Epidemiology and characterization of type 1 diabetes mellitus in children in Gran Canaria

UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA FACULTAD DE MEDICINA. DEPARTAMENTO DE CIENCIAS CLÍNICAS “Epidemiology and characterization of Type 1 Diabetes Mellitus in children in Gran Canaria” Tesis Doctoral Yeray Nóvoa Medina Las Palmas de Gran Canaria Noviembre 2015 Anexo 1 DR. JUAN FRANCISCO LORO FERRER DIRECTOR DEL DEPARTAMENTO DE CIENCIAS CLINICAS DE LA UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA, CERTIFICA, Que el Consejo de Doctores del Departamento en su sesión de fecha 14 de octubre de 2015, tomó el acuerdo de dar el consentimiento para su tramitación, a la tesis doctoral titulada "EPIDEMIOLOGY ANO CHARACTERIZATION OF TYPE 1 DIABETES MELLITUS IN CHILDREN IN GRAN CANARIA" presentada por el doctorando D. Yeray Nóvoa Medina y dirigida por la Dra. Dña. Ana Wargner Fahlin y el Dr. D. Francisco Nóvoa Mogollón. Y para que así conste, y a efectos de lo previsto en el Artº 6 del Reglamento para la elaboración, defensa, tribunal y evaluación de tesis doctorales de la Universidad de Las Palmas de Gran Canaria, firmo la presente en Las Palmas de Gran Canaria, a 14 de octubre de dos mil quince. UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA Facultad de Ciencias de la Salud. Departamento de aenclas Clínicas Programa de Doctorado "Perspectivas actuales en la investigación pediátrica" Título de la Tesis "Epidemiology and characterization of Type 1 Diabetes Mellitus in chlldren in Gran Canaria'' Tesis Doctoral presentada por Don Veray Nóvoa Medina El presente trabajo ha sido realizado bajo mi dirección y autorizo su presentación an te el tribunal que la ha de juzgar. Las Palmas de Gran Canaria. a 11 de Noviembre de 2015 Fdo. L os directores Fdo. El doctorando Prof. Dña. Ana Wii811er Fahlin Prof. D. Francisco Javier Nóvoa Mogollón D. Verav Nóvo• Medina Dña. A~A. WXGNF:R FAHLIN, Profesora Titular vinculada de Medicina adscrita al Depa1tamento de Ciencias Médicas y Quirúrgicas <le la Un iversidad de Las Palmas de Gran Canaria y endocrinóloga adjunta al servivio de Endocrinología y nutrición del Complejo Hospitalario Universitario lnsulru· .\1aterno de Las Paltnas de GC, CER11FlCA: Que Don Yeray >Jóvoa Medina, licenciado en Medicina y especialista en Pediatría y sus A.reas füpecíficas, ha realizado bajo mi dirección y supervisión d trabajo de Tesis Doctoral titulado: ''Epidemiology and characterization of Type 1 Diabetes Mellitus in children in Gran Canaria". La aportación mús relevante de dicho trabajo es su contribución al conocimiento acerca de la epidemiología de la Diabetes Mellitus tipo 1 en Gran Canaria y la caracterización de los pacientes al debut. Confirma la elevada incidencia de diabetes tipo 1 en el archipiélago y ayuda a profundizar en d conocimiento de las características clínicas y genéticas de nuestros pacienlt:s con diabetes. Dado que la presente memoria reúne las con diciones para ser defendida como Tesis Doctoral ante tribunal y para optar a Mención Internacional, en cump1im.icnto de las disposiciones vigentes, firmo el presente certificado en Las Palmas, a 12 ele noviembre de 2015. Fdo.: Ana Wagncr Fahlin Don FHAl\CJSCO .JAVIER NÓVOA MO(;OLLÓN, Profesor Titular de :V1edicina adscrilo al departamento d..: Ciencias Médicas y Quirúrgicas de la Universidad de Las Palmas de Gran Canaria y Je fe de sección de Endocrinología y nutrición del Complejo Hospitalario Universita:io Insular Materno de Las Pa lmas de GC, CERT1FICA: Que Don Yeray Nóvoa Medina, licenciado en Medicina y especialista en Pediatría y sus Arcas Específicas, ha realizado bajo mi dirección y supervisión el trabajo de Tesis Doctoral titulado: "Epidemiology and characterization of Type 1 Diabetes Mellitus in children in Gran Canaria''. La aportación más relevante de dicho trabajo es su contribución al <.:0nocimiento acerca de Ja epidemiología de la Diabetes Mellitus tipo 1 en niños en Gran Canaria y la caracterización de los pacientes al debut. Confirma la elevada incidencia de diabetes Lipo l en el archipiélago y ayuda a profundizar en el conocimiento de las características clínicas y genéticas de nuestros pacientes con diabetes. Dado que la presente memoria reúne las condiciones para ser defendida como Tesis Doctoral ante tribunal y para optar a Mención Internacional, en cump limiento de las disposiciones vigentes, firmo el pr esente certificado en Las Palmas, a 12 de noviembre de 2015. Fdo.: i''rancisco Javier Nóvoa Mogollón Anexo 1 DR. JUAN FRANCISCO LORO FERRER DIRECTOR DEL DEPARTAMENTO DE CIENCIAS CLINICAS DE LA UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA, CERTIFICA, Que el Consejo de Doctores del Departamento en su sesión de fecha 14 de octubre de 2015, tomó el acuerdo de dar el consentimiento para su tramitación, con mención internacional a la tesis doctoral titulada "EPIDEMIOLOGY ANO CHARACTERIZATION OF TYPE 1 DIABETES MELLITUS IN CHILDREN IN GRAN CANARIA" presentada por el doctorando D. Yeray Nóvoa Medina y dirigida por la Dra. Dña. Ana Wargner Fahlin y el Dr. D. Francisco Nóvoa Mogollón. Y para que así conste, y a efectos de lo previsto en el Artº 6 del Reglamento para la elaboración, defensa, tribunal y evaluación de tesis doctorales de la Universidad de Las Palmas de Gran Canaria, firmo la presente en Las Palmas de Gran Canaria, a 14 de octubre de dos mil quince. Nic.:olás :\1 Suárcz , Ph.D . 464 Hearsden Koad Glasgo\v, G61 lQH (0141) 330 4638 ni [email protected] To whom iL may conccrn: CVR Mt'd1col Rer.(lorch Councll Unlvcr\lty of Glo<;gow Centre for Virus R1?$Nlrch I, Nicolás M Suárez, as a Research Associate at the MRCUniversity of Glasgow Centre for Virus Research, Glasgow, United Kingdom, hereby declare that after reading and assessing the potential contribution to the scientific community of the Ph.D. thesis "Epidemiology and Characterization of Type 1 Diabetes Mellitus in Children in CTran C:anaria ", hy Lhc T>h.D . carnlid a Lc Ycray Núvoa Medin a, T consickr it to he o f' intcm ational standa rd , and thc rc fon: I supporl Íl s c1uali l'icalion for an Tnlcmalio na l :Mcntion. Y ours tmly , 9tb Nnvemher 2015 ,, F~RENDO National Healthcare Pr ogramme for Rare En docrine Diseases Cochin Hospital, 27 ruede Faubourg Saint-Jacques, 75014 París, France maria.givony@aphp . fr . 01 58 41 33 77 París, 10/11/2015 To whom it may concern : 1, Ma ria Givony, Project Ma n ager at the" FI RENDO : Nat i onal Healthcare Program for Rare E ndocrine Diseases" in France, hereby dec l are that 1 read and assessed the sc i ent ific qua lity of the Ph .D. thesis "Epid emiology and Characterizati on of Type 1 Diabetes Mellitus in Chi ldren in Gran Canaria", by the Ph. D. candidate Yeray Nóvoa Med ina. In my opin i on, t hi s manuscr i pt has a potentia l of bring ing an adde d value to the qual ity and organ ization of care for Type 1 Diabetes Mellitus pedi atric pat ients in Gran Canaria. 1 th erefore co nsi de r it to be of in ternational standa r d, and the r efo re 1 support its qualificat i on for an l nternational Mention . Best regards, Ma ria Givony, PhD TABLE OF CONTENTS 1. Introduction 1.1 Diabetes Mellitus: History, definition and diagnostic criteria ....................................1 1.1.1 Type 1 Diabetes Mellitus ...........................................................................6 1.1.2 Natural History............................................................................................10 1.1.3 Genetic determinants .................................................................................13 1.1.4 Environmental factors ................................................................................16 1.1.5 Epidemiology .............................................................................................20 1.1.6 Clinical Presentation ..................................................................................21 1.1.7 Associated Autoimmune Diseases .............................................................22 1.2 Ethnic origin of the study population ........................................................................23 1.3 Justification for the research .......................................................................................24 2. Hypothesis and Objectives 2.1 Hypothesis .................................................................................................................26 2.2 Objectives ..................................................................................................................27 3. Material and Methods 3.1 Sampling .....................................................................................................................29 3.2 Incidence ....................................................................................................................30 3.3 Characterization of T1D at onset ................................................................................32 4. Results 4.1 Incidence ...................................................................................................................37 4.2 Age and sex distribution at onset ...............................................................................42 4.3 HbA1c values at onset ...............................................................................................43 4.4 Acute complications at onset: Diabetic ketoacidosis ................................................44 4.5 HLA characterization ................................................................................................46 4.6 Autoantibodies ...........................................................................................................52 4.7 Associated autoimmunity ...........................................................................................54 4.8 Regression analysis ....................................................................................................55 5. Discussion ....................................................................................................................57 6. Conclusion ..................................................................................................................68 7. References ...................................................................................................................122 Tables Table 1. Diagnostic Criteria for Diabetes .....................................................................4 Table 2. Types of insulin and pharmacodynamics........................................................8 Table 3. Multicenter studies regarding diabetes ..........................................................9 Table 4. Summary of risk and protective haplotypes ..................................................14 Table 5. Differences in the microbiome of seroconverted vs High-risk non diabetic individuals .....................................................................................................................17 Table 6. Incidence rates. Temporal trends. Age and sex distribution ..........................38 Table 7. Monthly distribution of cases ........................................................................39 Table 8. Number of onsets and cases of flu for the 2006-2013 period .........................40 Table 9. Distributions of onset in the different municipalities ......................................41 Table 10. Summary of descriptive features of participants at onset ............................42 Table 11. Age distribution ............................................................................................42 Table 12. HbA1C at onset ............................................................................................43 Table 13. Distribution of DKA at onset distributed by age groups .............................44 Table 14. 2x3 table. Distribution of DKA among age groups ......................................44 Table 15. Distribution of DKA among gender .............................................................44 Table 16. DKA at onset and increased risk haplotype ..................................................45 Table 17. DKA and auto-antibodies .............................................................................45 Table 18. 2x2 table for DRB*03 & DQB*02 ...............................................................46 Table 19. 