Autoimmune (Type 1) Diabetes

Autoimmune (Type 1) Diabetes

Chapter 41 Autoimmune (Type 1) Diabetes Ahmed J. Delli and A˚ke Lernmark Lund University, Clinical Research Center, Department of Clinical Sciences, ...

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Chapter 41

Autoimmune (Type 1) Diabetes Ahmed J. Delli and A˚ke Lernmark Lund University, Clinical Research Center, Department of Clinical Sciences, Ska˚ne University Hospital SUS, Malmo¨, Sweden

Chapter Outline Introduction Clinical and Pathologic Features Epidemiologic Features Genetic Features HLA Genetic Factors: The DR-DQ Alleles Non-HLA Genetic Factors Autoimmune Features

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INTRODUCTION Autoimmune (type 1) diabetes (which will be referred to in this chapter as autoimmune diabetes mellitus: AI-DM) is a disease of undetermined etiology and mode of inheritance, in which genetically predisposed individuals are exposed to a group of putative environmental exposures that trigger an aggressive and selective autoimmune response against beta cells. Our understanding of this type of diabetes has improved through years of research along with several alterations in nomenclature (Table 41.1). AI-DM is a multi-stage disease characterized by a complex and prolonged autoimmune prodrome (pre-diabetes phase) that develops over months to years (Figure 41.1). Several autoimmune markers circulate the peripheral blood and are readily detectable during the prodrome. However, it is only near the end of stage III (Figure 41.1), when the beta cells’ secretory reserves are lost, that signs of dysglycemia and eventually hyperglycemia become evident. At this point, the only possible measure to take is to restore euglycemia and its subsequent metabolic disturbance through provision of exogenous insulin. More attention and research needs to be directed to both stage II and the pre-diabetes autoimmune stage III aiming at properly identifying determinants of autoimmune response in order to improve the diagnosis and classification of autoimmune diabetes. Primary prevention should be approached in stage II. However, the only possible cure for this form of diabetes is to halt or reverse the

Pathologic Effector Mechanisms Triggering Autoimmunity APCs in Genetically Predisposed Subjects In Vivo and In Vitro Models Autoantibodies as Potential Immunologic Markers Concluding Remarks—Future Prospects References

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autoimmune response through prevention or intervention measures rather than just replacing the insulin deficiency.

CLINICAL AND PATHOLOGIC FEATURES The clinical onset of AI-DM follows after a prodromal phase of beta cell autoimmunity that is not associated with any symptoms (Figure 41.1). The diagnosis of diabetes is based on blood glucose level determinations according to combined recommendations of the World Health Organization (WHO) and the American Diabetes Association (ADA, 2013) (Table 41.2). The loss of beta cells varies with age. Young children have lost proportionally more beta cells compared to teenagers, young adults, and adults. The clinical onset is therefore not only a function of a reduced beta cell mass but also of insulin resistance. Young age at onset patients may show classic symptoms such as weight loss, thirst, frequent urination, and hunger. Adult subjects may develop diabetes symptoms that would be consistent with type 2 diabetes (T2D). It is not until beta cell autoantibodies are measured that AI-DM is properly classified. The so-called latent autoimmune diabetes in the adult (LADA) may represent 5 10% of all diabetes diagnosed above 35 years of age (Leslie et al., 2008). LADA is a useful clinical classification that requires the analysis of autoantibodies against the 65K variant of glutamic acid decarboxylase

N. Rose & I. Mackay (Eds): The Autoimmune Diseases, Fifth edition. DOI: http://dx.doi.org/10.1016/B978-0-12-384929-8.00041-1 © 2014 Elsevier Inc. All rights reserved.

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TABLE 41.1 Variants of Diabetes Mellitus and Nomenclature Used to Describe Autoimmune (Type 1) Diabetes Nomenclature/Acronym

Period

Basis of Classification

Juvenile diabetes Insulin-dependent diabetes (IDDM) Type 1 diabetes Latent autoimmune diabetes in adults (LADA) Latent autoimmune diabetes in the young (LADY) Slowly progressive insulin-dependent diabetes mellitus (SPIDDM) Autoimmune diabetes mellitus (AI-DM)

until 1970 until 1980 ongoing ongoing ongoing ongoing Proposed

Classification by age Classification by treatment method Classification by clinical features Classification by GAD65A positivity in adult T2DM Classification by GAD65A positivity in young T2DM Classification by clinical features Classification by immunogenetics

FIGURE 41.1 Stages of Autoimmune Diabetes Mellitus (AI-DM). The preclinical prodrome, which may last several months up to years, represented by three overlapping stages. Stage I represents genetic predisposition, which interacts with putative environmental factors to trigger autoimmune response in stage II. During stage III, islet autoimmunity is initiated and propagated, marked by detectable islet autoantibodies. During stages I III, prediction, primary, and secondary prevention trials may be initiated to halt autoimmunity and progression to DM. Stage IV represents the final stage of clinical diabetes marked by metabolic consequences of insulin deficiency.

