HbA1c thesis final format 2016

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!A comparative study using HbA1c as an analytical tool in assessing the progression of Type 2 Diabetes in a Swedish

obese population !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

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By!

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James!D.!Sullivan!

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!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!A!thesis!presented!towards!the!degree!

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!BSc.!(Hons)!Biomedical!Science!!

At!!

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!Dublin!Institute!of!Technology!2016!

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!School of Biological Science

Dublin Institute of Technology

Kevin Street

Dublin 8

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Abstract !Obesity is a growing problem amongst developed countries with its prevalence

doubling in the last 20 years. Obesity remains the leading preventable cause of death

across the world with a 20 year reduction in life expectancy. It results in life

threatening complications such as cardiovascular disease, diabetes mellitus, liver and

renal failure. There is a significant link between obesity and the development of type

2 diabetes mellitus. The Swedish Obese Subjects Study (SOS) was a prospective

interventional trial which established the clinical effect that bariatric surgery had on

mortality rates and obesity related complications.

This follow on study aims to investigate whether bariatric surgery is a more favorable

treatment than conventional weight loss interventions in the prevention of diabetes

progression, through the use of HbA1c analytical follow-up data. The aim of this

analytical study is to guide future treatment options to effectively reduce the onset of

diabetes and other long-term life threatening complications that may arise as a result

of obesity. This study noted that Hba1c was more sensitive and specific when

compared to fasting blood glucose as a diagnostic tool in assessing the risk of diabetes

progression from non-diabetic and pre-diabetic states following bariatric surgery. It

also demonstrated there was an increased benefit of bariatric surgery in the prevention

of diabetes at 2-years and a lesser benefit at 10-years compared to the conventional

treatment group. Overall this study enhances our knowledge and supplements current

scientific literature on obesity intervention and diabetic monitoring options.

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Acknowledgements

I would like to take this opportunity to thank my research supervisor Dr Carel Le

Roux whose expertise; understanding and continuous support throughout was second

to none. He demanded high standards and his constructive feedback, which I feel

enabled me to fulfill the biggest challenge that I have encountered in the 4 years of

my Biomedical Science degree.

I would also like to thank fellow research student Lyndsey Kane, Lab supervisor Julie

O Riordan, medical scientist Julie Fitzpatrick and the rest of the Biochemistry

department in St Vincent’s Private Hospital. Without their assistance and patience I

would never have managed to complete the practical component of this project.

The enormity of the project was very challenging with the volume of samples I had to

process in a limited space of time. Therefore, I express my sincere gratitude to Mr.

Frank Clarke for his awareness and understanding of the problems that I faced along

the way.

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Abbreviations

T2DM Type 2 Diabetes Mellitus

HSPH Harvard School of Public Health

SNP Single Nucleotide Polymorphisms

FTO Fat Mass and Obesity GWA Genome wide associated INSIG2 Insulin Induced Gene 2 BMI Body Mass Index HDL High-Density lipoprotein DM Diabetes Mellitus T1DM Type 1 Diabetes Mellitus NEFA Non-esterified fatty acids IGT Impaired Glucose Tolerance IFG Impaired Fasting Glucose VLCD Very low calorie diet MNT Medical Nutritional Therapy GLP Glucagon-like peptide VBG Vertical-banded gastroplasty ADA American Diabetes Association DCCT Control and Complications Trial UKPDS UK Prospective Diabetes Study HPLC High Pressure Liquid Chromatography HB Hemoglobin SOS Swedish Obese Subjects IQC Internal Quality Control

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QC Quality Control IFFC International Federation of Clinical Chemistry EDTA Ethylenediaminetetraacetic acid ID Identification number PPV Positive predictive value NPV Negative predictive value ROC Receiver operating characteristic ANOVA Analysis of variance FBG Fasting Blood Glucose AUC Area Under the Curve

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Table of Contents Pages

1.0 Introduction 01

1.1 Obesity 02

1.1.1 Obesity as a risk factor for diabetes 02

1.1.2 Contribution of genetics 03

1.1.3 Measuring obesity 04

1.1.4 Metabolic syndrome 05

1.2 Diabetes Mellitus 06

1.3 Pre-diabetes 08

1.4 Obesity Treatment 09

1.4.1 Lifestyle treatment 09

1.4.2 Pharmacological approaches 10

1.4.3 Bariatric surgery 11

1.4.3.1 Gastric bypass 12

1.4.3.2 Vertical-banded gastroplasty 13

1.4.3.3 Gastric banding 14

1.5 HbA1c Analysis 15

1.5.1 physiology 15

1.5.2 Diagnostic utility and clinical value 15

1.5.3 History of HbA1c 15

1.5.4 Diagnostic levels 16

1.5.5 Assays 17

1.5.6 Gold standard assays for HbA1c 18

1.5.7 Traditional assays for HbA1c 19

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1.6 Swedish Obese Subject (SOS) Study 20

1.6.1 Background of SOS 20

1.6.2 Implementing a more analytical approach 21

1.6.3 The impact and role of HbA1c in Diabetes prevention 21

2.0 Methods and Materials 23

2.1 study design and methodology 24

2.2 Sample Preparation 25

2.2 Test Method 25

2.2.1 Test principle 25

2.2.2 Reagents 26

2.2.3 Other materials 27

2.2.4 Instrumentation 27

2.2.5 Calibration and quality control 27

2.2.6 Test procedure 28

2.2.7 Data collection and preparation 29

2.2.8 Statistical analyses 29

3.0 Results 32

3.1 Diagnostic performance of Hba1c vs Fasting Blood Glucose(FBG) 33

3.1.1 HbA1c as diabetic predictor using FBG as ‘’gold standard’’ 33

3.2.2 FBG as diabetic predcitor using HbA1c as ‘’gold standard’’ 35

3.2 Preliminary data analysis of Diabetic diagnostic strategies 37

3.2.1 HbA1c Analysis 37

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3.1.2 Fasting blood glucose 39

3.3 Analytical correlations between diagnostic strategies 41

3.3.1 Pre-operation - HbA1c and FBG correlation for control 41

3.3.2 Pre-operation - HbA1c and FBG correlation for surgical Treatment 42

3.3.3 HbA1c and FBG correlation for conventional treatment 43

3.3.4 HbA1c and FBG correlation for bariatric surgical treatment 44

3.3.5 HbA1c and FG correlation for conventional treatment 45

3.2.6 HbA1c and FG correlation for bariatric surgical treatment 46

3.4 HbA1c data and diabetes prevention 47

4.0 Discussion 52

4.1 HbA1c analytical validity and precision 53

4.2 Diagnostic performance of HbA1c vs fasting blood glucose 53

4.3 Preliminary data analysis of diabetic diagnostic startegies 55

4.4 Analytical correlations between diagnostic strategies 57

4.5 HbA1c data and diabetes prevention 59

4.6 HbA1c-‘’an improved diagnostic tool for quantification of blood glucose’’ 63

4.7 Clinical interventions and their impact on diabetes using HbA1c 63

5.0 Bibliography 66

6.0 Appendix 72

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Ethical Approval statement Informed consent was obtained through previous SOS studies and therefore several regional ethical review boards such as the University of Gothenburg, Sweden, ethically approved this study.

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1.0!!!!

Introduction!!!!!!!!!!!!!!!!!!!!!!!!!!

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1.1 Obesity Obesity is a growing problem amongst developed countries in the western world that

ultimately leads to increased mortality and morbidity. (WHO, 2016). It is a term used

to describe a medical disorder where the accumulation of excess fat can impair human

health and result in some severe life threatening complications eg: cardiovascular

disease, diabetes, liver and renal failure. Almost 25% of the Irish population are

obese and up to 80000 people in Ireland are morbidly obese. The estimated health

expenditure on obesity related issues has amounted to €1.13 billion (Carroll and

O’Carroll, 2012). A combination of excessive food intake, physical inactivity and

genetic risk factors are the most common causes of obesity (Shahian, 2015). The

energy imbalance that results from a combination of inactivity and a net surplus

energy intake leads to an accumulation of adipose tissue development. This tissue has

a limited expandability however; with increased amount of intra-abdominal fat

deposition an inappropriate expansion of adipocytes occurs. This mechanism is

referred to as hypertrophic obesity and its ectopic fat accumulation in the abdominal

and visceral areas is considered a major contributing factor in the development of

obesity related metabolic complications such as Type 2 Diabetes Mellitus (T2DM)

(Gustafson et al., 2015).

1.1.1 Obesity as a risk factor for diabetes

Walter Willet and the Harvard School of Public Health (HSPH) outlined the strength

of this relationship between excessive weight gain and diabetes in a nutritional study

with 30% of overweight people developing T2DM (Powell and Writer, 2012). There

has been no sign of this twin epidemic slowing as the prevalence of obesity cases

along with diabetes has almost doubled over the past two decades (Powell and Writer,

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2012). Prior to detection of type 2 diabetes mellitus, pre-diabetes can be identified in

obese patients. Pre-diabetes is defined as impaired glucose tolerance or impaired

fasting blood glucose where the blood glucose level is elevated but does not meet the

criteria to be diagnosed as type 2 diabetes mellitus. This condition in a prolonged state

is associated with systemic circulatory and cardiovascular problems.

1.1.2 Contribution of genetics

In terms of genetic risk factors, Single Nucleotide Polymorphisms (SNPs) in the Fat

Mass and Obesity associated (FTO) gene region on chromosome 16 have been shown

to have a strong influence on the development of obesity (Frayling et al., 2007). It has

been shown that overexpression of FTO leads to increase fat mass and obesity via

hyperphagia in animal studies (Church 2010). Genome wide associated (GWA)

studies have confirmed an interaction between the non-coding region of the FTO

region and promoter genes IRX3 and IRX5. A single nucleotide abnormality in this

genetic component enhances IRX3 and IRX5 expression thereby causing excessive

weight gain due to a shift to energy-storing white adipocytes and a significant

reduction in energy dissipation (Smemo, Tena et al. 2014). This study also identified

a direct association between these genes in the central nervous system with an

increased intake of food. Furthermore a reduction in energy expenditure was noted

with expression of these genes (Frayling, Timpson et al. 2007). Another GWA study

also proved that people carrying two copies of the FTO gene allele are susceptible to a

1.67 fold higher risk of obesity development than people who do not possess this gene

abnormality (Frayling, Timpson et al. 2007). Although no direct correlation with

diabetes progression has been recognized, this FTO gene alteration in combination

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with Insulin Induced Gene 2 (INSIG2) SNPs has also shown a strong association with

the predisposition of human obesity (Chu, Erdman et al. 2008).

1.1.3 Measuring obesity

In the measurement of obesity, Body Mass Index (BMI) is commonly used in

providing an accurate diagnosis as it takes a person’s weight and height into account.