2x2 table for DRB*04 & DQB*03 ...............................................................46 Table 20. Increased risk alleles and age-groups (3 categories) ....................................47 Table 21. Increased risk alleles and age-groups (2 categories) ....................................47 Table 22. Increased risk alleles and gender ..................................................................47 Table 23. Protective alleles and age-groups .................................................................48 Table 24. Protective alleles and gender ........................................................................48 Table 25. Distribution of DR .......................................................................................49 Table 26. Distribution of DQ ........................................................................................50 Table 27. Distribution of increased risk alleles/genotypes ...........................................51 Table 28. Distribution of anti-islet antibodies ..............................................................52 Table 29. Sex distribution of anti-pancreatic autoantibodies........................................53 Table 30. Distribution of anti-IA2 antibodies among the three age categories ............53 Table 31. Relationship between DRB*04 and anti-IA2 ...............................................53 Table 32. AAIDs ...........................................................................................................54 Table 33. Results from the model studying relationship between age of onset and risk HLA, antibodies and gender .........................................................................................55 Table 34. Results from model studying relationship between presenting with DKA and risk HLA, antibodies, gender and age of onset. ............................................................55 Table 35. OR ................................................................................................................55 Figures Figure 1. Physiological secretion of insulin in response to circulating glucose levels .3 Figure 2. Schematic representation of normal and affected islets in the pancreas .......6 Figure 3. Physiologic insulin secretion .........................................................................7 Figure 4. . Histopathology of islets of Langerhans from a two year old female patient with recent onset T1D ...................................................................................................10 Figure 5. Schematic representation of the pathogenesis of T1D ........................................ 11 Figure 6. Natural History of T1D .................................................................................12 Figure 7. Schematic representation of the HLA region in Cr. 6 ...................................13 Figure 8. Human Type 1 diabetes susceptibility regions ..............................................15 Figure 9. Frequency of T1D worldwide .......................................................................18 Figure 10. Incidence of childhood diarrheal diseases and prevalence of tuberculosis worldwide .....................................................................................................................18 Figure 11. Symptoms of T1D .......................................................................................21 Figure 12. Municipalities in Gran Canaria ...................................................................40 Figure 13. Distribution of AAIDs among sex ...............................................................54 Annex tables Annex I. Comparison of frequency (%) of HLA DR in different populations .............70 Annex II. Comparison of frequency (%) of HLA DQ in different populations ...........71 Annex III. Comparison of frequency (%) of increased risk HLA genotypes in different populations ....................................................................................................................71 Annex IV. Comparison of antibodies frequencies ........................................................72 Annex V. Traducción al español ...................................................................................73 Érase una vez un niño muy dulce… I. INTRODUCTION Introduction 1 The first written records regarding Diabetes were found in the Eber´s Papyrus in Egypt, and date back to 1500 BC. The name comes from the Greek word Diabetes (meaning to pass through) and the Latin word Mellitus (meaning honeyed or sweet) and clearly refers to the most common symptom of the disease. The term was first used by the Greek physician Aretaeus, who was the first to recognize the excessive sugar levels in the urine. However, these high sugar levels in the urine where not confirmed until 1776 by Matthew Dobson. The late 1800s and early 1900s witnessed the discovery of important landmarks in Diabetes research like the recognition of the role of the pancreas in glucose metabolism by Joseph von Mering and Oscar Minkowski in 1889 or the first reference to the deficiency of a single product from the pancreas as the origin of Diabetes by Edward Albert Sharpey-Schafer in 1910. He hypothesized that diabetes was caused by the deficiency of a single product from the pancreas, and called it Insulin, from the Latin Insula making reference to the islet cells of Langerhans. But it wasn’t until the Nobel Prize winning discovery of Insulin in 1921 by Frederick Banting and Charles Best, that diabetes due to insulin deficiency stopped being a fatal disease within weeks to months after diagnosis. Soon after their discovery, they reversed diabetes induced in dogs with an extract from the pancreatic islet cells of healthy dogs. Treatment in humans was started soon after that, and marked the start of a new era in the management of insulin deficiency 1 . Current technological advances have allowed for the use of more efficient monitoring and treatment methods. From urine testing to glucose meters and continuous glucose monitoring, measurement of blood glucose has changed dramatically. Even more impressive advances have led to the use of synthetic insulin (resulting in a decrease of adverse local reactions), more comfortable and efficient delivery methods (pens and pumps) and better detection and treatment of complications secondary to chronic hyperglycemia (kidney transplantation for diabetic renal disease, laser therapy for retinopathy….). All of them have contributed to increased life expectancy and improved quality of life in patients with diabetes. However, the overall threat diabetes represents for public health has only increased. One-two hundred years ago, symptoms of insulin deficiency were the main clinical presentation of diabetes, with milder forms mostly escaping clinical detection. Advances in biochemistry and genomics have resulted in the recognition of other forms of diabetes produced by single gene disorders (monogenic diabetes) that affect the pancreatic β-cell, but only account for about 1-2% Introduction 2 of cases. Changes in lifestyle in developed countries, increased life expectancy and the great surge in obesity have enormously increased the prevalence of diabetes and changed the spectrum of the overall clinical presentation. As a result, severe insulin deficiency only accounts for about 10% of all cases of diabetes. Most other cases are due to a combination of insulin resistance and impaired insulin secretion resulting from increased body fat1. The result is a worldwide epidemic that has made diabetes one of the most common and most serious threats to public health. 1.1 Diabetes Mellitus: Definition and diagnostic criteria According to the American Diabetes Association 2 , diabetes can be defined as a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. The resulting hyperglycemia can have acute and chronic consequences. Insulin is normally secreted by the β-cells located in the islets of Langerhans in the pancreas. Its secretion is normally regulated by their ability to “sense” circulating blood glucose levels and respond with an adequate insulin secretion in order to maintain glycemic levels within the normal range. Different mechanisms can alter this physiologic process, resulting in increased insulin secretion than can produce hypoglycemia, or decreased insulin secretion in the case of diabetes. Glucose normally enters the β-cells through specific channels (GLUT-2 transporters) and is then phosphorylated by the enzyme glucokinase. Phosphorylation prevents glucose from leaving the cell, and allows the phosphorylated glucose molecule to enter the Krebs cycle, resulting in an increased ATP/ADP ratio inside the cell. This increased ratio results in closure of K+ channels, producing membrane depolarization that in turn opens calcium channels in the cell membrane. Increased intracytoplasmic calcium levels result in the liberation of stored insulin into the blood stream (Figure 1). Inability to normally produce and secrete insulin in response to increased blood glucose levels can result in abnormally high glycemic values. Introduction 3 Figure 1. Physiological secretion of insulin in response to circulating glucose levels. Taken from Steven E. Kahn, Rebecca L. Hull and Kristina M. Utzschneider. Mechanisms linking obesity to insulin resistance and type 2 diabetes. 2006. Nature 444, 840-846. Different pathogenic mechanisms can alter the pancreatic ability to secrete insulin. In recent guidelines, the ADA classifies diabetes into 4 general categories according to the underlying mechanisms producing it: 1. Type 1 diabetes (T1D): due to autoimmune β-cell destruction, usually leading to absolute insulin deficiency. It appears more frequently in children and requires insulin replacement. 2. Type 2 diabetes: Due to progressive insulin secretory defect secondary to prior insulin resistance. This resistance is related to the presence of increased adiposity, especially the presence of abdominal fat. Introduction 10 1.2.1 Natural History T1D is a complex chronic disease triggered by not well known factors that exert their effect on genetically predisposed individuals. It starts with a preclinical T-cell mediated autoimmune destruction of the insulin producing β-cells in the islets of Langerhans in the pancreas. This can be seen in the form of characteristic lymphocytic infiltration limited to the islets and more prominent in early stage disease in children 22 (Figure 4). Figure 4. Histopathology of islets of Langerhans from a two year old female patient with recent onset (9 days) type 1 diabetes (case SP57/130 from W Gepts collection): insulitis in an islet immunohistochemically stained for insulin (A), pseudoatrophic islet stained for glucagon (B), islet with normal architecture stained for insulin (C). Section of pancreas from a three year old male patient with recent onset (60 days) type 1 diabetes (case ChHB 60/184 from W Gepts collection), showing marked islet hyperplasia in a single lobe (D). Insulitis in a 59 year old potentially prediabetic male organ donor with serum positivity for multiple autoantibodies against islet cell antigens and a susceptible HLA-DQ genotype (case two from ref. 40) (E). Immunofluorescent staining showing infiltrating CD8+ T-cells (red) and residual b-cells stained for insulin (blue) in islets from case two (F)22. After the immune attack has started, Islet autoantibodies are measurable in most individuals and their presence is a helpful tool in the differential diagnosis of T1D 23 . They are believed to be markers of the disease, appearing as a result of the exposure of intracellular β-cell proteins to the defensive cells after their destruction during the autoimmune attack. Anti-GAD (anti Glutamic Acid Decarboxylase), anti-IA2 (anti tyrosine-phosphatase), anti-insulin (IAAs) and, in more Introduction 11 recent years, Zinc transporter 8 antibodies (ZnT8A) 24 are the most frequently measured ones. They can appear as early as six months of age, with a peak of appearance between 9 months and two years of age in children at an increased genetic risk 25 . Also, the order of appearance varies, with IAA appearing earlier than GADA. Testing for at least two of them at diagnosis is now considered standard of care in T1D23. Their presence can also be used to predict the risk of developing T1D: The TEDDY study follows children at increased risk for developing T1D. They have recently reported expression of two or more autoantibodies in children progressing to T1D, and an association between high levels of IAA and IA-2 and an increased risk of presenting the disease 26 . The appearance of the immune response precedes the destruction of the insulin secreting β-cells in the pancreas. The rate of destruction is determined by unknown environmental and genetic factors, presenting great variation among individuals. This asymptomatic phase can last from months to years. When β-cell destruction reaches such a degree (usually 80-90% of β-cell mass) that it impairs insulin response, blood sugar levels start to rise until reaching overt diabetes (Figure 5). It is important to be aware of the fact that the presence of autoimmunity doesn´t necessarily predict the development of T1D. In fact, only about 5% of individuals who express a single autoantibody go on to develop it 27 . Figure 5. Schematic representation of the pathogenesis of T1D. Taken from Atkinson et al 28 Introduction 12 There are still important gaps in knowledge regarding the autoimmune attack and the progression to overt diabetes. Studies examining twin siblings have not shown complete concordance in the appearance of diabetes among monozygotic twins and, in cases were both twins develop T1D, the timing of the onset of the disease varies among them 29 . These findings suggest the presence of environmental factors influencing the appearance and the rate of progression of the disease. What triggers the autoimmune response? What influences the progression to disease? Recent epidemiological studies show an increase in the incidence of T1D worldwide and link it to the growing influence of environmental factors 30 . Some authors propose an accelerated progression from islet autoimmunity to overt T1D as the underlying mechanism 31 (opposed to an increase in the incidence of islet autoimmunity). Birth cohort studies (see table 3) are providing new insights into the natural history of autoimmunity and T1D (Figure 6). Figure 6. Natural History of T1D Autoimmunity Overt T1D β -cell destruction in the pancreas Genetic background Environental factors Pre-clinical phase Clinical phase Introduction 13 1.2.2 Genetic determinants T1D is partly determined by