F O R S C H E R G R U P P E D I A B E T E S E . V .
Neuherberg near Munich
A n n u a l R e p o r t 2 0 1 2
1. Working group Prof. Ziegler...1
I. Members of the working group...1
II. Scientific Reports and Future Perspectives of the working group...1
A.Research Group T1D cohort studies; biobank – Dr. Christiane Winkler...2
B.Research Group T1D immune phenotyping – Dr. Peter Achenbach ...18
C.Research Group Gestational diabetes mellitus – Dr. Sandra Hummel...21
D. Research Group T1D Prevention; clinical studies – Prof. Anette-G. Ziegler ...28
E. Research Group Epidemiology – Dr. Andreas Beyerlein ………. 33
III. Publications 2012 ...36
IV. Diploma Thesis/ PhD Projects / Honors and Awards 2012 ...39
V. Third-party funds 2012 ...39
VI. Guest Speakers 2012...40
VII. Press work and Public Relations 2012 ...42
2. Working group Prof. Standl………..………...44
I. Report 2012…….………..44
II. Meetings and Symposia ………..45
III. Positions………..46
IV. Publications..………..46
3. Study group Prof. Schnell ………...47
I. Progress report..………..47
II. Educational activities, symposia and congresses ………...52
4. Working group Prof. Schaaf………57
I. Report 2012………...57
II. Literature………...58
5. Working group Prof. Haslbeck………59
I. Members of group………59
II. Report 2012………...59
III. References………...…60
1. Working group Prof. Ziegler
I. Members of the working groupScientists: Prof. Dr. med. Anette-Gabriele Ziegler, Prof. Dr. phil. Ezio Bonifacio, Dr. hum. biol. Alexandra Achenbach, Dr. med. Peter Achenbach, Dr. oec. troph. Kerstin Adler, Dr. rer. biol. hum. Andreas Beyerlein, Dr. rer. nat. Caroline Daniel, Dr. rer. nat. Orietta D’Orlando, PD Dr. med. Martin Füchtenbusch, Dr. med. Minna Harsunen, Dr. rer. nat. Florian Haupt, PD Dr. med. Michael Hummel, Dr. oec.troph. Sandra Hummel, Dr. med. Anna Huppert, Dr. rer. nat. Maren Pflüger, Dr. rer. nat. Ramona Puff, Dr. med. Katharina Warncke, Dr. med. Markus Walter, Dr. hum. biol. Christiane Winkler, Dr. rer. nat. Daniela Much
PhD/MD students: Johannes Försch, Katharina Förtsch, Eleni Giannopoulou, Imme Kaiser, Miriam Krasmann, Stephanie Krause, Jörg Maier, Jennifer Raab, Michaela Roßbauer, Michaela Schaller, Carolyn Schendell-Gröling, Fabienne Wehweck
Study coordinator, study nurses, technical personnel, and administration: Nadja Antl, Petra Becker, Melanie Bunk, Verena Cermak, Vanessa Dietrich, Bettina Drobnitzky-Kemmerzell, Cordula Falk, Anita Gavrisan, Lydia Henneberger, Melanie Herbst, Susanne Hummel, Susanne Kapfer, Annette Knopff, Oliver Knopff, Gerson Kurz, Dennis Kusian, Lorenz Lachmann, Ramona Liedtke, Claudia Matzke, Claudia Pecher, Claudia Peplow, Claudia Ramminger, Sarah-Maria Riedel, Julia Schenkel, Lisa Schneider, Simone Schneider, Marlon Scholz, Elisabeth Strauss, Joanna Stock, Tuan Tran, Anja Wosch.
II. Scientific Reports and Future Perspectives of the working group The working group of Prof. Ziegler consists of five sub-groups, led by senior scientists or senior post-docs on the following areas of research:
A. T1D cohort studies; biobank (Dr. hum.biol.Christiane Winkler), B. T1D immune phenotyping (Dr. med. Peter Achenbach),
C. Gestational diabetes mellitus (Dr. oec.troph.Sandra Hummel),
D. T1D prevention; clinical studies (Prof. Dr. med. Anette-G. Ziegler, Dr. med. Minna Harsunen and Dr. rer. nat. Florian Haupt)
E. Epidemiology (Dr. rer. biol. hum. Andreas Beyerlein).
A. Research Group T1D cohort studies; biobank – Dr. hum. biol. Christiane Winkler
Overview
The etiology of T1D as well as factors that influence T1D incidence is largely unknown. The overall objective of this research group is to build cohorts and bio-resources to study the natural history of T1D and to identify genetic and environmental factors which are associated with disease etiology. For many years, it is known that the clinical manifestation of T1D is preceded by the presence of autoantibodies to islet antigens and that this period of ‘pre-diabetes’ is of variable duration. However, it was not clear when islet autoimmunity initiated, whether there was a critical age of initiation, and what the primary targets of autoreactivity were. Therefore the group of Prof. Ziegler initiated, already in 1989, the first birth cohort study in diabetes, the BABYDIAB study. The BABYDIAB study with initial funding from German Ministry (BMBF) was the pioneer study of the natural history of T1D in children. Other landmark studies followed in the USA (DAISY), and Finland (DIPP) and eventually in 2002, the NIDDK took the model and started the multicenter TEDDY study which through a projected 19 year recruitment and investigational period will follow over seven thousand genetically susceptible children from birth to autoimmunity and diabetes.