BMI helps clinicians categorize a patient as under-weight, healthy, overweight and

obese by utilizing height: weight ratio. A BMI range of 25-30kg/ !! is considered

overweight. Any BMI value exceeding 30kg/!! is classified as obese with a BMI

>40kg/!! as morbidly obese (Gibbons, 2013). Other relatively simple assessments of

obesity includes waist circumference and waist to hip ratio measurements. The waist

circumference is the most straightforward estimation of obesity though it may be

subject to human measurement error. The size of a particular subject’s waist

circumference is indicative of abdominal obesity and there is a high risk of obesity

related conditions in men and women if their respective waist circumference

measurement is greater than 102cm and 88cm (President and Harvard, 2012). The

waist to hip ratio is a simple convenient measurement however it is observer

dependent and may be inaccurate. With regards to solely examining body fat

composition, a bio-impedance method is used. The principle behind this procedure is

to calculate the total body water through an indirect measurement of opposition or

impedance to the flow of electric current as it passes through the body’s tissues.

Although this form of obesity evaluation is easily assessed through body fat meters, it

is still not considered a ‘’gold standard’’ method due to its high variability and

inaccuracy in providing an overall measure of body composition (Khalil, Mohktar,

and Ibrahim, 2014).

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Fig.1A BMI Chart displaying BMI height: weight ratio in categorizing different

patients (var et al., 2016).

1.1.4 Metabolic syndrome

A combination of critical risk factors that may contribute to further disease

development is known as the metabolic syndrome. A collection of three out of the

five of the following symptoms results in a confirmatory diagnosis of metabolic

syndrome: raised blood pressure, abdominal obesity, increased fasting plasma glucose

(5.6mmol/l), high triglyceride level (>1.7mmol/L) and a low level of High-Density

lipoprotein (HDL)(Men<1.0mmol/L)(Female<1.3mmol/L). Metabolic Syndrome or

often known as ‘’Pre-Diabetes”, is considered a precursor stage in the development of

Type 2 Diabetes Mellitus due to increased blood glucose as a result of insulin

resistance (Grundy, 2012).

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1.2 Diabetes Mellitus

Diabetes Mellitus (DM) is a metabolic disorder of impaired carbohydrate, fat and

protein metabolism caused by a lack or reduced effectiveness of insulin on tissues

leading to elevated blood glucose levels. In Type 1 Diabetes Mellitus (T1DM), the

pancreatic β cells are incapable of producing sufficient insulin to transport glucose

from the bloodstream into nearby cells. In Type 2 Diabetes Mellitus (T2DM), there is

an increased resistance of insulin activity, as the body’s cells are unable to respond to

the normal levels of insulin leading to an inappropriate level of glucose in the

bloodstream. (Tidy, 2013).

T2DM accounts for 90% of diabetes cases worldwide and it is known to develop later

in life between the age of 50 to 60 years due to physical inactivity and excessive

weight gain. This chronic metabolic disorder is therefore referred to as ‘’adult onset

diabetes ‘’ and it is associated with a shortened life expectancy of 10 years. In spite of

the increased secretion of insulin by the pancreas, the diminished insulin sensitivity of

the peripheral tissues leads to a deregulation in glucose metabolism and

hyperglycemia occurs as a result. As T2DM progresses, the pancreatic beta cells

ultimately become ‘’exhausted’’ and are unable to produce sufficient insulin causing

severe abnormalities of glucose metabolism. Hyperglycemia (high glucose level in

blood) can predispose the individual to severe micro vascular and macro vascular

complications such as retinopathy, nephropathy and angiopathy (Ozougwu et al.,

2013). There is a strong correlation with obesity and T2DM as it has been shown that

intentional weight loss can ameliorate glycemic control.

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Type 2 Diabetes Mellitus can develop in those who lack sufficient insulin secretion to

overcome the degree of insulin resistance. In the obese population, adipose tissue

releases increased amounts of non-esterified fatty acids (NEFA), glycerol, hormones,

pro-inflammatory cytokines and other factors that are involved in the development of

insulin resistance. Excessive exposure of NEFA leads to a dysfunction in insulin

secretion. This dysfunction, in spite of the key role NEFA plays in insulin synthesis,

is in response to high blood glucose levels (Karpe, Dickmann et al. 2011). The

dysfunctional pancreatic beta cells lead to an impairment of blood glucose regulation

which increases the likelihood of diabetes mellitus development (Goblan, Alfi, and

Khan, 2014). Chronic insulin resistance may also arise as a result of obesity-promoted

systemic inflammation in response to a high calorific intake. A study carried out by

Karasik et al. (2006), noted that pro inflammatory cytokines such as TNF-α, IL-6 and

resistin combine to activate other chemokines that are involved in the recruitment of

macrophages to the adipose tissue. These recruited chemokines induce an intracellular

signal cascade resulting in a progressive decrease in insulin sensitivity thereby

promoting T2DM development (Shoelson, Lee, and Goldfine, 2006). Another

significant factor that determines the link between insulin resistance and weight gain

is body fat distribution. Insulin sensitivity is very much dependent on the distribution

of adipose tissue throughout the body due to the contrasting metabolic activity of

intra-abdominal and subcutaneous fat. For example, truncal obesity is associated with

increased insulin resistance compared to peripheral obesity as a result of the lipolytic

nature of intra-abdominal fat. The anti-lipolytic activity of insulin is therefore unable

to exert its effects on the insulin-insensitive abdominal tissue thus leading to a

malfunction in glucose regulation and potential risk of diabetes progression (Goblan,

Alfi, and Khan, 2014).

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1.3 Pre-diabetes

Pre-Diabetes is the asymptomatic stage of diabetes mellitus in which blood glucose

levels are higher than normal but have not reached the diagnostic cut off for diabetes

mellitus. Without clinical intervention, pre-diabetes will likely develop into T2DM.

Pre-diabetes also leads to an increased risk of cardiovascular diseases within 10 years

(Dagogo-Jack 2005). Pre-Diabetes can be clinically identified through a HbA1c

analytical value between 5.7%- 6.4% or 42-47.9 mmol.mol-1. Any elevated HbA1c

level exceeding this pre-diabetic range is diagnostic of T2DM. Pre-Diabetes is

categorized into two separate conditions:

Impaired Glucose Tolerance (IGT) and Impaired Fasting Glucose (IFG). IGT reflects

a hyperglycemic state associated with insulin resistance. IGT is identified with an

elevated serum glucose 2 hours following an oral glucose tolerance test that doesn’t

meet the criteria for the diagnosis of T2DM. Fasting glucose levels can be normal or

high.

Impaired Fasting Glucose (IFG) is a consistently elevated level of fasting blood

glucose that hasn’t reached the required diagnostic level for diabetes mellitus. The

HbA1c cut off values for these conditions have been determined as 6.0% and 5.9%

respectively (Rao, Disraeli et al. 2004). By obtaining HbA1c data it allows clinicians

to predict the likelihood of pre-diabetes and the potential development of both micro

and macro vascular diabetic complications amongst obese patients.

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1.4 Obesity Treatment

There have been ongoing clinical trials to determine which clinical intervention is the

most effective in reducing obesity prevalence worldwide. Despite a significant

growth in physical activity programmes and dietary support, obesity remains the

leading preventable cause of death across the world. The three primary clinical

interventions to date are lifestyle changes, pharmacotherapy and surgery such as

gastric bypass, vertical-banded gastroplasty and banding.

1.4.1 Lifestyle Treatment

Lifestyle changes require strict dietary plans, regular physical activity and

psychological support. One must also acknowledge social-economical factors as well

as the level of education of the individual when introducing lifestyle interventions.

Optimizing energy intake and expenditure balance play a key role in providing a

lifestyle treatment from a dietary perspective. This treatment option requires strict

adherence and dedication from the obese patient in order to achieve the desired

outcome. It may require a significant decrease in caloric intake . In order to achieve

the desired weight loss in a safe manner, obese patients must lower their daily caloric

intake. One such extreme diet, the very low calorie diet (VLCD) restricts calorie

intake to 1000kcal. This diet consists of a unique nutritional product containing

greater than 15% of high quality proteins and essential vitamins and minerals. Such a

limited amount of calories induces a state of ketosis which may diminish and suppress

the patient’s appetite. However, this intentional weight loss method is very difficult to

adhere to in the long term and hence most patients regain their weight.

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In an attempt to try and keep patients in a physiological state of starvation, education

of these patients are often advocated. Diet plans devised by personal nutritionist

through Medical Nutritional Therapy (MNT) are often tried. This therapy involves the

recognition of a wide range of factors that may contribute to some nutritional

imbalances and further health concerns associated with obesity. MNT allows a

nutritionist to work with an obese patient to improve their quality of health and help

them reduce and maintain their blood glucose level to a healthy asymptomatic state.

Nutritionists will also help them devise strategies to address the economic expensive

of healthy eating.

Patients often try to address their psychological issues in order to change their

behavior and develop strategies to improve their lifestyle. This approach is largely

unsuccessful because most patients consume too many calories because their appetite

centers in the subcortical areas of the brain makes them hungrier or less satisfied with

smaller quantities of food.

1.4.2. Pharmacological approaches

The most common pharmacological approaches in obesity treatment include

prescribed drugs such as orlistat, amylase inhibitors and liraglutide.

Orlistat is prescribed to morbidly obese patients and acts as a lipase inhibitor whereby

it prevents the absorption of fats thereby reducing a patient’s calorie intake. This leads

to poor nutritional absorption and excess lipid content remaining in the colon

resulting in side effects such as steatorrhoea, nausea, fatigue, abdominal pain and

anorexia. Consequently, this drug is poorly tolerated by patients and therefore it is not

used as a first line treatment option for obesity (Tidy, 2016). Another drug that may

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be used but not commonly recommended for obesity is liraglutide, a glucagon-like

peptide (GLP)-1 agonist. The function of this intravenous drug is to reduce the level

of blood glucose through the stimulation of insulin release into the bloodstream in

T2DM and obese patients. Although it has a common mechanism of action to other

GLP-1 agonists there are many adverse effects for its use in weight loss treatment.

Clinical trials have demonstrated an increase in thyroid T4 receptor carcinomas in

patients with high exposure to liraglutide. Furthermore, a research study performed by

Johns Hopkins et al. (2013), reported clinically significant associations between this

pharmacological approach and pancreatitis development.