(GAD65A) as it identifies AI-DM that will require treatment with insulin within 5 years of the diagnosis of diabetes (Leslie et al., 2008). The pancreatic islet beta cells are specifically destroyed in the AI-DM. Replacement therapy with insulin is therefore introduced immediately in children and young adults diagnosed with diabetes along with symptoms of thirst, weight loss, and frequent urination. In LADA, insulin therapy will be needed after about 5 years when all other treatment approaches have failed. Insulin therapy is not a cure in AI-DM but a replacement therapy. Currently, there is no alternative to insulin replacement therapy and the many thousands of patients diagnosed each year throughout the world will all be dependent on daily insulin treatment for the rest of their lives.

Historically, the involvement of immune cells in AIDM was described when inflammatory cell infiltrates, fibrosis, and atrophy of the islets were demonstrated in postmortem pancreatic tissues obtained from some children who died soon following diagnosis (Gepts, 1965). The pathological feature of AI-DM is the conspicuous loss of the pancreatic islet beta cells (Pipeleers et al., 2008). An infiltration of mononuclear cells in islets is often but not always observed (In’t Veld, 2011). Insulitits ($2 mononuclear immune cells per islets) appears as a late manifestation and is observed primarily in multiple autoantibody positive subjects prior to clinical diagnosis (In’t Veld et al., 2007). Quantitative immunocytochemistry reveals a specific loss of beta cells and that the neighboring cells producing glucagon, somatostatin, pancreatic

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TABLE 41.2 Basis of Diagnosis and Classification of Diabetes Mellitus (Current and Proposed) Current criteria of diabetes diagnosis by the ADA (2013) At least one of the following criteria is required A1C $ 6.5% (NGSP & DCCT standardized) Repeated in absence of unequivocal hyperglycemia FPG $ 126 mg/dl (7.0 mmol/l) Repeated in absence of unequivocal hyperglycemia 2-h PG $ 200 mg/dl (11.1 mmol/l) during a WHO Repeated in absence of unequivocal hyperglycemia standardized OGTT RPG $ 200 mg/dl (11.1 mmol/l) In presence of hyperglycemia or hyperglycemia crisis Criteria for increased risk of diabetes (prediabetes) by the ADA (2013) FPG $ 100 125 mg/dl (5.6 6.9 mmol/l) Impaired fasting glucose (IFG) 2-h PG $ 140 199 mg/dl (7.8 11.0 mmol/l) Impaired glucose tolerance (IGT) during OGTT (WHO) A1C $ 5.7 6.4% Proposed criteria for prediction and diagnosis classification of AI-DM (ongoing research) HLA class II DR-DQ alleles DR4 (B1*04), DR3 (B1*03), DQ8 (B1*03:02), DQ2 (B1*05:02) (Erlich et al., 2008) Standardized autoimmune markers (Since 1985 through IDW, DASP and currently IASP)

Autoantigen

Marker

Abbreviation

Insulin GAD65 Islet antigen-2 Zinc transporter 8 three variants

insulin autoantibodies GAD IA-2 and IA-2β ZnT8-R (arginine), W (tryptophan) and Q (glutamine)

IAA GAD65A IA-2A ZnT8-RA, ZnT8-WA, ZnT8-QA

polypeptide, or ghrelin are not affected. In subjects with insulitis, the islets of Langerhans may be infiltrated by T and B lymphocytes as well as monocytes and dendritic cells supporting a state of chronic inflammation (Eizirik et al., 2009). In some insulitis-positive islets it has been possible to demonstrate markers of inflammation along with viral antigens (Foulis et al., 1991; Foulis, 2008). The known insulitis characteristics are derived from specimens that mostly reflect an advanced stage of the disease or more extensive form of it when obtained from postmortem autopsies. Little is understood about early stages of propagation of autoimmune pathologic features in the prolonged pre-diabetes phase. T lymphocytes, especially the CD41 and CD81 cell subsets, dominate the insulitits (Pinkse et al., 2005), compared to B lymphocytes, and may be found in larger populations in the islets (Kent et al., 2005). Therefore, the infiltration of pancreatic islets by inflammatory cells, beta cell destruction and the resulting insulitis is a multi-step process, which may vary widely in duration and intensity before diabetes becomes clinically manifest. Furthermore, in recent-onset AI-DM, residual beta cell function was temporarily preserved through the use of monoclonal antibodies targeting CD3 on T lymphocytes (Herold et al., 2002), CD20 on B lymphocytes (Pescovitz et al., 2009), or drugs such as cyclosporine targeting monocyte/macrophage populations (Bougneres et al., 1988). These observations in addition to the significant role of cellular immunological pathway, predominantly CD81 lymphocytes, suggest that AI-DM is primarily a cell-mediated autoimmune disease (Notkins

