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Linköping University Medical Dissertations
No. 1218
Autoantibodies related to type 1 diabetes
in children
Camilla Skoglund
Division of Pediatrics
Department of Clinical and Experimental Medicine
Faculty of Health Sciences, Linköping University
SE-581 85 Linköping, Sweden
Linköping 2011
© Camilla Skoglund 2011
ISBN: 978-91-7393-276-9
ISSN: 0345-0082
Paper I has been reprinted with permission of John Wiley and Sons.
Paper II and IV have been reprinted with permission of Elsevier Ltd.
During the course of the research underlying this thesis, Camilla Skoglund (formerly
Gullstrand) was enrolled in Forum Scientium, a multidisciplinary doctoral programme at
Linköping University, Sweden.
Printed in Sweden by Liu-Tryck, Linköping 2011
To my family
Abstract
Type 1 diabetes is an autoimmune disease resulting from destruction of the insulin producing
beta cells in the pancreas. The patients need life-long heavy treatment and still complications,
both acute and later in life, are common. The incidence of type 1 diabetes has increased
rapidly during the last decades, especially among young children. The disease can be
predicted by genes predisposing type 1 diabetes, mainly human leukocyte antigen (HLA)
genes, together with presence of autoantibodies to beta-cell antigens, where multiple
autoantibodies confer the highest risk. A number of immune system intervention trials are
now ongoing aiming to halt the progression of the inflammatory process in the beta cells.
This thesis aimed to investigate the prevalence and levels of autoantibodies in healthy
children and in children with type 1 diabetes. Another aim was to study different properties of
one of these autoantibodies, such as to which epitopes the antibodies bind and the distribution
of immunoglobulin (Ig)-G subclasses, after immunomodulatory treatment in children with
type 1 diabetes.
We found that positivity to autoantibodies against glutamic acid decarboxylase (GADA) and
tyrosine phosphatase like protein islet antigen-2 (IA-2A) was associated with HLA risk
genotypes in 5-year old children from the general population. HLA risk genotypes seemed
important for persistence of autoantibodies and for development of type 1 diabetes, while
emergence of autoantibodies, especially transient autoantibodies, seemed to be more
influenced by environmental factors. Improved methods for detection of autoantibodies are
needed, for prediction of diabetes and for identification of high-risk individuals suitable for
prevention treatments. Therefore, an assay for measurement of insulin autoantibodies (IAA),
based on surface plasmon resonance (SPR), was developed. The main advantages of this
method are that there is no need for labelling and that it is time-saving compared to the
traditionally used radioimmunoassay (RIA), but further development of the method is needed.
Treatment with GAD-alum (Diamyd) in children with type 1 diabetes has shown to preserve
residual insulin secretion. This clinical effect was accompanied by an increase in GADA
levels. We investigated the epitope reactivity of GADA in both GAD-alum and placebo
treated children, and found that binding to one of the tested epitopes was temporarily
increased after injection of GAD-alum. This result suggests that the quality of GADA was, to
some extent, transiently affected by the treatment. On the other hand, no changes in binding to
epitopes associated with stiff person syndrome (SPS) were observed, which together with the
lack of change in GAD65 enzyme activity further strengthens the safety of the treatment. We
also observed that the distribution of IgG subclasses was changed by GAD-alum treatment,
with a lower proportion of IgG1 and higher IgG3 and IgG4. Lower IgG1 and higher IgG4
suggest a temporary switch towards a protective Th2 immune response, which has previously
been observed in the same individuals for other immunological markers.
In conclusion, measurement of autoantibodies related to type 1 diabetes is an important tool
for studying the autoimmune process in pre-diabetic and type 1 diabetic children. In addition
to the use as markers of disease progression, the autoantibodies may be used for studying the
effects of immunomodulatory treatments on the humoral immune response.
Sammanfattning
Typ 1 diabetes är en allvarlig sjukdom som uppkommer när de insulinproducerande
betacellerna i bukspottskörteln förstörs via en autoimmun process. Patienterna måste
behandlas hela livet, med bl a dagliga insulininjektioner, och komplikationer är vanliga.
Förekomsten av typ 1 diabetes har ökat den senaste tiden, och den kraftigaste ökningen har
setts hos små barn. Det går att förutspå vilka individer som har störst risk att utveckla typ 1
diabetes, genom att studera vissa gener, framförallt de som kodar för humant leukocyt-antigen
(HLA), i kombination med mätning av autoantikroppar mot olika ämnen som finns i
betacellerna. Autoantikroppar kallas de antikroppar som reagerar mot kroppsegna ämnen.
Förekomst av flera olika autoantikroppar mot de insulinproducerande cellerna medför högst
risk att utveckla typ 1 diabetes. Ett antal studier pågår, som genom att påverka
immunsystemet med specifika ämnen, har som mål att stoppa den inflammatoriska processen
som dödar betacellerna hos de barn som håller på att få diabetes och hos de som redan har fått
diabetes.
Målet med den här avhandlingen var att undersöka förekomst och nivåer av diabetesrelaterade
autoantikroppar hos friska barn och hos barn med typ 1 diabetes. Därutöver studera olika
egenskaper hos en av dessa autoantikroppar, t e x olika bindningsställen (epitoper) på ett
kroppseget protein (antigen) dit antikroppar kan binda, och subklasser av immunoglobulin G
(IgG)-antikroppar, hos barn med typ 1 diabetes som genomgått en immunmodulerande
behandling.
Resultaten visade att förekomst av autoantikroppar mot glutaminsyredekarboxylas (GADA)
och mot det tyrosinfosfatas-liknande proteinet IA-2 (IA-2A) var associerat med HLA-
riskgener hos 5-åriga barn från den allmäna befolkningen. HLA-riskgener verkade viktiga för
bestående autoantikroppar samt för utveckling av typ 1 diabetes. Uppkomsten av
autoantikroppar verkade däremot påverkas mer av miljöfaktorer än av HLA-riskgener, vilket
gällde särskilt för de så kallade övergående (transienta) autoantikropparna, dvs de som senare
försvann.
Det behövs bättre metoder för att mäta autoantikroppar, både för att kunna förutspå typ 1
diabetes och för att hitta personer med ökad risk för sjukdomen som skulle kunna delta i
förebyggande behandlingar. Därför utvecklade vi en metod för mätning av autoantikroppar
mot insulin (IAA) som bygger på en metod som kallas ytplasmonresonans (SPR). Fördelarna
med vår nyutvecklade metod är att provet inte behöver märkas in med något radioaktivt ämne
före analys samt att det går åt mindre tid att köra ett enskilt prov jämfört med den traditionellt
använda metoden immunoprecipitation (RIA), men metoden behöver utvecklas ytterligare.
Barn som nyligen fått typ 1 diabetes och som har behandlats med GAD bundet till aluminium
hydroxid som vaccin-adjuvans (GAD-alum; Diamyd), har visats kunna behålla sin egen
insulinproduktion bättre än de som inte fått denna behandling. Förutom denna kliniska effekt
observerades också att GADA-nivåerna ökade. Vi undersökte vilka epitoper GADA band till
både hos de barn som fått behandling och de utan behandling. GADA-bindning till en av de
testade epitoperna visade sig öka efter GAD-alumbehandling, vilket skulle kunna tyda på att
antikropparnas kvalitet till viss del har ändrats av behandlingen. Däremot var det ingen
förändring i GADA-bindning till de epitoper som är kopplade till sjukdomen stiff person
syndrome (SPS), vilket tillsammans med oförändrad enzymatisk aktivitet hos GAD65
ytterligare bekräftar behandlingens säkerhet. Fördelningen av IgG-subklasser ändrades efter
behandling med GAD-alum, med en lägre andel IgG1 och högre IgG3 och IgG4. IgG1 och
troligtvis även IgG3 är kopplade till så kallade T-hjälpar 1 (Th1)-cellers immunsvar medan
IgG4 är kopplat till Th2-svar. Den lägre andelen IgG1 och högre IgG4 som observerades i vår
studie skulle kunna tyda på en tillfällig förändring av immunsvaret till ett skyddande Th2-
svar, vilket också har setts hos dessa individer för andra immunologiska markörer.
Sammanfattningsvis, att mäta autoantikroppar som är relaterade till typ 1 diabetes är viktigt
för att studera den autoimmuna processen hos barn som håller på att få diabetes och hos de
som redan har diabetes. Utöver att använda autoantikropparna som markörer för utveckling av
typ 1 diabetes kan de även användas för att studera immunmodulerande behandlingars
effekter på det humorala immunsvaret.
Contents
Abbreviations ................................................................................................................... 5
Original publications ......................................................................................................... 7
Review of the literature .................................................................................................... 9
INTRODUCTION TO THE IMMUNE SYSTEM ................................................................................ 9
T cells ........................................................................................................................................................... 10
B cells ........................................................................................................................................................... 11
Antibodies.................................................................................................................................................... 12
INTRODUCTION TO DIABETES MELLITUS ................................................................................. 16
Definition and diagnosis .............................................................................................................................. 16
Classification ................................................................................................................................................ 17
Type 1 diabetes ....................................................................................................................................... 17
Type 2 diabetes ....................................................................................................................................... 17
Gestational diabetes ............................................................................................................................... 18
Other specific types of diabetes ............................................................................................................. 18
Epidemiology of type 1 diabetes ................................................................................................................. 18
PATHOGENESIS OF TYPE 1 DIABETES ....................................................................................... 19
Genetic risk .................................................................................................................................................. 20
HLA .......................................................................................................................................................... 21
Other genetic factors .............................................................................................................................. 22
Environmental factors ................................................................................................................................. 23
Viral infections ........................................................................................................................................ 24
Dietary factors ........................................................................................................................................ 24
Beta-cell stress ........................................................................................................................................ 25
Beta-cell autoantigens and autoantibodies ................................................................................................. 26
Islet cell antibodies (ICA) ........................................................................................................................ 26
Glutamic acid decarboxylase (GAD) ........................................................................................................ 27
GAD autoantibodies (GADA) ................................................................................................................... 28
The tyrosine phosphatase like protein islet antigen-2 (IA-2) .................................................................. 29
IA-2 autoantibodies (IA-2A) .................................................................................................................... 30
Insulin ..................................................................................................................................................... 31
Insulin autoantibodies (IAA) ................................................................................................................... 32
Zink transporter 8 (ZnT8) and ZnT8 autoantibodies (ZnT8A) ................................................................. 33
PREDICTION AND PREVENTION OF TYPE 1 DIABETES ............................................................... 34
Prediction and prevention studies .............................................................................................................. 35
DETECTION OF AUTOANTIBODIES ........................................................................................... 40
Aims of the thesis ........................................................................................................... 43
Subjects and methods ..................................................................................................... 45
STUDY POPULATIONS ............................................................................................................. 45
The ABIS study ........................................................................................................................................ 45
The phase II clinical trial with GAD-alum ................................................................................................ 46
Type 1 diabetic patients .......................................................................................................................... 47
METHODS .............................................................................................................................. 47
RIA for detection of GADA and IA-2A (Paper I and IV) ................................................................................ 47
HLA genotyping (Paper I) ............................................................................................................................. 49
RIA for detection of IAA (Paper II) ............................................................................................................... 50
Surface plasmon resonance (SPR) (Paper II) ................................................................................................ 51
Competitive epitope-specific RIA for determination of GADA epitopes (Paper III) .................................... 53
A modified RIA for detection of IgG subclasses of GADA (Paper IV) ........................................................... 54
GAD65 enzymatic activity assay (Paper IV) ................................................................................................. 55
Analysis of tetanus toxoid antibodies (Paper IV) ......................................................................................... 56
Analysis of IgE (Paper IV) ............................................................................................................................. 56
C-peptide measurement (Paper IV) ............................................................................................................. 56
STATISTICS ............................................................................................................................. 57
ETHICAL CONSIDERATIONS ..................................................................................................... 58
Results and discussion ..................................................................................................... 61
AUTOANTIBODY POSITIVITY AND RELATION TO HLA GENOTYPES (PAPER I) ............................. 61
GADA and IA-2A in children at three different ages .................................................................................... 62
Permanent and transient autoantibodies ................................................................................................... 63
Children with type 1 diabetes...................................................................................................................... 65
DEVELOPMENT OF AN IMMUNOASSAY BASED ON SPR (PAPER II) ............................................ 66
Attempts to develop an SPR-based immunoassay for GADA ...................................................................... 67
Development of the SPR-based immunoassay for IAA ................................................................................ 68
Efforts to reduce non-specific binding of serum proteins to the sensor surface ................................... 69
Detection of IAA in serum using the 3-day and 5-min methods ............................................................. 70
High-affinity IAA ...................................................................................................................................... 75
The 5-min method compared to RIA ...................................................................................................... 75
Type 1 diabetes-related analytes measured with SPR: Results from other studies ............................... 77
Further and future experiments with the 5-min method ....................................................................... 78
EFFECTS OF THE GAD-ALUM TREATMENT ON THE HUMORAL IMMUNE RESPONSE (PAPER III AND
IV) .......................................................................................................................................... 78
Increased levels of GADA ............................................................................................................................. 78
Temporary increase in binding to one rFab-defined epitope ...................................................................... 79
Changes in IgG subclasses of GADA ............................................................................................................. 84
No changes in SPS-related epitope binding or inhibition of GAD65 enzymatic activity .............................. 88
Antigen-specific effect ................................................................................................................................. 90
The role of GADA in the effect of GAD-alum ............................................................................................... 91
Summary and conclusions ............................................................................................... 93
Acknowledgements ........................................................................................................ 95
References ...................................................................................................................... 97
5
Abbreviations
ABIS all babies in southeast of Sweden
alum aluminum hydroxide
CM carboxymethylated
C-peptide connecting peptide
cpm counts per minute
CTLA-4 cytotoxic T lymphocyte antigen-4
DAISY the diabetes autoimmunity study in the young
DASP diabetes autoantibody standardization program
DIPP the diabetes prediction and prevention study
DPT-1 the diabetes prevention trial
ELISA enzyme-linked immunosorbent assay
ENDIT the European nicotinamide diabetes intervention trial
GABA gamma-aminobutyric acid
GAD glutamic acid decarboxylase
GAD65 65 kDa isoform of GAD
GADA autoantibodies to GAD65
HLA human leukocyte antigen
IA insulin antibodies
IA-2 tyrosine phosphatase like protein islet antigen-2
IA-2A autoantibodies to IA-2
IAA autoantibodies to insulin
ICA islet cell antibodies
IDS immunology of diabetes society
IFN interferon
Ig immunoglobulin
IL interleukin
INS insulin gene
LADA latent autoimmune diabetes in adults
LPS lipopolysacharide
mAb monoclonal antibody
MHC major histocompatibility complex
Abbreviations ’
6
MODY maturity-onset diabetes of the young
mRNA messenger RNA
NOD non-obese diabetic
OGTT oral glucose tolerance test
PCR polymerase chain reaction
PEG poly(ethylene glycol)
PLP pyridoxal 5-phosphate
PPV positive predictive value
PTP protein tyrosine phosphatase
PTPN22 protein tyrosine phosphatase N22
rFab recombinant Fab
RIA radioimmunoassay
RU resonance units
SAM self-assembled monolayer
SD standard deviation
SPR surface plasmon resonance
SPS stiff person syndrome
SPSS statistical package for the social sciences
TCR T cell receptor
TEDDY the environmental determinants in diabetes of the young
Th T-helper
Treg naturally occurring regulatory T cells
TRIGR the trial to reduce type 1 diabetes in the genetically at risk
U/ml units/ml
VNTR variable number of tandem repeats
WHO the world health organization
ZnT8 zink transporter 8
ZnT8A autoantibodies to ZnT8
7
Original publications
This thesis is based on the following four papers, which are referred to in the text by their
roman numerals:
I. Camilla Gullstrand, Jeanette Wahlberg, Jorma Ilonen, Outi Vaarala and Johnny
Ludvigsson
Progression to type 1 diabetes and autoantibody positivity in relation to HLA-risk
genotypes in children participating in the ABIS study.
Pediatric Diabetes 2008; 9(Part I): 182-190
II. Jenny Carlsson, Camilla Gullstrand, Gunilla T. Westermark, Johnny Ludvigsson,
Karin Enander and Bo Liedberg
An indirect competitive immunoassay for insulin autoantibodies based on surface
plasmon resonance.
Biosensors and Bioelectronics 2008; 24: 882-887
III. Camilla Skoglund, Mikael Chéramy, Rosaura Casas, Johnny Ludvigsson and
Christiane S. Hampe
GAD-alum treatment-induced increase in GAD autoantibody (GADA) titers of type 1
diabetes children and adolescents is accompanied by a transient change in GADA
epitope pattern.
Submitted
IV. Mikael Chéramy, Camilla Skoglund, Ingela Johansson, Johnny Ludvigsson,
Christiane S. Hampe and Rosaura Casas
GAD-alum treatment in patients with type 1 diabetes and the subsequent effect on
GADA IgG subclass distribution, GAD65 enzyme activity and humoral response.
Clinical Immunology 2010; 137(1): 31-40
8
9
Review of the literature
INTRODUCTION TO THE IMMUNE SYSTEM
The human body is continuously surrounded by microorganisms, where some of them are
good and others harmful (Janeway et al. 2005; Mölne et al. 2007). The disease-causing
microorganisms have been divided into four categories: viruses, bacteria, pathogenic fungi
and parasites. The body has to defend itself against these pathogens. A well functioning
immune system has been developed, consisting of a wide range of different white blood cells.
These cells perform different tasks in the immune system. Communication between immune
cells occurs via receptors on the surfaces of the cells and via different signaling molecules,
such as cytokines and chemokines.
The immune system consists of three lines of defense (Janeway et al. 2005; Mölne et al.
2007). The first line of defense comprises mechanical and chemical barriers protecting the
skin and mucosa, together with a microbiological barrier of non pathogenic bacteria.
Epithelial cells are joined by tight junctions, mucus is transported from the lungs by cilia,
fatty acids and antibacterial peptides are produced etc. These barriers are very effective and
most of the microorganisms are therefore unable to enter the body, but those who manage to
break through encounter the second line of defense, namely the innate immune system. This
non-specific immune system consists of granulocytes (neutrophils, eosinophils, basophils and
mast cells) and macrophages. Macrophages have a wide variety of cell-surface receptors that
recognize microbial structures on the pathogen, such as lipopolysacharide (LPS), peptide
glucane and virus-RNA. The pathogen is attached to a receptor and then engulfed by
phagocytosis and eliminated by the macrophage. This is followed by activation of the
macrophage with release of cytokines, chemokines and other mediators that initiate
inflammation in the tissue and bring neutrophils and plasma proteins to the site of an
infection, aiming at elimination of the microorganisms.
The third line of defense is the adaptive immune system, which can be activated by almost
any microbial structure (Janeway et al. 2005; Mölne et al. 2007). The adaptive immune
system consists of lymphocytes (T cells and B cells) and provides a specific response. Mature
lymphocytes circulate between blood vessels, lymph vessels and lymph nodes. The antigen is
Review of the literature ’
10
transported to the lymph nodes by antigen presenting cells, primarily macrophages. The
environment of the lymph node is ideal for interaction between antigen and lymphocyte. The
lymphocytes interact with antigen presenting cells through their antigen-specific receptors.
Upon contact with the specific antigen, the lymphocytes are activated, mature and divide
creating a clone of active cells, which leave the lymph node and migrate to places in the body
where they can have their effect (MacLennan et al. 1997). The T cells mature into antigen-
specific effector cells, often producing cytokines, and the B cells into antibody-secreting
plasma cells.
T cells
Precursors of the T cells are developed in the bone marrow (Janeway et al. 2005; Mölne et al.
2007). They migrate to the thymus, where they mature into T cells and get their unique
specificity. The T cells are selected based on their binding affinity to major histocompatibility
complex (MHC)-peptide complexes. If T cell receptors (TCRs) on a T cell recognize and bind
antigen presented by MHC molecules the cell will survive positive selection, while the other
T cells will die. However, if the T cell reacts strongly with self antigens, it will instead die by
negative selection, thereby maintaining tolerance to self antigens.
T cells can be divided into CD4-positive T cells, which react with the antigen bound to MHC-
II molecules, and CD8-positive T cells, which recognize the antigen on MHC-I molecules
(Janeway et al. 2005; Mölne et al. 2007). MHC-I molecules are present on all kinds of cells in
the body, except red blood cells, while MHC-II are mainly expressed on antigen presenting
cells. Human MHC molecules are referred to as human leukocyte antigen (HLA). When CD4-
positive T cells are activated they become either helper T cells or regulatory T cells (Janeway
et al. 2005; Mölne et al. 2007). Helper T cells can be divided into T-helper 1 (Th1), T-helper
2 (Th2) and T-helper 17 (Th17) cells, depending on their function in the immune system
(Mosmann et al. 1996; Rautajoki et al. 2008; Korn et al. 2009).
Activation of helper T cells is central for all immune reactions, by producing cytokines and by
cell-to-cell interactions. Th1 cells are important in cell-mediated immunity against
intracellular pathogens, by activating and attracting macrophages and cytotoxic cells to the
site of infection (Szabo et al. 2003; Rautajoki et al. 2008). In addition, Th1 cells stimulate the
production of immunoglobulin (Ig)-G antibodies that are involved in opsonization and
Review of the literature
11
phagocytosis. Further, cytokines produced by Th1 cells, such as interferon-gamma (IFN-γ),
activate macrophages to increase their production of bactericidal substances, such as enzymes
and free radicals, to eliminate those microorganisms that survived inside the macrophage
(Mölne et al. 2007). Th2 cells are instead important in the humoral immune response (Mowen
et al. 2004). Cytokines produced by Th2 cells, such as interleukin-4 (IL-4), IL-5, IL-9 and IL-
13, assist B cells in their maturation and antibody production for elimination of extracellular
pathogens. Further, a predominant Th2 response is associated with atopic diseases and
allergies. The recently described Th17 cells produce cytokines including IL-17, IL-21 and IL-
22, and seem to have a proinflammatory role (Korn et al. 2009). In addition, IL-17 has been
shown to be important for host defense against some pathogens. Further, both Th17 and Th1
cells are associated with autoimmune diseases, including type 1 diabetes (Szabo et al. 2003).
Regulatory T cells control and down regulate various immune responses. Naturally occurring
regulatory T cells (Treg) constitutely express CD25 (a subunit of the IL-2 receptor) on their
surface and transcribe the FOXP3 gene (Sakaguchi 2004). Expression of the transcription
factor FOXP3 is vital to the development and function of Treg (Fontenot et al. 2003; Hori et
al. 2003). Defects in the function of Tregs have been hypothesized to be involved in the
pathogenesis of numerous autoimmune diseases, including type 1 diabetes (Sakaguchi 2004).
Activated CD8-positive T cells mature into cytotoxic T cells, which can eliminate infected
cells (Janeway et al. 2005; Mölne et al. 2007). Maturation of cytotoxic T cells is both
dependent on cytokines, such as IL-2 and IFN-γ, and on cell-to-cell contact with the helper T
cell.
B cells
B cells are developed in the bone marrow, where they also get their unique specificity
(Janeway et al. 2005; Mölne et al. 2007). The B cell receptor for recognition of an antigen
consists of a membrane bound antibody. When an antigen has bound to the B cell receptor,
the antigen is internalized and processed into peptides (Figure 1). The B cell presents the
peptides on MHC-II molecules and expresses cytokine receptors and the co-stimulatory
molecule CD40 on its surface. This enables a helper T cell that recognizes the antigen, to
attach to the B cell and then produce cytokines. The cell-to-cell contact between the B and the
T cell, together with cytokines binding to the receptors, activates the B cell to mature into a
Review of the literature ’
12
plasma cell that produces antibodies. These antibodies have the same specificity as the
membrane bound antibody used as a receptor.
Figure 1. Schematic illustration of the process for the maturation of a B cell into an antibody
producing plasma cell. The antigen is bound to the B cell receptor followed by internalization and
processing into peptides, which are then presented on MHC-II molecules on the surface of the B cell.
The interaction between an antigen-specific T cell and the B cell, together with cytokines produced by
the T cell, leads to activation of the B cell and maturation into a plasma cell that produces antibodies.
TCR = T cell receptor, CD40L = CD40 ligand, MHC-II = major histocompatibility complex-II.
Antibodies
Antibodies are gamma globulin proteins that are found in blood or other body fluids, and are
used by the immune system to identify and neutralize foreign objects, such as bacteria and
viruses (Cohen 1963; Janeway et al. 2005). The immune system creates billions of different
antibodies with a limited number of genes by rearranging DNA segments during B cell
development, prior to antigen exposure (Janeway et al. 2005). Mutation can also increase
genetic variation in antibodies. Antibodies can react with almost any chemical structure in
nature, including our own proteins. Antibodies that recognize self antigens are called
autoantibodies (Milgrom et al. 1963).
Antibodies consist of two heavy chains and two light chains of amino acids, in a Y shaped
form, with a molecular mass of approximately 150 kDa (Figure 2) (Janeway et al. 2005;
Schroeder et al. 2010). The two ends of the antibody, linked via flexible hinge regions, have
different functions. The Fc region determines the mechanisms used to destroy antigen, such as
activation of complement and binding to Fc receptors, and the two Fab regions bind to a
B cellB cell B cellB cell B cellT cell
CD4
MHC-II
peptideTCR
CD40 CD40Lcytokinereceptors
cytokines
B cellPlasma cell
B cell receptor
antigen
antibodies
Review of the literature
13
specific epitope on the antigen. Differences in antigenic and structural properties of the heavy
chains determine the class and subclass of the molecules. There are five antibody isotypes
known as immunoglobulin A (IgA), IgD, IgE, IgG and IgM, which have different roles in the
immune system.
Figure 2. A schematic presentation of an antibody. It is composed of two heavy chains and two light
chains. The Fab regions, containing the antigen-binding sites, are linked by hinge regions to the Fc
part of the antibody. Adapted from (Alberts et al. 2002).
IgM are relatively low-affinity antibodies that are associated with a primary immune response
and are found in blood and sometimes in extracellular fluids (Schroeder et al. 2010). IgM
consists of five antibody units in a pentamer structure, which makes it very efficient in
opsonizing antigens for destruction and fixing complement.
IgD antibodies are found at very low levels in serum and the function of IgD is unclear
(Schroeder et al. 2010).
IgG antibodies, which usually are of higher affinity, are produced later in the immune
response and this isotype is predominant in blood and extracellular fluids (Schroeder et al.
2010). IgG can be divided into four subclasses; IgG1, IgG2, IgG3 and IgG4, which are
numbered according to the rank order (IgG1>IgG2>IgG3>IgG4) of the serum levels of these
antibodies in the blood of healthy individuals (Figure 3). The major structural differences
between the IgG subclasses occur in the hinge region. IgG1 has a freely flexible hinge region
consisting of 15 amino acids (Brekke et al. 1995). IgG3 has an elongated hinge region of 62
amino acids, giving this subclass the greatest flexibility. The hinge region of IgG4 is shorter
FabFab
Fc
antigen-binding site
antigen-binding site
hingeregions
light chain
heavy chain heavy chain
light chain
HOOC COOH
HOOC COOH
H2N
H2N NH2
NH2
Review of the literature ’
14
than IgG1, and IgG2 has the shortest, making this molecule rigid (Roux et al. 1997). The
flexibility of the molecule may be important for the function of the antibody (Brekke et al.
1995). IgG can activate the complement system, neutralize toxins, viruses and bacteria, and
opsonize them for phagocytosis (Schroeder et al. 2010). They bind Fc-receptors on
neutrophils, monocytes and macrophages, which facilitates opsonization of particles that are
covered with IgG antibodies. The Fc regions of IgG, as well as IgM, can bind complement
factor C1 and thereby activate complement via the classical way, leading to that also
encapsuled bacteria can be opsonized by the complement system. Not all IgG subclasses can
bind complement; IgG1 and IgG3 are effective complement activators, IgG2 is a weak
complement activator and IgG4 is unable to activate complement.
IgG1
IgG2
IgG3
IgG4
Property
Proportion of total 67
22
7
4
IgG in serum (%)
Serum half life (days) 21
21
7
21
Complement activation +++
+
+++
-
Figure 3. Schematic structure and properties of IgG subclasses. IgG1 is predominant in serum, IgG3 is
the subclass with the longest hinge region and the shortest half life and IgG4 is unable to fix
complement. Adapted from (Hamilton 1987; Lin et al. 2010; Schroeder et al. 2010).
IgA antibodies are found as monomers in blood and extracellular fluids and as dimeric
molecules at mucosal surfaces and in secretions, such as saliva and breast milk (Schroeder et
al. 2010). IgA has two subclasses, which differ mainly in their hinge regions; IgA1 and IgA2,
where IgA2 is more resistant to proteolysis. IgA provides a first line of defense against a wide
variety of pathogens by protecting mucosal surfaces from toxins, viruses and bacteria, via
direct neutralization or prevention of binding to the mucosal surface.
Review of the literature
15
IgE is a very potent immunoglobulin, although it is present at the lowest serum concentration
of all isotypes (Schroeder et al. 2010). It binds with high affinity to FcεRI receptors on mast
cells, basophils, Langerhans cells and eosinophils. IgE antibodies are associated with
hypersensitivity and allergic reactions and with responses to parasitic worm infections.
During an antibody response the isotype and subclass of antibodies can be shifted without
changing the specificity (Janeway et al. 2005). The dominance of IgM in the beginning of an
antibody response is after re-exposure of the antigen switched into other classes of antibodies,
for example IgG in serum. Cytokines regulate the generation of different Ig isotypes and IgG
subclasses (Purkerson et al. 1992; Snapper et al. 1993). The distribution of various isotype-
specific antibodies may therefore reflect whether the immune response is Th1- or Th2-biased.
The Th2 cytokine IL-4 induces the synthesis of IgG4 and IgE (Lundgren et al. 1989; Gascan
et al. 1991), and the Th1 cytokine IFN-γ stimulates IgG1 and seems to stimulate IgG3
production (Widhe et al. 1998), but further studies are needed to clearly define the antibody
subclass association with Th1 or Th2 response in humans.
Antibodies can be monoclonal or polyclonal (Janeway et al. 2005). Polyclonal antibodies are
produced by different cells and are therefore immunochemically different from each other,
while monoclonal antibodies are the product of an individual clone of plasma cells and thus
immunochemically identical. Antibodies that bind to the same antigen but to different
epitopes, or that bind to similar antigens, have different binding capacity, so called specificity.
Affinity defines the strength of binding of the antibody to its antigen in terms of a single
antigen-binding site binding to a monovalent antigen. The total binding strength of a molecule
with more than one binding site is called its avidity. Anti-idiotypic antibodies recognise the
antigen-binding site of a specific antibody and can thereby interfere with the binding to the
corresponding antigen (Geha 1985). It has been suggested that there is a lack of anti-idiotypic
antibodies in type 1 diabetes (Oak et al. 2008).
In conclusion, T cells and B cells are important in the adaptive immune system. Activated T
cells become helper T cells or regulatory T cells, while activated B cells mature into plasma
cells that produce antibodies. The five antibody isotypes IgM, IgD, IgG, IgA and IgE have
different roles in the immune system.
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INTRODUCTION TO DIABETES MELLITUS
Definition and diagnosis
Diabetes mellitus is a group of metabolic disorders characterized by chronic hyperglycemia
with disturbances of carbohydrate, fat and protein metabolism resulting from defects in
insulin secretion, insulin action, or both (WHO 1999; American Diabetes Association 2008).
Several pathogenic processes are involved in the development of diabetes. These include
destruction of the beta cells of the pancreas with consequent insulin deficiency and
abnormalities that result in resistance to insulin action.
Symptoms indicating diabetes mellitus are signs of hyperglycemia, such as thirst, polyuria,
glucosuria, weight loss and others such as fatigue, blurred vision and recurrent infections
(WHO 1999). In severe forms, ketoacidosis or a non-ketotic hyperosmolar state may develop
and lead to drowsiness and coma, and in absence of effective treatment to death. Effects of
diabetes mellitus are long-term damage, dysfunction and failure of various organs, especially
the eyes, nerves, kidney, heart and blood vessels (Glastras et al. 2005; Melendez-Ramirez et
al. 2010; Skyler 2010).
