………… 5 2.2.2. Pregnancy induced hypertension……………………………………………………… 6 2.2.3. Gestational diabetes mellitus and Pregnancy induced hypertension… 6 2.2.4. Association………………………………………………………………………………………. 7

3. Literature Review……………………………………………………………………………………………… 8 3.1. Definition…………………………………………………………………………………………………….. 9 3.2. Pathogenesis……………………………………………………………………………………………….. 11 3.3. Prevalence…………………………………………………………………………………………………… 15 3.4. Risk factors………………………………………………………………………………………………….. 19 3.5. Screening and Diagnosis………………………………………………………………………………. 21 3.6. Complications of GDM and PIH……………………………………………………………………. 23 3.7. Management of GDM and PIH……………………………………………………………………… 25

3.7.1. Dietary therapy of GDM…………………………………………………………………… 26 3.7.2. Physical therapy of GDM………………………………………………………………….. 27 3.7.3. Pharmacotherapy of GDM……………………………………………………………….. 27 3.7.4. Pharmacotherapy of PIH………………………………………………………………….. 28

3.8. Association of PIH………………………………………………………………………………………… 29 3.9. Pharmacist and Patient education……………………………………………………………….. 30

4. Scope of the study……………………………………………………………………………………………. 32 4.1. Plan of the work…………………………………………………………………………………………… 33

5. Materials and Methods…………………………………………………………………………………….. 34 5.1. Study overview……………………………………………………………………………………………. 34 5.2. Gestational diabetes mellitus………………………………………………………………………. 35

5.2.1. General outcome……………………………………………………………………………… 35 5.2.2. Risk factors………………………………………………………………………………………. 35 5.2.3. Complications………………………………………………………………………………….. 35

5.3. Pregnancy induced hypertension…………………………………………………………………. 37 5.3.1. General outcome……………………………………………………………………………… 37 5.3.2. Risk factors………………………………………………………………………………………. 37 5.3.3. Complications………………………………………………………………………………….. 38

5.4. Gestational diabetes mellitus and Pregnancy induced hypertension……………. 39 5.4.1. General outcome……………………………………………………………………………… 39 5.4.2. Risk factors………………………………………………………………………………………. 39 5.4.3. Complications…………………………………………………………………………………… 39

5.5. Association………………………………………………………………………………………………….. 40

5.5.1. Assessment of association of complications GDM and PIH by risk factors………………………………………………………………………………………………

40

5.5.2. Assessment of association of complications GDM and PIH by pregnancy outcome………………………………………………………………………….

40

5.6. Definition and cutoff values for the Maternal and Fetal analysis…………………. 41 5.7. Statistical analysis……………………………………………………………………………………….. 43

6. Results……………………………………………………………………………………………………………… 44 6.1. Gestational diabetes mellitus………………………………………………………………………. 46 6.2. Pregnancy induced hypertension…………………………………………………………………. 55 6.3. Gestational diabetes mellitus and Pregnancy induced hypertension…………... 62 6.4. Analysis……………………………………………………………………………………………………….. 70

6.4.A. Individual analysis……………………………………………………………………………. 73 6.4.A.1. GDM…………………………………………………………………………………... 73

6.4.A.1.a. Diet vs. Insulin………………………………………………….. 73 6.4.A.1.b. 1st vs. 2nd vs. 3rd trimester………………………………… 76 6.4.A.1.c. Early vs. Late onset…………………………………………. 80 6.4.A.1.d. FBS ≤ 95 mg/dl vs. FBS ≥ 95 mg/dl…………………. 84

6.4.A.2. PIH…………………………………………………………………………………….. 89 6.4.A.2.a.Treatment comparison…………………………………….. 89 6.4.A.2.b.Disease severity comparison……………………………. 92

6.4.B. Comparison analysis………………………………………………………………………… 98 6.4.B.1. Risk factors………………………………………………………………………… 96

6.4.B.1.a. Risk factors and their values are not equally distributed between the groups……………………….

97

6.4.B.1.b. An increase in number of risk factors and interactions of one on another showed variation in the development of complications……………………………………………………

104

6.4.B.1.c. The increasing number and values of riskfactors causing earlier development of complications……………………………………………………

108

6.4.B.2. Complications………………………………………………………………….... 121 6.4.B.2.1. Maternal complications……………………………………. 121 6.4.B.2.2. Neonatal complications……………………………………. 122

6.4.B.3. Associations………………………………………………………………………. 127 6.4.B.3.1. Common risk factors……………………………………….. 129 6.4.B.3.2. Pregnancy outcome…………………………………………. 129

6.5. Development of prediction tool…………………………………………………………………… 131 6.5.1. Prediction tool an introduction………………………………………………………… 131 6.5.2. Limitations of tool……………………………………………………………………………. 132 6.5.3. How to use the prediction tool………………………………………………………… 133

7. Discussion…………………………………………………………………………………………………………. 138 7.1. Prevalence of GDM……………………………………………………………………………………… 136 7.2. Risk factors and complications of GDM……………………………………………………….. 136 7.3. Risk factors and complications of GDM……………………………………………………….. 139

7.4. Discussion for analysis…………………………………………………………………………………. 141 7.4.1. Gestational diabetes mellitus…………………………………………………………… 141

7.4.1.1. The duration of GDM is not significantly affecting the outcome of pregnancy……………………………………………………….

141

7.4.1.2. Types of treatment to GDM shown no significant difference in the outcome of pregnancy…………………………….

142

7.4.1.3. Control of glycemic level with in the normal range has given better pregnancy outcome……………………………………….

143

7.4.2. Pregnancy induced hypertension ……………………………………………………. 144 7.4.2.1. No significant difference in terms of pregnancy outcome

between different anti-hypertensive drugs used in the treatment of PIH…………………………………………………………………

144

7.4.2.2. Pregnancy outcomes were not differed much with the severities of PIH, same time no special risk factors can be identified corresponding to particular severity…………………..

145

7.4.3. Associations……………………………………………………………………………………… 146 8. Conclusion………………………………………………………………………………………………………… 148 9. Impact of the study…………………………………………………………………………………………… 152 Bibliography Appendix 1 Human Ethics Committee permission letter Appendix 2 List of Publications Appendix 3 Plagiarism report Appendix 4 Data collection form Errata

1. Introduction

Gestational diabetes mellitus (GDM) and Pregnancy induced hypertension (PIH) are the most

common complications throughout the world that affect 1-14%1 and 2-10%2 of all pregnancies

respectively. Both these complications arise during pregnancy and resolve after delivery.

GDM is a condition of elevated blood glucose level generally detected during pregnancy and

become normal soon-after delivery, resulting with immediate and long-term effects to both

mother and child.

The prevalence of diabetes is increasing enormously day by day. What is not as well recognized is

that a similar trend occurring to GDM. The incidence of GDM has increased worldwide by 35%

from 1991 to 20003. It is presently predicted, that GDM affects 7% of all pregnancies, which means

there are about 2,00,000 of GDM recorded each year. It occurs in almost 1 in 20 pregnant

women4.

The prevalence of GDM ranges from 1 to 14% of all pregnancies, and this variation is due to

various diagnostic methods applied in different etinic5. One survey taken in India has shown that

the prevalence of GDM was increasing from 2.1% in 1982 to 16.55% in 2002. The prevalence rate

of GDM in reproductive age women is similar to the rate of impaired glucose tolerance in the

general population. Over the next 2-3 decades 80 million women in the reproductive age will be

affected by diabetes in the world. Twenty million women expected to be affected in India.

Ethnically Indian women are considered more risks to develop diabetes and the relative risk of

developing diabetes mellitus in Indian women are 11.3 times more compared to western countries

women6. The prevalence is high across the Asian countries and studies about perinatal

consequences of these diseases are important from these countries7.

Screening is usually carried out around 24 – 28 weeks of gestational age. But GDM can manifest at

any stage of pregnancy. The factors that can influence the pregnant women to develop GDM in all

trimesters include age, BMI, positive family history of diabetes, previous history of GDM,

multiparity and irregular menstrual history8.

Indeed the hyperglycemia resolves in postpartum it causes cesarean delivery, preterm delivery

Macrosomia, hyperbilirubinemia, fetal hypoglycemia, shoulder dystocia and respiratory distress

syndrome. GDM affects mother as well as their child, at the age of 5 years, offspring of GDM

mothers are larger and have transformed glucose metabolism compared to offspring of non GDM

mothers9. The prevalence of childhood type-2 diabetes for past 30 years is attributable to

increasing exposure to maternal diabetes during pregnancy10. Women with GDM are at increased

risk and it may lead to have preeclampsia11 and cardiovascular complications12. In long term, GDM

women are more prone (20 – 50%) to develop type 2 diabetes mellitus in five years after

delivery13.

Diet and exercise are important elements in the treatment of GDM. Insulin and certain oral

hypoglycemic drugs can be used, separately or combined, to achieve normoglycemia14,15. Oral

anti-diabetic drugs are not recommended to treat GDM since these drugs are causing potential

teratogenicity and cross the placenta resulting with neonatal hyperinsulinism and

hypoglycemia4,16.

Hypertension in pregnancy, whether chronic or recently diagnosed is always a matter of concern

as they are associated with various pregnancy outcome. PIH is a condition of elevated blood

pressure and proteinuria during pregnancy and become normal after pregnancy. PIH is a generic

classification of hypertension disorders occurring during pregnancy that includes Gestational

hypertension (GH), Preeclampsia (PE) and eclampsia17.

GH is a hypertension without development of significant proteinuria (less than 0.3g/l), after 20th

week of gestation or during labour and/or within 48 h of delivery. Preeclampsia is the

development of GH and significant proteinuria after 20th weeks of gestation or during labour and

/or within 48 h of delivery. Eclampsia is the development of convulsions during or postpartum

associated with high blood pressure and proteinuria18.

Approximately 12 – 22% of pregnancies affected with hypertension and cause 17.6% of maternal

death in United States19.The prevalence of PIH in Europe was 3.1% to 9.6%20. Overall PIH affects

up to 10% of all pregnancies worldwide (desk Prev.Europ-1). Nulliparity, increasing maternal age,

multiple births, chronic hypertension, obesity, diabetes previous preeclampsia, family history, a

new partner and/or ≥ 10 years since last pregnancy, presence of antiphospholipid antibody, renal

diseases are the important risk factors to develop hypertension in pregnancy21,22,23,24,25.

Indeed the PIH resolves postpartum it causes various maternal and fetal morbidity and mortality.

(Seizure, stroke, intra uterine growth retardation (IUGR), preterm delivery, hepatic and renal

failure and death)26. Fetal mortality and low birth weight is high among the hypertensive related

pregnancies27. Preeclamptic women have high risk for cardiovascular diseases, and their offspring

may be at increased risk for cardiovascular diseases in adulthood28.

The aim of treatment is to control the blood pressure there by preventing further complication to

both mother and fetus29. A wide variety of antihypertensive drugs are used to treat PIH, the

rationale behind the antihypertensive treatment is to control the blood pressure thereby reduce

the hypertensive related problems and thereby improving fetal growth30. Methyldopa, calcium

channel blockers like nifedipine, amlodipine and beta blockers like labetalol are the

antihypertensive drugs frequently used in the treatment of PIH during pregnancy. Methyldopa and

beta-blockers are the drugs of choice for treating mild to moderate hypertension31.

The presence of PIH become high and reaches up to 20% among the pregnancy women having

diabetes mellitus32,33. Diabetes increases the risk of preeclampsia from two to three folds in

pregnant women34,35. It was perceived from 1960s, that the Preeclamptic women are at increased

risk for developing hypertension and cardiovascular diseases in later life36. The same was observed

with GDM women in their later life. The implication of this is that the women with GDM are at

increased risk of future diabetes as are their children.

The coexistence of GDM and PIH seems to be high frequency of complications in both stages,

immediate and long-term37. There were common risk factors and complications for both diseases

and both diseases seek treatment in order to achieve better pregnancy outcome. Many questions

are still arising in the usage of hypoglycemic drugs, antihypertensive drugs in the treatment of

GDM as well as PIH. There was lack and demand of prospective trials to confirm the optimal target

BP level for good pregnancy outcome in PIH treatment38.

The relationship of GDM and PIH is not well understood39. Many studies are suggesting that there

is an association between these two diseases40,41,42,43,44 and some other studies suggesting no

association45,46,47. However there was less studies in India about these diseases.

The future prospect is alarming and it is time to check the incidence of these two complications. A

better understanding of these complications and their relationship may suggest effective

strategies for natal care to women.

This study would give better understanding on risk factors, management, complications, relative

measures and relationship between GDM and PIH.

2. Aims and Objectives

The basic idea of the study is to understand and compare the two commonly encountered complications of women, GDM and PIH.

2.1. Aims

1) Understanding the relationship of GDM and PIH by comparing these complications.

2) Development of strategy to identify the women to particular risks by assessing the risk factors

2.2. Objectives

2.2.1. Gestational diabetes mellitus [GDM]

2.2.1.1. General outcome 1) Assessment of maternal and fetal characters of gestational diabetes mellitus.

2.2.1.2. Risk factors

1) Assessment of risk factors for the development of GDM.

2) Is there any difference in the risk factors of women those who developed GDM early and those who developed GDM late?

3) Whether Increase in number of risk factors causing earlier development of GDM?

4) Whether increasing values of risk factors causing earlier development of GDM?

2.2.1.3. Complications

1) Assessment of complication of GDM.

2) Whether the duration of GDM has any influence on the outcome of pregnancy.

3) Assessment of difference in the pregnancy outcome of GDM women who received controlled diet alone and who received diet along with Insulin as a treatment.

4) Assessment of difference in the pregnancy outcome of GDM women who controlled fasting blood glucose level on or below 95 mg/dl and who controlled above 95 mg/dl?

2.2.2. Pregnancy induced hypertension [PIH]

2.2.2.1. General Outcome

1) Assessment of maternal and fetal characteristics of Pregnancy induced hypertension

2.2.2.2. Risk factors

1) Assessment of risk factors for the development of PIH.

2) Whether increase in number of risk factors causing earlier development of PIH?

3) Whether increasing values of risk factors causing earlier development of PIH?

4) Are the risk factors causing variation in the severity of PIH?

2.2.2.3. Complications

1) Assessment of complications of PIH.

2) Assessment of difference between the pregnancy outcome of PIH women who received drug and who do not received drug for their treatment.

3) Is pregnancy outcome varies with severity of PIH?

2.2.3. Gestational diabetes mellitus and Pregnancy induced hypertension [GDM+PIH]

2.2.3.1. General analysis

1) Assessment of maternal and fetal characteristics of women diagnosed with both complications GDM and PIH.

2.2.3.2. Risk factors

1) Assessment of risk factors for the development of PIH and GDM

2.2.3.3. Complications

1) Assessment of complications of women diagnosed with PIH and GDM.

2.3.4. Association

2.3.4.1. Assessment of association of complications; GDM and PIH by risk factors

1) What are the common risk factors between GDM and PIH

2) Is there any association in the development of GDM and PIH?

2.3.4.2. Assessment of association of complications; GDM and PIH by pregnancy outcome

1) What are the common problems in the pregnancy outcome of GDM and PIH?

2) Is there any association in the outcome of pregnancy between GDM and PIH?

3. Literature Review

Pregnancy is a maturational crisis that can be stressful but rewarding as the woman prepares for a

new level of caring and responsibility. Moves from being self-contained and independent to being

committed to a lifelong concern for another human being48,49. The following are various traditional

cultural beliefs50,51,52,53,54,55,56.

Hispanic – members of Hispanic community have their origins in Spain, Cuba, Central and South

America, Mexico and other Spanish speaking countries.

‘Pregnancy is desired soon after marriage Prenatal care is sought late’

African American – Members of the African American culture have their origins in Africa

‘Pregnancy is thought to be state of ‘wellness’ Which is often reason for delay in seeking prenatal care; Pregnancy may be viewed by men as sign of virility’

Asian – The term Asian commonly refers to groups from China, Korea, the Philippines, Japan and South East Asia, particularly Thailand, Indochina and Vietnam

‘Pregnancy is considered time when mother has Happiness in her body’ and it is seen as natural process’

Caucasian/European-Americans –

‘Pregnancy is viewed as condition that requires medical attention to ensure health’

Native American –

‘Pregnancy is considered as normal natural process

Pregnancy spans 9 calendar months, 10 lunar months, or approximately 40 weeks, pregnancy is

divided into three month periods called trimesters. The first trimester lasts from weeks 1 through

13; the second weeks 14 through26; the third, weeks 27 through term gestation (38 to 40

weeks).Among the various health problems arising in pregnancy, one serious health problem is

GDM57.

3.1. Definition

Gestational diabetes is one of the types of diabetes mellitus which is recognized only during

pregnancy. Gestational diabetes mellitus suffers the body as like the other type of diabetes

mellitus do, resulted with increased blood glucose level. In 1824 Dr. Heinrich Gottleib Bennewithz,

from University of Berlin, reported a definition of the symptoms which are unchanged today

“urine differing in quality and quantity from the normal….Accompanied by unquenchable thirst

and eventual wasting” he summarized his observation from pregnancy, that the diabetes was a

symptom of the pregnancy or due to the pregnancy58.

The original concept of GDM goes back at least as early as 1946, when Miller reported a perinatal

mortality rate of 8% in infants delivered to mothers who subsequently developed diabetes in

middle age59.Dr. J.P. Hoet was first recognized and described the association of obstetrical risk and

diabetes. This French description was translated to English and published in Diabetes in 1954 by

Dr. F.D.W. Lukens. Hoet had used the term “Meta Gestational diabetes58,60,61.After various studies,

in 1979, in Chicago, the First International Gestational Diabetes Workshop Conference provided a

definition of Gestational diabetes mellitus;

‘Carbohydrate intolerance of varying severity with onset or first recognition during pregnancy’.

One of the concepts in late 1940s was the degrees of hyperglycemia is a risk factor to diabetes in

pregnancy. This led to the term “Prediabetes in pregnancy” and to the concepts of temporary and

latent diabetes. In 1964, the typical study of O’Sullivan highlighted the criteria for diabetes during

pregnancy. In 1967 Jorgen Pedersen was first used the term “Gestational diabetes”61,62.

Hypertension in pregnancy is a group of conditions associated with elevated blood pressure during

pregnancy, with or without proteinuria and in some cases convulsions were the clinical

manifestation occurs usually late in pregnancy and regress after delivery63. Hypertensive disorders

on pregnancy (HDP) refers to a broad spectrum of conditions from preexisting hypertension and

mild (pregnancy-induced) Gestational hypertension (GH) to severe gestational hypertension,

eclampsia, the hemolysis, elevated liver enzymes, low platelets (HELLP) syndrome and multi organ

failure. The most serious maternal and fetal consequence occurs due to Preeclampsia (PE) and

eclampsia64.

During the early weeks of normal pregnancy the blood pressure decline due to a general relaxation

of muscles within the blood vessels. From around the middle of pregnancy it increases gradually to

reach the pre-pregnancy levels at term. Physical activity, period of day and body position and

mental status are the factors also influencing the BP during pregnancy65.

Historically it didn’t take time to realize that there were certain conditions unique to pregnancy,

and what got the attention was death which seemed to be an accepted hazard of trying to

reproduce. Hippocrates in the 5th century noted the signs drowsiness, head pain and convulsions

during pregnancy. And since his time preeclampsia ‘the disease of theories’ has eluded the

physicians. The presence of a circulating toxin of fetal origin was postulated as the cause of

toxemia of pregnancy. This condition was known to the ancient Greeks, who named it eclampsia

which means a sudden flashing or on slaught. By 1619 Varandaeus coined the term eclampsia in a

treatise in gynecology. Before all these the term eclampsia was used only to refer to the visual

phenomena which accompanied the neurologic aspects of the malady. In the late 19th century, it

was recognized that increased BP and proteinuria occurs before seizures. Later the association of

these signs and syndrome of PE was realized66.

The classification of PIH is still confusion because of the limited knowledge on the etiology and due

to the continuous nature of the signs and symptoms used for the diagnosis. The classification of

severe disease is reserved for the development of additional maternal and fetal complications

relating to various organ systems. The differentiation of mild and severe disease is not definable or

predictable, except in retrospect, as mild disease can rapidly progress to eclampsia and may occur

in its severest form in postpartum period67.The International statistical classification of diseases

and related health problems 10th revision (ICD-10) classifies pregnancy induced hypertension as

gestational hypertension/mild preeclampsia without significant proteinuria Moderate

preeclampsia with proteinuria (013) Severe preeclampsia with proteinuria (014) Eclampsia with

convulsions (015).

PIH is defined as hypertension with or without proteinuria emerging after 20 weeks gestation, but

resolving up to 12 weeks postpartum68,69. PIH is also defined as new onset proteinuria (≥300

mg/24 hours) in hypertensive women who exhibit no proteinuria before 20 weeks gestation. GH is

diagnosed in women whose blood pressure reaches ≥140/90 mmHg for the first time during

pregnancy (after 20 weeks gestation), but without proteinuria. Preeclampsia (PE) is elevated blood

pressure (blood pressure ≥140/90 mmHg) accompanied with proteinuria exceeding 300 mg/24 h

emerges for the first time after 20 weeks gestation, but both symptoms normalize by 12 weeks

postpartum. Eclampsia (E) Eclampsia is defined as the onset of convulsions in a woman with PIH;

the same cannot be attributed to other reasons. This generalized seizure may appear during the

gestation, during the labor and even after the labor.

The severity of PIH is assessed by the extent of symptoms. Both blood pressure and proteinuria

are dependable indicators of severity. Mild PIH Blood pressure is ≥140/90 mm/Hg but <160/110

mm/Hg after 20 weeks gestation, and proteinuria is ≥300 mg/24 hours without exceeding 2.0 g/24

h or 3+ by dipstick method69,70,71,72. Severe PIH Blood pressure is ≥160/110 mm/Hg, and

proteinuria exceeds 2.0 g/24 h or 3+ by dipstick method73. PIH that emerges earlier than 32 weeks

gestation is referred to as early onset (EO) type, and PIH that emerges after 32 weeks gestation is

referred to as late onset (LO) type74,75.

3.2. Pathogenesis

Pathogenesis of GDM has been postulated and well recognized in the Third-workshop-conference

on GDM. Until then it was not known how the GDM developing in pregnanancy76. The exact cause

of this is unknown and may be due to genetic factors, as well as the hormones produced by the

placenta, progesterone, estrogen, human placental lactogen (HPL), and human chorionic

somatotropin (HCS). Even though, insulin production is increased, its effect is diminished, which is

indicative of insulin resistance with hyperinsulinemia and poor insulin response. The insufficient

insulin produced by the mother allows glucose to circulate in the blood stream and be passed on

to the fetus77.

Decreased maternal pregravid insulin sensitivity (insulin resistance) coupled with an inadequate

insulin response are the pathophysiological mechanisms underlying the development of GDM.

Insulin regulated carbohydrate, lipid and protein metabolism are affected and increases nutrient

availability to the fetus, possibly resulted with fetal overgrowth and overweight78.

Autoimmunity and heredity: Danish, Finnish, and US studies found that similar frequencies of HLA-

DR2, DR3 and antigens in normal and GDM pregnant women and little dominance of markers (islet

cell antibodies (ICAs) insulin auto antibodies (IAAs) and glutamyl acetate decarboxylase (GAD), are

the antibodies causing self-destruction of beta cells of GDM women. After this result the

autoimmune concept of GDM were ruled out79,80,81.

Insulin secretion and sensitivity: Proinsulin levels in individuals with fasting in the third trimester of

pregnancy are approximately double fold higher in GDM women than normal pregnant women

(MY-30). A number of clinical studies indicated that pregnancy is an insulin resistant state. GDM

women show a discrete reduction in the ability of beta subunit of insulin receptor to undergo

tyrosine phosphorylation. Further it reduces the 25% less glucose transport in GDM women82,83.

Insulin resistance generally begins in the second trimester and progresses during the remaining

period of pregnancy. Approximately 80% of Insulin sensitivity is reduced during the pregnancy.

Placenta secreting hormones, progesterone, lactogen, cortisol, prolactin, and growth hormone,

are key contributors to the insulin-resistant state gotten in pregnancy.

Women with GDM have a greater severity of insulin resistance compared to the insulin resistance

seen in non-GDM pregnancies. The reduction in this 1stphase insulin release is a marker for β-cell

dysfunction. In addition, the women with GDM had a 67% reduction in their β-cell compensation

compared with normal pregnant control subjects84.

Two forms of insulin resistance present in GDM women. The first one happened as a result of

normal physiology during pregnancy. This may be due to multiple factors, and are exerted at the

level of substrate -1 of the insulin receptor in β-subunit.

Figure 1 Pathophysiology of GDM

Physical

PCOS

Obesity

Race\Ethnicity

Pre-Conception

Insulin Resistance

Increased Demand on Beta Cells for Insulin Secretion

Hyperinsulinemia

Increased Maternal Weight Gain

Neonatal Macrosomia

Pre-eclampsia

Pregnancy Induced Hypertension

Insulin Resistance of Pregnancy

Pregnancy Increases Requirement for insulin Secretion

Reduction in Beta cell Reserve

Later Development of Type 2 Diabetes Mellitus

Fail to Overcome Insulin resistance

Glucose Intolerance

Gestational Diabetes

of skeletal muscle. Added to this the increased level of free intracytoplasmic p85α subunit of

phosphatidylinositol 3-kinase seems to be complicated. This type of alterations in insulin signaling

would give to reduced insulin-mediated glucose uptake in body cells.

The second form of insulin resistance in GDM is due to the susceptibility of women, means that

the diabetes happened even before the pregnancy and now it is exacerbated because of the

various physiological changes occurs in pregnancy. The uptake of glucose by cells is due to the

tyrosine phosphorylation, which gives signals to cells to recognize the glucose. This tyrosine

phosphorylation also considered as an important factors deciding insulin resistance84.

PE is characterized by vasospasm, pathologic vascular lesions in multiple organs, high platelet

activation along with platelet consumption and coagulation process in microvasculature. The

pathogenesis of PE has not yet been fully established, however in recent years, some hypotheses

have been proposed.

In normal pregnancy, the development of mutual immunologic tolerance between maternal tissue

and fetal (paternal) allograft is thought to lead to important morphologic and biochemical changes

in the-systemic and uteroplacental circulation. The endovascular trophoblast destroys the

muscular layer and autonomic innervations of the spiral arteries. Meanwhile, endothelium

increases the synthesis of prostacyclin and nitric oxide, vascular relaxing factors. These changes

result in vasodilation of the uterine circulation.