and Lernmark, 2001; Willcox et al., 2009). However, in the absence of specific cellular assays, stage III is defined by islet autoantibodies (Figure 41.1). The islet autoantibodies therefore represent predictive markers of an ongoing autoimmune response, yet their exact role in beta cell destruction remains to be clarified.

EPIDEMIOLOGIC FEATURES The epidemiology of stage III AI-DM (Figure 41.1) is essentially unknown. Studies such as DIPP, BABYDIAB (Schenker et al., 1999), DAISY (Rewers et al., 1996), and DiPiS (Larsson et al., 2005) have screened newborns for AI-DM high risk human leukocyte antigen (HLA). There are four standardized tests for diabetes-relevant autoantibodies: against insulin, glutamic acid decarboxylase (GAD65), islet antigen-2 (IA-2), and the zinc transporter 8 (ZnT8) (Pietropaolo et al., 2012). All have been used to follow high HLA risk children longitudinally. Children with low risk HLA genotypes are yet to be followed. Persistent autoantibody positivity (more than 3 months) would detect AI-DM stage III. However, its epidemiology remains unknown as there is a paucity of studies screening, e.g., schoolchildren (Schlosser et al., 2002). It is generally accepted that up to 10% of all diabetes in different age groups are classifiable as type 1 or autoimmune (ADA, 2013). In obese patients, it may sometimes be difficult to discriminate between AI-DM and T2D especially in adolescents and young adults (Pozzilli et al., 2011) and shared pathways in such patients may be

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present (Libman and Becker, 2003). The occurrence of AI-DM varies according to ethnic heritage and geographical locations where high-incidence regions are seen in populations of European descent especially Scandinavia (Karvonen et al., 2000). Recent estimates suggest that the annual rise in incidence of AI-DM among children under 15 years is 3% (2 5%) worldwide including low prevalence populations (IDF, 2011). AI-DM constitutes more than 85% of diabetes phenotypes in patients under 20 years in most populations (Vandewalle et al., 1997; Craig et al., 2006; Thunander et al., 2008) but it may also show a second peak between 50 and 65 years (Lorenzen et al., 1994). Nearly a quarter of patients become clinically evident during adulthood (Haller et al., 2005). In younger patients, most AI-DM patients become clinically evident around or shortly after puberty (12 15 years in most countries), but it may be diagnosed as early as 9 months of age. This peak incidence around puberty (EURODIAB, 2000; Dabelea et al., 2007) is proposed to be related to spur of growth and upsurge metabolic demand. The highest rise in incidence was seen in younger individuals, especially preschool children indicating an increasing role of environmental factors (EURODIAB, 2000; Pundziute-Lycka et al., 2002; Berhan et al., 2011). Only 15% of newly diagnosed AI-DM patients have a first degree relative with the disease. Overall, AI-DM shows a slight male preference, which becomes evident after puberty up to 35 years (Pundziute-Lycka et al., 2002; Kyvik et al., 2004), a phenomenon that was partly linked to lack of the high risk HLA DQ2/8 genotype (Weets et al., 2004). Another significant indicator of environmental factors to the autoimmunity is the seasonal patterns of birth (Samuelsson et al., 1999; Mckinney, 2001; Willis et al., 2002; Kahn et al., 2009) and timing of diagnosis (Padaiga et al., 1999; Willis et al., 2002). The higher frequency of incident patients in colder months (Padaiga et al., 1999; Willis et al., 2002). and season of birth in spring (Mckinney, 2001) were correlated to exposure to cycles of viral infections (Knip et al., 2005) during early childhood and gestational life, respectively.