1.4.3 Bariatric surgery

Bariatric surgery includes a range of weight loss surgical procedures performed on

severe obese patients. The aim of these treatments is to achieve the required weight

loss by reducing the size of the stomach. This results in reducing the onset of further

medical complications associated with obesity. The clinical outcome of these

treatments results in reduced absorption and gastric restriction thus assisting the

patient achieve their desired long-term weight loss. Several research studies have

outlined the success of bariatric surgery as a treatment option for obesity due to the

significant reduction in the incidence of diabetes and vast improvement in obesity

comorbidities such as dyslipidemia, hyperuricemia and also reducing cardiovascular

risk factors. Despite being the only modality in providing a sustained weight loss for

clinically obese patients, short and long term complications may arise as a result of

this invasive procedure. Potential short-term health risks associated with bariatric

surgery include anastomotic leaks, band erosions or band slippage, port and tubing

problems, wound infection, excessive bleeding, deep vein thrombosis and electrolyte

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abnormalities (Madura and DiBaise 2012). In addition to these short-term potential

side effects, long-term complications may occur as a result of this type of treatment.

These include incisional hernias, gastro-oesophageal reflux, gallstones, gastric

perforations, stomal stenosis, short bowel syndrome, metabolic and nutritional

derangements (Madura and DiBaise 2012).

1.4.3.1 Gastric bypass

Gastric bypass is the most common weight loss surgical procedure accounting for

40% of all surgically weight loss treatments internationally. This form of weight loss

surgery is used for clinically obese patients with significant amounts of weight to be

lost that may not be achievable by intentional weight loss methods. It involves

dividing the stomach into two sections; smaller thumb sized upper pouch and a larger

lower remnant pouch. The surgeon then reconnects the small intestine to each section

to enable drainage of both stomach segments. The stomach volume and size is

reduced but the anastomosis between the stomach pouch and small bowel is large thus

not restricting the amount of food that enters the small bowel, but rather enhancing

the signals in the small bowel when large amounts of undigested food suddenly

appears (Gastric bypass surgery, 2014).

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Fig 1B: Gastric bypass procedure Generation of a smaller stomach pouch and

bypassing of stomach and duodenum limits calorie absorption (Foundation,

Education, and Research, 1998).

1.3.3.2 Vertical-banded gastroplasty

Vertical-banded gastroplasty (VBG) also known as stomach sampling is an operation

no longer performed although it was popular during the 1980s and 1990s. This

operation involves the use of bands and staples to create a small pouch in the upper

part of the stomach. This procedure creates a feeling of fullness for the patient due to

the limited elasticity of propylene mesh band surrounding the pouch thus resulting in

smaller amounts of food intake. VBG was developed to be a safer clinical

intervention than Gastric Bypass due to the reduced complications that may arise post

surgery with a lower mortality rate. There is a decreased incidence of malnutrition

due to the enhanced absorption of key nutrients and minerals (Khader and Thabet,

2005). Many patients were not able to tolerate the symptoms of delayed transit of

food through the upper part of the stomach as they didn’t have the feeling of enhanced

fullness and therefore this procedure has lost popularity.

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1.4.3.3 Gastric Banding

Gastric Banding involves a laparoscopic procedure where a fluid filled band is placed

around the stomach creating a small pouch and a narrow passage into the larger

remainder of the stomach. This band is connected to an access point under the

abdominal wall where it can be inflated by means of a solution being injected into the

port. This solution adjusts the passageway by either tightening or loosening the

adjustable band depending on the size of the food content passing through the

alimentary canal (Rogers et al., 2014). After gastric banding, patients have reduced

hunger, which is most likely related to pressure on the vagus nerve by the band.

Unfortunately, up to 20% of patients do not feel less hungry after the band and they

experience dysphagia if the band becomes too tight

Figure 1C: Gastric Band Procedure lacroscopic adjustable gastric band induces

weight loss by reducing capacity of stomach (MacGill and Webberley, 2016)

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1.5 HbA1c Analysis

1.5.1 Physiology

The hemoglobin A1c (HbA1c) value is a mean glycemic measurement of

glycosylated hemoglobin over a 120-day period. HbA1c values are directly

proportional to the degree of glucose exposure over a period of time and further

diabetic treatment can be adjusted depending on the patient’s HbA1c data (Stöppler,

2016).

1.5.2 Diagnostic utility and Clinical Value

HbA1c is now well established as the most reliable means of assessing chronic

hyperglycemia. This analytical test has shown a strong association with the risk of

developing long term type 2 diabetic complications through many observational

studies. A study performed over a six-year period demonstrated improved blood

glucose control through the use of HbA1c analysis.

Wilf-Miron et al. (2014) showed that the ‘’improvement in HbA1c control was

associated with an annual average of 2% reduction in hospitalisation days’’. This

further emphasizes how this approach has revolutionized the management of diabetes

mellitus since its discovery. It has lead to tighter glycemic control and facilitated

earlier detection, diagnosis and reduction in diabetes associated complications.

1.5.3 History of HbA1c

In 1968, Samuel Rahbar, a member of the American Diabetes Association (ADA),

discovered the clinical significance of the HbA1c analytical test. Although not

broadly appreciated initially, it gradually became the most apparent clinical indicator

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of glucose metabolism allowing a clinician to critically assess the potential effects of

glycemic control and the risk of developing type 2 diabetes. During the 1960s, further

understanding of the hemoglobin protein structure led to researchers, like Rahbar,

discovering hemoglobin structural variants and their relative functions (PERUTZ et

al., 1960). Rahbar in particular, identified a distinctive haemoglobin band in the

electrophoresis study by Holmquist and Schroeder, which found five different

structural hemoglobin variants; HbA1a, HbA1b, HbA1c, HbA1d and HbA1e. The

outcome of a study by Rahbar et al. (1969) noted the distinctive electrophoretic

mobility and chromatographic separation of the diabetic hemoglobin between 7.5 and

10.6% in comparison to normal subjects where the HbA1c accounted for only 4-6%

(Rahbar et al. 1969). These results offered molecular evidence that HbA1c may be

considered a marker of glycemic status over time in diabetic patients.

In 1978, Cerami discovered that HbA1c levels have a direct correlation with urinary

glucose levels, further compounding the link between HbA1c and diabetes (Koenig et

al., 1976). In 1998, As a result of these findings, The Diabetes Control and

Complications Trial (DCCT) and the UK Prospective Diabetes Study (UKPDS)

established HbA1c as a valuable clinical marker in patients with types 1 and 2

diabetes due to its key role in blood glucose control and in the prevention of potential

long-term complications of diabetes (Gebel, Association, and Alexandria, 2012).

1.5.4 Diagnostic levels

HbA1c can be expressed as a percentage of the Hemoglobin that is glycosylated

(DCCT unit) or as a value in mmol.mol-1 (IFCC unit) and 6.5% / 48 mmol.mol-1 are

the respective cut off points for a diabetes mellitus diagnosis. HbA1c analysis has

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been considered a better diagnostic tool and biochemical marker of diabetes in

comparison to other glucose clinical parameters as it is not influenced by daily

fluctuations in blood glucose concentration, thus a non-fasting sample may be

collected from the subject. This analytical procedure also only requires one single

blood sample and the day-to-day variability of HbA1c is significantly lower than

fasting plasma glucose measurements thus reducing the likelihood of false negatives

and false positives with repeat testing (Foundation, Education, and Research, 1995).

In spite of the many benefits of HbA1c analysis, many concerns and limitations still

remain in terms of its accuracy and sensitivity as a screening and diagnostic tool in

diabetes worldwide. HbA1c is limited in its use as a monitor of regular day-to-day

blood glucose concentrations and as a detection method in the acute presence of

hyperglycemia (Landgraf, 2004). HbA1c monitoring is not suitable in patient;s with

hemoglobinapathies, thalassemia and other red cell turnover abnormalities (hemolytic

anemia, chronic malaria and blood transfusions), due to a shorter lifespan of the red

blood cell resulting in a falsely decreased HbA1c (Lippi and Targher, 2010).

1.5.5 Assays

For years the lack of assay standardization posed a serious problem for HbA1c

analysis. National programmes such as the National Glycohaemoglobin

Standardization Program were put in place to achieve a uniform standardization of

HbA1c measurements on a global level. A major concerning feature associated with

HbA1c is that it primarily represents the glycation of proteins in the body instead of

an elevated blood glucose level.

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Analytical Test Fasting Blood Glucose HbA1c

Duration of Blood

Glucose Monitoring

8-12 hours post fasting 3 Months

Timing Prospective Retrospective

Variability Moderate Variation No biological variation

Patient Preparation Strict adherence to

fasting guidelines

None

Table 1A: Comparing Fasting Blood Glucose and HbA1c in terms of glycemic

control and diabetes diagnosis.

1.5.6 Gold standard assays for HbA1c

High Pressure Liquid Chromatography (HPLC) is considered the ‘’gold standard’’

method for the determination of HbA1c. Since its introduction 57 years ago, the

HPLC procedure has proven to be successful for clinical laboratories and healthcare

professionals in achieving the required standards in monitoring glycemic control for

diabetes patients. In spite of being a highly reliable diagnostic tool, this ion-exchange

procedure separates hemoglobin (Hb) species based on their charge and their affinity

to the ion exchanger integrated into a hematological automated analyzer. This

particular process provides an added advantage compared to other traditional assays

due to its ability to identify the presence of most common Hb variants (HbS, HbC,

HbD, HbE) in their heterozygous state.

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1.5.7 Traditional assays for HbA1c

Traditional methods for HbA1c analysis are based on standard antigenic and

antibodies immunoassay interactions. A latex bead enhanced immunoassay method

involves antibodies with latex bead coated antibodies specific for HbA1c combining

with HbA1c molecules forming a cross-linked reaction. As a result, a HbA1c value

can be determined through the measurement of solution turbidity due to the directly

proportional relationship with the amount of HbA1c protein present in the patient

sample.

Fig 1D: Latex enhanced immunoassay illustrating the cross-linked reaction

between antigenic HbA1c proteins and HbA1c specific antibodies (2016).

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1.4. Swedish Obese Subject (SOS) study

Background of SOS

Throughout the 21st century the prevalence of obesity has been increasing rapidly

throughout the western world. With regards to the United States, this significant

increase has amounted to approximately one third of the entire population suffering

from obesity. Several epidemiologic studies performed have recognized a strong

correlation between clinical obesity and increasing mortality rates with up to a 20 year

reduction in life expectancy. It has been well established that weight loss treatment

procedures have been considered to be effective in improving clinical outcomes by

reducing long-term health complications amongst obese individuals. Clinical trials

have been carried out to determine the relationship between weight loss and reduced

mortality. Unfortunately, these particular trials were unsuccessful in differentiating

between intentional and unintentional weight loss due to underlying co-morbidities

with an associated mortality increase. Due to these limitations there have been no

reported interventional studies that identify a reduced risk of mortality with an

intentional weight loss surgical treatment.

Bariatric surgery has been utilized more frequently as a form of weight loss treatment,

as evident in the United States where 100,000 procedures were carried out in 2003. It

was unknown if bariatric surgery would lead to a long-term reduction in mortality

rates associated with obesity and its complications. The Swedish prospective

interventional trial was established in order to examine the influence that surgery had

on mortality rates.