genetic factors, as shown by association studies that prove the increased risk of acquiring diabetes for first degree relatives of patients with the disease 32 . Recent studies suggest that the weight of the genetic load in determining the acquisition of T1D is as high as 80%29. Different loci have been associated with an increased risk of acquiring T1D 33 . The Human Leucocyte Antigen (HLA), encoded in chromosome 6, accounts for 40-50% of that genetic susceptibility according to data from the T1DGC 34 . The HLA region has three main coding areas: HLA I, coding for A, B and C molecules; HLA II region, coding for DQ, DR and DP molecules; and the HLA III region, coding for some immunologically relevant genes, but not classical HLA genes (Figure 7). Proteins encoded mainly by HLA regions I and II are responsible for binding antigens and presenting them to defensive T-cells, and have an influential role in the development of autoimmune diseases like T1D 35 . Figure 7. Schematic representation of the HLA region in Cr. 6 36 HLA type II Molecules DR and DQ account for most of the risk encoded in HLA (Table 4), even though the role other HLA molecules play is gaining recognition 37 . Not just their presence but the combination of certain DR-DQ haplotypes, their interaction with other HLA and non-HLA molecules and the ethnic background are factors influencing the risk for the disease 38 . Introduction 14 Table 4. Summary of risk and protective haplotypes34 Risk Haplotypes OR Protective Haplotypes OR DRB1*0405-DQA1*0301-DQB1*0302 (DR4) 11.37 DRB1*0701-DQA1*0201-DQB1*0303 (DR7) 0.02 DRB1*0401-DQA1*0301-DQB*0302 (DR4) 8.39 DRB1*1401-DQA1*0101-DQB1*0503 (DR6) 0.02 DRB1*0301-DQA1*0501-DQB1*0201 (DR3) 3.64 DRB1*1501-DQA1*0102-DQB1*0602 (DR2) 0.03 DRB1*0402-DQA1*0301-DQB1*0302 (DR4) 3.63 DRB1*1104-DQA1*0501-DQB1*0301 (DR11) 0.07 DRB1*0404-DQA1*0301-DQB1*0302 (DR4) 1.59 DRB1*1303-DQA1*0501-DQB1*0301 0.08 DRB1*0801-DQB1*0401-DQB1*0402 (DR8) 1.25 Other genes linked to T1D and described based on a candidate gene approach are the Insulin gene (INS) and PTPN22. The insulin promoter region was linked with predisposing or protecting genotypes to T1D depending on the genotype. Their effect is speculated to affect susceptibility by modulating thymic expression of insulin and affecting T-cell “education”. The PTPN22 gene encodes for Protein tyrosine phosphatase, non-receptor type 22. It has an effect on responsiveness of T and B cell receptors, and mutations are associated with increases or decreases in risk of autoimmune diseases (rheumatoid arthritis, Grave´s disease, vitiligo…). Genome-Wide Association Studies (GWAS) allow for testing of thousands of candidate genes at the same time. Since achieving significance for such a large number of genes requires very large numbers of cases and controls, large collaborative studies are the best strategy to achieve significant results. Type 1 Diabetes Genetics Consortium (T1DGC) is the one generating the largest body of data up to date. They have identified more than 40 different genetic regions with significant association with T1D, and confirmed the importance of the INS gene, finding it presents the second highest OR (2.38) for T1D 39 . They have also proven useful to confirm the relevance of the HLA region, showing the biggest association with T1D in the DR-DQ region, followed by the HLA-B, HLA-A and HLA DP regions 40 . A schematic representation of chromosomes and regions described as predisposing to T1D can be found in figure 8. Introduction 15 Figure 8. Human Type 1 diabetes susceptibility regions 41 From www.t1dbase.org (September 2015) Introduction 16 1.2.3 Environmental factors The appearance of T1D is not completely genetically determined. The influence environmental factors have on the development of T1D is being increasingly acknowledged. The fact that the concordance rate for T1D for monozygotic twins is only about 20-50%, reveals that other factors different to genetics play a role in the development of T1D. Environmental factors can exert their influence even before birth 42 , and are currently thought to be responsible for the increasing trend in incidence in T1D worldwide 43 , 44 . The DAISY study group (Diabetes Autoimmunity Study in the Young) recruited children at increased risk of developing T1D from 1993 to 2004 and has followed them since, measuring the appearance of autoimmunity and overt T1D. They have reported a relationship between the age of food introduction in toddlers 45 and the presence of infections in susceptible individuals and the development of autoimmunity 46 . Not surprisingly, factors that increase the demand for insulin like increased intake of sugars in the diet 47 or a greater height growth velocity 48 have also been related to a faster progression from autoimmunity to T1D. Vitamin D has also been extensively studied in relationship with T1D with varying results. A recent meta-analysis reports lower 25(OH) vitamin D concentrations in children with T1D than in healthy controls 49 whereas other authors report no association between the development of pancreatic autoimmunity or the progression from pancreatic autoimmunity to overt T1D with vitamin D intake or 25(OH) vitamin D levels in children 50 . Vitamin D supplementation has also been associated with an improved suppressive capacity of regulatory T-cells in children with new onset T1D 51 , thus adding new lines of research in the therapy of the disease and showing the influence of nutritional factors in the progression of autoimmune diabetes. The influence of the microbiome on the development of T1D is being increasingly researched. The presence of certain bacteria in the gut has been related to the appearance of autoimmunity and T1D, but it is not yet clear whether their presence is the cause of the appearance of the disease or it is simply a marker of the development of diabetes (table 5). The presence of healthy microbiota in the gastrointestinal tract has been linked to the education and maturation of the immune system for self/non-self immunoregulation early in life and also to the “leakiness” of the gut epithelial barrier. Alteration in these two necessary functions for the adequate development Introduction 17 of the immune system are two of the main mechanisms by which alterations in the microbiome could influence the appearance of autoimmune diseases such as T1D 52 . Table 5. Differences in the microbiome of seroconverted vs high-risk non diabetic individuals52 Property Seroconverted subjects High Risk controls Dominant Phylum Bacteroides Firmicutes Short Chain Fatty Acid producers Succinate, Acetate Butyrate Bacterial diversity Low High Functional diversity Low High Genus differences Bacteroides Clostridium Veillonella Bifidobacterium Faecalibacterium Lactobacillus Community stability Low High Also, the increase of allergic and autoimmune diseases in developed countries has led to increasing recognition of the hygiene hypothesis as a possible model that could partially account for the increase in T1D 53 : lower exposure to pathogens during early years in life favors the increase in number of lymphocyte subtypes that predispose to the development of autoimmune disorders. This hypothesis is supported by the distribution of T1D and other autoimmune diseases worldwide. It contrasts widely with the countries with the highest prevalence of infectious disease (figures 9&10). Introduction 18 High (>16) Intermediate (8-16) Low (0-8) No estimate Figure 9. Frequency of T1D worldwide. Incidence per 100,000 children 0-14 yr. Data from www.eatlas.idf.org. October-2015 High (≥100) Intermediaate (25-99) Low (0-24) No estimate Incidence rate per 100,000 (2004). Childhood diarrheal diseases High Intermediate Low Prevalence of Tuberculosis Figure 1053. Incidence of childhood diarrheal diseases and prevalence of tuberculosis worldwide. Data used in map creation was obtained from www.cdc.gov. October-2015. Introduction 19 Careful design of prospective studies with long term follow up is needed to gain deeper understanding of the role environmental factors play in the appearance of T1D. A large international study group, the Environmental Determinants of Diabetes in the Young (TEDDY)12, is currently researching their role in predisposing or protecting the development of autoimmunity and T1D. They screened newborns for high-risk HLA DR and DQ genes and follow them long term to study the development of islet autoantibodies and T1D. Participants are carefully assessed for environmental exposures with questionnaires and carefully kept records for events like vaccinations, allergies, diet, school… Their final goal is to identify environmental triggers of T1D that could be targeted in primary prevention trials. Hypothesis and Objectives 26 2.1 Hypothesis 2.1.1 Null Hypothesis: - Incidence of T1D is not higher than in the rest of Spain. - There hasn’t been an increase in incidence in recent years in Gran Canaria. - Our values are similar to those expected by looking at the rest of Spain and neighboring European and African countries and support the classically described North-South gradient for the incidence of T1D in Europe. - Distribution of cases among different municipalities in Gran Canaria is similar - There are no differences in the presentation of T1D when looking at gender and different age groups - There is no relationship between flu the previous or the same year and the onset of T1D in Gran Canaria. - There is no seasonality in the appearance of cases - Genetic and immune characterization of our patients is not different from what has been reported for Caucasian populations. - The presence of AAIDs in our populations is similar to that described in the literature. 2.1.2 Alternative Hypothesis: - Incidence of T1D is higher in Gran Canaria than in the rest of Spain - There has been an increasing trend in the number of cases with T1D presenting in Gran Canaria for the last 9 years. - Our values are different to those expected by looking at the rest of Spain and neighboring European and African countries. - Our incidence values do not support the classically described North-South gradient for the incidence of T1D in Europe. - Distribution of cases differs throughout the island - Distribution of cases and characteristics at onset among gender and age groups differ significantly. - The presence of flu the previous or the same year influences the appearance of T1D in Gran Canaria. - There is seasonality in the appearance of cases. - Genetic and immune characterization of our patients is different from what has been reported for Caucasian population. - The presence of AAIDs in our populations is different to that described in the literature Hypothesis and Objectives 27 2.2 Objectives The main objective is to learn about the incidence of T1D in Gran Canaria and to better comprehend factors leading to T1D in order to be able to more effectively decrease morbidity and mortality. The main operative objectives for the study are: 2.2.1 Incidence.  To define the incidence of T1D in the pediatric population (children under 14 years of age) of Gran Canaria for the last 9 years (2006-2014) and evaluate temporal trends.  To compare our incidence to that of the rest of Spain and neighboring European and African countries.  To evaluate if our incidence values respect the classically described North-South gradient for European countries.  To determine the possible existence of variability among 3 different age groups (0-4; 5-9 and 10-14 years of age) and between boys and girls.  To determine the distribution of onsets among the different municipalities in an attempt to find factors that might be influencing its appearance.  To evaluate the existence of a possible relationship between incident cases of flu and onset of T1D.  To evaluate the influence of seasonality in the appearance of cases throughout the 9 year period. 2.2.2 Clinical characterization at onset  To describe the following characteristics at onset: age of diagnosis, sex, mean HbA1C, severity of onset (DKA) and presence of AAID. 2.2.3 Genetic (HLA) and Autoimmune (anti-islet autoantibodies) characterization.  To assess the frequency of previously described high-risk HLA alleles in our T1D population.  To assess the frequency of anti-GAD, anti-IA2 and anti-insulin antibody positivity in our T1D population.  To assess possible factors influencing age of onset and severity of presentation. 