GENETIC FEATURES AI-DM-associated genetic factors are essential yet not sufficient causal factors (Notkins and Lernmark, 2001). Among these there are more than 50 different loci on 12 chromosomes linked to AI-DM (Table 41.3). HLA class II alleles of the major histocompatibility complex (MHC) on chromosome 6p21 are the most important (Todd et al., 2007). The majority of implicated genes are essentially related to the immune response rather than predisposing to diabetes and metabolic derangements. Only few genes such as INS and ERBB3 may modulate beta cell function through affecting insulin secretion and

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metabolism. Other genes such as TNFAIP3 are related to mechanisms of cell apoptosis (Todd et al., 2007; Concannon et al., 2009).

HLA Genetic Factors: The DR-DQ Alleles The HLA-DR-DQ alleles are linked to susceptibility to AI-DM (Todd et al., 2007) and also several other autoimmune diseases such as celiac disease, multiple sclerosis, and rheumatoid arthritis, but some alleles provide protection from these diseases (Nerup et al., 1974; Owerbach et al., 1983). The AI-DM risk alleles may be found in up to 50% of the general population but only a smaller proportion develops the disease (Knip et al., 2005). The DQ8 (A1*03:01-B1*03:02) and DQ2 (A1*05:01-B1*02:01) haplotypes confer the strongest risk (Schranz and Lernmark, 1998; Erlich et al., 2008). These haplotypes share a linkage disequilibrium (LD) relationship with the DR4 (B1*04) and DR3 (B1*03), respectively (Hermann et al., 2004). Almost 90% of AI-DM patients diagnosed before 35 years of age carry one or both of these two haplotypes (Kockum et al., 1999; Komulainen et al., 1999; Graham et al., 2002). The DR3-DQ2/DR4-DQ8 genotype may be detected in 30% of patients ,15 years and up to 50% of autoantibody-positive children under 5 years (AIDM stage III) but only 2.4% of the general population (Rewers et al., 1996; Thomson et al., 2007). A sibling who is DR3-DQ2/DR4-DQ8 identical to a diabetic proband has almost 80% risk to develop islet autoantibodies and 60% for diabetes by the age of 15 years. The other main susceptibility genotypes are the homozygous DR4-DQ8/ DR4-DQ8 and DR3-DQ2/DR3-DQ2 (Erlich et al., 2008). It is frequently tested whether HLA class I genes confer risk for AI-DM independent of HLA class II (Noble et al., 2002; Aly et al., 2006; Nejentsev et al., 2007). The HLA-A*24, A*02:01, A*03:02, A*01:01, and also B*39 were associated with a lower age of onset. The A*02 allele was found to add to the class II risk haplotypes such DR4-DQ8 (Fennessy et al., 1994), while others such as the (B18 Ah 18.2) haplotype may modulate the risk of DR3-DQ2. However, as HLA class I and II genes are in strong LD, it is problematic to prove independent contributions to AI-DM risk.

Non-HLA Genetic Factors A larger group of non-MHC genes were implicated to contribute by up to half of the AI-DM genetic risk in families with AI-DM (Risch, 1989). As indicated in Table 41.3, the majority of these genes function through regulating autoimmune responses; however, the risk of each single gene is markedly less than HLA class II (Todd et al., 2007; Todd, 2010). The genetic marker of the candidate gene/region, its chromosome location, and

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TABLE 41.3 The Significant HLA and Non-HLA Genes (Confirmed Associations Loci) Associated with Autoimmune Diabetes Mellitus, Listed by Relative Risks (RR) between Homozygous Gene/Region Chromosome Location

Marker(s)

MHC/HLA-DRB1, rs9268645 DQB1, A, B. Chr.6p21 PTPN22 Chr.1p13 rs2476601, rs6679677

INS, INS-IGF2 Chr.11p15 IL2, IL21 Chr.4q27

rs689, rs7111341

rs13119723, rs13132308, rs17388568, rs2069762, rs2069763, rs4505848, rs6822844 CCR5 Chr.3p21 rs11711054, rs2097282, rs333 SH2B3 Chr.12q24 rs3184504, rs653178, rs7137828 PTPN2 Chr.18p11 rs1893217, rs2847293, rs45450798, rs478582 IL2RA Chr.10p15 rs11594656, rs12251307, rs12722495, rs2104286, rs7090512, rs7909519 CD55, IL10 rs3024505 Chr.1q32 CTLA-4 Chr.2q33 rs11571297, rs11571302, rs3087243 IFH1 (MDA-5) rs1990760, rs2111485 Chr.2q24