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Implementing a more analytical approach

A number of previous SOS epidemiological studies looked at the reduced incidence

of obesity related complications following bariatric surgery. They recognized the key

relationship between obesity and diabetes onset along with hyperglycemia-associated

complications. However these previous studies were limited due to a lack of an

analytical approach on specifically assessing the progression of diabetes.

This current study is a continuum of the original SOS study. It consists of a

prospective, matched, controlled clinical interventional trial consisting of morbidly

obese subjects. This study involved a series of HbA1c measurements over a follow up

period of 10 years post treatment and it aims to analytically demonstrate a more

accurate relationship between obesity and progression to diabetes.

The impact and role of HbA1c in Diabetes prevention

Many research studies have been carried out with a series of fasting glucose

concentrations highlighting the key relationship between bariatric surgery and

reduction in mortality rates and several hard-end points such as hyperglycemia,

hypertriglyceridemia and diabetes (Sjöström et al., 2004). This research project aims

to investigate whether bariatric surgery is a more favorable treatment option than

conventional weight loss in the prevention of diabetes, through the use of HbA1c

analytical follow-up data. However through HbA1c analysis rather than fasting blood

glucose measurements in this Swedish Obese Subject (SOS) study, the primary

objective is to outline the significant difference in pre-diabetes to diabetes progression

and development between the two contrasting treatment groups. In conjunction with

the HbA1c data, this study correlates the diagnostic performance of HbA1c when

compared with fasting glucose. This study will enable scientific analysis and the value

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of these diagnostic strategies. The aim of this analytical study is to guide future

treatment options to effectively reduce the onset of diabetes and other long-term life

threatening complications that may arise as a result of obesity.

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Materials!and!Methods!

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2.0 Materials and Methods

2.1 SOS study design and methodology

Several regional ethical review boards have ethically approved the Swedish Obese

Subjects (SOS) study’s protocol across Sweden. All subjects agreed to participate in

this study provided written informed consent. When the study was conceived between

1970 and 1980, a high operative mortality was observed in various surgical groups.

As a result, the ethics committees would not allow randomization as it was deemed

that the risks of surgery were too high to allow equipoise. Participants recruited to the

SOS study were given a free choice between surgical and conventional treatment thus

making it a non-randomized study. Through mass media and 480 primary health care

centers throughout Sweden, 11,453 subjects submitted their standardized application

forms to SOS secretariat from September 1987 to January 2001 (Sjöström , Narbro et

al. 2007). In total, 2010 underwent bariatric operations and 2037 received

conventional treatment. Furthermore, a large proportion of the respective treatment

groups also consented to participate in follow up examinations at 2 and 10 years

(1471 bariatric and 1444 conventional)(Sjöström , Narbro et al. 2007). Of the 2010

subjects in the surgical group, 1369 patients received vertical banded gastroplasty,

376 underwent adjustable and nonadjustable gastric banding and 265 received a

gastric bypass procedure. In stark contrast, participants involved in the conventional

controlled group received lifestyle intervention and behavioral modification

programmes upon registration to the SOS study (Sjöström et al., 1992).

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2.2 Sample Preparation

Samples obtained from patients were transported to Goteborg University, Sweden

where they were processed and stored. The samples that were sent for Hba1c analysis

were collected in vacutainer tubes, which consist of whole blood preserved in

Ethylenediamnetetracetic acid (EDTA). Samples were snap frozen and stored for up

to 25 years in some cases. Frozen samples were sent from Gothenburg University to

St Vincent’s Private Hospital by courier overnight packed in dry ice and maintained at

-80 °C. Samples were then stored in a -80 °C freezer until analysis.

Samples were removed one hour prior to analysis from the -80 °C refrigerator before

being thawed at room temperature for one hour. Samples were subjected to a tube

roller mixer for five minutes and samples were loaded onto the analyzer. The Cobas

6000 analyzer underwent daily maintenance procedures to prevent any interference

with the immunoassay. Analyzer capacity was rated at eighty samples per hour.

2.3 Test Method

2.3.1 Test Principle

The Cobas 6000 analyzer incorporates a Turbidimetric inhibition immunoassay

(TINIA) for the determination of HbA1c in whole blood. The provided R1 reagent by

Cobas Roche system contains the relevant HbA1c antibodies. When R1 reagent is

introduced to the sample of whole blood preserved in EDTA, the HbA1c N-terminus

structure reacts with the R1 antibodies. The complex formed in this reaction is

soluble. Since soluble products cannot be detected under ultraviolet light, R2 reagent

is introduced to form insoluble complex of free antibodies specific to HbA1c from R1

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reagent (Roche Roche Diagnostics Ltd, 2013). R2 reagent consists of polyheptans,

which form an insoluble antibody polyheptan complex. This antibody-polyheptan

complex can be detected turbidimetrically (Roche Roche Diagnostics Ltd, 2013).

Fig 2A: Turbidimetric inhibition immunoassay (TINA) reaction pathway for HbA1c

determination in hemolyzed whole blood.

2.3.2 Reagents

• R1- Antibody Reagent

MES buffer :0.025mol/L; TRIS buffer 0.015mol/L, pH 6.2;HbA1c antibody

(ovine serum) : >0.5mg/ml; detergent; stabilizers; preservatives

Sample'Hemoglobin'(HB)' Sample1glycohemoglobin'(HbA1c)'

Sample1hemoglobin'(Hb)'

An71HbA1c'an7body'

Insoluble'complex'of'polyhaptens'and'excess'an71HbA1c'an7bodies'

Photometric'measurement'of'Hb''

Turbidimetric'measurement'of'an7body1polyhapten'

complex'

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• R2-Polyhapten Reagent

MES buffer: 0.025mol/L; TRIS buffer 0.015mol/L, pH 6.2; HbA1c polyhapten :

>8 ug/mL; detergent; stabilizers; preservatives

• Haemolyzing reagent Gen.2

2.3.3 Other materials

• Cobas C Special Cell Cleaning Solution (51mL)

• 5ml Greiner test tubes

• Greiner stopper lids

• Roche Cobas 6000 loading racks

• Distilled water for calibrator reconstitution

2.3.4 Instrumentation

• Roche Cobas 6000 chemistry analyses

• Laboratory sample roller

2.3.5 Calibration and Quality Control

The C.f.a.s. HbA1c-2ml of lyophilized calibrator material was maintained in a stable

state for 2 days at 2-8° C prior usage. Prior to any sample processing, two levels of

Internal quality control (IQC) were run. This consists of running 1ml of PreciControl

HbA1c normal quality control (QC) and 1ml PreciControl pathological QC before and

every two hours after the first set of samples have been processed.

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Assay type 1-point

Reaction time 10/23

Wavelength 660/376nm

Reaction direction Increase

Unit mmol/mol (%)

Table 2A: TINA Assay key units and measurements (Roche Roche Diagnostics Ltd,

2013)

2.3.6 Test procedure

Eighty samples were loaded at one reaction cycle to the Cobas 6000 automated

analyzer. Reagent R1, R2 and haemolyzing reagent were incorporated into reagent

port prior to the analyzer cycle selection. One cassette of reagent is rated to carry out

150 samples. Then the automated Cobas analyzer pipettes 5ul of sample to 500µl of

haemolyzing reagent. The above described step is performed by the red blood cell

lysate prior to reagent introduction. The next process of the automated analyzer is to

introduce R1 and R2 reagents to the cell lysate. Automated liquid handler of the

Cobas analyzer introduces 120µl of R1 reagent to the cell lysate and then 24µl of R2

reagent to cell lysate and then the reaction of the antibody and polyheptan complex

takes place. Detection is then carried out by a turbidimetric approach of measuring

light absorption through the sample to determine the total HbA1c concentration. The

dataset is then generated by the Cobas automated analyzer in two measurements

respectively as millimols per mol(mmol/mol) and as a percentage of A1c/Hb (%).

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2.3.7 Data Collection and Preparation

Once blood samples were processed on the Roche Cobas 6000 analyzer, the HbA1c

results were collected onto the analyzer interface. The data obtained was measured in

the two following units: the Diabetes Control and Complications Trial (DCCT) units

which consists of the percentage of Hemoglobin that accounts for HbA1c (%) and the

International Federation of Clinical Chemistry (IFCC) unit of millimoles of HbA1c

per mole of Hb (mmol/mol). Both sets of units are commonly used in clinical

practice to make diagnostic measurements on blood glucose however there has been

a recent shift in HbA1c reporting from HbA1c percentages to mmols/mol. From an

epidemiological point of view, the more frequent use of SI units across Europe

allows the UK and Ireland to make key glycemic comparisons and differences

between morbidly obese patients. Once a particular batch of

Ethylenediaminetetraacetic acid (EDTA) blood samples were completed, the dataset

generated were released onto a Windows Xcel file. The HbA1c results with the

assigned patient identification number (ID) were then arranged according to the

patients gender, BMI, respective treatment groups and the different time periods

when samples were taken pre treatment or follow up periods 2 and 10 years post

treatment. After matching up the correct HbA1c data for each patient, comparative

statistical analysis between the two contrasting treatment groups was performed.

2.3.8 Statistical Analyses

All statistical analysis on the obtained HbA1c data was performed using PRISM and

SPSS software systems. To determine whether bariatric surgery resulted in a better

clinical outcome than usual medical care at preventing non diabetics and patients with

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prediabetes at baseline to progress to type 2 diabetes the following analysis was

performed:

• Descriptive statistical analysis and normality tests for both HbA1c and

fasting blood glucose diagnostic tools prior treatment.

• Fishers Exact tests comparing control and surgery for Non Diabetics and

patients with Diabetes 2 and 10 years follow up.

• 2 way ANOVA bonferoni correction for follow up time of Hba1c data

versus treatment type.

• Non-parametric Spearman rank correlation of HbA1c and fasting blood

glucose for both types of treatment at each time point.

To determine the sensitivity, specificity, false positives and false negatives of HbA1c

as a diagnostic tool,, fasting blood glucose values were defined as the gold standard

although it should be appreciated that no single test for diabetes is superior to each

another on all parameters. Sensitivity, specificity, and false positive and false negative

parameters were defined as major statistical indictors to interpret data that has been

collected.

I then compared HbA1c as the only diagnostic strategy for diabetes against the “gold

standard” of fasting glucose and calculated the positive predictive value (PPV) and

negative predictive value (NPV) as follows:

• Performed ROC curve of HbA1c analytical test as predictor of diabetes with

fasting glucose as established cut off for Diabetes.

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I then reversed my assumptions and compared fasting glucose as the only diagnostic

strategy for diabetes against the “gold standard” of HbA1c and calculated positive

predictive value and negative predictive value as follows:

• Performed receiver operating characteristic (ROC curve) of fasting glucose as

predictor of diabetes with HbA1c as established cut off for diabetes.