The criteria for the diagnosis of diabetes are symptoms of hyperglycemia and a plasma
glucose value of ≥11.1 mmol/l or fasting plasma glucose of ≥7.0 mmol/l (in whole blood 6.1
mmol/l) or 2-h plasma glucose ≥11.1 mmol/l during an oral glucose tolerance test (OGTT)
(American Diabetes Association 2008). In the absence of symptoms, the diagnosis must be
confirmed another day by one of the three methods above. Diabetes may present differently in
different individuals, ranging from severe symptoms and gross hyperglycemia to a lack of
symptoms and with blood glucose values just above the diagnostic cut-off value. In children,
diabetes often presents with severe symptoms, very high blood glucose levels, marked
glycosuria and ketonuria (WHO 1999). Diagnosis is usually confirmed with blood glucose
measurements and treatment is initiated immediately. Rarely, children and adolescents lack
symptoms and are then diagnosed with a fasting blood glucose measurement and/or an
OGTT. This happens, for example, when patients are found by screening of autoantibodies to
identify individuals with increased risk of developing type 1 diabetes.
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Classification
Diabetes mellitus can be classified into four types based on etiology; type 1 diabetes, type 2
diabetes, gestational diabetes and other types of diabetes. The majority of cases of diabetes
fall into type 1 diabetes or type 2 diabetes (American Diabetes Association 2008).
Type 1 diabetes
Type 1 diabetes is characterized by an absolute deficiency of insulin secretion (Notkins et al.
2001; American Diabetes Association 2008). The most common form of type 1 diabetes is
immune-mediated diabetes and accounts for 5-10% of all individuals with diabetes. This
disease is considered autoimmune (Rose et al. 1993; Bach 1994) and results from a cellular-
mediated autoimmune destruction of the beta cells of the pancreas (Knip 1997; Knip et al.
2008). Autoimmune destruction of beta cells has multiple genetic predispositions, mainly
strong HLA associations, and is influenced by environmental factors (Knip 1997; Akerblom
et al. 2002; Ilonen et al. 2002; Achenbach et al. 2005). Autoantibodies against beta-cell
proteins, such as glutamic acid decarboxylase (GAD) (Baekkeskov et al. 1990), the tyrosine
phosphatase like protein islet antigen-2 (IA-2) (Lan et al. 1996; Notkins et al. 1996), insulin
(Palmer et al. 1983) and zink transporter 8 (ZnT8) (Wenzlau et al. 2007), are produced during
the autoimmune destruction of the beta cells. One or more of these autoantibodies are present
in 90-95% of individuals when hyperglycemia is initially detected (Notkins et al. 2001). Type
1 diabetes occurs predominantly in children and adolescents, usually as a rapidly progressive
form, but may also occur in adults, often as a slowly progressive form, referred to as latent
autoimmune diabetes in adults (LADA). A minority of patients with type 1 diabetes fall into
the category named idiopathic diabetes (American Diabetes Association 2008). This form of
diabetes is most common in non-Caucasians and is strongly inherited, lacks immunological
evidence for beta-cell autoimmunity, and is not HLA associated.
Type 2 diabetes
In type 2 diabetes, the cause is a combination of resistance to insulin action and defects in
insulin secretion (American Diabetes Association 2008). In adults, type 2 diabetes is much
more prevalent than type 1 diabetes, but it is rare in children and adolescents in Sweden.
Individuals with type 2 diabetes do initially not need insulin treatment to survive, and often
not later in life either. Most patients are obese, which causes some degree of insulin
resistance. Type 2 diabetes is often associated with a strong genetic predisposition and the risk
Review of the literature ’
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of developing this disease increases with age, obesity and lack of physical activity.
Individuals with this form of diabetes are often undiagnosed for many years because their
hyperglycemia is not severe enough to present symptoms, but they have increased risk to
develop macrovascular and microvascular complications. Insulin sensitivity may be increased,
but not restored to normal, by weight reduction, increased physical activity and/or
pharmacological treatment of hyperglycemia.
Gestational diabetes
Any degree of glucose intolerance with onset or first recognition during pregnancy is referred
to as gestational diabetes (American Diabetes Association 2008).
Other specific types of diabetes
Some forms of diabetes may be associated with genetic defects of the beta cell, which are
often referred to as maturity-onset diabetes of the young (MODY) (Stride et al. 2002). These
individuals have impaired insulin secretion with minimal or no defects in insulin action. Other
types of diabetes are associated with genetic defects in insulin action or diseases of the
exocrine pancreas, endocrinopathies, drug- or chemical-induced diabetes, infections, other
genetic syndromes sometimes associated with diabetes, and uncommon forms of immune-
mediated diabetes (American Diabetes Association 2008). The last category includes patients
with stiff person syndrome (SPS) who also have developed diabetes. SPS is a rare neurologic
disorder characterised by muscle rigidity and episodic spasms involving axial and limb
musculature, and the patient has usually very high levels of GADA (Levy et al. 1999). About
one third of patients with SPS will develop diabetes.
Epidemiology of type 1 diabetes
The International Diabetes Federation has estimated the number of children globally aged 0-
14 years with type 1 diabetes to be 480 000 in 2010, with 76 000 newly diagnosed cases a
year (IDF 2009). One quarter of the cases come from South East Asia and more than one fifth
from Europe. The incidence of childhood onset type 1 diabetes is increasing worldwide, with
an overall annual increase of about 3% (Onkamo et al. 1999; The DIAMOND Project Group
2006; IDF 2009; Patterson et al. 2009). The DIAMOND project group has examined
incidence and trends of type 1 diabetes worldwide for the period of 1990-1999, giving an
annual increase in incidence of 2.8% (The DIAMOND Project Group 2006), and the
Review of the literature
19
EURODIAB study group has done the same for Europe during 1989-2003, resulting in an
annual increase of 3.9% (Patterson et al. 2009).
The increase in incidence has been observed in countries with both high and low prevalence,
with a more steeply increase in some of the low prevalence countries, such as those in central
and eastern Europe (IDF 2009). The highest increase was found in children younger than 5
years of age, particularly in European populations (Karvonen et al. 1999; Green et al. 2001;
2006; Patterson et al. 2009). Finland has the highest incidence in the world; in 2005 the
annual incidence was 64.2/100 000 per year in children before 15 years of age (Harjutsalo et
al. 2008). Sweden has the second highest incidence, with an incidence in 2009 of 44.0/100
000 per year in children 0-14.9 years of age (SWEDIABKIDS 2009).
In general, the incidence of type 1 diabetes increases with age, peaking at puberty (Soltesz et
al. 2007). The overall sex ratio for type 1 diabetes is roughly equal in children, with a minor
male excess in incidence in Europe. A seasonality of onset has been reported, with a peak
occurring in winter, and it is more pronounced in countries with marked differences between
summer and winter temperatures (Dahlquist et al. 1994; Soltesz et al. 2007).
In conclusion, type 1 diabetes is a serious disease resulting from destruction of the beta cells
in the pancreas. The incidence of type 1 diabetes is increasing worldwide and Sweden has the
second highest incidence in the world.
PATHOGENESIS OF TYPE 1 DIABETES
The etiology of type 1 diabetes is largely unknown, but a combination of genetic
predisposition, environmental factors and a dysregulated immune system is believed to play
an important role for development of the autoimmune process leading to the disease (Figure
4). The genetic susceptibility of type 1 diabetes is mainly dependent of HLA class II genes,
but other genes are also involved (Rich et al. 2009). Environmental factors that are suggested
to influence the development of type 1 diabetes include viral infections, early infant diet,
toxins and psychological stress (Peng et al. 2006). The autoimmune process, causing an
inflammation in the beta cells (insulitis), is characterized by infiltration of CD8-positive
cytotoxic T cells, CD4-positive T cells, B cells and macrophages (Imagawa et al. 1999;
Moriwaki et al. 1999). This process, which may be initiated several years before clinical onset
Review of the literature ’
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of type 1 diabetes, leads to decreased beta-cell mass, reduced insulin production and finally to
clinical type 1 diabetes. During the pre-clinical period, autoantibodies against beta-cell
antigens often circulate in the peripheral blood, and measurement of these autoantibodies can
be used for identification of individuals at risk for the disease.
Figure 4. Schematic illustration of the development of type 1 diabetes. Interaction between genetic
predisposition and environmental triggers, together with a dysregulated immune system, may induce
an autoimmune response with autoantibody production, leading to loss of beta cells and progression to
type 1 diabetes. Modified from (Atkinson et al. 2001).
Genetic risk
Most of the children who develop type 1 diabetes have genetic predisposition for the disease.
The HLA gene complex is responsible for about 50% of the genetic risk for type 1 diabetes
and the remaining genetic susceptibility is conferred by a large number of loci, where most of
them have minor effects (Ilonen et al. 2002; Rich et al. 2009). Other genes known to have
effect on the risk of type 1 diabetes include insulin (INS), cytotoxic T lymphocyte antigen-4
(CTLA-4) and protein tyrosine phosphatase N22 (PTPN22) (Redondo et al. 2002; Onengut-
Gumuscu et al. 2004).
Pre-diabetic phase Overt diabetes
Time
Bet
a-c
ellm
ass
Geneticpredisposition
Environmental triggers
Immune dysregulation
Insulitis
Beta-cell autoimmunity
Glucose intolerance
Loss of C-peptide
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HLA
The HLA gene complex is located on the short arm of chromosome 6 (6p21) and contains
genes encoding HLA class I (HLA-A, -B and -C) and class II (DR, DQ and DP) molecules,
which are peptide presenting molecules for T cells, consisting of an α- and a β-chain (She
1996; Undlien et al. 2001). These genes are the most polymorphic genes known in humans
and there is a strong linkage disequilibrum between the genes, which means that the alleles at
one HLA locus are non-randomly associated with alleles at other HLA loci. Polymorphisms
in the HLA genes affect the conformation of the molecule and especially the peptide binding
groove, and define which peptide that will be bound and presented to T cells, thus shaping the
T cell repertoire (Ilonen et al. 2002).
Type 1 diabetes has strong HLA associations, with linkage to the DQA and DQB genes, and
is influenced by the DRB genes (She 1996; Ilonen et al. 2002). The strongest determinant of
genetic risk is the presence of risk associated HLA class II haplotypes DR3-DQ2
(DRB1*0301-DQA1*0501-DQB1*0201) and DR4-DQ8 (DRB1*04-DQA1*0301-
DQB1*0302) or especially the combination of them both (Table 1) (Sanjeevi et al. 1995; She
1996; Ilonen et al. 2002; Redondo et al. 2002). About 90% of individuals with type 1 diabetes
have at least one of these two high-risk haplotypes compared to approximately 20% of the
general population (Redondo et al. 2002). The DRB1 allele modifies the risk conferred by the
DQ8 molecule; the DRB1*0401, *0402 and *0405 are associated with high susceptibility, the
DRB1*0404 with moderate susceptibility and DRB1*0403 with protection against type 1
diabetes.
Other class II haplotypes are protective, such as DR2-DQ6 (DRB1*15-DQA1*0102-
DQB1*0602), which is the most significant in the Northern European population (Table 1)
(Baisch et al. 1990; Redondo et al. 2002). This haplotype seems to confer dominant
protection, since it is protective also in the presence of a high-risk haplotype in the same
individual (Baisch et al. 1990; Pugliese et al. 1995; Redondo et al. 2002). About 20% of
Europeans have DR2-DQ6 while less than 1% of children with type 1 diabetes have this allele
(Redondo et al. 2002). Besides the susceptible and protective haplotypes, there are also
neutral HLA class II haplotypes, which do not affect the risk of type 1 diabetes (Table 1).
Review of the literature ’
22
Table 1. Susceptibility, protective and neutral associated haplotypes (according to Jorma Ilonen,
personal communication 2005).
Susceptibility DRB1*0401/2/5-DQB1*0302 (DR4-DQ8) DRB1*0404-DQB1*0302 (DR4-DQ8) DRB1*0301-DQA1*0501-DQB1*0201 (DR3-DQ2)
Protective DRB1*15-DQA1*0102-DQB1*0602 (DR2-DQ6) (DR5)-DQA1*05-DQB1*0301 (DR7)-DQA1*0201-DQB1*0303 (DR14)-DQA1*0101-DQB1*0503 DRB1*0403-DQB1*0302 (DR1301)-DQB1*0603
Neutral (DR4)-DQA1*0301-DQB1*0301 (DR4-DQ7) (DR1)-DQB1*0501 (DR7)-DQA1*0201-DQB1*02 (DR1302)-DQB1*0604 (DR9)-DQA1*03-DQB1*0303 (DR8)-DQB1*04 (DR7)-DQA1*02-DQB1*02 (DR4)-DQA1*03-DQB1*0301 (DR2)-DQB1*0601 (DR16)-DQB1*0502 DQB1*0609
Other genetic factors
INS: The insulin gene (INS) is located on chromosome 11p15 and includes a non-coding
region with a variable number of tandem repeats (VNTR) (Bennett et al. 1995; Redondo et al.
2002; Ziegler et al. 2010). Polymorphisms in this region are associated with risk of diabetes
and influence thymic insulin messenger RNA (mRNA). There are three main types of the
insulin VNTR defined by the number of repeats, class I, class II and class III, where class III
has the highest number. The class I VNTRs are most common in Caucasians and the class II
alleles are rare (Stead et al. 2002). Homozygosity for class I alleles is associated with high
risk for diabetes, while class III alleles confer dominant protection (Redondo et al. 2002).
Class III alleles are associated with higher expression of insulin mRNA within the thymus and
high concentration of thymic insulin might lead to negative selection of high-avidity
autoreactive T cells and thus to the development of tolerance.
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23
CTLA-4: The cytotoxic T lymphocyte antigen 4 (CTLA-4) gene is located on chromosome
2q33 and encodes a receptor expressed by activated T cells (Redondo et al. 2002). This
receptor can limit the proliferative response of activated T cells, some of which could be
autoreactive, upon binding to B7 molecules, and can also mediate T cell apoptosis.
Polymorphisms or mutations that alter the activity of CTLA-4 are believed to play a role in
the risk for developing autoimmunity. A polymorphism in the first exon of CTLA-4 (49 A/G)
results in an amino acid change (threonine/alanine) in the leader peptide of the expressed
protein. The presence of an alanine at codon 17 of CTLA-4 has been associated with genetic
susceptibility to type 1 diabetes (Nistico et al. 1996; Donner et al. 1997).
PTPN22: The protein tyrosine phosphatase N22 (PTPN22) gene, located on chromosome
1p13, encodes a lymphoid-specific phosphatase known as Lyp (Onengut-Gumuscu et al.
2004; Ladner et al. 2005). This protein is a negative regulator of T cell activation by
dephosphorylating T cell receptor activation-dependent kinases. The single nucleotide
polymorphism C1858T of the PTPN22 gene have been associated with type 1 diabetes.
Individuals lacking the C allele of PTPN22 may have reduced capacity to downregulate T cell
responses and may therefore be more susceptible to autoimmunity.
Environmental factors
The rapid increase in incidence of type 1 diabetes cannot be explained by changes in genetic
predisposition, but rather by environmental factors. In addition, the relatively low
concordance (with both twins affected) in monozygotic twins, 21-70%, and 6% of siblings to
type 1 diabetic patients that develops the disease (Redondo et al. 1999; Redondo et al. 2002;
Aly et al. 2006), further emphasize a role of environmental factors in the etiology of type 1
diabetes. Several environmental factors, including viral infections, cow´s milk, gluten and
psychological stress, have been suggested to trigger the autoimmune response and the
development of type 1 diabetes, as reviewed in (Akerblom et al. 2002; Peng et al. 2006;
Soltesz et al. 2007). A number of studies are ongoing aiming to further investigate the role of
different environmental factors on the development of type 1 diabetes, see the section
“Prediction and prevention studies”.
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24
Viral infections
Viral infections have been suggested to trigger autoimmunity and type 1 diabetes (van der
Werf et al. 2007). Enterovirus infections, including Coxsackie B4, are the most studied, and
have been associated with seroconversion to islet autoantibody positivity and with diabetes
onset (Hyoty et al. 1995; Salminen et al. 2003; Moya-Suri et al. 2005). Congenital rubella
infection has been found to associate with a high rate of subsequent type 1 diabetes, but
effective immunization programs have eliminated this virus in most Western countries
(Menser et al. 1978). Enteroviral infections during pregnancy have been associated with
increased risk for type 1 diabetes in the offspring (Dahlquist et al. 1995; Hyoty et al. 1995).
Interestingly, it has been observed that in populations with high incidence of type 1 diabetes,
the frequency of enterovirus infections has tended to decrease over the last decades and is
lower than in populations with low incidence of type 1 diabetes (Viskari et al. 2000; Viskari
et al. 2004). This is in line with the polio hypothesis, which suggests that the complications of
enterovirus infections become more common in an environment with a decreased rate of
infections leading to a lack of immunity in the population.
The hygiene hypothesis suggests that the increased hygiene, leading to a lack of normal
background infections, predisposes the immune system to autoimmunity, including type 1
diabetes (Ludvigsson 2006). Probably, changes in the gut bacterial flora influence the
maturation of the immune system, leading to imbalance and thereby autoimmune reactions in
genetically predisposed individuals (Vaarala 1999b). Further, the increased hygiene might
lead to a lower immunity against certain viruses (Viskari et al. 2005).
Dietary factors
A number of dietary factors have been suggested to influence the development of beta-cell
autoimmunity and type 1 diabetes, as reviewed in (Virtanen et al. 2003). Factors that seem to
decrease development of autoimmunity include greater intake of breast milk, nicotinamide,
zinc, and vitamins C, D and E. On the other hand, increased islet autoimmunity seems to be
influenced by factors as early introduction of cow´s milk, gluten, nitrate, nitrite, and increased
calories causing increased linear growth and weight. Nitrate and nitrite are mainly found in
food, but may also originate from cigarettes, car interiors and cosmetics.
Breast-feeding has been suggested to have a protective effect; high frequency of breast-
feeding has been associated with low incidence of type 1 diabetes and short duration of
Review of the literature
25
breast-feeding has been reported to increase the risk of type 1 diabetes (Borch-Johnsen et al.
1984; Virtanen et al. 1993; Virtanen et al. 2003). In a Finnish study, the effects of duration of
breast-feeding and the age at introduction of supplementary milk were studied (Virtanen et al.
1993). The results indicated that early introduction of cow´s milk increased the risk for type 1
diabetes, and that this factor may overcome the protective effects of breast-feeding. Further,
Wahlberg et al found that early introduction of cow´s milk based formula and high
consumption of cow´s milk were associated with higher levels of beta-cell autoantibodies
(Wahlberg et al. 2006). It has been proposed that an early exposure to cow´s milk formula
may result in an immune response to bovine insulin, and that this could trigger an immune
response to human insulin, which has a very similar amino acid sequence as bovine insulin
(Vaarala et al. 1999a). In addition, feeding with cow´s milk formula during infancy has been
associated with increased weight gain (Johansson et al. 1994), which might induce beta-cell
stress, see section below. Further, introduction of gluten too early (before 3 months) (Ziegler
et al. 2003), or too late (after 6 months) (Wahlberg et al. 2006), seems to be a risk factor for
induction of autoantibodies.
Beta-cell stress
Beta-cell stress has been suggested as a risk factor for the development of type 1 diabetes
(Ludvigsson 2006). During psychological stress and periods of rapid growth, such as infancy
and puberty, the beta cells have to work hard to produce insulin. This increased insulin
production may result in beta-cell stress and stimulation of the autoimmune process, leading
to overt diabetes. This beta-cell stress hypothesis is an extension of the accelerator hypothesis
(Wilkin 2001), which states that increased insulin resistance, associated with childhood
overweight and obesity, creates greater insulin secretory demand on the islets, leading to
acceleration of beta-cell destruction and type 1 diabetes. The accelerator hypothesis suggests
that the increased incidence of type 1 diabetes may be caused by an accelerated progression
rather than by an increase in the absolute lifetime risk.
Psychological stress may, in certain individuals, cause insulin resistance and thereby beta-cell
stress and diabetes-associated autoimmunity. Psychological factors that have been found to
influence the development of autoantibodies, in one or two and a half year old children of the
general population, include high parental stress and serious life events (Sepa et al. 2005; Sepa
et al. 2005).
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In conclusion, genetic risk factors, mainly HLA, together with environmental factors, such as
viral infections and dietary factors, seem to play an important role for the development of
type 1 diabetes.
Beta-cell autoantigens and autoantibodies
Pancreatic beta-cell autoantigens are the targets of immune-mediated destruction of beta cells
(Yoon et al. 2005). One of the most common immunologic markers of individuals with
autoimmune diabetes is the presence of autoantibodies against beta-cell autoantigens. These
autoantibodies can also be used for prediction of type 1 diabetes, both in high-risk individuals
and in the general population, where positivity to multiple autoantibodies confer the highest
risk (Bingley et al. 1994; Bingley et al. 1997; Verge et al. 1998; Kulmala et al. 2001; LaGasse
et al. 2002). The autoantibodies most commonly studied are directed against glutamic acid
decarboxylase (GADA) (Baekkeskov et al. 1990), the tyrosine phosphatase like protein islet
antigen-2 (IA-2A) (Lan et al. 1996; Notkins et al. 1996) and insulin (IAA) (Palmer et al.
1983). Islet cell antibodies (ICA) (Bottazzo et al. 1974) were previously widely used to study
the clinical course and pathogenesis of type 1 diabetes, but were to a large extent replaced by
GADA and IA-2A, when methods for detection of these autoantibodies were developed.
Recently, autoantibodies against ZnT8 (ZnT8A) were discovered as an additional marker for
type 1 diabetes (Wenzlau et al. 2007).
Islet cell antibodies (ICA)
Islet cell antibodies (ICA), recognizing islet cytoplasmic antigens, were detected many years
ago in newly-diagnosed type 1 diabetic patients (Bottazzo et al. 1974) and comprise
autoantibodies to a number of antigens, with a predominance of GADA and IA-2A (Notkins
et al. 1996; Notkins et al. 2001). The presence of organ-specific pancreatic antibodies
provides evidence for type 1 diabetes as an autoimmune disease (Bottazzo et al. 1974;
MacCuish et al. 1974). ICA is measured using immunofluorescence, by incubation of sera
from type 1 diabetic patients with frozen tissue sections of normal blood group 0 pancreas,
which leads to staining of the pancreatic islets (Notkins et al. 2001). IAA is not recognized in
the ICA test, because insulin and c-peptide leach out from the unfixed frozen tissue sections
during sample preparation.
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27
Glutamic acid decarboxylase (GAD)
Glutamic acid decarboxylase (GAD) is a biosynthesizing enzyme of the inhibitory
neurotransmitter gamma-aminobutyric acid (GABA) and has been the most extensively
studied beta-cell autoantigen (Baekkeskov et al. 1990; Yoon et al. 2005). This antigen was
first discovered in serum from children with type 1 diabetes in Linköping, and was described
as an antigen with the weight of 64kDa (Baekkeskov et al. 1982). Later, Baekkeskov et al
discovered that 64 kDa actually was GAD (Baekkeskov et al. 1990). This enzyme is
expressed at high levels by pancreatic beta cells and a subpopulation of central nervous
system neurons. There are two isoforms of GAD, GAD65 and GAD67, with molecular
masses of 65kDa and 67kDa (Bu et al. 1992). In human pancreatic islet cells only the GAD65
isoform is expressed (Hagopian et al. 1993). GAD65 is an intracellular membrane anchored
protein consisting of 585 amino acids (Figure 5) and the GAD65 gene is located on
chromosome 10p11 (Bu et al. 1992; Christgau et al. 1992). The protein is synthesized within
the cytoplasm as a soluble hydrophilic molecule, but becomes membrane anchored after a
two-step modification at the NH2-terminal domain. GAD65 is located to the membrane of
small synaptic-like microvesicles in the islet beta cells.
Autoantibodies in type 1 diabetes primarily recognize the GAD65 isoform (Hagopian et al.
1993). In addition to autoantibodies, GAD-specific CD4-positive T cells and HLA-A*0201-
restricted CD8-positive cytotoxic T cells reactive against GAD have been observed in
recently diagnosed type 1 diabetic patients and in high-risk individuals (Yoon et al. 2005).
These results indicate that GAD may be an important target antigen and GAD-reactive T cells
may play a pathogenic role in the destruction of pancreatic beta cells. Further, there is
molecular mimicry between GAD (amino acids 247-279) and Coxsackie B4 virus (amino
acids 32-47 of the P2-C protein of Coxsackie B virus), which might be a link between
enterovirus infections and development of type 1 diabetes (Atkinson et al. 1994).
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Figure 5. Schematic representation of the GAD65 protein. The regions of membrane anchoring,
maximium divergence with GAD67, Coxsackie virus similarity and PLP binding are shown in the
figure, as well as the cysteine rich regions (CYS) and the major conformational epitope regions
recognized by GADA. Adapted from (Leslie et al. 1999).
GAD autoantibodies (GADA)
GADA has been found in approximately 50-80% of newly diagnosed type 1 diabetic patients
(Bonifacio et al. 1995; Sabbah et al. 1999; Strebelow et al. 1999; Winter et al. 2002;
Holmberg et al. 2006), and often persists in sera for many years after the diagnosis (Savola et
al. 1998). Besides the presence of GADA in type 1 diabetes, this autoantibody has also been
found in other autoimmune diseases, such as SPS (Levy et al. 1999), where approximately
80% of the patients have GADA (Rakocevic et al. 2004).
Epitopes: Diabetes-related GADA bind mainly to conformation dependent epitopes on GAD
(Figure 5), where the middle region of the protein is the most important (Padoa et al. 2003;
Ronkainen et al. 2004; Schlosser et al. 2005a). However, the GADA response in the
preclinical stage of type 1 diabetes is dynamic, with epitope spreading accompanied by an
increase in the number of epitopes recognized (Schlosser et al. 2005a). Initial reactivity of
GADA appears to target mainly the middle region of GAD (Bonifacio et al. 2000) and
spreads often rapidly to the C-terminal region (Ronkainen et al. 2006). Children with high risk
of developing type 1 diabetes, defined by having more than one autoantibody, often show
GADA reactivity both to epitopes in the middle and C-terminal part of the GAD molecule
(Hoppu et al. 2004a; Schlosser et al. 2005a). In children with both autoantibodies and genetic
risk for the disease this GADA reactivity generally spreads towards epitopes on the N-
terminal part and other epitopes located in the middle (Schlosser et al. 2005a). However, the
Review of the literature
29
most important epitope region on GAD at diagnosis and the following years after diagnosis is
the middle region, often in combination with the C-terminal region (Falorni et al. 1996;
Ronkainen et al. 2004). After disease onset the GADA epitope binding pattern seems to be
quite stable (Hampe et al. 2002).
Subclasses: IgG1 is the dominant IgG subclass of GADA during the early antibody response
in prediabetic individuals and in newly diagnosed type 1 diabetic individuals (Bonifacio et al.
1999; Hoppu et al. 2004a; Ronkainen et al. 2006). Antibodies of subclasses IgG2 and IgG3
often appear together with IgG1 or soon after the initial IgG1 response, while IgG4 is the last
subclass to appear (Ronkainen et al. 2006). In addition, a broad initial response with three or
four IgG subclasses and the lack of an emerging IgG4 response during follow-up have been
associated with increased risk for progression to type 1 diabetes.
The tyrosine phosphatase like protein islet antigen-2 (IA-2)
The tyrosine phosphatase like protein islet antigen-2 (IA-2) is a transmembrane protein
consisting of 979 amino acids with a molecular mass of 106 kDa and is a major autoantigen in
type 1 diabetes (Lan et al. 1996). IA-2 belongs to the protein tyrosine phosphatase (PTP)
family and is expressed in pancreatic islets and brain tissues (Notkins et al. 1996). The IA-2
gene is located on chromosome 2q35 and encodes a protein consisting of a signal peptide and
an extracellular, a transmembrane and an intracellular domain (Figure 6) (Lan et al. 1994;
Notkins et al. 2001). The PTP core sequence, a highly conserved region of 11 amino acids
located within the intracellular domain of IA-2, differs from other PTPs primarily in the
substitution of aspartic acid for alanine, which might be the cause of the lack of enzyme
activity for IA-2 (Notkins et al. 1996). IA-2β, which is closely related to IA-2, is another
member of the PTP family and does also act as an autoantigen (Lu et al. 1996). ICA 512
represents a fragment of IA-2, 453 amino acids shorter, and covers the region from amino
acid 389 to amino acid 914 (Notkins et al. 1996). The extracellular domain of ICA 512
appears to reside within secretory granules and the intracellular domain is located in the
cytoplasm (Solimena et al. 1996).
Review of the literature ’
30
Figure 6. Schematic representation of the IA-2 protein. The signal peptide and the extracellular,
transmembrane and intracellular domains are shown in the figure, as well as the cysteine rich regions
(CYS), the PTP core sequence and the region containing epitopes recognized by IA-2A. Adapted from
(Leslie et al. 1999).
IA-2 autoantibodies (IA-2A)
IA-2A is found in about 55-80% of newly diagnosed type 1 diabetic patients (Bonifacio et al.
1995; Sabbah et al. 1999; Strebelow et al. 1999; Winter et al. 2002). The frequency of IA-2A
varies with age and HLA genotype. In young children and in patients with HLA DR4-
DQA1*0301-DQB1*0302 genotype, the frequency and/or level of IA-2A is highest
(Bonifacio et al. 1995; Genovese et al. 1996; Gorus et al. 1997; Savola et al. 1998). The close
association between IA-2A and HLA DR4 indicates that IA-2A may be a more specific
marker of beta-cell destruction than GADA. IA-2A seems to be a strong predictor for
development of type 1 diabetes (Achenbach et al. 2004b). Further, high levels of IA-2A seem
to be correlated with rapid progression of disease (Bingley et al. 1994; Christie et al. 1994;
Kulmala et al. 1998).
Autoantibodies to IA-2β are found in 35-50% of patients with type 1 diabetes. Since more
than 95% of the patients who have autoantibodies to IA-2β also have IA-2A, screening for
IA-2beta autoantibodies for diagnosis is often omitted (Leslie et al. 1999). However, it has
been shown that presence of IA-2β autoantibodies in IA-2A positive individuals can increase
prediction of type 1 diabetes (Achenbach et al. 2004b).
Epitopes: IA-2A bind exclusively to epitopes located in the cytoplasmic domain of the
molecule (amino acids 601-979) (Figure 6) (Zhang et al. 1997; Dromey et al. 2004). Two
linear epitopes within the juxtamembrane domain (amino acids 611-620 and 621-630,
Epitope recognition
Extracellular Transmembrane Intracellular(cytoplasmic)
PTP core
907-917979 COOH750653500
577-60044838625026NH2 1
IA-2
Signal peptide
CYS
Review of the literature
31
respectively) and conformational epitopes in the PTP domain toward the C terminus of the
molecule (aa 931-979) and within the central region (aa 795-889) of the PTP domain have
been identified (Dromey et al. 2004). The disulphide bonds within the intracellular domain
seems important for maintaining the antigenic structure of IA-2, since loss of these bonds
result in almost total loss of reactivity with type 1 diabetic autoantibodies (Xie et al. 1997).
IA-2A present early in the disease process are often directed to juxtramembrane domain
epitopes (Dromey et al. 2004), and IA-2A reactivity to these epitopes have been associated
with an increased risk of progression to type 1 diabetes (Hoppu et al. 2004b). By the time of
diabetes onset, IA-2A reactivity has spread to epitopes predominantly in the PTP domain and
reactivity to the juxtamembrane domain epitopes are less frequent (Dromey et al. 2004).
Subclasses: IA-2A of the IgG1 subclass dominates the autoantibody response in prediabetic
individuals and in newly diagnosed type 1 diabetic individuals (Bonifacio et al. 1999;
Achenbach et al. 2004b; Hoppu et al. 2004b). One study showed a correlation between IA-2A
response of IgG4 subclass and protection from diabetes (Seissler et al. 2002), but others do
not (Bonifacio et al. 1999; Achenbach et al. 2004b; Hoppu et al. 2004b). High risk of
progression to type 1 diabetes was in one of these studies associated with IgG2, IgG3 and/or
IgG4 subclasses of IA-2A (Achenbach et al. 2004b).
Insulin
Insulin, one of the major autoantigens in type 1 diabetes, is a hormone that regulates energy
and glucose metabolism in the body. The blood glucose level is lowered by insulin, by
accelerating the transport of glucose into cells. Insulin is secreted by the beta cells, which
constitute about 70% of the pancreatic islet cells. The protein consists of 51 amino acids and
is encoded on chromosome 11p15 (Notkins et al. 2001). The insulin molecule is a
heterodimer consisting of an A and a B chain of 21 and 30 amino acids, respectively, linked
by two disulfide bridges (Figure 7). Insulin is synthesized as pre-proinsulin by the beta cells
of the pancreas, and after cleavage of an NH2-terminal sequence, proinsulin is formed
(Eisenbarth 2008). After folding of proinsulin into its correct secondary and tertiary structure,
the 5.8 kDa insulin protein is formed by proteolytic cleavage and removal of the connecting
peptide (C-peptide), which is a large peptide in the middle of the proinsulin molecule.