GLIS3 Chr.9p24

rs7020673

IKZF1 Chr.7p12

rs10272724

UBASH3A Chr.21q22 BACH2 Chr.6q15

rs11203203, rs3788013

TNFAIP3 Chr.6q23

rs10806425, rs11755527

rs10499194, rs13192841, rs17264332, rs2327832, rs6920220 ERBB3 Chr.12q13 rs2292239

Proposed Function

Mechanism of Action in AI-DM

Role in other AI Diseases

Expressed on surface of APCs, present antigen to T lymphocytes through interaction with TCR

Immune response

Modulates protein kinases activation through encoding protein tyrosine kinase leading to negative regulation of T cell activation

Immune response

Other markers: CD, RA, MS, SLE ATD, RA, SLE, JIA, Crohn’s, vitiligo

Facilitate central tolerance through expression of insulin in the thymus Maintain regulatory T lymphocytes and T and B lymphocyte proliferation. Stimulate B lymphocytes and other APCs

Glucose metabolism Immune response

Chemokine receptor 5, may be important in regulating lymphocyte trafficking Encodes a negative regulator (LNK) of cell-signaling events from some receptors, including the TCR Possible association with growth factor mediated cell signaling pathway Serves as a receptor for interleukin-2, may have a significant role in proliferation of immune cells populations Causes polymorphism in the decay accelerating factor that restricts complement activation Minimize tissue damage and AI response through regulating the activation of T lymphocytes When MDA5 binds to viral RNA, it may trigger production of type 1 interferon (a and b), which may enhance CD81 activity in islets. GLIS3 transcription factor that is thought to be an autoantigen Encodes Ikaros, which is an essential regulator of lymphopoiesis and immune homeostasis Enhances accumulation of activated target receptors, such as TCRs, EGFR, and PDGFRB Encodes a B cell-specific transcription factor

Immune response Immune response Immune response Immune response

CD

Immune response Immune response Immune response

Crohn’s, SLE, UC ATD, RA CD

May be related to mechanisms of beta cells apoptosis

Modulation of beta cell function and insulin secretion and metabolism

Immune response Immune response Immune response Immune response Cellular apoptosis

Celiac, RA, UC

JIA, MS, RA CD, JIA, Crohn’s, UC JIA, MS, RA, vitiligo

Graves’, SLE, UC

RA, vitiligo CD CD, MS, RA, SLE, UC

Beta cell function

For detailed information see http://www.t1dbase.org/. APCs: antigen presenting cells; ATD: autoimmune thyroiditis; CD: celiac disease; JIA: juvenile idiopathic arthritis; MS: multiple sclerosis; RA: rheumatoid arthritis; SLE: systemic lupus erythematosus; UC: ulcerative colitis. After Todd (2010).

proposed function are all summarized in Table 41.3. The possible importance of the non-HLA genetic factors in relation to AI-DM stage II (Figure 41.1) is yet to be investigated. However, it cannot be excluded that the virus-response gene IFIH1, also known as MDA-5 and

INS controlling insulin gene expression in the thymus, may contribute. It will also be important to dissect to what extent the different non-HLA gene variants contribute to the variable rate of progression during AI-DM stage III.

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AUTOIMMUNE FEATURES The defining feature of AI-DM is islet (beta cell) autoimmunity (Notkins and Lernmark, 2001). The detection of islet autoantibodies in peripheral blood is currently considered as the earliest sign of AI-DM (Achenbach et al., 2005). The availability of standardized islet autoantibody tests may be used to distinguish AI-DM from other forms of diabetes (Table 41.2). Four major autoantigens have been identified in AI-DM along with a growing list of minor autoantigens (Hirai et al., 2008). Proinsulin is the exclusive beta cell-specific antigen (Pugliese et al., 2001) and insulin was described as a major target for the T lymphocyte attack especially in young children. GAD65 is specific for beta cells but is expressed in other cells as well (Karlsen et al., 1991). The IA-2 and the isoform IA2β are important antigens especially in carriers of the HLA-DQ8 haplotype (Delli et al., 2010). The development of persistent (.3 months) single or multiple ($2) islet autoantibodies is thought to occur shortly following killing of beta cells but this seems to occur regardless of insulitis (In’t Veld et al., 2007). A larger group of minor autoantigens have also been proposed; however, the roles of T and B lymphocyte reactivity as well as autoantibodies against most of these autoantigens are not fully determined. Among these autoantigens are the secretory vesicle-associated proteins, chromogranin A, VAMP2 and NPY, HSP-60 and HSP-70, IGRP, Glima-38, and many others (for a detailed list see Wenzlau et al., 2008). It cannot be excluded that autoimmunity against these minor autoantigens reflects antigen and epitope spreading (Morran et al., 2008), meaning presentation of new antigen to inflammatory cells of the immune system leading to activation of new T lymphocytes. The central role in autoimmune responses against islet autoantigens is related to the structural features of the HLA class II molecules and their interaction with T lymphocytes. Some of the HLA alleles coding these molecules may modify the timing, intensity, and rate of an autoimmune response and thereby affect the autoimmune feature of AI-DM. The DQA and DQB loci code for the alpha and beta subunits, the two chains of the HLA-DQ heterodimer molecules. These molecules are expressed on the cell surfaces of antigen presenting cells (APCs) and allow presentation of peptide antigens to T lymphocytes through the T lymphocyte antigen receptor (TCR). The binding of islet autoantigen peptides to HLA-DQ molecules was found to modulate the autoimmune response differentially (Eerligh et al., 2011). Binding insulin peptides to DQ8 molecules induced proinflammatory responses while binding to DQ6 molecules induced regulatory T lymphocyte responses (Eerligh et al., 2011). These differences may be related to structural differences in the DQ molecules and the affinity of these molecules to accommodate certain antigens and