28 III. MATERIAL AND METHODS Material and Methods 29 3.1 Sampling We used two different sampling procedures for the study: - First, we used “census sampling” to describe the incidence of T1D in Gran Canaria for the 2006-2014 period, description of its distribution among age groups and sex, and to evaluate the relationship between incident cases of flu the previous year and the onset of T1D. This sample is representative and has enough size to reach meaningful conclusions regarding the population that is the object of our study, since to the best of our knowledge, we had the complete census of all children diagnosed with T1D in the island during the given period. - In order to ensure availability of records, to describe the analytical characteristics of children with T1D in Gran Canaria at onset (HLA, anti-islet autoantibodies, prevalence of DKA and AAIDs) we decided to take a convenience sample: cross-sectional sample of patients with T1D under our care during the 2013-2014 period and retrospectively examined their characteristics at onset. After age 14, patients and their records are moved for care to a different hospital. Until recently, records were kept only in paper format. The study was accepted by the Ethics Committee for Clinical Research from our Hospital. Material and Methods 30 3.2 Incidence In order to evaluate incidence, we included all patients living on the island at the time of diagnosis during the 2006-2014 period. Record of new T1D patients for each year was kept in our Unit and was contrasted with data obtained from the hospital's pharmacy (new insulin prescriptions) and from the local Diabetes Association as described by the capture-recapture method 86 . Diagnosis was made according to ADA's diagnostic criteria. The date in which the first insulin dose was administered was considered as the date of onset. Data was collected retrospectively from hospital records. Incidence was calculated as the number of new cases identified per year for every 100,000 inhabitants younger than 14 years of age. For some of those whose onset was in 2006, we lacked access to the exact month of diagnosis. Thus, seasonality was assessed for the whole period except for 2006. Census data was provided by the Canarian Institue of Statistics. For the assessment of climate parameters and flu incidence, we used data from the Meteorological Agency and from the Office of Epidemiology and Prevention of the Department of Public Health of the Canary Islands respectively. Geographical distribution was assessed by classifying onsets by municipality and evaluating possible differences in incidence throughout the island. Statistical analysis - SPSS vs 22 (SPSS Inc, Chicago, IL) was used for most of the analysis of data.. Results for continuous variables are described as mean (SD) or median (range), depending on distribution (Gaussian or not, respectively) and qualitative variables, as N or percentage. 95% Confidence Intervals were computed when considered appropriate. - Temporal trend analysis was performed using Poisson regression, with the following modeling: log(μt)=log(N)+β0+β1t where “t” is the year, “μt” is the expected number of cases for that year and “N” is the exposed population. We assume that the number of new cases in the year “t” follows a Poisson distribution of “μt” value. This model can be expressed as: log(μtN)=β0+β1t meaning that the log of the expected incidence is a lineal function of time. Material and Methods 31 - Analysis of seasonality was performed using Cosinor test. Both Poisson regression analysis and Cosinor test were performed using computing environment R v3.0.2 (R Foundation for Statistical Computing, Vienna, Austria) - Correlation analysis was performed to look for relationship between number of new T1D cases and flu prevalence the previous year as well as to look for relationships between the monthly onsets and possible relationship with temperature, humidity and hours of sunlight. Number of cases per month was computed by obtaining the mean value for each month throughout the whole 2006-2014 period. Mean temperature, humidity and hours of sunlight values used were the mean values per month for the 2000-2008 period. - Chi-squared was used to assess differences in the distribution of cases (in the different municipalities). Observed cases were calculated counting the total number of cases for each municipality for the 2006-2013 period only (due to lack of data for all municipalities for the year 2014). Expected number of cases was calculated for each municipality using the overall incidence for the island and the census for the population of the island of 2013. We only had access to the total population per municipality, no data for the under 14 year old population per municipality was found. Thus, we are assumed that the distribution in the number of the children less than 14 is similar to the distribution of the total population per municipality. - Chi-squared was also used to assess the differences in cases across age groups. The observed value used was the global % of cases found for each age group, and the expected value was 33% for each group. A bilateral p <0.05 was considered significant. - Yearly incidence was computed dividing the number of cases by the total population for the age group and adjusting for 100,000 children at risk. Incidence for the 9 year period was computed by dividing the total number of cases by the total number of children at risk for the whole period and adjusting for 100,000 children. Material and Methods 32 3.3 Characterization of T1D at onset Data at onset of patients with T1D was collected retrospectively from 277 patients followed at the time of the study in the only Pediatric Endocrinology Unit in the Island of Gran Canaria, located at the “Hospital Materno-Infantil de Canarias” (Table 6). To calculate the mean number of years with T1D we used the last day at which data was entered (august 1st 2014). Patients' records were examined and the following variables were recorded: Date of birth, sex, date of diabetes onset, weight and height at onset, analytical data from the onset: HbA1c; pH; Class II HLA DQ-DR; anti-GAD, anti-insulin and anti-IA2 antibodies; TPO and anti-Transglutaminase Antibodies. BMI was calculated by dividing weight in Kg by squared-height in meters (Kg/m2). T1D was defined according to ADA diagnostic criteria. Monogenic diabetes was excluded by performing genetic characterization in those patients in which we had reasons to suspect its presence: a 3-generation family history of diabetes and negative autoimmunity. Associated autoimmune diseases were defined by repeatedly elevated TSH values (higher than 5 mIU/L) and positive anti-TPO antibodies for autoimmune hypothyroidism; repeatedly low TSH values (lower than 0.5 mIU/L) along with positive anti-TSH receptor antibodies for hyperthyroidism; repeatedly positive Ig A auto-transglutaminase antibodies (titer > 4-10 U/mL) and confirmation by intestinal biopsy when required for celiac disease 87 . The presence of clinical symptoms was not required for diagnosis. - Genomic DNA was extracted (QIAamp, Qiagen) and HLA-DRB1 / DQB1 was genotyped by Single Specific Primer-Polymerase Chain Reaction (SSP-PCR) (INNOTRAIN, DiagnostikGmbH). We have results for HLA-DRB1 in 114 patients and for HLA-DQB1 in 118 patients. Data is presented at the two digit level (i.e DRB*03) so synonymous polymorphisms which are documented in the 3rd, 4th, 5th and 6th digit are not reported. Given the absence of parental genotyping, no haplotypes were inferred. Characterization is presented in two forms. 1. The number and the percentage of patients presenting the corresponding allele at least once. 2. On the 2N column, allele frequencies are based on the occurrence of a given allele out of the total number of alleles (2N), as seen in the analysis done by Black et al in the SEARCH for diabetes in the Youth study 88 . For analysis, high risk HLA was defined by the presence of the DRB*03 and/or DRB*04 alleles. Protective HLA was defined by the presence of the DRB*07, DRB*11, DRB*13 or DRB*15 alleles. When doing regression analysis looking at the impact of Material and Methods 33 “high risk HLA” on different variables, both the presence of the risk genotype and the risk alleles were evaluated separately. We performed hypothesis testing in order to evaluate the existence of significant differences in the frequency of appearance of DRB*03 and DRB*04 alleles and between DQB*02 and DQB*03 alleles in our population. - Anti-islet autoantibodies were measured using radioimmunoanalysis by Reference Laboratory (Barcelona). IA2 autoantibody RIA kit from RSR with 125I-labeled IA2 was used for the detection of IA2 antibodies 89 . GAD autoantibody RIA kit from RSR with 125I-labeled GAD was used for the detection of GAD antibodies 90 . DIAsource AIA-100 kit was used for the detection of anti-insulin antibodies via determination of the binding of 125I-Tyr-A14-insulin to the serum fraction precipitated by the polyethylene glycol. In the 2005 DASP study 91 , RSR´s RIA kit showed 100% specificity (n=100) and 70% sensitivity (n=50) for IA-2 autoantibodies and 95% specificity (n=100) and 84% sensitivity (n=50) for GAD autoantibodies. Normal reference values for anti-GAD/64k and anti-IA2 are considered < 1 U/ml. For anti-insulin antibodies, positivity is considered when binding is > 8.2% (mean binding value + 3 SDS). Frequencies were determined taking into account the number of positive antibodies in the population number for which that antibody was measured: antiGAD and anti-insulin were measured in 261 patients, anti-IA2 only in 231 of our children and all three of them only in 228 of our patients. Absence of antibodies as a proportion of the population was computed using 228 as the denominator (number of children with all 3 antibodies measured). Anti-TPO antibodies were measured using electrochemiluminescence immunoassay. Anti-transglutaminase recombinant IgA was measured using enzyme-immunoassay. - HbA1c was measured using turbidimetric inhibition immunoassay (TINIA) for hemolyzed whole blood in auto analyzer “Cobas 6000” (Roche Diagnostics). EDTA was used as anticoagulant. The method meets DCCT standards. Overall mean values as well as mean values for subgroups with and without DKA were determined. Statistical analysis SPSS vs 22 (SPSS Inc, Chicago, IL) was used for most of the analysis of data. Data at onset was described using basic descriptive tools (mean, standard deviation and 95% CI when appropriate). Chi square was used to evaluate the existence of relationship among categorical variables. Material and Methods 34 Fisher´s exact test was used when 1 observed value was <5. Linear and logistic regression to evaluate relationship between independent and dependent variables were performed using computing environment R v3.0.2 (R Foundation for Statistical Computing, Vienna, Austria). 95% CI and hypothesis testing were calculated with the following assumptions and formulas 92 : 1. 95% CI for one sample, continuous variables Formula X ± z* 𝑠 √𝑛 Assumptions to be met N ≥ 30 2. 95% CI for population proportions (one sample, dichotomous outcome): Formula p ± z*√𝑝∗(1−𝑝) 𝑛 Assumptions to be met At least five successes (n*p) and at least five failures (n(1-p)) in the sample 3. Hypothesis testing with a continuous outcome variable in a single population: Test statistic n≥30 z = 𝑋−µ0 𝑠/√𝑛 n<30 t= 𝑋−µ0 𝑠/√𝑛 Assumptions to be met For both, the mean specified in the Ho is a fair and reasonable comparator. Appropriate use of the t distribution assumes that the outcome of interest is approximately normally distributed. 4. Hypothesis testing with a dichotomous outcome variable in a single population: Test statistic Z = 𝑝−𝑝0 √𝑝0(1−𝑝0) 𝑛 Assumptions to be met The smallest of n*po or n(1po)≥5 5. Hypothesis testing with a categorical outcome variable in a single population: Test statistic X2 = ∑(𝑂−𝐸)2 𝐸 Assumptions to be met Expected frequency in each response category is ≥5 Material and Methods 35 Other variables analyzed: - Mean age, age category distribution, weight, height and BMI at onset. Mean values and 95% CI were determined. Percentiles for weight, height and BMI were calculated using the 2008 growth charts developed by Carrascosa et al 93 . Chi-square was used to evaluate differences in distribution among age categories and gender. - Overall mean HbA1c and values for children presenting with and without DKA. 95% CI were determined for all three groups. - Presence/absence of DKA. Data was presented as % of cases and 95% CI for the 3 described age groups. Differences among groups were evaluated using chi-square. - Prevalence of other autoimmune diseases associated with T1D and distribution among males and females. 95% CI were determined. Chi-square was used to evaluate the existence of significance in the distribution of AAIDs among males and females. Results 42 4.2 Age and sex distribution and physical characteristics In the cross-sectional study, a total of 277 children were included (134 boys and 143 girls), aged 6.4 years at onset, with a mean duration of T1D of 4.6 years by the time the study was started. We found that there was no significant difference in gender distribution. Also, we found no significant differences regarding weight, height or BMI at onset (Table 10). Looking at age groups, we found significantly more children presenting with T1D in each of the categories 0-4.9 years and 5-9.9 years than in the 10-13.9 category (p < 0.01) (Table 11). Table 10. Summary of descriptive features of participants at onset Overall Age Weight Height BMI HbA1c Overall Value (95% CI) 6.4 years (5.99– 6.81) 24.38 kg (22.89 – 25.87) 1.16 mt (1.13 – 1.19) 16.3 Kg/m2 (15.78 – 16.8) 10.5% (10.3 – 10.8) Boys 48.4% (43%-54%) 6.2 24.8 (P67) 1.16 (P48) 16.15 (P48) 10.5% Girls 51.6% (46%-57%) 6.44 23.92 (P51) 1.17 (P39) 16.4 (P52) 10.6% Table 11. Age distribution 0 – 4.99 years 5 – 9.99 years 10-13.99 years % (95% CI) 40.3 (34 – 46) 41.8 (36 – 48) 17.9* (13.5 – 22.5) *p < 0.01 Results 43 4.3 HbA1c values at onset -We found that, on average, HbA1c at onset was 10.55%, higher for children with than for children without DKA at onset (table 12). Table 12. HbA1C at onset Mean HbA1c Mean HbA1c without DKA Mean HbA1c with DKA HbA1C (95%CI) 10.55% (10.3-10.8) 10.2% (9.99 – 10.63) 11.23% (10.96 – 11.44) Results 44 4.4 Acute complications at onset: Diabetic ketoacidosis -In our sample, we found that 34.2% (95% CI= 29% – 40%) of our patients presented with DKA at onset. -We found a statistically significant difference in the proportion of DKA among age groups (X2=8.201; df=2; p<0.05), pointing at a higher incidence in children aged less than 5 years (table 13&14). Table 13. Distribution of DKA at onset distributed by age groups Age 0-4.99 Age 5-9.99 Age 10-13.99 % of children presenting with DKA in each age-group (95% CI) 43.7 (34 - 53) 28.1 (19.6 - 36.4) 27.6 (14.6 - 40) Table 14. 