Through the BABYDIAB study, this group showed that there is a peak incidence of seroconversion to islet autoimmunity around 1 to 2 years of age (Ziegler AG et al., Immunity 2010), that the initial target of the autoimmunity is insulin in this age group (Ziegler AG et al., Diabetes 1999), that there is relatively quick spreading to other antigens such as GAD and IA-2 (Hummel M et al., Ann of Internal Med 2004), defined the molecular targets of these antigens during the initial response, and determined that the intensity of the immune response to the antigens is a predictor of the rate of progression to diabetes (Achenbach P et al, Diabetes 2004). This group also defined the genetic risks of beta cell autoimmunity in children (Schenker M et al., Diabetologia 1999, Walter M et al., Diabetologia 2003) and was able to identify a target group of newborn children in whom immune intervention to prevent autoimmunity and T1D is possible (Bonifacio E et al., Diabetes Care 2004). The group also took the opportunity provided by BABYDIAB to examine early environmental influences on the development of beta cell autoimmunity, demonstrating associations with diet and with maternal environment (Ziegler AG et al., Jama 2003, Bonifacio E et al., Diabetologia 2008). As a result of these findings, the group executed a dietary intervention study in high risk newborns (BABYDIET, see 2.4), and recently in collaboration with Ezio Bonifacio (Dresden) and George Eisenbarth (USA) has begun an international primary vaccination trial in children with extreme genetic risk for T1D (Pre-POINT, see 2.4).
Currently, our research group is following children and adults from the BABYDIAB, BABYDIET, TEENDIAB, DiMelli, TEDDY and ImmunDiabRisk cohorts with biomaterial and exposure data.
The following objectives are currently addressed through these cohorts: BABYDIAB/BABYDIET
Identify phenotypes of islet autoimmunity and T1D and compare characteristics of islet autoimmunity initiating early (within the first two years of life) and late (at and after puberty). This is done within the BABYDIAB and BABYDIET cohorts where children are followed from birth until age 22 years. Our data suggest that early autoimmunity differs substantially from ‘puberty’ autoimmunity with respect to primary antigen reactivity, genetic susceptibility, spreading, and multiple antibody frequency (Ziegler AG et al., Immunity 2010)
Study and characterize children with different rates of disease progression from seroconversion (multiple islet autoantibody-positive subjects who do not develop diabetes compared to fulminant progressors).
Model initiation of islet autoimmunity and progression to T1D in a systems biology approach by combining genetic and environmental data, and data on islet autoantibody characteristics (number, affinity, epitope reactivity, isotype reactivity, and change). This is done in collaboration with W. v. Castell (HMGU) by using the BABYDIAB cohort (see preliminary results presented by P. Achenbach, research group 2.2).
Study metabolomic pathways and bacterial diversity (microbiome) relative to the initiation of islet autoimmunity.
TEENDIAB
Assess beta cell function, obesity, and insulin resistance and their contribution to autoimmunity and T1D. These studies include prospective assessment of beta cell function (OGTT and IVGTT), growth, insulin resistance, hormone status, and vitamin D levels in the TEENDIAB study.
DiMelli
Investigate incidence trends in diabetes in youth and changes in diabetes phenotypes within the Bavarian diabetes register cohort DiMelli.
TEDDY
Identify infectious agents, dietary factors, or other environmental exposures that modify the risk of autoimmunity and T1D.
ImmunDiabRisk
Analyze differences in the maturation of the immune system between children of mothers with T1D and children of fathers with T1D, and to identify mechanisms which can be used to prevent the development of islet autoimmunity.
Cohort descriptions:
BABYDIAB cohort: The BABYDIAB study follows 1650 offspring of a mother or father with T1D from birth to age 22 years. Venous blood samples and questionnaires are obtained from the children at study visits scheduled at age 9 months, 2 year, and in three years intervals thereafter. If children have a positive autoantibody result, visit frequencies and islet autoantibody measurements are subsequently performed at 6 to 12 month intervals. Children were recruited between 1989 and 2000; 440 children dropped out of the study: 161 children developed persistent islet autoantibodies, 63 developed T1D during follow-up.
BABYDIET cohort: The BABYDIET dietary intervention study includes 150 offspring or siblings of patients with T1D who have one of the following high-risk HLA genotype (DRB1*03-DQA1*0501-DQB1*0201/DRB1*04-DQA1*0301-DQB1*0302; DRB1*04-DQA1*0301-DQB1*0302/DRB1*04-DQA1*0301-DQB1*0302 or DRB1*03-DQA1*0501-DQB1*0201 /DRB1*03-DRB1*03-DQA1*0501-DQB1*0201). Children were first exposed to gluten either around age 6 months (control) or age 12 months (intervention). BABYDIET children were followed intensively (3-monthly) until the age of 3 years with blood and stool collection at each visit, and 6-monthly as part of a natural history follow-up protocol thereafter. Islet autoantibodies (IAA, GADA, IA-2A, ZnT8A) were measured on all blood samples. Infections, medication and the introduction of new food groups were recorded daily.
TEENDIAB cohort: The TEENDIAB study is a puberty cohort and aims to recruit 1500 children aged 8-12 years who have a first-degree relative with T1D; the children will be followed 6-monthly/yearly until age 18 years for the development of islet autoantibodies and diabetes. The first study visit and the visit at the age of 14 years take place in the study centers Munich (FD-TUM) or Hannover; remaining follow-up visits are performed as remote visits by local pediatricians or family doctors. Beta cell function will be assessed in all 1500 children twice during the study by IVGTT and OGTT and 6-monthly in children developing islet autoantibodies. Biomaterial (serum, PBMCs, stool, RNA) is collected 6-monthly as well as clinical information regarding growth, obesity, diet, demographic information, psychosocial factors, and physical exercise. The recruitment started in 2009 and 397 children have been enrolled (December 2012).
DiMelli cohort: DiMelli is a Bavarian diabetes register cohort with biomaterial collected. The register was initiated in 2009 and has collected samples from 746 children and adolescents aged <20 years. A questionnaire collecting data on medication, family history, and socioeconomic status is available from all registered patients. Phenotyping includes central measurement of islet-, celiac-, and thyroid-autoantibodies, C-peptide, and lipids.