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thereby facilitate thymic recognition of such antigens as “self” or propagate proinflammatory response leading to activation of autoreactive T lymphocytes. Structural studies of autoantigen peptide binding to HLA-DQ2, 8, and 6.4 have suggested differences that may be related to features of AI-DM (Delli et al., 2012). It was proposed that the DQ2 heterodimer has the ability to bind multiple peptides due to a wider binding groove compared to DQ8 which is less accommodating (Lee et al., 2001; Suri et al., 2005). Structural differences between the risk-conferring alleles such as DQ8 and DQ2 and the protective allele DQ6 were suggested to modify their binding properties through modification of the volume and polarity of binding grooves (Jones et al., 2006). Similarly, HLA-DQ6.4, which is strongly associated with ZnT8 autoantibodies, showed an epitope binding pattern that would be consistent with a reduction in ZnT8 peptide presentation in the thymus. It has been speculated that a reduction in thymic presentation increases the risk for autoimmunity (Pugliese et al., 1997; Delli et al., 2012).

PATHOLOGIC EFFECTOR MECHANISMS Triggering Autoimmunity Normally, the HLA autoantigen complex in the thymus presents weak and low affinity signals to T lymphocytes, which will be educated (positive selection) to identify self-antigen as “self.” If these signals were deficient or were too strong, these T lymphocytes will be deleted (negative selection) as part of central tolerance induction (Ohashi, 2003). In the periphery, T regulatory (Treg) cells help to maintain normal non-responsiveness to “self” antigens through eliminating autoreactive T lymphocytes that escape negative selection in the thymus by a process called “clonal deletion,” which is part of peripheral tolerance. In AI-DM, there is loss of the normal regulatory immune mechanisms, which assist in recognition of selfantigen (Morran et al., 2008). An imbalance between regulatory (Treg) and effector T lymphocytes has been described (Brusko et al., 2008), and the balance between regulatory CD41CD251 and CD41 and CD81 T lymphocytes is distorted (Brusko et al., 2008; Morran et al., 2008). The interaction between environmental and genetic factors (risk-conferring HLA alleles) may disrupt normal tolerance mechanisms and initiate or promote an aggressive autoimmune response. Many environmental factors, most importantly virus infections, have been incriminated in triggering such autoimmune response (Knip et al., 2005). It cannot be excluded that molecular mimicry may contribute. Coxsackievirus shares the sequence PEVKEK with GAD65 and it has been proposed that this type of molecular mimicry may explain the association between the virus and AI-DM (Atkinson et al., 1994). Direct viral

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infection of beta cells may induce local inflammatory mechanisms, secretion of proinflammatory cytokines, and involvement of APCs. This activation may elicit autoimmune response and autoreactive T lymphocytes activation; however, it has been suggested that these T lymphocytes need to be provoked by molecular mechanisms before being able to propagate autoimmunity (von Herrath et al., 2003). Other mechanisms of triggering islet autoimmunity are yet to be elucidated.