Review of the literature ’
32
Figure 7. The structure of proinsulin and insulin. Proinsulin is formed from pre-proinsulin and after
removal of the connecting peptide (C-peptide) the insulin protein is formed, consisting of an A and a B
chain of 21 and 30 amino acids (aa), respectively.
Insulin autoantibodies (IAA)
In 1983, IAA was identified in newly diagnosed untreated diabetic patients (Palmer et al.
1983). This is often the first autoantibody to appear in young children as a sign of beta-cell
autoimmunity and the levels correlate inversely with age (Vardi et al. 1988; Ziegler et al.
1999). IAA is found in about 40-70% of newly diagnosed type 1 diabetic children (Sabbah et
al. 1999; Strebelow et al. 1999; Winter et al. 2002; Williams et al. 2003; Holmberg et al.
2006).
Epitopes: The epitopes on insulin recognized by IAA are not well characterized. However, it
has been observed that IAA specific to human insulin seems to bind epitopes located on the B
chain of insulin, while antibodies cross-reactive to insulin of other species recognize both the
B chain and conformational epitopes involving both the A and B chains (Potter et al. 2000).
Further, IAA that bind epitopes dependent on threonine B30 seem not to be associated with
type 1 diabetes, while those who bind conformational epitopes incorporating the A and B
chains frequently are. Epitope analysis of IAA using a recombinant Fab (rFab) revealed that
IAA in type 1 diabetic patients can bind an epitope located predominantly on the A chain
(Padoa et al. 2005).
Subclasses: IAA of the IgG1 subclass is often predominant in both pre-diabetic and type 1
diabetic individuals, but the IgG3 subclass is also frequently occurring in pre-diabetic
individuals (Bonifacio et al. 1999; Potter et al. 2000; Hoppu et al. 2004c). In one study,
A chain
B chain
C-peptide
Proinsulin Insulin
(110 aa) (51 aa)
Pre-proinsulin A chain
B chain
B1 B30
A1A21
Review of the literature
33
genetically susceptible young children who progressed rapidly to clinical type 1 diabetes were
characterized by strong IgG1 and IgG3 responses to insulin, whereas a weak or absent IgG3
response was associated with relative protection from disease (Hoppu et al. 2004c). In another
study, the risk of progression to type 1 diabetes was higher in individuals with IAA of
subclasses IgG2, IgG3 or IgG4 than in those without these IgG subclasses (Achenbach et al.
2004b).
Affinity: In IAA positive children from the general population, antibody affinity can identify
those at high and low risk (Schlosser et al. 2005b). High affinity has been associated with
HLA DRB1*04, young age of IAA appearance and progression to multiple autoantibodies or
type 1 diabetes (Achenbach et al. 2004a). In addition, the A8-A13 region on insulin is
important for binding of high-affinity IAA.
Zink transporter 8 (ZnT8) and ZnT8 autoantibodies (ZnT8A)
ZnT8, a multispanning transmembrane protein belonging to a large cation efflux family, was
recently discovered to be a major autoantigen in type 1 diabetes (Wenzlau et al. 2007).
Approximately half of the ZnT8 molecule comprises six membrane-spanning regions, which
might hinder the folding of the protein in an aqueous environment. Therefore, C-terminal, N-
terminal and N- and C-terminal fusion proteins have been constructed for use in
radioimmunoassays to detect ZnT8A (Wenzlau et al. 2007; Achenbach et al. 2009).
Autoantibodies to the C-terminal part of ZnT8 have shown a strong relation to disease
development. ZnT8A has been found in about 60% of children with new onset type 1
diabetes. In the first report, ZnT8A was found in 26% of type 1 diabetic patients previously
classified as autoantibody negative (Wenzlau et al. 2007). Both the levels and the prevalence
of ZnT8A increase with age, and the antibodies appear frequently in children by 3 years of
age. ZnT8A usually precedes the disease by many years and emerges often after GADA and
IAA.
In conclusion, autoantibodies against beta-cell proteins can be used as markers for the
autoimmune process, where GADA, IA-2A, IAA and ZnT8A are the most commonly used
today.
Review of the literature ’
34
PREDICTION AND PREVENTION OF TYPE 1 DIABETES
The risk of developing type 1 diabetes is increased in first degree relatives of type 1 diabetic
patients compared to the general population. The risk of type 1 diabetes in the general
population is approximately 0.5%, or even higher in high incidence countries, while the risk
for siblings is about 6%, the risk for offspring of a diabetic mother is 1-4% and of a diabetic
father 6-9% (Hawa et al. 2002; Redondo et al. 2002). For prediction of type 1 diabetes, a
combination of genetic and immunological markers is useful.
Presence of HLA susceptibility genes in an individual increases the risk of developing type 1
diabetes, but only to a small extent, as reviewed in (Ziegler et al. 2010). For example, risk of
type 1 diabetes in children without a family history of this disease can vary from about 0.01%
up to more than 5%, depending on their HLA class II genotypes. Further, some of the HLA
genotypes confer extreme protection, such as DQB1*0602. The risk of type 1 diabetes in a
child carrying this allele and with a family history of type 1 diabetes is reduced to about 1%
of the risk in children with similar family history but without this allele.
The most important increase in type 1 diabetes risk of a child occurs when beta-cell
autoantibodies develop. Autoantibodies to insulin, GAD, IA-2 and ZnT8 are good markers for
prediction of type 1 diabetes in children and young adults (Bingley et al. 1994; Bingley et al.
1997; Kulmala et al. 1998; Verge et al. 1998; LaGasse et al. 2002; Wenzlau et al. 2007).
Presence of only one of these four autoantibodies is associated with a marginally increased
risk, both in individuals with and without a family history of type 1 diabetes. However,
positivity to two or more autoantibodies is highly predictive of type 1 diabetes (Verge et al.
1996; Bingley et al. 1997; Eising et al. 2010; Knip et al. 2010). Further, the risk of type 1
diabetes varies dependent on which of the antibodies that is present, and IA-2A has been
associated with the highest risk (Achenbach et al. 2004b).
In addition to autoantibody positivity, characteristics of the autoantibodies such as greater titer
and affinity, broadness of epitope reactivity and certain distribution of IgG subclasses are also
associated with high risk of type 1 diabetes (Achenbach et al. 2004b; Achenbach et al. 2006;
Achenbach et al. 2007; Mayr et al. 2007). For example, in one study, the risk of progression
to type 1 diabetes was higher in individuals with autoantibodies binding multiple epitopes
than single epitope reactivity, and in individuals with IA-2β autoantibodies than in those
Review of the literature
35
without these autoantibodies (Achenbach et al. 2004b). The risk of developing type 1 diabetes
can be stratified from <1% up to >70% by using various combinations of risk markers (Figure
8) (Ziegler et al. 2010).
Figure 8. Stratification of type 1 diabetes risk by islet autoantibody properties together with other risk
markers, such as HLA genotype and age. Characteristics of the antibody response can increase
prediction for disease progression. Ab = antibody. Adapted from (Ziegler et al. 2010).
Prediction and prevention studies
Accurate assessment of risk of type 1 diabetes in non-diabetic individuals is necessary to
identify those individuals suitable for recruitment into intervention trials aiming to prevent the
clinical onset of diabetes. A number of intervention trials have recently been performed and
others are ongoing (Ludvigsson 2010a), e.g. in pre-diabetic individuals; the trial to reduce
type 1 diabetes in the genetically at risk (TRIGR) (Knip et al. 2010), the European
nicotinamide diabetes intervention trial (ENDIT) (Gale et al. 2004), the Diabetes Prevention
Trial (DPT-1) (DPT-1 2002) and The Diabetes Prediction and Prevention study (DIPP)
(Kupila et al. 2001), and in newly diagnosed type 1 diabetic patients; DiaPep277 (Schloot et
<5
10
40
30
20
80
70
60
50
Risk categories
Low Very highHighIntermediate
Single islet AbLow-affinity
Older ageNonsusceptibleHLA genotype
Single islet Abwith high affinity
Proinsulin reactive IAA, middleor C-terminal reactive GAD65
Young ageHLA DR3 or DR4
Two or three islet Abs(IA-2A, ZnT8A)
Young ageLow first-phase insulin(HLA risk genotypes)
Four islet AbsAbs to IA-2β epitopes
Higher titerMultiple IgG subclasses responses
Young age at initiationImpaired glucose tolerance
(HLA risk genotype)
Review of the literature ’
36
al. 2007), anti-CD3 (Herold et al. 2009), anti-CD20 (Pescovitz et al. 2009), IFN-α (Rother et
al. 2009) and GAD-alum (Ludvigsson et al. 2008). Some of these studies are described below.
In several prediction studies, children at increased genetic risk of type 1 diabetes are followed
with frequent sampling and autoantibody analysis. In the All Babies in Southeast Sweden
(ABIS) study (Ludvigsson et al. 2001), children from the general population are followed
without selection for genetic risk, see the section “Study populations” for more information
about this study. Ongoing studies, such as the Diabetes Autoimmunity Study in the Young
(DAISY) (Barker et al. 2004), the Environmental Determinants in Diabetes of the Young
(TEDDY) (The TEDDY Study Group 2008) and ABIS, investigate the role of different
environmental factors on the development of autoimmunity and type 1 diabetes.
DAISY
The Diabetes Autoimmunity Study in the Young (DAISY) is a prospective study in Denver,
Colorado, USA that follows young first-degree relatives (siblings or offspring) of persons
with type 1 diabetes and newborn children with high or moderate risk HLA genotypes. It was
shown that the predictive value of the autoantibody measurements could be increased by
blinded duplicate and independent sample retesting together with the level of the
autoantibody. Approximately one third of the autoantibody positive individuals were false
positive, one third transiently positive and one third persistently positive (Barker et al. 2004).
Further, early or late introduction of cereal seemed to increase the risk of islet cell
autoimmunity in children younger than 3 years of age (Norris et al. 2007), while no
association was found for early introduction of cow´s milk protein (Norris et al. 1996).
TEDDY
The Environmental Determinants in Diabetes of the Young (TEDDY) study is an
observational clinical trial that started 2004 and involves six international centres; Seattle,
Denver and Augusta in North America, and Germany, Finland and Sweden in Europe
(Hagopian et al. 2006; The TEDDY Study Group 2007). The aim is to identify environmental
factors, such as infectious agents, dietary factors and psychosocial factors, which contribute to
or protect from islet autoimmunity and risk of type 1 diabetes. Newborns with HLA risk
genotypes or who are first-degree relatives of type 1 diabetic patients were recruited and will
be followed regularly from birth to the age of 15 years. By the age of 15, it is estimated that
Review of the literature
37
800 children will develop islet autoimmunity and 400 will progress to type 1 diabetes (The
TEDDY Study Group 2008).
BABYDIAB
The BABYDIAB study is a German prospective study in which newborn offspring of parents
with type 1 diabetes were followed for development of autoantibodies and diabetes (Hummel
et al. 2004). GADA, IA-2A and IAA were measured at 9 months and at 2, 5 and 8 years of
age. By the age of 5 years, 24 (1.5%) children had developed type 1 diabetes, and all but one
had multiple autoantibodies before the onset of the disease. In addition, the frequency of
autoantibodies at this age was 5.9% and the frequency of multiple autoantibodies was 3.5%.
Children with multiple autoantibodies before the age of 2 years were at the greatest risk of
developing type 1 diabetes.
DIPP
The Diabetes Prediction and Prevention study (DIPP) is a Finnish trial in which genetically
predisposed children from the general population were closely followed for signs of diabetes-
associated autoimmunity (Kupila et al. 2001). Children who developed signs of beta-cell
autoimmunity, with at least two autoantibodies in two consecutive samples, were invited to a
separate prevention trial, with administration of intranasal insulin. The first autoantibody that
emerged was predominantly IAA, and in the majority of the children autoantibodies appeared
in the fall and winter, indicating that infectious agents may play a role in the induction of
beta-cell autoimmunity (Kimpimaki et al. 2001). By the age of 5 years, 13 (1.3%) children
had presented with type 1 diabetes, and all of them had developed at least two autoantibodies
before onset (Kukko et al. 2005). Further, it was found that nasal insulin did not prevent or
delay development of type 1 diabetes (Nanto-Salonen et al. 2008).
TRIGR
In the trial to reduce type 1 diabetes in the genetically at risk (TRIGR) study, which is a
multinational, randomized, double-blind, controlled trial, newborns with HLA-conferred
susceptibility to type 1 diabetes and with at least one family member with type 1 diabetes
were recruited. If exclusive breast-feeding was not possible during the first 6-8 months of life,
the child was randomly assigned either hydrolyzed cow´s milk formula or a standard cow´s
milk based formula. The participants will be monitored up to the age of 10 years for the
appearance of autoantibodies and clinical type 1 diabetes. Final results of the study will be
Review of the literature ’
38
available in 2017 (TRIGR 2010). Autoantibodies against GAD, IA-2, IAA, ZnT8 and islet
cells are analyzed to investigate if the elimination of cow´s milk proteins during infancy can
decrease the development of beta-cell autoimmunity during the first years of life. Results
from a median observation of 10 years have shown that the hazard ratio for positivity for one
or more autoantibodies was reduced in the group that received hydrolyzed formula (Knip et
al. 2010).
ENDIT
The European nicotinamide diabetes intervention trial (ENDIT) was a double blind placebo-
controlled trial that aimed to assess whether high dose nicotinamide during 5 years could
prevent or delay clinical onset of diabetes in ICA positive individuals with a first-degree
family history of type 1 diabetes (Gale et al. 2004). The results showed that there were no
differences in development of diabetes between the nicotinamide and placebo treated
individuals.
DPT-1
The Diabetes Prevention Trial (DPT-1) was started in 1994 with the aim of determining
whether antigen based treatment with insulin would prevent or delay diabetes. Relatives to
type 1 diabetic patients were treated with oral or parenteral insulin. The results showed that
these treatments did not overall slow the progression to type 1 diabetes (DPT-1 2002; Skyler
et al. 2005). However, a beneficial effect of treatment with oral insulin was observed in a
subgroup of the treated individuals with higher IAA levels.
Anti-CD3 monoclonal antibody intervention studies
The humanized Fc mutated anti-CD3 monoclonal antibody OKT3γ (Teplizumab) was tested
in a clinical trial, in which newly diagnosed type 1 diabetic patients were randomly assigned
the antibody or placebo for 14 days (Herold et al. 2002; Herold et al. 2005). Still after two
years, a majority of the treated patients had a maintained or improved C-peptide response, as
well as reduced usage of insulin and improved HbA1c levels. Adverse events were common,
often mild, but some patients had serious adverse events. Further, a study with high-dose
Teplizumab in newly diagnosed type 1 diabetic patients showed to preserve the endogenous
insulin secretion still 5 years after intervention (Herold et al. 2009). However, the recruitment
of patients in that study was stopped after treatment of only 6 patients, due to more adverse
events, probably as a result of the high dose. Very recently, a phase II/III randomized double-
Review of the literature
39
blind controlled multicenter study with intravenous administration of Teplizumab for 14 days
in two rounds was performed, in children and adults with recent-onset type 1 diabetes
(http://clinicaltrials.gov 2010a). This trial was a 4-armed dose-ranging study, with one group
of patients receiving placebo and the other three groups receiving different doses of
Teplizumab. The aims were to assess the efficacy, tolerability and safety of Teplizumab.
Efficacy was defined primarily by the capacity of Teplizumab to markedly reduce insulin
requirements while maintaining relatively normal blood sugar levels. In October 2010,
MacroGenics and Lilly announced that the analysis of one-year safety and efficacy data of the
phase III trial of Teplizumab was completed with the result that the primary efficacy endpoint
of the study was not met, indicating a lack of efficacy of Teplizumab (Press release
Macrogenics and Lilly 2010). As a consequence, the companies have decided to suspend
further recruitment and dosing of patients in two ongoing trials of Teplizumab in type 1
diabetes.
GAD-alum intervention studies
In non-obese diabetic (NOD) mice, one of the animal models for type 1 diabetes, several
studies have shown that diabetes can be delayed or prevented via administration of GAD
(Tisch et al. 1993; Tian et al. 1996; Tisch et al. 1999; Jun et al. 2002). This preventive effect
is probably due to restoration of self-tolerance, which seems not to be restricted to the
tolerogen, but spreads to other beta-cell autoantigens (Bach et al. 2001). Tian et al showed
that injection of GAD65 in an adjuvant that induced Th2 responses to GAD65 could greatly
inhibit the progression of the autoimmune process in NOD mice that already had an
established autoimmune process (Tian et al. 1996). This and other experiments led to the
GAD65 vaccine (GAD-alum; Diamyd) that DIAMYD is using in clinical trials on humans.
Aluminum hydroxide (alum) is a commonly used vaccine adjuvant that preferentially induces
a humoral Th2 response and a Th2-driven antibody isotype production against co-injected
antigens, rather than a cellular immune response (McKee et al. 2008).
The safety of GAD-alum was first tested in a phase I trial in healthy humans (Diamyd
Medical, unpublished). Then, the efficacy of GAD-alum treatment as well as further
evaluation of the safety and investigation of the immunological effect was tested in a phase
IIa dose-finding study in a group of LADA patients (Agardh et al. 2005). This study was
followed by a phase IIb clinical trial in newly diagnosed type 1 diabetic children (further
described in the “Study populations” section) (Ludvigsson et al. 2008). Results from the latter
Review of the literature ’
40
study showed a better preservation of fasting and stimulated C-peptide in the individuals with
less than six months duration of disease at inclusion. The effect persisted still 4 years after
treatment (Ludvigsson et al. 2010b). At present, phase III clinical trials with GAD-alum are
ongoing in both Europe and the USA. Results from the European study will be available in
the spring or summer of 2011 (www.diamyd.com). The study comprises 334 type 1 diabetic
patients (10-20 years of age) diagnosed within the last three months. They have GADA
positivity and fasting C-peptide levels above 0.1 pmol/ml (http://clinicaltrials.gov 2010b).
This is a three-armed trial with one group receiving four subcutaneous injections with
placebo, another group GAD-alum at two occasions and placebo at the other two visits, and
the last group four injections of GAD-alum. It will be interesting to follow the results from
the GAD-alum phase III clinical trial.
In conclusion, improved prediction strategies for type 1 diabetes facilitate identification of
individuals suitable for intervention studies. Unfortunately, the ENDIT, DPT-1 and anti-CD3
intervention studies had no effect on the development of type 1 diabetes, but other treatments,
such as GAD-alum, are still promising.
DETECTION OF AUTOANTIBODIES
Autoantibodies related to type 1 diabetes are traditionally detected using radioimmunoassays
(RIAs). The first radioassays for detection of IAA in prediabetic and new onset diabetic
patients used 125
I-labelled insulin in fluid phase assays, with poly(ethylene glycol) (PEG) for
precipitation and relatively large volumes of serum were needed. Antibodies induced by
insulin injection were generally measured with enzyme-linked immunosorbent assays
(ELISAs). However, the detection limit of the ELISAs was higher than of the radioassays,
and the levels of the insulin antibodies induced by insulin injections are approximately 10-
fold higher than the commonly detected levels of IAA in individuals at diagnosis of type 1
diabetes (Winter et al. 2002). In 1992, Greenbaum et al showed that IAA measured by RIA
were more related to type 1 diabetes than those measured by ELISA, and thus recommending
RIA as the method of choice for measuring IAA associated with type 1 diabetes (Greenbaum
et al. 1992). This could be explained by the fact that in RIA, the ligand (insulin) is limiting
and the antibodies are in excess, favoring the binding of high-affinity IAA, and it has been
observed that IAA of high affinity is disease specific (Achenbach et al. 2004a; Schlosser et al.
2005b). On the other hand, in ELISA, an excess of the antigen is fixed to the plate, enabling
Review of the literature
41
binding of low-affinity antibodies as well (Sodoyez-Goffaux et al. 1988; Wilkin 1990).
Further, almost all of the epitopes on the insulin molecule would be available for binding in
the fluid phase RIA, while in ELISA some epitopes might be blocked for antibody binding
due to binding of insulin to the solid surface (Greenbaum et al. 1992). To improve sensitivity
and specificity for detection of IAA with RIA, competition with unlabelled insulin was
introduced and micro-assays using smaller volumes of serum and protein A sepharose for
precipitation were developed (Williams et al. 1997) and this method is the standard assay for
IAA today.
When RIA had been established as the most suitable method for detection of IAA related to
type 1 diabetes, RIAs were also developed for the detection of GADA and IA-2A. These
immunoassays, which are widely used today, are based on in vitro synthesized radioactively
labelled (35
S-methionine) antigen and protein A sepharose for precipitation of antibody-
antigen complexes (Grubin et al. 1994). These fluid phase RIA’s have generally demonstrated
higher disease sensitivity compared to ELISAs, which are solid phase assays (Schmidli et al.
1995; Bingley et al. 2003). More recently the ELISAs have improved and some assays with
the same or better sensitivity and specificity than the conventional RIAs have been developed
(Ankelo et al. 2003; Brooking et al. 2003; Luo et al. 2004; Mueller 2007). Two ELISAs are
based on a divalent action of GADA that forms a bridge, between immobilized GAD65 and
liquid-phase GAD65-biotin in one assay (Brooking et al. 2003) and between immobilized
GAD65-biotin and liquid-phase europium-labelled GAD65 in the other (Ankelo et al. 2003).
Another assay uses a biotin-GAD65 fusion protein, which is free in the liquid-phase during
the incubation with antibody-containing serum, and is then bound to the surface for detection
of GADA (Luo et al. 2004). In The Diabetes Autoantibody Standardization Program (DASP)
2007, one laboratory using ELISA for detection of GADA and IA-2A had the highest
sensitivities and specificities among all participating laboratories (Mueller 2007). The high
sensitivity and specificity of these assays opens the question whether new ELISA assays
might replace RIAs for determination of GADA and IA-2A in the future.
RIAs for GADA, IA-2A and IAA are today widely used in laboratories around the world. To
improve and standardize the measurement of type 1 diabetes related autoantibodies, a series
of international workshops have been held (Greenbaum et al. 1992; Schmidli et al. 1994;
Schmidli et al. 1995; Verge et al. 1998) and were followed by the establishment of The
Diabetes Autoantibody Standardization Program (DASP). DASP is a collaboration of the
Review of the literature ’
42
Immunology of Diabetes Society (IDS) and the US Centers for Disease Control and
Prevention. The major goals of DASP are to help laboratories to improve methods for
detection of autoantibodies by providing technical support, training and information, to
organize workshops that evaluate laboratory performance of the assays and to provide
reference material for the development of new measurement technologies (Bingley et al.
2003; Torn et al. 2008). The aim of the first DASP evaluation was to improve comparability
of the autoantibody measurements between laboratories and to evaluate the new WHO
international reference reagent for GADA and IA-2A (Mire-Sluis et al. 2000; Bingley et al.
2003).
There are some disadvantages with the RIAs for detection of GADA, IA-2A and IAA,
including the use of radioactively labelled material, low sensitivity for the IAA assay and long
assay times for a sample. Of the three RIAs, the assay for IAA has been and is still the most
difficult for new laboratories to perform and to standardize (Liu et al. 2007; Schlosser et al.
2010). This could be due to the very low levels of IAA that are present in serum of both
prediabetic and newly diagnosed individuals with type 1 diabetes, and the fact that there is a
very small difference in concentration between autoantibody positive individuals and normal
control subjects. Thus, there is a need for better methods to measure autoantibodies against
GAD, IA-2 and insulin. Surface plasmon resonance (SPR) is an interesting candidate
technique for this purpose (Shankaran et al. 2007). Biacore’s SPR technology is an efficient
method that investigates biomolecular interactions in real-time and uses label-free detection
(Liedberg et al. 1995) (www.biacore.com). This technique can identify binding partners to
target molecules, but also provide quantitative information on specificity of the binding
between two molecules, the concentration of the molecule, rate of association and dissociation
and strength of the binding. Comparison of SPR with two conventional immunoassays, i.e.
RIA and ELISA, has shown that SPR is a more powerful technique with several advantages,
such as the low quantities of reagents needed, no labelling of interactants and measurements
in real time (Revoltella et al. 1998).
In conclusion, RIAs are commonly used for detection of GADA, IA-2A and IAA. There is a
need for improved methods for detection of these autoantibodies, and SPR is an interesting
candidate.
43
Aims of the thesis
The general aims of this thesis were to investigate prevalence and levels of autoantibodies
related to type 1 diabetes, in healthy children and in children with type 1 diabetes. In addition,
we wanted to study different properties of GADA, such as binding epitopes and IgG
subclasses, after immunomodulatory treatment of children with type 1 diabetes.
The specific aims were:
I. To elucidate the influence of HLA genotypes on the development of diabetes-related
autoantibodies and manifest type 1 diabetes in children participating in the ABIS
study. We therefore decided to study the longitudinal changes of GADA and IA-2A
and their relation to HLA genotypes, both in healthy children and in children with
type 1 diabetes.
II. To develop a sensitive and specific method for detection of diabetes-related
autoantibodies based on the surface plasmon resonance (SPR) technique. We thought
that SPR might be a suitable technique due to its label-free detection, the short
analysis time for each sample and the possibility to simultaneously measure multiple
antibodies.
III. To study how treatment with GAD-alum in children with recent onset of type 1
diabetes influences GADA epitope binding. We hypothesized that the epitope
binding pattern would not be influenced by the treatment, since unaltered epitope
binding was previously observed after GAD-alum treatment in LADA patients.
IV. To study if treatment with GAD-alum in children with recent onset of type 1 diabetes
influences IgG subclasses of GADA and GAD65 enzyme activity. We hypothesized
that the IgG subclass distribution would be altered towards a Th2-like phenotype
with more IgG4, since an early GAD65 specific Th2 cytokine response has
previously been observed following this treatment.
44
45
Subjects and methods
STUDY POPULATIONS
The ABIS study
The All Babies In Southeast of Sweden (ABIS) study is a prospective population-based
follow-up study, in which 17055 out of 21700 infants (78.6%) born between 1 October 1997
and 1 October 1999 in Southeast of Sweden were included (Ludvigsson et al. 2001)
(www.abis-studien.se). The aim was to study the role of environmental factors for
development of type 1 diabetes, but also for other diseases, such as celiac disease,
inflammatory bowel disease and allergy. A second aim was to increase the knowledge of how
to identify individuals with risk of developing type 1 diabetes in the general population, using
markers such as autoantibodies and HLA. In ABIS I, 1997-2005, children were followed from
birth up to 5 years of age. The parents were asked to fill in comprehensive questionnaires and
biological samples, such as blood and hair samples, were taken from the children at birth and
at 1, 2.5 and 5 years of age. In the follow-up study, ABIS II, which started 2006, all children
participating in ABIS I were asked to participate. The children were asked to fill in
questionnaires and to contribute with biological samples at 8, 11 and 14 years of age. The
prevalence of diabetes in the ABIS cohort will be investigated, by registration of the diagnosis
from the clinics, until the end of 2017. Results from the study so far include a number of
factors that have been related to development of beta-cell autoantibodies, such as virus
infections early in life (Sepa et al. 2005), short duration of breast-feeding (Wahlberg et al.
2006; Holmberg et al. 2007), early introduction of cow’s milk and late introduction of gluten
during the first year of life (Wahlberg et al. 2005; Wahlberg et al. 2006) and psychological
stress (Sepa et al. 2005; Sepa et al. 2005).
In paper I, samples from subgroups of children who participated in the ABIS study were used
for analyses. Samples were grouped as follows:
1) GADA and IA-2A were investigated in all children who had given at least one serum or
whole blood sample at any age (1, 2.5 or 5 years of age). The relationship between HLA
alleles and GADA and IA-2A was investigated.
Subjects and methods ’
46
2) The occurrence of permanent vs transient autoantibodies was studied in 2329 children from
whom samples at all three ages were available, and the association to HLA genotype was
investigated. In this group, children who had developed type 1 diabetes were excluded.
3) Analyses of GADA, IA-2A and HLA genotype were performed in samples from 1, 2.5
and/or 5 years of age in children who developed type 1 diabetes in the ABIS study.
In paper II, serum samples from children in the ABIS study who had not developed type 1
diabetes were used for measurement of IAA in an indirect competitive immunoassay based on
SPR.
The phase II clinical trial with GAD-alum
A phase II clinical trial with injections of recombinant aluminum formulated human GAD65
(GAD-alum) has been performed in type 1 diabetic patients (Ludvigsson et al. 2008). The aim
of the study was to investigate if GAD-alum treatment could preserve beta cell function in
children with type 1 diabetes. The participants (n=70) were 10-18 years old at diagnosis of
type 1 diabetes and had disease durations of <18 months, fasting c-peptide levels of >0.1
nmol/l and tested positive for GADA at the initial screening. In this randomized, double-
blinded placebo-controlled trial, recent onset type 1 diabetic children and adolescents were
treated with either 20 μg GAD-alum (Diamyd®, Diamyd Medical, Stockholm, Sweden)
(n=35) or placebo (alum-formula alone) (n=35) subcutaneously in a prime and booster
injection four weeks apart. At day 1 and at 3, 9, 15, 21 and 30 months, the patients underwent
a mixed-meal tolerance test to stimulate residual insulin secretion (measured as C-peptide
level). Adverse events were regularly registered throughout the study, and neurological
examination was performed by a pediatrician at regular intervals. The outcome of the study
demonstrated that GAD-alum treatment had a clinical effect on the preservation of beta-cell
function in children with shorter disease duration, and immunomodulated the humoral
immune response, inducing GADA levels in GAD-alum treated individuals (Ludvigsson et al.
2008).
In addition, the effect of GAD-alum on the immune system was studied. Blood samples from
69 patients (one placebo patient dropped out after the first injection) were collected before the
first injection (day 0) and after 1, 3, 9, 15, 21 and 30 months. All samples were transported to
Linköping within 24 hours, and serum samples were stored at −70°C for simultaneous
analysis to avoid inter-assay variation.
Subjects and methods
47
In paper III, serum samples taken before the first injection (baseline) and at 1, 3, 9, and 15
months after the initial injection were included. Nine patients (7 from the placebo group and 2
from the GAD-alum group) with baseline GADA levels <60 units/ml (U/ml), were excluded
because measurement of competition with rFab in samples with low GADA levels is
unreliable. One additional patient in the placebo group was excluded, since this individual
dropped out after the first injection. Thus, GADA epitope specificity was analyzed in 27
patients from the placebo group and 33 from the GAD-alum group using rFab fragments in an
epitope-specific radioligand binding assay.
In paper IV, the IA-2A and tetanus toxoid titers were measured at baseline, 3 and 9 months.
GAD65 enzyme activity and IgG subclass measurements were performed at baseline, 3 and
15 months. Total IgE was analysed at baseline and 21 months.
Type 1 diabetic patients
In paper II, samples taken from children with manifest type 1 diabetes were studied. These
children have been recruited at the Pediatric Clinic at Linköping University hospital and the
diagnosis was based on the WHO criteria from 1999. All samples used were taken within 10
days after diagnosis, to be sure that the IAA measured were antibodies against endogenous
insulin and not antibodies produced after encounter of exogenous insulin.
METHODS
RIA for detection of GADA and IA-2A (Paper I and IV)
GADA and IA-2A were determined using a RIA with 35
S-labelled human recombinant
GAD65 and IA-2ic, respectively. These antigens were produced by in vitro transcription and
translation of complementary DNAs of GAD65 and IA-2ic, cloned into vectors (pcDNA2 and
pSP64 poly (A), respectively; kindly supplied by Prof. Å Lernmark, Seattle), in the presence
of 35
S -labelled methionine. Serum or whole blood samples diluted 1:25 were incubated in
duplicates with GAD or IA-2 antigen at +4°C over night. This was followed by precipitation
with protein A sepharose (Amersham Biosciencies, Uppsala, Sweden) to separate free
labelled antigen from antibody bound labelled antigen. A standard curve was included on
each plate, as well as a blank (containing only buffer) and positive and negative controls. The
Subjects and methods ’
48
immunoprecipitated radioactivity was measured in a Wallac 1450 Microbeta Liquid
Scintillation Counter (Perkin Elmer Life and Analytical Sciences), and the results were
expressed as U/ml in relation to the standard curve.
In paper I, cut-off for positivity at the 98th
percentile for GADA and IA-2A was used.