TEDDY cohort: The TEDDY consortium comprises six clinical centers located in the USA and Europe: Washington (Seattle), Colorado (Denver), and Georgia (Augusta); Finland (Turku); Sweden (Malmo); and Germany (Munich, FDeV), and a data coordinating center in Tampa, Florida. The primary objective of the multicenter, multinational, epidemiological TEDDY study is the identification of infectious agents, dietary factors, or other environmental exposures that are associated with increased risk of autoimmunity and T1D. The TEDDY study has recruited 8,668 children (7,751 from the general population, and 917 with a first-degree relative with T1D). Newborns were eligible for TEDDY if they were younger than 4.5 months of age and had HLA DR-DQ T1D risk genotypes. Participants are seen every 3 months up to 4 years of age, with subsequent visits every 6 months until the subject is 15 years of age. Blood samples are collected at each visit for detection of islet autoantibodies, candidate infectious agents and nutritional biomarkers; monthly stool samples are collected for infectious agents. Demographic data, information about the child's diet, illnesses, vaccination, allergies and psychosocial factors are obtained by interviews and questionnaires. Recruitment began in 2004 and ended in 2010. The German center follows 595 TEDDY children, including 220 with a first-degree relative with T1D. The TEDDY study has a central repository of data and biologic samples for subsequent hypothesis based research.
The TEDDY consortium comprises six clinical centers located in the USA and Europe: Washington (Seattle), Colorado (Denver), and Georgia (Augusta); Finland (Turku); Sweden (Malmo); and Germany (Munich, Forschergruppe Diabetes e.V.), and a data coordinating center in Tampa, Florida. The primary objective of this multicenter, multinational, epidemiological study is the identification of infectious agents, dietary factors, or other environmental exposures that are associated with increased risk of autoimmunity and type 1 diabetes. Factors affecting specific phenotypic manifestations such as early age of onset or rate of progression or with protection from the development of type 1 diabetes will also be identified. Identification of such factors will lead to a better understanding of disease pathogenesis and result in new strategies to prevent, delay, or reverse type 1 diabetes.
Newborns were eligible if they were younger than 4.5 months and had high-risk human leukocyte antigen alleles (HLA-DR,DQ) in the general population or having a first degree relatives (FDRs) of patients affected with type 1 diabetes. From 2004-2010, TEDDY screened more than 420,000 newborns from both the general population and families already affected by type 1 diabetes and identified 21,577
children with high-risk HLA-DR,DQ genotypes. Of those, 8,668 (917 first-degree relatives and 7,751 newborns from the general population) are enrolled in the prospective follow-up beginning before the age of 4.5 months.
Participants are seen every 3 months up to 4 years of age, with subsequent visits every 6 months until the subject is 15 years of age. Blood samples are collected at each visit for detection of islet autoantibodies, candidate infectious agents and nutritional biomarkers; monthly stool samples are collected for infectious agents. Demographic data, information about child`s diet, illnesses, vaccination, allergies and psychosocial factors are obtained by interviews and questionnaires.
The primary outcome is the development of persistent autoantibodies to one or more of the antigens insulin, GAD, IA-2. The secondary outcome is the development of type 1 diabetes according to the criteria drawn up by the American Diabetes Association (ADA) (Expert Committee on the Diagnosis and Classification of Diabetes Mellitus, 1997).
As of December 31, 2012, 459 children have developed persistent islet autoantibodies (38 from Germany) and 138 children have developed type 1 diabetes (21 from Germany).
ImmunDiabRisk: Pregnant women with T1D, pregnant women with a child with T1D, or pregnant women carrying a baby from a father with T1D, are invited to participate in the ImmuneDiabRisk study prior to week 20 of gestation. At week 20 and 28 of gestation, and at the age of two weeks, six months and twelve months postpartum, volume and size of the fetal thymus and pancreas are assessed by fetal ultrasound, and a maternal blood sample taken for DNA, RNA, PBMC, HbA1c, blood count, plasma and serum storage. At birth, and three-monthly thereafter until the age of 24 months, blood samples are collected from the child for immediate T and B cell subset analysis, and for storage (DNA, RNA, PBMCs, blood count, plasma/serum). Demographic data and data on perinatal factors (C section, infections, medication, vaccinations, diet) will be analyzed using questionnaires. Stool samples are collected from the mother during pregnancy (gestation week 20 and 24) and from the child until the age of 24 months.
Biobank: The biobank of the IDF contains samples from 4000 patients with T1D, 8000 relatives of patients with T1D, 600 people with positive islet autoantibodies, 600 women with gestational diabetes (GDM), 200 patients with T2D, and 960 healthy control subjects.
The numbers of samples are: 89,000 plasma/serum, 8200 DNA, 2300 RNA, 1800 stool, 733 urine, and 12,000 PMBC samples, 600 IVGTTs and/or OGTTs.
Main Results
BABYDIAB/BABYDIET cohort:
1) Age-related islet autoantibody incidence in offspring of parents with type 1 diabetes. The development of type 1 diabetes, one of the most common chronic diseases in childhood and adolescence, is preceded by a pre-clinical period of islet autoimmunity. Within the BABYDIAB and BABYDIET cohort we were able to identify periods of high seroconversion incidence, which could be targeted for mechanistic and therapeutic studies. Here we could show that the incidence of islet autoimmunity has a peak between the age of nine months and two years. This is in contrast to thyroid autoimmunity which peaks at around puberty. Children who develop autoantibodies at this early age have a very high risk of developing type 1 diabetes by the age of ten. The other new piece of knowledge we acquired is that autoantibodies at ages 6 months or younger are rare. Seroconversion to insulin autoantibodies occurred earlier than other autoantibodies (against glutamic acid decarboxylase [GAD]-, insulinoma-associated protein 2 [IA-2]- and zinc transporter 8 [ZnT8]-autoantibodies). These results clearly demonstrate the need to develop preventive strategies and immunotherapies for young children in order to help to reduce the incidence of type 1 diabetes. (Ziegler et al. Diabetologia 2012).