In preeclampsia, immunologic maladaptation might lead to a disturbance of trophoblast invasion

in the spiral arteries. The insufficient invasion and in growth of the trophoblast inhibits vessel

dilation, reducing maternal blood supply to the intervillous space and thus reducing perfusion or

causing hypoxia. It is believed that poorly perfused trophoblast elaborates an unknown substance

that is toxic to endothelial cells, causing endothelial dysfunction and damage and ultimately

leading to the clinical syndrome of preeclampsia. The most commonly suspected endothelial toxin

is oxygen free radicals. Injured endothelial cells release endothelin, a potent vasoconstrictor, and

produce less nitric oxide. These changes, coupled with endothelial damage, cause progressive

vasoconstriction and platelet aggregation. Damaged vascular endothelium expresses antigens

which induce endothelial cell antibodies. Binding of these antivascular antibodies and immune

complexes to resting endothelial cell monolayer might be involved in altered prostacyclin

secretion, increased platelet adherence, activation of the complement cascade, and disruption of

the monolayer. Coagulation will be triggered, and thrombin will be generated locally, contributing

to platelet aggregation. With endothelial damage and attenuation of the normal vasodilation of

pregnancy, a decrease in glomerular filtration rate and renal blood flow and enhanced sensitivity

to angiotensin are observed. These events may lead to hypertension, edema, and proteinuria85.

Susceptibility to preeclampsia is dependent upon a single receding gene. Endothelins are potent

vasoconstrictors; possibly have a role in the development of PIH. Plasma endothelin-1 has been

reported to be increased in normotensive laboring and non-laboring women and even higher

levels have been reported in Preeclamptic women. Wang and colleagues reported that

normotensive pregnancies are characterized by progressive increases in the ratios of

prostacyclin/thromboxane and vitamin E/lipid peroxides86.

3.3. Prevalence

The prevalence of GDM diverges with different ethnic within a country87.It affects 1 to 14% of

pregnancies depending on the ethnic and diagnostic methods88. It was estimated that 366 million

people will be affected with diabetes globally in 2011. The prediction to rise is 552 million people

by 2030 with half of these living in Asia89.

Ethnicity is a particularly important factor determining incidence of GDM. Very high risk group

includes – Australian indigenous, Polynesian and South Asian groups. Moderate high risk includes

– Middle eastern and other Asian groups90,91.

Table 1Global prevalence of GDM across different countries.

Country Year Prevalence

Australia92 2010 9.5

Bahrain93 2001-2002 13.5

Belgium94 2002-2004 1.0

Canada95 2010 17.8

China96 2008 6.8

Finland97 2009 8.9

France98 2006 12.1

Hong Kong99 2006 10.4

Ireland100 2006-2007 10

Japan101 2008-2010 1.6

Korea102 1993-1997 2.4

Malaysia103 2006 11.4

Qatar104 2010-2011 16.3

Saudi Arabia105 2000 12.5

Sweden106 1998-2002 2.28

Thailand107 2001-2002 3.0

UnitedArab Emirates108 2007 20.6

US109 2002 4.1

The prevalence of GDM in India varies with different parts of the country. High to low rates of

GDM have been documented. Depending upon the diagnostic methods used the prevalence varied

from 3.8% to 21%110. From the random survey performed in various cities in India, 2002 to 2003,

recorded the prevalence of GDM was 16.2% in Chennai, 15% in Thiruvanathapuram, 21% in

Alwaye, 12% in Bangalore, 18.8% in Erode and 17.5% in Ludhiana111.In Chennai, Tamilnadu, the

prevalence was recorded with 16.5% in 2004112.The trend was 2.1% in 1982, which was increased

to 7.62% in 1991, which further increased to 16.55% in 2002113. One other study in Tamilnadu was

found that the prevalence of GDM was high in urban population, 17.8% in urban, 13.8% in semi-

urban and 9.9% in rural areas114.In Belgaum, Karnataka, the prevalence was found to be 16% for

the period of 2008 to 2010115.In Maharashtra the prevalence was found to be 7.7%116. In Rohtak,

Haryana, the prevalence was found to be 7.1%117. In Kashmir, the prevalence was found to be

3.8%118. In Rajasthan, the prevalence was found to be 6.6%119. In Hydrabad, Andhraprdesh, the

prevalence was found to be 8.43%120.

Hypertension in women of child bearing age has become a challenging medical problem with its

increasing prevalence121.Preeclampsia affects 2% to 10% of pregnancies worldwide. WHO

estimates developing countries are seven times higher, compared to developed countries, in

incidence of preeclampsia122.Recently it was reported that in USA, the pregnancy associated

maternal deaths are majorly caused by hypertensive disorders, which is accounts for 12.3%123.

InLatin-America the number one cause of maternal death is preeclampsia124.

The average prevalence of preeclampsia from 1980 to 2010 in United States was 3.4%125. In

Australia it was found to be 3.3% for the period of 2000 to 2008126. In Nigeria the prevalence

ranges from 2% to 16.7%127. In Tanzania, South Africa, and Ethiopia and in Egypt it ranges from

1.8% to 7.1%128,129. Canada and Western Europe ranges from 2% to 5%130,131. In Iran the

prevalence of preeclampsia was 2.32%132. The prevalence was found to be 7.5% in Brazil133 and

3.3% in Turkey134.

In India the prevalence was 8% to 10%.135. The incidence was recorded with 3.2% in Orissa136,

and4.18% in Mumbai137. The prevalence of PIH in other states of India as follows.

Table 2: Prevalenceof Pre-eclampsia by region and state, India, 2005-06138.

India/States Pre-eclampsia

Urban Rural Total N (%) N (%) N (%) India 5738 54.0 16323 56.2 22,061 55.6 Northern region Delhi 403 50.2 30 43.5 433 49.7 Haryana 88 37.3 201 31.8 289 33.3 Himachal Pradesh 36 47.4 313 46.9 349 46.9 Jammu and Kashmir 113 58.5 402 58.2 515 58.3 Punjab 197 56.4 306 52.8 503 54.1 Rajasthan 207 67.2 496 45.3 703 50.1 Uttaranchal 145 67.1 467 71.2 612 70.2 Central region Chhattisgarh 127 59.3 501 50.9 628 52.4 Madhya Pradesh 327 59.8 1027 59.8 1354 59.8 Uttar Pradesh 529 51.4 2110 53.8 2639 53.3 Eastern region Bihar 150 75.4 1143 77.8 1293 77.5 Jharkhand 150 64.1 748 77.4 898 74.8 Orissa 106 52.2 679 58.9 785 57.9 West Bengal 297 63.7 1015 63.4 1312 63.5 Northeastern region Arunachal Pradesh 121 75.6 275 63.4 396 66.7 Assam 85 52.5 641 58.1 726 57.4 Manipur 161 36.7 426 41.3 587 39.9 Meghalaya 99 68.8 399 59.4 498 61.0 Mizoram 203 70.5 201 63.6 404 66.9 Nagaland 167 50.0 535 49.0 702 49.2 Sikkim 54 59.3 306 67.4 360 66.1 Tripura 73 89.0 383 87.2 456 87.5 Western region Goa 253 56.9 209 59.7 462 58.1 Gujarat 298 69.5 407 61.8 705 64.8 Maharashtra 531 46.1 435 33.6 966 39.5 Southern region Andhra Pradesh 208 36.7 426 36.3 634 36.4 Karnataka 231 38.0 355 36.9 586 37.3 Kerala 207 78.4 431 76.4 638 77.1 Tamil Nadu 290 47.4 346 48.7 636 48.1

3.4. Risk factors

A number of risk factors are associated with the development of GDM. The factors include

glucosuria, age over 30 years, family history of diabetes mellitus, previous history of GDM,

previous history of glucose intolerance, Previous macrosomic baby, treatment for infertility,

polyhydromnios, frequent UTI, frequent monliasis, previous history of still birth, unexplained

neonatal death, prematurity, pre-eclampsia in multipara139. Women with a record of gestational

diabetes have also better risk of developing gestational diabetes in succeeding pregnancies. The

delivery of a macrocosmic infant or a suspected glucose intolerance test in a previous pregnancy is

also risk factors for gestational diabetes140.

The risk of GDM becomes significantly and progressively increased from the age of 25 years

onwards. In clinical practice, maternal age of ≥25 years should be adopted instead of ≥35 years or

40 years as a risk factor for the development of GDM141.

The biological characteristics such as elderly maternal age, a high maternal BMI, and multiparity

are significant risk factors to develop GDM. A previous history of Caesarean delivery or

macrosomia was similarly associated. But the histories of previous early pregnancy or perinatal

loss, or congenital anomalies were not associated with GDM142.

History of irregular menstrual cycle was found to be an independent risk factor for the

development of GDM. It should be consider in the process of screening for GDM143.

The blood hemoglobin level of a woman also shows influence on the development of GDM. The

HB concentration more than 13% shows an increased risk for GDM144.

Systolic pressure more than 110 mmHg at or before 12 weeks of pregnancy also has an increased

risk of GDM145.

Physically inactive, smoking habits, food habits and drugs that affect glucose metabolism are also

other risk factors146.Some researchers speculated that PE is a disease of the upper class, others

more recently believe it is a disease of the poor status, and still others think all social classes are at

equal risk147.

PE is considered as important cause to perinatal problems. The following are the risk factors

causing PE: Genetic factors: family history, immune response genes, antioxidant enzymes and the

hypertension genes. Immunological factors: Unprotected sexual intercourse, parity, previous

abortion and paternal change, assisted reproductive technology, antiphospholipid antibodies.

Physiological factors: age, pre pregnancy BMI, multiple gestations, polyhydromnios,

hydropsfetalis, ethnicity. Environmental factors: smoking, high altitude, physical activity and job

stress during pregnancy148.

Among these, nulliparity, previous preeclampsia, high maternal weight, hypertension, diabetes

and twin pregnancies are the significant risk factors to cause PIH147.

A case control study done by Odegard et al on 12,804 pregnant women to examine the risk factors

and clinical manifestation of PE found out the increased risk for PE to nulliparous, high BP, over

maternal weight and with previous PE149. Women with PE in a previous pregnancy had a strongly

increased risk of PE in the current pregnancy compared to women to parous women with no

previous PE149. Pregnancies with multiple gestations have a higher incidence of PIH than do those

with singleton gestation. Pregnant woman with twin gestation has three times the risk of

developing PIH than does a woman with a singleton pregnancy. The incidence of eclampsia is also

three times higher in twin pregnancies than in singleton pregnancies148.

High maternal weight was positively associated with the risk of PE were the risk was confined to

mild to moderate PE and was not present for severe disease. This observation was different from

that which was reported by stone et al who found that maternal obesity may also increase the risk

of developing severe PE149. The association between prepregnancy BMI, pregnancy weight gain

and risk for PIH was recognized. It was also observed that a greater weight gain during pregnancy

increases PIH risk. A significant weight gain of 12.2 kg in the first two trimesters was found in

women who had PE and not among those who had GH150.

Nulliparous women were at increased risk of PE compared to primiparous women. The strength of

the association increased slightly with disease severity. Working during pregnancy doubled the risk

for PE. Working during pregnancy has been implicated as a risk factor for preeclampsia in a variety

of studies147.

It has been reported that a man who fathered a preeclamptic pregnancy in one woman had an

increased risk of fathering a preeclamptic pregnancy in other women, although statistically

significant, the increased paternal recurrence risk was small151.

GDM has been frequently reported as a complication of PE. Common risk factors such as elevated

body mass index and advanced age has been noted for these two conditions152. A large population

based study on the association between GDM and PIH found a significant increased risk of GH,

mild PE, severe PE among women with GDM and the risk of developing this disorder was 1.5 times

greater among women with GDM153.

3.5. Screening and Diagnosis

Ever since the introduction of the concept of GDM, there has been controversy about the

importance of this condition and the appropriateness of screening for it154. Assessment of risk

factors is the usual method to identify the GDM, unfortunately this type of methods identify about

50% of women with GDM87,155.

The oral glucose tolerance test (OGTT) is the method mostly used to diagnose GDM. Based on

ADA, the diagnostic criteria is 100gm oral glucose tolerance test in 3- h and GDM is diagnosed if

two or more plasma glucose levels meet or exceed the following thresholds: FBS of 95 mg/dl, 1-h

glucose level of 180 mg/dl, 2-h glucose level of 155 mg/dl, or 3-hglucose level of 140 mg/dl156.

The World Health Organization (WHO) diagnostic criteria are based on a 2-h 75-g OGTT.

Gestational diabetes mellitus is diagnosed by WHO criteria if either the fasting glucose is > 126

mg/dl or the 2-h glucose is > 140 mg/dl. If they found negative for initial screening, they should be

rescreened again between 24 and 28 weeks of gestation157.

Table 3: Diagnostic criteria according to different organizations

Plasma glucose (mg/dl)

Organization OGTT glucose load Fasting 1-h 2-h 3-h

ADA* 100g 95 180 155 140

ACOG* 100g 105 190 165 145

WHO# 75g 126 - 140 -

IADPSG# 75g 92 180 153 -

* Diagnosis of Gestational diabetes mellitus if two or more glucose values equal to or exceeding

the threshold values, OGTT: oral glucose tolerance test, ADA: American diabetes association,

ACOG: American council of obstetricians and gynecologists, WHO: World health organization,

IADPSG: International association of diabetes and pregnancy groups.

GDM occurs only in pregnancy; usually the diagnosis of GDM will be done by glucose tolerance

test. The suitable method for identifying GDM is still controversial158. The screening of GDM is

carried out at 24th to 28thweeks of pregnancy. During pregnancy, placenta produce many

hormones which are prevent the insulin from its action and give rise to insulin resistance, and even

more hormone secretion and resistance in the second and third trimesters since placenta grows

larger through trimester. The need of extra insulin, up to three times normal, may not be

produced by the maternal pancreas. At this situation, very low amount of glucose reached inside

the cells and plenty stays back in blood itself. This is the condition called GDM and usually occurs

around 20th and 24th week of gestation and can be recognized during 24th to 28thweek of

gestation159.

3.6. Complications of GDM and PIH

The presence of GDM has been associated with several adverse outcomes, to both mother and

baby160,161. The complications are significant and often potential to both mother and child.

Most women who have diabetes give birth to healthy babies, especially when they keep their

blood sugar under control, eat a healthy diet, get regular and moderate physical activity and

maintain a healthy weight. In some cases, though the condition can affect the pregnancy. Keeping

glucose levels under control may prevent certain problems related to GDM162.

Some of the problems associated with GDM are as follows158,162,163,164.

Maternal – miscarriages, polyhyrdromnios, preterm labor, infection, preeclampsia, growth

acceleration, cesarean, birth trauma, puerperal sepsis, failing lactation and perinatal death.

Fetal –macrosomia, hypoglycemia, jaundice, kernicterus, polycythemia, respiratory distress

syndrome, hypocalcemia, hypomagnesimia, hyperbilirubinemia, birth injury, neonatal death.

The GDM are very prone to develop preeclampsia particularly with the women diagnosed earlier

to 24th week of gestation165. Carr et al166 found that GDM women are more risk to develop

cardiovascular disease at young age if they have family history of diabetes mellitus. GDM mothers

have high risk to develop type – 2 diabetes mellitus. Hence, the early detection of risk factors

which are adjustablemay delay or prevent the disease development, thereby improving their

quality of life. Ghattu V. Krishnaveni et al167carried out a study on gestational diabetes to find out

the rate of diabetes in the 5 years follow-up among South Indian women and concluded that the

incidence of diabetes was significantly high with women who had previous GDM.

Poorly controlled maternal type-1 diabetes and fetal macrosomia were associated with lipoprotein

abnormalities in infants and this has implications for later metabolic diseases168. GDM increases

cardio metabolic risks of offspring, and the hyperinsulinemia in uterus state is found to be

independent forecaster to childhood glucose intolerance169. Anders Aberg et al170 in their study on

congenital malformations among infants whose mothers had gestational diabetes or preexisting

diabetes found a total malformation rate of 9.5% while the rate of gestational diabetes was found

to be 5.7%.

Egeland et al171 carried out a study to determine effects GDM on women and their girl children for

future metabolic and cardiovascular risks. Gestational diabetes mellitus (GDM) pregnancies led to

increased conversion to glucose intolerance in mothers, minimal in daughters. The girl children of

GDM women are more prone to develop central adiposity and insulin resistance. Control over the

blood glucose level and BMI may prevent the obesity and glucose intolerance in offspring’s of

GDM women.

The various maternal impacts of PIH are HELLP, glomerular endotheliosis, hemorrhage, ischemia

and edema of cerebral hemispheres that leads to seizures, frontal headaches, occipital headaches,

hemiplegia, visual disturbances, cardiomyopathy and pulmonary oedema172.

Cardiovascular dysfunction with reduced cardiac output and vascular resistance were occurring

largely in preeclamptic women. The PE women and their off springs were also having high risk to

develop cardiovascular disease during their adulthood173.

Stroke is a feared, but fortunately infrequent complication of severe PE or eclampsia. Cerebral

hemorrhage has been reported to be the most common cause of death in patients with

eclampsia174. One of the chief aims in the use of antihypertensive drugs in the management of

severe preeclampsia and eclampsia is to reduce the risk of (fatal cerebral) hemorrhages175. The

death happened in eclampsia is importantly because of cerebral hemorrhage176. A study on 31

patients with PE or eclampsia from 1980 to 2003 reported that hemorrhage in cerebral area is a

vital cause of maternal morbidity and mortality in severe PE174. The HELLP syndrome is a variant of

severe PE causes maternal mortality of 0% to 24%, and perinatal mortality is between 6.6% and

60%. HELLP syndrome is reported to occur in 20% of women with severe PE and in 10% of women

with eclampsia177.

Incidence, etiology, and course of pulmonary edema in all obstetric patients were studied

prospectively among 29,621 subjects for 3.5 years. It was found out that pulmonary edema

developed in 18 cases (0.06%), and it was considered of cardiogenic origin178.

Fetal morbidity and mortality rates are directly related to the severity of hypertension. Adverse

perinatal outcomes of mild and severe PE explained that women who developed severe GH had

higher rates of both spontaneous and indicated preterm deliveries at <37 weeks of gestation and

had more infants that were small for gestational age (SGA).Women with severe PE were more

than two folds increased risk to have preterm delivery and fourfold increased chance to have fetus

of <10th percentile birth weight for gestational age179.

In a population-based retrospective cohort study of 16,936 pregnant women in China the mean

birth weights of babies born to mothers with GH, PE, and severe PE were compared with birth

weights of infants born to mothers with normal blood pressure. There were no differences in

mean birth weight between women with GH and women with normal BP180.

A study also noted that preterm PE was associated with lighter, shorter, and leaner newborns,

whereas late PE had increased rates of both larger and smaller newborns180. Proteinuria in

pregnancies complicated by hypertension was associated with increased rates of stillbirth and

delivery of low-birth-weight infants181. The risk of SGA in a pregnancy with PIH increased with the

severity of PIH180. Severe GH was associated with a high rate of low birth-weight infants and lower

gestational age at delivery when compared to mild GH or mild PE182.

3.7. Management of GDM and PIH

Treating GDM, means taking steps to keep blood glucose levels in a target range. Treatment will

include self-testing of blood glucose, nutritional assessment and counseling with appropriate meal

plan, physical activity, and insulin if needed. Medical nutrition therapy and physical activity are the

primary intervention or management strategies for gestational diabetes mellitus183. The main aim

of the management of GDM is preventing the perinatal morbidity and mortality, which can be

achieved through normalizing blood glucose level as well as other metabolites like lipids and

amino acids to normal range87.

Most of the GDM women were controlling blood glucose by prescribed diet and exercise but for

20-50% of women with GDM medication is needed to control their diabetes. In either case

monitoring blood sugar is a key part of the treatment program76.

The GDM women strictly controlled the glucose level by treatment showed significantly lower

problems to child birth. Therapy includes the following 3 steps, dietary therapy, physical therapy

and pharmacological therapy.

3.7.1. Dietary therapy of GDM

A healthy diet is important for every woman. Eating the correct and right amount of diet were the

best way to normalize the sugar level in blood. The first line management of women with

gestational diabetes mellitus consists of medical nutrition therapy with adjunctive exercise for at

least 30 min per day. Patients who fail to maintain glycaemia targets through diet and exercise

therapy receive insulin injections or other anti-hyperglycemic medications184.

The aim of medical nutritional therapy are to provide adequate nutrition for the mother and

fetus, provide sufficient calories for appropriate maternal weight gain, maintain normoglycemia,

and avoid ketosis. Women who are in first trimester do not require any increased energy. Normal

weighted women who are in second and third trimester require additional 300 kcal/day. A

minimum of 1700 to 1800 kilocalories are needed to prevent ketosis. Carbohydrate foods should

be distributed throughout the day. Small to moderate-sized meals has to be taken three to four

times a day. Limiting carbohydrates to 40% of the total daily caloric intake has been shown to

decreased postprandial glucose values185.

The level of weight gain indicates sufficiency of nutritional food for mother. Mother’s tissue

growth, which supports the baby growth, and body growth are the reasons of weight gain during

pregnancy which includes added blood, increased size of breast, fat store, the placenta, baby, and

the increased amniotic fluid volume57.

3.7.2. Physical therapy of GDM

Physical activity can be helpful in lowering blood glucose levels and reducing stress. It also

increases the insulin sensitivity. In addition, regular exercise help in the management of back pain,

muscle-cramps, swelling, disturbances in sleep and constipation. Physical activity prepares the

body for delivery, improve the insulin sensitivity, and improve the glucose uptake and glycogen

synthesis. Around 40% of women need physical therapy along with dietary therapy in order to

meet the pleasant delivery186.

3.7.3. Pharmacotherapy of GDM

Use of oral hypoglycemic agents to treat GDM has not been recommended because of concerns

about potential teratogenicity and transport of glucose across the placenta resulting in

hyperinsulinism, causing prolonged neonatal hypoglycemia.

First generation hypoglycemic agents like chlorpropamide, tolbutamide, have been shown to cross

the placenta187. Glyburide does not cross the placenta and stimulate fetal hyperinsulinism if

started after the first trimester of pregnancy188.Langer et al189 found that glyburide was as

effective as insulin for the treatment of GDM. Eighty percent of GDM patients were found to be

achieved the target levels of glucose with glyburide; most of the patients required 10 mg of

glyburide on daily basis. But FDA was not approved the glyburide for the treatment of GDM and

yet to have to prove the safety of drugs in large population.

Rowan et al190 on 363 GDM patient who received metformin, found that, there were no difference

in pregnancy outcomes between the patient who treated with insulin and metformin.

A randomized controlled trail by Langer et al191has found that there is no difference in large for

gestational age, macrosomia, neonatal hypoglycemia, admission to neonatal intensive care, or

fetal anomalies between GDM women who were treated with insulin and Glyburide.

In a prospective study conducted by Rai et al192metformin was found to be cheap and effective

alternate to insulin in GDM women. Management of GDM by oral hypoglycemic agent found to be

safe and there are no differences in glycemic control or pregnancy outcomes compared to

insulin167.

In GDM insulin therapy traditionally has been started when blood glucose levels exceed 105gms/dl

in the fasting state and 120 mg/dl, two hours after meals. In aggressive treatment the fasting state

value of 95 mg/dl is taken193.Insulin therapy to GDM constantly reduces the fetal morbidities and

mortalities. Maternal glucose level is the deciding factors to choose the insulin as a

treatment194,195. The following steps were using in the insulin therapy for GDM.

A. Dose of 30/70 insulin: start with 4 units in the morning then based on the requirements add

up the dose of 2 units up to 10 units. The total need of insulin can be divided like 2/3rd in the

morning and 1/3rd in the evening. Insulin 50/50 can be considered if PPBS is high.

B. For the GDM developed in third trimester: MNT is the initial therapy then addition of

premixed insulin can be considered. Dose of insulin can be initiated with 8 units in the

morning.

3.7.4. Pharmacotherapy of PIH

The pharmacologic treatment of hypertensive disorders is a medical and obstetric challenge197.

During pregnancy the aim of antihypertensive treatment is avoidance of cerebrovascular

complications and that drug should give effective vasodilation29. It should not cause fetal

bradycardia or teratogenicity. Therefore ACE inhibitors, thiazides diuretics and beta blockers

should be avoided197.

Methyldopa, labetalol and calcium channel antagonists are frequently used in the treatment of

PIH. Methyldopa and beta-blockers are used in the mild to moderate PIH. Prazosin and hydralazine

are used in the moderate and severe PIH. For emergency PIH Hydralazine, urapidil and labetalol

were used. The PIH developed during the 1st trimester of pregnancy, methyldopa will be the drug

of choice for the treatment198.

Magee et al in their study on oral beta-blockers for mild to moderate hypertension in pregnancy

suggested that long-acting oral antihypertensive drugs, beta-blockers causes Intra uterine growth

retardation (IUGR) and reduced birth weights199.

Alpha methyldopa is the drug of choice for long-term control in PIH pregnancy. There are

restricted indications for Nifedipine29. But a study on the safety of calcium channel blockers

concluded that there were no major teratogenicity risk199. According to meta-analyses and

Cochrane reviews reported the fetal complications with hydralazine29.

3.8. Association of GDM and PIH

To date, most epidemiologic studies exploring the relationship of gestational diabetes with

preeclampsia have considered GDM as a risk factor for preeclampsia200,201. Though both diseases

arise because of placental substances, the association between these diseases was also evaluated

by some of the studies. Women with GDM have1.5 folds increased risk for developing

hypertension disorders in pregnancy202. Several studies are suggesting the common

pathophysiology, including insulin resistance, chronic inflammation and endothelial

dysfunction200,203,204,205,206.

Acohort study analyzed the risk factors for PE and GH with 10,666 pregnant women in Sweden and

found; the risk for development of preeclampsia is high with GDM mother207. Study by Ingrid

Ostland et al208, with 430,852 women, suggests that there is an independent and significant

association between GDM and preeclampsia. In GDM patients the high blood pressure early in

pregnancy, even prior to gestational diabetes diagnosis, were associated with the subsequent

development of preeclampsia209. In a retrospective cohort study of more than 111,000

pregnancies found that women with GDM were 2.5 times more likely to experience preeclampsia

than without diabetes210.One other large, population-based study conducted in Latin America with

878,680 pregnancies demonstrated an association between GDM and preeclampsia211. Vambergue

et al212from France studied in 15 maternity units found an association between PIH and GDM.

Since some of the risk factors are common for both disease the coexistence of both disease may

worsen the pregnancy outcome. Increased maternal age, nulliparity, and multiple gestation

pregnancies could be identified as common risk factors for both diseases213. one study with 24,290

singleton pregnancies, reported that carbohydrate intolerance of varying severity are increased

risk factors for developing PIH and the same time both conditions share the common etiology214.

Preeclampsia has been frequently reported as a complication of GDM215. But the relationships

between these two conditions were not well established.

Short term cohort studies reports that there was no correlation between insulin resistance and

hypertension but post pregnancy studies reports the association of insulin resistance with both

inflammatory deregulation and vascular dysfunction216.