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3.0!!!!

Results!!

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3.1 Diagnostic performance of HbA1c vs Fasting Blood Glucose (FBG) 3.1.1 HbA1c analysis as predictor of diabetes with FBG as established cut-off.

Table 3A: Measuring the predictive values, accuracy and validity of HbA1c analysis using FBG as established cut off.

Type 2 Diabetes

YES NO TOTAL

HbA1c Above 517 171 A+B Below 4 2913 C+D TOTAL 521 3101 3622

Outcome Prevalence (%) 14.38% Sensitivity (%) 99.23% Specificity (%) 99.42%

Positive Predictive Value (PPV-%) 75.15% Negative Predictive Value (NPV-%) 99.99%

Likelihood Ratio (LR) 171.09:1

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Fig 3.1: ROC curve of HbA1c performance holding Fasting Glucose as gold standard for diabetes diagnosis.

Area Under the Curve

Area Std. Errora Asymptotic

Sig.b

Asymptotic 95% Confidence Interval

Lower Bound Upper Bound .969 .007 .000 .955 .982

The test result variable(s): HbA1c has at least one tie between the positive actual state group and the negative actual state group. Statistics may be biased. a. Under the nonparametric assumption b. Null hypothesis: true area = 0.5

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3.1.2 Fasting Glucose as predictor of diabetes with HbA1c as established cut-off.

Table 3B: Measuring the predictive values, accuracy and validity of Fasting Glucose testing.

Type 2 Diabetes

YES NO TOTAL

Fasting Glucose

Above 301 20 321 Below 214 3122 3336 TOTAL 515 2928 3657

Outcome Prevalence (%) 14.08% Sensitivity (%) 58.45% Specificity (%) 99.36%

Positive Predictive Value (PPV-%) 93.77% Negative Predictive Value (NPV-%) 93.59%

Likelihood Ratio (LR) 91.33:1

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Fig 3.2: ROC curve of Fasting Glucose performance holding HbA1c as gold standard for diabetes diagnosis.

Area Under the Curve

Area Std. Errora Asymptotic

Sig.b

Asymptotic 95% Confidence Interval

Lower Bound Upper Bound .942 .007 .000 .928 .955

The test result variable(s): FBG has at least one tie between the positive actual state group and the negative actual state group. Statistics may be biased. a. Under the nonparametric assumption b. Null hypothesis: true area = 0.5

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3.2 Preliminary Data Analysis of Diabetic diagnostic strategies

3.2.1 HbA1c analysis

The descriptive statistics of the obtained HbA1c analytical data (Table 3A) showed obese populations were not taken from a Gaussian (normal) distribution as a result of a failed D’Agostino and Pearson normality test (P= <0.0001).!!

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Table 3C: Descriptive statistics summarizing HbA1c data at each time point for Conventional versus Surgical patient groups.

Time!! ! Stat!

Pre-Operation! 2!Years! 10!Years!Control! Surgery! Control! Surgery! Control! Surgery!

n=!!

1739! 1707! 1083! 1201! 1152! 1351!

Mean!!

40.66! 42.64! 44.05! 38.22! 45.87! 41.69!

Median(IQR)!!

37.60(6.9)! 38.50!(7.8)! 39.9!(7.7)! 36.9(5)! 40.9!(13.4)! 38.6(7.2)!

Min(±SD)!!

25.30(±10.95)!

24.7!(±13.04)! 27.7!(±13.41)! 20.8!(±8.3)! 21.9(±13.89)! 24.9(±10.9)!

Max!!

!!!!108.90! 138.2! 119.7! 152.2! 136.70! 136.40!

SEM! 0.26! 0.32! 0.41! 0.24! 0.41! 0.30!!CV!

!26.94!

!30.58!

!30.45!

!21.71!

!30.28!

!26.18!

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!Figure!3.3:!Comparing*HbA1c*analytical*values*between*the*two*contrasting*treatments*at*preoperation,*2*and*10*years*follow*up."Patients"that"underwent"bariatric"surgeries"resulted"in"significantly"lower"HbA1c"values"2"and"10"years"post"operation"in"comparison"to"obese"patients"that"endured"lifestyle"changes."

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50

100

150

200

Treatment

Descriptive Statistics - HbA1c

Contro

lPreop

Surger

yPreo

p

Contro

l2yea

rs

Contro

l10Ye

ars

Surger

y2Ye

ars

Surger

y10Y

ears

HbA1

c(m

edian+IQR)

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3.2.2 Fasting Blood Glucose

The descriptive statistics of the previously obtained Fasting Glucose analytical data (Table 3B) showed that obese populations were not taken from a Gaussian (normal) distribution as a result of a failed D’Agostino and Pearson normality test (P= <0.0001).

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Table!3D:!Descriptive!statistics!summarizing!Fasting!Glucose!data!at!each!time!point!for!Conventional!versus!Surgical!patient!groups!

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Time Stat

Pre-Operation 2 Years 10 Years Control Surgery Control Surgery Control Surgery

n=

1738! 1705! 1083! 1204! 1150! 1352!

Mean

4.97! 5.19! 4.64! 4.59! 5.54! 4.68!

Median (IQR)

4.42(1.07)! 4.54!(1.29)! 4.20!(1.19)! 4.13(0.86)! 4.8!(2)! 4.3(1.1)!

Min (±SD)

2.43(±1.86)! 2.19(±2.03)! 2.58(±2.04)! 2.14(±1.09)! 2.5(2.22)! 1.5(±1.66)!

Max

18.22! 20.05! 18.62! 19.67! 21.9! 23.8!

SEM 0.04! 0.05! 0.05! 0.05! 0.07! 0.05!

CV% !

37.41!!

39.17!!

36.21!!

35.98!!

40.20!!

35.41!

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Fig!3.4:!Comparing!Fasting!Glucose!(Median+IQR)!values!between!the!two!contrasting!treatments!at!preoperation!,2!and!10!years!follow!up.!Patients!that!underwent!bariatric!surgical!procedures!showed!a!lower!FG!value!2!and!10!years!post!operation!in!comparison!to!obese!patients!that!endured!lifestyle!changes.!!

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10

15

20

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Descriptive Statistics - FBG

Treatment

FBG(m

edian+IQR)

Contro

lPreop

Surger

yPreo

p

Contro

l2yea

rs

Contro

l10Ye

ars

Surger

y2Ye

ars

Surger

y10Y

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3.3 Analytical correlations between diagnostic strategies !

3.3.1 Pre operation - HbA1c and FBG correlation for conventional treatment

There were 1738 analytical values that were utilized to determine correlation between Fasting Glucose and HbA1c diagnostic methods. The non-parametric spearman rank coefficient assessing the statistical dependence between the two diagnostic variables was 1. This statistical value represents a perfect Spearman correlation as Fasting glucose measurements and HbA1c values were monotonically related. The recorded P value was <0.0001 which was statistically significant (<0.05).

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Fig 3.5: Rank Correlation between Fasting blood glucose and HbA1c of the Controlled Matched group at preoperation.

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0 50 100 1500

5

10

15

20

Preoperation - Control - FG vs HbA1c

HbA1c (mmol/mol)

Fasti

ng G

lucos

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3.3.2!Pre!operation@!HbA1c!and!FG!correlation!for!bariatric!surgical!treatment!

There! were! 1705! XY! pairs! that! were! used! to! determine! the! rank! correlation!between!Fasting!Glucose!and!HbA1c!analytical!tests!in!predicting!the!outcome!of!Type!2!DM!in! the!bariatric!surgical!group!at!preoperation.!The!non@parametric!spearman!rank!coefficient! (r)!assessing! the!statistical!dependence!between! the!two!variables!was!1.!This!represents!a!perfect!Spearman!correlation!coefficient.!The!recorded!P!value!was!<0.0001!suggesting!a!statistical!significance!between!the!two!diagnostic!variables.!!

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Fig 3.6: Rank Correlation between Fasting blood glucose and HbA1c of the Bariatric Surgical group at preoperation.

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0 50 100 1500

5

10

15

20

25

Preoperation - Surgery - FG vs HbA1c

HbA1c (mmol/mol)

Fast

ing

Gluc

ose(

mm

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3.3.3 Year 2- HbA1c and FG correlation for conventional treatment

There were 1083 XY analytical pairs used to determine the rank correlation between Fasting glucose and HbA1c testing in the conventionally treated group at 2 years follow up of treatment. The non-parametric spearman rank correlation coefficient (r) assessing the statistical dependence between the two variables was 0.9999. This displays a near perfect positive correlation coefficient. The recorded P value was <0.0001 suggesting a statistical significance between the two diagnostic strategies.!

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Fig$3.7:$Rank Correlation between Fasting blood glucose and HbA1c of the controlled matched group at 2 years.$$

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0 50 100 1500

5

10

15

20

Year 2 - Control - FG vs HbA1c

HbA1c (mmol/mol)

Fast

ing

Gluc

ose(

mm

ol/l)

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3.3.4 Year 2- HbA1c and FG correlation for bariatric surgical treatment

The number of XY pairs used to determine the rank correlation between the Fasting glucose and HbA1c diabetic measurements for the bariatric surgical group at 2 years follow up was 1210. The non-parametric spearman rank correlation coefficient (r) assessing the statistical dependence between the two variables was 0.9999. This displays a near perfect positive correlation coefficient. The recorded P value was <0.0001 suggesting a statistical significance between the two diagnostic strategies.!!

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Fig! 3.8:$ Rank Correlation between Fasting blood glucose and HbA1c of the Bariatric Surgical group at 2 years.$$

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0 50 100 150 2000

5

10

15

20

Year 2 - Surgery - FG vs HbA1c

HbA1c (mmol/mol)

Fast

ing

Glu

cose

(mm

ol/l)

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3.3.5!Year 10- HbA1c and FG correlation for conventional treatment

There were 1150 XY analytical pairs used to determine the rank correlation between Fasting glucose and HbA1c testing in the conventionally treated group at 10 years follow up of treatment. The non-parametric spearman rank correlation coefficient (r) assessing the statistical dependence between the two variables was 0.9999. This displays a near perfect positive correlation coefficient. The recorded P value was <0.0001 suggesting a statistical significance between the two diagnostic strategies.

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Fig$ 3.9:$ Rank Correlation between Fasting blood glucose and HbA1c of the controlled matched group at 10 years.$$

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0 50100

1500

5

10

15

20

25

Year 10 - Control - FG vs HbA1c

HbA1c (mmol/mol)

Fast

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Gluc

ose(

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3.3.6 Year 10 – HbA1c and FG correlation for bariatric surgical treatment

There were 1351 XY analytical pairs used to determine the rank correlation between Fasting glucose and HbA1c testing in the surgically treated group at 10 years follow up of treatment. The non-parametric spearman rank correlation coefficient (r) assessing the statistical dependence between the two variables was 0.9997. This displays a positive correlation coefficient. The recorded P value was <0.0001 suggesting a statistical significance between the two diagnostic strategies.