However, at 5 years of age, the 99th
percentile was used for IA-2A since less than 2% of the
samples had values above the detection limit. In the DASP workshop 2005, our GADA assay
had, at the 98th
percentile, a specificity of 95% and a sensitivity of 76% (Table 2). For IA-2A,
at the 99th
percentile, the specificity was 100% and the sensitivity 72%. Interassay variations
for negative and positive controls were 10% and 8% for GADA and 11% and 12% for IA-2A,
respectively.
Table 2. Specificities and sensitivities for our autoantibody assays from the Diabetes Autoantibody
Standardization Program (DASP) during the years 2003-2010. Measurements of GADA, IA-2A and
IAA were included all time points, while the three variants of ZnT8A; ZnT8(Arg)A, ZNT8(Gln)A and
ZnT8(Trp)A, were included for the first time in DASP 2010. The percentiles used as cut-off for
positivity varied between antibodies and years and are shown in the table. * = In 2007, the cut-off
value used for IA-2A was 9.9 U/ml, which exceeded the 99th percentile.
Year GADA IA-2A IAA ZnT8(Arg)A ZnT8(Gln)A ZnT8(Trp)A
Sensitivity 74% 48% 24% - - -
Specificity 98% 100% 100% - - -
Percentile 98th 98th 99th - - -
Sensitivity 76% 72% 28% - - -
Specificity 95% 100% 100% - - -
Percentile 98th 99th 98th - - -
Sensitivity 82% 65% 25% - - -
Specificity 94% 98% 99% - - -
Percentile 98th >99th* 98th - - -
Sensitivity 66% 64% 40% - - -
Specificity 99% 99% 97% - - -
Percentile 98th 99th 98th - - -
Sensitivity 80% 66% 34% 52% 28% 46%
Specificity 100% 100% 98% 97% 100% 91%
Percentile 98th 99th 98th 99th 99th 99th
2003
2005
2007
2009
2010
Subjects and methods
49
In paper IV, the cut-off for positivity determined at the 95th
percentile was regarded as 67.3
U/ml (corresponding to 23.1 WHO units) based on measurements from 1700 children, aged 5-
6 years, participating in the ABIS study. Validation of our assay in DASP 2007 showed 94%
specificity and 82% sensitivity for GADA and 98% specificity and 65% sensitivity for IA-2A
(Table 2).
HLA genotyping (Paper I)
HLA DQB1, DQA1 and DRB1 alleles were determined with a time-resolved fluorometry
based sandwich hybridization assay, and performed by the research group of Professor Jorma
Ilonen in Turku, Finland. Details of the method have been described earlier (Sjoroos et al.
1998; Nejentsev et al. 1999; Laaksonen et al. 2002; Hermann et al. 2003). Briefly, the
polymerase chain reaction (PCR) was carried out using a 3 mm in diameter disc from whole
blood spots on a filter paper punched directly into the PCR reagent mixture or using DNA
extracted from whole blood. For amplification of the genes, specific HLA-DQA1, DQB1 or
DRB1 primer pairs, of which one was biotinylated at the 5’-end, were used. The PCR product
was transferred into a streptavidin coated microtiter plate and heat denatured, followed by
addition of detection probes labelled with various lanthanide chelates (Europium, Samarium
or Terbium). After an incubation step, allowing hybridization of the PCR product and the
probes, the time-resolved fluorescence was detected in a VictorTM
1420 Multi label Counter
(Wallac Oy, Turku, Finland) (Figure 9). Differences in the emission wave lengths of the
lanthanides and the usage of a time delay before measurement enable the detection of the
three lanthanide labels.
For genotype analysis, HLA haplotypes were categorized into susceptibility associated (S),
neutral (N) and protective (P) ones. This classification was done based on the results of
haplotype-conferred risk in 622 Finnish families with one child affected by type 1 diabetes
(Hermann et al. 2003), but essentially similar results have been described in other European
populations (Koeleman et al. 2004; Lambert et al. 2004). Susceptibility associated haplotypes
included DR4-DQ8 (DRB1*0401/2/4/5-DQB1*0302) and DR3-DQ2 (DQA1*05-DQB1*02),
protective haplotypes included DR2-DQ6 (DQB1*0602), DR11/12/1303-DQ7 (DQA1*05-
DQB1*0301), DR7-DQ3 (DQA1*0201-DQB1*0303), DR14-DQ5 (DQB1*0503), DR403-
DQ8 (DRB1*0403-DQB1*0302) and DR1301-DQ6 (DQB1*0603) (Table 1). Other
haplotypes were defined as neutral.
Subjects and methods ’
50
Figure 9. Principle of time-resolved fluorometry based sandwich hybridization assay. PCR
amplification of DNA with a biotinylated primer was followed by hybridization with specific
detection probes on streptavidin coated microtiter plates and thereafter measurement of time-resolved
fluorescence. SA = streptavidin, bio = biotin.
RIA for detection of IAA (Paper II)
A competitive RIA, based on the method developed by Williams et al (Williams et al. 1997)
with some modifications (Holmberg et al. 2006), was used for detection of IAA. Serum
samples in quadruplicates (7.5µl/well) were incubated in a 96 deep-well plate with either 125
I-
insulin alone (2 wells) (25 000-30 000 cpm/well of human recombinant insulin, mono-iodated
at thyrosine position A14, Amersham Biosciences, Buckinghamshire, Great Britain) or with
125I-insulin together with an excess of unlabelled insulin (2 wells) (human recombinant
insulin, Sigma-Aldrich, St. Louis, MO, USA) shaking for 72 h at +4°C. A standard curve as
well as positive and negative controls were included on each plate. After incubation, the
insulin-antibody complexes were precipitated with Protein A Sepharose (Zymed, San
Francisco, CA, USA) and washed five times by centrifugation at +4°C, followed by
measurement of the radioactivity in a gamma counter (1282 CompuGamma, Wallac, Turku,
Finland). Specific antibody binding were calculated by subtraction of mean counts per minute
(cpm) of duplicate samples with an excess of unlabelled insulin from mean cpm of duplicate
samples with labelled insulin. The results were expressed as U/ml calculated in relation to a
standard curve. The cut-off limit for positivity corresponding to the 98th
percentile of 114
healthy Swedish children was 6.45 U/ml. Validation of our IAA assay in DASP 2003 showed
100% specificity and 24% sensitivity (Table 2).
SA
bio
Europium
SASASA SASA
probe
SASASA
bio
Samarium
bio
Terbium
Subjects and methods
51
Surface plasmon resonance (SPR) (Paper II)
Surface plasmon resonance (SPR) is an optical surface phenomenon that can be used to
monitor biomolecular interactions in real-time without the need for labelling (Liedberg et al.
1995; Shankaran et al. 2007) (www.biacore.com). The instrument used in our study, Biacore
X, is one of the commonly used instruments for SPR-based assays. One of the interacting
molecules, the ligand, is immobilized on a sensor chip consisting of a glass surface coated
with a thin layer of gold usually covered with a dextran hydrogel. The sample containing the
other interacting molecule, the analyte, is then injected over the surface in a controlled flow.
The SPR phenomenon occurs when light, under conditions of total internal reflection, strikes
a thin electrically conducting gold layer (Figure 10). Here, this gold layer is at the interface
between a glass slide and the flow channel with buffer, which have different refractive
indexes. When light strikes the gold film through a glass prism docked to the slide, an electric
field is generated, which extends from the opposite film interface into the buffer. At a certain
angle of incidence, the resonance angle, this so-called evanescent field excites the surface
plasmon, which is a collective oscillation (wave) of electrons that propagates along the gold
interface. This excitation is called surface plasmon resonance. When monitoring the intensity
of the reflected light as a function of the angle of incidence, the resonance angle can be
identified from a minimum. This is because most of the light energy has coupled into the
surface plasmon upon excitation at this angle. The resonance angle is dependent upon the
refractive index of the medium (the buffer or analyte solution) close to the gold layer. In this
way, changes of the refractive index at or close to the surface, for example due to binding of
biomolecules, results in displacement of the reflectance minimum (Figure 10a), which is
monitored as a function of time in a so-called sensorgram (Figure 10b). The displacement is
expressed in resonance units (RU, 1 RU ~1 pg/mm2 of any biomolecule). The contribution to
the refractive index change by any biomolecule binding to or dissociating from the surface is
usually proportional to the molecular mass. After the analysis, analyte molecules can be
removed using an appropriate regeneration solution that, ideally, does not affect the activity of
the immobilized ligand.
Subjects and methods ’
52
Figure 10. The principle of surface plasmon resonance, with a protein immobilized on the sensor
surface and a sample containing antibodies injected over the surface. Polarized light that strikes the
surface at a certain angle, the resonance angle, generates an electric field that excites a surface
plasmon, resulting in that the intensity of reflected light is at a minimum (I). When antibodies bind to
the protein, the refractive index close to the surface is changed, resulting in displacement of the
reflectance minimum (from I to II in graph a), which is displayed in a sensorgram (b), expressed as
resonance units (RU).
The SPR technique can identify binding partners to molecules of interest and monitor binding
specificity, but also provide quantitative information on analyte concentration as well as
kinetic and thermodynamic parameters describing the interaction.
The design of the sensor chip differs depending on the application. The carboxymethylated
(CM) dextran chip CM5 is a commonly used commercially available general-purpose chip for
interaction analysis involving all types of biomolecules (Biacore, Uppsala, Sweden). The
sensor chip is coated with gold and covered with a CM dextran layer (Figure 11). It is suitable
for concentration measurements, and is generally the first choice for protein immobilization
via amine coupling.
Sensor chip withgold film
Flow channel
Light-source
Polarizedlight
Prism
Opticaldetection
unit
III
Reflectedlight
I II
Intensity
Angle
Time
Resonancesignal
I
II
Sensorgram
a)
b)
Subjects and methods
53
Figure 11. The structure of a CM5 chip. At the bottom, the sensor chip coated with gold. On top of
that the dextran layer, which has been carboxylated.
Prior to immobilization of a ligand, a process called pre-concentration is often performed, to
get high absorption of the protein into the dextran matrix, and is optimized by adjusting the
pH-value. When using a CM5 sensor chip, the pH of the solution should be acidic to pre-
concentrate the protein in the negatively charged CM dextran matrix. This is usually obtained
at a pH of the antigen solution of approximately 1 pH unit below the isoelectric point of the
antigen.
Competitive epitope-specific RIA for determination of GADA epitopes
(Paper III)
The capacity of different rFab fragments to inhibit GADA binding to GAD65 was tested in a
competitive epitope-specific RIA. Serum samples were initially screened for GADA using the
traditional RIA, at a dilution of 1/25. Samples with a GADA titer exceeding 1000 U/ml were
diluted to <1000 U/ml and reanalyzed. This dilution was then used in the epitope-specific
RIA. Six GAD65-specific rFab were used to determine the GADA epitope specificity in
serum samples. The rFab were cloned, expressed and purified as described previously (Padoa
et al. 2003). Four of these have been related to type 1 diabetes (b96.11, DPA, DPD and
MICA3) (Richter et al. 1993; Padoa et al. 2003) and the other two (b78 and N-GAD65 mAb)
have been found in SPS patients (Raju et al. 2005). These rFab recognize epitopes on different
regions of GAD65, ranging from the N-terminal to the C-terminal part of the molecule
(Fenalti et al. 2008) (Figure 12). DPA and MICA3 are derived from two different patients
with type 1 diabetes, and recognize epitopes within the same epitope region on GAD65. The
insulin-specific rFab CG7C7 (Padoa et al. 2005) was included on each plate to determine non-
specific competition. The rFab were added at a concentration sufficient to compete binding of
the originating intact monoclonal antibody (mAb) to GAD65 by at least 80%, resulting in 4,
Sensor Chip CM5
Subjects and methods ’
54
0.6, 1.7, 1, 14, 7 and 2 μg/ml for rFab DPA, b96.11, DPD, MICA-3, N-GAD65 mAb, b78 and
CG7C7, respectively.
Figure 12. Illustration of the six rFab used in the competitive epitope-specific RIA, showing the
epitope regions on GAD65 recognized by each of them. The rFab recognize epitopes located at the N-
terminus (N-GAD65 mAb and DPD), middle region (b96.11) and C-terminus (DPA, MICA3 and
b78). DPA and MICA3 are derived from two different patients with type 1 diabetes, and recognize
epitopes within the same epitope region on GAD65.
Serum samples were incubated with rFab to inhibit GAD65 binding, or without rFab for non-
competed measurement, and the levels of GADA were determined by precipitation with
Protein A Sepharose. For each sample, non-competed GADA as well as GADA competed
with a GAD65-specific rFab, were measured in triplicates, from which a mean value was
calculated. This was done for each timepoint: baseline and month 1, 3, 9 and 15. The intra-
assay average coefficient of variation was 4%, with the highest value of 13% and the lowest
being 0.03%. Binding of GADA to GAD65 in the presence of rFab was expressed as follows:
ratio = GADA cpm in the presence of rFab (competed) / GADA cpm in the absence of rFab
(non-competed). A higher binding to GAD65 in the presence of an rFab indicates a lower
proportion of GADA binding to the respective epitope. The cutoff for specific competition
was determined as >15% by using the negative control rFab CG7C7.
A modified RIA for detection of IgG subclasses of GADA (Paper IV)
GADA of the subclasses IgG1, 2, 3 and 4 were measured using a modification of the
conventional RIA for GADA, based on a protocol kindly provided by Shilpa Oak (University
of Washington, Seattle, USA), with some further modifications in our lab. Briefly, serum
samples (5 µl) were incubated with 60 µl human recombinant 35
S-labelled GAD65 in a 96-
N-GAD65 mAb DPD DPAb96.11
96 173 308 365 483-499 556-585
b78
GAD65
483-499 556-585
532 540
MICA3
N-terminal C-terminal
224
Subjects and methods
55
well plate (Nunc 96 MicroWell plate, Nunc A/S, Roskilde, Denmark) under vigorous shaking
at 4°C over night. In parallel, biotinylated monoclonal mouse anti-human IgG1, IgG2 and
IgG4 (BD PharMingen, San Diego, CA, USA) and IgG3 (Southern Biotech, Birmingham,
AL, USA) were incubated with streptavidin agarose beads (Thermo Scientific, Rockford, IL,
USA), under vigorous shaking at 4°C over night. Thereafter, 50 µl of the mixture with 35
S-
labelled GAD65-GADA complexes and 50 µl of the biotinylated anti-human IgG antibody
coupled to streptavidin agarose beads were transferred into a 96-well filtration plate
(Millipore, Bedford, MA, USA), and incubated under vigorous shaking at 4°C for 2 h,
followed by washing 8 times with 150 µl/well assay buffer using a vacuum device
(Millipore). Then, scintillation liquid (OptiPhase Supermix, Perkin-Elmer Life Sciences
Wallac) was added to the wells, and the activity was measured in a liquid scintillation counter
(1450 Microbeta Trilux, Perkin-Elmer Life Sciences, Wallac). All samples were run in
duplicates. The cut-off value for each subclass was determined using a GADA negative
control, which was included in each assay. Results were expressed as cpm, and positivity of
each sample was calculated by subtraction of the mean cpm value plus three times the
standard deviation (SD) obtained for the negative control.
GAD65 enzymatic activity assay (Paper IV)
GAD65 enzyme activity was measured by a 14
CO2-trapping method based on the enzymatic
reaction of glutamate to GABA. GAD65 works as an enzyme together with the coenzyme
pyridoxal 5-phosphate (PLP). GADA-positive serum from SPS patients have shown to inhibit
this reaction (Raju et al. 2005). First, 150 l of a solution containing K2HPO4 (stock solution
50 mM), PLP (stock solution 3 mM, Fisher Scientific, Göteborg, Sweden) and GAD65 (stock
solution 0.2 mg/ml; Diamyd Diagnostics AB, Stockholm, Sweden) was mixed with 15 l
serum and incubated for 1 hour at room temperature. Thereafter, 28 l of a mix containing
K2HPO4 (stock solution 50 mM), L-glutamic acid (stock solution 5 mM, Fisher Scientific,
Göteborg, Sweden) and L-[14
C-(U)]-glutamic acid (0.4 Ci; Perkin Elmer, Boston, USA) was
added to each tube. All tubes were sealed with a rubber stopper attached with a center well
(Kimble Chase Kontes, Vineland NJ, USA), in which a NaOH-soaked (50 l of 1 M NaOH)
filter paper (Camlab Ltd, Cambridge, UK) was placed, and the reaction was carried out in a
water bath at 37°C for 1 hour with gentle agitation. The reaction was stopped by placing the
tubes on ice for 10 minutes. The radioactivity of the 14
CO2 captured on the filter papers was
measured in a Wallac Microbeta Liquid Scintillation Counter (Perkin Elmer Life and
Subjects and methods ’
56
Analytical Sciences, Inc, Boston, MA, USA). Serum from one SPS patient was included in all
assays as a positive control for inhibition.
Analysis of tetanus toxoid antibodies (Paper IV)
Determination of tetanus toxoid antibodies in serum was performed using an Immunozym
Tetanus Ab ELISA according to the manufacturer´s instructions (IBL, Hamburg, Germany).
The optical density was measured at 450 nm using a VersaMax microplate reader (MDS, Inc.,
Sunnyvale, CA, USA). Cut-off levels were determined at >0.070 IU/ml.
Analysis of IgE (Paper IV)
Total serum IgE was quantified using the ImmunoCap100 system (Phadia AB, Uppsala,
Sweden). In short, the principle of the method is binding of total IgE to anti-IgE covalently
coupled to ImmunoCap, followed by addition of enzyme-labelled antibodies against IgE,
addition of a developing agent and finally measurement of the fluorescence of the eluate. The
fluorescence is directly proportional to the concentration of IgE in the serum sample. The
measuring range for the assay was 2-50 000 kU/l, and calibrators were run in duplicates to
obtain a full calibration curve. Levels of total IgE ≥ 85 kU/l were regarded as positive.
C-peptide measurement (Paper IV)
C-peptide levels were measured in serum samples using a time-resolved fluoroimmunoassay
(AutoDELPHIATM
C-peptide kit, Wallac, Turku, Finland). Briefly, 25 µl of standards,
controls and patient serum samples were incubated together with anti-C-peptide-Eu tracer
solution in a microtiter plate coated with antibodies directed against C-peptide. After washing
and addition of enhancement solution, automatic measurement and result calculation was
performed using a 1224 MultiCalc® program (Wallac), with results expressed in pmol/ml.
The results in each assay were validated by inclusion of a C-peptide control module
containing a high, a medium and a low-level control (Immulite, DPC, UK).
Subjects and methods
57
STATISTICS
To elucidate if the immunological markers were normally distributed, the D’Agostino &
Pearson omnibus normality test was performed. One can also evaluate if a material seems to
be normally distributed by examining the distribution of the material in a histogram by
looking if the material has a similar shape as a normally distribution curve, or by comparing
mean and median values, which should be close to each other to support normally
distribution. When the material is large, the outcome data does not necessarily have to be
normally distributed for using parametric tests based on normally distributions (Lumley et al.
2002). A sufficiently large material in this context could be less than 100, but for
consideration of parametric tests, other parameters than actual size of the material are also
important, for example the shape of the distribution in the groups to be compared.
As the immunological markers in paper III and IV were not normally distributed, non-
parametric tests corrected for ties were used. In paper III, to investigate differences in
variability of the ratios over time, the sums of absolute differences between consecutive
measurements were compared using Mann-Whitney U-test. Unpaired analysis was performed
using Mann-Whitney U-test and correlations with Spearman’s rank order correlation test. Dot
plots and longitudinal plots were used to visualize the data. In paper IV, unpaired analyses
were performed using Kruskal-Wallis followed by the Mann-Whitney U-test. Differences
within the groups were analyzed by Friedman’s test followed by Wilcoxon-signed rank test.
In paper I, Fisher´s exact test was used to analyze frequencies of GADA and IA-2A, and the
relationship of HLA genotypes to autoantibodies and type 1 diabetes. Standard curves for RIA
were performed using GraphPad Prism 4 (GraphPad, Sotfware Inc., San Diego, California,
USA). Positive predictive value (PPV) in % was calculated from the formula: (antibody
positive who developed type 1 diabetes) / (antibody positive who developed type 1 diabetes +
antibody positive healthy individuals) * 100
In paper II, a cut-off for positivity for the 5-min method was calculated from the mean value
of a number of low IAA positive samples subtracted by 3 SD. In the standard curve plot with
error bars, the mean values are presented and the error bars show the range.
Subjects and methods ’
58
All statistical calculations were performed using the statistical package for the social sciences
(SPSS) for Windows (SPSS Inc., Chicago, Illinois, USA). In paper I version 13.0 was used, in
paper IV version 16.0 was used and in paper III the version named PASW Statistics 18 was
used. A two-tailed p-value of <0.05 was considered statistically significant.
ETHICAL CONSIDERATIONS
Since type 1 diabetes often develops in childhood, it is important to study environmental and
immunological factors influencing the development of type 1 diabetes in children, and not
only in adult individuals. It is very important to get more knowledge about why this disease
arises, to be able to prevent children from developing type 1 diabetes in the future.
Autoantibodies are used to predict the development of type 1 diabetes, which is crucial for the
identification of risk individuals to be included in prevention trials when candidate drugs are
available.
The protocol of the ABIS study was approved by the Research Ethics Committees of the
Medical Faculties of Linköping University, Linköping and Lund University, Lund. Parents
gave their informed consent to enter the study, and they were informed that the participation
was totally voluntary and could be discontinued at any time point without any specific reason.
The parents were told that they, upon active request, could receive information about genetic
risk genotypes or presence of autoantibodies, but that they were not going to be informed
about this automatically. The data from questionnaires and laboratory analyses were stored
without personal identifications, with only an ABIS code that identified the children in the
study. Ethical questions in the ABIS study have been studied separately (Stolt et al. 2002;
Stolt et al. 2005) as well as the maternal attitude to participation (Ludvigsson et al. 2001).
Collection of samples from children with manifest type 1 diabetes was approved by the
Research Ethics Committee of the Medical Faculty of Linköping University, Linköping and
parents gave their informed consent.
Approval for the phase II GAD-alum trial was obtained from the Swedish National
Regulatory authorities and the Research Ethics Committee of the Medical Faculty of
Linköping University, Linköping. Written informed consent was obtained from participants
and their parents according to the Declaration of Helsinki. The study was randomized and
Subjects and methods
59
double blinded. Since GAD-alum treatment seems to be safe, is easy to administrate and has
shown to be effective for preservation of endogenous insulin production compared to a
control group, we believe that the ethical benefits of the GAD-alum treatment exceed the
risks.
60
61
Results and discussion
AUTOANTIBODY POSITIVITY AND RELATION TO HLA GENOTYPES
(PAPER I)
The emergence of autoantibodies against beta-cell antigens is a dynamic process (Yu et al.
1996; Knip 1997). So far, there is no consensus on how often and at what age children in the
general population should be tested to achieve the best possible prediction value for clinical
type 1 diabetes. The emergence of autoantibodies has been investigated in different study
populations, including families with first-degree relatives of individuals having type 1
diabetes, and individuals with increased risk for type 1 diabetes determined by disease-
associated HLA genotypes. First-degree relatives of patients with type 1 diabetes have for
example been studied in the BABYDIAB (Hummel et al. 2004), DPT-1 (Krischer et al. 2003),
Childhood Diabetes in Finland (DiMe) (Savola et al. 2001) and the Bart´s Oxford and the
Munich family studies (Achenbach et al. 2004b). The DIPP study (Kimpimaki et al. 2001;
Kupila et al. 2001; Kukko et al. 2005) investigated instead populations with disease-
associated HLA genotypes. In the TEDDY (The TEDDY Study Group 2007) and DAISY
(Barker et al. 2004) studies, both individuals with HLA-risk genotypes and those with first-
degree relatives were included.
The rapid increase in the incidence of type 1 diabetes during the last decades cannot be
explained by genetic factors, but is probably instead related to changes in the environment.
Several environmental factors that seem to be related to development of type 1 diabetes have
been found, including viral infections, introduction of cow´s milk and gluten, beta-cell stress
and breast-feeding (Akerblom et al. 2002; Peng et al. 2006). To elucidate the influence of
environmental factors in the development of beta-cell autoimmunity and type 1 diabetes, it
would be appropriate to follow a general population that has not been selected for genetic
risk. Therefore, we studied the emergence of autoantibodies in children from the ABIS study,
and related the presence of autoantibodies to HLA-risk and -protective genotypes.
Results and discussion ’
62
GADA and IA-2A in children at three different ages
We started to investigate GADA and IA-2A in children from the ABIS study, from whom
samples were collected at 1, 2.5 and/or 5 years of age. Positivity to an autoantibody was then
related to HLA genotypes. We found that children positive for GADA or IA-2A at 5 years of
age had the HLA risk haplotype DR4-DQ8 or the high-risk genotype DR3-DQ2/DR4-DQ8
more often than autoantibody negative (GADA and IA-2A negative) children. In addition,
children at 5 years of age positive for IA-2A had the protective haplotype DR2-DQ6 more
seldom and the risk associated genotype DR4-DQ8/DR4-DQ8 more often than autoantibody
negative children. At 2.5 years of age, children positive for IA-2A had the protective genotype
DR2-DQ6/DR2-DQ6 more seldom than autoantibody negative children.
Similar relations between autoantibodies and HLA-risk and -protective alleles have been
found in other studies, as presented in Table 3. For example, GADA has been associated with
the HLA DR3-DQ2/DR4-DQ8 genotype (Kulmala et al. 2001; Bakhtadze et al. 2006) and the
DR4-DQ8 haplotype (Kulmala et al. 2001), which was also found in our study, as well as
associations for IA-2A with the DR4-DQ8 haplotype (Bakhtadze et al. 2006). An explanation
for differences in associations between autoantibodies and HLA alleles could be that in some
studies the autoantibodies were measured in individuals at onset of type 1 diabetes (Hagopian
et al. 1995; Genovese et al. 1996; Savola et al. 1998; Bakhtadze et al. 2006) while in other
studies in pre-diabetic individuals (Kulmala et al. 2000; Kulmala et al. 2001). Not all of the
healthy individuals with autoantibodies will develop diabetes, and therefore the HLA
genotypes in non-progressors might differ from the ones observed in individuals recently
diagnosed with type 1 diabetes. In our study, associations between beta-cell autoantibodies
and HLA-risk alleles were found only in children at 5 years of age. The lack of association at
1 and 2.5 years of age suggest that HLA-risk alleles related to type 1 diabetes are not
necessarily needed for presentation of beta-cell antigens and for induction of beta-cell
autoantibodies.
Results and discussion
63
Table 3. Associations for GADA or IA-2A positive children with HLA alleles, found in our study
and/or in previous studies. aab = autoantibody.
Permanent and transient autoantibodies
To further evaluate the associations of HLA genotypes with the presence of autoantibodies,
we investigated samples collected at all three time points (1, 2.5 and 5 years of age) from a
group of non-diabetic children in the ABIS study (Figure 13). Eight of 2329 (0.3%) children
were antibody positive at least at 2.5 and 5 years of age, i.e. had permanent autoantibodies. In
addition, 143 of 2329 (6%) children were autoantibody positive only at one time point
followed by autoantibody negativity, i.e. had transient autoantibodies. Children with
permanent autoantibodies had more often the risk associated DR4-DQ8 haplotype than
autoantibody negative children, while no associations with HLA risk or protective genotypes
were found for transient autoantibodies.
A previous study has shown that more than 50% of the children with permanent
autoantibodies had the high-risk genotype DR3-DQ2/DR4-DQ8, while no children with
transient autoantibodies had this genotype (Yu et al. 2000). That study demonstrated the
occurrence of permanent antibodies in 0.9% of children from the general population, which
was slightly higher than the percentage observed in our study (0.3%). However, it should be
noted that the study by Yu et al. differed in some aspects from our study. That study included
measurements of three autoantibodies; GADA, IA-2A and IAA, the age interval of the
children was 0-13 years, and the consecutive measurements were performed more often;
annually in autoantibody negative children and every 3-6 months after autoantibody positivity
was found. Further, in our study the group of children with permanent autoantibodies was
small, with only eight individuals, which makes it difficult to draw conclusions. In a recent
Autoantibody HLA alleles associated with aab's References
DR3 Genovese et al. 1996; Kulmala et al. 2000
DR3-DQ2/DR4-DQ8 Bakhtadze et al. 2006; Kulmala et al. 2001+our study
GADA DR4-DQ8 Kulmala et al. 2001+our study
DR3-DQ2 Savola et al. 1998; Hagopian et al. 1995
DR2-DQ6 (negatively associated) Kulmala et al. 2001
DR4 Genovese et al. 1996; Savola et al. 1998
DR4-DQ8 Bakhtadze et al. 2006; Kulmala et al. 2001+our study
IA-2A DR4-DQ8/DR4-DQ8 our study
DR2-DQ6 (negatively associated) our study
DR2-DQ6/DR2-DQ6 (negatively associated) our study
Results and discussion ’
64
study, both HLA risk genotypes and the T allele of the PTPN22 gene were shown to influence
progression to permanent islet autoimmunity (Steck et al. 2009).
Figure 13. Selection of children from the ABIS study for the follow-up of GADA and IA-2A at 1, 2.5
and 5 years of age. Thirty-two children had developed type 1 diabetes by the age of 6-7 years. Of the
2329 children from whom samples were available at all three ages, permanent and transient
autoantibodies were studied and related to HLA genotypes. A subgroup of autoantibody negative
children were randomly selected for HLA genotyping.
We found that children with transient autoantibodies and autoantibody-negative children had
a very similar pattern of HLA genotypes (Figure 14). This suggests that HLA-risk alleles are
not necessarily needed for the induction of beta-cell autoantibodies, but is likely rather
influenced by environmental factors or other genetic factors than HLA.
sample available
the occasion
before
diagnosis
All children in the ABIS study
n=17055
n=32developed
diabetes n=2329
n=22 n=10
GADA IA-2A
5
samples available
at all 3 ages
Yes No
54
n=8
aab
negative
n=14
n=678 n=143 n=8
aab
negative
transient
aab´s
permanent
aab´s
GADA and/or
IA-2A positive
n=2178 n=143 n=8
aab
negative
transient
aab´s
permanent
aab´s
HLA genotyped
Results and discussion
65
Figure 14. Percentages of individuals with risk, protective, a combination of risk and protective and
neutral HLA genotypes in children with type 1 diabetes, in healthy children with permanent
autoantibodies or transient autoantibodies and in healthy autoantibody-negative children.
Children with type 1 diabetes
By the age of 6-7 years we identified 32 children with type 1 diabetes among the 17055
participants in the ABIS study. HLA risk genotypes associated with type 1 diabetes were
frequently found in these children, whereas protective alleles were rare (Figure 14). The
percentages of HLA-risk and -protective haplotypes corresponded well with other studies,
showing that nowadays the contribution of high-risk HLA haplotypes is lower and the
proportion of protective haplotypes is higher among type 1 diabetes patients compared to 50
years ago (Hermann et al. 2003; Gillespie et al. 2004). This changed distribution of HLA
haplotypes could indicate that environmental factors are important for development of type 1
diabetes, not only in children at later ages, but also in young children as in our study. Three
percent (1/32) of the diabetic children in our study did not have any HLA-risk alleles, in
accordance with a study by Gillespie et al. where 5-8% of children diagnosed before 5 years
of age lacked risk-associated alleles (Gillespie et al. 2004).
By studying samples from the time point preceding the onset of type 1 diabetes we found that
64% of the children were positive for GADA and/or IA-2A. This is lower than what have
been reported by other studies (Bonifacio et al. 1995; Sabbah et al. 1999; Strebelow et al.
1999; Winter et al. 2002). The lower frequency of autoantibody positivity in our study might
be due to a longer interval between collections of samples, and in many individuals the last
sample before onset was taken a long time before onset (range 0-2.5 years). Thus, it cannot be
excluded that autoantibodies may have developed short after the last sampling. Another factor
that could contribute to the lower percentage of autoantibody positivity is that IAA was not
Type 1 diabetes Permanent aab´s Transient aab´s Negative aab´s
Risk
91%
Protective
0%
Neutral
3%
Risk and
protective
6%
Risk
50%
Protective
25%
Risk and
protective
25%
Neutral
0%Risk
28%
Protective
40%
Risk and
protective
19%
Neutral
13% Risk
27%
Protective
38%
Neutral
14%
Risk and
protective
21%
Results and discussion ’
66
measured in our study, because of the small volumes of EDTA blood that was collected from
the children at 1 and 2.5 years of age.