Figure 1: Autoantibody incidence (cases per 1000/year) in offspring of parents with type 1 diabetes (A. BABYDIAB study; B. BABYDIET study). Incidence is shown at the ages of islet autoantibody testing for the BABYDIAB study children (9 months, 2, 5, 8, 11, and 14 years) and at 6 months, 1, 2, 3, 4, 5, and 6 years for the BABYDIET study children and refer to the age intervals between these time points. The BABYDIET study children were selected for HLA DR/DQ genotypes conferring increased type 1 diabetes risk, hence the higher incidences in these children.
2) A strategy for combining minor genetic susceptibility genes to improve prediction of disease in type 1 diabetes. Genome-wide association studies have identified gene regions associated with type 1 diabetes. With the exception of the HLA and the INS gene regions, the contribution of any single locus to type 1 diabetes susceptibility is relatively small. Aim of this study was to determine how the combined
allele frequency of 12 type 1 diabetes susceptibility genes (ERBB3, PTPN2, IFIH1, PTPN22, KIAA0350, CD25, CTLA4, SH2B3, IL2, IL18RAP, IL10, COBL) can stratify type 1 diabetes risk in 1290 children of parents with type 1 diabetes. We could show that the non-HLA gene combinations were highly effective in discriminating type 1 diabetes and most effective in children with a high risk HLA genotype. We further provide a model for identifying combinations of genes to obtain maximal disease risk stratification. Using this model, we show that the sum of risk alleles derived from combinations of genes provided significant increased discrimination over that which could be achieved by any single gene. The greatest diabetes discrimination was obtained by the sum of risk alleles for 8 genes (IFIH1, CTLA4, PTPN22, IL18RAP, SH2B3, KIAA0350, COBL, ERBB3) in the HLA risk children. Categorizing a risk allele score from these 8 genes in low, moderate and high (scores: <6, 6-9, >9) was able to stratify the risk for developing islet autoantibody and for progression from islet autoantibody positivity to type 1 diabetes (by age 10 years: moderate scores 40%; high scores 80% P=0.03). Overall type 1 diabetes risk by age 14 years ranged from 0% in HLA risk children with low risk allele scores to 26.9% (95% CI, 15.2-38.6%; P<0.0001) in children with high risk allele scores. Therefore genotyping at multiple susceptibility loci in children from affected families can identify neonates with sufficient genetic risk of type 1 diabetes to be considered for early intervention.
(Winkler et al., Genes and Immunity 2012).
A B C 0.45 0.50 0.55 0.60 0.65 0.70 0.75 1 2 3 4 5 6 7 8 9 1011 12 Number of SNPs used P<0.001 P<0.000 P<0.0000 P<0.01 HLA risk children
ROC AUCs for all 4095 possible combinations
A U C 100 S e n si ti v it y 1-Specificity 0 20 40 60 80 0 20 40 60 80 100 >9 alleles 5-9 alleles <5 alleles AUC: 0.73 P<0.00001 Score <5 Score 5-9 Score >9 Age (years) 0 2 4 6 8 10 12 14 P ro b a b ili ty o f D ia b e te s % 30 25 20 15 10 5 0 P<0.001
Figure 2: Area under the curve (AUC) calculated from the receiver operator curve analysis (ROC) for all 4095 possible combinations of the 12 SNPs using diabetes as outcome in children with HLA risk genotypes (A), Receiver Operator Curve (sensitivity vs 1-specificity) for type 1 diabetes outcome using risk allele scores of 8 gene SNPs in all genotyped children (B). Cumulative risk for the development of type 1 diabetes and islet autoantibodies by the 8-gene combined risk allele score (C). (Winkler C et al. Genes and Immunity 2012).
3) Obesity, T2D associated genes and T1D risk. It has been suggested that type 1 diabetes and type 2 diabetes may share pathophysiological and genetic aetiology, leading to the concept of double diabetes. In particular, increasing population weight and body mass index (BMI) has been linked to increasing type 1 diabetes trends. Our
previous studies in children who are offspring of patients with type 1 diabetes found no association of body weight or insulin resistance with islet autoimmunity.
We examined the associations of type 2 diabetes susceptibility genotypes on the development of islet autoimmunity and investigated the effect of these genotypes and body weight on progression to type 1 diabetes after islet autoantibody seroconversion in 1350 children from parents with type 1 diabetes participating in the BABYDIAB study. We could show that none of type 2 diabetes risk alleles at the CDKAL1, CDKN2A/2B, FTO, HHEX-IDE, HMGA2, IGF2BP2, KCNJ11, KCNQ1, MTNR1B, PPARG and SLC30A8 loci were associated with the development of islet autoantibodies or type 1 diabetes. Overweight children at seroconversion did not progress to diabetes faster than non-overweight children.
Our findings suggest that type 2 diabetes risk factors are not a common feature of type 1 diabetes occurring in first degree relatives of patients with type 1 diabetes and do not support a significant role of these type 2 diabetes associated factors in the pathogenesis of diabetes in families affected with type 1 diabetes. (Winkler C et al., PlosOne 2012). b) Age in years 0 5 10 15 644 615 544 548 354 336 142 132 P ro ba bi lit y of Is le t A ut oa nt ib od y (% ) P=0.015 0 8 10 12 14 6 4 2 16 TCF7L2 TT CT/CC
Follow-up after islet autoantibodies (years)
0 5 10 15 n 47 47 47 25 23 23 9 4 9 3 3 0 10 20 30 40 70 50 60 P ro b ab ili ty o f d ev e lo pi ng di ab e te s (% ) P>0.05
Figure 3: Cumulative risk for the development of autoantibodies by the TCF7L2 genotypes. Children are grouped with respect to TCF7L2 SNP rs7901695 genotype into those carrying TT genotype (solid line) and the CT or CC genotype (dashed line). Follow-up (axis) is from birth. Numbers below the x-axis indicate the number of autoantibody negative children remaining on follow-up with respect to age. (b) Cumulative risk for the progression from islet autoantibody seroconversion to type 1 diabetes. Children are divided into 3 groups: lowest tertile (dashed line), second tertile (dotted line) and highest tertile (solid line). Follow-up (x-axis) is from the age of islet autoantibody seroconverison.