Fig 3.10: Rank Correlation between Fasting Glucose and HbA1c of the bariatric surgical group at 10 years.$$

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0 20 40 60 80 1000

5

10

15

20

25

Year 10 - Surgery - FG vs HbA1c

HbA1c (mmol/mol)

Fasti

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lucos

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3.4 HbA1c data and Diabetes prevention

$$$$$

3.4.1!Fishers!Exact!test!for!0@2!years!$

Treatment Control Surgery Total 427 536 Non Diabetic

367

531

Diabetic

60

35

% Diabetes Prevalence

14.05%

6.53%

$Table$3E:$2x2$contingency$table$comparing the glycemic outcome from 0-2 years between Control and Surgical patient groups.$$!Fisher Exact Test statistical p value for 0-2 years is 0.0001.!!!!!!!!!!$$$3.4.2 Fishers Exact test for 0-10 years !!Treatment Control Surgery Total Non Diabetic Diabetic % Diabetes Prevalence

957 723 234 24.45%

1077 918 159 14.76%

!Table 3F: 2x2 contingency table comparing the glycemic outcome from 0-10 years between Control and Surgical patient groups. Fisher Exact Test statistical p value for 0-10 years is 0.0001. !

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3.4.3 Pre Diabetic progression to Type 2 Diabetes Mellitus

Out of the total number of patients participating in this SOS study, a small proportion of patients who received 2 years follow up had blood glucose levels at baseline within Pre Diabetic range (42-47.9mmol/mol). The number of Pre Diabetic obese cases amounted to 119 patients in the conventional treated group and 136 patients in the bariatric surgical group prior to undergoing their respective treatment procedures. Bariatric Surgery proved to be a more favourable outcome as only 2.94% of Pre Diabetic patients progressed to diagnostic levels of T2DM after two years of follow up. In stark contrast, 42.02% of Pre Diabetic patients within the control treatment group developed Diabetes after 2 years.

Treatment Pre Diabetes Non Diabetes Diabetes Control 119 69 50

Surgery

136

57.98%

132

42.02%

4

97.06%

2.94%

Table 3G: Assessing Pre Diabetic development and remission from 0-2 Years

Furthermore, Pre Diabetes development to T2DM was also quantified over a period of preoperation to 10 years of follow up. At baseline, there was 127 prediabetic obese patients amongst lifestyle change treatment programmes and 167 as part of the bariatric surgical group who had follow up blood samples taken after 10 years. Corresponding with 0-2 years, Bariatric Surgery proved to be more successful in preventing diabetes progression as only 2.27% reached diabetes diagnostic levels in comparison to 60.63% of patients amongst the controlled matched group.

Treatment Pre Diabetes Non Diabetes Diabetes Control 127 50 77

Surgery

167

39.37%

136

60.63%

31

81.44%

2.27%

Table 3H: Assessing Pre Diabetic development and remission from 0-10 Years

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Control Surgery

Figure 3.11: Outlining the Pre-Diabetic progression and regression between the contrasting treatment groups at 2 and 10 years of follow up.

0"

20"

40"

60"

80"

100"

120"

140"

160"

0" 2" 10" 0" 2" 10"

Pre$Diabe)c+Progression+and+Regression+ """""Pre+Diabetes"""""""Non"Diabetes"""""""""""""""""""Diabetes"

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3.4.4 - 2 Way Measured ANOVA for Time versus Treatment

Control Non Diabetics Control Diabetics

Mean SD N Mean SD N

Preop 36.16 3.0 395 61.66 12.54 62

2 Years 39.40 7.123 395 65.89 23.05 62

10 Years 42.13 9.983 395 62.48 18.81 62

Surgery Non Diabetics Surgery Diabetics

Mean SD N Mean SD N

Preop 36.16 2.8 422 67.17 17.78 113

2 Years 36.71 7.786 422 43.75 11.01 113

10 Years 39.16 8.643 442 51.16 15.08 113

Table 3I: Analytical data obtained for the assessment of diabetes progression and regression for obese patients at baseline

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Fig 3.12A: 2 way repeated measures ANOVA statistical test in diabetic and non-diabetic patients that either underwent bariatric surgery or lifestyle changes (*see appendix for 2 way ANOVA for patients in full profile in each respective treatment)

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Time (years)

HBA1C - 2 way repeated Measures ANOVA

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HbA

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10 Years

2 Years

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4.0 !!!!!!!!!!!!!!!!!!Discussion !

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4.0 Discussion

4.1 HbA1c Analytical Validity and Precision

The Roche TINA assay for HbA1c determination in vivo whole blood proved to be a

valid procedure and also provided high levels of reproducibility (Fleming 2007). The

evaluation of this new whole blood HbA1c immunoassay has been compared and

contrasted with Cobas INTEGRA 800 and Hitachi Tina-quant methods. In addition,

results were published with 1.7% mean biased against national glycohemoglobin

standardization programme. The overall study also concluded that this Hba1c assay is

accurate in detecting with common hemoglobin variants such as HbS, HbE, HbC and

HbD (Fleming 2007). Another beneficial aspect of this assay was that it increased

sample testing and reduced sample handling thereby maximizing the overall

efficiency of the test.

4.2 Diagnostic performance of HbA1c vs Fasting Glucose

In the obese subjects, the Hba1c and fasting glucose measurements were strongly

associated with each other. According to the data representation in Table 3A and

Table 3B, our statistical analysis showed that HbA1c is a more sensitive diabetic

diagnostic tool compared to fasting blood glucose despite both parameters being

considered to be highly specific. The recorded sensitivity values for these diabetic

diagnostic strategies were 99.23% and 58.45% respectively. Therefore, this further

emphasizes the superior clinical value of the Hba1c analytical test by including a

higher proportion of patients that have reached the required levels for diabetic

diagnosis. With regards to the determination of obese patients that don’t have diabetes

prior to treatment, the respective specificity values were 99.42% and 99.36% for

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HbA1c analysis and fasting blood glucose. These particular findings show that both of

these diagnostic predictors are clinically effective in ruling out patients that haven’t

reached the clinically significant blood glucose levels for diabetes diagnosis.

Through examining the data obtained for the respective diagnostic markers on table

3A and table 3B, the PPV were 75.15% and 93.77%. Although the low PPV for

HbA1c highlights the low analytical precision of this test, the obtained HbA1c data is

associated with a NPV of 99.99%. This statistical value suggests that negative

HbA1c analytical test patients are identified with high degree of specificity. A study

published by Ghazanfari et al in 2010 agrees with the produced statistical analysis

from this SOS study. The PPV for HbA1c analysis using FG as gold standard was

36% whereas the PPV for FG using HbA1c as ‘’gold standard’’ was 86%

(Ghazanfari, Haghdoost et al. 2010) . These particular findings coincide with this SOS

study’s calculated statistical values due to the high proportion of observed false

negatives when assessing HbA1c for diabetes prediction while utilizing FG as

established diabetes cut off. This elevated false negative value is possibly due to the

poor post prandial control in some obese patients leading to large glucose excursions

and ultimately elevating HbA1c status while FBG levels still remain at a normal

glycemic state. Another explanation for this high level of false negatives is the

possible underestimation of hyperglycemic status by FBG when defined by HbA1c

diagnostic cut off.

The diagnostic performance of the HbA1c and FBG diagnostic markers were further

assessed through ROC curves of each analytical tool holding the other as ‘’gold

standard’’ in diabetes diagnosis. Both ROC curves illustrated a near perfect

performance for their corresponding diagnostic test as an excellent accuracy

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measurement was observed. This accuracy evaluation is dependent on how successful

these diagnostic tests differentiate obese patients with and without diabetes. To

determine the accuracy of the analytical test the area under the curve (AUC) is

measured. With regards to HbA1c and FBG testing, the reported AUC values were

0.969 and 0.942 respectively. As a result, this provides further evidence that HbA1c

analysis is more accurate than the alternative FBG test in separating diabetics and non

diabetics due to the closer proximity of the ROC to the optimal point of perfect

clinical prediction (0,1). In spite of the slightly bigger AUC for HbA1c in comparison

to the AUC for FBG there is still no statistical difference between the two analytical

tests.

4.3 Preliminary Data Analysis of Diabetic diagnostic strategies

The descriptive statistics for the contrasting diagnostic strategies were performed to

summarize the size of the particular population and to describe quantitative

measurements in a structured and feasible format. These statistical values also

allowed for key comparisons and differences between the two types of diabetic tests

analyzing the same obese population at baseline.

The D’Agostino and Pearson normality tests for both HbA1c analysis and FBG at

each time point produced failed outcomes (p<0.0001). This rejected hypothesis was

statistical significant in indicating that the total patient sample size analyzed by the

contrasting diagnostic tools did not come from a normally distributed population.

Through further analysis of the box and whiskers plots from Fig 3.3 and Fig 3.4 for

the respective diagnostic tests, it can be concluded that the distribution of these obese

population is positively skewed at each time point as the upper whisker tail is longer

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(upper limit of max) than the lower tail (lower limit of min) and all median glycemic

values lie closer to the first quartile (Lower 25%) than the third quartile (upper 25%).

In relation to the HbA1c data representation for pre-operation on table 3C and Fig 3.3,

there is an obvious reduction in HbA1c values from pre operation to 2 Years of follow

up within the bariatric surgical group while also identifying a slight increase in

glycemic values amongst the control group. The mean HbA1c values for the total

obese population undergoing bariatric surgery was 42.64 mmol/mol prior to surgery

which decreased significantly to 38.22 mmol/mol at 2 years follow up but then

showed a slight increase to 41.69 mmol/mol after 10 years post treatment. In

conjunction with these observed changes in the mean HbA1c values, there was a

recorded drop in mean FBG levels from 5.19mmol/l to 4.59mmol/l after 2 years

follow up of bariatric surgery. However over the course of 2 and 10 years follow up

of this surgical procedure, the mean HbA1c value increased to 46.8. In spite of these

corresponding elevated mean HbA1c and FBG levels over the 2 to 10 years period of

follow up, the rapid reduction in these respective diagnostic measurements within 2

years of treatment outlines the effectiveness of bariatric surgery as a short term

procedure to combat obesity and diabetes onset. With regard to the descriptive

statistical values at follow up of obesity control treatment consisting of lifestyle

changes, a 5.21mmol/mol (40.66-45.87mmol/mol) increase in the mean HbA1c

throughout the full ten years of follow up was observed. According to the data

represented on table 3D, a reduction in the mean FBG level within 2 years follow up

was recorded, but over a longer follow up period of 10 years, an elevated mean

glycemic measurement from 4.97 at baseline to 5.54mmol/L was obtained. Using this

statistical evidence and boxplot findings it proves that lifestyle changes was less

successful than bariatric surgery in providing a more favorable clinical outcome for

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obese pre-diabetic patients through the prevention of T2DM progression. A possible

explanation behind the poor diabetes prevention for participants involved in the

conventional treatment programme was the difficulty facing obese subjects in

adhering to strict lifestyle changes and dietary plans over longer periods of time to

ensure significant improvements in glycemic control. An observational study

performed by Bente et al 2014, obtained similar statistical values in signifying the

added clinical benefit of bariatric surgery over conventional treatment for short

periods of time. The clinical findings of this study were in agreement with this SOS

study, as there was an observed diminished plasma glucose and elevated high-density

lipoprotein cholesterol (HDL) amongst the gastric banding surgical group at 5 years

follow up compared with all lifestyle groups (all p<0.05) (Øvrebø 2014).