The inadequate perinatal care also is a reason to develop preeclampsia in GDM women. The better

perinatal care may reduce the occurrence of preeclampsia in GDM women by 30 percent217,218.

One other studies suggests that the aggressive early treatment of GDM might reduce the risk of

preeclampsia219,220. The glucose control in pregnancy may reduce the rate of preeclampsia even in

severe GDM condition221.

The coexistence of both diseases increases the risk of complicated pregnancy as well as future risk

for cardiovascular diseases and type 2 diabetes mellitus. Women complicated with preeclampsia

or GDM had an increased risk for developing diabetes later in life, especially those having GDM222.

Many studies were reported the linkage of GDM and preeclampsia to type 2 diabetes in later

life223,224,225,226,227. In Norwegian hospital-based clinical study, with in the 16 years of GDM

pregnancy 60% of women developed diabetes228.

Comparison of the risk factors for pre-eclampsia and gestational diabetes may provide vision into

the etiologic and mechanisms related to these conditions. Several studies focused on GDM as a

risk factor for pre-eclampsia, but not directly compared the risk factors of these complications.

There was no documentation on which risk factors are similar and to what way they are related.

3.9. Pharmacist and Patient education

Pharmacists have an important role in the management of GDM. The importance of screening for

GDM and the management of GDM are the area where pharmacist can educate the patients. GDM

women have the huge chance to develop type 2 diabetes mellitus later in their life; hence a

pharmacist should educate them on the need of life style modification and continue follow-up for

checking blood glucose level195.

The main role of pharmacist in GDM management is patient education. Education for GDM

women can includes dietary counseling, life style modification, insulin administration, and self-

glucose monitoring. Pharmacist should educate the patients on the symptoms of hypoglycemia

and also the management of hypoglycemia. Pharmacist should also educate the women about the

importance of exercise, diet and treatment196.

4. Scope of the study

GDM and PIH are the serious complications that occur during pregnancy which affects both the

mother and fetus. The coexistence of these seems to cause high complications in both immediate

and long-term. But the relationships of these pregnancy problems were not well established.

The future prospect is alarming and it is the time to check the incidence of these two

complications. A better understanding of these complications and their relationship may suggest

effective strategies for natal care to women.

Development of easy assessing methods, to understand these complications, like self-monitoring

of glucose, will be helpful to women and their family in the assessment of their risk level and

future prospects to control these complications.

This study would give better understanding on risk factors, management, complications, relative

measures and relationship between GDM and PIH.

4.1. Plan of the work

Figure 2: Plan of work

Explanations for terms used in the figure 2.

Early vs Late- analysis of risk factors and outcome of GDM diagnosed early and late of gestation.

Trimester – analysis of risk factors and outcome of GDM developed in three trimesters.

Treatment – analysis of outcome of pregnancy by diet vs insulin

Blood glucose – analysis of pregnancy outcome against Fasting blood glucose level

Severity – analysis of risk factors and outcome of PIH based on severity of PIH

Treatment – analysis of pregnancy outcome of PIH by anti-hypertensive drugs

5. Materials and Methods

Eligible pregnant women

(n=517)

GDM (Primary Diagnosis)

PIH (Primary Diagnosis)

(n=330)

(n=187)

GDM alone

GDM+PIH

PIH alone

(n=277)

(n=68)

(n=172)

GDM analysis

PIH analysis

Early vs late

Risk factors analysis

Severity

Trimester

Treatment

Treatment

Blood Glucose

Pregnancy outcome analysis

Developing strategies

Conclusion

5.1. Study overview

This is an observational study; the pregnant women who diagnosed and treated for Gestational

diabetes mellitus [GDM] and or Pregnancy induced hypertension [PIH] were included in the study

for the period of 10 years from January 2003 to December 2012.

The study was carried out at Kovai Medical Center and Hospital(KMCH), Coimbatore, Tamilnadu,

India, a 750 bedded multispecialty hospital. Study was approved by Institutional ethics committee.

(Ref.No. EC/AP/102/09-2009, Date - 12-08-2009)

A pregnant woman diagnosed by physician as GDM and or PIH is the basic criteria to include in the

study. Diabetes mellitus, hypertension, renal disorder, autoimmune disease woman were not

included in the study.

Totally 517 women were met the criteria and included for the study. Out of 517, 345 women were

diagnosed as GDM, 240 women were diagnosed as PIH and 68 women were diagnosed as both

GDM and PIH. Women who complicated with GDM alone are 277 and 172 women were

complicated with PIH alone.

Antenatal, perinatal and neonatal data were collected from the patients, family members, and

patient medical records and from hospital database. Maternal data includes demographic details,

family history, past medical history, obstetric history and ultra sound scanning report, laboratory

investigations and current diagnosis. Neonatal details included sex, weight, height, length, blood

group, head circumference, apgar scores, blood sugar level and bilirubin level. Data were collected

retrospectively for a period of 6 years from January 2003 to December 2008 and prospectively for

4 years from January 2009 to December 2012.

The study is divided into three sections in order to deal with the individual diagnosis. Selection

and number of study population varied with the objective of particular section.

5.2. Gestational diabetes mellitus

5.2.1. General outcome

1) Assessment of maternal and fetal characters of gestational diabetes mellitus.

Study population: - For the analysis of general characters of GDM, all the women who diagnosed

with GDM and all the GDM women who developed PIH as a complication of GDM were included in

the study. Totally 330 eligible women were taken to the analysis.

5.2.2. Risk factors

1) Assessment of risk factors for the development of GDM.

2) Is there any difference in the risk factors of women those who developed GDM early and those who developed GDM late?

3) Whether Increase in number of risk factors causing earlier development of GDM?

4) Whether increasing values of risk factors causing earlier development of GDM?

Study population: - for the analysis of all above; the GDM women and women has the

complication of both GDM and PIH, later one is developed after GDM, were included into the

study. But women who developed GDM after the PIH were not taken into the analysis, since the

risk factors may vary for primary development of complication. Totally 330 GDM women (277

GDM alone women and 53 PIH developed GDM women) were taken into the analysis.

5.2.3. Complications

1) Assessment of complication of GDM.

2) Whether the duration of GDM has any influence on the outcome of pregnancy.

3) Is there any difference between the pregnancy outcome of GDM women who treated with diet alone and along with Insulin?

4) Is there any outcome difference between the GDM women those who controlled fasting blood glucose level on or below 95mg/dl and who controlled above 95mg/dl?

5) Is there any outcome difference between early and late developed GDM?

Study population: - for the analysis of general complications, women diagnosed with GDM alone

were included in the study. Women who developed GDM along with PIH were not included in the

study, since PIH also can cause GDM like problems to pregnancy. Totally 277 GDM alone women

were taken into the analysis.

For the analysis of duration of GDM and its effect on pregnancy; women diagnosed with GDM

alone were included into the analysis (n=277). The GDM women who developed PIH were also

included while analyzing the PIH as a complication of GDM (n=330, 277 GDM alone women and 53

PIH developed GDM women). Women were categorized into three groups based on onset of GDM

through trimester of pregnancy. The groups are as follows

First Trimester Group (Group 1) – Women diagnosed with GDM through week 14.

Second trimester Group (Group 2) – Women diagnosed with GDM from week 15 to week 28.

Third trimester Group (Group 3)–Women diagnosed with GDM from week 29 through labor and

delivery, which varies considerably but average at week 40.

For the analysis of GDM treatment and its outcome; GDM only diagnosed women were taken into

the study. The eligible women were divided into two groups, one is women who received diet

alone treatment, and another is women who received insulin along with dietary treatment.

(Regular or NPH or both of the insulin were used in the individual for treatment). Totally 277 GDM

alone women were included into the analysis.

For the analysis impact of fasting blood glucose level on pregnancy outcome; women diagnosed

with GDM alone were included into the analysis. Since PIH also can cause GDM like problems to

pregnancy, GDM women who developed PIH were not included in the analysis. Mean FBS value is

considered to categorize the women into two groups. Women who controlled mean average FBS

level below 95 mg/dl is one group and another group is women who controlled mean average FBS

level on or above the 95 mg/dl. Total of 277 GDM alone women were included for grouping and

analysis. The mean FBS value is calculated from the value taken on the day next to diagnosis of

GDM to the value taken last or before the delivery.

For the analysis of outcome difference between early and late developed GDM; the GDM alone

(n=277) were included into the analysis. Since PIH also can cause GDM like problems to pregnancy,

GDM women who developed PIH were not included in the analysis.

Following are the risk factors and complications taken for the analysis

Risk factors Complications

Age Weight Body Mass Index [BMI] Previous history of GDM Family history of GDM Gravidity Parity HB Irregular menstrual cycle

PIH Cesarean section Pre term delivery Apgar score ≤7 at 1st min Apgar score ≤7 at 5th min Low birth weight (LBW) Macrosomia Large for gestational age (LGA) Small for gestational age (SGA) Hypoglycemia Hyperbilirubinemia

5.3. Pregnancy induced hypertension

5.3.1. General outcome

1) Assessment of maternal and fetal outcome of Pregnancy induced hypertension

Study population: - for the analysis of general maternal and fetal characters, all the women

diagnosed with PIH were taken into the analysis, and from the both complicating women, those

developed PIH first were considered for the analysis. Totally 187 PIH women were taken into the

analysis.

5.3.2. Risk factors

1) Assessment of risk factors for the development of PIH.

2) Assessment of risk factors for the development of PIH.

3) Whether increase in number of risk factors causing earlier development of PIH?

4) Whether increasing values of risk factors causing earlier development of PIH?

5) Are the risk factors causing variation in the severity of PIH?

Study population: - for the analysis of all above; women diagnosed with PIH and women have the

complication of both PIH and GDM, later one is developed after PIH, were included in the analysis.

But women who developed the PIH after GDM were not taken into the analysis, since the risk

factors may vary for primary development of complication. Totally 187 PIH women (172 PIH alone

Women and 15 GDM developed PIH women) were taken into the analysis.

5.3.3. Complications

1) Assessment of complications of PIH.

2) Assessment of difference between the pregnancy outcome of PIH women those who treated

with drug and those not treated with drug

Study population: - for the analysis of general complications, women diagnosed with PIH alone

were included in the study. Women who developed PIH along with GDM were not included in the

study, since GDM also can cause PIH like problems to pregnancy. Totally 172 PIH alone women

were included in the analysis.

For the analysis of treatment outcome of PIH; women diagnosed with PIH alone were included in

the study. Since GDM complications may interfere with outcome results, women diagnosed with

PIH and GDM were not included in the analysis. The PIH alone women were categorized according

to the drug they received for their treatment. The groups are as follows

Group 0 – women received none of the drug

Group 1 – women received Nifidepine

Group 2 – women received Methyldopa

Group 3 – women received Nifidepine and Methyldopa

Out of total 172 eligible women, 161 PIH women were included in the study. 7 women who

received atenolol and 2 women who received ecosprin were excluded from the analysis, 2 women

found with fetal death were also excluded from the analysis.

Following risk factors and complications were taken into analysis.

Risk factors Complications

Age Weight Body Mass Index [BMI] Previous history of PIH Family history of PIH Gravidity Parity HB Irregular menstrual cycle

Cesarean section Eclampsia Pre term delivery Apgar score ≤7 at 1st min Apgar score ≤7 at 5th min Low birth weight (LBW) IUGR Large for gestational age (LGA) Small for gestational age (SGA) Hypoglycemia Hyperbilirubinemia

5.4. Gestational diabetes mellitus and Pregnancy induced hypertension

5.4.1. General analysis

1) Assessment of maternal and fetal characteristics of women diagnosed with both complication GDM and PIH

Study population: - all the women diagnosed with PIH and GDM were included into the study.

Totally 68 women diagnosed with GDM and PIH were included in the study.

5.4.2. Risk factors

1) Assessment of risk factors for the development of both GDM and PIH together

Study population: - for the analysis of risk factors, women diagnosed with both GDM and PIH were

included in the study. Totally 68 eligible women were taken into the analysis.

5.4.3. Complications

1) Assessment of complications of women diagnosed with both PIH and GDM together.

Study population: - for the analysis of complications; women diagnosed with both GDM and PIH

were included in the study. Women diagnosed with PIH or GDM alone were excluded. Totally 68

eligible women were taken into the analysis.

Following risk factors and complications were taken into analysis.

Risk factors Complications

Age Weight Body Mass Index [BMI] Previous history of GDM and PIH Family history of GDM & PIH Gravidity Parity HB Irregular menstrual cycle

PIH Cesarean section Eclampsia Pre term delivery Apgar score ≤7 at 1st min Apgar score ≤7 at 5th min Low birth weight (LBW) Macrosomia IUGR Large for gestational age (LGA) Small for gestational age (SGA) Hypoglycemia Hyperbilirubinemia

5.5. Association

5.5.1. Assessment of association of complications GDM and PIH by risk factors

3) What are the common risk factors between GDM and PIH

4) Is there any association in the development of GDM and PIH?

5.5.2. Assessment of association of complications GDM and PIH by pregnancy outcome

3) What are the common problems in the pregnancy outcome of GDM and PIH?

4) Is there any association in the outcome of pregnancy between GDM and PIH?

Study population – for the analysis of association between GDM and PIH; women diagnosed with

GSM alone (n=277), women diagnosed with PIH alone (n=172) and women diagnosed with both

GDM and PIH (n=68) were taken into the analysis.

5.6. Definition and cutoff values for the Maternal and Fetal Analysis

1. Age – The cutoff value is 25 years(≥ 25 years of age is considered high risk)

2. BMI – The cutoff value 25 kg/m2 (≥25 is considered as high risk)

3. Previous history of GDM – woman has already experienced the GDM during her previous

pregnancy

4. Gravidity – number of pregnancy

a. Primigravida – first pregnancy

b. Multigravida – has been pregnant once (from second to all pregnancy)

5. Parity– number of delivery

a. Nulliparity – women has not given birth previously (regardless of outcome)

b. Primiparity –has given birth once

c. Multiparity – has given birth more than once

6. Family history of DM – parent of women has diabetes mellitus either mother or father

a. Paternal history – father of women has diabetes mellitus

b. Maternal history – mother of women has diabetes mellitus

7. HB – Hemoglobin more than 13 mg/dl consider as high risk group for GDM

8. Proteinuria – presence of protein in the urine more than 0.3 g/l

9. Gestational hypertension – pregnant women diagnosed after 20th week of gestation or during

labor or within 48 h of delivery with 20% increased BP but no proteinuria

10. Preeclampsia – pregnant women diagnosed after 20th week of gestation or during labor or

within 48 h of delivery with 20% increased BP and proteinuria

11. Eclampsia – pregnant women diagnosed after 20th week of gestation or during labor or within

48 h of delivery with 20% increased BP and proteinuria with seizure

12. Trimester – division of pregnancy into three months

13. The cut of values for FBS and PPBS level of GDM women are 95 mg/dl and155mg/dl (2 h post

meal) according to ADA (above this value consider as above normal)

14. Preterm delivery - the delivery before 37th week of gestation.

15. Macrosomia - the birth weight of 4000 grams or greater.

16. Hyperbilirubinemia- increase of total bilirubin level in the blood more than 12 mg/dl.

17. Neonatal hypoglycemia - capillary heel blood glucose levels of 40 mg/dl or less.

18. Maternal weight gain – is the amount of weight gained by the mother during the pregnancy

period. According to Institute of medicine and nutrition the normal maternal weight gain

during pregnancy is 11.5 – 16 kg.

19. Large for gestational age (LGA) – baby birth weight lies above the 90thpercentile of weight for

that gestational age.

20. Appropriate for gestational age (AGA) – baby birth weight lies above the 10th percentile for

that gestational age and below the 90th percentile of weight for that gestational age.

21. Small for gestational age (SGA) – baby birth weight, length or head circumference lies below

the 10th percentile of weight for that gestational age.

22. IUGR – Intrauterine growth restriction; baby birth weight is below the tenth percentile of the

average for the gestational age.

23. HELLP syndrome – hemolysis, elevated liver enzyme and low platelet count

24. Early and late GDM – universal screening for GDM is between 24 – 28 weeks. The GDM

diagnosed before 28th week of gestation is considered as ‘early GDM’ and which is after 28th

week of gestation is considered as ‘late GDM’.

25. Apgar score – is determined by evaluating the new born baby in five simple criteria on a scale

from zero to two, then summing up the five values thus obtained. The resulting apgar score

ranges from 0 to 10. Apgar score of 8 or above indicates it’s a good healthy baby. Apgar score

may be made at 1st and 5th minutes after delivery.

26. Caesarean section – is a surgical procedure in which one or more incisions are made through

mother’s abdomen (laparotomy) and uterus (hysterotomy) to deliver one or more babies or

rarely to remove dead fetus.

5.7. Statistical analysis

SPSS package version 20.0 for windows was used to do the statistical analysis. Paired‘t’ test was

used to find out the control of blood glucose level and blood pressure level before and after

treatment. Bivariate correlation (2-tailed) analysis was done to find out the correlation between

the risk factors and complications; GDM and PIH. One way ANOVA was done to find out the

significance for risk factors / outcome, between the risk factors on development of GDM and PIH,

between early and late diagnosis of GDM, between 1st, 2nd and 3rd trimester developed GDM,

between diet and insulin treatment, between the FBS level, between the severity of PIH, and

between the treatments of PIH. Non parametric Mann-Whitney U test was done to assess the risk

factors and complications between the GDM and PIH. Nonparametric Krushkal-Wallis one way

ANOVA test was used to find the relationship of risk factors and complications between GDM, PIH

and both. Regression – Curve Estimation was done to find the significance, for increasing numbers

of risk factors and effects on development of complications, increasing values of risk factors and

effects on development of complications. Multinomial Logistic Regression analysis was done to

develop the modal for prediction of complications against risk factors.

6. Results

Total of 15623 pregnant women were given birth for the period of 10 years from January 2003 to

December 2012, of which 517 women were complicated with GDM and or with PIH. The

prevalence of GDM was 2.20% (n=345) and PIH was 1.53% (n=240). Overall percentage of

coexisting of both of these complications was 0.43% (n=68).

Out of 517 complicated women, 277 women were diagnosed only with GDM and 172 women

were diagnosed only with PIH. Sixty eight (68) women were developed both GDM and PIH, of

which 53 women were developed PIH after the GDM complication and 15 women were developed

GDM after the PIH complication. Totally 517 women given birth to 537 babies. Twin pregnancy

accounted for 22 women. All neonates were normally in good except for 2 fetal deaths occurred in

PIH pregnancy. Totally 290 babies were born to GDM women and 177 babies were born to PIH

women and 70 babies were born to both complicated women. Of which 537 babies, 316 (59%)

were male babies 221 (41%) were female babies.

The prevalence rates of both complications were reduced from 2003 to 2012. The prevalence of

GDM was 3.28% in 2003 which was reduced to 2.11% in 2012 and the prevalence of PIH was 2.87%

in 2003 to 1.16% in 2012. The incidence of GDM was increases from 24 cases to 60 cases. The

same trend was found in PIH also, from 21 cases to 33 cases. – Table 4 & Graph 1.

Table – 4: Prevalence and number of cases of GDM and PIH: 2003-2012

S.no Years Number of deliveries per year

Number of GDM

cases

Number of PIH cases

Prevalence (%) PIH

Prevalence (%)

GDM 1 2003 730 24 21 2.87 3.28 2 2004 745 30 35 4.69 4.02 3 2005 867 28 24 2.76 3.22

4 2006 1075 29 19 1.76 2.69

5 2007 1273 28 22 1.72 2.19 6 2008 1554 37 22 1.41 2.38

7 2009 1897 27 21 1.1 1.42 8 2010 2187 36 21 1.0 1.64

9 2011 2452 46 25 1.01 1.87

10 2012 2843 60 33 1.16 2.11

Graph – 1: Expressing the prevalence of GDM and PIH: year vs. percentage

3.28 4.02 3.22

2.69

2.19 2.38

1.42 1.64

1.87 2.11 2.87

4.69

2.76

1.76 1.72 1.41

1.1 1 1.01 1.16

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

GDM

PIHPrevalence

Perc

enta

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>

Year -->

6.1. Gestational diabetes mellitus.

Women primarily diagnosed as GDM were taken in to analysis (n=330). Maternal and fetal

characteristics were shown in – Table 5, 5A, 5B, 5C, 5D, 5E, 5F, 5G, 5H, 5I, 5J, & Graph 2, 2A, 2B,

2C, 2D, 2E, 2F, 2G, 2H.

The mean age was 27.75 ± 3.90 years. Most of the women fall between the age group of 25 to 29

years of age, which accounts for 49%. Around 6% of women were more than 35 years of old. The

mean BMI was 27.71 ± 3.61 kg/m2 of which 51% was found with obese and 20% was found to be

ideal body weight. The average weight at the time of confirmation of pregnancy was 68.05 ± 9.12

kg. The average mean weight gain was 11.77 ± 3.59 kg.

The patient history revealed that 58% of population has family history of diabetes. Paternal history

of diabetes (39%) was more compared to 31% of maternal history of diabetes. Eight percent of

population had previous history of GDM and 6% had previous history of PIH where as 93% of

population doesn’t have any previous history.

Thirty six percentage women had one previous delivery and 4% had more than one previous

delivery, where as 60% had no previous delivery. Gravidity data shows 52% of women had at least

one previous pregnancy and this is the first pregnancy for 48% of women.

Confirmation of GDM differed from woman to woman; the average week of diagnosis was 27.17 ±

8.23 week. Fifty seven percent of GDM diagnosed after the 28th week of gestation and 31% of

GDM found between 14 to 28th week of gestation where as 12% of GDM diagnosed before the 14th

week of gestation.

Women were treated with diet and as well as insulin. Regular insulin (24%), Intermediate insulin

(NPH)(42%) and sometime both insulin (15%) were used to treat. 2000 kcal to 3800 Kcal of food

were given as the diet treatment. 80% of women were received insulin and 20% of women were

received only diet treatment.

For all the GDM women glucose level was monitored regularly and was controlled well. - Table 5B,

Graph 2. The last four values of mean fasting and post prandial glucose levels were 96.67 ± 14.46

mg/dl and 140.16 ± 28.94 mg/dl respectively. The mean baseline values of, which are taken at the

time of GDM diagnosed, fasting blood sugar and post prandial blood sugar were 100.55 ± 21.81

mg/dl and 144.73 ± 34.33 mg/dl respectively. After the treatment, the mean end values of, which

are taken prior to delivery, fasting and postprandial blood glucose were 95.99 ± 24.37 mg/dl and

138.15 ± 29.70 mg/dl respectively.

The average week of delivery was 35.87 ± 2.05 weeks. Fifteen delivery was twin babies and totally

345 babies were delivered. Cesarean delivery was more with 89% and 11% of delivery was through

vaginal. Among 345 babies 208 were male and 137 were female. Mostly all babies were normally

in good health. The mean birth weight of baby was 2.72 ± 1.32 kgs. Mean Apgar score at 1st min

was 7.69 ± 0.65 and 8.58 ± 0.52 for the same at 5th min. Fifty five percentages of babies were

found with hypoglycemia and 59% of babies were found with hyperbilirubinemia.

Table – 5: Maternal Characters of GDM [n=330]

Characters Number of woman Values [Mean ± SD] / %

Age 27.75 ± 3.90 Weight 68.05 ± 9.12 Height 156.95 ± 5.34 BMI 27.71 ± 3.61 Gravidity 1.91 ± 1.10 Primigravida 159 48.18 Multigravida 171 51.81 Parity 0.46 ± 0.63 Nulliparity 196 59.39 Primiparity 120 36.36 Multiparity 14 4.24 Previous GDM 25 7.57 Family History of DM 191 57.87 No family history of DM 96 29.09 Insulin 266 80.60 NPH Insulin 139 52.25 Regular Insulin 79 29.69 Both 48 18.05 diet 64 19.40 Cesarean delivery 295 89.40 Vaginal delivery 35 10.60 PIH 53 16.06 PIH diagnosis week 33.45 ± 2.79 Marital period 4.05 ± 3.28 MWG 11.77 ± 3.59 Week of GDM diagnosis 27.17 ± 8.23 Week of delivery 35.87 ± 2.05

Table – 5A: Neonatal Characteristics of GDM [n=330]

Characters Number of Babies Values [Mean ± SD] / % Baby 345 Male baby 208 63.03 Female baby 137 41.51 Weight 2.72 ± 1.32 Term baby 168 50.90 Preterm baby 177 53.63 Apgar score at 1st minute 7.69 ± 0.65 Apgar score at 5th minute 8.58 ± 0.52 Twin babies 15 5.41 LBW 111 32.17 NBW 234 67.82 SGA 14 4.05 LGA 41 11.88 AGA 290 84.05 Macrosomia 8 2.31 Apgar score <7 at 1st minute 119 34.49 Hypoglycemia 190 55.07 Hyperbilirubinemia 202 58.55

Table – 5B: Maternal blood glucose levels

Base value mg/dl End value mg/dl

FBS 100.55 ± 21.81 95.99 ± 24.37

PPBS 144.73 ± 34.33 138.15 ± 29.70

Graph – 2 Expressing of Blood Glucose level of GDM women

100.55

144.73

95.99

138.15

0

20

40

60

80

100

120

140

160

180

FBS PPBS

Bloo

d gl

ucos

e va

lue

mg/

dl

Base

End

Table – 5C: Distribution of Sample According to the Age

S .no Age Number of patients Percentage (%) 1 ≤ 24 69 20.90 2 25 – 29 162 49 3 30 – 34 79 23 4 ≥ 35 20 6.06

Graph – 2A: Expressing percentage of woman and their age range

Table – 5D: Distribution of Sample According to the BMI

S .no BMI (kg/m2) Number of patients Percentage (%) 1 18.59 to 24.99 66 20.00 2 25 to 29.99 169 51.21 3 ≥ 30 95 28.78

Graph – 2B: Expressing percentage of woman and their BMI range

20.9

49

23

6.06

0

10

20

30

40

50

60

≤ 24 25 – 29 30 – 34 ≥ 35

% o

f wom

en

Age range

% of women/ Age

20

51.21

28.78

0

10

20

30

40

50

60

18.59 - 24.99 25 - 29.99 ≥ 30

BMI

BMI of Woman

Perc

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Table – 5E: Average diagnosis and delivery week

S .no Diagnosis Week

1 GDM 27.17 2 PIH 33.45 3 Delivery 35.87

Graph – 2C:

Table – 5F: Family history of diabetes mellitus

S .no Family History Percentage (%) 1 Family history of DM 57.87 2 No family history of DM 29.09

Graph – 2D: Expressing Family history of diabetes mellitus

27.17

33.45 35.87

0

5

10

15

20

25

30

35

40

Week of GDM diagnosis Week of PIH diagnosis Week of Delivery

Week of diagnosis & delivery

Aver

age

Wee

k

57.87

29.09

0

10

20

30

40

50

60

70

Family history of DM No Family history

Percentage of Family history

Perc

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Table – 5G: Types of Treatment and types of Insuli n

S .no Treatment Percentage (%) 1 Insulin 80.60 2 NPH Insulin 52.25 3 Regular Insulin 29.70 4 Both Insulin 18.05 5 Diet 19.40

Graph – 2E: Expressing Type of treatment and Type of Insulin

Table – 5H: Mode of delivery S .no Delivery Percentage (%)

1 Cesarean 89.40 2 Normal 10.60

Graph – 2F: Expressing Mode of Delivery

80.6

19.4

52.25

29.7

18.05

0

10

20

30

40

50

60

70

80

90

Insulin NPH Insulin Regular Insulin Both Insulin Diet

Type of TreatmentType of Insulin

Perc

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ge

80.6

10.4

Cesarean

Normal

Percentage of Mode of Delivery

Table – 5I: Preterm and term delivery

S .no Delivery Percentage (%)

1 Preterm 51 2 Term 49

Graph – 2G: Expressing preterm and term delivery

Table – 5J: Distribution of baby according to Size

S .no Size of baby Percentage (%)

1 SGA 4.05 2 LGA 11.88 3 AGA 84.05 4 Macrosomia 2.31

Graph – 2H: Expression percentage of baby according to Size

term 49% preterm

51%

Type of delivery

4.05 11.88

84.05

2.31 0

10

20

30

40

50

60

70

80

90

SGA LGA AGA Macrosomia

Perc

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->

Size of Baby

6.2. Pregnancy induced hypertension

Women primarily diagnosed as PIH were taken in to analysis (n=187). Maternal and fetal

characteristics were shown in – Table 6, 6A, 6B, 6C, 6D, 6E, 6F, 6G & Graph 3,3A, 3B, 3C, 3D.