In conclusion, the strong relation between HLA risk alleles and type 1 diabetes observed in
our study confirms that HLA risk genotypes play an important role for development of type 1
diabetes. In contrast, HLA genotypes seem not to explain induction of autoantibodies,
especially transient autoantibodies, in the general population. Our results indicate that other
factors than HLA are involved in the initiation of autoimmunity.
DEVELOPMENT OF AN IMMUNOASSAY BASED ON SPR (PAPER II)
Despite a lot of research in the field of type 1 diabetes, there is still no prevention for the
disease. However, a number of ongoing and planned trials raise the hope for successful
prevention strategies (Schloot et al. 2007; Pescovitz et al. 2009; Rother et al. 2009; Tian et al.
2009; Ludvigsson 2011). If any treatment demonstrates to be beneficial for prevention, the
need to identify risk individuals is large. Although HLA-risk genotypes define some of the
risk individuals (Ilonen et al. 2002), the best way to identify risk individuals is through
autoantibodies (Verge et al. 1996; Bingley et al. 1997). Thus, large scale screening of
autoantibodies in children from the general population will be necessary to identify high-risk
individuals suitable for prevention treatment. At present, screening for autoantibodies is
commonly performed by the well established RIA methods. However, there are some
disadvantages with these methods, including the use of radioactively labelled material,
variations among the “in-house” batches of the GAD and IA-2 proteins, low sensitivity for the
IAA assay and long assay times for a sample. Thus, there is a need for better methods for
detection of autoantibodies.
The surface plasmon resonance (SPR) technology is an interesting candidate for detection of
autoantibodies, mainly due to the label-free detection, the short analysis time for each sample
and the possibility to simultaneously measure multiple antibodies. We therefore decided to
develop a method based on this technology, by using a Biacore instrument, for detection of
three of the autoantibodies commonly related to type 1 diabetes; GADA, IA-2A and IAA. We
aimed to decrease the analysis time per sample, and if possible, to increase the sensitivity of
the autoantibody measurement compared to RIA. We decided to start with GADA, since this
autoantibody is one of the most commonly observed in pre-diabetic children. In addition, a
Results and discussion
67
better method for GADA was of special interest for us since the GAD-alum trial was about to
start. This treatment might be a good candidate to be used in prevention trials with GADA
positive individuals.
Attempts to develop an SPR-based immunoassay for GADA
We immobilized GAD65 at pH 5.0 via amine coupling, resulting in moderate levels of
immobilized protein at the dextran surface. However, the injections of GAD65 mAbs and a
GADA positive serum, to the surface immobilized with GAD65, induced low and inconsistent
binding responses. A possible explanation for this could be that the conformation of the
GAD65 protein might have changed upon binding to the surface. In addition, after
regeneration with NaOH, the binding responses were different from the ones observed before
regeneration. We tried to overcome these problems by attaching the GAD65 protein to the
surface using another coupling chemistry. Instead of immobilizing GAD65 directly to the
sensor surface, we immobilized streptavidin to the surface and then added biotinylated
GAD65, which binds to streptavidin. However, this setup did not perform better than GAD65
immobilized directly to the surface.
A number of different regeneration solutions and cocktails, such as NaOH (pH ranging from
10.7 to 12.7), glycine pH 3.0 and different ionic solutions, were tested without finding any
solution that regenerated the surface sufficiently. David Myszka, in cooperation with Chris
Hampe, has previously observed difficulties to regenerate the surface by using acidic or basic
regeneration solutions after interaction with GAD65 (unpublished report, partially published
in (Raju et al. 2005)). The problems with the low binding of antibodies to GAD65 and the
altered binding after regeneration, suggest that GAD65 is a pH-sensitive and unstable protein.
Consequently, since the GAD65 protein seemed to be problematic in this kind of assay, we
did not continue the development of the assay.
We did not develop an SPR-based method for detection of IA-2A, mainly due to difficulties
to obtain the IA-2 protein and antibodies towards IA-2 to test the assay.
Results and discussion ’
68
Development of the SPR-based immunoassay for IAA
Pre-concentration of human recombinant insulin to the dextran matrix was optimized. Then,
insulin (100 μg/ml) at pH 5.0 was immobilized via amine coupling to a CM5 chip. This
procedure was started by activation of the carboxyl groups at the sensor chip with 0.2 M N-
ethyl-N-(3-diethylaminopropyl) carbodiimide (EDC) and 0.05 M N-hydroxysuccinimide
(NHS) during 7 minutes (Figure 15). Thereafter, insulin was injected during 7 minutes for
immobilization, followed by blocking of excess activated carboxyl groups with ethanolamine
hydrochloride (1 M, pH 8.5) for 7 min. The surface was finally washed twice with glycine-
HCl (10 mM, pH 1.8) for 1 min. The level of immobilized insulin varied to some extent
between different sensor chips, but stayed at 1300 ± 200 RU. This was the highest level
possible to immobilize, and could not be increased by injection of higher insulin
concentrations or by increasing the time that insulin was allowed to pass the surface.
Figure 15. Immobilization of insulin followed by regeneration and binding of a monoclonal anti-
human insulin antibody. Injection of EDC/NHS (1), insulin (2), ethanolamine (3), glycine-HCl (4) and
finally a monoclonal anti-human insulin antibody (5) are shown in the sensorgram.
To assure that binding to the immobilized insulin worked properly, a standard curve, with a
monoclonal anti-human insulin antibody (Art. No. I7660-14, Biosite, recognizing an epitope
including amino acid B30) diluted in running buffer, was run once (Figure 16A). In following
experiments, new surfaces immobilized with insulin were checked for activity with the same
insulin antibody at a concentration of 20 µg/ml.
1
2
5
3
4
Time
s
Results and discussion
69
Figure 16. The anti-human insulin antibody concentrations injected were 10000, 5000, 2500, 1250,
625, 313, 156 and 78 ng/ml, resulting in responses of 1352, 630, 290, 137, 66, 31, 14 and 6 RU,
respectively, as shown in the standard curve (A). In panel B, the anti-human insulin antibody at
concentrations of 10000, 5000, 2500, 1250, 625 and 313 ng/ml were diluted in an IAA-negative serum
instead of buffer. The responses were 948, 361, 163, 102, 41 and 4 RU, after subtraction of the
response for the serum alone (169 RU), and were presented in panel B (black squares) together with
the standard curve from panel A (black circles).
Efforts to reduce non-specific binding of serum proteins to the sensor surface
The next step in the assay development was to introduce serum to the immobilized surface.
Non-specific binding of serum components to the surface of the sensor chip often gives a
large SPR signal, which masks the analyte response. This is especially evident when
measuring molecules of low concentration in serum, which is the case for IAA. We observed
high non-specific binding of serum proteins during initial experiments. It has been reported
that when the concentration of a molecule is high enough to be detected after diluting serum
1/20, the problem with non-specific binding of serum matrix proteins can be overcome
(Karlsson et al. 1993). However, when detecting IAA in human serum, the generally very low
concentrations do not allow the use of diluted samples in the assays.
Other ways to overcome the problem with non-specific binding of serum proteins are to use a
proper design of the surface chemistry on the sensor chip and/or to introduce additives to the
analyte solution. Different non-sticky surface coatings, based on dextran (Löfås et al. 1990) or
PEG (Herrwerth et al. 2003; Gobi et al. 2007; Larsson et al. 2007; Shankaran et al. 2007),
have been developed. For the development of our SPR-based immunoassay we used the CM5
chip (Figure 11). A CM4 chip, with low carboxylation, is sometimes used to reduce non-
A.
050
0
1000
1500
2000
2500
0
50
100
150
200
250
2600
1000
0
3001400
Insulin ab (ng/ml)
Resp
on
se (
RU
)B.
0
2000
4000
6000
8000
1000
00
500
1000
1500
Insulin ab (ng/ml)
Resp
on
se (
RU
)
Results and discussion ’
70
specific binding (Raju et al. 2005) (www.biacore.com). However, since assays for
determination of the concentration of an analyte generally require a high surface
concentration of the immobilized molecule, this chip with fewer immobilization sites was not
a suitable choice for our assay. Addition of CM dextran to a serum sample has shown to
reduce the non-specific binding to CM-coated surfaces with up to 75% via a competition
effect (Karlsson et al. 1993). We therefore chose to use CM dextran and NaCl for reduction of
non-specific binding. When the concentrations of these additives were optimized (1 mg/ml
CM dextran and 0.35 M NaCl) an evident reduction of the non-specific binding signal was
observed.
After reduction of the non-specific binding it was possible to detect the insulin antibodies in
serum. A standard curve, with different concentrations of the monoclonal anti-human insulin
antibody diluted in an IAA-negative serum sample, was run. The shape of the curve was
similar, but with lower responses, than the standard curve obtained with different
concentrations of insulin antibody diluted in buffer (Figure 16B).
Detection of IAA in serum using the 3-day and 5-min methods
Since the non-specific binding fluctuated among sera from different individuals, it was
difficult to measure IAA directly in serum. To overcome this problem, an indirect competitive
immunoassay was developed. In this method, each serum sample was divided in two equal
aliquots; one was mixed with buffer and the other with an excess amount of insulin, similarly
to the procedure for detection of IAA by RIA. In RIA, the following step is incubation of the
serum solutions for 72 hours. Since one of our objectives was to develop a method which was
not as time consuming as RIA, we aimed to reduce the time required for analysis of one
sample. Thus, the serum mixtures were incubated under agitation either for 72 hours at 4ºC
(according to the 3-day method) or for 5 minutes at room temperature (according to the 5-min
method). Thereafter, each serum mixture was diluted with CM dextran and NaCl for reduction
of non-specific binding, and then injected over the sensor surface immobilized with insulin.
When the portion of serum mixed with buffer was introduced to the surface, IAA in the
sample was free to bind the immobilised insulin on the surface (Figure 17, lower left). In
contrast, when serum mixed with insulin was injected, competition for binding of IAA
between insulin in solution and insulin immobilised on the surface took place. However, due
to the large excess of insulin molecules in solution, very little binding of IAA to the surface-
Results and discussion
71
bound insulin would occur. The resonance signal obtained from this sample is instead a
measure of non-specific binding to the immobilized insulin and to the dextran matrix.
The levels of IAA interacting with insulin were too low to be directly quantified. To enhance
the response signal, a secondary polyclonal anti-human IgG antibody (50 µg/ml in HBS-EP)
was injected immediately after the serum injection was completed. After a short regeneration-
pulse with glycine-HCl at pH 1.8 the surface was ready to be used for a new serum injection.
Regeneration of the surface removed both specifically and non-specifically bound serum
components from the surface. The regeneration was very reliable, providing a stable baseline,
and a chip could be used for numerous (at least 50) repeated cycles without any visible
decrease in response.
To calculate the level of IAA in a serum sample (R), the secondary antibody response from
serum mixed with buffer (Ba-Bb) was subtracted from the response obtained when the serum
was mixed with excess insulin (Ia-Ib) (Figure 17), according to the formula:
)()( baba IIBBR ,
where Ba = response for sample incubated with buffer after secondary antibody, Bb = before
secondary antibody, Ia = response for sample incubated with excess insulin after secondary
antibody and Ib = before secondary antibody.
To assess if we were able to detect different levels of IAA in serum, we run a standard curve,
using both the 3-day and the 5-min method. A pooled high IAA-positive serum was diluted to
different concentrations of IAA (0-40 U/ml) in pooled serum from IAA-negative non-diabetic
individuals. This was done to yield a range of positive samples with almost identical serum
matrix composition. The SPR-run showed that the responses increased with increasing IAA
concentrations, both for the 3-day (Figure 18A) and the 5-min method (Figure 18B).
Results and discussion ’
72
Figure 17. The principle of IAA detection with the indirect competitive immunoassay based on SPR
(lower part of the figure) as well as a full response cycle for a serum sample mixed with buffer (upper
left) and with insulin (upper right) is shown. In the figure, the different steps during the cycle are
illustrated as follow: 1 = start of injection of serum mixed with buffer, 2 = end of serum injection, 3 =
start of secondary antibody injection, 4 = end of secondary antibody injection, 5 = start of
regeneration, 6 = end of regeneration, 7 = start of injection of serum mixed with insulin, 8 = end of
serum injection, 9 = start of secondary antibody injection, 10 = end of secondary antibody injection,
11 = start of regeneration and 12 = end of regeneration. The difference in secondary antibody response
from a serum sample incubated in buffer (Ba-Bb) and insulin (Ia-Ib), respectively, gives a measure of
the level of IAA in the sample.
100 200
Results and discussion
73
Figure 18. Standard curves from (A) the 3-day and (B) the 5-min methods. A pooled high IAA
positive serum was diluted to concentrations of 0, 4, 6, 10, 20 and 40 U/ml for the 3-day method,
resulting in SPR responses of -4, 8, 15, 17, 20 and 29 RU. For the 5-min method, the concentrations of
the pooled high IAA positive serum were 0, 4, 6, 7.5, 10, 20 and 40 U/ml, giving SPR responses of 45,
44, 47, 54, 61, 67 and 80 RU. For both methods, the IAA positive serum was diluted in pooled serum
from IAA negative non-diabetic individuals. U/ml is an arbitrary unit from the reference RIA method.
Since the non-specific binding of serum proteins to the surface is very different among
different individuals, we wanted to evaluate if a similar standard curve could be obtained
when using sera from 7 non-diabetic individuals instead of one pooled serum as background
for the standard curve. Therefore, another set of standard curves was run, with both the 3-day
and the 5-min method. Each IAA-containing sample was diluted in a single IAA-negative
serum, and different single IAA-negative serum samples were used for the different
concentrations of the IAA-positive sample. Again, the SPR responses increased with
increasing IAA concentrations (Figure 19 and 20), confirming that the SPR-based indirect
competitive immunoassay was suitable for the measurement of IAA in serum from different
individuals. To determine variations in the method, we run the standard curve with the 5-min
method three times (Figure 20). Reproducibility was found to be excellent, with low variation
between the runs (Table 4).
A
5 10 15 20 25 30 35 40 45
-10
-5
0
5
10
15
20
25
30
35
40
U/ml
RU
B
0 5 10 15 20 25 30 35 40 4540
45
50
55
60
65
70
75
80
85
U/ml
RU
Results and discussion ’
74
Figure 19. Standard curve from the 3-day method. Different concentrations of a pooled high IAA-
positive sample (0, 4, 6.6, 10, 20 and 40 U/ml) were diluted in different single IAA negative serum
samples from non-diabetic individuals, resulting in SPR responses of -8, 4.9, 15.9, 23.2, 37.6 and 45.2
RU.
Figure 20. Standard curve from the 5-min method, based on three repeated runs. A high IAA-positive
sample was diluted to different concentrations in different single serum samples from IAA-negative
non-diabetic individuals. The values of the mean responses from the SPR run for these samples are
shown below in Table 4. Sample A, B, C and D are non-pooled sera from type 1 diabetic patients (A =
32.5 U/ml, B = 24 U/ml, C = 11.3 U/ml and D = 5.2 U/ml).
5 10 15 20 25 30 35 40 45
-20
-10
0
10
20
30
40
50
60
U/ml
RU
Results and discussion
75
Table 4. Test of reproducibility of a standard curve from the 5-min method, with different
concentrations of an IAA-positive sample diluted in different single serum samples from IAA-negative
non-diabetic individuals. The corresponding SPR responses for the three runs are shown, as well as
mean and CV (coefficient of variation).
High-affinity IAA
It has been shown that IAA of high affinity is related to progression to type 1 diabetes
(Achenbach et al. 2004a; Schlosser et al. 2005b). Thus, methods for detection of IAA should
be designed to measure high-affinity antibodies. We were therefore interested to find out if
the IAA measured with the SPR-based immunoassay were of high affinity. The sites for
antigen immobilization in an immunoassay might affect the nature of the antibodies being
captured. In our experiments, possible coupling sites on the insulin molecule to the dextran
matrix are the N-termini of the A and B chains and the lysine residue at position B29, since
these positions contain amino groups. Thus, the A8-A13 region, which is important for
binding of high-affinity IAA (Achenbach et al. 2004a), should be available for binding after
immobilization of the insulin molecule. This speaks in favor of that the observed response in
the SPR-based assays can be originated from high-affinity IAA. Other important arguments
supporting this idea are that the same conditions as in the incubation step in RIA were used in
our 3-day method, and that all the IAA-positive serum samples measured in the SPR-based
system were from newly diagnosed type 1 diabetic patients, and consequently should be
disease-specific and thereby IAA of high affinity.
The 5-min method compared to RIA
We evaluated differences between the responses detected by the 3-day and the 5-min
methods. The methods showed to perform similarly, suggesting that the 5-min method
probably also detect high-affinity IAA. Thus, we believe the 5-min method is suitable for
SPR-based detection of IAA in serum. In RIA, the mixture of IAA-containing serum and
IAA (U/ml) Mean CV (%)
0 46.7 46.5 44.7 46.0 2.4
2 48.9 35.3 37.6 40.6 17.9
4 41.1 39.9 38.4 39.8 3.4
6 64.7 61.7 61.4 62.6 2.9
8 84.7 85.8 83.5 84.7 1.4
10 86.0 88.8 90.0 88.3 2.3
20 92.0 87.8 91.8 90.5 2.6
40 136.0 132.6 135.3 134.6 1.3
Response (RU)
Results and discussion ’
76
labelled insulin is incubated for 3 days, capturing the high-affinity antibodies (Wilkin 1990).
In the Biacore system, the continuous flow enabling repeated associations and dissociations
between antigens and antibodies, might explain the functionality of the 5-min method,
although the incubation time is shortened to only 5 minutes.
By using a calculated cut-off value for the 5-min method, the serum samples were classified
as IAA-positive or -negative in accordance with the results obtained from the measurement
with RIA (Figure 20). To determine the cut-off level for the 5-min method, a number of IAA-
negative samples from non-diabetic individuals as well as low IAA-positive samples from
newly diagnosed type 1 diabetic patients were measured with the SPR method. The cut-off for
positivity was set to 6 U/ml, by calculating the mean value of the low IAA positive samples
subtracted by 3 SD. None of the IAA-negative samples exceeded this value, indicating that
this cut-off value was suitable for our assay. Since the RU value for the same sample might
differ to some extent between different runs, depending on the amount of insulin immobilized
on the surface, the cut-off value cannot be expressed as RU. In RIA, the cut-off for positivity
for IAA was 6.45 U/ml, corresponding to the 98th
percentile of 114 healthy Swedish children
(Holmberg et al. 2006).
The similar cut-off levels, i.e. 6 and 6.45 U/ml, for the SPR-based method and RIA
respectively, indicate that the sensitivity for detection of IAA with both methods was similar.
However, the IAA levels differed to some extent between the 5-min indirect competitive
immunoassay and the RIA. A possible explanation might be that with the SPR-based assay
we probably detected IAA of the IgG3 subclass, which cannot be detected in RIA using
protein A sepharose for precipitation. In a study by Hoppu et al, IAA of IgG1 and IgG3
subclasses were predominant in genetically susceptible young children who rapidly
progressed to clinical type 1 diabetes, while a weak or absent IgG3 response was associated
with relative protection from disease (Hoppu et al. 2004c).
The SPR-based immunoassay for detection of IAA was time-saving compared to RIA. The
RIA procedure takes 4 days to perform, and 15 samples can be run simultaneously in one
plate. The same number of samples in the SPR-based immunoassay takes only approximately
13 hours to run, since the time to run one sample in this system is about 50 minutes and the
samples are run after each other. One disadvantage with the SPR-based immunoassay
compared to RIA is the larger volume of serum needed for the assay; 100 µl compared to only
Results and discussion
77
30 µl in RIA. However, the advantages of the 5-min method, with no need for labelling of
interactants and that it is time-saving compared to RIA, speak in favor for the use of the SPR-
based assay when sample volume is not a limiting issue.
Type 1 diabetes-related analytes measured with SPR: Results from other
studies
SPR has been used in several studies to measure biomolecules in diluted serum (Vikinge et al.
1998; Gomara et al. 2000; Jongerius-Gortemaker et al. 2002) and some have detected
biomolecules in undiluted serum (Masson et al. 2007; Phillips et al. 2007). A number of
studies have measured type 1 diabetes-related analytes with SPR. For example, Ayela et al
detected a polyclonal antibody against a synthetic peptide from the juxtamembrane region of
IA-2, in diluted serum (1/100), using mixed self-assembled monolayers (SAM) on a gold
surface as the sensor surface (Ayela et al. 2007). Lee et al measured monoclonal anti-GAD65
antibodies diluted in buffer, using an alkanethiol SAM sensor surface, by immobilizing
streptavidin on the surface followed by injection of biotin-GAD (Lee et al. 2005). Another
study focused on measurement of insulin, both in buffer and in diluted serum (1/4 and 1/10),
using a PEG-containing SAM sensor surface (Gobi et al. 2007). They used an indirect
competitive immunoreaction principle, in which a monoclonal anti-insulin antibody was
mixed with different concentrations of insulin prior to injection into the SPR instrument.
Thereafter, the insulin concentration was indirectly measured by detecting the binding of the
anti-insulin antibody to the sensor surface immobilized with insulin. In another study, the
level and affinity of insulin antibodies (IA) in diabetic patients after receiving exogenous
insulin were measured with SPR, using a CM5 chip (Kure et al. 2005). Since the levels of IA
in diabetic patients after receiving insulin are generally much higher than the levels of IAA
commonly observed in newly diagnosed type 1 diabetic patients and non-diabetic individuals,
their assay did not detect low concentrations of antibodies, as we aimed to do. Also, due to the
relatively high concentrations of IA, they were able to overcome the problems with non-
specific binding of serum proteins to the dextran matrix by diluting the serum. Our SPR-based
indirect competitive immunoassay for IAA is, to our knowledge, the only published report on
IAA quantification in serum samples using SPR.
Results and discussion ’
78
Further and future experiments with the 5-min method
After publication of the methodological paper with the SPR-based indirect competitive
immunoassay for detection of IAA (Paper II), further experiments were performed. To
evaluate the capability of the method for large scale screening of serum samples, 86 serum
samples were analysed. The method detected the majority of the IAA-positive samples,
previously classified as positive by RIA, and also most of the IAA-negative samples were
correctly classified. However, we observed that some serum samples showed aberrant high
responses, higher than what is commonly observed in positive samples with high IAA levels.
To date, we have no explanation for this result, but it was interesting to observe that some of
the sera were collected from diabetic patients while others came from non-diabetic
individuals. Further experiments are needed to clarify the reason for the aberrant high
responses.
In conclusion, we have developed an SPR-based indirect competitive immunoassay for
detection of IAA in serum. The main advantages with this assay are that there is no need for
labelling of interactants and it is time-saving compared to RIA, but further development of the
method is needed.
EFFECTS OF THE GAD-ALUM TREATMENT ON THE HUMORAL
IMMUNE RESPONSE (PAPER III AND IV)
Increased levels of GADA
In the phase II clinical trial performed in children and adolescents with type 1 diabetes,
treatment with GAD-alum resulted in preservation of residual insulin secretion in children
with short duration of the disease, shown as a declined decrease in C-peptide levels. A
number of effects on the immune system have been observed after this treatment (Axelsson et
al. 2010; Hjorth et al. 2010), including increased levels of GADA (Ludvigsson et al. 2008)
(Figure 21). The GADA levels started to increase already after the first injection of GAD-
alum, and peaked at 3 months, after the booster dose. Further, still 30 months after the first
injection the levels were higher compared to the placebo group.
Results and discussion
79
Figure 21. Median levels of GADA for the GAD-alum group (black circles) and the placebo group
(white circles) at baseline and at 1, 3, 9, 15, 21 and 30 months after initial injection. GADA titers
increased after injections of GAD-alum, and at month 3, the titers were significantly higher in the
GAD-alum compared to the placebo group. This difference persisted throughout the 30 months study
period.
In a GAD-alum dose-finding study in LADA patients, GADA levels did not increase after
injection of 20 μg of GAD-alum, the dose that resulted in clinical effect (Agardh et al. 2005)
and was used in our study. However, in the same study, increased GADA levels were
observed in patients treated with 500 μg of GAD-alum, and were accompanied by a
temporary increase in GADA-binding to one epitope specificity often related to type 1
diabetes (Bekris et al. 2007). We were therefore interested to study whether the epitope
binding pattern of GADA was affected by the GAD-alum treatment in type 1 diabetic
patients.
Temporary increase in binding to one rFab-defined epitope
To investigate if the epitope specificity of GADA was affected by the GAD-alum treatment,
we used six rFab (b96.11, DPA, DPD, MICA3, b78 and N-GAD65 mAb) recognizing
different epitope regions on GAD65 (Paper III). The b96.11-defined epitope was generally
0
1000
2000
3000
4000
P=0.001
N.S
N.S
P=0.01
P=0.04P=0.01
P=0.03
0 1 3 9 15 21 30
Month
GA
DA
(U
/ml)
↑ ↑Injections
Results and discussion ’
80
predominant and observed in the majority of the tested sera of both GAD-alum and placebo
treated children (Figure 22). This was expected, since this epitope specificity has previously
been shown to be predominant in type 1 diabetic patients (Padoa et al. 2003). Further, binding
to the DPD- and MICA3-defined epitopes was also observed in our study, previously found in
other studies with type 1 diabetic individuals (Richter et al. 1993; Padoa et al. 2003).
( )
Results and discussion
81
Figure 22. Binding to GAD65 in the presence of rFab b96.11 (A), DPD (B), MICA-3 (C), DPA (D),
b78 (E) and N-GAD65 mAb (F) at baseline, 3 months and 15 months for GAD-alum and placebo
groups, presented as ratio of competed / non-competed. A higher binding to GAD65 in the presence of
an rFab indicates a lower proportion of GADA binding to the respective epitope. Ratios above 1.4 are
placed within brackets outside the range of the y axis.
Overall, there were no longitudinal changes in binding to any of the epitopes, including
b96.11, over the 15 months study period (Table 5). Binding levels to the DPA-, N-GAD65
mAb- and b78-defined epitopes were very low throughout the study (Figure 22) and were
therefore excluded from the statistical analyses.
Table 5. The variability over time (baseline to 15 months) was investigated by calculation of the sum
of absolute differences between consecutive measurements, and compared for the GAD-alum and
placebo groups.
Epitope Sum of absolute differences (min, max) p-value
GAD-alum Placebo
b96.11 40.0 (3.6, 108.2) 30.1 (3.9, 78.9) 0.07
DPD 37.3 (17.3, 76.6) 46.3 (13.9, 92.8) 0.48
MICA3 42.1 (16.3, 108.7) 40.8 (11.1, 90.4) 0.75
Next, as GADA titers peaked at 3 months, we were interested to study whether this increase
in GADA titer was accompanied by any change in GADA epitope recognition between two
visits. For this analysis, binding of GADA to rFab-defined epitopes was compared between
samples collected at two time points (baseline to 1 month, baseline to 3 months, and 3 months
( ) ( ) ( ) ( )
Results and discussion ’
82
to 15 months). Our results showed that binding to the b96.11-defined epitope changed
temporarily, with increased binding between baseline and 3 months in the GAD-alum treated
group compared to the placebo group (Table 6). This effect was especially evident in the
group of GAD-alum treated patients who experienced an increase of more than 100% in their
GADA titer from baseline to 3 months. The increase in binding to the b96.11-defined epitope
was followed by decreased binding between 3 and 15 months, resulting in similar levels at 15
months as the ones observed at baseline. Similar results were observed in the GAD-alum
dose-finding study on LADA patients, in which treatment with the highest dose of GAD-alum
(500 µg) was accompanied by increased GADA levels (Agardh et al. 2005), and temporarily
increased binding to the b96.11-defined epitope (Bekris et al. 2007). The changes in b96.11
epitope recognition after treatment with 20 µg GAD-alum found in our study, was not seen in
the group of LADA patients receiving the same dose of GAD-alum resulting in clinical effect.
This might be due to the small number of individuals in the LADA study, or alternatively,
changes in b96.11 epitope recognition might be explained by the increased GADA levels in
the type 1 diabetic patients, which was not observed in LADA patients after the same dose of
GAD-alum.
Higher GADA titers have previously been associated with broader epitope reactivity (Gilliam
et al. 2004; Ronkainen et al. 2004). In our study, we found no correlation between GADA
titer and epitope binding at 3 and 15 months in the GAD-alum group, but in the placebo group
binding to the b96.11- and DPD-defined epitopes correlated with GADA titer (Table 7). We
cannot explain the lack of correlation in the GAD-alum group after treatment. The treatment
induced very high GADA titers, often much higher than the ones detected in the majority of
GADA positive diabetic individuals. We could speculate that the binding to the b96.11-
defined epitope did not increase in the same proportion as the induced GADA. It could also be
possible that some of the induced GADA bind to other epitopes on GAD65 than those tested
in our study. The temporary increased binding to the b96.11-defined epitope could be due to
the activation and further upregulation of the already existing memory B cell responses.
Although the increase in binding to the b96.11-defined epitope was only temporary, the
GADA levels remained higher in the GAD-alum group compared to placebo even 30 months
after treatment. It cannot be excluded that as a part of the immunomodulatory effect of the
treatment, GAD-alum might have induced memory B cells producing GADA with other
epitope specificities than those tested in our study. This is an interesting hypothesis to be
confirmed in future studies.
Results and discussion
83
Table 6. Differences in epitope binding between GAD-alum and placebo patients, from baseline to 1
month, baseline to 3 months, 3 months to 15 months and baseline to 15 months. Changes in median
binding to GAD65 in the presence of rFab b96.11, DPD and MICA3 between two time points were
calculated and the differences in median response were compared between the GAD-alum and placebo
groups. This was also done for individuals in the GAD-alum group who experienced an increase in
GADA titer of more than 100% between baseline and 3 months (high GADA increasers, HGI)
compared to individuals in the same group without this increase (non-HGI).
Epitope Months compared Difference in median response % (min, max) p-value
GAD-alum Placebo
0-1 -2.8 (-54.4, 52.6) 0.5 (-28.4, 28.2) 0.45
b96.11 0-3 -8.1 (-72.4, 39.6) 1.5 (-28.3, 28.6) 0.02 *
3-15 8.3 (-17.1, 36.7) -2.4 (-32.8, 30.1) <0.05 *
0-15 -5.6 (-62.5, 62.4) 1.6 (-25.6, 35.7) 0.24
0-1 0.9 (-24.1, 23.3) 3.2 (-15.3, 32.5) 0.59
DPD 0-3 -1.8 (-43.6, 41.5) 2.4 (-28.6, 32.7) 0.87
3-15 3.2 (-15.9, 24.3) 2.6 (-37.4, 26.7) 0.66
0-15 -2.5 (-42.3, 42.1) -0.1 (-49.8, 37.3) 0.85
0-1 5.6 (-21.8, 51.7) 1.2 (-9.5, 49.4) 0.30
MICA3 0-3 3.1 (-31.1, 56.8) 0.1 (-36.6, 25.4) 0.18
3-15 5.3 (-34.3, 20.7) -0.9 (-42.1, 36.1) 0.20
0-15 0.1 (-33.9, 39.2) -0.1 (-36.2, 25.3) 0.89
GAD-alum HGI GAD-alum non-HGI
b96.11 0-3 -10.8 (-72.4, 30.5) 3.1 (-7.9, 39.6) 0.04 *
Results and discussion ’
84
Table 7. Correlations between GADA titer and binding of GADA to GAD65 in the presence of rFab
b96.11 or DPD for all individuals, and for the GAD-alum and placebo groups, at baseline, 3 months
and 15 months after the first injection.
Epitope Time point Individuals Correlation with GADA titer
p-value r
b96.11 baseline all individuals 0.001 ** -0.423
GAD-alum 0.021 * -0.400
placebo 0.073 -0.350
3 months all individuals 0.018 * -0.305
GAD-alum 0.688 -0.073
placebo 0.005 ** -0.524
15 months all individuals 0.017 * -0.308
GAD-alum 0.283 -0.193
placebo 0.019 * -0.448
DPD baseline all individuals 0.003 ** -0.377
GAD-alum 0.011 * -0.436
placebo 0.088 -0.335
3 months all individuals 0.075 -0.232
GAD-alum 0.840 0.036
placebo 0.004 ** -0.538
15 months all individuals 0.025 * -0.289
GAD-alum 0.254 -0.205
placebo 0.048 * -0.385
Changes in IgG subclasses of GADA
Since the GAD-alum treatment affected GADA levels and temporarily increased binding to
the b96.11 defined epitope, we were interested to investigate if there were any further effects
of the GAD-alum treatment on GADA. In humans, Th1 cells and their related cytokines seem
to stimulate the production of IgG1 while Th2 cells stimulate the production of IgG4 and IgE
(Gascan et al. 1991; Couper et al. 1998; Bonifacio et al. 1999; Hawa et al. 2000). Based on
results from animal studies, a Th2 profile has been suggested to be protective against the
aggressive damage inflicted on the insulin-producing cells (Cameron et al. 1997; Petrovsky et
al. 2003). The GAD-alum treatment in type 1 diabetic children induced a Th2 response, with
increased levels of IL-5 and IL-13 already 1 month after the first injection (Axelsson et al.