4.4 Analytical correlations between diagnostic strategies

In order to determine the statistical relationship between established HbA1c and FBG

diagnostic cut offs, non-parametric rank correlations were performed between the two

types diagnostic strategies for both forms of treatments at each time point of follow

up. By organizing the corresponding obtained data for HbA1c and FBG at each

follow up time into ordinal rank scales, a Spearman rank correlation coefficient R was

computed to assess how statistical dependent both analytical tests are in achieving

diabetic diagnosis amongst the obese population.

Through completion of rank correlations in both Fig 3.5 and Fig 3.6 between HbA1c

and FBG at pre-operation for the respective controlled matched treatment group and

bariatric surgical group, a perfect monotonic relationship in each case was confirmed

as the calculated R value was 1. This positive linear correlation rejects the null

hypothesis (p<0.0001) thus showing there is a statistically significant association

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between the HbA1c and FBG data prior patients receiving lifestyle alterations, newly

devised dietary plans or before undergoing gastric bypass, gastric banding or vertical

banded gastroplasty.

In terms of 2 year follow up of bariatric surgeries and lifestyle changes, there was a

strong positive statistical dependence between both diagnostic tools in the

determination of diabetes diagnosis as the Spearman correlation R values were both

0.9999. Nathan et al further emphasized this analytical association between these

diagnostic strategies as their respective glycemic readings for a select group of T1DM

and T2DM patients after 12 weeks were strongly correlated (Nathan, Turgeon et al.

2007) . Although a statistically significant association (p<0.0001) between the ordinal

HbA1c and FBG data amongst the conventionally treated group was recorded (Fig

3.7), the statistical curve displays a slight shift towards the x-axis of HbA1c

diagnostic testing. This shift further supports the argument to hold HbA1c as a more

accurate diagnostic utility in predicting the onset of T2DM over other alternatives

such as FBG.

The analytical interpretation of the non parametric Pearson rank correlation between

both diagnostic tools for the final period of follow up of 10 years showed some

statistical differences to the shorter time period follow up of 2 years. Fig 3.9 and Fig

3.10 correlation curves displaying the representative data for controlled treatment and

bariatric surgical treatment respectively expressed both diagnostic analytical data as

monotonically related. The respective R values for control and surgical groups were

0.9999 and 0.9997 thereby confirming the statistical significance of the correlation

coefficient (p<0.0001). However, amongst patients that underwent the more invasive

bariatric procedure instead of the conventional treatment option there is a slight shift

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in the trend progression towards HbA1c analysis x-axis. As a result, this emphasizes

the added advantage that the HbA1c data has over other diabetes diagnostic

performance indicators as it provides greater accuracy in detecting patients that have

reached hyperglycemic diagnostic levels or some that have even developed

consequential diabetic associated complications over longer periods of time.

4.5 HbA1c data and diabetes prevention

For the statistical assessment of the glycemic outcomes between the surgical and

controlled group for 2 and 10 years follow up of treatment, the respective diabetic

percentage prevalence was measured as outlined in the 2x2 contingency tables.

According to the data representation (Table 3E) assessing the glycemic outcome

through HbA1c measurements for a period of pre-operation to 2 years, there was a

14.05% diabetes prevalence amongst a total number of 957 patients who received

conventional treatment. In stark contrast, the diabetes prevalence of patients who

underwent bariatric surgery was significantly smaller as only 6.53% out of 536

patients were diagnosed with diabetes after 2 years. This statistical difference between

the two treatment groups expresses a more favorable clinical outcome for patients that

underwent bariatric surgeries compared to conventional treatments over a short period

of time. Furthermore the performed fishers exact statistical test rejected the null

hypothesis (p<0.0001) as there was a statistical significance between the control and

bariatric surgical treatments. This statistical value indicates that bariatric surgery had

a better glycemic outcome using HbA1c as an analytical diagnostic marker for

diabetes after 2 years of follow up.

In terms of examining the glycemic outcome after 10 years of follow up, the 2x2

contingency table (Table 3F) showed that there was a slight disimprovement in

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glycemic outcome over a longer period of time for both types of treatment. There

were 957 obese patients within the control group and 1077 bariatric surgical patients

that received follow up HbA1c examinations after 10 years. The calculated

percentage diabetes prevalence for the different treatments was 24.45% and 14.76%

respectively. Although these statistical findings represent an increased number of

patients for both treatment groups that have developed diabetes over a longer period

of time, a 9.96% statistical difference shows that bariatric surgery was more

successful in providing improved glycemic control in order to prevent the rising

number of new cases of diabetes among the Swedish obese population. These two

different therapeutic approaches were also deemed statistically significant (p<0.0001)

through a calculated fishers exact test. Holding a HbA1c level of 48 mmol/mol as the

diagnostic cut off marker, the fishers exact statistical value identifies the statistical

difference in glycemic outcome between the two treatments over a longer period of

time.

There were many obese patients that had elevated HbA1c levels prior to any treatment

but hadn’t quite reached the diagnostic cut off point for diabetes. These pre-diabetic

patients that were within the HbA1c range of 42-47.9 mmol/mol at baseline were

assessed for diabetic progression and regression over a time period of 2 and 10 years.

For this research study, there were 119 prediabetic patients in the control group and

136 prediabetics in the surgical group that received 2 years HbA1c analytical follow

up. Through extensive analysis of table 3G and Fig 3.11, there was a significant

difference observed between the two weight loss procedures as only 2.94% of the

surgical group developed diabetes whereas, 42.02% of prediabetic patients developed

diabetes in the control group after 2 years. Therefore the statistical data obtained in

this study suggest that bariatric surgery is very effective in reducing the progression to

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diabetes in the pre diabetic group. Due to the restriction of food digestion and

absorption after the bariatric surgery, the majority of pre diabetic patients show a

rapid decrease in blood glucose levels within a short period of time. According to the

data obtained by a study carried out by Pories et al, the clinical findings coincide with

this SOS study, as a number of pre-diabetic patients with elevated blood glucose

levels at pre-operation returned to and remained euglycemic 10 years after a gastric

bypass procedure (Pories, Swanson et al. 1995)

At 10 years follow up, there was 127 prediabetics in the controlled matched group and

167 subjects in the bariatric surgical group. Similarly to the results at 2 years follow

up; there was only 2.27% of pre-diabetic patients diagnosed with diabetes amongst

the surgical group while there was a 60.63% incidence of diabetes in the controlled

group. This statistical difference between the two weight loss treatments showed how

bariatric surgery is more successful in providing a favourable clinical outcome by

effectively attaining a desirable level of weight loss and consequently providing a

sustained improvement in glycemic control. As a result this triggers a tighter

regulation in glucose metabolism thus leading to a decreased number of pre diabetic

patients progressing to T2DM after 10 years. To support these clinical outcomes

illustrated in Fig 3.11, Buchwald et al also recorded a large number of pre-diabetic

patients that remitted to a normal healthy state due to the resolved clinical

manifestations following 2 years of bariatric surgery (Buchwald, Avidor et al. 2004) .

Two-way ANOVA statistical examinations were completed to compare and contrast

the mean glycemic HbA1c measurements between the different forms of weight loss

treatments. Through classifying obese patients at baseline into diabetics and non-

diabetics for each respective treatment, the 2 way ANOVA curve illustrated in Fig

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3.12A showed a significant decrease in mean HbA1c level for diabetic patients

amongst the surgical group after 2 years. This drop in mean HbA1c levels from 67.17

mmol/mol to 43.75 mmol/mol within 2 years suggests a considerable improvement in

glycemic control and type 2 diabetes remission over a short period of time. A 2010

study performed by Pournaras et al, reinforced this clinical suggestion as the HbA1c

analytical measurements 5 years after gastric bypass and gastric banding surgeries

showed a significant reduction by 2.9% and 1.9% respectively (Pournaras, Osborne et

al. 2010).

Furthermore, with regard to the non diabetic patients prior to surgery, their mean

HbA1c slightly increased by 0.55 mmol/mol after 2 years. This minimal statistical

change at 2 years represented a successful bariatric surgical treatment in achieving a

sufficient level of weight loss in order to effectively reduce the development of

diabetes onset in the short term. In the assessment of diabetes onset after a longer

period of follow up, Fig 3.12A demonstrates an elevation in mean HbA1c between 2

and 10 years for patients who had diabetes at baseline within the bariatric surgical

group. Although this increase of HbA1c to 51.17 mmol/mol after 10 years follow up

of treatment is diagnostic of diabetes, an overall reduction in mean HbA1c between

pre-operation and 10 years was observed highlighting the clinical success of this anti-

obesity procedure. The improved clinical outcome after 2 years of bariatric surgery

follow up compared to 10 years demonstrates the long term difficulty facing obese

patients in adhering to the strict post operative lifestyle modifications to ensure the

impact of the surgery is clinically effective in combatting obesity and preventing the

progression of diabetes.

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4.6 HbA1c - ‘’an improved diagnostic tool for quantification of blood glucose’’

This SOS study demonstrated how the introduction of a series of HbA1c analytical

measurements compared to FBG provides a more sensitive and equally specific

diagnostic indicator for diabetes.

HbA1c showed to be a more accurate marker of glycemic control compared to FBG

in obese patients, as FBG doesn’t take into account postprandial glucose excursions.

Therefore fasting blood glucose doesn’t provide an accurate reflection of high glucose

measurements in obese patients upon clinical presentation.

Especially over long term periods following on from bariatric surgery, Hba1c is more

accurate in capturing the true glycemic state of the obese patient.

4.7 Clinical interventions and their impact on diabetes using HbA1c

Clinical interventions play a key role in reducing diabetes prevalence and other

associated comorbidities amongst obese subjects worldwide. Through implementing

HbA1c as a diagnostic analytical tool, this research study highlighted the positive

clinical effect bariatric surgery had on obese non-diabetic and pre-diabetic patients.