Out of the 187 PIH diagnosis 61 (33.62%) was gestational hypertension and 125 (66.84%) was

preeclampsia. Based on the severity of disease 61 (33.62%) had gestational hypertension, 38

(20.32%) had mild-preeclampsia, 67 (35.82%) had severe-preeclampsia and 20 (11.69%) had

eclampsia.

The mean age of women was 26.55 ± 4.55 years. Most of the women fall between the age group

of 22 to 25 years of age, which accounts for 40% of population. Five percentages of women were

more than 35 years of old. The mean BMI was 25.60 ± 4.57 kg/m2. Forty five percent of women

found with obese and 37% women were found to be ideal body weight.

The patient history revealed that 30.48% of population has family history of DM and 31% has

family history of HTN. Eleven percent of women had previous history of PIH and 2% women had

previous history of GDM where as 55% of women doesn’t have any previous history.

Regarding parity, 20% of women had one previous delivery and 6% of women had more than one

previous delivery, where as 74% women have no previous history of delivery. Gravidity data

showed 42% of women had at least one previous pregnancy and 58% of women had the first

pregnancy. The average weight at the time of confirmation of pregnancy was 63.51 ± 12.06 kg. The

average maternal weight gain was 13.40 ± 4.6 kg.

Confirmation of PIH differed from woman to woman; the average week for diagnosis of PIH was

31.76 ± 6.71 weeks. The diagnoses of PIH before and after the 20th week of gestation were found

to be 12% and 88% respectively.

Eighty two percent of women were treated by drugs for PIH complication and 18% of women were

not treated by any drugs. The drugs used for the treatment are methyldopa-250 mg, 500 mg

(28.35%), nifedipine- 5 mg, 10 mg (26.73%), atenolol- 50 mg and ecospirin- 75 mg. around 21% of

women received both the drugs methyldopa and nifedipine.

Blood pressure (BP) for all the PIH women was monitored regularly and it was controlled well. The

overall mean morning, mid-day and night time blood pressure levels were 133.62/88.98 ±

(15.44/9.02), 132.62/88.57 ± (16.32/9.88) and 133.77/89.03 ± (1.99/7.71) respectively.

The mean baseline blood pressure values of, which are taken at the time of PIH diagnosed,

morning, mid-day and night time were 133.44/88.74 ± (16.01/10.26), 134.66/90.20 ±

(20.20/11.37) and 132.44/88.37 ± (14.95/10.00) respectively. The mean end values were which are

taken prior to delivery, 134.06/89.72 ± (17.59/10.71), 134.35/88.75 ± (16.32/9.88) and

131.13/86.72 ± (14.21/10.17) respectively. Post-partum BP was taken immediately, 3 h and 7 h of

post-partum; the values are 133.44/88.85 ± (15.83/9.95), 131.14/85.77 ± (13.31/9.08) and

130.33/84.73 ± (12.52/6.47) respectively.

Total of 187 PIH women were given birth to 194 infants. The average week of delivery was 34.74 ±

3.70 weeks. Ninety five percent of women given birth through cesarean delivery whereas only 5%

of women delivered through vaginal. Of the 194 babies 7 were twin babies. Two fetal deaths were

reported and totally 192 live babies were delivered. About 108 male babies and 84 female babies

were born. Babies mean birth weight was 2.20 ± 0.75kgs. Mean Apgar score at 1st min was 7.77 ±

0.78 and 8.36 ± 0.59 for the same at 5th min. Preterm babies were accounted to 65.62%. Small for

gestational age babies (SGA) were 36.45% and 26% of IUGR was recorded.

Table – 6: Maternal Characteristics of PIH (n=187)

Characters Number of woman Values [Mean ± SD] / %

Age (year) 26.55 ± 4.55 Weight (kg) 63.51 ± 12.6 Height (cm) 157.36 ± 6.33 BMI (kg/m2) 25.60 ± 4.579 Gravidity 1.63 ± 0.97 Primigravida 109 58.28 Multigravida 78 41.71 Parity 0.35 ± 0.69 Nulliparity 138 73.79 Primiparity 37 19.78 Multiparity 12 6.41 Previous PIH 21 11.22 Family History of HTN 57 30.48 No family history of HTN 130 69.51 Marital period (year) 3.79 ± 3.09 MWG (kg) 13.40 ± 4.60 Week of PIH diagnosis 31.76 ± 6.74 Week of delivery 34.74 ± 3.70 Methyldopa 53 28.35 Nifedipine 50 26.73 Both (M.Dopa+Nifedipine) 39 20.85 None (No drug) 34 18.18 Cesarean delivery 177 94.65 Vaginal delivery 10 5.34

Table – 6A: Neonatal Characteristics of PIH [n=187]

Characters Number of Babies Values [Mean ± SD] / % Baby 192 Male baby 108 56.25 % Female baby 84 43.75 % Weight 2.20 ± 0.75 Term baby 66 34.37 % Preterm baby 126 65.62 5 Apgar score at 1st minute 7.77 ± 0.78 Apgar score at 5th minute 8.36 ± 0.59 Twin babies 7 3.74 % LBW 112 58.33 % NBW 80 41.67 % SGA 70 36.45 % LGA 8 8.69 % AGA 114 59.37 % IUGR 26 26 % Apgar score <7 at 1st minute 62 32.29 % HELLP 3 1.56 %

Table – 6B: Maternal Blood pressure level

Blood pressure Base line value (mmHg) systolic/diastolic ± (SD)

End value (mmHg) systolic/diastolic ± (SD)

BP – morning 133.44/88.74 (16.01/10.26) 134.06/89.72 (17.59/10.71)

BP – midday 134.66/90.20 (20.20/11.37) 134.35/88.75 (16.97/10.78)

BP – night 132.44/88.37 (14.95/10.00) 131.13/86.72 (14.21/10.17)

BP – delivery (postpartum) 134.67/87.85 (15.85/10.21) BP – delivery (3 h postpartum) 131.14/86.37 (14.27/9.71) BP – delivery (7 h postpartum) 130.33/84.86 (12.10/7.98)

Table – 6C: Distribution of Sample According to the Age

S .no Age Number of patients Percentage (%)

1 ≤ 24 69 20.90 2 25 – 34 103 55.38 3 ≥ 35 14 7.53

Graph – 3: Expressing percentage of woman and their age range

Table – 6D: Distribution of sample according to the BMI

S .no BMI (kg/m2) Number of patients Percentage (%)

1 <18.59 2 1.07%

2 18.59 to 24.99 96 51.61%

3 25 to 29.99 58 31.18%

4 ≥ 30 30 16.12%

Graph – 3A: Expressing percentage of woman and their BMI range

37.09%

55.38%

7.53%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

≤ 24 25 – 34 ≥ 35

Percentage (%)

1.07%

51.61%

31.18%

16.12%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

<18.59 18.59 to 24.99 25 to 29.99 ≥ 30

Percentage (%)

BMI of Women (PIH)

Age of women (PIH)

Table – 6E: Type of Gravidity S .no Gravidity Percentage

1 Primigravidity 60.96% 2 Multigravidity 39.04%

Graph – 3B: Expressing type of gravidity in percentage

Table – 6F: Type of Parity

S .no Parity Percentage

1 Primiparity 19.78% 2 Multiparity 6.41% 3 Nulliparity 73.79%

Graph – 3C: Expressing type of Parity in percentage

60.96%

39.04%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

Primigravidity Multigravidity

Perc

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->

19.78%

6.41%

73.79%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

Primiparity Multiparity Nulliparity

Perc

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->

Parity

Type of Gravidity (PIH)

Table – 6G: Family History of HTN

S .no Family History Percentage (%)

1 Family history of HTN 30.48%

2 No family history of HTN 69.52%

Graph – 3D: Expressing family history of HTN of PIH women in percentage

30.48%

69.52%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

Family history of HTN No family history of HTN

Perc

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6.3. Gestational diabetes mellitus and Pregnancy induced hypertension

Women diagnosed with GDM and PIH were taken in to analysis (n=68). Maternal and fetal

characteristics were shown in – Table 7, 7A, 7B, 7C, 7D, 7E, 7F, 7G, 7H, 7I & Graph 4, 4A, 4B, 4C,

4D, 4E, 4F, 4G.

Total of 68 women found with GDM and PIH of which 53 women were developed GDM first then

PIH whereas 15 women were developed PIH first then GDM. The mean age of women was 29.60 ±

4.52 years. Most of the women fall between the age group of 28 to 32 years of age, which

accounts for 46%. Twenty four percent of women were more than 33 years of old. About 90% of

women’s age was more than 25 years. The mean BMI was 27.95 ± 3.88 kg/m2. Obese population

accounts for 79% and 20% were found to be ideal body weight.

The patient history revealed that 70.58% of women had family history of diabetes and 47% women

had family history of hypertension. Seventeen percent of women don’t have any family history of

diabetes or hypertension. Nine percent of women had previous history of GDM and 20.58% of

women had previous history of PIH where as 70.58% of women doesn’t have any history of GDM

or PIH.

Regarding parity, 59% of populations are nulliparous, 37% are primiparous and 4% were

multiparous. Gravidity data shows 59% of women had at least one previous pregnancy and for

41% of women this is the first pregnancy. The average weight at the time of confirmation of

pregnancy was 70.49 ± 10.43 kg. The average maternal weight gain was 10.48 ± 3.60 kg.

Confirmations of complications were differed from woman to woman; the average week for

diagnosis of GDM was 27.10 ± 7.46 weeks and for PIH diagnosis was 29.89 ± 8.32 weeks. GDM was

diagnosed first for 78% women whereas 22% of women were diagnosed PIH first.

GDM was managed with diet and insulin. Insulin was given to 75% of women and diet was used to

25% of women as a treatment. Regular insulin, Intermediate insulin (NPH) and sometime both

insulin were used to treat GDM women. All the dietary treated GDM were received 2000 kcal to

3800 Kcal of food the diet treatment. For the management of PIH Methyldopa (250 mg, 500 mg)

and Nifedipine (5 mg, 10 mg) were used. Thirty seven percent of women were received

Methyldopa, 20.58% of women were received Methyldopa and Nifedipine, 7.75% of were received

Nifedipine and 35.29% of women were received none of the drug for their PIH treatment.

The glucose level was monitored regularly and was controlled well. The last four values of mean

fasting and post prandial glucose levels were 100.83 ± 10.22 mg/dl and 144.10 ± 30.05 mg/dl

respectively. The mean baseline values of, which are taken at the time of GDM diagnosed, fasting

blood sugar and post prandial blood sugar are 101.76 ± 19.28 mg/dl and 135.57 ± 28.09 mg/dl

respectively. The mean end values of, which are taken prior to delivery, fasting and postprandial

blood glucose were 101.54 ± 16.83 mg/dl and 134.15 ± 24.99 mg/dl respectively. The mean

baseline BP taken in the morning was 134.60/89.19 ± (21.18/12.55) mm Hg and end value was

135.98/86.10 ± (16.67/12.16) mm Hg. The immediate post-partum BP was 137.85/89.14 ±

(16.48/7.82) mm Hg and the same was reduced to 129.64/85.05 ± (8.66/7.28) 7 h of post-

partum.

The average week of delivery was 34.60 ± 4.00 weeks. Totally 70 babies were delivered of which 2

deliveries were twin babies. About 40 male babies and 30 female babies were born. Babies mean

birth weight was 2.45 ± 0.81 kgs. Mean Apgar score at 1st min was 7.55 ± 0.67 and 8.38 ± 0.58 for

the same at 5th min. Preterm delivery was 61.76%. SGA was 17.64% and LGA was 11.76%. Fifteen

percent of IUGR was recorded. 45.58% of babies were found with hypoglycemia and 61.76% of

babies were found with hyperbilirubinemia.

Table – 7: Maternal characters of GDM + PIH [n=68]

Characters Number of woman Values [Mean ± SD]/ %

Age (year) 29.60 ± 4.52 Weight (kg) 70.49 ± 10.43 Height (cm) 158.65 ± 5.7 BMI (kg/m2) 27.95 ± 3.88 Gravidity 2.0 ± 1.13 Primigravida 28 41.17 % Multigravida 40 58.82 % Parity 0.45 ± 0.58 Nulliparity 40 58.82 % Primiparity 25 36.76 % Multiparity 3 5.88 % Previous GDM 6 8.82 % Previous HTN 14 20.58 % Family History of DM 48 70.58 % Family History of HTN 32 47.05 % No family history of DM/HTN 12 17.64 % Marital period (year) 5.80 ± 4.15 MWG (kg) 10.48 ± 3.6 Week of GDM diagnosis 27.10 ± 7.46 Week of PIH diagnosis 29.89 ± 8.32 Average Interval for diagnosis 5.79 ± 5.58 week Week of delivery 34.60 ± 4.00 Insulin 51 75 % diet 17 25 % Antihypertensive drugs 44 64.70 % Cesarean delivery 68 100 % Vaginal delivery 0 0 %

Table – 7A: Neonatal Characteristics of GDM and PIH [n=68]

Characters Number of Babies Values [Mean ± SD] / % Baby 70 Male baby 40 57.14 % Female baby 30 42.85 % Weight 2.45 ± 0.81 Term baby 26 38.23 % Preterm baby 42 61.76 % Apgar score at 1st minute 7.55 ± 0.67 Apgar score at 5th minute 8.33 ± 0.58 Twin babies 2 2.94 % LBW 32 47.05 % NBW 36 51.42 % SGA 12 17.64 % LGA 8 11.76 % AGA 50 73.52 % IUGR 10 14.70 % Macrosomia 2 2.94 % Apgar score <7 at 1st minute 62 32.29 % HELLP 3 1.56 % Hypoglycemia 31 45.58 % Hyperbilirubinemia 42 61.76 %

Table – 7B: Distribution of Sample According to the Age

S .no Age Number of patients Percentage (%)

1 ≤ 24 7 10.29%

2 25 – 34 50 73.52%

3 ≥ 35 11 16.17%

Graph – 4: Expressing distribution of age in percentage in GDM+PIH women

Table – 7C: Distribution of Sample According to the BMI

S .no BMI (kg/m2) Number of patients Percentage (%)

1 18.59 to 24.99 13 19.11%

2 25 to 29.99 34 50%

3 ≥ 30 21 30.88%

Graph – 4A: Expression of GDM+ PIH women by BMI (n=68)

10.29%

73.52%

16.17%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

≤ 24 25 – 34 ≥ 35

Perc

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% of Age

19.11%

50%

30.88%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

18.59 to 24.99 25 to 29.99 ≥ 30

Perc

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BMI

Table – 7D: Average diagnosis and delivery week

S .no Diagnosis Week 1 GDM 30.16 ± 5.18 2 PIH 33.71 ± 4.25 3 Delivery 34.60 ± 4.00

Graph – 4B: Distribution of diagnosis and delivery of GDM+ PIH women

Table – 7E: Family history of DM and HTN

S .no Family History Percentage (%)

1 Family history of DM 70.58%

2 Family history of HTN 47.05%

3 Family history of DM/HTN 80.88%

Graph – 4C: Positive Family history of GDM+ PIH women

30.16 33.71 34.6

0

5

10

15

20

25

30

35

40

45

GDM PIH Delivery

Wee

k

Week

DM 35%

HTN 24%

Both 41%

Family history in percentage

Table – 7F: Mode of delivery

S .no Delivery Percentage (%)

1 Cesarean 100%

2 Normal 0%

Graph – 4D: Expressing mode of delivery

Table – 7G: Distribution of sample according to parity

S .no Parity Percentage

1 Primiparity 36.76%

2 Multiparity 4.41%

3 Nulliparity 58.82%

Graph – 4E: Expressing Type of Parity

Cesarean, 100%

Cesarean

36.76%

4.41%

58.82%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

Primiparity Multiparity Nulliparity

Perc

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->

Percentage

Table – 7H: Distribution of sample according to Gravidity

S .no Gravidity Percentage

1 Primigravidity 41.17%

2 Multigravidity 58.83%

Graph – 4F: Expressing Type of Gravidity

Table – 7I: Distribution of baby by gender

S .no Baby Number of babies

1 Male 40

2 Female 30

3 Twin MM 1

4 Twin MF 1

Graph – 4G: Expressing Number of babies by gender

41.17%

58.83%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

Primigravidity Multigravidity

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Percentage

40

30

1 1 0

5

10

15

20

25

30

35

40

45

Male Female Twin MM Twin MF

Num

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Number of babies

6.4. Analysis

6.4.A. Individual analysis

6.4.A.1. Gestational diabetes mellitus

6.4.A.1.a. Diet vs. Insulin 6.4.A.1.b. 1st vs. 2nd vs. 3rd trimester 6.4.A.1.c. Early vs. Late onset 6.4.A.1.d FBS ≤ 95 mg/dl vs. FBS ≤ 95 mg/dl

6.4.A.2. Pregnancy induced Hypertension (PIH)

6.4.A.2.a. Treatment comparison 6.4.A.2.b. Severity comparison

6.4.B. Comparison analysis

6.4.B.1. Risk factors 6.4.B.2. Complications 6.4.B.3. Association

General characters of Maternal and neonatal were presented in - Table 8 and 8A.

Table – 8: Maternal characteristics of GDM, PIH and GDM + PIH women

values are expressed in percentage and mean ± SD

Characters GDM n=277) PIH (n=172) GDM & PIH (n=68)

Age (year) 27.45 ± 3.78 26.55 ± 4.55 29.60 ± 4.52 Weight (kg) 67.93 ± 9.26 63.51 ± 12.6 70.49 ± 10.43 BMI (kg/m2) 27.77 ± 3.94 25.60 ± 4.579 27.95 ± 3.88 Gravidity 1.91 ± 1.11 1.63 ± 0.97 2.0 ± 1.13 Primigravida 40.09 % 62.79 % 41.17 % Multigravida 50. 90 % 37.20 % 58.82 % Parity 0.46 ± 0.65 0.35 ± 0.69 0.45 ± 0.58 Nulliparity 60.28 % 73.83 % 58.82 % Primiparity 35.01 % 20.34 % 36.76 % Multiparity 13 % 5.81 % 5.88 % Previous GDM 7.94% 0.581% 8.82% Previous HTN 3.24% 11.22% 20.58% Family History of DM 57.03% 26.16% 8.82% Family History of HTN 30.68% 26.74% 20.58% Marital period (year) 3.79 ± 3.09 3.79 ± 3.09 5.80 ± 4.15 MWG (kg) 11.87 ± 3.65 13.40 ± 4.60 10.48 ± 3.6 Week of GDM diagnosis 27.01 ± 8.51 30.16 ± 5.18 Week of PIH diagnosis 31.76 ± 6.74 29.89 ± 8.32 Week of delivery 36.28 ± 1.99 34.74 ± 3.70 34.60 ± 4.00 Hb (gm/dl) 11.45 ± 1.24 11.54 ± 1.52 11.87 ± 1.03 Irregular Menstrual cycle 36.10 % 17.63 % 44.11 % Insulin 80.50% 75% Diet 19.49% 25% Methyldopa 28.355% Nifedipine 26.73% Methyldopa + Nifedipine 20.85% None (No drug) 18.18% Caesarean delivery 87.36% 94.65% 100% Vaginal delivery 12.63% 5.81 % 0%

Table – 8A: Neonatal characters of women of GDM, PIH and GDM+PIH

Characters Neonates of GDM alone (n=290)

Neonates of PIH alone (n= 177)

Neonates of GDM+PIH (n=68)

Male baby 60 % 57.62 % 57.14 % Female baby 40 % 43.50 % 42.85 % Weight 2.67 ± 0.65 2.22 ± 0.73 2.45 ± 0.81 Term baby 50.34 % 35.02 % 38.23 % Preterm baby 48.96 % 64.97 % 61.76 % Twin babies 4.69 % 4.06 % 2.94 % LBW 33.79 % 55.86 % 47.05 % NBW 66.2 % 43.5 % 51.42 % SGA 2.75 % 37.28 % 17.64 % LGA 12.06 % 2.82 % 11.76 % AGA 80.68 % 59.88 % 73.52 % IUGR 0 % 12.99 % 14.70 % Macrosomia 2.06 % 0.56 % 2.94 % Apgar score <7at 1st mint 24.13 % 28.81 % 32.29 % HELLP 0 % 1.12 % 1.56 % Hypoglycemia 55.51 % 0 45.58 % Hyperbilirubinemia 56.55 % 0 61.76 %

values are expressed in percentage and mean ± SD

6.4.A. Individual analysis 6.4.A.1. GDM 6.4.A.1.a. Diet vs. Insulin

GDM diagnosed women treated with diet alone and along with insulin also. To analyze the

maternal and fetal outcome of GDM treatment, GDM alone diagnosed (n=277) populations were

categorized into two groups as diet alone and along with insulin received groups. For the analysis

of PIH as a complication of GDM, the GDM women who developed PIH were also included to the

analysis (n=330).

Among 277 GDM cases 57 (20.57%) were received diet alone as a treatment and 223 (80.50%)

were received insulin along with diet as a treatment. The glucose level controlled well for both

groups. The base and end value of FBS for diet and insulin groups were 96.70 ± 17.03, 92.12 ±

28.16 and 101.26 ± 24.76, 94.95 ± 23.21 respectively. The base and end value of PPBS for diet and

insulin groups were 143.96 ± 30.50, 142.03 ± 31.36 and 146.73 ± 39.14, 136.10 ± 28.70

respectively.

Maternal and fetal outcome of these two groups doesn’t showed significant difference except for

cesarean delivery and preterm delivery. Types of treatment not have shown any difference in

pregnancy outcome. Cesarean deliveries were more with insulin group, nearly 93% of women

given birth through cesarean delivery. Only 19% were cesarean delivery from diet alone group.

Around 62% of babies were delivered as preterm in insulin group and 43% of babies were

delivered as preterm in diet alone group. Average week of diagnosis and delivery were 29.88 ±

6.25, 36.62 ± 1.95 and 26.31 ± 8.85, 35.78 ± 2.05 respectively for diet and insulin groups. Around

3% of macrosomic babies were found with insulin group whereas no babies were found with

macrosomia in diet alone group. Hypoglycemic and hyperbilirumic state of neonates were equally

distributed between the groups around 56% of neonates were found with hypoglycemia and

hyperbilirubinemia in both groups. In diet alone group 22% of women developed PIH and 18% of

women developed PIH in insulin group. Details were recorded in Table 9, 9A, 9B & Graph 5, 5A.

Table – 9: Details of GDM women treated with diet and Insulin.

Statistics value for ANOVA – P value expressed for significance. * P value is significant,

Diet(54) Insulin(223) P - value Age (year) 27.62 ± 4.06 27.40 ± 3.72 BMI (kg/m2) 26.86 ± 3.92 27.99 ± 3.52 week of diagnosis 29.88 ± 6.25 26.31 ± 8.85 week of delivery 36.62 ± 1.95 35.78 ± 2.05 weight of baby 2.69 ± 0.52 2.67 ± 0.68 MWG (kg) 12.09 ± 3.21 11.84 ± 3.74 FBS (mg/dl) Base 96.70 ± 17.03 101.26 ± 24.76 End 92.12 ± 28.16 94.95 ± 23.21 FBS L4 98.32 ± 20.79 96.52 ± 18.62 P = 0.534 PPBS Base 143.96 ± 30.50 146.73 ± 39.14 End 142.03 ± 31.36 136.10 ± 29.20 PPBS L4 140.33 ± 28.77 139.04 ± 28.70 P = 0.767 Caesarean delivery 10 (18.51%) 207 (92.82%) P = 0.000* Vaginal delivery 44 (81.48%) 16 (7.17%) Apgar score <7at1st min 20 (34.48%) 82 (35.34%) P = 0.971 Preterm delivery 25 (43.1%) 143 (61.63%) P = 0.016* Term delivery 33 (61.11%) 89 (39.91%) LBW (kg) 19 (32.75%) 73 (31.46%) P = 0.733 NBW (kg) 39 (72.22%) 159 (71.30%) SGA 1 (1.72%) 7 (3.01%) LGA 5 (8.62%) 30 (12.93%) P = 0.407 AGA 52 195 Macrosomia 0 6 (2.58%) P = 0.224 Hypoglycaemia 32 (55.17%) 129 (55.6%) P = 0.851 Hyperbilirubinemia 30 (51.62%) 134 (57.75%) P = 0.545

Table – 9A: Distribution of Mode of delivery between Diet and Insulin

Diet Insulin Statistics value Cesarean delivery 18.51 % 92.82 % P = 0.000

Graph – 5: Expressing of Mode of delivery between Diet and Insulin

Table – 9B: Distribution of Preterm delivery between Diet and Insulin Diet Insulin Statistics value Preterm 43.1 % 61.63 % P = 0.01

Graph – 5A: Distribution of Preterm deliveries between Diet and Insulin

Diet 17%

Insulin 83%

Cesarean Delivery

Diet 41%

Insulin 59%

Preterm

6.4.A. Individual analysis 6.4.A.1. GDM 6.4.A.1.b. 1st vs. 2nd vs. 3rd trimester

Usually GDM diagnosis at 24th to 28th week of gestation but during the gestation it can occur any

time. The duration of GDM is more with women who diagnosed GDM at 1st trimester of pregnancy

than women who diagnosed GDM at 2nd or 3rd trimester of pregnancy. This change in the length of

GDM may or may not influence the outcome of pregnancy. To analyze the impact of duration of

GDM on pregnancy outcome population (n=277) were categorized into three groups according to

the week of diagnosis through trimester basis. Women diagnosed GDM before the 14th week of

gestation grouped as 1st trimester group, and those diagnosed between 14th and 28th week of

gestation comes under 2nd trimester group, women diagnosed after the 28th week of gestation

through delivery were grouped as 3rd trimester group.

