HbA1c thesis final format 2016

!
!
i!
!
A comparative study using HbA1c as an analytical tool in
assessing the progression of Type 2 Diabetes in a Swedish
obese population
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!
By!
!!
James!D.!Sullivan!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!A!thesis!presented!towards!the!degree!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!BSc.!(Hons)!Biomedical!Science!!
At!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!Dublin!Institute!of!Technology!2016!
!
!!!!!!
!
School of Biological Science
Dublin Institute of Technology
Kevin Street
Dublin 8

!
!
ii!
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.

!
!
iii!
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.

!
!
iv!
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

!
!
v!
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

!
!
vi!
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

!
!
vii!
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

!
!
viii!
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
!
!
!

!
!
ix!
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.
!
!
!
!
!
!
!
!
!

!
!
1!
!
!
!
1.0!
!
!
!
Introduction!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!

!
!
2!
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,

!
!
3!
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

!
!
4!
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).

!
!
5!
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).

!
!
6!
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.

!
!
7!
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).

!
!
8!
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.

!
!
9!
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.

!
!
10!
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

!
!
11!
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

!
!
12!
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).

!
!
13!
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.

!
!
14!
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)

!
!
15!
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

!
!
16!
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

!
!
17!
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.

!
!
18!
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.

!
!
19!
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).

!
!
20!
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.

!
!
21!
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

!
!
22!
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.
!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!
!
!
!
!
!
!
!
!
!
!
!
!
!

!
!
23!
!
!
2.0!
!
Materials!and!Methods!
!
!
!
!
!
!
!
!
!
!

!
!
24!
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).

!
!
25!
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

!
!
26!
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'

!
!
27!
• 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.

!
!
28!
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 (%).

!
!
29!
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

!
!
30!
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.

!
!
31!
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.
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!!!!!!!!!!!!!!!!!!!!!!
!
!!!!!!!!!!!!!!!!!!!!!!
!!!!!!!!!!!!!!!!!!!!!!

!
!
32!
!!!!!!!!!!!!!!!!!!!!!!
!
!
3.0!
!
!
!
Results!
!
!

!
!
33!
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

!
!
34!
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

!
!
35!
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

!
!
36!
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
!
!

!
!
37!
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).!!
!
!
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!

!
!
38!
!
!
!
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."
!
!
!
0
50
100
150
200
Treatment
Descriptive Statistics - HbA1c
Control Preop
Surgery Preop
Control 2 years
Control 10 Years
Surgery 2 Years
Surgery 10 Years
HbA1c (median + IQR)

!
!
39!
!
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).
!
!
!
!
!
!
!
!
Table!3D:!Descriptive!statistics!summarizing!Fasting!Glucose!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= 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!

!
!
40!
!
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.!!
0
5
10
15
20
25
Descriptive Statistics - FBG
Treatment
FBG (median + IQR)
Control Preop
Surgery Preop
Control 2 years
Control 10 Years
Surgery 2 Years
Surgery 10 Years

!
!
41!
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).
!
!
!
Fig 3.5: Rank Correlation between Fasting blood glucose and HbA1c of the
Controlled Matched group at preoperation.
!
!
!
!
!
!
!
!
0 50 100 150
0
5
10
15
20
Preoperation - Control - FG vs HbA1c
HbA1c (mmol/mol)
FastingGlucose(mmol/l)

!
!
42!
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.!!
!
!
Fig 3.6: Rank Correlation between Fasting blood glucose and HbA1c of the
Bariatric Surgical group at preoperation.
!
!
!
!
!
!
!
!
!
0 50 100 150
0
5
10
15
20
25
Preoperation - Surgery - FG vs HbA1c
HbA1c (mmol/mol)

!
!
43!
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.!
!
!
Fig$3.7:$Rank Correlation between Fasting blood glucose and HbA1c of the
controlled matched group at 2 years.$$
$
$
$
$
$
$
!
!
!
!
!
0 50 100 150
0
5
10
15
20
Year 2 - Control - FG vs HbA1c
HbA1c (mmol/mol)

!
!
44!
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)
<0.0001 suggesting a statistical significance between the two diagnostic strategies.!!
!
!
Fig! 3.8:$ Rank Correlation between Fasting blood glucose and HbA1c of the
Bariatric Surgical group at 2 years.$$
!
!
!
!
!
!
!
!
!
!
0 50 100 150 200
0
5
10
15
20
Year 2 - Surgery - FG vs HbA1c
HbA1c (mmol/mol)

!
!
45!
3.3.5!Year 10- HbA1c and FG correlation for conventional treatment
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)
<0.0001 suggesting a statistical significance between the two diagnostic strategies.
!
!
$
Fig$ 3.9:$ Rank Correlation between Fasting blood glucose and HbA1c of the
controlled matched group at 10 years.$$
!
!
!
!
!
!
!
!
!
0
50
100
150
0
5
10
15
20
25
Year 10 - Control - FG vs HbA1c
HbA1c (mmol/mol)

!
!
46!
3.3.6 Year 10 – HbA1c and FG correlation for bariatric surgical treatment
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)
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.$$
!
!
!
!
!
!
!
!
!
0 20 40 60 80 100
0
5
10
15
20
25
Year 10 - Surgery - FG vs HbA1c
HbA1c (mmol/mol)

!
!
47!
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.
!
!
!

!
!
48!
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

!
!
49!
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"

!
!
50!
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

!
!
51!
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)
!
0
20
40
60
80
100
!
!
!! !! !!
!!! !! !!
!!! !! !!
!!!
!!
!!
Time (years)
HBA1C - 2 way repeated Measures ANOVA
Control_ND
! !!
Control_D
! !!
Surgery_ND
! !!
Surgery_D
! !!
HbA1c(mean)
Preop
10 Years
2 Years

!
!
52!
4.0
!
!
!!!!!!!!!!!!!!!!Discussion
!
!
!

!
!
53!
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

!
!
54!
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

!
!
55!
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

!
!
56!
(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

!
!
57!
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

!
!
58!
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

!
!
59!
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

!
!
60!
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

!
!
61!
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

!
!
62!
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.

!
!
63!
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

!
!
64!
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,

!
!
65!
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.

!
!
67!
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/

!
!
73!
6.0 Appendix a
Figure 3.12B: Comparing the changes in HbA1c between obese patients that either
underwent Control or Surgical treatment.
!
35
40
45
50
2 Way ANOVA 2 lines-HbA1c
Control
Surgery
Preop
2 Years
10 Years
HbA1c (Mean)

HbA1c thesis final format 2016

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (11)

Similar to HbA1c thesis final format 2016

Similar to HbA1c thesis final format 2016 (20)

HbA1c thesis final format 2016