APCs in Genetically Predisposed Subjects The cellular pathway of the immune system plays a more significant role than the humoral pathway; the CD81 autoreactive T lymphocytes are the most abundant (Pinkse et al., 2005) and the most active in beta cell destruction (Imagawa et al., 2001). It is proposed that following exposure to a putative antigen, the APCs residing in the islet process and present the autoantigen and drive CD41 T lymphocyte activation through expression of surface markers (Gepts, 1965). These APCs, including macrophages, monocytes, and dendritic cells, also express immunological abnormalities (Jansen et al., 1995; Litherland et al., 1999; Plesner et al., 2002). Antigen presentation is facilitated by the HLA class II heterodimer molecules expressed on the surfaces of these cells. The presence of risk alleles such as DQB1*03:02 or predominantly non-risk alleles such as DQ6B1*06:02 determines the magnitude and pace of beta cell killing. CD41 cells drive and activate CD81 cells in the regional lymph nodes through formation of complexes between the autoantigen and the TCR. Once activated these autoreactive T lymphocytes will invade islets and attack and destroy beta cells (Baekkeskov et al., 1990). This is possible as antigen presentation occurs directly on the cell surface of the beta cells with the help of HLA class I molecules. CD41 may initiate direct killing of beta cells through secretion of proinflammatory mediators (Amrani et al., 2000; Plesner et al., 2002) or through inviting autoreactive CD81 T lymphocytes. Activated T lymphocytes may also secrete inflammatory mediators such as cytokines, chemokines, and perforin, which are toxic to beta cells. Simultaneously, macrophages invading the islets produce proinflammatory cytokines (such as interferon (IFN)-γ, tumor necrosis factor (TNF)-α, and interleukin (IL)-1β) and chemokines (Plesner et al., 2002) thereby recruiting additional T lymphocytes, macrophages, and dendritic cells. Cytokines are short polypeptides that serve as signaling mediators of inflammatory processes. The destruction of beta cells will expose intracellular “antigenic” component to APCs, which will further induce antigen presentation and T lymphocyte activation. This cycle of complex autoimmune interactions may be present for variable duration before the reserves of beta cells are severely diminished and hyperglycemia becomes inevitable. It is proposed

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that the balance between the CD41 T-helper (Th1) and the (Th2) pathways may also be distorted by various factors including infections and stress (Ernerudh et al., 2004). Whereas the cellular mechanisms of the Th2 pathway appear to conserve beta cells, cytokines of the Th1 pathways are the likely contributors to the inflammatory process in the islets as combinations of cytokines may be toxic to beta cells and enhance the immune reactions. B lymphocytes can also act as APCs and may present putative islet autoantigen epitopes to CD41 helper cells, which in turn invite CD81 T lymphocytes from regional lymph nodes to invade the islets. The possible role of B lymphocytes in early AI-DM stage III and late stage IV has been understudied clearly demonstrated by the ability of the CD20 monoclonal antibody rituximab that was shown to delay the loss of endogenous insulin production following the clinical onset of AI-DM (Pescovitz et al., 2009). The Rituximab-treated AI-DM patients failed to mount an immune response to neoantigens including insulin, which may temporarily have reduced disease progression.

IN VIVO AND IN VITRO MODELS Several animal species were studied as models for AIDM, although their diabetes phenotype was found to differ from the human type. Nevertheless, research on these animals has yielded valuable guidance to human diabetes, where ethical issues or difficulties in obtaining human pancreatic samples may limit research. AI-DM-like syndromes develop spontaneously in the BB rat (Mordes et al., 2004), the LEW.1AR1-iddm rat (Jorns et al., 2010), and the Komeda diabetes-prone (KDP) rats (Yokoi et al., 2003). All three strains of rat have features similar to human AI-DM; however, the cause (stage II, Figure 41.1) is mutations in different genes. Diabetes in all three rats is RT1.u, an ortholog of HLA-DQ2. None of the rats develop islet autoantibodies that predict AI-DM (stage III, Figure 41.1). The non-obese diabetes (NOD) mouse, which also develops diabetes with features comparable to human AI-DM, has been studied extensively (for reviews see Mathis et al., 2001; Thayer et al., 2010; Driver et al., 2011). The NOD mouse and its many congenic lines may be useful to dissect the genetic and pathogenic basis for T lymphocyte-mediated AI-DM. However, while sharing many similarities, it is becoming increasingly clear that there are not trivial but major differences in immunopathogenesis between humans and NOD mice. Combination therapy with rapamycin and IL-2 prevented NOD mouse diabetes (Rabinovitch et al., 2002) but accelerated the loss of residual beta cell function in newly diagnosed AI-DM patients (Long et al., 2012). Wild bank voles (Myodes glareolus) were reported to develop diabetes in laboratory captivity in association with

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autoantibodies against GAD65, IA-2, and insulin in standardized radioligand-binding assays as well as antibodies to in vitro transcribed and translated Ljungan virus antigens. It was speculated that bank voles may have a possible zoonotic role as a reservoir and vector for a virus that may contribute to human AI-DM (Niklasson et al., 2003).