Among 277 GDM cases 36 (13%) were found at 1st trimester, 88 (32%) were found at 2nd trimester

and 153 (55%) were found at 3rd trimester of the pregnancy. The average week of diagnosis of

each group was 10.00 ± 2.87, 23.63 ± 4.10 and 33.05 ± 2.34 respectively. The average delivery

week of each group was 35.61 ± 1.85, 36.06 ± 1.99 and 36.00 ± 2.10 respectively. Maternal and

fetal data were shown in – Table 10, 10A, 10B, 10C, 10D & Graph 6, 6A, 6B, and 6C. Age,

Gravidity and Parity shows significant influence on the early development of GDM, other risk

factors doesn’t show significant influence on the early development of GDM. The average age of

1st trimester group women was 28.88 ± 4.25 years, which is high to compare 2nd (27.59 ± 3.77) and

3rd (27.02 ± 3.81) trimester groups’ women. The average number of risk factors for 1st trimester

group was 4.22 which were high to compare with 3.88 and 3.45 for 2nd and 3rd trimester groups

respectively.

The maternal and fetal outcome doesn’t show significant difference between these three groups

except for LGA and AGA. The duration or length of GDM doesn’t affect the outcome of

pregnancy. The 1st trimester group had more LGA babies (19.44%) than 2nd (5.49%) and 3rd

(14.54%) trimester groups. All babies were normally in good health, the average baby weight of

each group was 2.75 ± 0.54, 2.63 ± 0.60 and 2.70 ± 0.69 years respectively.

Table – 10 Details of women diagnosed GDM at 1st, 2nd and 3rd trimester

1st trim(36) 2nd trim(88) 3rd trim(153) Value

Age 28.88 ± 4.25 27.59 ± 3.77 27.02 ± 3.81 P = 0.024* BMI 27.78 ± 2.91 27.68 ± 3.82 27.74 ± 3.59 P = 0.997 Gravidity 2.38 ± 1.07 1.76 ± 1.10 1.88 ± 1.09 Primigravida 22.23 % 56.82 % 50.31 % P = 0.002* Multigravida 77.77 % 43.18 % 49.69 % P = 0.002* Parity 0.63 ± 0.48 0.43 ± 0.60 0.44 ± 0.71 Nulliparity 36.11 % 62.5 % 64.51 % P = 0.005* Primiparity 63.88 % 31.81 % 30.32 % P = 0.001* Family History DM 61.11 % 50 % 59.35 % P = 0.306 Irreg. Menstrual 33 % 39.77 % 34.64 % P = 0.680 FBS

Base 106.69 ± 34.44 98.96 ± 24.33 99.62 ± 21.47 End 102.36 ± 32.95 93.27 ± 18.91 93.03 ± 24.95 FBS L4mean 103.33 ± 31.00 95.63 ± 14.63 95.98 ± 12.08 P = 0.095 PPBS

Base 155.13 ± 31.00 145.37 ± 35.67 145.00 ± 37.64 End 136.52 ± 26.93 133.76 ± 25.37 139.46 ± 32.36 PPBS L4mean 136.25 ± 24.89 135.37 ± 22.67 142.01 ± 32.22 P = 0.674 Caesarean 31 (86.11%) 72 (81.11%) 141 (92.15%) P = 0.084 Vaginal 5 16 14 Term 14 37 70 P = 0.706 Preterm 22 (61.11%) 54 (59.34%) 95(58.28%) P = 0.515 LBW 8 (22.22%) 38 (41.75%) 52 (33.54%) P = 0.084 NBW 28 53 111 SGA 0 2 (2.19%) 6 (3.72%) P = 0.414 LGA 7 (19.44%) 5 (5.49%) 24 (14.54%) P = 0.046* AGA 29 84 135 P = 0.034* Macrosomia 0 1(1.09%) 5 (3.06%) P = 0.792 Apgar<7at 1st min 12 (33.33%) 35 (38.46%) 52 (31.05%) P = 0.527 HypoGlycemia 18 (50%) 50 (54.94%) 97 (60.24%) P = 0.277 HyperBilirubinemia 15 (41.66%) 57 (62.66%) 91 (56.52%) P = 0.059 Insulin 33 (91.66%) 74 (84.09%) 118 (74.12%) P = 0.074 Diet 3 14 37 Baby 36 91 163 twin 0 3 10

Statistics value for ANOVA – P value expressed for significance. * P value is significant,

Table – 10A: Distribution of Gravidity of all trimesters GDM women 1st trim(36) 2nd trim(88) 3rd trim(153) Value

Gravidity 2.38 ± 1.07 1.76 ± 1.10 1.88 ± 1.09 Primigravida 22.23 % 56.82 % 50.31 % P = 0.002* Multigravida 77.77 % 43.18 % 49.69 % P = 0.002*

Graph – 6: Expressing gravidity of all trimesters GDM women

Table – 10B: Distribution Parity of all trimesters GDM women

1st trim(36) 2nd trim(88) 3rd trim(153) Value

Parity 0.63 ± 0.48 0.43 ± 0.60 0.44 ± 0.71 Nulliparity 36.11 % 62.5 % 64.51 % P = 0.005* Primiparity 63.88 % 31.81 % 30.32 % P = 0.001*

Graph – 6A: Expressing parity of all trimesters GDM women

22.23

77.77

56.82

43.18 50.31 49.69

0

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20

30

40

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70

80

90

Primigravida Multigravida

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1st

2nd

3rd

36.11

63.88 62.5

31.81

64.51

30.32

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10

20

30

40

50

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70

Nulliparity Primiparity

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1st

2nd

3rd

% of women

Table – 10C: Average age of all trimesters GDM women

1st trim(36) 2nd trim(88) 3rd trim(153) Value

Age (year) 28.88 ± 4.25 27.59 ± 3.77 27.02 ± 3.81 P = 0.024*

Graph – 6B: Expressing average age of all trimesters GDM women

Table – 10D: Distribution of LGA, AGA babies of all trimesters GDM women

1st trim(36) 2nd trim(88) 3rd trim(153) Value

LGA 7 (19.44%) 5 (5.49%) 24 (14.54%) P = 0.046* AGA 29 (80.55%) 84 (95.45%) 135 (88.23%) P = 0.034*

Graph – 6C: Expressing percentage of LGA, AGA babies of all trimesters GDM women

28.88 27.59 27.02

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Average Age

Age

19.44

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95.45

14.54

88.23

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6.4.A. Individual analysis 6.4.A.1. GDM 6.4.A.1.c. Early vs. Late onset

The outcome of GDM pregnancy varies with early and late onset of GDM. Same way the

influencing factors also varies for early and late onset. This analysis was done in order to

understand the difference in risk-factors and outcomes between early and late onset of GDM.

Based on the week of diagnosis women were categorized into two groups. Women identified GDM

before 28th week of gestation considered as early onset and women identified GDM after 28th

week of gestation considered as late onset. For the comparison of risk-factors and outcome

women diagnosed with GDM alone (n=277) were taken to analysis.

Maternal and fetal characteristics of population were shown in – Table 11, 11A, 11B, 11C & Graph

7, 7A. Among 277 GDM cases 124 (45%) had the early onset and 153 (55%) had late onset.

Advancing age and primiparity shows significant influence on early development of GDM

whereas other risk-factors doesn’t show influence on early development. The average age of early

onset group was high (27.96 ± 3.68) compared to late onset group (27.02 ± 3.81). Forty two

percent of primiparity women had early onset of GDM and 31% of women had late onset of GDM.

The average parity of early onset women was 0.49 ± 0.57 and for late onset women was 0.44 ±

0.71.

Maternal and fetal outcome of these two groups don’t have significant difference except for

cesarean delivery and the insulin usage. The pregnancy outcome was same between early and

late developing GDM. Cesarean delivery was more with late onset of GDM (92%) compared to

early onset of GDM (83%). In early onset group around 86% of women received insulin for their

treatment whereas 76% of women received insulin in late onset group. Around 21% of women

from late onset group developed PIH whereas 17% of women from early onset group developed

PIH.

Table – 11: Details of women developed GDM early and late

Statistics value for ANOVA – P value expressed for significance.* P value is significant

Early(124) Late(153) P – value

Age (year) Risk level

27.96 ± 3.68 100 (80.64%)

27.02 ± 3.81 113 (73.85%)

P = 0.030*

BMI (kg/m2) Risk level

27.71 ± 3.57 100 (80.64%)

27.74 ± 3.59 121 (79.08%)

P = 0.933

Gravidity 1.94 ± 1.12 1.88 ± 1.09 P = 0.627 Primigravida 58 (46.77%) 78 (50.98%) P = 0.318 Multigravida 66 (53.22%) 75 (49.01%) P = 0.318 Parity 0.49 ± 0.57 0.44 ± 0.71 P = 0.140 Nulliparity 68 (54.83%) 99 (64.7%) P = 0.767 Primiparity 51 (42.12%) 47 (30.71%) P = 0.005* Multiparity 5 (4.03%) 7 (4.47%) Family History DM 66 (53.22%) 72 (47.05%) P = 0.308 Father DM 48 (38.7%) 54 (35.29%) P = 0.690 Mother DM 31 (25%) 50 (32.67%) P = 0.110 Previous GDM 8 (6.45%) 13 (8.49%) P = 0.523 HB 9 (7.25%) 13 (8.49%) P = 0.368 Irregular Menstrual cycle 47 (37.9%) 53 (34.64%) week of diagnosis 19.66 ± 7.69 33.05 ± 2.34 week of delivery 35.92 ± 1.97 35.99 ± 2.09 weight of baby 2.66 ± 0.58 2.69 ± 0.69 P = 0.173 MWG (kg) 12.09 ± 3.21 11.84 ± 3.74 Insulin used 107 (86.29%) 118 (76.12%) P = 0.025* PIH developed (n=330) 24 (16.55%) 39 (21.08%) Caesarean delivery 103 (83.06%) 141 (92.15%) P = 0.020* Vaginal delivery 21 (16.93%) 14 (9.15%) Apgar score <7at1st min 47 (37.9%) 52 (37.9%) P = 0.500 Preterm delivery 76 (61.29%) 95 (61.29%) P = 0.892 Term delivery 51 (41.12%) 70 (56.45%) LBW (kg) 46 (37.09%) 52 (37.09%) P = 0.591 NBW (kg) 81 (65.32%) 111 (72.54%) SGA 2 (1.61%) 6 (1.61%) LGA 12 (9.67%) 24 (9.67%) P = 0.140 AGA 113 (91.12%) 135 (88.23%) Macrosomia 1 (0.80%) 5 (0.8%) P = 0.162 Hypoglycaemia 68 (54.83%) 97 (54.83%) P = 0.150 Hyperbilirubinemia 72 (58.06%) 91 (58.06%) P = 0.813

Table – 11A: Distribution of blood glucose value of women developed GDM early and late

Statistics value for ANOVA – P value expressed for significance.* P value is significant

Table – 11B: Age and Primiparity of women developed GDM early and late

Graph – 7: Expressing percentage of Age, Primiparity of early and late GDM women

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Early(124) Late(153) P – value FBS (mg/dl) Base 96.70 ± 17.03 101.26 ± 24.76 End 92.12 ± 28.16 94.95 ± 23.21 FBS L4 98.32 ± 20.79 96.52 ± 18.62 P = 0.534 PPBS Base 143.96 ± 30.50 146.73 ± 39.14 End 142.03 ± 31.36 136.10 ± 29.20 PPBS L4 140.33 ± 28.77 139.04 ± 28.70 P = 0.767

Early(124) Late(153) P – value Age (year) Risk level

27.96 ± 3.68 100 (80.64%)

27.02 ± 3.81 113 (73.85%)

P = 0.030*

Primiparity 51 (42.12%) 47 (30.71%) P = 0.005*

Table – 11C: Percentage of Cesarean and Insulin use in early and late GDM women

* P value is significant

Graph – 7A: Percentage of Cesarean, use of Insulin in early and late GDM women

83 86 92

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Early(124) Late(153) P – value Insulin used 107 (86.29%) 118 (76.12%) P = 0.025* Caesarean delivery 103 (83.06%) 141 (92.15%) P = 0.020*

6.4.A. Individual analysis 6.4.A.1. GDM 6.4.A.1.d. FBS ≤ 95 mg/dl vs. FBS ≥ 95 mg/dl

Variation in blood glucose level of mother causes considerable changes in fetal health. Fetus

insulin stimulation will be more when he/she receives mother blood with excess glucose level,

which further cause hypoglycemic state of fetus and further complications as a consequences for

the same. This analysis was done to understand, what will be the outcome difference if blood

glucoses controlled under normal range and not. Women who controlled the FBS level ≤ 95 mg/dl

(144) were considered as control group and women who controlled the FBS level > 95 mg/dl (133)

were considered as non-control group.

Maternal and fetal characteristics were shown in – Table 12, 12A, 12B, 12C, 12D, 12E & Graph 8,

8A, 8B, 8C, 8D. There was significant difference in terms of pregnancy outcome between the

controlled and non-controlled groups. Strict control of blood glucose level under the normal

range gives significant improvement in the pregnancy outcome of GDM. The following

complications were significantly improved in control group, mode of delivery, preterm delivery,

term delivery, LBW, NBW, LGA and week of delivery.

The average end FBS values of control and non-control group were 82.95 ± 12.44 and 106.68 ±

27.69 mg/dl respectively. Cesarean section and preterm delivery were less in number with control

group. Around 83% of women given birth through cesarean delivery the same was 93% with non-

control group of GDM. BMI have significant role in maintenance of blood glucose, larger the BMI

requires more amount of insulin and further it may leads to insulin resistance. The average BMI of

non-controlled group women was 28.33 ± 8.59 kg/m2 which is higher than controlled group

women (27.23 ± 3.61). Around 63% of delivery was preterm delivery in no-controlled group

whereas 52% of preterm deliveries were recorded in controlled group. The average week of

delivery for controlled group was 36.30 ± 1.85, which less for non-controlled group (35.61 ± 2.16).

Term babies were more with controlled group which accounts for 51%. Only 38% were recorded

as term babies in non-controlled group. LBW babies were high (38%) with non-controlled group,

the same was accounts for 26% in controlled group. Double the amount of LGA recorded with non-

controlled group compared to controlled group, the values are 17% and 8% respectively.

Table – 12: Blood glucose level of women who controlled FBS ≤ 95 mg/dl and above 95 mg/dl.

Measurement ≤ 95(144) >95 (133) P – value FBS

Base value 90.86 ± 12.86 110.43 ± 27.82 End value 82.95 ± 12.44 106.68 ± 27.69

FBS Last 4 values mean 83.93 ± 7.68 110.87 ± 17.71 P = 0.000* PPBS Break Fast

Base value 137.32 ± 32.90 155.21 ± 40.05 End value 128.34 ± 24.41 146.87 ± 31.88

PPBS Last 4 values mean 129.80 ± 24.77 149.22 ± 29.41 P = 0.000* PPBS LUNCH

Base value 143.27 ± 33.05 160.84 ± 35.95 End value 136.84 ± 30.03 145.81 ± 24.27

PPBS Last 4 values mean 132.09 ± 21.76 148.50 ± 23.88 PPBS DINNER

Base value 143.94 ± 28.04 163.59 ± 36.39 End value 140.06 ± 25.76 152.57 ± 33.50

PPBS Last 4 values mean 131.46 ± 23.38 146.32 ± 26.03 Statistics value for ANOVA – P value expressed for significance. * P value is significant.

Graph – 8: Expressing Blood glucose level of women controlled FBS below and above 95 mg/dl

91 83 84

137 128 130

110 107 111

155 147 149

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<95 mg/dl group (FBS) >95 mg/dl group (FBS)

Table – 12A: Details of women who controlled FBS ≤ 95 mg/dl and above 95 mg/dl

Character FBS ≤ 95 mg/dl (n=144)

FBS >95 mg/dl (n=133) P Value

Age (year) 27.07 ± 3.42 27.78 ± 4.08 P = 0.243 BMI (kg/m2) 27.23 ± 3.61 28.33 ± 8.59 P = 0.037* Week of diagnosis 27.20 ± 8.59 26.87 ± 8.47 P = 0.532 week of delivery 36.30 ± 1.85 35.61 ± 2.16 P = 0.004* MWG 11.87 ± 3.51 11.92 ± 3.77 Diet 32 (22.22%) 23 (17.29%) Insulin 112 (77.77%) 110 (82.70%) P = 0.305 Caesarean delivery 119 (82.63%) 123 (92.48%) P = 0.014* Vaginal delivery 25 (17.36%) 10 (7.51%) P = 0.014* Apgar<71t 49 (32.23%) 48 (34.78%) P = 0.720 Preterm 79 (51.97%) 87 (63.04%) P = 0.044* Term 73 (51%) 51 (38%) P = 0.021* LBW 39 (25.65%) 52 (37.68%) P = 0.034* NBW 113 (78.47%) 86 (64.66%) SGA 1 (0.65%) 7 (5.07%) P = 0.011* LGA 12 (7.89%) 23 (16.66%) P = 0.025* AGA 139 (96.52%) 108 (81.20%) P = 0.000* Macros 3 (1.97%) 3 (2.17%) P = 0.922 Hypoglycaemia 90 (59.21%) 71 (51.44%) P = 0.125 Hyperbilirubinemia 92 (60.52%) 72 (52.17%) P = 0.100 Baby 152 138 twin 8 5 Baby Weight 2.74 ± 0.57 2.64 ± 0.67 P = 0.079

Statistics value for ANOVA – P value expressed for significance. * P value is significant

Table – 12B: Distribution of BMI of women: FBS below and above the 95 mg/dl Character FBS ≤ 95 mg/dl FBS >95 mg/dl P Value BMI (kg/m2) 27.23 ± 3.61 28.33 ± 8.59 P = 0.037*

Graph – 8A: Expressing BMI of women: FBS below and above the 95 mg/dl

Table – 12C: Week of diagnosis and delivery of women: FBS below and above the 95 mg/dl Week (average) FBS ≤ 95 mg/dl FBS >95 mg/dl P Value Week of diagnosis 27.20 ± 8.59 26.87 ± 8.47 P = 0.532 week of delivery 36.30 ± 1.85 35.61 ± 2.16 P = 0.004*

Graph – 8B: Week of diagnosis & delivery of women: FBS below and above the 95 mg/dl

27.23 28.33

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Table – 12D: Term and preterm delivery of women: FBS below and above the 95 mg/dl Character FBS ≤ 95 mg/dl FBS >95 mg/dl P Value Preterm 79 (51.97%) 87 (63.04%) P = 0.044* Term 73 (51%) 51 (38%) P = 0.021*

Graph – 8C: Week of diagnosis & delivery of women: FBS below and above the 95 mg/dl

Table – 12E: Distribution of SGA, LGA babies of women: FBS below and above the 95 mg/dl

Character FBS ≤ 95 mg/dl (number/%) FBS >95 mg/dl (number/%) P Value SGA 1 (0.65%) 7 (5.07%) P = 0.011* LGA 12 (7.89%) 23 (16.66%) P = 0.025*

Graph – 8D: Expressing % of LGA, SGA babies of women, FBS below and above the 95 mg/dl

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6.4.A. Individual analysis 6.4.A.2. PIH 6.4.A.2.a. Treatment comparison

Uncontrolled blood pressure caused complications both in mother and fetus. Growth retardation

and fetal mortality are the serious complications in PIH pregnancy. The elevated BP also caused

the early and cesarean delivery. Antihypertensive drugs are used to control the BP within the

normal range which further reduces the PIH related complications. Various drugs are used to treat

the PIH; this analysis was done to find the treatment outcome differences between different types

of drugs used in the treatment of PIH.

The PIH diagnosed women (n=161) were categorized according to the drug that they received for

their treatment. The groups are Group 0 (n=30) – women who received none of the drug, Group 1

(n=50) – women who received Nifidepine, Group 2 (n=46) – women who received Methyldopa and

Group 3 (n=35) – women who received Nifidepine and Methyldopa.

Maternal and fetal outcome data were presented in – Table 13, 13A, 13B, 13C & Graph 9, 9A. For

the entire group BP was significantly reduced from base value to end value. All the drugs

significantly reduced the BP level from base to end. But there was no significant difference in the

reduction of BP between the drugs. The pre and post BP value of all the drugs were given in the –

Table 13A. No significant difference was found in terms of pregnancy outcome between these

drugs treatment except for eclampsia. Eclampsia was affected with 14% women in group 2

(women received both type of drugs) whereas no women was affected with eclampsia in group 0

(women received no drug). Cesarean delivery was high across the group, all the group 0 women

has given birth through cesarean delivery.

Table – 13: Distribution of complications of PIH women based on the drugs received

Complications Nifidepine (n=50)

Methyldopa (n=46)

Nife+M.dop (n=35)

No drug (n=30)

P Value

Cesarean delivery 94 91.3 94.28 100 P = 0.454 Vaginal delivery 6 8.69 5.71 0 P = 0.454 Term delivery 32 41.30 25.71 46.66 P = 0.265 Preterm delivery 69.26 60.41 75 56.25 P = 0.376 LBW 61.53 56.25 61.11 43.75 P = 0.457 NBW 40 45.65 40 60 P = 0.313 SGA 42 37.5 41.66 21.87 P = 0.234 LGA 2 4.16 5.55 0 AGA 40 60.86 54.28 83 P = 0.002* IUGR 20 14.58 11.11 6.25 P = 0.391 Apgar <7 at 1st mint 36 25 41.66 15.62 P = 0.101 HELLP 0 0 2 0 P = 0.428 Eclampsia 6 2.17 14.28 6.66 P = 0.049*

Statistics value for ANOVA. All values are expressed in percentage. * P value is significant, Nife+M.dopa – Nifidepine and Methyldopa

Table – 13A: Distribution of BP values of PIH women based on the drugs received

Measurement Nifidepine (n=50)

Methyldopa (n=46)

Nife+M.dop (n=35)

No drug (n=30)

Pre BP Systolic 147.38±15.15 140.57±13.29 137.10±9.59 133.16±15.29 Diastolic 93.31±11.95 90.38±7.86 90.07±6.56 85.26±9.64

Post BP Systolic 143.25±15.86 141.71±11.40 136.69±10.42 135.63±10.73 Diastolic 92.88±8.62 89.81±5.65 88.72±5.73 88.63±7.29

Postpartum BP Systolic 133.44±10.46 130.43±8.58 131.52±11.14 131.89±9.92 Diastolic 88.56±5.97 85.62±6.80 85.07±5.38 84.63±6.19

P = 0.001* P = 0.000* P = 0.000* P = 0.001* Statistics value for ANOVA. All values are expressed by mean±SD mmHg. * P value is significant

Table – 13B: Distribution of AGA babies of PIH women based on the drugs received Complications Nifidepine Methyldopa Nife+M.dop No drug P Value AGA (%) 40 60.86 54.28 83 P = 0.002*

Graph – 9: Expression of AGA babies of PIH women based on the drug received.

Table – 13C: Distribution of Eclampsia in PIH women based on the drugs received Complications Nifidepine Methyldopa Nife+M.dop No drug P Value Eclampsia (%) 6 2.17 14.28 6.66 P = 0.049*

Graph – 9A: Expression of eclampsia in PIH women based on the drug received

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Eclampsia

6.4.A. Individual analysis 6.4.A.2. PIH 6.4.A.2.b. Disease severity comparison

The increase in severity changes the outcome of pregnancy. Disease Severity increases the rate of

morbidity and mortality. PIH further classified to various types according to the severity and

clinical features of disease. The severity of the diseases is also not predictable and no identifiable

causation factors. This analysis was done to understand the causing factors and changes in

pregnancy outcome by changes in severity of PIH. Women diagnosed as GH (n=54), Pre-eclampsia

– mild (PE-M, n=37), Pre-eclampsia – severe (PE-S, n=63) and eclampsia (n=18) were taken into

analysis (n=172).

Maternal characteristics were shown in – Table 14. The risk factors are not different and doesn’t

show any influence to develop particular type of disease. No significant risk factors were found to

be an important to develop one particular severity of PIH. The age was around 25 years across

the groups of women. Nulliparity women were more in all group compare to primiparity women.

Primigravida women were more in all severity comparing to multigravida.

Outcome results were present in – Table 14A, 14B, 14C, 14D, 14E & Graph 10, 10A, 10B, 10C. The

pregnancy outcome results showed significant difference between the severities of PIH. The

significant difference can be found in the outcome variables of preterm delivery, SGA, LBW and

baby weight. High percentage of preterm was (79%) found for eclampsia women, the same was

recorded low with GH women. SGA babies were more with severe-preeclampsia (50%) and

recorded low with mild-preeclampsia. LBW baby was more with eclampsia and preeclampsia-

severe followed by preeclampsia-mild and gestational hypertension. The average baby weight was

reduced against increasing severity. The average weight of baby was 2.01 ± 0.84 kg for eclampsia,

2.08 ± 0.96 kg for PE-S, 2.33 ± 0.86 kg for PE-M and 2.61 ± 0.62 kg for GH subjects. It was

understood, the increasing severity of PIH will worsen the outcome of pregnancy.