2x3 table. Distribution of DKA among age groups age category age 0-4 age 5-9 age 10-13 Total Presence of DKA yes 49 31 12 92 no 62 79 35 176 Total 111 110 47 268 -We found no significant difference when comparing distribution by gender (X2=0.109; df=1; p=0.741) (Table 14). Table 15. DKA and gender sex Boys Girls Total Presence of DKA yes 43 49 92 no 86 90 176 Total 129 139 268 Results 45 -Also, we found no significant relationship between the presence of DKA at onset and having increased risk HLA (Fisher´s exact test, p=1 (2 sided)) (Table 16) Table 16. DKA at onset and increased risk haplotype Presence of DKA Total yes no Increased risk HLA no 4 8 12 yes 32 67 99 Total 36 75 111 -Nor with the presence of islet auto-antibodies (X2=3.124; df=5; p=0.681) (Table 17). Table 17. DKA and auto-antibodies Anti-IA2 only AntiGAD only antiGAD+antiinsulina antigad+antiIA2 three antibodies present Total DKA yes 26 12 9 16 12 87 no 38 19 20 40 31 171 Total 64 31 29 56 43 258 Results 46 4.5 HLA characterization -HLA characterization for DR (table 25), DQ (table 26) and “increased risk genotypes” (table 27) are described below. DRB*03 and DRB*04 are clearly the most frequent ones among the DR alleles (63.16% of the possible alleles), with significantly more children presenting at least one DRB*04 allele than one DRB*03. However, when looking at the overall allelic frequency, the difference between DRB*04 and DRB*03 is not significant. DRB*03, DRB*04 or both are present in 89.47% of our patients. Regarding DQ: DQ*02 and DQ*03 are the most frequent ones, with overlapping CIs and no significant differences in their appearance. A total of 96.61% of our children present either one or both of them. The presence of at risk genotypes is described in table 19. Regarding protective alleles (DRB*07, DRB*11, DRB*13, DRB*15), we found that they add up to 19.73% of the total alleles reported for our patients. -When looking at the co-appearance of DRB*03 with DQB*02, we found that the presence of DRB*03 is accompanied by the appearance of DQB*02 in 100% of our patients (table 18). When looking at DRB*04 with DQB*03, we found that the presence of DRB*04 predicts the presence of DQB*03 in 87.5% of our patients (table 19). Table 18. 2x2 table for DRB*03 & DQB*02 HLA DQB*02 Total No Yes HLA DRB*03 No 34 23 57 Yes 0 57 57 Total 34 80 114 Table 19. 2x2 table for DRB*04 & DQB*03 HLA DQB*03 Total No Yes HLA DRB*04 No 31 5 36 Yes 8 70 78 Total 39 75 114 Results 47 -We found a non-significant difference in the distribution of higher risk alleles among age categories, with a slightly bigger proportion in the younger group (Freeman-Halton extension of Fisher´s exact test; p=0.25) (Table 20). We then categorized age into children younger and older than 5 years of age, and still found no significant differences (Fisher´s exact test; p= 0.125) (table 21) Table 20. Increased risk alleles and age-groups (3 categories) age category age 0-4 age 5-9 age 10-13 Total Increased risk HLA alleles No 2 6 4 12 Yes 41 35 24 100 Total 43 41 28 112 Table 21. Increased risk alleles and age-groups (2 categories) age category age 0-4 age 5-13 Total Increased risk HLA alleles No 2 10 12 Yes 41 59 100 Total 43 69 112 -We didn´t find any differences when stratifying by sex (X2=0.004; df=1; p=0.949) (Table 22). Table 22. Increased risk alleles and gender sex Male Female Total Increased risk HLA alleles No 6 6 12 Yes 52 50 102 Total 58 56 114 Results 48 -When looking at the protective alleles, we found no difference in their distribution among age categories (X2= 0.761 ; df=2; p= 0.684) (table 23) or gender (X2= 0.846; df=1 ; p=0.358) (table 24). Table 23. Protective alleles and age-groups age category age 0-4 age 5-9 age 10-13 Total Protective HLA alleles No 29 24 17 70 Yes 14 17 11 42 Total 43 41 28 112 Table 24. Protective alleles and gender sex Male Female Total Protective HLA alleles No 39 33 72 Yes 19 23 42 Total 58 56 114 Results 49 Table 25. Distribution of DR Individuals with at least one of the following DR values % (95% CI) allele present % (2N) DR*1 24 21.05 (13.5 - 28.5) 24 10.53 DR*3 57 50 (41 - 59) 63 27.63 DR*4 78 68.42 (59 – 77) 81 35.53 DR*7 19 16.67 19 8.33 DR*8 6 5.26 6 2.63 DR*9 5 4.39 5 2.19 DR*10 1 0.88 1 0.44 DR*11 6 5.26 6 2.63 DR*13 15 13.16 15 6.58 DR*15 5 4.39 5 2.19 DR*16 3 2.63 3 1.32 Results 50 Table 26. Distribution of DQ Individuals with at least one of the following DQ values % (95C% CI) % (2N) DQ*02 81 69.49 (61.5 -77.5) 40.25 DQ*03 78 66.1 (58 - 74) 36.44 DQ*04 6 5.08 2.54 DQ*05 33 27.97 (20 – 36) 14.83 DQ*06 14 11.86 5.93 Results 51 Table 27. Distribution of increased risk alleles/genotypes Increased risk alleles and genotypes N % (95C% CI) Individuals with DR3 or DR4 or both 102 89.47 (83.9 – 95) Individuals with DQ2 or DQ3 or both 114 96.61 Genotype Individuals with DR3-DQ2 in homozygosis 5 4.39 Genotype Individuals with DR4-DQ3 in homozygosis 4 3.51 Genotype Individuals with DR3-4/DQ2-3 31 27.19 (19 – 35) Discussion 58 the number of cases. This last part of the analysis must be interpreted with care, since in order to be able to analyze the trend we are assuming that the incidence of T1D in Gran Canaria in 199596 is the same as the mean incidence described for the archipelago by Carrillo et al65. In the rest of Spain we find heterogeneity in the reports63. Some regions like Malaga 99 , Aragón 100 and Cantabria 101 report increasing trends whereas others maintain uniform rates 102 . Similar heterogeneity is reported internationally. Patterson et al report non-uniformity in rates of increase over Europe in a recent review of data from Eurodiab60. Countries like Czech Republic 103 , Finland57, and Sweden58 report plateaus in their previously increasing trends after 2005. Australia61 reported a plateau after 2005, but a more recent analysis by Hynes et al98 reports a sinusoidal pattern with 5-yearly peaks and troughs for the 2000-2011 period. On the other hand, countries like Germany 104 and the US97 recently reported an ongoing increasing trend. We found no significant difference in gender distribution, in line with data published from international reviews for children55,66, or other population studies from Australia61, Sweden67 and Germany104. Some studies seem to show a slight difference in this respect, and always reporting a higher incidence in boys: in Sweden, in patients aged between 15-3568; in Finland with boys over 13 years of age 105 ; in Massachusetts with children under 6 106 and in other reports from Italy59 and other parts of Spain100. Mean age at onset of our patients is younger than that reported by other authors, but effective comparisons cannot be done due to differences in the age of patients included in the studies. Belinchón et al64 reported a mean age at onset of 8.5 years for the study in La Palma, including children aged less than 15 years. Lawrence et al presented data from the SEARCH study group in 201497 reporting a mean age of diagnosis of 9.9 years in patients younger than 15 years of age; Fröhlich-Reiterer et al reported a mean age at diagnosis onset of 8.7 years in Austrian/German patients from the DPV database aged less than 20 years 107 . In Korea, Jung et al 108 reported a mean age at diagnosis of 8.3±3.7 years for a sample including both children and adolescents. Weight, height and BMI have been studied as both a determinant and a consequence of the appearance of T1D48,112. Both girls and boys with T1D onset in our population present, on average, height and BMI in the 50th percentile. Comparison with a control group would be Discussion 59 necessary to reach meaningful conclusions regarding the possible influence of BMI in the development of T1D for our population. Regarding age distribution at onset, we found different results for the two sampling methods: while we found no differences using the census sample, we did find significantly less children aged 10-13.99 than in either of the 0-4.9 and 5-9.99 groups when looking at the cross-sectional sample. The difference is probably due to the fact that older children spend less time in our Unit since they are switched to adult care after they turn 14 years of age. By taking a cross-sectional sample, the probability of children aged 10-13.99 years at onset still being in the unit is smaller than for younger children. Other authors have classically described the age distribution as bimodal, with peaks at 4-6 years and 10-14 years 109 , 110 . Data from national registries from Finland57 , Australia61 and Germany104 report greater incidence in children older than 4 years of age. Our results do show a greater number of children in the older age groups, but without reaching statistical significance. Belinchón et al64 reported similar findings for the island of La Palma. Our conclusions are limited by the low number of subjects in the study. Perhaps greater numbers would have allowed for more significant results. We found no significant difference in seasonality, with small non-significant peaks in September and February. Similar findings were reported by Carrillo et al65 and Belinchon et al64 in the Canary Islands. International reports describe a non-generalized seasonal pattern in the distribution of cases62, especially in areas of high incidence. In our case, perhaps the number of cases wasn’t enough to reach statistical significance, or maybe the climatic stability allows for less variability in Gran Canaria. In any case, studies including larger number of patients would be needed to reach meaningful conclusions. Viral infections have been previously described as a potential trigger of T1D 111 and some reports mention increased numbers of T1D in children after H1N1 epidemics 112 . We found an interesting, albeit non-significant, correlation between the number new T1D cases and the incidence of flu the previous year. Once again, we are limited by the low number of years analyzed. Kondrashova et al 113 , from the DIPP study group in Finland, recently published a paper looking into the relationship between antibodies against influenza A and islet autoimmunity and concluded that in children with increased genetic susceptibility for T1D, influenza A infections were not associated with the development of islet autoimmunity. Studies Discussion 60 in children with a different genetic predisposition to T1D need to be done before any definitive conclusions can be reached regarding the influence of flu in the appearance of T1D. Like Fourlanos et al43 concluded in their paper, perhaps it is in children without high genetic risk where the environmental factors have a greater impact. Regarding the severity of presentation, we found that 34.2% of our patients presented with DKA at onset. In Spain, Oyarzabal et al recently published the results of a multicenter study, reporting a mean incidence of DKA during T1D onset in children of 39.5% 114 . Lévy-Marchal et al report similar incidence in a 2001 report from EURODIAB 115 , finding its appearance in about 40% of new onsets in 90% of the centers that are part of EURODIAB. Other multicenter studies in European countries show great variation, with incidence ranging from 7.3% in Sweden to 21.3% in Lithuania 116 , 37.2% in Austria 117 and 43.9% in France 118 . Other studies from reference centers within countries show values ranging from 15.2% in northern Finland 119 , 18.2% in Bulgaria 120 , 23% in Poland 121 , 26.3% in Germany 122 , 27% in New Zealand 123 , around 30% in the US 124 , 31% in Oman 125 , 41.9% in China 126 , 48.5% -50.8% in Turkey 127 , 128 and 55.2% in Saudi Arabia 129 . We found a significantly greater proportion of DKA in the younger age group, similarly to other authors117,120,121,122,124. We were surprised by the results obtained in the regression analysis looking at factors influencing the appearance of DKA. When adjusting for sex, having high risk HLA and three antibodies positive, the OR of DKA increased 1.16 times for every one year increase in age. We have found no authors reporting similar findings. The lack of difference regarding sex distribution contrasts to that reported by other authors who report a greater incidence of DKA in females122,124,127. Also, we found no significant relationship between the presence of DKA at onset and having increased risk HLA or with the presence of antibodies. Comparison of our results regarding the genetic component of our patients with those reported for different ethnic groups is aligned with results from studies reporting the complex ancestry influencing the inhabitants of Gran Canaria. Regarding DRB* (Annex 1), we found that DRB*03 and DRB*04 are the two most frequent alleles, with no statistically significant Discussion 61 difference in their allelic frequency. However, different proportions have been reported in different ethnic groups. Studies in Caucasians and Hispanics by the SEARCH study88 and in Caucasians by the T1DGC34 show DRB*04 as the most common allele in those groups, followed by DRB*03, with slight differences in their frequency. On the other hand, studies done in African Americans 130 and T1D patients from the north of Africa (Tunisia)38 report reversed results, with DRB*03 presenting more frequently. Japanese patients 