The study consisted of a control obese population group, which was compared and

contrasted to a group of obese patients which underwent bariatric surgeries. The

outcome of the control group participating in the conventional treatments such as diet,

exercise and pharmacotherapy were largely ineffective in preventing diabetes through

conventional weight loss measures. Although these treatments have been recognized

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in facilitating weight loss and preventing diabetes progression in the short term, this

study showed at 2 and 10 years there was a variability in efficiently improving

glycemic control and remitting type 2 DM. There was an unsuccessful outcome

associated with the controlled treatment group and this could be attributed to poor

compliance with treatment or due to the limitations in the design of the conventional

treatment group. There was no standardization of the conventional treatment group

whether they received diet, exercise or pharmacotherapy. This was not specified in

the study. As a result, a wide range of variable factors could potentially have had an

effect on the overall clinical outcome. These may include the number of visits the

patients made to their physician or nutritionist or the varying exercise training

programmes. In addition, if an obese patient received pharmacological therapy, it

remained unclear whether they were under constant review by a particular physician

or if the anti-obesity medication was up titrated to the highest possible dose that they

can tolerate without side effects.

Compared to these conventional treatment options, bariatric surgery proved to be the

more effective short-term treatment option in reducing obesity and progression to

diabetes. This was shown by the significant reduction in the prevalence of diabetes

after 2 and 10 years in the non-diabetic group.

Clinical benefits of bariatric surgery were clearly evident in the pre-diabetic patients.

In comparison to the control group, there was a significant reduction in diabetes

progression after 2 and 10years.

In the diabetic group that had bariatric surgery, there was a reduction in Hba1c over

10 years. Although in this group, their hba1c was still within the diabetic range,

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overall their mean hba1c was lower than at baseline. This would result in a reduction

in diabetic related complications.

However over longer periods of time (10 years follow up) bariatric surgery is not as

clinically effective in ameliorating glycemic control and preventing T2DM due to the

required difficulty amongst obese subjects in adhering and maintaining strict post

operative lifestyle changes for a sustained period of time. A further study could be

undertaken to determine if aggressive conventional treatments would significantly

cause a remission in T2DM using HbA1c analytical tool in patients 5 years post

bariatric surgery.

Another possible limiting aspect of the research study was that there were a small

proportion of patients that underwent gastric banding and gastric bypass treatments in

comparison to VBG surgeries. As a result the study was statistically unable to

accurately determine the differences in clinical outcomes between the three types of

treatments within the bariatric surgical group. This proved to be a slight drawback, as

the clinical effectiveness of each respective bariatric surgery couldn’t be distinguished

in reducing the onset of diabetes and its related long-term comorbidities.

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5.0 Bibliography

!

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Bibliography

Association, AD, Nutrition recommendations and interventions for diabetes, Diabetes Care 2008. 1: 61–78.

BMI calculator - body mass chart, BMI formula and history, 2016, Retrieved March 7,2016 from World Wide Web: http://www.diabetes.co.uk/bmi.html

Buchwald H et al, "Bariatric surgery: a systematic review and meta-analysis." Jama 2004; 292: 1724-1737.

Carroll, O’Carroll, S, Obesity costs Ireland over €1.1 billion per year, 2012, Retrieved March 7, 2016 from World Wide Web:http://www.thejournal.ie/obesity-costs-ireland-over-e1-billion-per-year-692596-Nov2012

Chu X, et al , "Association of morbid obesity with FTO and INSIG2 allelic variants." Arch Surg 2008; 143: 235-240 Church C, "Overexpression of Fto leads to increased food intake and results in obesity." 2010; 42: 1086-1092.

Dagogo-Jack, S, "Primary Prevention of Cardiovascular Disease in Pre-Diabetes." Diabetes Care 2005; 28: 971-972. Direct!Enzymatic!HbA1c!Methods,!2016,!Retrieved!on!April!6,!2016!from!World!Wide!Web:!http://www.diazyme.com/direct@enzymatic@hba1c@methods.!! Foundation, Mayo , Education, M. and Research HBA1C - clinical: Hemoglobin A1c, blood, 2016, Retrieved March 6, 2016 from World Wide Web: http://www.mayomedicallaboratories.com/test

Foundation M, Education M, Research, ‘Gastric bypass surgery’, Mayoclinic, 2016

!

!

68!

Fleming JK , "Evaluation of HbA1c on the Roche COBAS Integra 800 closed tube system." Clin Biochem 2007; 40: 822-827. Frayling TM et al, "A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity." Science 2007; 316: 889-894. Gastric bypass surgery, 2014 , Retrieved on March 6, 2016 from World Wide Web: http://www.webmd.com/diet/obesity/gastric-bypass-operations Gebel, E., Association, AD, The start of something good: The discovery of HbA1c and the American diabetes association Samuel Rahbar outstanding discovery award, Diabetes Care, 2012; 12: 2429–2431

Ghazanfari Z, et al, "A Comparison of HbA1c and Fasting Blood Sugar Tests in General Population." Int J Prev Med 1: 187-194.

Gibbons GH, Related director’s message, 2013, Retrieved on March 6,2016 from World Wide Web:http://www.nhlbi.nih.gov/health/health-topics/topics/obe/diagnosis

Goblan, AS Al, Alfi, MA, AL Khan, MZ, Mechanism linking diabetes mellitus and obesity, 2014; 1: 7

Grundy SM, Pre-Diabetes, metabolic syndrome, and cardiovascular risk, Journal of the American College of Cardiology, 2012; 59: 635–643.

Gustafson B, Hammarstedt A, Hedjazifar S, Smith U, ‘Restricted adipogenesis in hypertrophic obesity’, Diabetes, 2013; 62: 2997–3004.

Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK prospective diabetes study (UKPDS) group, Lancet (London, England) 1998; 9131: 837–53.

Karpe F et al, "Fatty acids, obesity, and insulin resistance: time for a reevaluation." Diabetes 2011; 60: 2441-2449.

!

!

69!

Khader MA, Thabet O, Vertical-banded gastroplasty | Radiology reference article ,2005, Retrieved March 6, 2016 from World Wide Web: http://radiopaedia.org/articles/vertical-banded-gastroplasty-1

Khalil SF, Mohktar MS, Ibrahim F , The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases, 2014; 4:1

Koenig RJ, Peterson, CM, Jones, RL, Saudek, C, Lehrman, M, Cerami A, ‘Correlation of glucose regulation and hemoglobin A Ic in diabetes mellitus’, New England Journal of Medicine, 1976; 295:417–420 Landgraf, R., The relationship of postprandial glucose to HbA1c, Diabetes/metabolism research and reviews. 2004; 20: 1

Lippi G, Targher, G, Glycated hemoglobin (HbA1c): Old dogmas, a new perspective?, Clinical chemistry and laboratory medicine., 2010 ; 48: 609–14.

MacGill, M, Webberley H, Gastric bands: How does a gastric band work?, no date, retrieved March 7, 2016 from World Wide Web: http://www.medicalnewstoday.com/articles/298313.php

Madura, JA and JK. DiBaise. "Quick fix or long-term cure? Pros and cons of bariatric surgery." F1000 Med Rep 2004; 4. Nathan, DM et al. "Relationship between glycated haemoglobin levels and mean glucose levels over time." Diabetologia 2007;50: 2239-2244.

Ogbru, O PharmD, Liraglutide, Victoza: Drug facts, side effects and Dosing, 1996, Retrieved April 18, 2016 from World Wide Web: http://www.medicinenet.com/liraglutide/article.htm

Øvrebø, B, "Bariatric Surgery versus Lifestyle Interventions for Morbid Obesity: 5-Year Changes in Body Weight, Risk Factors and Comorbidities." 2014

!

!

70!

Ozougwu, JC., Obimba, KC, Belonwu, CD, Unakalamba (2013) ‘Journal of physiology and Pathophysiology the pathogenesis and pathophysiology of type 1 and type 2 diabetes mellitus’2013; 4: 46–57

Perutz, MF., Rossmann, MG, Cullis AF, Muirhead H, Will G et al, ‘Structure of hæmoglobin: A three-dimensional fourier synthesis at 5.5-Å. resolution, obtained by x-ray analysis’, 1960; 185: 416–422

Powell, A, Writer, HS, Obesity? Diabetes? We’ve been set up , 2012, retrieved March 7, 2016 from World Wide Web:http://news.harvard.edu/gazette/story/2012/03/the-big-setup

President T, Harvard F, Measuring obesity, Retrieved April 6,2016 from world wide web: http://www.hsph.harvard.edu/obesity-prevention-source/obesity-definition/how-to-measure-body-fatness

Pories WJ et al, "Who would have thought it? An operation proves to be the most effective therapy for adult-onset diabetes mellitus." Ann Surg 1995;222: 339-352. Pournaras DJ et al, "Remission of type 2 diabetes after gastric bypass and banding: mechanisms and 2 year outcomes." Ann Surg 2010;252: 966-971. Rao SS et al, "Impaired glucose tolerance and impaired fasting glucose." Am Fam Physician 2004;69: 1961-1968. Shahian T, Obesity, 2015, retrieved March 6, 2016 from World Wide Web: https://www.innerbody.com/diseases-conditions/obesity

Shoelson, SE, Lee J, Goldfine AB , ‘Inflammation and insulin resistance’, 2006; 116: 1

Sjöström L et al, "Effects of Bariatric Surgery on Mortality in Swedish Obese Subjects." New England Journal of Medicine 2007;357: 741-752.

!

!

71!

Smemo S et al, "Obesity-associated variants within FTO form long-range functional connections with IRX3." Nature 2014; 507: 371-375. . Sjöström L, Lindroos, AK., Peltonen, M et al, ‘Lifestyle, diabetes, and cardiovascular risk factors 10 years after Bariatric surgery’, New England Journal of Medicine, 2004; 351: 2683–2693

Stöppler MC, HbA1c, hemoglobin A1c, 2016 retrieved on March 6,2016 from World WideWeb:http://www.emedicinehealth.com/hemoglobin_a1c_hba1c/article_em.htm

Tidy C, Type 2 diabetes. Symptoms and Info on diabetes, 2013, retrieved March 6, 2016 from World Wide Web: http://patient.info/health/type-2-diabetes

Vertical banded Gastroplasty ,no date, Retrieved March 6,2016 from World Wide Web:http://www.mcallenbariatric.com/procedures/restrictive-surgery/vertical-banded-gastroplasty

WHO , Obesity and overweight, no date, Retrieved March 6,2016 from World Wide Web: http://www.who.int/mediacentre/factsheets/fs311/en/

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6.0

Appendix

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6.0 Appendix a

Figure 3.12B: Comparing the changes in HbA1c between obese patients that either underwent Control or Surgical treatment.

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35

40

45

50

2 Way ANOVA 2 lines-HbA1c

Control Surgery

Preop

2Year

s10Y

ears

HbA1

c(Mean)