Table – 14: Characteristics of PIH women based on the severity of complication

GH (n=54)

PE-M (n=37)

PE-S (n=63)

ECLAMPSIA (n=18) P value

Average age 25.42 ± 3.18 26.62 ± 4.62 27.16 ± 4.80 24.22 ± 3.81 P = 0.109 BMI 24.92 ± 4.11 26.66 ± 4.97 25.94 ± 3.97 25.41 ± 5.03 P = 0.279 Gravidity 1.51 ± 0.81 1.51 ± 0.67 1.63 ± 1.05 1.66 ± 1.08 Parity 0.31 ± 0.66 0.29 ± 0.51 0.41 ± 0.81 0.33 ± 0.59 Diagnosis week 34.25 ± 4.74 28.51 ± 9.28 31.30 ± 6.25 31.38 ± 4.28 Primigravida 34 (62.96%) 23 (62.16%) 39 (69.0%) 12 (66.66%) P = 0.986 Multigravida 20 (37.03%) 14 (54.05%) 24 (38.09%) 6 (33.33%) P = 0.986 Nulliparity 40 (74.07%) 27 (72.97%) 47 (74.6%) 13 (72.22%) P = 0.996 Primiparity 13 (24.07%) 9 (24.32%) 9 (14.28%) 4 (22.22%) P = 0.517 Multiparity 1 (1.85%) 1 (2.70%) 7 (11.11%) 1 (5.55%) Previous PIH 8 (14.8%) 4 (10.81%) 4 (6.34%) 2 (11.11%) P = 0.526 Family.Hist.HT 13 (24.07%) 12 (32.43%) 17 (26.98%) 3 (16.66%) P = 0.631 Family.Hist.DM 19 (35.18%) 9 (24.32%) 14 (22.22%) 3 (16.66%) P = 0.300

GE – Gestational hypertension, PE-M – Pre-eclampsia mild, PE-S – Pre-eclampsia sever, Statistics value for ANOVA. Values are expressed in mean ± SD and %, * P value is significant Table – 14A: Pregnancy outcome of PIH women based on the severity of complication

GH (n=54)

PE-M (n=37)

PE-S (n=63)

ECLAMPSIA (n=18) P value

Caesarean 49 (90.74%) 36 (97.29%) 59 (93.65%) 7 (94.44%) P = 0.922 Vaginal 5 (9.26%) 1 (2.71%) 4 (6.35%) 11 (5.56%) Preterm 27 (49.09%) 24 (61.53%) 48 (75%) 15 (78.94%) P = 0.014* Term 27 (50.91%) 13 (39.47%) 15 (25%) 3 (21.06%) Delivery week 36.59 ± 1.85 34.97 ± 2.80 34.17 ± 3.68 33.47 ± 2.48 Apgar <7 1 min 11 (20%) 9 (23.07%) 22 (34.37%) 7 36.84%) P = 0.237 SGA 16 (29.09%) 10 (25.64%) 32 (50%) 9 (47.36%) P = 0.019* IUGR 6 (10.9%) 5 (12.82%) 11 (17.18%) 3 (15.78%) P = 0.788 Baby weight 2.61 ± 0.62 2.33 ± 0.86 2.08 ± 0.96 2.01 ± 0.84 P = 0.000* Twin babies 1 2 3 1 Foetal death 0 0 2 0 LBW 21 (38.18%) 23 (58.97%) 44 (68.75%) 13 (68.42%) P = 0.006*

Statistics value for ANOVA. Values are expressed in mean ± SD and %, * P value is significant

Table – 14B: Preterm delivery of PIH women based on the severity of complication

GH PE-M PE-S ECLAMPSIA P value Preterm 27 (49.09%) 24 (61.53%) 48 (75%) 15 (78.94%) P = 0.014*

Graph – 10: Expressing preterm deliveries of PIH women based on the severity of complication

Table – 14C: SGA babies of PIH women based on the severity of complication

GH PE-M PE-S ECLAMPSIA P value SGA 16 (29.09%) 10 (25.64%) 32 (50%) 9 (47.36%) P = 0.019*

Graph – 10A: Percentage of SGA babies of PIH women based on the severity of complication

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Table – 14D: Mean Baby weight of PIH women based on the severity of complication

GH PE-M PE-S ECLAMPSIA P value Baby weight 2.61 ± 0.62 2.33 ± 0.86 2.08 ± 0.96 2.01 ± 0.84 P = 0.000*

Graph – 10B: Mean baby weight of PIH women based on the severity of complication

Table – 14E: Distribution of LBW of PIH women based on the severity of complication

GH PE-M PE-S ECLAMPSIA P value LBW 21 (38.18%) 23 (58.97%) 44 (68.75%) 13 (68.42%) P = 0.006*

Graph – 10C: Expressing LBW of PIH women based on the severity of complication

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6.4.B. Comparison analysis 6.4.B.1. Risk factors

Maternal characteristics, family history, personal lifestyle and obstetric history are the likelihoods

to develop some determinable changes in pregnancy. Those likelihoods causing complications to

pregnancy can be considered as risk factors. Age, BMI, gravidity, parity, irregular menstrual cycle

history, family history of diabetes, previous history of GDM and PIH are the risk factors shown

significant correlation with complications – Table S1.

These risk factors were taken to analysis. As a single or coupled, each risk factor has their own

strength to develop complications to pregnancy. GDM and PIH are the complications having

unique risk factors for the development as well as share some common risk factors also. This

analysis would give better idea on risk factors and their influence. Women diagnosed with GDM

(n=277), PIH (n=172) and both GDM & PIH (n=68) were grouped and taken into the analysis. Risk

factors for all the complications were detailed in the Table 15.

Table S1: Correlation between risk factors and complications; GDM and PIH Correlation coefficient {Pearson Correlation – sig. (2-tailed)} values for Risk factors.

** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the o.05 level (2-tailed)

Risk factors P Value

Age (year) P = 0.009** BMI (kg/m2) P = 0.000** Gravidity P = 0.002** Primigravida P = 0.020* Multigravida P = 0.020* Parity P = 0.002* Nulliparity P = 0.005** Primiparity P = 0.002** Multiparity P = 0.615** Previous GDM P = 0.001** Previous PIH P = 0.002* Family History of DM P = 0.000** Family History of HTN P = 0.540* Hb (gm/dl) P = 0.396* Irreg. Menstrual cycle P = 0.007**

6.4.B.1.a. Risk factors and their values are not equally distributed between the groups.

Complications are varying with risk factors. All the risk factors taken for the analysis showed

significant difference between the groups of women except for multiparity and HB level. Few

women has the risk level of hemoglobin (>13 gm/dl) same way, the overall percentage of

multiparity women were less across the groups.

Age – details shown in Table 15A, Graph 11, 11A. Average age of all the group women are >25

years, which is the age to develop any of these complications. When comparing the age between

the groups, women who developed both were elder than any other groups. PIH complicated

women are younger compare to any other group. From the mean value and SD values, it can be

understand that advancing age from the risky age (>25 years) shows high influence on

development of both complications together. The entire groups have highest percentage of

women between the ages in the range of 25 to 29 years. Percentage of PIH complicated women

are more for the age in the range of 20 to 24 years compare to any other groups. It can be

understand that younger age women are prone to develop PIH compared to GDM and GDM+PIH,

and when age is advancing the risk for developing complications becomes linear from PIH then

GDM then both together.

BMI – details shown in Table 15B, Graph 11B, 11C. Average BMI of all the group women was >25

kg/m2. The BMI value of above 25 was considered as overweight and the risks for developing any

of these complications. When comparing the value between groups, women who developed GDM

and GDM+PIH were having equally high BMI value than PIH group women. From the mean value

and SD values, it can be understood that advancing BMI beyond overweight (>25 kg/m2), shows

high influence on development of GDM and along with PIH, and when BMI reducing below 25 risks

of developing PIH is more. The entire groups have highest percentage of women for the BMI range

of 25 to 29 except for PIH group women. PIH group have more percentage of women between the

BMI ranges of 20 to 24 and below 20 kg/m2. More than half the populations (54%) were below the

BMI of 25 in PIH group. lean women or women with BMI value less than 25, are under the risk to

develop PIH compared to GDM and GDM+PIH, and when BMI is advancing to overweight (>25-29)

the risk for developing complications becomes linear from PIH to GDM then both together, but

when BMI value advancing to obese (>30) the risks to develop GDM and both together are more

and not to PIH.

Gravidity – details shown in Table 15C, Graph 11D, 11E. The average gravidity of all the group

women was 1.81 ± 1.6. Primigravida and multigravida women are equally spread across the

groups. The average gravidity was less with PIH compare to GDM or both group women.

GDM+PIH group women have higher gravidity value than GDM or PIH. Around 63% women are

Primigravida in PIH group. Around 60% women are multigravida in GDM+PIH group, primigravida

women are more prone to develop PIH, and when gravidity increases the risks are more to

develop GDM and both together, whereas multigravida women are more prone to develop both

GDM & PIH complications together.

Parity – Multiparity doesn’t have significant difference between the groups. Nulliparity and

primiparity were not equally distributed. The average parity value is less with PIH group women

compared to other groups. Percentage of nulliparous women are more in the entire group. Around

74% of PIH group women are nulliparous. Women who experienced one previous delivery are

risks to developing PIH then GDM for current pregnancy.

Previous history of GDM / PIH – overall percentage of previous history of either GDM or PIH is less

across the groups. Less than 1% of women have previous history of GDM in PIH group. GDM+PIH

group have more number of women with previous history of either GDM or PIH. The previous

history may not be considered as under significant risk but when combined with other risk factors

this may boost the risks for developing any other complications. Previous history of PIH showed

significant influence to develop both complications together.

Family history of DM / HTN – around 57% of GDM women have the family history of DM either

mother or father or both. This also influences the women to develop PIH as well. Family history of

hypertension doesn’t show the significant correlation to develop any complication. Around 71%

and 47% of women in GDM+PIH group have the family history of DM and HTN respectively.

Women who had family history of DM are more prone to develop both the types of

complications. Paternal history of DM is more in GDM and GDM+PIH group. Around 40% of

women in GDM and GDM+PIH groups have paternal history of DM. Maternal history of DM is

around 30% in both GDM and GDM+PIH group. Paternal history of DM has influence on

development of GDM.

Menstrual cycle – Irregular menstrual cycle women are found to be more prevalent with

GDM+PIH group and less with PIH group. Around 37% of women are having irregular menstrual

cycle in GDM group. It can be understood that irregular menstrual history women are prone to

develop GDM and more prone to develop GDM and PIH together than PIH alone.

Table – 15: Risk factors of GDM, PIH and GDM + PIH women Statistics – P value for non-parametric Kruskal-wallis test given at significance level of 0.05.

Risk factors GDM (n=277) PIH (n=172) GDM & PIH (n=68)

P Value

Age (year) 27.45 ± 3.78 26.55 ± 4.55 29.60 ± 4.52 P = 0.000* ≤ 24 23.82% 38.95% 10.29% 25 - 29 50.18% 43.02% 39.70% ≥ 30 21.66% 17.44% 49.99% Weight (kg) 67.93 ± 9.26 63.51 ± 12.6 70.49 ± 10.43 BMI (kg/m2) 27.77 ± 3.94 25.60 ± 4.579 27.95 ± 3.88 P = 0.000* ≤ 24 .99 19.48% 54.06% 20.58% 25 – 29.99 51.26% 30.23% 50.0% ≥ 30 29.23% 15.10% 30.88% Gravidity 1.91 ± 1.11 1.63 ± 0.97 2.0 ± 1.13 P = 0.001* Primigravida 40.09 % 62.79 % 41.17 % P = 0.009* Multigravida 50. 90 % 37.20 % 58.82 % P = 0.009* Parity 0.46 ± 0.65 0.35 ± 0.69 0.45 ± 0.58 Nulliparity 60.28 % 73.83 % 58.82 % P = 0.008* Primiparity 35.01 % 20.34 % 36.76 % P = 0.002* Multiparity 13 % 5.81 % 5.88 % P = 0.843* Previous GDM 7.94% 0.581% 8.82% P = 0.002* Previous HTN 3.24% 11.22% 20.58% P = 0.000* Family History of DM 57.03% 26.16% 8.82% P = 0.000* Family History of HT 30.68% 26.74% 20.58% P = 0.009* Marital period (year) 3.79 ± 3.09 3.79 ± 3.09 5.80 ± 4.15 MWG (kg) 11.87 ± 3.65 13.40 ± 4.60 10.48 ± 3.6 Hb (gm/dl) 11.45 ± 1.24 11.54 ± 1.52 11.87 ± 1.03 P = 0.135* Irreg. Menstrual cycle

36.10 % 17.63 % 44.11 % P = 0.026*

Table – 15A: Distribution of age as risk factors for all the complications

Graph – 11: Expression

of age as risk factors for all the complications

Graph 11A - Distribution pattern of complication against Age by number of women. (Graph contains line and logarithmic curves. Log curves represent the pattern of distribution of complications for age)

23.82

50.18

21.66

38.95 43.02

17.44

10.29

39.7

49.99

0

10

20

30

40

50

60

≤24 25-29 ≥30

Age

Perc

enta

ge --

->

GDM

PIH

G+P

-5

0

5

10

15

20

25

30

35

17 19 21 23 25 27 29 31 33 35 37 39

Num

ber o

f wom

en

GDMPIHG+PLog. (GDM)Log. (PIH)Log. (G+P)

Age in years

AGE in Years GDM (n=277) PIH (n=172) GDM & PIH(68) P Value ≤ 24 23.82% 38.95% 10.29%

P = 0.000* 25 - 29 50.18% 43.02% 39.70% ≥ 30 21.66% 17.44% 49.99%

Table – 15B: Distribution of BMI as risk factors for all the complications

Graph – 11B: Expression

of BMI as risk factors for all the complications

Graph 11C - Distribution pattern of complication against BMI by number of women. (Graph contains line and logarithmic curves. Log curves represent the pattern of distribution of complications for BMI)

19.48

51.26

29.23

54.06

30.23

15.1

20.58

50

30.88

0

10

20

30

40

50

60

≤24 25-29 ≥30

BMI

Perc

enta

ge --

->

GDM

PIH

G+P

% of women for BMI

-5

0

5

10

15

20

25

30

35

40

45

17 19 21 23 25 27 29 31 33 35 37 39

Num

ber o

f wom

en

GDMPIHG+PLog. (GDM)Log. (PIH)Log. (G+P)

BMI in kg/m2

BMI (kg/m2) GDM (n=277) PIH (n=172) GDM & PIH (68) P Value ≤ 24 .99 19.48% 54.06% 20.58%

P = 0.000* 25 – 29.99 51.26% 30.23% 50.0% ≥ 30 29.23% 15.10% 30.88%

Table – 15C: Distribution of Gravidity as risk factor for all the complications Graph –

11D: Expression of gravidity as risk factors for all the complications

Graph 11E - Distribution pattern of complication against gravidity by percentage of women. (Graph contains line and logarithmic curves. Log curves represent the pattern of distribution of complications for BMI)

40.09

50.9

62.79

37.2 41.17

58.82

0

10

20

30

40

50

60

70

Primigravida Multigravida

Perc

enta

ge --

->

GDM

PIH

G+P

% of women in gravida

-20

-10

0

10

20

30

40

50

60

70

1 2 3 4 5 6

GDM

PIH

G+P

Log. (GDM)

Log. (PIH)

Log. (G+P)

Gravidity

% o

f wom

en

Gravidity GDM (n=277) PIH (n=172) GDM & PIH(68) P Value Primigravida 40.09 % 62.79 % 41.17 % P = 0.009* Multigravida 50. 90 % 37.20 % 58.82 % P = 0.009*

6.4.B.1.b. An increase in number of risk factors and interactions of one on another showed

variation in the development of complications; GDM / PIH / GDM+PIH

Women having number of risk factors are more prone to develop any complications. When a

woman having number of risk factors, probability of developing a particular complication is varies,

the combined effect of those risk factors may influence the women to develop a particular

complication. This analysis was done to understand whether increasing number of risk factors

having any influence on development of particular complication or not.

Details described in Table 16 showed that all the groups of women have at least 3 risk factors. The

average number of risk factors for women who developed both complications was high (4.16 ±

1.19) compare to GDM (3.54 ± 1.48) or PIH (3.27 ± 1.21) group women. PIH group women have

less number of risk factors compared to other groups. Nearly 72% of women had more than 3 risk

factors and 28% had risk factors from 1 to 3 in G+P group. Around 48% of women had the risk

factors from 1 to 3 and 53% of women had the risk factors above 3 in GDM group. Only 41%

women had more than 3 risks factors and 58% of women have from 1 to 3 risk factors in PIH

group. Only 16% women have more than 4 risk factors in PIH group compared to GDM (26%) and

GDM+PIH (43%) group. The increase in the number of risk factors caused a parallel increase of

risks to PIH, GDM and GDM+PIH was followed to be increased.

An influence of risk factors one on another to develop these complications was analyzed, which

showed, the additional risk factors significantly changed the probability of developing

complications. The combination of risk factors and their risks for the development of

complications are varied – Table S2 – Prediction Tool.

A primigravida woman under the age of 25 years, had the probability of developing GDM is 46%

and for PIH is 50% and for developing both together is 4%. When age is increasing probability of

developing GDM (56%) is more than PIH (36%). Primigravida women are prone to develop PIH,

when their age is increasing the probability is increasing to develop GDM than PIH. Addition to

this when BMI is below 25, as a risk factor, the probability varies with 70% to PIH and 26% to GDM.

When BMI increases the probability is more to develop GDM (56%) than PIH (36%).

When family history of DM added to above circumstance, considering age and BMI is under the

risk range, risks to develop PIH is more (54%) compared to GDM or both. When age is increasing

up to 29 years, risks to develop GDM is more (50%) and age increasing above 30 years, risks to

develop GDM+PIH is more (40%) whereas the probability to develop PIH is less. When BMI is also

increasing along with age, risks follow the same pattern as age does. Addition of family history of

HTN doesn’t changes the probability but the percentages of chances to develop the complications

were increased. Addition of irregular menstrual cycle history and risk of HB were also not changed

the probability of developing complications, but varied with the percentages of chances.

A multigravida woman with age under 25 years, has the probability of developing GDM is 47% and

for PIH is 45% and for developing both together is 8%. When age is increasing the probability of

developing GDM (64%) is more than PIH (21%). Multigravida women are prone to develop GDM,

when their age is increasing the probability of developing GDM is also increasing. Leaner body

weight multigravida women have great risks to develop PIH (80%) but not to GDM (16%) when her

age is below 25. when BMI is getting increases the probability of developing GDM is also increases

and reaches high level of 73% when BMI is above 30.

The nulliparous, multigravida women of normal body weight (BMI <25) and age below 25 years

have great risks to develop PIH (74%). The probability is changing with increasing age and also with

increasing BMI, when age is increasing probability to GDM is also increasing, age reaches above 30

the probability of GDM+PIH is great compared to anyone. When BMI increases, same way the

probability to GDM+PIH is more (66%) but very less to PIH (5%).

Addition of family history of DM assures the same trend with changes in the percentage level,

when BMI and age are increases probability for developing GDM+PIH (71%) is great and is very less

to PIH (1%). Addition of irregular menstrual history and risk of HB doesn’t alter the pattern of

development but boosts the percentages of probability for the same trend.

Primiparous, multigravida women of normal body weight (BMI <25) and below 25 years of age

have great risks to develop PIH (87%) than GDM (13%). When BMI and age are advancing, the

probability is increasing to develop first GDM (90%) than PIH (10%). Addition of family history of

diabetes mellitus doesn’t changed the probability, instead greatly increased the risks to develop

complications.

Nulliparous women having more chances to develop PIH (44%) than GDM (38%), and when their

age is increasing probability to develop GDM is increasing and when age is above 30 years, the

probability is increasing (58%) to exist both together.

Primiparous women having more chances to develop GDM (55%) than PIH (46%), and when their

age is increasing probability to develop GDM is increasing and when age is above 30 years, the

probability is still increasing (78%) to develop GDM alone.

Table – 16: Details of number of risk factors women having in each complication No. of Risks Complications

1 2 3 4 5 6 7

GDM 24 50 58 73 49 17 7 PIH 11 33 56 42 27 1 0 G+P 3 5 11 20 21 7 1 Risks Range GDM PIH G+P

1 – 3 47.65% 58.13% 27.94% 4 – 6 50.18% 40.69% 70.58% > 6 2.52% 0% 1.47% No of Risks(avg) 3.54 ± 1.48 3.27 ± 1.20 4.10 ± 1.35

Values expressed in numbers/percentage/mean ± SD.

6.4.B.1.c. The increasing number and values of risk factors causing earlier development of

complications; GDM / PIH

Women having number of risk factors are more prone to develop any complications. An increase

in number of risk factors and their values has significant influence on the early development of

GDM or PIH. This analysis was done to understand the risk factors and their influence on early

development of complications. Details are presented in – Table S2, S3 & Plot S1, S2.

Details were described in – Table 17 & Graph 12. All the GDM and PIH women had at least 3 risk

factors. The maximum of 7 risk factors was recorded with women. For a GDM woman who had 3

risk factors, the average week of diagnosis was 28.48 ± 7.24, the same for PIH woman was 33.25 ±

4.24. Most of the women are having 4 risk factors in GDM group and 3 risk factors in PIH group.

Advancing age, BMI, gravidity and advancing parity significantly changed the development of

complications. When the values of above mentioned risk factors are increasing, it causes earlier

onset of complications of GDM and PIH. Increasing values of risk factors causes earlier

development of complications. Details are presented in – Table S5, S6, S7, S8, S9, S10, S11, S12 &

Plot S3, S4, S5, S6, S7, S8, S9, S10.

Table – 17: Details of number of risk factors and average week of diagnosis for GDM and PIH Values expressed in mean ± SD.

No. of Risk factors Week of Diagnosis GDM (n=277) PIH (n=172)

1 28.23 ± 6.19 33.54 ± 2.97 2 28.85 ± 7.22 30.48 ± 7.28 3 28.48 ± 7.24 33.25 ± 4.24 4 26.50 ± 9.02 29.71 ± 9.01 5 24.77 ± 9.80 31.96 ± 6.77 6 26.11 ± 8.65 NA 7 23.71 ± 10.61 NA

Average Number of Risk factors

3.54 ± 1.48 3.27 ± 1.20

Week of Diagnosis 27.01 ± 8.51 31.76 ± 6.74

Graph 12 - Week of diagnosis of complication against number of risk factors. (Graph contains line and logarithmic curves. Log curves represent the frequency pattern of diagnosis of week for number of risk factors)

18

21

24

27

30

33

36

1 2 3 4 5 6 7

Perc

enta

ge --

->

Number of risk factors --->

Diagnosis week GDMDiagnosis week PIHLog. (Diagnosis week GDM)Log. (Diagnosis week PIH)

The average number of risk factors for women who developed GDM was 3.54 ± 1.48 and the

average week of diagnosis of GDM was 27.17 ± 8.23 weeks – Table 17, S3 & Plot S1.

Table S3 - Model Summary and Parameter Estimates the Number of Risks vs week of diagnosis (GDM) Dependent Variable: GDM Week / The independent variable is Number of Risks. Equation Model Summary Parameter Estimates

R Square

F df1 df2 Sig. Constant b1 b2

Linear .025 8.579 1 328 .004 30.430 -.889 Quadratic .033 5.526 2 327 .004 27.713 .935 -.255

From – Table S3, the estimation of F statistics is significant (P=0.04), which indicated that the

estimation is not due to chance. In the linear and quadratic model the b1 value is less than 1,

indicated, the increase in number of risk factors would actually reduce the week of diagnosis.

An increase of risk factors by 1 reduce the diagnosis of week by 30.430 + (- .889) and 27.713

+0.935. The b2 value is squared value of increasing risk factors, for each increase of number of risk

factors; diagnosis of week reduces by 0.935/2*-0.255.

Plot S1 - Linear and quadratic curve-fit for number of risks against the week of diagnosis

From this – plot S1, the quadratic model curve matches exactly with values and visually showed

the changes of weeks against number of risks.

The average number of risk factors for women who developed PIH was 3.27 ± 1.21and the average

week of PIH was 31.76 ± 6.74 weeks. Details were recorded in – Table 17, S4 & Plot S2

From the – Table S4, it can be understood, the estimation of F statistics significant value is greater

than 0.05, which indicates that the estimation model have some variations in the value. In the

linear model the b1 value is less than 1, which indicates, the increase in number of risk factors

reduces the week of diagnosis. In quadratic model the b1 value is greater than 1 which indicated

the increase in number of risk factors and not reduced the week of diagnosis. An increase of risk

factors by 1 reduced the week of diagnosis by 32.879 + (- .697) – linear model. The b2 value is

squared value of increasing risk factors, for each increase of number of risk factors; diagnosis of

week reduces by 1.410/2*-0.318.

Table S4 - Model Summary and Parameter Estimates the Number of risks vs. Week of diagnosis (PIH) Dependent Variable: PIH diagnosed week, The independent variable; number of risk. Equation Model Summary Parameter Estimates

R Square F df1 df2 Sig. Constant b1 b2 Linear .012 2.235 1 185 .137 32.879 -.697 Quadratic

.018 1.700 2 184 .185 29.878 1.410 -.318

Plot S2 - Linear and quadratic curve-fit for number of risks against the week of diagnosis

From the above – plot S2, the quadratic model curve matches with values and visually shows the

changes of weeks against number of risks.

From the analysis, it can be understood that, the increase of risk factors by 1 would reduce the

week of diagnosis by 0.889 (linear model) or 1.86 weeks (quadratic model) for the GDM

complication. For the PIH complication; increase of risk factors by 1 would reduce the week of

diagnosis by 0.697 week (linear model) or 2.21 weeks (quadratic model). It can be understood

that, the increasing number of risk factors would actually increases the risks for women to

develop the complications early. Increases in number of risk factors causing early development

of GDM may not cause the early development of PIH.

Table S5: Model Summary and Parameter Estimates Advancing age Vs. Diagnosis week: GDM Dependent Variable: Diagnosis week of GDM/ The independent variable is Age. Equation Model Summary Parameter Estimates

R Square

F df1 df2 Sig. Constant b1 b2

Linear .015 5.148 1 328 .024 34.456 -.262

Quadratic .018 2.933 2 327 .055 48.740 -1.274 .018

From – Table S5, the estimation of F statistics is significant (P=0.024, linear modal) with actual

value, which indicates that the estimation is not due to chance. In the linear and quadratic modal

the b1 value is less than 1, indicates, the advancing age would actually reduce the week of

diagnosis. An increase of age by 1 reduce the diagnosis of week by 34.456 + (-.262) and

48.740+1.274. The b2 value is squared value of advancing age, for increase of age by each year;

diagnosis of week reduces by 1.274/2* 0.018. Plot – S3: Linear and quadratic curve-fit for advancing age Vs week of diagnosis: GDM

From this – plot S3, the quadratic modal curve matches exactly with actual values and visually

shows the changes of weeks against advancing age.

Table S6: Model Summary and Parameter Estimates Advancing BMI Vs. Diagnosis week: GDM Dependent Variable: Diagnosis week of GDM / The independent variable is BMI.

Equation Model Summary Parameter Estimates

R Square F df1 df2 Sig. Constant b1 b2

Linear .003 .927 1 328 .336 30.530 -.121

Quadratic .003 .501 2 327 .607 25.325 .267 -.007

From – Table S6, the estimation of F statistics is not significant (P>0.05), which indicates that the

estimation modal has some variation values with actual value. In the linear and quadratic modal the

b1 value is less than 1, indicates, the advancing BMI would actually reduce the week of diagnosis.