4) Respiratory infections in early life predict the development of islet autoimmunity in children at increased type 1 diabetes risk: Evidence from the BABYDIET study. We examined whether early, short term, or cumulative exposures to episodes of infection or fever during the first three years of life were associated
with the initiation of persistent islet autoimmunity in children at increased T1D risk. We had data available of 1,245 infectious events recorded for 90,750 person days documented during the first three years of life in high T1D risk children followed up since 2000 in the prospective BABYDIET study. An increased rate of islet autoantibody seroconversion was associated with respiratory infections during the first 6 months of life (HR, 2.18; 95% CI, 1.28-3.70) and in the age of 6.0-11.9 months (HR, 1.18; 95% CI, 1.02-1.36). During the second year of life, no meaningful effects were detected for any infectious category. A higher number of respiratory infections in the six months prior to islet autoantibody seroconversion was also associated with an increased hazard rate (HR, 1.32; 95% CI, 1.08-1.61). These findings were mainly attributed to infections of the upper respiratory tract. Our study identified respiratory infections in early childhood as a potential risk factor for the development of T1D. We found evidence for both early exposure and short term impact effects (Beyerlein A et al. in press)
5) Age of adiposity peak in the first year of life determine risk of islet autoimmunity in susceptible children. We evaluated the associations between growth velocity in infancy and the risk of islet autoimmunity and type 1 diabetes. The adiposity peak was significantly associated with the development of islet autoantibodies. There was a linear correlation between the adiposity peak and the risk for the development of one or more islet autoantibodies (any islet autoantibody: HR 0.6 [95% CI 0.4-0.9]; p=0.018 and multiple islet autoantibodies: HR 0.4 [95% CI 0.2-0.8] per 2SD increase; p=0.006). Children´s adiposity peak were classified in quartiles which showed that children with a adipositiy peak in the first quartile have a significant higher risk for the development of multiple islet autoantibodies compared to children in the 2nd, 3rd and 4th quartile (at age 10 years: 1st quartile 8.1% vs 2nd and 3rd quartile 4.4% vs 4th quartile 2.9%, p=0.017). The adiposity peak was not associated with the progression to type 1 diabetes. These data suggest that an early relative weight gain in the first years of life increase the risk for the development of islet autoimmunity in childhood. The exact mechanisms involved are not yet clear and further examinations are required.
6) Differences between fast and slow progressors. We compared islet autoantibody characteristics, T1D associated genotypes, and environmental factors between children who progressed to T1D within 2 years after seroconversion and children who remained non-diabetic for at least 10 years after seroconverion. We find that the presence of IA-2A at seroconversion is exclusively found in children with fast progression. Furthermore IFIH1, CTLA4, ERBB3 as well as spreading of
autoantibody responses to IA-2 were significantly different between fast and slow progressors (Achenbach P et al., submitted).
T1D within 2 yrs diabetes free > 10 yrs
Figure 4: Risk factors, who differentiate between fast and slow progressors in BABYDIAB offspring who developed multiple islet autoantibodies.
TEENDIAB cohort:
Evaluating the Diet of Children at Increased Risk for Type 1 Diabetes by Using the Diet History Interview DISHES Junior
The development of islet autoimmunity and T1D is potentially influenced by nutrition. Up to now, there are only few studies investigating the diet of children at increased risk of T1D, which was the aim of this analysis. Dietary intake of the last four weeks was assessed using a diet history interview in 268 first degree relatives of people with T1D, aged 8 – 12 years, who are participating in the TEENDIAB study. The daily nutrient and food intake of these children were compared current German recommendations, the German Dietary Reference Intakes on the nutrient-level as well as the Optimized Mixed Diet developed by the Research Institute of Child Nutrition in Dortmund on the food-level. The macronutrient ratio was within the
4 2 0 4 4 2 6 2 6 8 7 9 4 8 4 9 4 0 0 5 4 1 7 1 1 6 2 8 2 2 7 7 7 0 1 2 5 0 5 0 8 0 3 3 4 1 6 1 4 4 7 3 1 9 4 8 1 7 2 4 4 6 2 5 1 6 4 9 6 7 9 6 6 9 1 2 3 4 2 4 5 7 2 0 1 0 6 8 5 0 0 6 5 2 6 2 6 0 8 1 6 6 3 7 4 4 1 2 6 2 2 6 p Chi² Sectio yes no o o o o o o o o o o o o o o o o o o o o o o o o o o o o 0.02 IFIH1 GG highrisk GA AA lowrisk o o o o o o o o o o o o o o o o o o o o o o o o 0.02 CD25 T highrisk TA TT lowrisk o o o o o o o o o o o o o o o o o o o o o o o o 0.02 CTLA4_rs3087243 GG highrisk GA AA lowrisk o o o o o o o o o o o o o o o o o o o o o o o o 0.04 IGIH1_proxy_rs2111485 GG highrisk GA AA lowrisk o o o o o o o o o o o o o o o o o o o o o o o o 0.02 ERBB3_rs2292239 AA highrisk CA CC lowrisk o o o o o o o o o o o o o o o o o o o o o o o 0.01 IA2A at seroconversion positive negative o o o o o o o o o o o o o o o o o o o o o o o o o o o o 0.001
Time between first ab and IA2A
<1.5yrs >1.5yrs
o o o o o o o o o o o o o o o o o o o o o o o o o o o o
< 0.001
Ab-spreading to multiple abs
<1.5yrs >1.5yrs
o o o o o o o o o o o o o o o o o o o o o o o o o o o o 0.01
recommended range. The children took up 52.0 % of their energy by carbohydrates (24 % of mono- and disaccharides and 28 % by polysaccharides), 32.6 % from fat (14 % from saturated fatty acids (SFA), 11 % from monounsaturated fatty acids (MUFA), 5 % from polyunsaturated fatty acids (PUFA) and the rest from glycerol and lipoids) and 14 % by protein. In line with the expectations, dietary fiber only made up approximately 1 % of the total energy intake The children had intakes above the reference values for all minerals and vitamins with the exception of iodine with 58.1%, vitamin D with 8.9 % and folate with 30.0% of the recommended intake. The intake for non-desirable food groups (meat, meat products, sweets, snacks, sweetened beverages) was above the recommendations and the consumption for desirable food groups (fruits, vegetables, carbohydrate-rich foods) was below the recommendations. (Weber K et al., manuscript in preparation)