131 present DRB*04 as their most frequent risk-allele, but almost non-existing presence of DRB*03 (and this is true for both acute-onset and fulminant T1D 132 ). Philippines’ patients present a lower presence of both DRB*03 and DRB*04, and a greatly increased presence of DRB*1534. Our results are aligned with those reported for the Caucasian and Hispanic population by the SEARCH study88, and only hold small differences in proportion with those reported by the T1DGC34 (27.63% for DRB*03 vs 34.1% in T1DGC data and 35.53% for DRB*04 vs 42.7%). DRB*07, however, presents more similarities with results reported from African Americans130 and T1D patients from Tunisia38 (8.33% in Gran Canaria vs 3.6% in the T1DGC study, 10.2% in African Americans and 6.82% in Tunisia). Regarding DQB* (Annex 2), our population presents DQB*02 and DQB*03 as the most frequent alleles. The third more frequent allele is DQB*05, followed by DQB*06 with a similar presence. The least frequently found allele in our population was DQB*04. When compared to other ethnic groups, we find that DQB*02 and DQB*03 are still the most frequent ones in Caucasian, Hispanic88, African American130 and North African populations38. Caucasian and Hispanic populations seem to have a higher frequency of DQB*03. However, African American and North African groups seem to show an inversion in frequency, with DQB*02 clearly being the most frequent one in African Americans, and not so clearly in North African populations. Similarly to what happened with DRB*, Japanese population131 presents a distinct haplotype, with DQB*03 being the most frequent allele, followed by DQB*04. Philippines’ patients34 present DQB*05 as their most frequent allele (38%), followed by DQB*02 and DQB*03 (with lower frequencies than in the rest of the non-Asian populations). Also, they present a high prevalence of DQB*04 (11.9%), even though not as high as that presented by Japanese population. Discussion 62 When looking at genotypes (Annex 3), we observed that we present a lower prevalence for the DRB1*03-DQB1*02/DRB1*04-DQB1*03 genotype than reported for Caucasian population34 (27% vs 38-40%) and higher than reported for Tunisian38 and African American patients130 (27% vs 11% and 10.8% respectively) (this comparison is done under the assumption of a linkage disequilibrium of 100% whenever we have DRB1*03-04 and DQB1*02-03. This assumption is based on non-published data analyzed by the T1DGC from T1D children from Gran Canaria showing 100% linkage disequilibrium between DRB*03 and DQB*02 and DRB*04 and DQB*03). In general, our results present similarities to those reported for Caucasian population34,88 but also to African American130 and North African reports38, in line with reports from other studies supporting the mixed genetic pool for inhabitants of the Canary Islands79,80,81. However, differences in the selection methods limit the ability to reach meaningful conclusions due to the possibility of selection bias that could interfere in the comparisons. Age of participants varies among studies: patients from Gran Canaria presented with T1D before age 14, while patients from the SEARCH study did so before 20 years of age; patients from the T1DGC were under 35 years of age; African American patients (coming from the SEARCH, T1DGC, DCT/EDIC and GoKinD databases) were both adults and children also with onset under 35 years of age; the study from Tunisia also included children and adults with a mean age of 16 years; and, finally, patients from Japan also included children and adults, with a mean age of 16 years. Furthermore, patients from the T1DGC were selected only if they had siblings with T1D, which might add further bias to the age difference among the studies. Regarding distribution of high risk alleles between sex and age groups, we found a nonsignificant difference in the distribution of high risk alleles among the three age categories, with a slightly higher proportion of children in the younger age group presenting with high risk alleles (95.4% for children aged 0-4.9 years vs 85.7% for children aged 5-9.9 and 10-14). Other authors have reported higher incidence of high risk alleles and haplotypes in younger children30. Differences in results might be due to the low number of children in our sample. We found no difference in the distribution between males and females. Discussion 63 Regarding anti-islet autoantibodies, and similarly to reported values in the literature108, 86.8% of our patients were positive for at least one autoantibody. Savola et al 133 reported 97.8% of patients from the Childhood Diabetes in Finland Study Group presenting with at least one positive anti-islet antibody at onset, but they screened for 4 antibodies (GADA, IA-2, IAA and ICA) whereas we only did for three. We found positivity for two or more autoantibodies in about 55-60% of our study population. Data from the DAISY study differs from ours, with 89% of their selected children 134 presenting it. Data from TEDDY (Annex 4) is also somewhat different 135 : they report higher levels of antiinsulin antibodies (81% vs 28.4%) and lower levels of GAD-antibodies (44% vs 62.1% (p<0.001)) than those obtained for patients in Gran Canaria. Also, in their population sample insulin-antibodies appear in isolation (as well as in combination with others), whereas in patients from Gran Canaria they only do so with GAD or with both GAD and IA-2 antibodies. In TEDDY they only appear once in isolated combination with IA-2, not very different from our results in which it did not appear in isolated combination with IA-2 at all. The difference in findings might be due to the fact that the population for DAISY and TEDDY is selected based on their high risk of developing T1D and our sample is taken from the general T1D population. Also, their mean age at diagnosis is younger (2.3 vs 6.4 years of age). Both factors might explain the lower presence of autoimmunity in our population. The presence of different environmental factors might also be an influencing factor. In the U.S. SEARCH for Diabetes in Youth study, 52% of newly diagnosed children were positive for GAD-antibodies, 60% were positive for IA–2-antibodies, and 38% were positive for both 136 . The Childhood Diabetes in Finland Study Group (DIPP) found that 91% of children with newly diagnosed T1D were positive for at least two autoantibodies, and 71% for three or more. IA–2-antibodies were detected in 86% of cases133. ZnT8-antibodies are present in 60–80% of new-onset T1D patients, and in 25% of those who are negative for GAD, IA2, anti-insulin and islet cell autoantibodies (ICA)24. We cannot provide our own estimates since the measurement of ZnT8 antibodies was not part of our protocol. T1DGC data shows a prevalence of 47.4% for GADA and 45.7% for IA-2 antibodies73, both of them lower than those reported for our population. Differences in selection criteria (siblings with Discussion 64 T1D for the T1DGC sample), likely differences in environmental factors in both populations, and longer time since diagnosis probably account for the reported differences. We found no relationship between high risk alleles and genotypes and the presence of anti-islet autoantibodies. Finnish studies present opposite results. The DIPP study reports a relationship between high risk HLA (defined for this analysis as DQB*02) and autoimmunity in children that haven’t developed T1D 137 . Similar results are reported by the Childhood Diabetes study group in children that have gone to develop T1D 138 . Differences might be due to the inherent differences and environmental factors surrounding both populations and/or the small number of cases in our study. Regarding the higher frequency of anti-islet autoimmunity in women, we haven’t found other authors reporting similar findings. However, autoimmunity in general and in association with T1D has been often associated with female gender73. When looking at AAIDs, the nonsignificant predominance we found in girls vs boys is aligned with findings reported by the majority of authors, stablishing a clear predominance of Celiac disease 139 and autoimmune thyroid dysfunction in females 140 . The low number of subjects with T1D and autoimmunity in our sample might explain the lack of significance in our results. In adults thyroid dysfunction has been described as the most frequent AAID associated with T1D. Its presence grows in frequency with longer duration of T1D140, later age at diagnosis, and female gender73. In pediatric population celiac disease and thyroid disease have been described as the most frequent ones 141 . Prevalence of celiac disease ranges from around 0.5 to 4% of children in the general population, according to reports from Sweden 142 and the USA 143 , depending on the presence (increased risk) or absence of family relatives with the disease. In children with T1D the prevalence ranges from 4 to 11%139, and the risk has been characterized as greater for those diagnosed younger than 5 years of age141. In Saudi Arabia, Al-Agha et al recently reported results from a cross-sectional study in which they found celiac disease in 19.7% of patients aged 0-18 years of age 144 . In Spain, Discussion 65 a 2008 report from Madrid reported co-occurrence of T1D and celiac disease in 8% of their patients 145 . Regarding autoimmune thyroid disease, the study regarding Arab population from Al-Algha et al reported a prevalence of 4.8%144. Brazilian studies report a prevalence of 7.9% 146 , and European multicenter studies report a prevalence of 10% among children with T1D140. Some report prevalence as high as 25%76, 147 . All those studies present higher prevalence than the one found in our population. The results might be explained by the fact that they have an older population and with longer duration of T1D compared to ours. Overall, we present a lower prevalence of both celiac disease and autoimmune thyroid dysfunction than that reported by other authors. The fact that celiac disease presents a higher prevalence than thyroid dysfunction in our sample (5.7% vs 1.8%) compared to other reports might be explained by the young age of our patients and the short time since diagnosis. Recent publications reinforce the evidence supporting the increased development of thyroid dysfunction with older age 148 . Our study has several strengths, as well as weaknesses. We found that the utilization of two different sampling methods (“census sampling” to compute the incidence and cross-sectional sampling to characterize T1D onset in our patients) allowed for better exploitation of the strengths and minimization of the weaknesses of our study. Regarding the study of the incidence in the island of Gran Canaria, the main strengths of our study are the utilization of “census sampling” (allowing for a high degree of ascertainment) and the inclusion of a 9 consecutive years cycle. On the other hand, the low number of cases overall limits the conclusions that can be drawn about age distribution, temporality, seasonality and association with flu. Also, we used the total population per municipality to compute the expected number of cases per municipality, since no data for less than 14 years of age was available at the time of the analysis. Thus, in order for the derived conclusion to be true we are assuming that the distribution in the number of the children less than 14 is similar to the distribution of the total population per municipality. The utilization of a cross-sectional sample for the characterization of T1D patients from Gran Canaria at onset allows us to obtain information from a larger group of children, allowing for more significant conclusions in the analysis. On the other hand, it limits our ability to reach Discussion 66 conclusions given the possible influence of bias affecting our results. A clear example is the difference in age group distribution observed between the sampling methods. Careful interpretation of our results is required, and confirmation from prospective studies would give more reliability to the obtained results. Lack of use of high resolution HLA genotyping and lack of results from a control group limits our ability to reach conclusions regarding the genetic background of the whole population as well as our capacity to identify the most at risk and protective alleles/genotypes present in our sample. 67 VI. CONCLUSIONS Traducción 74 Introducción De acuerdo con la Asociación Americana de Diabetes (AAD), la Diabetes Mellitus se define como un grupo de trastornos metabólicos caracterizados por la presencia de hiperglucemia secundaria a defectos en la secreción de insulina, a defectos en su acción o a ambos. La AAD clasifica la Diabetes en 4 categorías, de acuerdo a los mecanismos subyacentes que la producen: 1. Diabetes Tipo 1 (T1D): secundaria a la destrucción de las células-β pancreáticas, normalmente desencadenando un déficit absoluto de insulina. 2. Diabetes Tipo 2: debida a un defecto secretor progresivo de insulina secundario a la presencia previa de resistencia a su acción. 3. Diabetes Gestacional: diabetes diagnosticada en el segundo o tercer trimestre del embarazo, sin llegar a ser un caso claro de diabetes manifiesta. 