An increase of BMI by 1 reduce the diagnosis of week by 30.530 + (-.121) and 25.325+0.267. The b2

value is squared value of increasing risk factors, for increase of BMI by each value; diagnosis of

week reduces by 0.267/2*-0.007.

Plot – S4: Linear and quadratic curve-fit for advancing age Vs week of diagnosis: GDM

From this – plot S4, the quadratic modal curve matches exactly with actual values and visually

shows the changes of weeks against advancing BMI.

Table S7: Model Summary and Parameter Estimates advancing gravida Vs diagnosis week:GDM Dependent Variable: Diagnosis week of GDM / The independent variable is Gravidity.

Equation Model Summary Parameter Estimates

R Square F df1 df2 Sig. Constant b1 b2

Linear .014 4.576 1 328 .033 28.847 -.874

Quadratic .021 3.526 2 327 .031 31.097 -3.208 .454

From – Table S7, the estimation of F statistics is significant (P=0.03) with actual value, which

indicates that the estimation modal is not due to the chance. In the linear and quadratic modal the

b1 value is less than 1, indicates, the advancing gravidity would actually reduce the week of

diagnosis. An increase of gravidity by 1 reduce the diagnosis of week by 28.847 + (-.874) and

31.097+3.208. The b2 value is squared value of increasing risk factors, for increase of gravidity by

each value; diagnosis of week reduces by 3.208/2* 0.454.

Plot S5: Linear and quadratic curve-fit for advancing gravida Vs week of diagnosis: GDM

From this – plot S5, the quadratic modal curve matches exactly with actual values and visually

shows the changes of weeks against advancing gravidity.

Table S8: Model Summary and Parameter Estimates advancing parity Vs diagnosis week: GDM Dependent Variable: Diagnosis week of GDM / The independent variable is Parity.

Equation Model Summary Parameter Estimates

R Square F df1 df2 Sig. Constant b1 b2

Linear .017 5.667 1 328 .018 27.952 -1.681

Quadratic .042 7.190 2 327 .001 28.434 -4.830 1.575

From – Table S8, the estimation of F statistics is significant (P=0.01) with actual value, which

indicates that the estimation modal is not due to the chance. In the linear and quadratic modal the

b1 value is less than 1, indicates, the advancing gravidity would actually reduce the week of

diagnosis. An increase of parity by 1 reduce the diagnosis of week by 27.952 + (-1.681) and

28.434+4.830. The b2 value is squared value of increasing risk factors, for increase of parity by each

value; diagnosis of week reduces by 4.830/2* 1.575.

Plot S6: Linear and quadratic curve-fit for advancing parity Vs week of diagnosis: GDM

From this – plot S6, the quadratic modal curve matches exactly with actual values and visually

shows the changes of weeks against advancing parity.

Table S9: Model Summary and Parameter Estimates advancing age Vs diagnosis of week: PIH Dependent Variable: Week of PIH / The independent variable is Age (PIH)

Equation Model Summary Parameter Estimates

R Square F df1 df2 Sig. Constant b1 b2

Linear .129 27.299 1 185 .000 47.088 -.626

Quadratic .137 14.637 2 184 .000 26.314 .859 -.026

From – Table S9, the estimation of F statistics is significant (P=0.000) with actual value, which

indicates that the estimation is not due to chance. In the linear and quadratic modal the b1

value is less than 1, indicates, the advancing age would actually reduce the week of diagnosis.

An increase of age by 1 reduce the diagnosis of week by 47.088 + (-.626) and 26.314+0.859.

The b2 value is squared value of increasing risk factors, for increase of age by each year;

diagnosis of week reduces by 0.859/2* -0.026. .

Plot S7: Linear and quadratic curve-fit for advancing age Vs week of diagnosis: PIH

From this – plot S7, the quadratic modal curve matches exactly with actual values and visually

shows the changes of weeks against advancing age.

Table S10 Model Summary and Parameter Estimates advancing BMI Vs Diagnosis week: PIH Dependent Variable: Week of PIH / The independent variable is BMI (PIH).

Equation Model Summary Parameter Estimates

R Square F df1 df2 Sig. Constant b1 b2

Linear .046 9.012 1 185 .003 40.057 -.375

Quadratic .046 4.482 2 184 .013 39.536 -.335 -.001

From – Table S10, the estimation of F statistics is significant (P=0.003) with actual value, which

indicates that the estimation is not due to chance. In the linear and quadratic modal the b1 value is

less than 1, indicates, the advancing BMI would actually reduce the week of diagnosis. An increase

of BMI by 1 reduce the diagnosis of week by 40.057 + (-.375) and 39.536+0.335. The b2 value is

squared value of increasing risk factors, for increase of BMI by each value; diagnosis of week

reduces by 0.335/2* -0.01.

Plot S8: Linear and quadratic curve-fit for advancing BMI Vs week of diagnosis: PIH

From this – plot S8, the quadratic modal curve matches exactly with actual values and visually

shows the changes of weeks against advancing BMI.

TableS11:Model Summary and Parameter Estimates advancing gravidaVs diagnosis of week:PIH Dependent Variable: Week of PIH / The independent variable is Gravidity (PIH).

Equation Model Summary Parameter Estimates

R Square F df1 df2 Sig. Constant b1 b2

Linear .046 8.988 1 185 .003 33.308 -1.750

Quadratic .067 6.643 2 184 .002 37.021 -5.917 .854

From – Table S11, the estimation of F statistics is significant (P=0.003) with actual value, which

indicates that the estimation is not due to chance. In the linear and quadratic modal the b1 value is

less than 1, indicates, the advancing BMI would actually reduce the week of diagnosis. An increase

of gravidity by 1 reduce the diagnosis of week by 33.308 + (-1.750) and 37.021+5.917. The b2 value

is squared value of increasing risk factors, for increase of gravidity by each value; diagnosis of week

reduces by 5.917/2*-0.854.

Plot S9: Linear and quadratic curve-fit for advancing gravida Vs week of diagnosis: PIH

From this – plot S9, the quadratic modal curve matches exactly with actual values and visually

shows the changes of weeks against advancing gravidity.

Table S12:Model Summary and Parameter Estimates advancing parity Vs diagnosis of week: PIH Dependent Variable: Week of PIH / The independent variable is Parity PIH.

Equation Model Summary Parameter Estimates

R Square F df1 df2 Sig. Constant b1 b2

Linear .045 8.769 1 185 .003 31.318 -2.446

Quadratic .049 4.780 2 184 .009 31.462 -3.950 .646

From – Table S12, the estimation of F statistics is significant (P=0.003) with actual value, which

indicates that the estimation is not due to chance. In the linear and quadratic modal the b1

value is less than 1, indicates, the advancing BMI would actually reduce the week of diagnosis.

An increase of parity by 1 reduce the diagnosis of week by 31.318 + (-2.446) and 31.462+3.950.

The b2 value is squared value of increasing risk factors, for increase of parity by each value;

diagnosis of week reduces by 3.950/2*-0.646.

Plot S10: Linear and quadratic curve-fit for advancing parity Vs week of diagnosis: PIH

From this – plot S10, the quadratic modal curve matches exactly with actual values and visually

shows the changes of weeks against advancing parity.

6.4.B. Comparison analysis 6.4.B.2. Complications

Whether GDM or PIH once developed, the pregnancy may affect and leaves some traces on

pregnancy life. A good pregnancy outcome is a measure of uncomplicated delivery to mother.

Complications affect both mother and fetus. Outcome of pregnancy varies with complications,

severity of diseases and lifestyle of women. This analysis was done to understand the outcome of

pregnancy for the respective complications. Women diagnosed with GDM (n=277), PIH (n=172)

and both GDM & PIH (n=68) were grouped and taken into the analysis. Pregnancy outcome results

were recorded in the – Table 18, 18A, 18B, 18C, 18D & 13, 13A, 13B.

6.4.B.2.1. Maternal complications

Cesarean delivery: – All the group of women has high percentage of cesarean delivery. In GDM

group 87% of women given birth through cesarean section which was 94% to PIH group women.

But for the GDM+PIH group all the women (100%) given birth through cesarean delivery.

Significant difference was found between the groups. Comparatively GDM group women showed

less number of cesarean deliveries. Both GDM and PIH complications are suspected for the cause

to the mode of delivery, when a woman having both the complications together the chances are

very great to have cesarean delivery. In GDM group 46% of cesarean delivery was emergency

delivery whereas 30% of cesarean delivery was emergency delivery in PIH group.

Insulin administration: Around 81% of GDM women and 75% of GDM+PIH women received

insulin as a treatment for gestational diabetes. Regular and NPH type insulin are used for the

treatment. Most of the GDM women received NPH insulin (48%) where as 21% of women received

both types of insulin.

Drug administration: Around 82% of PIH women and 65% of GDM+PIH women received

antihypertensive drugs as their treatment for hypertension.

Eclampsia: Around 18% PIH women developed eclampsia and 1% of GDM+PIH group women

developed eclampsia.

Mortality: one woman have died because of eclampsia and 2 fetal deaths were occurred in PIH

complicating women.

6.4.B.2.2. Neonatal complications

All the babies were normal and good in health. Two fetal deaths and one maternal death were

occurred as a result of PIH complication. No neonatal birth injury was reported. Macrosomia and

HELLP syndrome were very less with the study population.

Twins: Totally 22 twins were born, 13 twins were born to GDM complication women and 7 were

for PIH alone and 2 twins were born to GDM+PIH women.

Preterm: Around 65% of babies were preterm babies in PIH and GDM+PIH group, which is less

(49%) in GDM group. PIH group women have more number of preterm babies.

Baby weight: Average baby weight of GDM women was 2.67 ± 0.65 kg. Average baby weight of

PIH women was 2.22 ± 0.73 kg and the same was 2.45 ± 0.81 kg. Significantly PIH women’s’ baby

weight was low compared to other group women. GDM complication tends to increase the weight

of baby and PIH tend to decrease the weight of baby, pathological reasons, significant result was

seen in our group’s women. When PIH combined with GDM the baby weight was significantly

altered, the baby weight is above the PIH complicating group babies’ weight and below the GDM

complicating group babies’ weight.

Apgar score at 1st min: Average Apgar score of GDM group babies average Apgar score was 7.69 ±

0.66 and for PIH group was 7.72 ± 0.78 and for GDM+PIH group was 7.55 ± 0.67. Added number

babies (34%) with Apgar score below 7 were seen in GDM+PIH group compare to GDM (24%) and

PIH (28%). Significantly PIH group women have less number of babies with Apgar score below 7.

LBW: GDM group’s women have less number of LBW babies (34%) compare to PIH and GDM+PIH,

whereas GDM+PIH group women had more number of LBW babies.

LGA: LGA babies were more with GDM group (12%) and less with PIH group (3%) women.

SGA: More number of SGA babies was seen in PIH group (37%) women, which is less in GDM group

women (3%).

Macrosomia: Around 2% of GDM and GDM+PIH women had macrosomic babies.

IUGR: Percentage of IUGR was more with GDM+PIH group (15%), which is high compare to PIH

group (13%).

Hypoglycemia: Around 56% babies were experienced hypoglycemia in GDM group and 46% of

babies were experienced hypoglycemia in GDM+PIH group.

Hyperbilirubinemia: Around 62% of babies were experienced hyperbilirubinemia in GDM+PIH

group where as 57% of babies were experienced in GDM group.

Table – 18: Perinatal outcome of GDM, PIH and GDM + PIH women

P values for non-parametric Mann-Whitney U, Kruskal-wallis tests were given at the significance level of 0.05.

Characters GDM alone (n=277)

PIH alone (n= 172)

GDM+PIH (n=68) P value

Maternal complications

Cesarean delivery 87.36% 94.65% 100% P = 0.001* Vaginal delivery 12.63% 5.81 % 0% P = 0.001* Insulin usage m 80.50% 75% P = 0.315 Week of delivery 36.28 ± 1.99 34.74 ± 3.70 34.60 ± 4.00 Neonatal complications (n=290) (n= 177) (n=70)

Term delivery 50.34 % 35.02 % 38.23 % P = 0.001* Preterm delivery 48.96 % 64.97 % 61.76 % P = 0.004* Baby Weight 2.67 ± 0.65 2.22 ± 0.73 2.45 ± 0.81 P = 0.001* Twin babies 4.69 % 4.06 % 2.94 % LBW 33.79 % 55.86 % 47.05 % P = 0.000* NBW 66.2 % 43.5 % 51.42 % P = 0.517 SGA m 2.75 % 37.28 % 17.64 % P = 0.002* LGA m 12.06 % 2.82 % 11.76 % P = 0.847 AGA 80.68 % 59.88 % 73.52 % P = 0.000* IUGR m 0 % 12.99 % 14.70 % P = 0.787 Macrosomia m 2.06 % 0.56 % 2.94 % P = 0.704 Apgar<7at 1st mint 24.13 % 28.81 % 32.29 % P = 0.000* HELLP 0 % 1.12 % 1.56 % Hypoglycemia m 55.51 % 0 45.58 % P = 0.063 Hyperbilirubinemiam 56.55 % 0 61.76 % P = 0.700

Table – 18A: Mode of delivery of GDM, PIH and GDM + PIH women

Characters GDM alone (n=277)

PIH alone (n= 172)

GDM+PIH (n=68) P value

Cesarean delivery 87.36% 94.65% 100% P = 0.001* Vaginal delivery 12.63% 5.81 % 0% P = 0.001*

Graph 13: percentage of Mod of delivery of GDM, PIH and GDM+PIH women

Table 18B: Size of the baby of GDM, PIH, GDM+PIH women complications (n=290) (n= 177) (n=70) P value SGA 2.75 % 37.28 % 17.64 % P = 0.002* LGA 12.06 % 2.82 % 11.76 % P = 0.847

Graph 13A: Percentage of Size of the baby of GDM, PIH and GDM+PIH women

87.36

12.63

94.65

5.81

100

0 0

20

40

60

80

100

120

Cesarean delivery Normal delivery

Perc

enta

ge --

-.

GDMPIHGDM+PIH

2.75

12.06

37.28

2.82

17.64

11.76

0

5

10

15

20

25

30

35

40

LGA SGA

Perc

enta

ge --

->

GDM

PIH

GDM+PIH

Table – 18C: Birth weight of baby of GDM, PIH and GDM + PIH women

Characters GDM alone (n=277)

PIH alone (n= 172)

GDM+PIH (n=68) P value

Baby Weight 2.67 ± 0.65 2.22 ± 0.73 2.45 ± 0.81 P = 0.001*

Graph 13B: Average birth weight of baby of GDM, PIH and GDM+PIH women

Table – 18D: Percentage of babies having apgar <7 at 1st min of GDM, PIH and GDM + PIH

Characters GDM alone (n=277)

PIH alone (n= 172)

GDM+PIH (n=68) P value

Apgar<7at 1st min 24.13 % 28.81 % 32.29 % P = 0.000*

Graph 13C: Average birth weight of baby of GDM, PIH and GDM+PIH women

2.67

2.22 2.45

0

0.5

1

1.5

2

2.5

3

3.5

GDM PIH GDM+PIH

Wei

ght i

n kg

--->

Birth weight in kg Birth weight

24.13

28.81 32.29

0 0

5

10

15

20

25

30

35

GDM PIH GDM+PIH

apga

r val

ue --

->

Apgar<7 at 1stmin Apgar<7 at 1stmin

6.4.B. Comparison analysis 6.4.B.3. Associations

Around 13% of women from total population have both GDM and PIH complications. Where 78%

of GDM women developed PIH and 22% of PIH women developed GDM. The complication which is

developed first is considered as primary diagnosis and the follow up is considered as effect of

that first complication. Average diagnosis week of GDM as a primary diagnosis was 28.01 ± 6.58

week and as a secondary to PIH was 23.86 ± 9.55 week. Average diagnosis week of PIH as a

primary diagnosis was 17.40 ± 9.40 week and as a secondary to GDM was 33.43 ± 2.78 week. The

average time period for developing the second one was 5.78 ± 5.98 week. Details were present in

– Table 19 & Graph 14.

For the comparison of primary diagnosis of GDM, the average week of diagnosis was higher than

GDM population (27.01 ± 8.51) (women who have developed GDM alone, n=277), but as a

secondary diagnosis of GDM, the average week of diagnosis was earlier than GDM alone

population. It may be understood, the development of GDM as a secondary may be influence of

PIH or risk factors.

For the comparison of primary diagnosis of PIH, the average week of diagnosis was lesser than PIH

population (31.76 ± 6.74), but as a secondary diagnosis of PIH, the average week of diagnosis was

later than PIH alone population. It may be understood, the development of GDM as a secondary

may be influence of GDM or risk factors.

For the comparison of primary diagnosis of GDM and PIH, the average age of women was

respectively 29.33 ± 4.52 and 30.53 ± 5.73 years for GDM and PIH, which was higher than GDM

alone and PIH population. Same way the gravidity value also was high for these groups of women

compared to GDM or PIH complicated women.

The development of GDM as a primary diagnosis follows the common GDM development pattern

(GDM alone development) for age as a risk factor. But the same trend was not seen in PIH as a

primary diagnosis case, since the probability of development for common PIH (PIH alone) was

huge with younger women (<25years of age),here the women’s average age was very high

compare to any other women. Development of secondary GDM in these women not may be due

to the influence of PIH, but the secondary development of PIH may be due to the influence of

GDM.

Table 19: Average diagnosis week of complications GDM and PIH in GDM + PIH women

Primary diagnosis – GDM (n=53)/ (week)

Primary diagnosis – PIH (n=15)/ (week)

Week of primary diagnosis 28.01 ± 6.58# 17.40 ± 9.40^ Week of secondary diagnosis 23.86 ± 9.55^ 33.43 ± 2.78# Interval from primary to secondary diagnosis

5.98 ± 5.47$ 5.13 ± 4.06≠

Average interval 5.79 ± 5.78 Primary diagnosis – For a woman which complication is diagnosed first Secondary diagnosis – For a woman which complication is diagnosed second # GDM diagnosis, ^ PIH diagnosis, ≠ - Interval from PIH to GDM, $ - Interval form GDM to PIH

Graph 14: Expressing Average diagnosis week of each complication of GDM+PIH women

28.01

17.4

23.86

33.43

0

5

10

15

20

25

30

35

40

GDM PIH

Wee

k ---

>

Primary diagnosis

Secondary diagnosis

6.4.B.3.1. Common risk factors

GDM and PIH are linked each other as both are the complications of pregnancy. Since pregnancy is

the inducer risk factors may be common for both the complications. This analysis was done to

understand whether the both complications are sharing the common risk factors or not. Details

were presented in – Table S1.

A common pattern of development can be seen between GDM and PIH. Both complications are

developed early when neither risk factors nor their values are increasing. Age, BMI, gravidity and

parity are the risk factors shows common pattern of development for both complications. –Table

S5,S6,S7,S8,S9,S10,S11,S12 & Plot S3,S4,S5,S6,S7,S8,S9,S10. Age, BMI, gravidity, Family history of

DM, irregular menstrual cycle and previous history of GDM and PIH are the common risk factors

for both GDM and PIH complications.

The frequencies of developing complications are common but varied with the values of risk

factors. For PIH, the higher frequency starts from younger age (below 25) to elder age (above 30)

and for GDM the higher frequency starts from risky age (from 25 to 29) to elder age. The same

kind of trend can be seen in BMI also – Table S2 – Prediction Tool. From the common risk factors

analysis; the frequency pattern of development of complications were same but the values of

risk factors varied.

6.4.B.3.2. Pregnancy outcome

Outcome of pregnancy varies with complications, their severity and lifestyle of women. Based on

the pathology and manifestations of disease the outcome complications may varies. Even the

treatments of diseases also worsen the quality of life of pregnant women, and further the

medicines and procedures used in the treatment may also produce some unwanted results.

Cesarean delivery is performed to complicated delivery in order to save the mother and child from

complications, since it is invasive procedure, further worsen the quality of life of women.

Various pregnancy problems can be found for both GDM and PIH. Among the all cesarean delivery,

preterm delivery, LGA, SGA, LBW and baby birth weight showed correlation between GDM, PIH

and GDM+PIH. It can be understood, that these complications are producing some similar

problems to pregnancy outcome.

Cesarean delivery is common problems of both PIH and GDM, 94% of PIH women and 87% GDM

women have given birth through cesarean delivery, whereas all the women in GDM+PIH category

given birth through cesarean delivery. Cesarean delivery will reach maximum, when both

complications are coexists. It can be understood, that the effect of individual complications may

triggered when both are combined.

Preterm delivery is more with PIH and as well as GDM+PIH group, which is less with only GDM

group. By the correlation and common complication analysis it can be understood, that preterm

delivery is an independent problem of PIH complication.

SGA babies are more (38%) with PIH complicating women, whereas GDM women have only 3% of

SGA babies and GDM+PIH women have, more compared to GDM, 17% of SGA babies. It can be

understood that, the SGA outcome is a unique problem of PIH and when it occurs along with GDM,

increases the probability of SGA in GDM populations.

LGA babies are more (13%) with GDM complicating women, whereas PIH women have only 3% of

LGA babies and GDM+PIH women have, more compared to GDM, 12% of LGA babies. It can be

understood that, the LGA outcome is a unique problem of GDM and when it occurs along with PIH,

increases the probability of LGA in PIH populations.

PIH population doesn’t have hypoglycemic or hyperbilirumic babies but the PIH women who

developed GDM later have the hypoglycemic as well as hyperbilirumic babies. GDM+PIH women

have more number of hyperbilirumic babies (66%) than GDM women (56%). Although the

hyperbilirubinemia is unique GDM complication, it is increased in GDM+PIH complicated women.

It can be understood, that the unique problems of particular complications may triggered by

coexistence of both complications.

In general, the pregnancy outcome may vary with complications that women experienced, and

further the outcome may worsen if both complications coexist.

6.5. Development of prediction tool

The probability of developing particular complication in a woman is based on their risk factors.

Maternal characteristics, family history, personal lifestyle and obstetric history are the likelihoods

to develop some determinable changes in pregnancy. Those likelihoods causing complications to

pregnancy are considered as risk factors. Age, BMI, gravidity, parity, irregular menstrual cycle

history, family history of diabetes, previous history of GDM and PIH are the risk factors shown

significant correlation with complications – Table S1. As a single or coupled, each risk factor has

their own strength to develop complications to pregnancy. GDM and PIH are the complications

having unique risk factors for the development and have some common risk factors also. Assessing

these risk factors can provide some understanding on these complications and would help in the

development of tool to assess the women for a particular complication.

6.5.1. Prediction tool an introduction

A prediction tool was developed to assess the women for a complication of GDM and PIH. This is

basically a table that contains percentage values and risk factors, where the values represent the

chances to develop a complication of GDM/PIH for a woman. (Table S2) This tool was developed

by using study data, all the women risk factors were analyzed and compared with corresponding

complications. By using Multinomial logistic regression method this tool was developed. Based on

the observed data, one prediction data will be developed by this method which is then finally

compiled for a single table. The prediction data is basically a percentage value represents

probability of particular complication.

Tool table is basically divided by three major rows as primigravida, multigravida – nulliparity and

multigravida – primiparity. The area in between these rows are relates to particular type of above

said gravidity. The left side first column represents risk factors and right side last column

represents complications, in between this, the 3 columns and 9 sub-columns represent age and

BMI respectively. The meeting point of rows and columns of the tool is the percentage value for

particular complication corresponding to particular risk factor.

6.5.2. Limitations of tool

Since the tool is prepared on the basis of our study population, this tool contains some limitations.

Data for number of samples having more than 5 risk factors was less; hence one cannot be able to

successfully generate the prediction model to access the women having more than 4 risk factors.

The multigravida women having more than 4 risk factors were less hence the prediction values

were not able to be generated successfully.

This tool gives the prediction value up to 5 risk factors for the analysis of primigravida women,

where the risk factors includes age, BMI, family history of diabetes, family history of hypertension

and menstrual history.

This tool gives the prediction value up to 5 risk factors for the analysis of multigravida nulliparous

women, where the risk factors includes age, BMI, family history of diabetes, family history of

hypertension and irregular menstrual history.

This tool gives the prediction value up to 3 risk factors for the analysis of multigravida primiparous

women, where the risk factors includes age, BMI, family history of diabetes, family history of

hypertension and irregular menstrual history.

6.5.3. How to use the Prediction tool

Approaching the prediction tool is explained with following example…

Example: probability of developing complications for a 24 year old overweight, primigravida

woman is as follows-

1) Select age of <25 years column

2) Select BMI of 25-29 column

3) Drag down through BMI column up to meet the PIH row

In Image 1, the circled meeting point is the prediction value for above said woman; she is having

47% of chance to develop PIH, 51% of chance to develop GDM and 2% of chance to have both

together.

Figure 3: Expressing the assessment of prediction tool

A woman of less than 25 years old and BMI

between 25 to 29 kg/m2 having 47% of chance to

develop PIH

AGE = In Years BMI = kg/m2 FM.DM = Family history of Diabetes FM.HTN = Family history of Hypertension G+P = GDM+PIH IRR.MENS = History of Irregular Menstrual cycle ALL = including all risk factors Prev.GDM, Prev.PIH = Previous history of GDM , Previous history of PIH Risk factors: age, BMI, FM.DM, FM.HTN, IRR.MENS, Prev.GDM, Prev.PIH

7. Discussion

7.1. Prevalence of GDM and PIH

The prevalence of GDM was 2.20% for the period of 10 years from January 2003 to December

2012. The prevalence of GDM may range from 1 to 14% of all pregnancies worldwide; the variation

is due to different screening methods used with different ethnic to diagnose GDM12. In India the

prevalence of GDM varies with different areas of country, the range is from 3.8% to 21%110. In

south India, the prevalence of GDM has increased from 1% in 1998 to 16.55% in 2004229. The

prevalence of GDM in Tamilnadu was increasing from 2.1% in 1982, which increased to 7.62% in

1991, which further increased to 16.55% in 20026. We found from our study that the prevalence of

GDM is declined from 3.28% in 2003 to 2.11% in 2012.

The prevalence of PIH was 1.53%for the period of 10 years from January 2003 to December 2012.

The prevalence of PIH may range from 5 to 10% of all pregnancies2. The prevalence of PIH is

increasing and affects about 5 to 8% of pregnancies230. In India the prevalence was recorded with

8% to 10%231. We have found from our study that the prevalence PIH is declining from 2.87% in

2003 to 1.16% in 2012. But the incidence GDM and PIH was found to be increased from 24 to 60

and 21 to 33 cases respectively.

7.2. Risk factors and complication of GDM

Age 25 years and above was considered as risk for development of GDM as many studies reported

that the risk of age greater than 25 years of old232,233. The prevalence of gestational diabetes

mellitus is largely driven by the increase in 25-35 years age group234. The risk of GDM becomes

significantly and progressively increased from the age of 25 years onwards141.We found similar

results in our study. The average of study population age was more than 25 (27.75 ± 3.90) and

half of the population was 25-29 of age, 72% of women were between the age group of 25-34.