DIMELLI cohort:
Confirmation of a new Tool to define Insulin Resistance
Diabetes incidence in children and youth is increasing worldwide, including autoimmune and non-autoimmune cases. The DiMelli study aims to establish a diabetes incidence cohort registry and to characterize diabetes phenotypes by immunologic, metabolic and genetic markers. The insulin-sensitivity score established within the SEARCH study was reassessed in the DiMelli cohort. From 2009 to 2011, 496 patients (54% male) were registered. 81.7% were positive for multiple islet autoantibodies (Aabs; type 1 diabetes), 10.3% for one Aab (intermediate cases), and 8% were islet Aab-negative (type 2 diabetes). In SEARCH, people with diabetes and a score of less than 8.15 were classified as insulin resistant (score = exp [4.64725-0.02032x(waist [cm]) - 0.09779x(HbA1c [%]) 0.00235x(triglyceride [mg/dl])]; Dabelea et al., Diabetes Care 2011). This score was reassessed in DiMelli. It ranged from 1.6 to 20.3 (median 8.6, IQR 6.4-11.7). By using the SEARCH cut-off, 38.8% of DiMelli patients with multiple islet AAbs, 52.9% with one AAb, and 75% with no AAbs were insulin resistant. The score was inversely correlated with age (r= -0.6; p=0.01) and body mass index (r = -0.4, p=0.01). Children with islet Aabs had higher scores than children without Aabs (median 8.8, IQR 6.9-11.8 vs. 6.2, IQR 3.7-8.3; p=0.02), and children with c-peptide > 2 ng/ml had lower scores than children with c-peptide ≤ 2 ng/ml (median 4.0, IQR 3.6-8.7; vs 8.9, IQR 7.1-11.9; p=0.03). Application of the SEARCH Insulin-sensitivity score in a second cohort confirms its applicability as a surrogate marker of insulin resistance in diabetes patients under the age of 20 years.
TEDDY cohort:
Performance of the German Clinical Center within the TEDDY consortium General Operations of the German Clinical Site
with her team that includes Drs. Bonifacio (co-chair immune markers committee), Hummel (diet committee), Winkler (coordinator and diet committee), Koletzko (celiac disease committee), Roth (psychosocial committee), and Pflüger (infectious agents committee), plus a number of staff dedicated to the follow-up of TEDDY children and specimen and data handling. Enrolled subjects at the GER site either have their study visits at the Munich TEDDY clinic (20%) or are followed by the Long Distance Protocol (80%). Long Distance visits are performed by the family pediatrician who performs anthropometric measurements and draws blood which is sent by overnight courier to the processing site. Questionnaires are completed by the family and returned to the clinical center in the appropriate time-window.
Final Results: Screening and Cohort Enrollment
The German clinical center was expected to screen 30,177 newborns, and screened of 36,105, of which 1679 were eligible for enrollment. The German center was expected to enroll 563 children by July 31, 2010, with a special focus on first degree relatives (FDR) and enrolled 595 children, including 375 general population (GP) and 220 FDR. As of December 2012, 38 children (24 FDR) have developed persistent confirmed islet autoantibodies, and 21 children (15 FDR) T1D. Thus, Germany has contributed 8% of all TEDDY children with islet autoantibodies, 8% with multiple islet autoantibodies, and 16% of children with T1D.
Retention of the Study Cohort
Since inclusion, 161 (27%) have withdrawn (121 GP; 40 FDR) and 19 (6%) are lost to follow-up. Common reasons for withdrawal from the study are the blood draws, visit frequency, 3-day diet records, families are too busy. Most families participate with the Long Distance Protocol and we have developed strategies and attitudes to enhance participation of the families and local pediatrician to improve retention. Examples include feedback questionnaires to families and pediatricians, newsletters to keep families and pediatricians informed of results, promotional items and modest reimbursement for time and effort, assigning one contact person to a family, taking note of family events and congratulate or respect them.
All is facilitated by a log and detailed annotation in the family file. The GER team meets weekly to review current families and families noted as “Active Retention Cases” or “Watch Cases”, and plans made for each family on the list.
To increase compliance to blood samples we are now offering study visits in our clinical center in Munich and visiting families with the “TEDDY mobil” for families following the long distance protocol. Additionally we developed the “TEDDY mobil” also for families on Long Distance Protocol, as a special assistance. Especially families who have problems with the blood draw it is very helpful. TEDDY staff and TEDDY physician visit the family at their home to provide help, explain study procedures and perform the blood draw.
To increase the pediatrician involvement and motivation, we have also sent out a Feedback Questionnaire where they indicated how the organization of the follow-up
visits can be improved. Furthermore we also sent out a newsletter to the pediatricians containing current study results.
Completeness of Key Data Collected
GER site compliance to the study components was high and similar to other centers until age 1 year, but has lagged behind some of the centers after that age. This is particularly so for blood draws reaching a minimum at the 3 year visit. We observed lower blood volumes from children visited by family pediatricians as compared to those seen at the Munich Clinical Center. Hence, more attention was placed on communication to the pediatrician with respect to blood draw procedures and the importance of visits and collection. Offers to bring more families to our Munich clinical center and home visits are now implemented to improve compliance and retention. Incentives to increase compliance for dietary recall include rewards for completion and the option to estimate rather than weigh food amounts. These efforts are working as the blood draw compliance has steadily increased since and near now the overall average. These efforts remain high priority.