2010). A broad range of cytokines were induced at 3 and 9 months, but only the two Th2
associated cytokines IL-5 and IL-13 together with FOXP3 increased continuously over time.
Results and discussion
85
Therefore, we were interested to study the IgG subclass distribution of GADA. Samples from
patients in the GAD-alum and placebo groups were investigated at baseline and at 3 and 15
months after initial treatment (Paper IV). Frequencies of the different IgG subclasses were
calculated with respect to total IgG in each sample at every time point. IgG1 was the
predominant IgG subclass of GADA, both in GAD-alum and placebo individuals (Table 8).
This is in accordance with previous studies showing a predominance of IgG1 both in
prediabetic individuals and in newly diagnosed type 1 diabetic individuals (Bonifacio et al.
1999; Hoppu et al. 2004a; Ronkainen et al. 2006).
At baseline, the percentages of IgG1, 2, 3 and 4 were similar in both groups. Three months
after initial injection of GAD-alum a reduced percentage of IgG1 and increased IgG3 and
IgG4 were detected in GAD-alum treated individuals (Figure 23). Emergence of the IgG4
subclass of GADA has been observed in genetically susceptible children who remained non-
diabetic, while children who developed type 1 diabetes often had a broad initial response of
IgG subclasses and rarely developed IgG4 before onset of the disease (Ronkainen et al. 2006).
GADA of the IgG4 subclass has been found to appear more often in LADA patients than in
type 1 diabetic patients (Hillman et al. 2009), indicating that IgG4 might be associated with
slower progression to clinical diabetes, perhaps due to a more balanced immune response in
the pancreatic tissue (Couper et al. 1998; Hillman et al. 2004). Thus, an increased percentage
of IgG4 and decreased IgG1 after GAD-alum treatment might further support the suggestion
that a predominant protective Th2 response was induced as a result of the treatment. While
IgG1 has been associated with Th1 and IgG4 with Th2 responses (Gascan et al. 1991; Couper
et al. 1998; Bonifacio et al. 1999; Hawa et al. 2000; Hillman et al. 2004), the relation to
specific T cell subsets of cytokines for IgG2 and IgG3 in humans is not well-defined. There
are some studies supporting that IgG3 might be associated with Th1 responses (Hussain et al.
1995; Widhe et al. 1998).
Results and discussion ’
86
Ig
G1
Ig
G2
Ig
G3
Ig
G4
G
AD
-alu
m
Pla
ceb
o
GA
D-a
lum
P
lace
bo
G
AD
-alu
m
Pla
ceb
o
GA
D-a
lum
P
lace
bo
Ba
seli
ne
33
94
(1
10
-12
199
)
35
/35
37
54
(9
7-1
15
15)
34
/34
50
(4
-240
)
25
/35
35
(4
-291
)
24
/34
80
(1
-735
)
31
/35
90
(3
-271
4)
29
/34
47
(3
-688
)
22
/35
65
(1
-109
9)
24
/34
3 m
on
ths
74
20
(7
60
-12
569
)
35
/35
30
24
(1
19
-10
811
)
34
/34
96
(18
-93
7)
31
/35
47
(3
-252
)
24
/34
42
5 (
15
-40
86
)
32
/35
87
(1
-209
4)
31
/34
23
1 (
5-3
01
7)
29
/35
67
(6
-668
)
27
/34
15
mo
nth
s 6
06
3 (
265
-11
311
)
35
/35
34
68
(6
5-9
79
5)
34
/34
61
(5
-164
6)
29
/35
40
(5
-696
)
21
/34
23
7 (
13
-21
86
)
30
/35
10
9 (
7-1
01
6)
29
/34
10
3 (
6-2
76
2)
28
/35
57
(7
-542
)
27
/34
Ta
ble
8.
GA
DA
su
bcl
ass
dis
trib
uti
on
. T
he
med
ian
lev
els
and
ran
ge
of
IgG
are
rep
rese
nte
d in
cp
m.
Th
e n
um
ber
s o
f in
div
idu
als
wit
h d
etec
tab
le le
vel
s ar
e sh
ow
n f
or ea
ch t
ime
po
int.
To
det
erm
ine
po
siti
vit
y f
or
the
sam
ple
s, t
he
mea
n c
pm
val
ue
for
a G
AD
A
neg
ativ
e co
ntr
ol
and
3x
SD
of
the
sam
e co
ntr
ol
was
su
btr
acte
d f
rom
eac
h s
amp
le.
All v
alu
es a
bo
ve
0 a
fter
th
is c
alcu
lati
on
wer
e
reg
ard
ed a
s p
osi
tiv
e.
Results and discussion
87
At 15 months, the distribution of IgG subclasses in the samples from GAD-alum individuals
had returned to that observed at baseline, and no differences in distribution between the GAD-
alum and placebo groups were observed. It will be interesting to see if this transient effect of
GAD-alum on IgG subclass distribution is prolonged by treatment with more than two GAD-
alum injections in the ongoing phase III GAD-alum trial.
A.
B.
% o
f to
tal
IgG
GA
DA
Baseline 3 m 15 m
IgG 1
80
85
90
95
100
p= 0.005 n.sn.s
IgG 2
0
1
2
3
4
n.sn.s
n.s
IgG 3
0
2
4
6 p= 0.01
n.sn.s
IgG 4
0
1
2
3
4
p= 0.048
n.s
n.s
Baseline 3 m 15 m
Baseline 3 m 15 m
Baseline 3 m 15 m
% o
f to
tal
IgG
GA
DA
% o
f to
tal
IgG
GA
DA
% o
f to
tal
IgG
GA
DA
GAD-alum
IgG 1 IgG 2 IgG 3 IgG 40
2
4
6
8
1080
90
100
Baseline
3 month
15 month
ns
p<0.001 p=0.001p<0.001 p=0.001
ns
ns p=0.039
ns
p=0.002 p=0.003
ns
p=0.017 p=0.04
% o
f to
tal Ig
G
Results and discussion ’
88
C.
Figure 23. The effect of GAD-alum in the GADA subclass distribution. Differences in IgG1, 2, 3 and
4 subclasses between GAD-alum (black circles) and placebo (white circles) (panel A), and within the
groups (panels B and C). The results are presented as median percentage of total IgG GADA. Total
IgG was calculated with respect to the combined sum of all four subclasses in each sample.
No changes in SPS-related epitope binding or inhibition of GAD65
enzymatic activity
There has been concern that induction of high GADA levels by GAD-alum treatment might
cause adverse events, especially neurological symptoms. However, no treatment-related
adverse events have been observed during the trial (Ludvigsson et al. 2008). High GADA
levels leading to neurological symptoms have been associated with a decreased production of
GABA in the nervous system of SPS patients (Murinson 2004; Murinson et al. 2004; Raju et
al. 2008). GADA in SPS patients inhibits the enzymatic activity of GAD65, which is not the
case for GADA in type 1 diabetic patients (Raju et al. 2005). We therefore investigated the
capacity of serum from patients in the GAD-alum trial to inhibit the enzyme activity of
GAD65 in vitro (Paper IV). The results showed that serum from GAD-alum treated
individuals did not affect the enzymatic activity of GAD65, at baseline, 3 or 15 months after
treatment (Figure 24).
Placebo
IgG 1 IgG 2 IgG 3 IgG 40
2
4
6
8
1080
90
100 Baseline
3 month
15 month
ns
ns ns
ns
ns ns
ns
ns nsns
ns ns
% o
f to
tal Ig
G
Results and discussion
89
Figure 24. GAD65 enzymatic activity assay for serum from patients treated with GAD-alum (black
circles) and placebo (white circles). GAD65 enzyme activity is reported as radioactivity (cpm) from
14CO2 released from 14C-glutamic acid measured at baseline and after 3 and 15 months. A control
sample from one SPS patient was included in each run (triangles).
Further, GADA in SPS patients recognizes mainly linear epitopes while GADA in type 1
diabetic patients generally recognizes conformational epitopes (Kim et al. 1994; Li et al.
1994; Daw et al. 1996; Raju et al. 2005). As a part of the epitope binding study (Paper III), we
therefore investigated if the treatment affected the epitope specificity of GADA regarding two
SPS associated epitopes (by using rFab b78 and N-GAD65 mAb). Sustained low binding to
the b78- and N-GAD65 mAb-defined epitopes throughout the study indicates that these
antibody specificities did not contribute to the increased GADA levels after GAD-alum
treatment. Thus, the treatment did not affect either the GAD65 enzyme activity or the binding
of GADA to SPS associated epitopes, further strengthening the safety of the treatment.
GAD-alum
Bas
elin
e
3 m
onths
15 m
onths
SPS
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
7000
7500
8000
8500
9000
9500
10000
ns ns
ns
p<0.001
p<0.001
p<0.001
cp
m
Placebo
Bas
elin
e
3 m
onths
15 m
onths
SPS
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
7000
7500
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8500
9000
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10000ns ns
ns
p<0.001
p<0.001
p<0.001
cp
m
Results and discussion ’
90
Antigen-specific effect
Another result that speaks in favor of the safety of the GAD-alum treatment was that the
immunomodulatory effect of GAD-alum was antigen-specific (Paper IV). The levels of IA-
2A, tetanus toxoid and IgE antibodies did not change after treatment, neither in the GAD-
alum nor in the placebo group (Figure 25), indicating a GAD65 specific effect on the immune
system. At all time points the levels of IA-2A were higher in the placebo group than in the
GAD-alum group, but there were no changes over time. In addition, levels of IA-2A did not
correlate to C-peptide levels, GADA levels or responsiveness of the treatment.
A.
B.
Baseline Baseline 3 months 3 months 9 months 9 months0
500
1000
1500
20002000
4000
6000
8000
GAD-alum
Placebo
IA-2
A t
ite
r (U
/ml)
p< o.o5
p< o.o5
p< o.o5
Baseline Baseline 3 months 3 months 9 months 9 months0.0
0.5
1.0
1.5
2.0GAD-alum
Placebo
Te
tan
us
tit
er
(IU
/ml)
n.s
Results and discussion
91
C.
Figure 25. Levels of IA-2A (A), tetanus toxoid antibodies (B) and IgE antibodies (C) before and after
treatment with GAD-alum. IA-2A and tetanus toxoid antibodies were analyzed at baseline, 3 and 9
months. IgE was analyzed at baseline and at 21 months. Antibody levels for each patient in the GAD-
alum (black circles) and placebo (white circles) groups are shown as dot plots.
The role of GADA in the effect of GAD-alum
The role of positivity to autoantibodies in the effect of autoantigen immunomodulation in type
1 diabetes is unclear. In our study, it was interesting to observe that baseline GADA levels
were higher in patients with better C-peptide preservation 15 months after the first injection of
GAD-alum (Paper IV). Our results are in line with those from the DPT-1 study, where a
beneficial effect of treatment with oral insulin was observed in individuals with high levels of
IAA pre-treatment, although this study failed to prevent type 1 diabetes (Skyler et al. 2005). A
possible explanation for the association between the response and GADA titres might be that
higher levels of GADA in circulation resulted in abundant antigen-antibody complex
formation. Complexes circulate effectively, resulting in a more efficient antigen processing
and presentation. In an experimental model of autoimmune disease, injection of specific
immune complexes was able to powerfully redirect immune responses (Barabas et al. 2007).
Thus, it is possible that induction of a more potent immune response by GAD65-antibody
complexes might be responsible for an increased response to GAD65. It cannot be excluded
that GADA levels simply reflect the current immunological status, and patients with higher
autoantibody levels may have more beta-cells left. However, this seems less likely, as it has
n.s
Results and discussion ’
92
been shown that after the diagnosis of type 1 diabetes the presence of GADA is independent
of whether or not patients still have measurable C-peptide levels (Jensen et al. 2007). Also,
another study showed that presence of GADA at onset of type 1 diabetes was not correlated
with rate of beta-cell destruction after clinical onset (Batstra et al. 1997). We have to be
cautious for drawing further conclusions from our results regarding better C-peptide
preservation in individuals with higher GADA levels, based on a very small group of patients.
However, this is an interesting observation that should be further addressed in ongoing trials
including larger number of patients.
In conclusion, we found that the increase in GADA levels after GAD-alum treatment was
accompanied by a temporarily increased GADA-binding to the b96.11-defined epitope. In
addition, a decreased percentage of GADA of the IgG1 subclass and increased IgG4 were
observed, which might suggest a temporary switch towards a protective Th2 response. The
safety of the treatment was further confirmed by the antigen-specific effect together with the
lack of changes in SPS-related epitope binding and GAD65 enzymatic activity.
93
Summary and conclusions
Autoantibodies related to type 1 diabetes are commonly measured in healthy children, often
with increased genetic risk, to predict the disease, and in new onset diabetic individuals for
classification of type 1 diabetes. In this thesis, we investigated the prevalence and persistence
of autoantibodies against GAD and IA-2, and studied the relation between these
autoantibodies and HLA genotypes, both in healthy children and in newly diagnosed type 1
diabetic children from the ABIS study. Our results confirmed the importance of HLA risk
genotypes for the development of type 1 diabetes, and indicated that HLA risk genotypes play
an important role for persistence of autoantibodies. On the other hand, the induction of
autoantibodies in the general population, especially transient autoantibodies, seems to be more
influenced by environmental factors. It is possible that other genetic factors than HLA might
also play a role in the induction of autoantibodies.
Radioimmunoassays (RIAs) are commonly used for detection of autoantibodies and are
regularly validated and compared between research laboratories around the world. However,
there are some disadvantages with these methods, such as the use of radioactively labelled
material, low sensitivity for the IAA assay and that they are time consuming. Due to the need
for better methods we developed an SPR-based assay for detection of IAA. The main
advantages of this method are that there is no need for labelling, and thus no work with
radioactivity, and it is time-saving compared to RIA. We were able to measure IAA with a
similar sensitivity as RIA, but further optimization of the SPR-based method is required. We
also made attempts to develop a method for detection of GADA, but unfortunately without
successful results. I believe that this is an area where further efforts should be done in the
future. It is important to have fast and well-functioning methods for detection of
autoantibodies, especially in the view of the increased possibilities for prevention, when there
will be a need to screen the general child population for identification of high-risk individuals.
Besides measuring the prevalence and levels of autoantibodies in type 1 diabetes-related
studies, it is also of interest to investigate their properties, such as subclass distribution,
affinity and epitope reactivity. During the time from emergence of autoantibodies to the
clinical manifestation of diabetes, the properties of the autoantibodies changes gradually,
generally with increasing affinity, changed distribution of subclasses and epitope spreading. A
Summary and conclusions ’
94
new field of interest is to study autoantibodies induced by immunomodulatory treatments with
autoantigens. The fact that GADA levels were increased after two injections of 20 µg GAD-
alum in type 1 diabetic children, raised the question whether the antibodies were different
after treatment than before. Our results showed a temporary increased binding to the b96.11-
defined epitope in the GAD-alum treated type 1 diabetic children, which was also the most
prevalent epitope for GADA binding before treatment. No other changes in epitope
specificities were observed, including the two SPS-related epitopes, which together with the
antigen specific effect of the treatment and the lack of changes in GAD65 enzymatic activity,
further speak in favor of the safety of GAD-alum.
Since the GAD-alum treatment induced an early Th2-directed cytokine response, we wanted
to clarify if the distribution of IgG subclasses was changed upon treatment. We investigated
the effect of GAD-alum on the distribution of the subclasses IgG1, 2, 3 and 4. A decreased
proportion of the predominantly occurring IgG1 subclass and increased IgG4, associated in
humans with Th1 and Th2 responses respectively, suggested a switch towards a protective
Th2 response. It was interesting to observe that the change in subclass distribution was only
temporary, and occurred when GADA titers were highest.
Concluding remarks and future perspectives
This thesis mainly comprises measurements of autoantibodies related to type 1 diabetes. We
observed that HLA risk genotypes play an important role for development of type 1 diabetes,
while induction of transient autoantibodies seems to be more influenced by environmental
factors. New improved methods for detection of autoantibodies are needed, where our SPR-
based immunoassay seems to be a good candidate, but further development is required. The
GAD-alum treatment induced changes in the humoral immune response, with increased levels
of GADA and temporary changes in epitope binding and IgG subclass distribution. GAD-
alum seems to be a promising treatment for type 1 diabetes, and hopefully many of the
research questions originated from our results will be answered during the ongoing phase III
trials.
95
Acknowledgements
I would like to end this thesis by expressing my sincere gratitude and thanks to all who
contributed and helped me to accomplish this. In particular, I would like to thank:
My supervisors: Johnny Ludvigsson for giving me the opportunity to do research within the
field of diabetes and to visit and do laboratory work at the University of Washington in
Seattle for 6 weeks during 2006.Thank you for your enthusiasm and helpfulness. Then I
would like to thank Rosaura Casas, my assistant supervisor during the second half of my
research studies. Your enthusiasm and support have helped me a lot and you were always
willing to discuss small and big matters whenever I needed. Outi Vaarala, my supervisor
during the first half of my research studies. Thank you for your ideas and discussions and for
sharing your knowledge in the field of immunology.
All children and parents participating in this study.
Special thanks to Jenny Carlsson, my twinning partner within the Strategic Area for
Prevention of Diabetes and its Complications (a joint strategy area for the County Council of
Östergötland and Linköping University), working at the Division of Molecular Physics at the
Department of Physics, Chemistry and Biology. Thank you for all collaboration and our
discussions about trouble-shooting and new ideas during all the hours in front of the Biacore
instrument, and for your friendship.
I would like to thank Chris Hampe, at the Univeristy of Washington in Seattle, for the time I
stayed in Seattle, for learning me the laboratory methods and for taking care of me. Thank
you for your kindness, helpfulness and our nice chats, and also for all e-mail communication
and discussions after the visit. Åke Lernmark, for letting me stay at your laboratory.
All friends and present and former colleagues at the Pediatric Laboratory in Linköping and
at the Clinical and Experimental Research for interesting discussions and nice chats in the
coffee room.
Ingela Johansson for your friendship, for scientific and non-scientific discussions and for all
refreshing chats and nice Friday “fika”.
My former and present roommates Maria Hjorth, Anna Rydén, Cecilia Bivik, Petra
Wäster and Christina Bendrik for all nice chats and interesting discussions, both regarding
research and more personal matters.
For excellent technical skills I would like to thank Gosia Smolinska-Konefal, Ingela
Johansson and Lena Berglert.
Mikael Chéramy for our discussions and chats about matters concerning the GAD-alum trial.
Maria Jenmalm for reading and giving constructive comments on the frame work of this
thesis.
Acknowledgements ’
96
My collaborators in different projects, Jeanette Wahlberg, Jorma Ilonen, Gunilla
Westermark, Karin Enander and Bo Liedberg for your invaluable knowledge you shared
with me and for your comments on manuscripts.
Ann-Christine Gilmore-Ellis for always being helpful and for your great service with
administrative issues, and Madeleine Örlin for your great service with administrative issues
regarding the dissertation.
Forum Scientum, the graduate school at Linköping University, for providing a
multidisciplinary research environment within the Diabetes Research Centre.
This thesis was supported by grants from the Swedish Child Diabetes Foundation
(Barndiabetesfonden), Swedish Research Council (Vetenskapsrådet), Swedish Diabetes
Association, Medical Research Fund of the County of Östergötland, the National Institutes of
Health, a basic research award from the American Diabetes Association, the Strategic Area
for Prevention of Diabetes and its Complications, and an unrestricted grant from Diamyd
Medical AB.
Till sist och viktigast av alla:
Mina föräldrar Inger och Lars. Tack för allt stöd och uppmuntran genom åren och att ni alltid
tror på mig och är stolta över mig.
Min storebror Magnus, för alla trevliga stunder tillsammans och samtal om allt från politik
till fritid och barn.
Min kusin Helene med familj, och Mariana för att ni är min lilla men naggande goda släkt.
Anna-Lisa och Göran samt Ulrika med familj, för att jag alltid känner mig välkommen hos
er.
Gamla och nya vänner, i och utanför Linköping, för allt roligt vi har hittat på under åren!
Min man Johan, tack för ditt stöd och uppmuntran. Jag älskar dig!
Våra barn Anton och Amanda. Ni är två underbara solstrålar!
97
References
Achenbach, P., Bonifacio, E., Koczwara, K. and Ziegler, A. G. (2005). Natural history of type 1
diabetes. Diabetes; 54 Suppl 2: S25-31.
Achenbach, P., Koczwara, K., Knopff, A., Naserke, H., Ziegler, A. G. and Bonifacio, E. (2004a).
Mature high-affinity immune responses to (pro)insulin anticipate the autoimmune cascade that leads to
type 1 diabetes. J Clin Invest; 114(4): 589-597.
Achenbach, P., Lampasona, V., Landherr, U., Koczwara, K., Krause, S., Grallert, H., Winkler, C.,
Pfluger, M., Illig, T., Bonifacio, E. and Ziegler, A. G. (2009). Autoantibodies to zinc transporter 8 and
SLC30A8 genotype stratify type 1 diabetes risk. Diabetologia; 52(9): 1881-1888.
Achenbach, P., Schlosser, M., Williams, A. J., Yu, L., Mueller, P. W., Bingley, P. J. and Bonifacio, E.
(2007). Combined testing of antibody titer and affinity improves insulin autoantibody measurement:
Diabetes Antibody Standardization Program. Clin Immunol; 122(1): 85-90.
Achenbach, P., Warncke, K., Reiter, J., Naserke, H. E., Williams, A. J., Bingley, P. J., Bonifacio, E.
and Ziegler, A. G. (2004b). Stratification of type 1 diabetes risk on the basis of islet autoantibody
characteristics. Diabetes; 53(2): 384-392.
Achenbach, P., Warncke, K., Reiter, J., Williams, A. J., Ziegler, A. G., Bingley, P. J. and Bonifacio, E.
(2006). Type 1 diabetes risk assessment: improvement by follow-up measurements in young islet
autoantibody-positive relatives. Diabetologia; 49(12): 2969-2976.
Agardh, C. D., Cilio, C. M., Lethagen, A., Lynch, K., Leslie, R. D., Palmer, M., Harris, R. A.,
Robertson, J. A. and Lernmark, A. (2005). Clinical evidence for the safety of GAD65
immunomodulation in adult-onset autoimmune diabetes. J Diabetes Complications; 19(4): 238-246.
Akerblom, H. K., Vaarala, O., Hyoty, H., Ilonen, J. and Knip, M. (2002). Environmental factors in the
etiology of type 1 diabetes. Am J Med Genet; 115(1): 18-29.
Alberts, B., Bray, D., Lewis, J., Raff, M., Roberts, K. and Watson, J. D. (2002). Molecular biology of
the cell. New York, Garland Science.
Aly, T. A., Ide, A., Jahromi, M. M., Barker, J. M., Fernando, M. S., Babu, S. R., Yu, L., Miao, D.,
Erlich, H. A., Fain, P. R., Barriga, K. J., Norris, J. M., Rewers, M. J. and Eisenbarth, G. S. (2006).
Extreme genetic risk for type 1A diabetes. Proc Natl Acad Sci U S A; 103(38): 14074-14079.
American Diabetes Association (2008). Diagnosis and classification of diabetes mellitus. Diabetes
Care; 31 Suppl 1: S55-60.
Ankelo, M., Westerlund-Karlsson, A., Ilonen, J., Knip, M., Savola, K., Kankaanpaa, P., Merio, L.,
Siitari, H. and Hinkkanen, A. (2003). Time-resolved fluorometric assay for detection of autoantibodies
to glutamic acid decarboxylase (GAD65). Clin Chem; 49(6 Pt 1): 908-915.
Atkinson, M. A., Bowman, M. A., Campbell, L., Darrow, B. L., Kaufman, D. L. and Maclaren, N. K.
(1994). Cellular immunity to a determinant common to glutamate decarboxylase and coxsackie virus
in insulin-dependent diabetes. J Clin Invest; 94(5): 2125-2129.
Atkinson, M. A. and Eisenbarth, G. S. (2001). Type 1 diabetes: new perspectives on disease
pathogenesis and treatment. Lancet; 358(9277): 221-229.
References ’
98
Axelsson, S., Hjorth, M., Akerman, L., Ludvigsson, J. and Casas, R. (2010). Early induction of
GAD(65)-reactive Th2 response in type 1 diabetic children treated with alum-formulated GAD(65).
Diabetes Metab Res Rev; 26(7): 559-568.
Ayela, C., Roquet, F., Valera, L., Granier, C., Nicu, L. and Pugniere, M. (2007). Antibody-antigenic
peptide interactions monitored by SPR and QCM-D. A model for SPR detection of IA-2
autoantibodies in human serum. Biosens Bioelectron; 22(12): 3113-3119.
Bach, J. F. (1994). Insulin-dependent diabetes mellitus as an autoimmune disease. Endocr Rev; 15(4):
516-542.
Bach, J. F. and Chatenoud, L. (2001). Tolerance to islet autoantigens in type 1 diabetes. Annu Rev
Immunol; 19: 131-161.
Baekkeskov, S., Aanstoot, H. J., Christgau, S., Reetz, A., Solimena, M., Cascalho, M., Folli, F.,
Richter-Olesen, H. and De Camilli, P. (1990). Identification of the 64K autoantigen in insulin-
dependent diabetes as the GABA-synthesizing enzyme glutamic acid decarboxylase. Nature;
347(6289): 151-156.
Baekkeskov, S., Nielsen, J. H., Marner, B., Bilde, T., Ludvigsson, J. and Lernmark, A. (1982).
Autoantibodies in newly diagnosed diabetic children immunoprecipitate human pancreatic islet cell
proteins. Nature; 298(5870): 167-169.
Baisch, J. M., Weeks, T., Giles, R., Hoover, M., Stastny, P. and Capra, J. D. (1990). Analysis of HLA-
DQ genotypes and susceptibility in insulin-dependent diabetes mellitus. N Engl J Med; 322(26): 1836-
1841.
Bakhtadze, E., Borg, H., Stenstrom, G., Fernlund, P., Arnqvist, H. J., Ekbom-Schnell, A., Bolinder, J.,
Eriksson, J. W., Gudbjornsdottir, S., Nystrom, L., Groop, L. C. and Sundkvist, G. (2006). HLA-DQB1
genotypes, islet antibodies and beta cell function in the classification of recent-onset diabetes among
young adults in the nationwide Diabetes Incidence Study in Sweden. Diabetologia; 49(8): 1785-1794.
Barabas, A. Z., Cole, C. D., Kovacs, Z. B. and Lafreniere, R. (2007). Elevated antibody response by
antigen presentation in immune complexes. Med Sci Monit; 13(5): BR119-124.
Barker, J. M., Barriga, K. J., Yu, L., Miao, D., Erlich, H. A., Norris, J. M., Eisenbarth, G. S. and
Rewers, M. (2004). Prediction of autoantibody positivity and progression to type 1 diabetes: Diabetes
Autoimmunity Study in the Young (DAISY). J Clin Endocrinol Metab; 89(8): 3896-3902.
Batstra, M. R., Pina, M., Quan, J., Mulder, P., de Beaufort, C. E., Bruining, G. J. and Aanstoot, H. J.
(1997). Fluctuations in GAD65 antibodies after clinical diagnosis of IDDM in young children.
Diabetes Care; 20(4): 642-644.
Bekris, L. M., Jensen, R. A., Lagerquist, E., Hall, T. R., Agardh, C. D., Cilio, C. M., Lethagen, A. L.,
Lernmark, A., Robertson, J. A. and Hampe, C. S. (2007). GAD65 autoantibody epitopes in adult
patients with latent autoimmune diabetes following GAD65 vaccination. Diabet Med; 24(5): 521-526.
Bennett, S. T., Lucassen, A. M., Gough, S. C., Powell, E. E., Undlien, D. E., Pritchard, L. E.,
Merriman, M. E., Kawaguchi, Y., Dronsfield, M. J., Pociot, F. and et al. (1995). Susceptibility to
human type 1 diabetes at IDDM2 is determined by tandem repeat variation at the insulin gene
minisatellite locus. Nat Genet; 9(3): 284-292.
Bingley, P. J., Bonifacio, E. and Mueller, P. W. (2003). Diabetes Antibody Standardization Program:
first assay proficiency evaluation. Diabetes; 52(5): 1128-1136.
References
99
Bingley, P. J., Bonifacio, E., Williams, A. J., Genovese, S., Bottazzo, G. F. and Gale, E. A. (1997).
Prediction of IDDM in the general population: strategies based on combinations of autoantibody
markers. Diabetes; 46(11): 1701-1710.
Bingley, P. J., Christie, M. R., Bonifacio, E., Bonfanti, R., Shattock, M., Fonte, M. T., Bottazzo, G. F.
and Gale, E. A. (1994). Combined analysis of autoantibodies improves prediction of IDDM in islet
cell antibody-positive relatives. Diabetes; 43(11): 1304-1310.
Bonifacio, E., Genovese, S., Braghi, S., Bazzigaluppi, E., Lampasona, V., Bingley, P. J., Rogge, L.,
Pastore, M. R., Bognetti, E., Bottazzo, G. F. and et al. (1995). Islet autoantibody markers in IDDM:
risk assessment strategies yielding high sensitivity. Diabetologia; 38(7): 816-822.
Bonifacio, E., Lampasona, V., Bernasconi, L. and Ziegler, A. G. (2000). Maturation of the humoral
autoimmune response to epitopes of GAD in preclinical childhood type 1 diabetes. Diabetes; 49(2):
202-208.
Bonifacio, E., Scirpoli, M., Kredel, K., Fuchtenbusch, M. and Ziegler, A. G. (1999). Early
autoantibody responses in prediabetes are IgG1 dominated and suggest antigen-specific regulation. J
Immunol; 163(1): 525-532.
Borch-Johnsen, K., Joner, G., Mandrup-Poulsen, T., Christy, M., Zachau-Christiansen, B., Kastrup, K.
and Nerup, J. (1984). Relation between breast-feeding and incidence rates of insulin-dependent
diabetes mellitus. A hypothesis. Lancet; 2(8411): 1083-1086.
Bottazzo, G. F., Florin-Christensen, A. and Doniach, D. (1974). Islet-cell antibodies in diabetes
mellitus with autoimmune polyendocrine deficiencies. Lancet; 2(7892): 1279-1283.
Brekke, O. H., Michaelsen, T. E. and Sandlie, I. (1995). The structural requirements for complement
activation by IgG: does it hinge on the hinge? Immunol Today; 16(2): 85-90.
Brooking, H., Ananieva-Jordanova, R., Arnold, C., Amoroso, M., Powell, M., Betterle, C., Zanchetta,
R., Furmaniak, J. and Smith, B. R. (2003). A sensitive non-isotopic assay for GAD65 autoantibodies.
Clin Chim Acta; 331(1-2): 55-59.
Bu, D. F., Erlander, M. G., Hitz, B. C., Tillakaratne, N. J., Kaufman, D. L., Wagner-McPherson, C. B.,
Evans, G. A. and Tobin, A. J. (1992). Two human glutamate decarboxylases, 65-kDa GAD and 67-
kDa GAD, are each encoded by a single gene. Proc Natl Acad Sci U S A; 89(6): 2115-2119.
Cameron, M. J., Arreaza, G. A., Zucker, P., Chensue, S. W., Strieter, R. M., Chakrabarti, S. and
Delovitch, T. L. (1997). IL-4 prevents insulitis and insulin-dependent diabetes mellitus in nonobese
diabetic mice by potentiation of regulatory T helper-2 cell function. J Immunol; 159(10): 4686-4692.
Christgau, S., Aanstoot, H. J., Schierbeck, H., Begley, K., Tullin, S., Hejnaes, K. and Baekkeskov, S.
(1992). Membrane anchoring of the autoantigen GAD65 to microvesicles in pancreatic beta-cells by
palmitoylation in the NH2-terminal domain. J Cell Biol; 118(2): 309-320.