4. Tipos de diabetes específicos, debidos a otras causas: por ejemplo, diabetes monogénicas (como la diabetes neonatal o la diabetes tipo MODY), enfermedades del páncreas exocrino (como la fibrosis quística), y la diabetes secundaria a tratamiento farmacológico o a sustancias químicas (como en el tratamiento del VIH/SIDA o tras trasplante de órganos). Traducción 75 La Diabetes Mellitus Tipo 1 (DM1) es el tipo de diabetes más frecuentemente encontrado en la edad pediátrica, y el objeto de estudio del presente trabajo. La DM1 es una enfermedad crónica compleja, desencadenada por factores no del todo conocidos que ejercen su efecto en individuos con una cierta predisposición genética. Comienza con una fase preclínica, que puede durar desde meses hasta años, en la que aparece una destrucción autoinmune de las células β productoras de insulina en los Islotes de Langerhans en el páncreas. El ritmo de destrucción presenta gran variabilidad entre individuos, dependiendo de factores genéticos y ambientales no del todo conocidos. Cuando la destrucción de la masa de células-β alcanza un nivel lo suficientemente elevado como para afectar la secreción de insulina (destrucción del 80-9% de las células), los niveles de azúcar en sangre comienzan a elevarse hasta alcanzar niveles de diabetes manifiesta. La elevación crónica de los niveles de azúcar en sangre conlleva la aparición de complicaciones microy macrovasculares a medio y largo plazo como la retinopatía, nefropatía y neuropatía diabéticas, así como un aumento en la incidencia de problemas cardiovasculares. Estudios previos en nuestro archipiélago sugieren que la incidencia de DM1 en población pediátrica en Canarias se encuentra entre las más altas publicadas para España y para el resto de Europa. Con el presente estudio se pretende profundizar en el conocimiento de la DM1 en nuestro entorno, con la finalidad de identificar Traducción 76 posibles factores desencadenantes y mejorar la asistencia que se presta a nuestros pacientes. Figure 6. Natural History of T1D Autoimmunity Overt T1D β -cell destruction in the pancreas Genetic background Environental factors Pre-clinical phase Clinical phase Traducción 77 Justificación del estudio La DM1 es una enfermedad crónica que requiere que los individuos afectos y sus familias cambien por completo sus hábitos y estilo de vida. Requiere tratamiento mantenido, y conlleva riesgo de aparición de complicaciones crónicas que pueden afectar la calidad y la esperanza de vida de las personas afectadas. También se ha descrito un exceso de mortalidad en estos pacientes antes de la aparición de complicaciones tardías,. En las Islas Canarias, la prevalencia de enfermedad renal terminal relacionada con la diabetes es la más alta reportada hasta la fecha en España. Las complicaciones agudas como la cetoacidosis diabética también pueden tener consecuencias nefastas. Durante los últimos 8-9 años, nos ha impresionado el aparentemente creciente número de pacientes con DM1 en nuestro Hospital. El volumen de publicaciones internacionales y locales haciendo referencia a la creciente incidencia de la diabetes tipo 1 en todo el mundo nos llevaron a evaluar la incidencia durante los últimos 9 años en Gran Canaria y caracterizar a nuestros pacientes en un intento por adquirir conocimientos sobre las tendencias de incidencia actual, entender mejor los factores epidemiológicos y describir características clínicas y analíticas al debut de nuestros pacientes. Traducción 78 Dada la importante carga que la diabetes supone en las Islas Canarias, nos parece necesario aumentar la investigación y el conocimiento en este campo. Un conocimiento más profundo de la diabetes tipo 1 nos permitirá aumentar nuestro conocimiento sobre los posibles mecanismos que conducen al aumento de la incidencia en nuestra región, así como diseñar mejores estrategias de prevención y tratamiento. El conocimiento es el primer paso para mejorar la calidad de la atención, para abogar por el cambio en la práctica actual y para orientar las políticas de salud en relación con el cuidado de pacientes con DM1. Como primer paso, se presenta la incidencia de la DM1 en la población pediátrica de Gran Canaria durante los últimos 9 años así como la caracterización clínica de nuestros pacientes en el momento de su debut. Traducción 79 Hipótesis y Objetivos Traducción 80 Hipótesis 1. Hipótesis nula - La incidencia de diabetes tipo 1 no es más alta que en el resto de España. - No ha habido un aumento de la incidencia en los últimos años en Gran Canaria. - Nuestros valores son similares a los encontrados en el resto de España y países europeos y africanos vecinos, y apoyan el gradiente norte-sur se describe clásicamente para la incidencia de diabetes tipo 1 en Europa. - La distribución de casos entre los diferentes municipios de Gran Canaria es similar - No hay diferencias en la presentación de diabetes tipo 1 en relación al sexo y los diferentes grupos etarios. - No existe relación entre la incidencia de gripe el mismo año o el previo y la incidencia de diabetes tipo 1 en Gran Canaria. - No existe estacionalidad en la aparición de casos - La caracterización genética e inmune de los pacientes no es diferente de lo que se ha reportado en poblaciones caucásicas. - La presencia de AAIDs en nuestras poblaciones es similar a la descrita en la literatura. Traducción 81 2. Hipótesis alternativa - La incidencia de diabetes tipo 1 es mayor en Gran Canaria que en el resto de España - Ha habido una tendencia creciente en el número de casos de diabetes tipo 1 en Gran Canaria durante los últimos 9 años. - Nuestros valores son diferentes a los encontrados en el resto de España así como en el resto de países europeos y africanos vecinos. - Nuestros valores de incidencia no son compatibles con el gradiente norte-sur clásicamente descrito para la incidencia de diabetes tipo 1 en Europa. - La distribución de casos no es homogénea en todos los municipios de la isla. -La distribución de los casos y las características al debut entre los grupos de sexo y edad difiere significativamente. - La incidencia de gripe en el año previo o el mismo año influye en la aparición de diabetes tipo 1 en Gran Canaria. - Existe estacionalidad en la aparición de casos. - La caracterización genética e inmune de nuestros pacientes es diferente de lo que se ha reportado para población caucásica. - La presencia de AAIDs en nuestras poblaciones es diferente a la descrita en la literatura. Traducción 82 Objetivos El objetivo principal del presente trabajo es conocer la incidencia de diabetes tipo 1 en Gran Canaria, así como obtener una comprensión más profunda sobre sus características al debut. Todo ello, con la finalidad de comprender mejor los factores que conducen a la aparición de diabetes tipo 1 en nuestro medio y ser capaces de disminuir de manera más efectiva la morbilidad y mortalidad que se le asocian. Los principales objetivos operativos para el estudio son: 1. Incidencia. • Definir la incidencia de diabetes tipo 1 en la población pediátrica (menores de 14 años de edad) de Gran Canaria en los últimos 9 años (2006-2014) y evaluar las tendencias temporales. • Comparar nuestra incidencia a la del resto de España así como a la de países europeos y africanos vecinos. • Evaluar si nuestros valores de incidencia respetan el gradiente norte-sur clásicamente descrito para los países europeos. • Determinar la posible existencia de variabilidad en la aparición de diabetes tipo 1 entre los 3 grupos etarios (0-4, 5-9 y 10-14 años de edad) así como entre niños y niñas. • Determinar su distribución entre los diferentes municipios en un intento de encontrar factores que pueden estar influyendo en su aparición. Traducción 83 • Evaluar la existencia de una posible relación entre los casos incidentes de gripe y la aparición de diabetes tipo 1. • Evaluar la influencia de la estacionalidad en la aparición de casos durante el período de 9 años. 2. Caracterización clínica al debut • Describir las características siguientes al inicio: la edad del diagnóstico, sexo, HbA1C media, la gravedad al debut (CAD) y la presencia de enfermedades autoinmunes asociadas. 3. Caracterización genética (HLA) y autoinmune (autoanticuerpos antiislote). • Evaluar la frecuencia de aparición de alelos HLA de riesgo en nuestra población DM1. • Evaluar la frecuencia de aparición de anticuerpos anti-GAD, anti-IA2 y antiinsulina en nuestra población con diabetes tipo 1. • Evaluar los posibles factores que puedan influir en la edad de inicio y la gravedad de la presentación de la diabetes tipo 1 en la isla de Gran Canaria. Traducción 90 3. Caracterización de la diabetes tipo 1 al debut Los datos al debut de los pacientes con diabetes tipo 1 se recogieron retrospectivamente de 277 pacientes seguidos en el momento del estudio en la única Unidad de Endocrinología Pediátrica en la Isla de Gran Canaria, localizada en el "Hospital Materno-Infantil de Canarias" (Tabla 6). Para calcular el número medio de años con diabetes, se utilizó el último día en el que se introdujeron datos para el estudio (01 de agosto 2014). Examinamos las historias clínicas de los pacientes y se registramos las siguientes variables: fecha de nacimiento, sexo, fecha de inicio de la diabetes, el peso y la altura al debut, los datos analíticos al debut: HbA1c; pH; Clase II HLA DQ-DR; anticuerpos anti-GAD, anti-insulina y anti-IA2; anticuerpos anti-TPO y anti-transglutaminasa. El IMC se calculó dividiendo el peso en Kg por la altura en metros al cuadrado (kg / m2). El diagnóstico de diabetes tipo 1 se realizó según los criterios diagnósticos de la Asociación Americana de Diabetes. El diagnóstico de diabetes monogénica fue excluido mediante la realización de estudio genético en aquellos pacientes que presentaron una historia familiar con presencia de diabetes en al menos tres generaciones y con autoinmunidad negativa. La presencia de enfermedades autoinmunes asociadas se definió ante la presencia de valores elevados de TSH en repetidas ocasiones (superior a 5 mUI / L) y anticuerpos anti-TPO positivos para el hipotiroidismo autoinmune; valores de TSH en repetidas ocasiones bajas Traducción 91 (inferiores a 0,5 mUI / L), junto con los anticuerpos anti-receptor de TSH positivos para el hipertiroidismo; valores de Ig A anti-transglutaminasa repetidamente positivos (título> 4-10 U / ml) y la confirmación mediante biopsia intestinal cuando fue considerado necesario por la unidad de Digestivo pediátrico para la enfermedad celíaca. La presencia de síntomas clínicos no se consideró necesaria para el diagnóstico. - Usamos genotipado mediante “Single Specific Primer-Polymerase Chain Reaction” (SSP-PCR) (INNOTRAIN, DiagnostikGmbH) para el estudio de los genes HLA-DRB1 / DQB1 tras extracción de ADN genómico (QIAamp, Qiagen). Obtuvimos resultados para HLA-DRB1 en 114 pacientes y para HLA-DQB1 en 118 pacientes. Los datos se presentan a nivel de dos dígitos (por ejemplo: DRB * 03) por lo que polimorfismos sinónimos documentadas en el tercero, cuarto, quinto y sexto dígito no se reportan. Dada la ausencia de determinación del genotipo de los padres, no realizamos inferencias sobre los haplotipos. La caracterización se presenta en dos formas. 1. El número y el porcentaje de pacientes que presentan los alelos correspondientes al menos una vez. 2. En la columna “2N” se presentan frecuencias alélicas basadas en la presencia de un alelo con respecto al número total de alelos (2N), de forma similar a la realizada por Black et al en el estudio SEARCH en EEUU. Para el análisis, HLA de riesgo se definió por la presencia de los alelos DRB * 03 y / o DRB * 04. Realizamos tests de hipótesis con el fin de Traducción 92 evaluar la existencia de diferencias significativas en la frecuencia de aparición de alelos DRB * 03 y DRB * 04 y entre los alelos DQB * 02 y DQB * 03 en nuestra población. -Los anticuerpos anti-islotes pancreáticos se midieron utilizando radioinmunoanálisis en el Laboratorio Reference (Barcelona). Para la detección de anticuerpos antiIA2 se usó el “IA2 autoantibody RIA kit” de RSR con IA2 marcado con 125I. Para la detección de anticuerpos antiGAD se usó el “GAD autoantibody RIA kit” de RSR con GAD marcado con 125I. Kit DIAsource AIA100 se utilizó para la detección de anticuerpos anti-insulina. En el estudio DASP 2005, el RIA kit de RSR mostró 100% de especificidad (n = 100) y 70% de sensibilidad (n = 50) para autoanticuerpos IA-2 y 95% de especificidad (n = 100) y 84% de sensibilidad (n = 50) para autoanticuerpos GAD. Se consideran valores de referencia normales para anti-GAD / 64k y anti-IA2 <1 U / ml. Para los anticuerpos anti-insulina, la positividad se define mediante una unión > 8,2%. Las frecuencias se determinaron teniendo en cuenta el número de anticuerpos positivos en la población en la que se midió el anticuerpo: antiGAD y anti-insulina se midieron en 261 pacientes, anti-IA2 sólo en 231 de nuestros niños y los tres anticuerpos sólo en 228 de nuestros pacientes. La ausencia de anticuerpos como proporción de la población se calculó utilizando 228 como denominador (número de niños con los 3 anticuerpos medidos). Los anticuerpos anti-TPO se midieron Traducción 93 utilizando inmunoensayo de electroquimioluminiscencia. Anti-transglutaminasa IgA recombinante se midió utilizando enzima-inmunoensayo. - La HbA1c se midió utilizando inmunoensayo de inhibición turbidimétrica (Tinia) a partir de sangre entera hemolizada en autoanalizador "Cobas 6000" (Roche Diagnostics). Se utilizó EDTA como anticoagulante. El método cumple con los estándares DCCT. Se determinaron los valores medios, así como los valores medios de los subgrupos con y sin cetoacidosis diabética al debut. Análisis estadístico Utilizamos SPSS vs 22 (SPSS Inc, Chicago, IL) para la mayor parte del análisis de los datos. Los datos al debut se describieron utilizando herramientas básicas descriptivas (media, desviación estándar y IC del 95% en su caso). Chi cuadrado se utilizó para evaluar la existencia de relación entre las variables categóricas. Se utilizó la prueba exacta de Fisher cuando uno de los valores observados fue <5. Utilizamos regresión lineal y regresión logística para evaluar la relación entre las variables independientes y dependientes usando entorno informático R v3.0.2 (Fundación R para la Computación de Estadística, Viena, Austria). Traducción 94 Test de hipótesis e intervalos de confianza del 95% se calcularon usaron las siguientes fórmulas y asunciones: 1. Intervalo de confianza del 95% para una muestra, con variables continuas: Formula X ± z* 𝑠 √𝑛 Asunciones N ≥ 30 2. Intervalo de confianza del 95% para proporciones poblacionales (una muestra, resultado dicotómico): Formula p ± z*√𝑝∗(1−𝑝) 𝑛 Asunciones Al menos 5 éxitos (n*p) y al menos 5 fracasos (n(1-p)) en la muestra 3. Test de hipótesis con una variable de resultado continua en una población: Test statistic n≥30 z = 𝑋−µ0 𝑠/√𝑛 n<30 t= 𝑋−µ0 𝑠/√𝑛 Asunciones Para los dos: la media especificada en Ho es un comparador justo y fiable. El uso apropiado de la distribución “t· assume que el resultado de interés sigue una distribución aproximadamente normal. 