Contributions of the German Clinical Site Investigators to TEDDY Achievements
GER site investigators and staff will continue to make substantial contributions to both study-wide and site-specific protocol/manual of operations documents and data analysis, and are actively involved in all TEDDY committees. The GER site has extensive experience in measuring and understanding changes in autoimmunity and factors which modify progression to disease. It uses findings generated in its BABYDIAB and BABYDIET studies to formulate and test hypotheses in TEDDY. This includes novel data on microbiome, metabolomics, and gene expression in the first years of life which are used to plan TEDDY investigations. Key GER site research area contribution will be 1) defining initiation of autoimmunity, 2) identifying factors affecting the rate of ‘progression’ from islet autoimmunity to diabetes in TEDDY children, 3) developing analytical methods to combine multiple parameters (genetic, immune, environment) 4) developing and validating assays for immune response and status; and 5) exploring novel interactions of infant dietary fiber and innate and adaptive immune responses. GER site investigators have been lead authors on two published TEDDY manuscripts (Ziegler et al. J Autoimmun. 2011; Bonifacio et al. J Clin Endocrinol Metab. 2010), two submitted TEDDY manuscripts, and six in progress manuscripts. The latter include novel work on infectious agent discovery in rapid onset diabetes TEDDY cases, and mathematical models of longitudinal autoantibody changes in relation to progression to diabetes, and confirmatory studies of associations of genes and cesarean section with progression to diabetes
Next generation sequencing for viruses in children with rapid onset type 1 diabetes
Viruses are candidate causative agents in the pathogenesis of type 1 diabetes. We hypothesized that children with a rapid onset of type 1 diabetes may have been exposed to such agents shortly before the initiation of islet autoimmunity, possibly at
high dose, and thus could be helpful for the identification of viruses involved in the development of autoimmune diabetes. We used next generation sequencing to search for viruses in plasma samples and examined the history of infection and fever in children enrolled in The Environmental Determinants of Diabetes in the Young (TEDDY) study who progressed to type 1 diabetes within 6 months from the appearance of islet autoimmunity, and in matched islet autoantibody negative controls. Viruses were infrequently detected in the period surrounding seroconversion in cases as well as controls. In addition, infection history was found to be similar between children with rapid onset diabetes and control children, although episodes of fever were less frequent in children with rapid onset diabetes. These findings do not support a major role of virus infection around the time of seroconversion in the pathogenesis of rapid onset type 1 diabetes in young children. (Ziegler AG et al., submitted).
Future Perspectives
1) Continuation of follow-up of BABYDIAB, BABYDIET, and TEDDY participants and implementation and development of retention strategies to assure low drop-out rate and high compliance to biomaterial collection and questionnaire data.
2) Continuation of recruitment of children for TEENDIAB, DiMelli and ImmunDiabRisk
3) Examination of infectious agents in fulminant diabetes cases. We reason that the fulminant autoimmune diabetes cases are an ideal source to identify causative infections. We have the opportunity to examine daily infection and medication protocols prior to the initiation of islet autoimmunity in children from the BABYDIET and TEDDY study, and via deep sequencing, search for virus in plasma, stool, and cells available from these children; and bacterial sequencing in stool samples. Deep sequencing will be done in collaboration with T. Briese and WI. Lipkin (Columbia University, USA).
4) Examination of cellular and humoral immune responses in children born via Cesarean section and having the IFIH1 protective and risk genotypes. We aim to test, whether cesarean section and IFIH1 genotype lead to a higher susceptibility towards virus infections (human enterovirus (HEV) and others) and whether children born by cesarean section are less protected through maternal antibody transfer. We also aim to investigate effects from sera pre-, and post-seroconversion on CD4 naive and memory T cells relative to cesarean section and IFIH1, and investigate whether there is an activation humoral 'milieu' prior to seroconversion and after seroconversion that increases progression.
5) Studies on beta cell function prior to and after seroconversion in relation to puberty, hormones, growth, and T1D and T2D associated genotypes in children participating in the TEENDIAB studies. We aim to examine whether beta cell function may be impaired in children at T1D risk prior to the initiation of islet autoimmunity and whether weight, insulin resistance, and factors influencing beta cell function affect risk of islet autoimmunity or progression to T1D.
6) Development of a bio-resource for ongoing and future collaborative research. We aim to establish an infrastructure for the use of study material from our ‘from birth’ and ‘puberty’ cohort bio-resources to facilitate ongoing and future collaborative research into the pathogenesis of T1D.
7) Create one database with results from all sub-projects (microbiome, metabolomics, enteroviruses, immunochip typing, nutritional, metabolic, autoantibody and diabetes status, anthropometric data, see collaborations below) for complex multi-dimensional approach (system biology tools), and provide the diabetes research community access to the data repository.
Our ongoing international and national collaborations with our cohorts are: Investigator: Matej Oresic, Helsinki, Finland; Project ‘Metabolomics’
Matej Oresic published intriguing novel metabolomic data from the Finnish DIPP study using high-end methods and analyses. We had the opportunity to take advantage of this technology by our co-participation in an EU project (DIAPREPP, funding until 4/2011). We specifically addressed the hypothesis that the age of development of islet autoimmunity was associated with different autoimmune and environment phenotype. A total of 133 serum samples from 70 children were sent, tested, and analyzed for 29 metabolites of the amino acid metabolism and 511 lipids.