The frequency of GDM was found to be increased from the age of 25. The prevalence of GDM is

increased with Increasing age235,236. Advancing age was found to increase the risks for earlier

development of GDM. Women diagnosed with GDM in first and second trimesters were elder than

the women diagnosed at third trimester237was in correlation with our study; the first trimester

group women were elder than second and third trimester group women. The average age of the

population was 28.88 ± 4.65, 27.59 ± 3.77 and 27.02 ± 3.81 respectively. The age of early

diagnosed GDM women was also higher than the age of late GDM diagnosed women. Thus,

advancing age increases the frequency and also causes the earlier development of GDM.

Obesity is one of the important risk factor for GDM238and one of the strongest predictor for GDM

is the BMI239.AbdulbariBener et al104 with 2056 pregnant women in Qatar reported that the

obesity is essential risk factor for GDM and BMI causes a steady increase in GDM. The advancing

BMI is also a risk factor for gestational diabetes mellitus229. Overweight and obesity are the

identifiable risk factors for GDM240. In our population the mean BMI was found to be risk with the

value of 27.71 ± 3.61kg/m2 and 80% of women were above the BMI of 25, and 51% of women

found overweight. The frequency of GDM is also increased from the BMI of 25 to 29 and above.

Thus, advancing BMI increases the frequency and also causes the earlier development of GDM.

Gravidity and Primiparity were found to be high risk to develop GDM241,242. Higher parity was

associated with more prevalence of GDM243,244.. Primigravida women have more risks to develop

GDM245. Several studies suggesting advancing in gravidity increases the prevalence of GDM. In our

study we found the same risk situation; about 52% of women found with multigravida and 36% of

women were primiparous. The average gravidity value was 1.91 ± 1.10 and average parity value

was 0.46 ± 0.63 which were showed the chances of risk. Multigravida and primiparity influences

the early development of GDM as independent risk factors. But multiparity did not show the

influence on the development of GDM, 77% of multigravida and 44% of primiparous women

developed GDM early in their gestation. Thus, increasing gravidity and primiparity are the risk

factors to GDM and also causes the earlier development of GDM.

Family history of diabetes mellitus (DM) has association in the development of GDM246. Family

history act as an independent risk factor for the development of GDM139,247. The maternal history

of diabetes is appears to be a stronger predictor of GDM than paternal history248. Williams et al249

reported in his study that the paternal only history showed two folds increased risk to GDM and

maternal history doesn’t showed risk to GDM. Many studies are stating about the risk of family

history. The above statement was similar with our study; in our study totally 58% of women had

family history of diabetes of which comparatively paternal history was more with 38% against 32%

with maternal diabetes history. Thus, the family history of diabetes is one of the important risk

factor for the development of GDM.

Previous history of GDM was found to be significant risk factor for GDM250. GDM can again lead to

gestational diabetes in the following pregnancies248,251, but in our study only 8% of women had

previous history of GDM and only 6% of women had previous history of PIH.

History of irregular menstrual cycle was found to be an independent risk factor for the

development of GDM43. The blood hemoglobin level of a woman also shows influence on the

development of GDM. The HB concentration more than 13% shows an increased risk for

GDM252.Our study was in correlation with this result, where 36% of women had irregular

menstrual cycle and showed significant influence on the development of GDM. Around 10% of

women had HB level more than 13 and doesn’t show significant influence on the development of

GDM.

The rate of cesarean delivery has increased significantly with GDM253. In our study 89% of women

given birth through cesarean delivery and only 11% of women were given birth through vaginal.

Xionget al254 considered preterm birth is a complication to the infants of GDM mother. Preterm

and term deliveries were not shown much difference in our study, 51% of preterm deliveries were

recorded against 49% of term deliveries.

We found that all new born babies were in good health and there were no reports of shoulder

dystocia, neonatal birth trauma and respiratory complications. The preterm deliveries and LGA

babies are significant to GDM women255. In our study we found that about 12% of babies born

were LGA, whereas 2% born macrosomic babies. The babies born to GDM mother are eight times

more likely to have hypoglycemia and three times more likely to have hyperbilirubinemia256. In our

study 55%of the babies were affected with hypoglycemia and 58% of babies were affected with

hyperbilirubinemia. Overall around 50% of babies were affected neither with hypoglycemia nor

with hyperbilirubinemia.

7.3. Risk factors and complications of PIH

A study done by Navascués et al in Spain on severe maternal complications associated with

preeclampsia found that women greater than 30-35 years of age had an increased risk of

complications257. A study done on 97,270 women with PIH found out that 37.4 % was over 30

years258. Several studies suggesting, younger and first pregnancy women are high risk to

developing PIH and largest number of primiparous women age from 20 found with

preeclampsia259. In our study, 17.44% of the study population was over 30 years of age, 39% of

women were below 24 years of old and 43% of women fall between the age group of 25 to 29. Our

result is similar with above mentioned study; women from the age of 20 to 24 years are more

prone to develop PIH. Thus, the frequency of PIH is more with younger women from the age of 20

to 24 and not much related with elder women age more than 30 years.

One cohort study with 16,582 women with PIH found that, obesity is not associated with the

development of PIH307. Other study conducted in Karachi hospital reported that the development

of PIH is high with obese women than non-obese women308. We found similar results like cohort

study; in our study the percentage of obese women are less compared to non-obese women. Lean

to ideal body weight and overweight women are more prone to develop PIH.

Primiparous women have 4 to 5 time’s more risk than multiparous women to develop PIH260. A

study on the risk factors and clinical manifestation of preeclampsia in Norway foundthat, out of

323 preeclampsia patients 64% were nulliparous261. Nulliparity, previous preeclampsia, high

maternal weight, hypertension, diabetes and twin pregnancies are the risk factors shown strong

relationship to develop PIH147,262. In our study, about 73.79% were nulliparous women and 19%

were primiparous women. Thus, the nulliparity women are more prone to develop PIH than

primiparity whereas multiparity women don’t show much influence to develop PIH. The

frequency of PIH increases with nulliparity compare to primiparity.

The development of preeclampsia is associated with family history of hypertension263. Women

having family history of hypertension are 10 times more risk to develop preeclampsia compared to

women not having family history of hypertension264. In our study, around 30% of PIH women had

family history of hypertension and 31% of women had family history of diabetes mellitus. From

our results, we found that the family history of hypertension doesn’t influence the women for the

development of PIH.

Women with PE in a previous pregnancy had strong risk to develop PE in the current pregnancy149.

Previous preeclampsia and previous diabetes have shown a strong relationship to develop PIH for

current pregnancy265. In our study, 11% of women had previous history of PIH and shown

significant association with current diagnosis of PIH.

Severity of PIH diseased women can be completely brought under control by voluntary delivery

process. The probability of vaginal delivery is very less for the women with preeclampsia

compared to non preeclamptic women266. PIH pregnancy is strongly associated with preterm

delivery182. In our study cesarean delivery was significantly high compared to vaginal delivery,

about 95% of delivery was done through cesarean section. The preterm delivery was also more in

our study, around 65% of women given birth as preterm delivery.

IUGR contributes to two-thirds of LBW babies born in India267. A retrospective study on impact of

PIH on birth weight by gestational age mentioned that the effect of decreased birth weight is

found mostly among preterm births258. The SGA, LBW and preterm birth were higher in PIH

women268. LBW and IUGR babies were significantly increased in women with PIH269. In our study

we found, the mean birth weight was low among the preterm births and overall the low birth

weight babies were more with record of 58%. IUGR was significantly increased to 26% in our

population. The maternal death due to hypertension is accounted for 13% of all pregnancy270.

Totally 2 fetal and one maternal death were occurred in our study due to pregnancy hypertension.

7.4. Discussion for analysis

7.4.1.Gestational diabetes mellitus

7.4.1.1. The duration of GDM is not significantly affecting the outcome of pregnancy.

The duration of GDM varies with women who developed GDM early and late, women who

developed GDM early exposed longer time to GDM and their treatment and progression of

pregnancy were also affected by GDM. Pathologically GDM would develop between the

gestational periods of 24 to 28th weeks but still women are developing GDM very early of gestation

and continuous along with GDM up to delivery. Hence we aimed to find out, whether the duration

of GDM influences the outcome of pregnancy and are there any cardinal risk factors to develop

GDM early. We grouped the women based on the diagnosis through trimester basis and week

basis, the cut off week for early and late diagnosis is – 28thweek.

Not many studies focused on the duration of GDM dividing by trimester and effects on outcome.

Riskin-Mashiahet al271 focused on first trimester glucose level and its effect on pregnancy

outcome, the study found that the elevated fasting glucose levels in first-trimester increases the

risk of adverse pregnancy outcome. Early diagnosis of GDM significantly has long duration of

pregnancy to develop adverse pregnancy outcome. Svareet al272 and Barthaet al273 concluded the

need of insulin, risk of overt diabetes mellitus and neonatal hypoglycemia were higher with

women diagnosed GDM early than women with GDM diagnosed later. Barahona et al274 resulted

that the earlier diagnosis of GDM in pregnancy is a predictor of two adverse pregnancy outcomes

namely PIH and insulin treatment, study also concluded higher incidence of poor pregnancy

outcome, except for PIH which is high in late GDM diagnosed group, with earlier diagnosis of GDM.

But Virally et al275 concluded that with intensive intervention there was no difference in the

pregnancy outcome between early and late diagnosed GDM women.

Similar observation found with our study where there was no significant difference in the

pregnancy outcome between the groups, the second trimester group women shown high

incidence of PIH. But contrary to both above mentioned Svare and Bartha studies, the third

trimester group women were higher in the need of insulin treatment. The onset of GDM doesn’t

show any influence on the development of PIH which is contrary to Barahona et al. Discussion to

neonatal outcome the LGA and macrosomic babies were found higher in women whose GDM

diagnosed early, Moses276 and Leipold277. We found similar observation with our study, as LGA

babies were more with first trimester group but contrary with macrosomia where third trimester

women had more. Contrary to Bartha study our study recorded more hypoglycemic babies with

third trimester followed to second and first trimester group.

Aqeela Ayaz et al278 in their study compared the GDM women on the basis of time they developed

GDM and concluded that, earlier diagnosis of GDM women were associated with less favorable

neonatal outcome. In our study, we found no significant difference in the pregnancy outcome

between the women who developed GDM at 1st, 2nd and 3rd trimester of gestation. Cesarean

delivery was more common for all trimester group women, 86%, 82% and 92% of cesarean

delivery was recorded with 1st,2nd and 3rd trimester group women respectively.

When compared the early and late diagnosis of GDM; results were found to be same with above

mentioned studies by Bartha et al and Barahona et al. Need of Insulin or insulin treatment was

significantly high with early developed group. The cesarean delivery was high with late GDM

developed women compared to early developed women. All the other complications were not

significantly differed between the groups of women.

7.4.1.2. Types of treatment to GDM shown no significant difference in the outcome of pregnancy

When we are comparing the outcome of pregnancy based on the treatment given; the overall

outcome shown no significant difference, except for cesarean delivery and preterm delivery,

between the women grouped according to the treatment they received.

A randomized controlled trial by Castilla et al279 with 152 GDM women were found, the low

carbohydrate diet treatment not influenced the pregnancy outcome and produced the similar

outcome compared to insulin treatment. But contrary to that, the other randomized controlled

trial conducted by Asemi et al280 found, that there was an improvement in the pregnancy outcome

for the women with GDM with DASH diet treatment. We found the similar result of Castilla et al

study, where the pregnancy outcome by insulin treatment not differed with diet treatment.

Study conducted at a maternity hospital, in Kuwait reported, that the rate of cesarean delivery was

increased with dietary treated GDM women and other perinatal outcomes were normal281. Some

studies have reported that the rate of cesarean delivery was reduced because of diet treament282.

Many studies are reporting that the cesarean delivery was higher with dietary treated GDM

women283,284 but other perinatal outcomes were normal to therm285,286. The study by Zanet al287

stated that1% babies delivered to insulin treated women had apgar score less than 7 at 1st min but

3% were reported with diet alone treated women.

We found from our study, nearly 91% of insulin treated women given birth through cesarean

section whereas 10% of diet alone women given birth through cesarean section. Preterm

deliveries are more with insulin treated women than diet alone women. Other than the cesarean

and preterm delivery there was no significant difference in outcome between these two groups of

women.

7.4.1.3. Control of glycemic level with in the normal range has given better pregnancy outcome.

When we compared the outcome of GDM women who controlled the FBS below 95 mg/dl with

GDM women who have not controlled FBS below 95 mg/dl, there was significant difference in the

cesarean delivery, term of delivery, weight of the baby and size of the baby.

Control of glucose level throughout the pregnancy in GDM contribute improvement in pregnancy

outcome288,289.290. If euglycemia is not achieved ultimately result with poor pregnancy

outcome291,292,293. Women with glucose tolerance value of 140 – 162 mg/dl have high rate of

cesarean delivery and macrosomic babies294. Similar results we have found with our study, in our

population the women who controlled glucose level above 95 mg/dl have high rate of cesarean

delivery.

The hyperglycemic and adverse pregnancy outcome (HYPO) study reported the significant

association between elevated glucose level and adverse pregnancy outcome, those are increased

birth weight, preterm delivery, cesarean delivery, hyperbilirubinemia and preeclampsia295,296. LGA

is strongly associated with GDM women of abnormal fasting glucose values297. We have found

similar results with our study; LGA babies and Low birth weight babies (LBW) were significantly

high with the women who have not controlled the blood sugar below 95 mg/dl. The perinatal

problems of cesarean delivery, preterm delivery and LGA will be more if women not treated for

abnormal glucose values298.

The obese women who have not controlled the blood glucose have twice the time of risk

compared to lean women to have LGA babies299. Same way many studies are suggesting the

increased rate of LGA in GDM women and the addition of maternal obesity again increases the

development of LGA. Similarly in our study we found that the BMI is significantly high with women

who have not controlled the glucose level below 95 mg/dl. If glucose level is not controlled under

the normal range the pregnancy outcome will be poorer and addition to this the maternal

obesity further worsen the outcome.

7.4.2. Pregnancy induced hypertension

7.4.2.1. No significant difference in terms of pregnancy outcome between different anti-

hypertensive drugs used in the treatment of PIH.

When we are comparing the outcome of pregnancy based on the treatment given; the overall

outcome shown no significant difference between the women grouped according to the drug

received for treatment. All the drugs have significantly reduced the BP from base to end. It was

similar to a study done on the comparison of Nifedipine with Methyldopa in Srilanka on 126 PIH

patients where the systolic and diastolic BP for nifedipine and methyldopa were significantly

reduced from base to end300. Same result found with the study conducted at National university

hospital, Singapore, concluded that short term treatment with methyldopa significantly reduced

the maternal blood pressure301.

Sibai et al302reported that (compared 300 PIH women for perinatal outcome of treatment by

methyldopa versus no drug) the treatment with methyldopa did not improved the pregnancy

outcome. The calcium channel blocker nifedipine controls the blood pressure as other anti-

hypertensive drugs do, but the advantages over the other drugs to pregnancy outcome need to be

established303. When compared to methyldopa the perinatal outcome was not changed with

nifedipine, both drugs were given similar pregnancy outcome300.

We also found the similar outcome as above mentioned; there were no significant changes in the

outcome of PIH between the methyldopa, nifedipine and no drug treatment. A study done on

methydopa versus no drug treatment in management of mild preeclampsia showed that there was

no much difference in the occurrence of IUGR between the methyldopa group and no drug

treatment group304. But in our study we found no difference in the incidence of IUGR between the

methyldopa group and no drug group.

Randomized clinical trial by Elhassan et al305 compared the treatment of preeclampsia between

methyldopa and no drug found; eclampsia and perinatal death were more with treatment group

than non-treatment group. Similar results found in our study, the eclampsia rate was high with no

drug compared to methyldopa group but contrary to death, one perinatal death occurred with

methyldopa group against no perinatal death with no-treatment group.

7.4.2.2. Pregnancy outcomes were not differed much with the severities of PIH, same time no

special risk factors can be identified corresponding to particular severity.

Alan Buchbinder et al179 compared the pregnancy outcome of preeclampsia with severe

hypertension and reported; there was significant difference in terms of preterm delivery and SGA,

both of this pregnancy problems are high with sever hypertension women. In our study, preterm

delivery and SGA are the important perinatal problems found with significant difference between

all the groups of women. The preterm delivery and SGA were high with mild preeclamptic women

not with gestational hypertension women. In other study the reports stated that the cesarean

delivery was more with sever preeclamptic women than chronic hypertension306, but in our study

the cesarean delivery was same across the groups.

MouniraHabli et al309 found that, other than the SGA all the perinatal outcomes were similar

between the hypertensive disorders in PIH women. We also found the same result in our study,

other than SGA and LBW the perinatal outcomes of all hypertensive disorder groups women were

same.

The assumption is that the etiology of preeclampsia, eclampsia and gestational hypertension

would vary since the definition and clinical symptoms are differs with each other. Former studies

were suggesting similar pattern of risk factors to develop these severities. A study by Rose et al310

reported that, the etiological factors are same between preeclampsia and gestational

hypertension. In our study we also found the same result as that of Rose et al, where no special or

associated risk factors was identified for any particular disorders of PIH.

7.4.3. Associations

Results showed the similarities in the underlying mechanism, and suggested the relationship

between these two complications. We found significant risk of PIH among women with GDM. Each

complication has its own influence in the pregnancy, like increase in blood pressure and increase

in sugar value.

To analyze the risk factors several studies suggested the age, family history, gravidity, parity and

multiple pregnancies has influence on development of GDM and PIH. Doherty et al311 found that

the age and prepregnancy BMI are the common risk factors for the development of both

complications GDM and PIH. Another study by Yariv Yogev et al312 reported that the prepregnancy

BMI and gestational diabetes are the important risk factors to develop preeclampsia.

Sven Schneider et al313 from Germany reported that the advanced age, BMI, multiple pregnancies

and nulliparity are the common risk factors to develop both GDM and PIH. Other study by Roach

et al314 found age, BMI and parity are the common risk factors to develop GDM and PIH. In our

study we found the similar result like above mentioned studies; advancing age, BMI, gravidity,

family history of diabetes mellitus and irregular menstrual cycle are the common risk factors for

the development of both of these complications, but contrary to Sven Schneider et al the parity

was not found to be a common risk factor to the development of GDM and PIH.

GDM women are having more risk to develop PIH. Joffe et al315 compared the non-diabetic women

and GDM women, found a higher risk for GDM women to develop PIH, 17% of GDM women

developed PIH compared to 12% from non-diabetic women. Many studies are suggesting the

influence of one complication on other. GDM women are one and half times more potent to

develop preeclampsia than non-GDM women316. The high blood pressure and the increased insulin

resistance in the GDM women are well associated with the development of preeclampsia317,318. In

our study, we found around 16% of GDM women developed hypertensive disorders of which 4% of

women developed preeclampsia and 9% of women developed severe hypertension.

Cesarean delivery was more and common in women who complicated with both of the

complications, all the women, complicated with both GDM and PIH, were delivered through

cesarean section followed to 94% from PIH alone complicated women and 86% from GDM alone

complicated women. Other perinatal problems like preterm delivery, LGA, SGA, LBW and baby

birth weight were also be the common perinatal problems of women who affected with GDM or

PIH.

8. Conclusion

Incidence of GDM in our study population was found to be high compared to PIH.

Prevalence of both the complications reduced from 2003 to 2012. Prevalence rate of GDM was

3.28% in 2003 which was reduced to 2.11% in 2012 and the prevalence rate of PIH was 2.87% in

2003 to 1.16% in 2012. But the incidence of both the complications was increasing from time.

GDM and PIH were found to be increased from 24 to 60 and 21 to 33 cases respectively.

Age, BMI, gravidity (primigravida, multigravida), parity (nulliparity, primiparity), previous GDM,

previous PIH, family history of DM and irregular menstrual cycle are found to be the risk factors for

GDM or PIH.

Advancing age, advancing BMI, advancing gravidity and advancing parity are the important and

common risk factors for causing GDM and PIH, and when this risk factor’s values reaches the

maximum greater the chances of developing both together.

The frequency patterns for development of both complications were similar, but the peak period

for occurrence is varies with values of risk factors. (For PIH, the higher frequency starts from younger

age to elder age (from 20 to linearly increase to age 25) and for GDM the higher frequency starts from

younger risky age to elder age (from 25 to linearly increase to age 29). The same kind of trend can be seen in

BMI also; the frequency of PIH increases from lean to ideal body weight (BMI of 18 to 24.99 kg/m2,), and

frequency of GDM increases form overweight to obese (BMI of 25 to 29.99 kg/m2,), when age and BMI

reaches above 30 the frequency is higher to develop both complications together).

Primigravida or the first pregnancy women are more prone to develop PIH, and when gravidity is

increasing, the risks are more to develop GDM and both together. (Significantly the average gravidity

was less with PIH women compared to GDM or both group women. GDM+PIH group women have higher

gravidity value than GDM or PIH alone women)

Nulliparity and primiparity are significant risk factors to develop both GDM and PIH, but for a

nulliparous woman the frequency is high to develop PIH first then GDM, and when parity is

increases the frequency is more with GDM, for primiparous women the frequency is high to

develop GDM than PIH. (The average parity value is less with PIH women compare to GDM women)

Women having the family history of DM are prone to develop both kind of complications, but the

frequency is significantly high with GDM than PIH. Family history of hypertension is not influence

the women to develop any complication. Paternal history of DM influences women more to

develop GDM than maternal history of DM do with women.

The previous history of GDM or PIH may not be consider as significant risk but when combined

with other risk factors this may enhance the risks for developing any complications. Previous

history of PIH significantly causes the development both complications together.

Women having more number of risk factors are more prone to develop any complication. The

increases in the number of risk factors may cause a parallel increase in the risks to develop PIH,

GDM then GDM+PIH.

When the risk factors are increasing the addition of risk factor significantly changes the probability

of developing complications. The increasing in number and values of risk factors would actually

increases the risks of women to develop the complications early. Increase in number of risk factors

may cause early development of GDM and not necessarily cause the early development of PIH.

Cesarean delivery is common in both complications, when a woman develops both complications

the greater the chances of cesarean delivery. (GDM alone women – 81% of cesarean delivery, PIH

alone women – 94% cesarean delivery, GDM+PIH woman – 100% cesarean delivery.)

All the new born babies were found to be normal and good in health. Two fetal deaths and one

maternal death were occurred as a result of PIH complication. No neonatal birth injury was

reported. The average baby weight of GDM women was within the normal range and below the

normal range for PIH women. (Average baby weight of GDM women was 2.67 ± 0.65 kg. Average baby

weight of PIH women was 2.22 ± 0.73 kg and the same was 2.45 ± 0.81 kg for GDM+PIH women).

Hypoglycemia and hyperbilirubinemia are very common in babies of GDM women. (Around 60% of

babies affected with both of above mentioned neonatal complications)

The length or duration of GDM doesn’t change the severity of perinatal outcome, whether it is

developed early or late the outcome result will be similar.

The type of treatment, whether it is diet or along with insulin, depends on the blood glucose

maintenance the perinatal outcome is similar for GDM women.

Pregnancy outcome was significantly improved with the GDM women who controlled the fasting

blood glucose level with in the normal range. Strict control of blood glucose level under the normal

range gives better pregnancy outcome in GDM. (The following perinatal complications were improved

in the GDM women who controlled fasting blood glucose level below 95 mg/dl, mode of delivery, term of

delivery, LBW, NBW, LGA and week of delivery).

Anti-hypertensive drugs nifidepine and methyldopa were significantly reducing the elevated blood

pressure to normal range and reduce the perinatal complications also as well, and pregnancy

outcome also same between these two drugs.

The increase in severity of PIH increases the perinatal complications as well. Greater the disease

severity, poorer the pregnancy outcome.

The duration, treatment and development or severity of diseases doesn’t produce significant

changes in the outcome of pregnancy, but the strict control over the disease would definitely give

the good outcome. The GDM women who strictly controlled the blood sugar level with in the

normal range showed significant improvement in the pregnancy outcome, hence, regardless to the

time of diagnosis and treatment of GDM, strict control of blood sugar level with in the normal

range would give beneficial effect to pregnancy outcome. Same way the pregnancy outcome is

worsening with the severity of PIH, and since the control of blood pressure improve the perinatal

outcome, the adequate BP control by any antihypertensive drugs within the normal range would

give a good pregnancy outcome.

The occurrence of GDM itself again act as a risk factor and causes PIH, the coexisting of these

complications influences each other and further worsen the pregnancy outcome. Since both

complications are having common risk factors and frequency of development, identifying the

women early, initiating the treatment early and strict control over the diseases definitely improve

the outcome of pregnancy.

9. Impact of the study

Gestational diabetes mellitus and pregnancy induced hypertension are the most commonly

encountered problems that occur during pregnancy and may lead to serious complications.

Maternal and neonatal morbidity and mortality were found to be high and potentially harm both

the fetus and mother. The impact of diseases continues even after the delivery to both mother

and child. The GDM affected women have 50% more chances to develop type 2 diabetes mellitus

than normal women. The offspring’s of GDM mother too affected with childhood obesity. The

preterm birth and intra uterine growth retardation leaves some traces on child hood growth,

those exposed children shows slow growth mentally and physically compared to non-exposed

children. The maternal mortality rate is still high with hypertensive disorders than any other

complications during pregnancy. But the recognition is still low to these complications.

First, this study would definitely create awareness about these complications in public. The

awareness will draw complete attention and cooperation in the management of diseases. Our

study suggests the possibilities of identifying the susceptible women by assessing the risk factors

very early even before the pregnancy. Early detection and early treatment gives a complete

understanding to health care professionals throughout the remaining pregnancy period.

Awareness and education about this model to society will make the women knowledgeable and

confident to fight against this illness.

We have aimed and developed a strategic tool to find out the susceptibility of women to have

these complications. This is basically a prediction tool assessing the biological risk factors of

women. Since the tool developed with small and single ethnic population it needs a widespread

data from different ethnic to become an adoptive model as a screening tool. Once this model is

successively developed, this will be an easy tool to screen the women in low resources settings

and rural areas.

Currently there were no universally adopted guidelines for the management of these

complications. This study results gives a broad idea about the progress and outcome of diseases. It

also suggests that control in GDM and PIH would give beneficial effect in the outcome of

pregnancy.

Education is a major part in the management of pregnancy complication which involves life style

modification, weight management, knowledge of drugs and self-monitoring of blood glucose and

blood pressure. Our study would give the information to women on all the areas of management

of these complications.

Outcome of the study provide the knowledge on cost-effective management and patient

education to the pharmacists.

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