4. Hypothesis testing with a dichotomous outcome variable in a single population: Test statistic Z = 𝑝−𝑝0 √𝑝0(1−𝑝0) 𝑛 Asunciones The smallest of n*po or n(1po)≥5 5. Hypothesis testing with a categorical outcome variable in a single population: Test statistic X2 = ∑(𝑂−𝐸)2 𝐸 Asunciones Expected frequency in each response category is ≥5 Traducción 95 Otras variables analizadas: - La edad media, la distribución según categorías de edad, el peso, la altura y el índice de masa corporal al debut. Se determinaron los valores medios y los intervalos de confianza del 95%. Los percentiles de peso, la altura y el IMC se calcularon utilizando las tablas de crecimiento de 2008 desarrollados por Carrascosa et al. Chi-cuadrado se utilizó para evaluar las diferencias en la distribución entre las categorías de edad y de sexo. - Valores medios de HbA1c así como para los niños que presentan con y sin CAD. Los intervalos de confianza del 95% se determinaron para los tres grupos. - Presencia / ausencia de la CAD. Los datos se presentan como% de los casos e IC 95% para los 3 grupos de edad descritos. Las diferencias entre grupos fueron evaluados usando chi-cuadrado. - La prevalencia de otras enfermedades autoinmunes asociadas con DM1 y la distribución entre hombres y mujeres. También se determinaron los IC del 95%. Chi-cuadrado se utilizó para evaluar la existencia de diferencias significativas en la distribución de enfermedades autoinmunes entre niños y niñas. Traducción 96 Resultados Traducción 97 1. Incidencia -Durante el período 2006-2014, un total de 316 niños debutaron con diabetes mellitus tipo 1 en Gran Canaria. El número de debuts por año durante todo el período, el número de niños menores de 14 años de edad en Gran Canaria, las incidencias anuales y medias se muestran en la Tabla 6. Las fuentes secundarias no proporcionaron nuevos casos (el grado de “seguridad” del Hospital es próximo al 100%). Como resultado, el método de captura-recaptura no proporcionó información adicional en nuestro caso. -No encontramos ninguna tendencia temporal estadísticamente significativa en el número de debuts desde 2006 hasta 2014 (F = 0,4982; gl = 1 y 6; p = 0,507; r = 0,077; r2 ajustado = -0,077). -Cuando comparamos con los datos de 1995-1996 (1995: CI 24,9 / 100.000 (95%: 19,34-30,46); 1996: CI 21,5 / 100.000 (95%: 16,23-26,77)) proporcionado por Carrillo et al, no encontramos tendencias significativas en el número de casos desde 1995 a 1996 hasta el período 2006-2014. El modelo inicial mostró resultados significativos en la tendencia (z = 2,467; df: 10; AIC: 81,86; p = 0,014). Cuando registramos la bondad del ajuste, se observó que el modelo no tenía un buen ajuste con los datos (p-valor de la bondad de ajuste de prueba, X2 = 0,044). La falta de ajuste podría ser debida a la dispersión excesiva de los datos. Para compensarlo, Traducción 98 reajustamos el modelo teniendo en cuenta el exceso de dispersión de los datos. Después de realizar el ajuste, los valores de los coeficientes no sufrieron cambios, pero se modificó el error estándar. Esto dio lugar a un valor de p diferente, que no resulta significativo (t = 1,744; df = 10; AIC: NA =; p = 0,115). -No encontramos diferencias significativas en la distribución por sexo o por grupos etarios al debut durante el período 2006-2014 (Tabla 1). - Al mirar el número de debuts por mes durante todo el período para evaluar la estacionalidad en la aparición de casos, no encontramos ninguna tendencia significativa en la distribución de los mismos. (Test de Cosinor; amplitud = 0,37; Fase: Mes = 4,3; Baja punto: Mes = 10,3; p> 0,05) (Tabla 2). - No se encontró correlación significativa entre el número de debuts por mes y la media de la temperatura, la humedad o la horas de luz solar para el mismo mes (Temperatura: r = -0,166; p = 0,606 Humedad: r = 0,064; p = 0,844.. horas de sol: r = -0,301; p = 0,341). Traducción 99 Tabla 1. Incidencia. Distribución por sexo y grupos etarios. *Noseencontró tendencia significativa. ** Ausencia de diferencia significativa entre nios y niñas *** La diferencia entre grupos etarios no es significativa 2006 2007 2008 2009 2010 2011 2012 2013 2014 Nº de debuts 24 35 33 39 53 31 44 27 30 Población < 14 años 117,299 118,064 119,177 119,827 119,867 118,757 117,699 116,063 113,304 Incidencia anual* (casos/100,000) (95%CI) 20.46 (12.28-28.64) 29.64 (19.82-39.46) 27.69 (18.24-37.14) 32.55 (22.34-42.76) 44.22 (32.32-56.12) 26.10 (16.91-35.29) 37.38 (26.34-48.42) 23.26 (14.49-32.03) 26.48 (17.01-35.95) Niñas (%) 63 49 40 49 53 52 50 59 43.3 Grupos etarios (%) (0-4, 5-9, 10-14) 29/21/50 32/34/34 28/39/33 31/49/20 30/38/ 32 26/35/39 27/34/39 18/41/41 28/38/34 Incidencia media (95% CI) 29.75/100,000 children (26.5-33.09) ** % medio de niñas 51 Distribución etaria global*** % (0-4, 5-9, 10-14) 27.6 / 36.5 / 35.8 Traducción 106 Tabla 11. CAD al debut y HLA de riesgo Presenia de CAD Total si no HLA de riesgo no 4 8 12 si 32 67 99 Total 36 75 111 -Tampoco con la presencia de anticuerpos antipancreáticos (X2=3.124; df=5; p=0.681) (Tabla 12). Tabla 12. CAD y anticuerpos anti-islotes pancreáticos Anti-IA2 Anti-GAD antiGAD+antiinsulina antigad+antiIA2 Presencia de los 3 anticuerpos Total CAD si 26 12 9 16 12 87 no 38 19 20 40 31 171 Total 64 31 29 56 43 258 Traducción 107 5. Caracterización HLA -La caracterización HLA para DR (tabla 20), DQ (tabla 21) y "genotipos de riesgo aumentado" (tabla 22) se describen a continuación. DRB * 03 y DRB * 04 son claramente los alelos más frecuentes entre los alelos DR (63,16% de los posibles alelos), con un número significativamente mayor de pacientes presentando al menos un alelo DRB * 04 que DRB * 03. Sin embargo, cuando se mira la frecuencia alélica en general, la diferencia entre DRB * 04 y DRB * 03 no es significativa. DRB * 03, DRB * 04 o ambos están presentes en 90,35% de nuestros pacientes. En cuanto a DQ: DQ * 02 y DQ * 03 son los más frecuentes, con solapamiento de los IC y sin diferencias significativas en su aparición. Un total de 96,61% de nuestros niños presentan uno o ambos alelos. La presencia de genotipos de riesgo se describe en la tabla 19. Con respecto a los alelos protectores (DRB * 07, DRB * 11, DRB * 13, DRB * 15), encontramos que suponen un 19,73% de los alelos totales reportados para nuestros pacientes. -Cuando miramos la co-aparición de DRB * 03 con DQB * 02, encontramos que la presencia de DRB * 03 se acompaña de la aparición de DQB * 02 en el 100% de nuestros pacientes (tabla 13). Al mirar DRB * 04 con DQB * 03, apreciamos que la presencia de DRB * 04 predice la presencia de DQB * 03 en el 87,5% de nuestros pacientes (tabla 14). Traducción 108 Tabla 13. Tabla 2*2 para ver concordancia entre DRB*03 & DQB*02 HLA DQB*02 Total No Si HLA DRB*03 No 34 23 57 Si 0 57 57 Total 34 80 114 Tabla 14.Tabla 2*2 para ver concordancia entre DRB*04 & DQB*03 HLA DQB*03 Total No Si HLA DRB*04 No 31 5 36 Si 8 70 78 Total 39 75 114 -Encontramos una diferencia no significativa en la distribución de alelos de riesgo para el desarrollo de DM1 entre las categorías de edad, con una proporción ligeramente mayor en el grupo más joven (extensión de la prueba exacta de Fisher Freeman-Halton, p = 0,25) (Tabla 15). Tras categorizar los grupos de edad en dos grupos (niños menores y mayores de 5 años de edad), tampoco encontramos diferencias significativas (prueba exacta de Fisher, p = 0,125) (tabla 16). Tabla 15. HLA de riesgo y grupos etarios (3 categorias) Categorías de edad 0-4 años 5-9 años 10-13 años Total HLA de riesgo No 2 6 4 12 Si 41 35 24 100 Total 43 41 28 112 Traducción 109 Tabla 16. HLA de riesgo y grupos etarios (2 categorias) Categorías de edad 0-4 años 5-13 años Total HLA de riesgo No 2 10 12 Si 41 59 100 Total 43 69 112 -No encontramos diferencias significativas al estratificar en función del sexo (X2=0.004; df=1; p=0.949) (Tabla 17). Tabla 17. HLA de riesgo y relación con sexo sexo Niños Niñas Total HLA de riesgo No 6 6 12 Si 52 50 102 Total 58 56 114 -Al estudiar la distribución de alelos protectores, no encontramos diferencias en su distribucion por categorías de edad (X2= 0.761 ; df=2; p= 0.684) (tabla 18) ni en su distribución en función del sexo (X2= 0.846; df=1 ; p=0.358) (tabla 19). Tabla 18. Alelos protectores y grupos etarios Categoría de edad 0-4 años 5-9 años 10-13 años Total HLA protector No 29 24 17 70 Si 14 17 11 42 Total 43 41 28 112 Table 19. Alelos protectores y sexo sex Male Female Total HLA protector No 39 33 72 Si 19 23 42 Total 58 56 114 Traducción 110 Tabla 20. Distribution de DR Individuos con al menos uno de los siguientes valores DR % (IC 95%) allelo presente % (2N) DR*1 24 21.05 (13.5 - 28.5) 24 10.53 DR*3 57 50 (41 - 59) 63 27.63 DR*4 78 68.42 (59 – 77) 81 35.53 DR*7 19 16.67 19 8.33 DR*8 6 5.26 6 2.63 DR*9 5 4.39 5 2.19 DR*10 1 0.88 1 0.44 DR*11 6 5.26 6 2.63 DR*13 15 13.16 15 6.58 DR*15 5 4.39 5 2.19 DR*16 3 2.63 3 1.32 Traducción 111 Tabla 21. Distribution de DQ Individuos con al menos uno de los siguientes valores DQ % (IC 95C%) % (2N) DQ*02 81 69.49 (61.5 -77.5) 40.25 DQ*03 78 66.1 (58 - 74) 36.44 DQ*04 6 5.08 2.54 DQ*05 33 27.97 (20 – 36) 14.83 DQ*06 14 11.86 5.93 Traducción 112 Table 22. Distribution of alelos/genotipos de riesgo Alelos de riesgo y genotipos N % (IC 95%) Individuos con DR3 o DR4 o los dos 102 89.47 (83.9 – 95) Individuos con DQ2 o DQ3 o los dos 114 96.61 Genotypo Individuos con DR3-DQ2 in homozygosis 5 4.39 Genotypo Individuos con DR4-DQ3 in homozygosis 4 3.51 Genotypo Individuos con DR3-4/DQ2-3 31 27.19 (19 – 35) Traducción 113 6. Autoanticuerpos La distribución de resultados positivos se describe a continuación (tabla 23). Es de destacar el hecho de que los anticuerpos anti-insulina siempre aparecieron en combinación con anti-GAD o junto con anti-GAD y anti-IA2, pero nunca por sí mismos o con anti-IA2. * 62,1% de los pacientes en los que se midió antiGAD fueron positivos. * 72,3% de los pacientes en los que se midió anti-IA2 fueron positivos. * 74% de los pacientes en los que se midió anti-insulina fueron positivos. * 19,7% de los pacientes en los que se midieron los 3 anticuerpos presentaron positividad para los tres. * 24,7% de los pacientes en los que se midieron anti-GAD y anti-IA2 presentaron positividad para ambos. * 11,1% de los pacientes en los que se midieron anti-GAD y anti-insulina presentaron positividad para ambos. Traducción 114 Tabla 23. Distribuciónn de anticuerpos anti-pancreáticos Pacientes % (95% CI) AntiGAD 162 62.1 (56 - 68) Anti-IA2 167 72.3 (66 - 78) Anti-insulina 74 28.4 (22 - 34) 3 anticuerpos presentes 45 19.7 (14.5 - 25) AntiGAD + anti-IA2 57 24.7 (19 - 31) AntiGAD + antiinsulina 29 11.1 Anti-IA2 + anti-insulina 0 0 Anticuerpos negativos 30 13.2 - Encontramos que la presencia de anticuerpos (todos los tipos) es significativamenet mayor en niñas (X2=16.889; df=5; p<0.05) (Table 24). Tabla 24. Distribución de anticuerpos antipancreáticos por sexo Anti-IA2 Anti-GAD antiGAD + antiinsulin Anti-GAD + anti-IA2 3 anticuerpos presentes Total sexo niños 39 16 10 23 15 128 niñas 26 15 19 34 30 136 Total 65 31 29 57 45 264 Traducción 115 - Nuestros datos también sugieren que, en nuestra población, la presencia de anticuerpos anti-IA2 es significativamente mayor en niños >5 años (X2=7.87; df=2; p<0.05) (Tabla 25). Tabla 25. Distribución de anticuerpos anti-IA2 en los tres gurpos de edad Presencia/Ausencia de anticuerpos anti-IA2 Ausencia de anti-IA2 Presencia de anti-IA2 Total Categoría de edad 0-4 años 36 60 96 5-9 años 20 77 97 10-13 años 8 30 38 Total 64 167 231 - Al estudiar la posible relación entre DRB * 03 y DRB * 04 alelos (por separado) y la positividad de autoanticuerpos (individualmente o en combinación), encontramos una relación significativa entre el alelo DRB * 04 y el anticuerpo anti-IA2 (X2 = 8,592; gl = 1; p = 0,03) (Tabla 26). No encontramos relación entre el alelo DRB * 03 y cualquiera de las posibles combinaciones de anticuerpos. Tampoco encontramos relación entre los dos alelos y la ausencia de autoinmunidad. Tabla 26. Relación entre DRB*04 y anti-IA2 Anti-IA2 positivo No Si Total HLA DRB*04 No 12 20 32 Si 9 63 72 Total 21 83 104 References 122 VII. REFERENCES References 123 1 Polonsky K.The Past 200 Years in Diabetes. N Engl J Med 2012;367:1332-40. 2 American Diabetes Association. 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