Christie, M. R., Genovese, S., Cassidy, D., Bosi, E., Brown, T. J., Lai, M., Bonifacio, E. and Bottazzo,
G. F. (1994). Antibodies to islet 37k antigen, but not to glutamate decarboxylase, discriminate rapid
progression to IDDM in endocrine autoimmunity. Diabetes; 43(10): 1254-1259.
Cohen, S. (1963). Antibodies. Mod Trends Immunol; 55: 25-52.
Couper, J. J., Harrison, L. C., Aldis, J. J., Colman, P. G., Honeyman, M. C. and Ferrante, A. (1998).
IgG subclass antibodies to glutamic acid decarboxylase and risk for progression to clinical insulin-
dependent diabetes. Hum Immunol; 59(8): 493-499.
References ’
100
Dahlquist, G. and Mustonen, L. (1994). Childhood onset diabetes--time trends and climatological
factors. Int J Epidemiol; 23(6): 1234-1241.
Dahlquist, G. G., Ivarsson, S., Lindberg, B. and Forsgren, M. (1995). Maternal enteroviral infection
during pregnancy as a risk factor for childhood IDDM. A population-based case-control study.
Diabetes; 44(4): 408-413.
Daw, K., Ujihara, N., Atkinson, M. and Powers, A. C. (1996). Glutamic acid decarboxylase
autoantibodies in stiff-man syndrome and insulin-dependent diabetes mellitus exhibit similarities and
differences in epitope recognition. J Immunol; 156(2): 818-825.
Donner, H., Rau, H., Walfish, P. G., Braun, J., Siegmund, T., Finke, R., Herwig, J., Usadel, K. H. and
Badenhoop, K. (1997). CTLA4 alanine-17 confers genetic susceptibility to Graves' disease and to type
1 diabetes mellitus. J Clin Endocrinol Metab; 82(1): 143-146.
DPT-1, D. P. T.-T. D. S. G. (2002). Effects of insulin in relatives of patients with type 1 diabetes
mellitus. N Engl J Med; 346(22): 1685-1691.
Dromey, J. A., Weenink, S. M., Peters, G. H., Endl, J., Tighe, P. J., Todd, I. and Christie, M. R.
(2004). Mapping of epitopes for autoantibodies to the type 1 diabetes autoantigen IA-2 by peptide
phage display and molecular modeling: overlap of antibody and T cell determinants. J Immunol;
172(7): 4084-4090.
Eisenbarth, G. S. (2008). The Pancreatic Beta-cell. In G.S. Eisenbarth (Ed), Type 1 diabetes:
Cellular, molecular and clinical immunology. Online Edition Version 3.0. Denver, Colorado,
USA: Barbara Davis Center for Childhood Diabetes.
Eising, S., Nilsson, A., Carstensen, B., Hougaard, D. M., Norgaard-Petersen, B., Nerup, J., Lernmark,
A. and Pociot, F. (2010). Danish children born 1981-2002 with GAD65 and IA-2 autoantibodies at
birth had an increased risk to develop type 1 diabetes before 2004. Eur J Endocrinol.
Falorni, A., Ackefors, M., Carlberg, C., Daniels, T., Persson, B., Robertson, J. and Lernmark, A.
(1996). Diagnostic sensitivity of immunodominant epitopes of glutamic acid decarboxylase (GAD65)
autoantibodies in childhood IDDM. Diabetologia; 39(9): 1091-1098.
Fenalti, G., Hampe, C. S., Arafat, Y., Law, R. H., Banga, J. P., Mackay, I. R., Whisstock, J. C.,
Buckle, A. M. and Rowley, M. J. (2008). COOH-terminal clustering of autoantibody and T-cell
determinants on the structure of GAD65 provide insights into the molecular basis of autoreactivity.
Diabetes; 57(5): 1293-1301.
Fontenot, J. D., Gavin, M. A. and Rudensky, A. Y. (2003). Foxp3 programs the development and
function of CD4(+)CD25(+) regulatory T cells. Nature Immunology; 4(4): 330-336.
Gale, E. A. M., Bingley, P. J., Knip, M., Emmett, C. L., Swankie, H., Fewell, S., Kearsey, P., Schober,
E., Gorus, F. K., Dupre, J., Mahon, J. L., Profozic, V., Reimers, J. I., Mandrup-Poulsen, T., Levy-
Marchal, C., et al. (2004). European Nicotinamide Diabetes Intervention Trial (ENDIT): a randomised
controlled trial of intervention before the onset of type 1 diabetes. Lancet; 363(9413): 925-931.
Gascan, H., Gauchat, J. F., Roncarolo, M. G., Yssel, H., Spits, H. and de Vries, J. E. (1991). Human B
cell clones can be induced to proliferate and to switch to IgE and IgG4 synthesis by interleukin 4 and a
signal provided by activated CD4+ T cell clones. J Exp Med; 173(3): 747-750.
Geha, R. S. (1985). Idiotypic-antiidiotypic interactions in man. Ric Clin Lab; 15(1): 1-8.
References
101
Genovese, S., Bonfanti, R., Bazzigaluppi, E., Lampasona, V., Benazzi, E., Bosi, E., Chiumello, G. and
Bonifacio, E. (1996). Association of IA-2 autoantibodies with HLA DR4 phenotypes in IDDM.
Diabetologia; 39(10): 1223-1226.
Gillespie, K. M., Bain, S. C., Barnett, A. H., Bingley, P. J., Christie, M. R., Gill, G. V. and Gale, E. A.
(2004). The rising incidence of childhood type 1 diabetes and reduced contribution of high-risk HLA
haplotypes. Lancet; 364(9446): 1699-1700.
Gilliam, L. K., Binder, K. A., Banga, J. P., Madec, A. M., Ortqvist, E., Kockum, I., Luo, D. and
Hampe, C. S. (2004). Multiplicity of the antibody response to GAD65 in Type I diabetes. Clin Exp
Immunol; 138(2): 337-341.
Glastras, S. J., Mohsin, F. and Donaghue, K. C. (2005). Complications of diabetes mellitus in
childhood. Pediatr Clin North Am; 52(6): 1735-1753.
Gobi, K. V., Iwasaka, H. and Miura, N. (2007). Self-assembled PEG monolayer based SPR
immunosensor for label-free detection of insulin. Biosens Bioelectron; 22(7): 1382-1389.
Gomara, M. J., Ercilla, G., Alsina, M. A. and Haro, I. (2000). Assessment of synthetic peptides for
hepatitis A diagnosis using biosensor technology. J Immunol Methods; 246(1-2): 13-24.
Gorus, F. K., Goubert, P., Semakula, C., Vandewalle, C. L., De Schepper, J., Scheen, A., Christie, M.
R. and Pipeleers, D. G. (1997). IA-2-autoantibodies complement GAD65-autoantibodies in new-onset
IDDM patients and help predict impending diabetes in their siblings. The Belgian Diabetes Registry.
Diabetologia; 40(1): 95-99.
Green, A. and Patterson, C. C. (2001). Trends in the incidence of childhood-onset diabetes in Europe
1989-1998. Diabetologia; 44 Suppl 3: B3-8.
Greenbaum, C. J., Palmer, J. P., Kuglin, B. and Kolb, H. (1992). Insulin autoantibodies measured by
radioimmunoassay methodology are more related to insulin-dependent diabetes mellitus than those
measured by enzyme-linked immunosorbent assay: results of the Fourth International Workshop on
the Standardization of Insulin Autoantibody Measurement. J Clin Endocrinol Metab; 74(5): 1040-
1044.
Grubin, C. E., Daniels, T., Toivola, B., Landin-Olsson, M., Hagopian, W. A., Li, L., Karlsen, A. E.,
Boel, E., Michelsen, B. and Lernmark, A. (1994). A novel radioligand binding assay to determine
diagnostic accuracy of isoform-specific glutamic acid decarboxylase antibodies in childhood IDDM.
Diabetologia; 37(4): 344-350.
Hagopian, W. A., Lernmark, A., Rewers, M. J., Simell, O. G., She, J. X., Ziegler, A. G., Krischer, J. P.
and Akolkar, B. (2006). TEDDY--The Environmental Determinants of Diabetes in the Young: an
observational clinical trial. Ann N Y Acad Sci; 1079: 320-326.
Hagopian, W. A., Michelsen, B., Karlsen, A. E., Larsen, F., Moody, A., Grubin, C. E., Rowe, R.,
Petersen, J., McEvoy, R. and Lernmark, A. (1993). Autoantibodies in IDDM primarily recognize the
65,000-M(r) rather than the 67,000-M(r) isoform of glutamic acid decarboxylase. Diabetes; 42(4):
631-636.
Hagopian, W. A., Sanjeevi, C. B., Kockum, I., Landin-Olsson, M., Karlsen, A. E., Sundkvist, G.,
Dahlquist, G., Palmer, J. and Lernmark, A. (1995). Glutamate decarboxylase-, insulin-, and islet cell-
antibodies and HLA typing to detect diabetes in a general population-based study of Swedish children.
J Clin Invest; 95(4): 1505-1511.
References ’
102
Hamilton, R. G. (1987). Human IgG subclass measurements in the clinical laboratory. Clin Chem;
33(10): 1707-1725.
Hampe, C. S., Hammerle, L. P., Bekris, L., Ortqvist, E., Persson, B. and Lernmark, A. (2002). Stable
GAD65 autoantibody epitope patterns in type 1 diabetes children five years after onset. J Autoimmun;
18(1): 49-53.
Harjutsalo, V., Sjoberg, L. and Tuomilehto, J. (2008). Time trends in the incidence of type 1 diabetes
in Finnish children: a cohort study. Lancet; 371(9626): 1777-1782.
Hawa, M. I., Beyan, H., Buckley, L. R. and Leslie, R. D. (2002). Impact of genetic and non-genetic
factors in type 1 diabetes. Am J Med Genet; 115(1): 8-17.
Hawa, M. I., Fava, D., Medici, F., Deng, Y. J., Notkins, A. L., De Mattia, G. and Leslie, R. D. (2000).
Antibodies to IA-2 and GAD65 in type 1 and type 2 diabetes: isotype restriction and polyclonality.
Diabetes Care; 23(2): 228-233.
Hermann, R., Knip, M., Veijola, R., Simell, O., Laine, A. P., Akerblom, H. K., Groop, P. H.,
Forsblom, C., Pettersson-Fernholm, K. and Ilonen, J. (2003). Temporal changes in the frequencies of
HLA genotypes in patients with Type 1 diabetes--indication of an increased environmental pressure?
Diabetologia; 46(3): 420-425.
Hermann, R., Turpeinen, H., Laine, A. P., Veijola, R., Knip, M., Simell, O., Sipila, I., Akerblom, H.
K. and Ilonen, J. (2003). HLA DR-DQ-encoded genetic determinants of childhood-onset type 1
diabetes in Finland: an analysis of 622 nuclear families. Tissue Antigens; 62(2): 162-169.
Herold, K. C., Gitelman, S., Greenbaum, C., Puck, J., Hagopian, W., Gottlieb, P., Sayre, P., Bianchine,
P., Wong, E., Seyfert-Margolis, V., Bourcier, K. and Bluestone, J. A. (2009). Treatment of patients
with new onset Type 1 diabetes with a single course of anti-CD3 mAb Teplizumab preserves insulin
production for up to 5 years. Clin Immunol; 132(2): 166-173.
Herold, K. C., Gitelman, S. E., Masharani, U., Hagopian, W., Bisikirska, B., Donaldson, D., Rother,
K., Diamond, B., Harlan, D. M. and Bluestone, J. A. (2005). A single course of anti-CD3 monoclonal
antibody hOKT3gamma1(Ala-Ala) results in improvement in C-peptide responses and clinical
parameters for at least 2 years after onset of type 1 diabetes. Diabetes; 54(6): 1763-1769.
Herold, K. C., Hagopian, W., Auger, J. A., Poumian-Ruiz, E., Taylor, L., Donaldson, D., Gitelman, S.
E., Harlan, D. M., Xu, D., Zivin, R. A. and Bluestone, J. A. (2002). Anti-CD3 monoclonal antibody in
new-onset type 1 diabetes mellitus. N Engl J Med; 346(22): 1692-1698.
Herrwerth, S., Rosendahl, T., Feng, C., Fick, W., Eck, M., Himmelhaus, R., Dahint, R. and Grunze,
M. (2003). Covalent coupling of antibodies to self-assembled monolayers of carboxy-functionalized
poly(ethylene glycol): Protein resistance and specific binding of biomolecules. Langmuir; 19: 1880-
1887.
Hillman, M., Torn, C. and Landin-Olsson, M. (2009). The glutamic acid decarboxylase 65
immunoglobulin G subclass profile differs between adult-onset type 1 diabetes and latent autoimmune
diabetes in adults (LADA) up to 3 years after clinical onset. Clin Exp Immunol; 157(2): 255-260.
Hillman, M., Torn, C., Thorgeirsson, H. and Landin-Olsson, M. (2004). IgG4-subclass of glutamic
acid decarboxylase antibody is more frequent in latent autoimmune diabetes in adults than in type 1
diabetes. Diabetologia; 47(11): 1984-1989.
References
103
Hjorth, M., Axelsson, S., Ryden, A., Faresjo, M., Ludvigsson, J. and Casas, R. (2010). GAD-alum
treatment induces GAD(65)-specific CD4(+)CD25(high)FOXP3(+) cells in type 1 diabetic patients.
Clin Immunol.
Holmberg, H., Vaarala, O., Sadauskaite-Kuehne, V., Ilonen, J., Padaiga, Z. and Ludvigsson, J. (2006).
Higher prevalence of autoantibodies to insulin and GAD65 in Swedish compared to Lithuanian
children with type 1 diabetes. Diabetes Res Clin Pract; 72(3): 308-314.
Holmberg, H., Wahlberg, J., Vaarala, O. and Ludvigsson, J. (2007). Short duration of breast-feeding
as a risk-factor for beta-cell autoantibodies in 5-year-old children from the general population. Br J
Nutr; 97(1): 111-116.
Hoppu, S., Harkonen, T., Ronkainen, M. S., Akerblom, H. K. and Knip, M. (2004b). IA-2 antibody
epitopes and isotypes during the prediabetic process in siblings of children with type 1 diabetes. J
Autoimmun; 23(4): 361-370.
Hoppu, S., Ronkainen, M. S., Kimpimaki, T., Simell, S., Korhonen, S., Ilonen, J., Simell, O. and Knip,
M. (2004c). Insulin autoantibody isotypes during the prediabetic process in young children with
increased genetic risk of type 1 diabetes. Pediatr Res; 55(2): 236-242.
Hoppu, S., Ronkainen, M. S., Kulmala, P., Akerblom, H. K. and Knip, M. (2004a). GAD65 antibody
isotypes and epitope recognition during the prediabetic process in siblings of children with type I
diabetes. Clin Exp Immunol; 136(1): 120-128.
Hori, S., Nomura, T. and Sakaguchi, S. (2003). Control of regulatory T cell development by the
transcription factor Foxp3. Science; 299(5609): 1057-1061.
http://clinicaltrials.gov (2010a). The Protégé Study - Clinical Trial of MGA031 in Children and Adults
With Recent-Onset Type 1 Diabetes Mellitus. From
http://clinicaltrials.gov/ct2/show/NCT00385697?term=MGA031-01&rank=2.
http://clinicaltrials.gov (2010b). A Phase III Study to Investigate the Impact of Diamyd in Patients
Newly Diagnosed With Type 1 Diabetes (EU). From
http://clinicaltrials.gov/ct2/show/NCT00723411?term=diamyd&rank=1.
Hummel, M., Bonifacio, E., Schmid, S., Walter, M., Knopff, A. and Ziegler, A. G. (2004). Brief
communication: early appearance of islet autoantibodies predicts childhood type 1 diabetes in
offspring of diabetic parents. Ann Intern Med; 140(11): 882-886.
Hussain, R., Kifayet, A. and Chiang, T. J. (1995). Immunoglobulin G1 (IgG1) and IgG3 antibodies are
markers of progressive disease in leprosy. Infect Immun; 63(2): 410-415.
Hyoty, H., Hiltunen, M., Knip, M., Laakkonen, M., Vahasalo, P., Karjalainen, J., Koskela, P.,
Roivainen, M., Leinikki, P., Hovi, T. and et al. (1995). A prospective study of the role of coxsackie B
and other enterovirus infections in the pathogenesis of IDDM. Childhood Diabetes in Finland (DiMe)
Study Group. Diabetes; 44(6): 652-657.
IDF (2009). International Diabetes Federation Diabetes Atlas online version: www.eatlas.idf.org.
Ilonen, J., Sjoroos, M., Knip, M., Veijola, R., Simell, O., Akerblom, H. K., Paschou, P., Bozas, E.,
Havarani, B., Malamitsi-Puchner, A., Thymelli, J., Vazeou, A. and Bartsocas, C. S. (2002). Estimation
of genetic risk for type 1 diabetes. Am J Med Genet; 115(1): 30-36.
Imagawa, A., Hanafusa, T., Itoh, N., Waguri, M., Yamamoto, K., Miyagawa, J., Moriwaki, M.,
Yamagata, K., Iwahashi, H., Sada, M., Tsuji, T., Tamura, S., Kawata, S., Kuwajima, M., Nakajima,
References ’
104
H., et al. (1999). Immunological abnormalities in islets at diagnosis paralleled further deterioration of
glycaemic control in patients with recent-onset Type I (insulin-dependent) diabetes mellitus.
Diabetologia; 42(5): 574-578.
Janeway, C., Travers, P., Walport, M. and Shlomchik, M. (2005). Immunobiology, Garland Science
Publishing.
Jensen, R. A., Gilliam, L. K., Torn, C., Landin-Olsson, M., Karlsson, F. A., Palmer, J. P., Kockum, I.,
Akesson, K., Lernmark, B., Lynch, K., Breslow, N. and Lernmark, A. (2007). Multiple factors affect
the loss of measurable C-peptide over 6 years in newly diagnosed 15- to 35-year-old diabetic subjects.
J Diabetes Complications; 21(4): 205-213.
Johansson, C., Samuelsson, U. and Ludvigsson, J. (1994). A high weight gain early in life is
associated with an increased risk of type 1 (insulin-dependent) diabetes mellitus. Diabetologia; 37(1):
91-94.
Jongerius-Gortemaker, B. G., Goverde, R. L., van Knapen, F. and Bergwerff, A. A. (2002). Surface
plasmon resonance (BIACORE) detection of serum antibodies against Salmonella enteritidis and
Salmonella typhimurium. J Immunol Methods; 266(1-2): 33-44.
Jun, H. S., Chung, Y. H., Han, J., Kim, A., Yoo, S. S., Sherwin, R. S. and Yoon, J. W. (2002).
Prevention of autoimmune diabetes by immunogene therapy using recombinant vaccinia virus
expressing glutamic acid decarboxylase. Diabetologia; 45(5): 668-676.
Karlsson, R., Fagerstam, L., Nilshans, H. and Persson, B. (1993). Analysis of active antibody
concentration. Separation of affinity and concentration parameters. J Immunol Methods; 166(1): 75-
84.
Karvonen, M., Pitkaniemi, J. and Tuomilehto, J. (1999). The onset age of type 1 diabetes in Finnish
children has become younger. The Finnish Childhood Diabetes Registry Group. Diabetes Care; 22(7):
1066-1070.
Kim, J., Namchuk, M., Bugawan, T., Fu, Q., Jaffe, M., Shi, Y., Aanstoot, H. J., Turck, C. W., Erlich,
H., Lennon, V. and Baekkeskov, S. (1994). Higher autoantibody levels and recognition of a linear
NH2-terminal epitope in the autoantigen GAD65, distinguish stiff-man syndrome from insulin-
dependent diabetes mellitus. J Exp Med; 180(2): 595-606.
Kimpimaki, T., Kupila, A., Hamalainen, A. M., Kukko, M., Kulmala, P., Savola, K., Simell, T.,
Keskinen, P., Ilonen, J., Simell, O. and Knip, M. (2001). The first signs of beta-cell autoimmunity
appear in infancy in genetically susceptible children from the general population: the Finnish Type 1
Diabetes Prediction and Prevention Study. J Clin Endocrinol Metab; 86(10): 4782-4788.
Knip, M. (1997). Disease-associated autoimmunity and prevention of insulin-dependent diabetes
mellitus. Ann Med; 29(5): 447-451.
Knip, M., Korhonen, S., Kulmala, P., Veijola, R., Reunanen, A., Raitakari, O. T., Viikari, J. and
Akerblom, H. K. (2010). Prediction of type 1 diabetes in the general population. Diabetes Care; 33(6):
1206-1212.
Knip, M. and Siljander, H. (2008). Autoimmune mechanisms in type 1 diabetes. Autoimmun Rev; 7(7):
550-557.
Knip, M., Virtanen, S. M., Seppa, K., Ilonen, J., Savilahti, E., Vaarala, O., Reunanen, A., Teramo, K.,
Hamalainen, A. M., Paronen, J., Dosch, H. M., Hakulinen, T. and Akerblom, H. K. (2010). Dietary
intervention in infancy and later signs of beta-cell autoimmunity. N Engl J Med; 363(20): 1900-1908.
References
105
Koeleman, B. P., Lie, B. A., Undlien, D. E., Dudbridge, F., Thorsby, E., de Vries, R. R., Cucca, F.,
Roep, B. O., Giphart, M. J. and Todd, J. A. (2004). Genotype effects and epistasis in type 1 diabetes
and HLA-DQ trans dimer associations with disease. Genes Immun; 5(5): 381-388.
Korn, T., Bettelli, E., Oukka, M. and Kuchroo, V. K. (2009). IL-17 and Th17 Cells. Annu Rev
Immunol; 27: 485-517.
Krischer, J. P., Cuthbertson, D. D., Yu, L., Orban, T., Maclaren, N., Jackson, R., Winter, W. E.,
Schatz, D. A., Palmer, J. P. and Eisenbarth, G. S. (2003). Screening strategies for the identification of
multiple antibody-positive relatives of individuals with type 1 diabetes. J Clin Endocrinol Metab;
88(1): 103-108.
Kukko, M., Kimpimaki, T., Korhonen, S., Kupila, A., Simell, S., Veijola, R., Simell, T., Ilonen, J.,
Simell, O. and Knip, M. (2005). Dynamics of diabetes-associated autoantibodies in young children
with human leukocyte antigen-conferred risk of type 1 diabetes recruited from the general population.
J Clin Endocrinol Metab; 90(5): 2712-2717.
Kulmala, P., Rahko, J., Savola, K., Vahasalo, P., Sjoroos, M., Reunanen, A., Ilonen, J. and Knip, M.
(2001). Beta-cell autoimmunity, genetic susceptibility, and progression to type 1 diabetes in
unaffected schoolchildren. Diabetes Care; 24(1): 171-173.
Kulmala, P., Savola, K., Petersen, J. S., Vahasalo, P., Karjalainen, J., Lopponen, T., Dyrberg, T.,
Akerblom, H. K. and Knip, M. (1998). Prediction of insulin-dependent diabetes mellitus in siblings of
children with diabetes. A population-based study. The Childhood Diabetes in Finland Study Group. J
Clin Invest; 101(2): 327-336.
Kulmala, P., Savola, K., Reijonen, H., Veijola, R., Vahasalo, P., Karjalainen, J., Tuomilehto-Wolf, E.,
Ilonen, J., Tuomilehto, J., Akerblom, H. K. and Knip, M. (2000). Genetic markers, humoral
autoimmunity, and prediction of type 1 diabetes in siblings of affected children. Childhood Diabetes in
Finland Study Group. Diabetes; 49(1): 48-58.
Kupila, A., Muona, P., Simell, T., Arvilommi, P., Savolainen, H., Hamalainen, A. M., Korhonen, S.,
Kimpimaki, T., Sjoroos, M., Ilonen, J., Knip, M. and Simell, O. (2001). Feasibility of genetic and
immunological prediction of type I diabetes in a population-based birth cohort. Diabetologia; 44(3):
290-297.
Kure, M., Katsura, Y., Kosano, H., Noritake, M., Watanabe, T., Iwaki, Y., Nishigori, H. and
Matsuoka, T. (2005). A trial to assess the amount of insulin antibodies in diabetic patients by surface
plasmon resonance. Intern Med; 44(2): 100-106.
Laaksonen, M., Pastinen, T., Sjoroos, M., Kuokkanen, S., Ruutiainen, J., Sumelahti, M. L., Reijonen,
H., Salonen, R., Wikstrom, J., Panelius, M., Partanen, J., Tienari, P. J. and Ilonen, J. (2002). HLA
class II associated risk and protection against multiple sclerosis-a Finnish family study. J
Neuroimmunol; 122(1-2): 140-145.
Ladner, M. B., Bottini, N., Valdes, A. M. and Noble, J. A. (2005). Association of the single nucleotide
polymorphism C1858T of the PTPN22 gene with type 1 diabetes. Hum Immunol; 66(1): 60-64.
LaGasse, J. M., Brantley, M. S., Leech, N. J., Rowe, R. E., Monks, S., Palmer, J. P., Nepom, G. T.,
McCulloch, D. K. and Hagopian, W. A. (2002). Successful prospective prediction of type 1 diabetes in
schoolchildren through multiple defined autoantibodies: an 8-year follow-up of the Washington State
Diabetes Prediction Study. Diabetes Care; 25(3): 505-511.
References ’
106
Lambert, A. P., Gillespie, K. M., Thomson, G., Cordell, H. J., Todd, J. A., Gale, E. A. and Bingley, P.
J. (2004). Absolute risk of childhood-onset type 1 diabetes defined by human leukocyte antigen class
II genotype: a population-based study in the United Kingdom. J Clin Endocrinol Metab; 89(8): 4037-
4043.
Lan, M. S., Lu, J., Goto, Y. and Notkins, A. L. (1994). Molecular cloning and identification of a
receptor-type protein tyrosine phosphatase, IA-2, from human insulinoma. DNA Cell Biol; 13(5): 505-
514.
Lan, M. S., Wasserfall, C., Maclaren, N. K. and Notkins, A. L. (1996). IA-2, a transmembrane protein
of the protein tyrosine phosphatase family, is a major autoantigen in insulin-dependent diabetes
mellitus. Proc Natl Acad Sci U S A; 93(13): 6367-6370.
Larsson, A., Ekblad, T., Andersson, O. and Liedberg, B. (2007). Photografted poly(ethylene glycol)
matrix for affinity interaction studies. Biomacromolecules; 8(1): 287-295.
Lee, J. W., Sim, S. J., Cho, S. M. and Lee, J. (2005). Characterization of a self-assembled monolayer
of thiol on a gold surface and the fabrication of a biosensor chip based on surface plasmon resonance
for detecting anti-GAD antibody. Biosens Bioelectron; 20(7): 1422-1427.
Leslie, R. D., Atkinson, M. A. and Notkins, A. L. (1999). Autoantigens IA-2 and GAD in Type I
(insulin-dependent) diabetes. Diabetologia; 42(1): 3-14.
Levy, L. M., Dalakas, M. C. and Floeter, M. K. (1999). The stiff-person syndrome: an autoimmune
disorder affecting neurotransmission of gamma-aminobutyric acid. Ann Intern Med; 131(7): 522-530.
Li, L., Hagopian, W. A., Brashear, H. R., Daniels, T. and Lernmark, A. (1994). Identification of
autoantibody epitopes of glutamic acid decarboxylase in stiff-man syndrome patients. J Immunol;
152(2): 930-934.
Liedberg, B., Nylander, C. and Lundstrom, I. (1995). Biosensing with surface plasmon resonance--
how it all started. Biosens Bioelectron; 10(8): i-ix.
Lin, R. Y., Schwartz, R. A. and Vafaie, J. (2010). Immunoglobulin G deficiency. From
http://emedicine.medscape.com/article/136897-overview.
Liu, E. and Eisenbarth, G. S. (2007). Accepting clocks that tell time poorly: fluid-phase versus
standard ELISA autoantibody assays. Clin Immunol; 125(2): 120-126.
Lu, J., Li, Q., Xie, H., Chen, Z. J., Borovitskaya, A. E., Maclaren, N. K., Notkins, A. L. and Lan, M. S.
(1996). Identification of a second transmembrane protein tyrosine phosphatase, IA-2beta, as an
autoantigen in insulin-dependent diabetes mellitus: precursor of the 37-kDa tryptic fragment. Proc
Natl Acad Sci U S A; 93(6): 2307-2311.
Ludvigsson, J. (2006). Why diabetes incidence increases--a unifying theory. Ann N Y Acad Sci; 1079:
374-382.
Ludvigsson, J. (2010a). Immune intervention in children with type 1 diabetes. Curr Diab Rep; 10(5):
370-379.
Ludvigsson, J. (2011). Diabetes vaccines: review of the clinical evidence. Clin. Invest.; E-pub ahead of
print.
Ludvigsson, J., Faresjo, M., Hjorth, M., Axelsson, S., Cheramy, M., Pihl, M., Vaarala, O., Forsander,
G., Ivarsson, S., Johansson, C., Lindh, A., Nilsson, N. O., Aman, J., Ortqvist, E., Zerhouni, P., et al.
References
107
(2008). GAD treatment and insulin secretion in recent-onset type 1 diabetes. N Engl J Med; 359(18):
1909-1920.
Ludvigsson, J., Hjorth, M., Cheramy, M., Axelsson, S., Pihl, M., Forsander, G., Nilsson, N. O.,
Samuelsson, B. O., Wood, T., Aman, J., Ortqvist, E. and Casas, R. (2010b). Extended evaluation of
the safety and efficacy of GAD treatment of children and adolescents with recent-onset type 1
diabetes: a randomised controlled trial. Diabetologia.
Ludvigsson, J., Ludvigsson, M. and Sepa, A. (2001). Screening for prediabetes in the general child
population: maternal attitude to participation. Pediatr Diabetes; 2(4): 170-174.
Lumley, T., Diehr, P., Emerson, S. and Chen, L. (2002). The importance of the normality assumption
in large public health data sets. Annu Rev Public Health; 23: 151-169.
Lundgren, M., Persson, U., Larsson, P., Magnusson, C., Smith, C. I., Hammarstrom, L. and
Severinson, E. (1989). Interleukin 4 induces synthesis of IgE and IgG4 in human B cells. Eur J
Immunol; 19(7): 1311-1315.
Luo, D., Rogers, C. N., Steed, J. T., Gilliam, L. K., Hampe, C. S. and Lernmark, A. (2004).
Development of glutamic acid decarboxylase 65 (GAD65) autoantibody assay using biotin-GAD65
fusion protein. J Biotechnol; 111(1): 97-104.
Löfås, S. and Johnsson, B. (1990). A novel hydrogel matrix on gold surfaces in surface plasmon
resonance sensors for fast and efficient covalent immobilization of ligands. J Chem Soc, Chem
Commun; 21: 1526-1528.
MacCuish, A. C., Irvine, W. J., Barnes, E. W. and Duncan, L. J. (1974). Antibodies to pancreatic islet
cells in insulin-dependent diabetics with coexistent autoimmune disease. Lancet; 2(7896): 1529-1531.
MacLennan, I. C. M., GulbransonJudge, A., Toellner, K. M., CasamayorPalleja, M., Chan, E., Sze, D.
M. Y., Luther, S. A. and Orbea, H. A. (1997). The changing preference of T and B cells for partners as
T-dependent antibody responses develop. Immunological Reviews; 156: 53-66.
Masson, J. F., Battaglia, T. M., Khairallah, P., Beaudoin, S. and Booksh, K. S. (2007). Quantitative
measurement of cardiac markers in undiluted serum. Anal Chem; 79(2): 612-619.
Mayr, A., Schlosser, M., Grober, N., Kenk, H., Ziegler, A. G., Bonifacio, E. and Achenbach, P.
(2007). GAD autoantibody affinity and epitope specificity identify distinct immunization profiles in
children at risk for type 1 diabetes. Diabetes; 56(6): 1527-1533.
McKee, A. S., MacLeod, M., White, J., Crawford, F., Kappler, J. W. and Marrack, P. (2008). Gr1+IL-
4-producing innate cells are induced in response to Th2 stimuli and suppress Th1-dependent antibody
responses. Int Immunol; 20(5): 659-669.
Melendez-Ramirez, L. Y., Richards, R. J. and Cefalu, W. T. (2010). Complications of type 1 diabetes.
Endocrinol Metab Clin North Am; 39(3): 625-640.
Menser, M. A., Forrest, J. M. and Bransby, R. D. (1978). Rubella infection and diabetes mellitus.
Lancet; 1(8055): 57-60.
Milgrom, F. and Witebsky, E. (1963). Antoantibody Formation as a Failure of "Self-Recognition".
Proc Can Cancer Conf; 5: 319-336.