AUTOANTIBODIES AS POTENTIAL IMMUNOLOGIC MARKERS Islet autoantibodies are an important indicator of progression to diabetes as well as the disease outcome (Harel and Shoenfeld, 2006). The type and number of these autoantibodies signify the advancement of islet autoimmunity and therefore predict AI-DM not only in first degree relatives (FDR) (Verge et al., 1996) but also in subjects from the general population (Bingley et al., 1997). Islet cell autoantibodies have been standardized in international workshops since 1985 (Gleichmann and Bottazzo, 1987). A WHO standard assay for autoantibodies against GAD65 and IA-2 was established by the Immunology of Diabetes Workshops (IDW) (Mire-Sluis et al., 2000) and further developed by the Diabetes Autoantibody (DASP) and Islet Autoantibody Standardization Programs (IASP) (Torn et al., 2008; Schlosser et al., 2010; Lampasona et al., 2011). The inter-laboratory variation in analyzing autoantibodies to insulin, GAD65, IA-2, and ZnT8 has been reduced through the use of common standards in subsequent workshops. Autoantibodies against all four autoantigens are used to follow children at increased genetic risk from birth to determine triggers of islet autoimmunity (stage II, Figure 41.1) (Hagopian et al., 2011; Ziegler et al., 2013), randomize subjects to secondary prevention trials (Yu et al., 2012) as well as to improve clinical classification (Delli et al., 2012). Interestingly, nearly half of all younger patients who were autoantibody negative at the time of diagnosis showed later sero-conversion (Hameed et al., 2011) indicating that islet autoantibodies may exist invariably in pre- and post- as well as at the time of diagnosis. Islet autoantibodies therefore remain robust markers of AI-DM and should prove useful to assist progress towards assays that better reflect environmental exposures (stage II, Figure 41.1) as well as sero-conversion (stage III, Figure 41.1). The mechanisms that are responsible for the variable progression to clinical onset during stage III are poorly understood. Standardized islet autoantibody tests, primarily in first degree relatives but also in subjects from the general population, have proven useful to predict the clinical onset (Yu et al., 2012; Ziegler et al., 2013). Parallel testing for autoantibodies against GAD65 and IA-2 followed by insulin autoantibodies was found to

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identify 50% of patients younger than 20 years and was associated with a 71% risk within 10 years (Bingley et al., 1999). In FDR at 20 39 years of age, this strategy conferred a 51% risk. Primary screening for antiIA-2 and anti-GAD65 autoantibodies followed by testing for insulin autoantibodies conferred a 63% risk to develop diabetes. Further studies also including subjects from the general population such as in studies of children followed since birth because of increased genetic risk for AI-DM have been reported (Hagopian et al., 2011; Ziegler et al., 2013).

CONCLUDING REMARKS—FUTURE PROSPECTS Our understanding of the etiology and pathogenesis of AI-DM is progressing rapidly through several major efforts. First, the sequencing of the human genome has made it possible to better define the genetic contribution to the risk of islet autoimmunity and diabetes (stage I, Figure 41.1). Further studies are needed to better understand the level of genetic propensity for AI-DM when moving between countries. Second, studies from birth may uncover triggers (stage II, Figure 41.1) that launch sero-conversion. Recent data that sero-conversion tends to occur during the first 3 years of life suggest that environmental exposures in early life may have a unique impact on children with increased genetic risk for islet autoimmunity and diabetes. Third, detection, characterization, and development of standardized autoantibody assays to islet autoantigens should prove useful to uncover part of the variable progress to the clinical onset of diabetes (stage III, Figure 41.1). At present, the larger the number of islet autoantibodies, the greater the risk for clinical onset. Fourth, a current challenge is therefore to remedy the paucity of cellular studies to better uncover the series of events that contribute to islet autoimmunity. It will also be a challenge to define the APC, T and B lymphocytes during the chronic stage III of islet autoimmunity and their contribution to the variable rate of beta cell destruction. Fifth, the more than 50 non-HLA genes should be scrutinized one by one to reveal their possible contribution to the variable progression. These nonHLA genetic factors may also represent potential drug targets for secondary prevention or intervention. The limited success in secondary prevention and intervention studies with immunosuppressive agents suggest that novel approaches perhaps in combination trials with islet autoantigens will be required to successfully halt progression to clinical onset or the loss of endogenous beta cell function that invariably takes place after the clinical diagnosis.

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