Mire-Sluis, A. R., Gaines Das, R. and Lernmark, A. (2000). The World Health Organization
International Collaborative Study for islet cell antibodies. Diabetologia; 43(10): 1282-1292.
References ’
108
Moriwaki, M., Itoh, N., Miyagawa, J., Yamamoto, K., Imagawa, A., Yamagata, K., Iwahashi, H.,
Nakajima, H., Namba, M., Nagata, S., Hanafusa, T. and Matsuzawa, Y. (1999). Fas and Fas ligand
expression in inflamed islets in pancreas sections of patients with recent-onset Type I diabetes
mellitus. Diabetologia; 42(11): 1332-1340.
Mosmann, T. R. and Sad, S. (1996). The expanding universe of T-cell subsets: Th1, Th2 and more.
Immunol Today; 17(3): 138-146.
Mowen, K. A. and Glimcher, L. H. (2004). Signaling pathways in Th2 development. Immunological
Reviews; 202: 203-222.
Moya-Suri, V., Schlosser, M., Zimmermann, K., Rjasanowski, I., Gurtler, L. and Mentel, R. (2005).
Enterovirus RNA sequences in sera of schoolchildren in the general population and their association
with type 1-diabetes-associated autoantibodies. J Med Microbiol; 54(Pt 9): 879-883.
Mueller, P. (2007). Workshop report: Autoantibody markers for Type 1 diabetes. Diabetes
Autoantibodies Standardization Program 2007. Discussion and future direction. 9th International
Congress of Immunology of Diabetes Society, Nov 14-18, 2007, Florida. Acta Diabetologica; 44
((Suppl 1) Nov).
Murinson, B. B. (2004). Stiff-person syndrome. Neurologist; 10(3): 131-137.
Murinson, B. B., Butler, M., Marfurt, K., Gleason, S., De Camilli, P. and Solimena, M. (2004).
Markedly elevated GAD antibodies in SPS: effects of age and illness duration. Neurology; 63(11):
2146-2148.
Mölne, J. and Wold, A. (2007). Inflammation, Liber.
Nanto-Salonen, K., Kupila, A., Simell, S., Siljander, H., Salonsaari, T., Hekkala, A., Korhonen, S.,
Erkkola, R., Sipila, J. I., Haavisto, L., Siltala, M., Tuominen, J., Hakalax, J., Hyoty, H., Ilonen, J., et
al. (2008). Nasal insulin to prevent type 1 diabetes in children with HLA genotypes and autoantibodies
conferring increased risk of disease: a double-blind, randomised controlled trial. Lancet; 372(9651):
1746-1755.
Nejentsev, S., Sjoroos, M., Soukka, T., Knip, M., Simell, O., Lovgren, T. and Ilonen, J. (1999).
Population-based genetic screening for the estimation of Type 1 diabetes mellitus risk in Finland:
selective genotyping of markers in the HLA-DQB1, HLA-DQA1 and HLA-DRB1 loci. Diabet Med;
16(12): 985-992.
Nistico, L., Buzzetti, R., Pritchard, L. E., Van der Auwera, B., Giovannini, C., Bosi, E., Larrad, M. T.,
Rios, M. S., Chow, C. C., Cockram, C. S., Jacobs, K., Mijovic, C., Bain, S. C., Barnett, A. H.,
Vandewalle, C. L., et al. (1996). The CTLA-4 gene region of chromosome 2q33 is linked to, and
associated with, type 1 diabetes. Belgian Diabetes Registry. Hum Mol Genet; 5(7): 1075-1080.
Norris, J. M., Beaty, B., Klingensmith, G., Yu, L. P., Hoffman, M., Chase, H. P., Erlich, H. A.,
Hamman, R. F., Eisenbarth, G. S. and Rewers, M. (1996). Lack of association between early exposure
to cow's milk protein and beta-cell autoimmunity - Diabetes autoimmunity study in the young
(DAISY). Jama-Journal of the American Medical Association; 276(8): 609-614.
Norris, J. M., Yin, X., Barriga, K., Eisenbarth, G. S. and Rewers, M. (2007). Timing of cereal
introduction in the infant diet is associated with earlier onset but not later onset islet autoimmunity:
The diabetes autoimmunity study in the young (DAISY). Diabetes; 56: A262-A262.
References
109
Notkins, A. L. and Lernmark, A. (2001). Autoimmune type 1 diabetes: resolved and unresolved issues.
J Clin Invest; 108(9): 1247-1252.
Notkins, A. L., Lu, J., Li, Q., VanderVegt, F. P., Wasserfall, C., Maclaren, N. K. and Lan, M. S.
(1996). IA-2 and IA-2 beta are major autoantigens in IDDM and the precursors of the 40 kDa and 37
kDa tryptic fragments. J Autoimmun; 9(5): 677-682.
Oak, S., Gilliam, L. K., Landin-Olsson, M., Torn, C., Kockum, I., Pennington, C. R., Rowley, M. J.,
Christie, M. R., Banga, J. P. and Hampe, C. S. (2008). The lack of anti-idiotypic antibodies, not the
presence of the corresponding autoantibodies to glutamate decarboxylase, defines type 1 diabetes.
Proceedings of the National Academy of Sciences of the United States of America; 105(14): 5471-
5476.
Onengut-Gumuscu, S., Ewens, K. G., Spielman, R. S. and Concannon, P. (2004). A functional
polymorphism (1858C/T) in the PTPN22 gene is linked and associated with type I diabetes in
multiplex families. Genes Immun; 5(8): 678-680.
Onkamo, P., Vaananen, S., Karvonen, M. and Tuomilehto, J. (1999). Worldwide increase in incidence
of Type I diabetes--the analysis of the data on published incidence trends. Diabetologia; 42(12): 1395-
1403.
Padoa, C. J., Banga, J. P., Madec, A. M., Ziegler, M., Schlosser, M., Ortqvist, E., Kockum, I., Palmer,
J., Rolandsson, O., Binder, K. A., Foote, J., Luo, D. and Hampe, C. S. (2003). Recombinant Fabs of
human monoclonal antibodies specific to the middle epitope of GAD65 inhibit type 1 diabetes-specific
GAD65Abs. Diabetes; 52(11): 2689-2695.
Padoa, C. J., Crowther, N. J., Thomas, J. W., Hall, T. R., Bekris, L. M., Torn, C., Landin-Olsson, M.,
Ortqvist, E., Palmer, J. P., Lernmark, A. and Hampe, C. S. (2005). Epitope analysis of insulin
autoantibodies using recombinant Fab. Clin Exp Immunol; 140(3): 564-571.
Palmer, J. P., Asplin, C. M., Clemons, P., Lyen, K., Tatpati, O., Raghu, P. K. and Paquette, T. L.
(1983). Insulin antibodies in insulin-dependent diabetics before insulin treatment. Science; 222(4630):
1337-1339.
Patterson, C. C., Dahlquist, G. G., Gyurus, E., Green, A. and Soltesz, G. (2009). Incidence trends for
childhood type 1 diabetes in Europe during 1989-2003 and predicted new cases 2005-20: a multicentre
prospective registration study. Lancet; 373(9680): 2027-2033.
Peng, H. and Hagopian, W. (2006). Environmental factors in the development of Type 1 diabetes. Rev
Endocr Metab Disord; 7(3): 149-162.
Pescovitz, M. D., Greenbaum, C. J., Krause-Steinrauf, H., Becker, D. J., Gitelman, S. E., Goland, R.,
Gottlieb, P. A., Marks, J. B., McGee, P. F., Moran, A. M., Raskin, P., Rodriguez, H., Schatz, D. A.,
Wherrett, D., Wilson, D. M., et al. (2009). Rituximab, B-lymphocyte depletion, and preservation of
beta-cell function. N Engl J Med; 361(22): 2143-2152.
Petrovsky, N., Silva, D. and Schatz, D. A. (2003). Vaccine therapies for the prevention of type 1
diabetes mellitus. Paediatr Drugs; 5(9): 575-582.
Phillips, K. S., Han, J. H. and Cheng, Q. (2007). Development of a "membrane cloaking" method for
amperometric enzyme immunoassay and surface plasmon resonance analysis of proteins in serum
samples. Anal Chem; 79(3): 899-907.
Potter, K. N. and Wilkin, T. J. (2000). The molecular specificity of insulin autoantibodies. Diabetes
Metab Res Rev; 16(5): 338-353.
References ’
110
Press release Macrogenics and Lilly (2010). MacroGenics and Lilly Announce Pivotal Clinical Trial
of Teplizumab Did Not Meet Primary Efficacy Endpoint. From
https://investor.lilly.com/releasedetail2.cfm?ReleaseID=521014.
Pugliese, A., Gianani, R., Moromisato, R., Awdeh, Z. L., Alper, C. A., Erlich, H. A., Jackson, R. A.
and Eisenbarth, G. S. (1995). HLA-DQB1*0602 is associated with dominant protection from diabetes
even among islet cell antibody-positive first-degree relatives of patients with IDDM. Diabetes; 44(6):
608-613.
Purkerson, J. and Isakson, P. (1992). A two-signal model for regulation of immunoglobulin isotype
switching. FASEB J; 6(14): 3245-3252.
Raju, R., Foote, J., Banga, J. P., Hall, T. R., Padoa, C. J., Dalakas, M. C., Ortqvist, E. and Hampe, C.
S. (2005). Analysis of GAD65 autoantibodies in Stiff-Person syndrome patients. J Immunol; 175(11):
7755-7762.
Raju, R. and Hampe, C. S. (2008). Immunobiology of stiff-person syndrome. Int Rev Immunol; 27(1-
2): 79-92.
Rakocevic, G., Raju, R. and Dalakas, M. C. (2004). Anti-glutamic acid decarboxylase antibodies in
the serum and cerebrospinal fluid of patients with stiff-person syndrome: correlation with clinical
severity. Arch Neurol; 61(6): 902-904.
Rautajoki, K. J., Kylaniemi, M. K., Raghav, S. K., Rao, K. and Lahesmaa, R. (2008). An insight into
molecular mechanisms of human T helper cell differentiation. Ann Med; 40(5): 322-335.
Redondo, M. J. and Eisenbarth, G. S. (2002). Genetic control of autoimmunity in Type I diabetes and
associated disorders. Diabetologia; 45(5): 605-622.
Redondo, M. J., Rewers, M., Yu, L., Garg, S., Pilcher, C. C., Elliott, R. B. and Eisenbarth, G. S.
(1999). Genetic determination of islet cell autoimmunity in monozygotic twin, dizygotic twin, and
non-twin siblings of patients with type 1 diabetes: prospective twin study. BMJ; 318(7185): 698-702.
Revoltella, R. P., Laricchia Robbio, L. and Liedberg, B. (1998). Comparison of conventional
immunoassays (RIA, ELISA) with surface plasmon resonance for pesticide detection and monitoring.
Biotherapy; 11(2-3): 135-145.
Rich, S. S., Akolkar, B., Concannon, P., Erlich, H., Hilner, J. E., Julier, C., Morahan, G., Nerup, J.,
Nierras, C., Pociot, F. and Todd, J. A. (2009). Current status and the future for the genetics of type I
diabetes. Genes Immun; 10 Suppl 1: S128-131.
Richter, W., Shi, Y. and Baekkeskov, S. (1993). Autoreactive epitopes defined by diabetes-associated
human monoclonal antibodies are localized in the middle and C-terminal domains of the smaller form
of glutamate decarboxylase. Proc Natl Acad Sci U S A; 90(7): 2832-2836.
Ronkainen, M. S., Hoppu, S., Korhonen, S., Simell, S., Veijola, R., Ilonen, J., Simell, O. and Knip, M.
(2006). Early epitope- and isotype-specific humoral immune responses to GAD65 in young children
with genetic susceptibility to type 1 diabetes. Eur J Endocrinol; 155(4): 633-642.
Ronkainen, M. S., Savola, K. and Knip, M. (2004). Antibodies to GAD65 epitopes at diagnosis and
over the first 10 years of clinical type 1 diabetes mellitus. Scand J Immunol; 59(3): 334-340.
Rose, N. R. and Bona, C. (1993). Defining criteria for autoimmune diseases (Witebsky's postulates
revisited). Immunol Today; 14(9): 426-430.
References
111
Rother, K. I., Brown, R. J., Morales, M. M., Wright, E., Duan, Z., Campbell, C., Hardin, D. S.,
Popovic, J., McEvoy, R. C., Harlan, D. M., Orlander, P. R. and Brod, S. A. (2009). Effect of ingested
interferon-alpha on beta-cell function in children with new-onset type 1 diabetes. Diabetes Care;
32(7): 1250-1255.
Roux, K. H., Strelets, L. and Michaelsen, T. E. (1997). Flexibility of human IgG subclasses. J
Immunol; 159(7): 3372-3382.
Sabbah, E., Savola, K., Kulmala, P., Veijola, R., Vahasalo, P., Karjalainen, J., Akerblom, H. K. and
Knip, M. (1999). Diabetes-associated autoantibodies in relation to clinical characteristics and natural
course in children with newly diagnosed type 1 diabetes. The Childhood Diabetes In Finland Study
Group. J Clin Endocrinol Metab; 84(5): 1534-1539.
Sakaguchi, S. (2004). Naturally arising CD4(+) regulatory T cells for immunologic self-tolerance and
negative control of immune responses. Annual Review of Immunology; 22: 531-562.
Salminen, K., Sadeharju, K., Lonnrot, M., Vahasalo, P., Kupila, A., Korhonen, S., Ilonen, J., Simell,
O., Knip, M. and Hyoty, H. (2003). Enterovirus infections are associated with the induction of beta-
cell autoimmunity in a prospective birth cohort study. J Med Virol; 69(1): 91-98.
Sanjeevi, C. B., Landin-Olsson, M., Kockum, I., Dahlquist, G. and Lernmark, A. (1995). Effects of the
second HLA-DQ haplotype on the association with childhood insulin-dependent diabetes mellitus.
Tissue Antigens; 45(2): 148-152.
Savola, K., Bonifacio, E., Sabbah, E., Kulmala, P., Vahasalo, P., Karjalainen, J., Tuomilehto-Wolf, E.,
Merilainen, J., Akerblom, H. K. and Knip, M. (1998). IA-2 antibodies--a sensitive marker of IDDM
with clinical onset in childhood and adolescence. Childhood Diabetes in Finland Study Group.
Diabetologia; 41(4): 424-429.
Savola, K., Laara, E., Vahasalo, P., Kulmala, P., Akerblom, H. K. and Knip, M. (2001). Dynamic
pattern of disease-associated autoantibodies in siblings of children with type 1 diabetes: a population-
based study. Diabetes; 50(11): 2625-2632.
Savola, K., Sabbah, E., Kulmala, P., Vahasalo, P., Ilonen, J. and Knip, M. (1998). Autoantibodies
associated with Type I diabetes mellitus persist after diagnosis in children. Diabetologia; 41(11):
1293-1297.
Schloot, N. C., Meierhoff, G., Lengyel, C., Vandorfi, G., Takacs, J., Panczel, P., Barkai, L., Madacsy,
L., Oroszlan, T., Kovacs, P., Suto, G., Battelino, T., Hosszufalusi, N. and Jermendy, G. (2007). Effect
of heat shock protein peptide DiaPep277 on beta-cell function in paediatric and adult patients with
recent-onset diabetes mellitus type 1: two prospective, randomized, double-blind phase II trials.
Diabetes Metab Res Rev; 23(4): 276-285.
Schlosser, M., Banga, J. P., Madec, A. M., Binder, K. A., Strebelow, M., Rjasanowski, I., Wassmuth,
R., Gilliam, L. K., Luo, D. and Hampe, C. S. (2005a). Dynamic changes of GAD65 autoantibody
epitope specificities in individuals at risk of developing type 1 diabetes. Diabetologia; 48(5): 922-930.
Schlosser, M., Koczwara, K., Kenk, H., Strebelow, M., Rjasanowski, I., Wassmuth, R., Achenbach, P.,
Ziegler, A. G. and Bonifacio, E. (2005b). In insulin-autoantibody-positive children from the general
population, antibody affinity identifies those at high and low risk. Diabetologia; 48(9): 1830-1832.
Schlosser, M., Mueller, P. W., Torn, C., Bonifacio, E. and Bingley, P. J. (2010). Diabetes Antibody
Standardization Program: evaluation of assays for insulin autoantibodies. Diabetologia; 53(12): 2611-
2620.
References ’
112
Schmidli, R. S., Colman, P. G. and Bonifacio, E. (1995). Disease sensitivity and specificity of 52
assays for glutamic acid decarboxylase antibodies. The Second International GADAB Workshop.
Diabetes; 44(6): 636-640.
Schmidli, R. S., Colman, P. G., Bonifacio, E., Bottazzo, G. F. and Harrison, L. C. (1994). High level
of concordance between assays for glutamic acid decarboxylase antibodies. The First International
Glutamic Acid Decarboxylase Antibody Workshop. Diabetes; 43(8): 1005-1009.
Schroeder, H. W., Jr. and Cavacini, L. (2010). Structure and function of immunoglobulins. J Allergy
Clin Immunol; 125(2 Suppl 2): S41-52.
Seissler, J., Eikamp, K., Schott, M. and Scherbaum, W. A. (2002). IA-2 autoantibodies restricted to
the IgG4 subclass are associated with protection from type 1 diabetes. Horm Metab Res; 34(4): 186-
191.
Sepa, A., Frodi, A. and Ludvigsson, J. (2005). Mothers' experiences of serious life events increase the
risk of diabetes-related autoimmunity in their children. Diabetes Care; 28(10): 2394-2399.
Sepa, A., Wahlberg, J., Vaarala, O., Frodi, A. and Ludvigsson, J. (2005). Psychological stress may
induce diabetes-related autoimmunity in infancy. Diabetes Care; 28(2): 290-295.
Shankaran, D. R. and Miura, N. (2007). Trends in interfacial design for surface plasmon resonance
based immunoassays. J Phys D Appl Phys; 40: 7187-7200.
She, J. X. (1996). Susceptibility to type I diabetes: HLA-DQ and DR revisited. Immunol Today; 17(7):
323-329.
Sjoroos, M., Ilonen, J., Reijonen, H. and Lovgren, T. (1998). Time-resolved fluorometry based
sandwich hybridisation assay for HLA-DQA1 typing. Dis Markers; 14(1): 9-19.
Skyler, J. S. (2010). Glycemia and cardiovascular diseases in type 2 diabetes. J Intern Med; 268(5):
468-470.
Skyler, J. S., Krischer, J. P., Wolfsdorf, J., Cowie, C., Palmer, J. P., Greenbaum, C., Cuthbertson, D.,
Rafkin-Mervis, L. E., Chase, H. P. and Leschek, E. (2005). Effects of oral insulin in relatives of
patients with type 1 diabetes: The Diabetes Prevention Trial--Type 1. Diabetes Care; 28(5): 1068-
1076.
Snapper, C. M. and Mond, J. J. (1993). Towards a comprehensive view of immunoglobulin class
switching. Immunol Today; 14(1): 15-17.
Sodoyez-Goffaux, F., Koch, M., Dozio, N., Brandenburg, D. and Sodoyez, J. C. (1988). Advantages
and pitfalls of radioimmune and enzyme linked immunosorbent assays of insulin antibodies.
Diabetologia; 31(9): 694-702.
Solimena, M., Dirkx, R., Jr., Hermel, J. M., Pleasic-Williams, S., Shapiro, J. A., Caron, L. and Rabin,
D. U. (1996). ICA 512, an autoantigen of type I diabetes, is an intrinsic membrane protein of
neurosecretory granules. EMBO J; 15(9): 2102-2114.
Soltesz, G., Patterson, C. C. and Dahlquist, G. (2007). Worldwide childhood type 1 diabetes
incidence--what can we learn from epidemiology? Pediatr Diabetes; 8 Suppl 6: 6-14.
References
113
Stead, J. D. and Jeffreys, A. J. (2002). Structural analysis of insulin minisatellite alleles reveals
unusually large differences in diversity between Africans and non-Africans. Am J Hum Genet; 71(6):
1273-1284.
Steck, A. K., Zhang, W., Bugawan, T. L., Barriga, K. J., Blair, A., Erlich, H. A., Eisenbarth, G. S.,
Norris, J. M. and Rewers, M. J. (2009). Do non-HLA genes influence development of persistent islet
autoimmunity and type 1 diabetes in children with high-risk HLA-DR,DQ genotypes? Diabetes;
58(4): 1028-1033.
Stolt, U. G., Helgesson, G., Liss, P. E., Svensson, T. and Ludvigsson, J. (2005). Information and
informed consent in a longitudinal screening involving children: a questionnaire survey. Eur J Hum
Genet; 13(3): 376-383.
Stolt, U. G., Liss, P. E., Svensson, T. and Ludvigsson, J. (2002). Attitudes to bioethical issues: a case
study of a screening project. Soc Sci Med; 54(9): 1333-1344.
Strebelow, M., Schlosser, M., Ziegler, B., Rjasanowski, I. and Ziegler, M. (1999). Karlsburg Type I
diabetes risk study of a general population: frequencies and interactions of the four major Type I
diabetes-associated autoantibodies studied in 9419 schoolchildren. Diabetologia; 42(6): 661-670.
Stride, A. and Hattersley, A. T. (2002). Different genes, different diabetes: lessons from maturity-
onset diabetes of the young. Ann Med; 34(3): 207-216.
SWEDIABKIDS (2009). The Swedish national pediatric diabetes registry. Yearly Report 2009. From
https://www.ndr.nu/ndr2/.
Szabo, S. J., Sullivan, B. M., Peng, S. L. and Glimcher, L. H. (2003). Molecular mechanisms
regulating Th1 immune responses. Annu Rev Immunol; 21: 713-758.
The DIAMOND Project Group (2006). Incidence and trends of childhood Type 1 diabetes worldwide
1990-1999. Diabet Med; 23(8): 857-866.
The TEDDY Study Group (2007). The Environmental Determinants of Diabetes in the Young
(TEDDY) study: study design. Pediatr Diabetes; 8(5): 286-298.
The TEDDY Study Group (2008). The Environmental Determinants of Diabetes in the Young
(TEDDY) Study. Ann N Y Acad Sci; 1150: 1-13.
Tian, J., Atkinson, M. A., Clare-Salzler, M., Herschenfeld, A., Forsthuber, T., Lehmann, P. V. and
Kaufman, D. L. (1996). Nasal administration of glutamate decarboxylase (GAD65) peptides induces
Th2 responses and prevents murine insulin-dependent diabetes. J Exp Med; 183(4): 1561-1567.
Tian, J., Clare-Salzler, M., Herschenfeld, A., Middleton, B., Newman, D., Mueller, R., Arita, S.,
Evans, C., Atkinson, M. A., Mullen, Y., Sarvetnick, N., Tobin, A. J., Lehmann, P. V. and Kaufman, D.
L. (1996). Modulating autoimmune responses to GAD inhibits disease progression and prolongs islet
graft survival in diabetes-prone mice. Nat Med; 2(12): 1348-1353.
Tian, J. and Kaufman, D. L. (2009). Antigen-based therapy for the treatment of type 1 diabetes.
Diabetes; 58(9): 1939-1946.
Tisch, R., Wang, B. and Serreze, D. V. (1999). Induction of glutamic acid decarboxylase 65-specific
Th2 cells and suppression of autoimmune diabetes at late stages of disease is epitope dependent. J
Immunol; 163(3): 1178-1187.
References ’
114
Tisch, R., Yang, X. D., Singer, S. M., Liblau, R. S., Fugger, L. and McDevitt, H. O. (1993). Immune
response to glutamic acid decarboxylase correlates with insulitis in non-obese diabetic mice. Nature;
366(6450): 72-75.
Torn, C., Mueller, P. W., Schlosser, M., Bonifacio, E. and Bingley, P. J. (2008). Diabetes Antibody
Standardization Program: evaluation of assays for autoantibodies to glutamic acid decarboxylase and
islet antigen-2. Diabetologia; 51(5): 846-852.
TRIGR. (2010). Trial to reduce type 1 diabetes in the genetically at risk, from
http://trigr.epi.usf.edu/index.html.
Undlien, D. E., Lie, B. A. and Thorsby, E. (2001). HLA complex genes in type 1 diabetes and other
autoimmune diseases. Which genes are involved? Trends Genet; 17(2): 93-100.
Vaarala, O. (1999b). Gut and the induction of immune tolerance in type 1 diabetes. Diabetes Metab
Res Rev; 15(5): 353-361.
Vaarala, O., Knip, M., Paronen, J., Hamalainen, A. M., Muona, P., Vaatainen, M., Ilonen, J., Simell,
O. and Akerblom, H. K. (1999a). Cow's milk formula feeding induces primary immunization to
insulin in infants at genetic risk for type 1 diabetes. Diabetes; 48(7): 1389-1394.
Wahlberg, J., Fredriksson, J., Nikolic, E., Vaarala, O. and Ludvigsson, J. (2005). Environmental
factors related to the induction of beta-cell autoantibodies in 1-yr-old healthy children. Pediatr
Diabetes; 6(4): 199-205.
Wahlberg, J., Vaarala, O. and Ludvigsson, J. (2006). Dietary risk factors for the emergence of type 1
diabetes-related autoantibodies in 21/2 year-old Swedish children. Br J Nutr; 95(3): 603-608.
van der Werf, N., Kroese, F. G., Rozing, J. and Hillebrands, J. L. (2007). Viral infections as potential
triggers of type 1 diabetes. Diabetes Metab Res Rev; 23(3): 169-183.
Vardi, P., Ziegler, A. G., Mathews, J. H., Dib, S., Keller, R. J., Ricker, A. T., Wolfsdorf, J. I.,
Herskowitz, R. D., Rabizadeh, A., Eisenbarth, G. S. and et al. (1988). Concentration of insulin
autoantibodies at onset of type I diabetes. Inverse log-linear correlation with age. Diabetes Care;
11(9): 736-739.
Wenzlau, J. M., Juhl, K., Yu, L., Moua, O., Sarkar, S. A., Gottlieb, P., Rewers, M., Eisenbarth, G. S.,
Jensen, J., Davidson, H. W. and Hutton, J. C. (2007). The cation efflux transporter ZnT8 (Slc30A8) is
a major autoantigen in human type 1 diabetes. Proc Natl Acad Sci U S A; 104(43): 17040-17045.
Verge, C. F., Gianani, R., Kawasaki, E., Yu, L., Pietropaolo, M., Jackson, R. A., Chase, H. P. and
Eisenbarth, G. S. (1996). Prediction of type I diabetes in first-degree relatives using a combination of
insulin, GAD, and ICA512bdc/IA-2 autoantibodies. Diabetes; 45(7): 926-933.
Verge, C. F., Stenger, D., Bonifacio, E., Colman, P. G., Pilcher, C., Bingley, P. J. and Eisenbarth, G.
S. (1998). Combined use of autoantibodies (IA-2 autoantibody, GAD autoantibody, insulin
autoantibody, cytoplasmic islet cell antibodies) in type 1 diabetes: Combinatorial Islet Autoantibody
Workshop. Diabetes; 47(12): 1857-1866.
WHO (1999). Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications:
Report of a WHO consultation Part 1: Diagnosis and Classifications of Diabetes Mellitus. Geneva,
World Health Organisation.
References
115
Widhe, M., Ekerfelt, C., Forsberg, P., Bergstrom, S. and Ernerudh, J. (1998). IgG subclasses in Lyme
borreliosis: a study of specific IgG subclass distribution in an interferon-gamma-predominated disease.
Scand J Immunol; 47(6): 575-581.
Vikinge, T. P., Askendal, A., Liedberg, B., Lindahl, T. and Tengvall, P. (1998). Immobilized chicken
antibodies improve the detection of serum antigens with surface plasmon resonance (SPR). Biosens
Bioelectron; 13(12): 1257-1262.
Wilkin, T. J. (1990). Insulin autoantibodies as markers for type I diabetes. Endocr Rev; 11(1): 92-104.
Wilkin, T. J. (2001). The accelerator hypothesis: weight gain as the missing link between Type I and
Type II diabetes. Diabetologia; 44(7): 914-922.
Williams, A. J., Bingley, P. J., Bonifacio, E., Palmer, J. P. and Gale, E. A. (1997). A novel micro-
assay for insulin autoantibodies. J Autoimmun; 10(5): 473-478.
Williams, A. J., Norcross, A. J., Dix, R. J., Gillespie, K. M., Gale, E. A. and Bingley, P. J. (2003). The
prevalence of insulin autoantibodies at the onset of Type 1 diabetes is higher in males than females
during adolescence. Diabetologia; 46(10): 1354-1356.
Winter, W. E., Harris, N. and Schatz, D. (2002). Type 1 diabetes islet autoantibody markers. Diabetes
Technol Ther; 4(6): 817-839.
Virtanen, S. M. and Knip, M. (2003). Nutritional risk predictors of beta cell autoimmunity and type 1
diabetes at a young age. Am J Clin Nutr; 78(6): 1053-1067.
Virtanen, S. M., Rasanen, L., Ylonen, K., Aro, A., Clayton, D., Langholz, B., Pitkaniemi, J., Savilahti,
E., Lounamaa, R., Tuomilehto, J. and et al. (1993). Early introduction of dairy products associated
with increased risk of IDDM in Finnish children. The Childhood in Diabetes in Finland Study Group.
Diabetes; 42(12): 1786-1790.
Viskari, H., Ludvigsson, J., Uibo, R., Salur, L., Marciulionyte, D., Hermann, R., Soltesz, G.,
Fuchtenbusch, M., Ziegler, A. G., Kondrashova, A., Romanov, A., Kaplan, B., Laron, Z., Koskela, P.,
Vesikari, T., et al. (2005). Relationship between the incidence of type 1 diabetes and maternal
enterovirus antibodies: time trends and geographical variation. Diabetologia; 48(7): 1280-1287.
Viskari, H., Ludvigsson, J., Uibo, R., Salur, L., Marciulionyte, D., Hermann, R., Soltesz, G.,
Fuchtenbusch, M., Ziegler, A. G., Kondrashova, A., Romanov, A., Knip, M. and Hyoty, H. (2004).
Relationship between the incidence of type 1 diabetes and enterovirus infections in different European
populations: results from the EPIVIR project. J Med Virol; 72(4): 610-617.
Viskari, H. R., Koskela, P., Lonnrot, M., Luonuansuu, S., Reunanen, A., Baer, M. and Hyoty, H.
(2000). Can enterovirus infections explain the increasing incidence of type 1 diabetes? Diabetes Care;
23(3): 414-416.
Xie, H., Zhang, B., Matsumoto, Y., Li, Q., Notkins, A. L. and Lan, M. S. (1997). Autoantibodies to
IA-2 and IA-2 beta in insulin-dependent diabetes mellitus recognize conformational epitopes: location
of the 37- and 40-kDa fragments determined. J Immunol; 159(7): 3662-3667.
Yoon, J. W. and Jun, H. S. (2005). Autoimmune destruction of pancreatic beta cells. Am J Ther; 12(6):
580-591.
Yu, J., Yu, L., Bugawan, T. L., Erlich, H. A., Barriga, K., Hoffman, M., Rewers, M. and Eisenbarth,
G. S. (2000). Transient antiislet autoantibodies: infrequent occurrence and lack of association with
"genetic" risk factors. J Clin Endocrinol Metab; 85(7): 2421-2428.
References ’
116
Yu, L., Rewers, M., Gianani, R., Kawasaki, E., Zhang, Y., Verge, C., Chase, P., Klingensmith, G.,
Erlich, H., Norris, J. and Eisenbarth, G. S. (1996). Antiislet autoantibodies usually develop
sequentially rather than simultaneously. J Clin Endocrinol Metab; 81(12): 4264-4267.
Zhang, B., Lan, M. S. and Notkins, A. L. (1997). Autoantibodies to IA-2 in IDDM: location of major
antigenic determinants. Diabetes; 46(1): 40-43.
Ziegler, A. G., Hummel, M., Schenker, M. and Bonifacio, E. (1999). Autoantibody appearance and
risk for development of childhood diabetes in offspring of parents with type 1 diabetes: the 2-year
analysis of the German BABYDIAB Study. Diabetes; 48(3): 460-468.
Ziegler, A. G. and Nepom, G. T. (2010). Prediction and pathogenesis in type 1 diabetes. Immunity;
32(4): 468-478.
Ziegler, A. G., Schmid, S., Huber, D., Hummel, M. and Bonifacio, E. (2003). Early infant feeding and
risk of developing type 1 diabetes-associated autoantibodies. JAMA; 290(13): 1721-1728.
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