Investigator: Ramnik Xavier, Curtis Huttenhower, Dirk Gevers, Broad Institute; Projekt: Understanding of the gut microbiome in children at increased risk of type 1 diabetes
Based on an initiative started by the Juvenile Diabetes Research Foundation (JDRF) in 2011, cooperation has been established between the Helmholtz Zentrum München and the Broad Institute to analyze the microbiome in stool samples of children at increased risk for type 1 diabetes. Samples will be selected from the ongoing prospective TEENDIAB study, the ‘from birth’ BABYDIET study, and our recently initiated pregnancy-birth cohort ImmunDiab study. As a first step, 71 islet autoantibody-positive children from the TEENDIAB/BABYDIET cohorts have been selected and matched in a case-control approach with two islet autoantibody-negative children per case; matched by gender, age, and duration of sample transport. An aliquot of ~100mg of stool has been aliquoted from each child and shipped to the Broad Institute for DNA extraction, and sequencing. In the next step,
samples from 150 children from BABYDIET with multiple 3-monthly collected stool samples and from 100 ImmunDiab children will be analyzed.
Investigator: Ezio Bonifacio; Project ‘Autoreactive T cell sequencing’; funding from BMBF
Ezio Bonifacio in Dresden has developed methodology that provides highly efficient sequencing and cloning of T cell receptor alpha and beta chains from single cells (with 60% efficiency to obtain sequences of both chains from a single cell), followed by expression of the TCR to identify target peptide. Moreover, this is now coupled to single cell cytokine/transcription factor profiling at the single cell level so that his group is able to provide TCR sequence and phenotype of single cells that respond to autoantigen. Validation studies from samples obtained from the BABYDIET study show that the method can distinguish pre-seroconversion from post-seroconversion samples, and sequential sample testing shows that it is possible to identify T cell clones with the same TCR in different samples. We consider this to be a novel approach to the identification of T cell antigen peptide targets in the pre-clinical period and which could eventually provide the possibility to develop molecular assays for tracking autoreactive T cells. Thus, we have initiated the process for collaborative use of the BABYDIET PBMC samples which are frozen at multiple time points from age 3 months.
Investigator: John Todd, Cambridge, UK; Project ‘RNA expression signatures’ The group of John Todd has produced functional data that support genetic and T1D associations with immune response phenotype. With the capacity to perform genotype-phenotype interaction studies, John Todd and David Clayton approached us to obtain samples suitable for RNA analyses from the pre-diabetic period. The project aims to identify peripheral blood RNA expression signatures associated with the development of autoimmunity, infection, and progression to diabetes. To validate sample quality an initial pilot shipment of 16 samples from BABYDIET children were shipped to Cambridge for preparation, QC, and measurement. The data confirmed that the study was feasible and over 700 samples of trizol-stored peripheral blood (approximately 1 million PBMC in each sample) from the BABYDIET study were shipped to Cambridge for RNA expression studies. Currently a database is being prepared in Munich with clinical data, confounding variable data, and islet autoantibody profiles to set up an RNA extraction plan so that a case control subset can be first tested in Cambridge followed by confirmation of the findings in the remaining samples. It will be further considered whether DNA samples will be sent for genotyping with the immune disease SNP chip, Immunochip, in order to correlate gene expression differences with SNP alleles and haplotypes.
Investigators: Polly Bingley and K. Gillespie; Project ‘characterization of long survivors’
The mechanisms which dictate why some individuals with ongoing islet autoimmunity do not develop diabetes, or do so only after many years, represent a critical gap in our understanding of the pathogenesis of T1D. The presence of two or more islet autoantibodies in unaffected first-degree relatives of individuals with T1D is associated with more than 25% risk of developing diabetes within 5 years. It is however increasingly clear that in some multiple islet autoantibody-positive individuals disease progression to diabetes is delayed for decades. To explore potential determinants of slow progression, we propose in depth characterization of immune and regulatory pathways in children from the Bart’s Oxford (BOX) family study, and the BABYDIAB and BABYDIET cohorts (funded by the JDRF).
B. Research Group T1D immune phenotyping – Dr. med.Peter Achenbach
Overview
Type 1 diabetes (T1D) is caused by an autoimmune destruction of the islet beta cells. Progression to disease is not uniform between affected individuals and islet autoimmunity can precede the development of clinical T1D by many years. Predicting the risk for progression of islet autoimmunity in pre-clinical T1D is important for identification of individuals that might profit from inclusion in interventional trials aiming to prevent the onset of disease.
Autoantibodies to beta cell antigens are a hallmark of T1D. They precede diabetes onset in >95% of children who develop disease and are frequent in insulin requiring diabetes in adults. Although they are not considered effectors of beta cell damage, they are established markers in the clinical classification of diabetes, prediction of the need for insulin treatment, identification of individuals at risk for developing T1D and as end-points in observational studies.
Our group has focused research on immunology of T1D, with a special emphasis on characterizing the humoral islet autoimmunity and predicting the disease. Specifically, we ask how autoantibody responses can best be applied to stratify, stage and monitor progression to T1D. The underlying hypothesis is that humoral autoimmune responses can be used to track T1D pathogenesis by identifying distinct immunization profiles at initiation of autoimmunity, demonstrating new autoimmunity on follow-up, and relating immune profiles/phenotypes to T1D development. The prospective cohorts at IDF (e.g. BABYDIAB and Munich family studies) provide unique opportunities to test this hypothesis. Furthermore, we work together with mathematicians at HMGU to modeling our high-dimensional datasets in order to get insight into complex interactions between immunologic, genetic and environmental
factors in T1D pathogenesis. Our long-term research goals are to contribute to a better understanding of the etiology and pathogenic mechanisms of T1D, and to ultimately find effective therapies that can prevent and cure this disease.
Main results
1) IA-2 autoantibody affinity in children at risk for type 1 diabetes. Autoantibodies to insulinoma-associated protein 2 (IA-2A) are associated with increased risk for type 1 diabetes. In this study, we examined IA-2A affinity and epitope specificity to assess heterogeneity in response intensity in relation to pathogenesis and diabetes risk in 50 children who were prospectively followed from birth. At first IA-2A appearance, affinity ranged from 107 to 1011 L/mol and was high (>1.0 x 109 L/mol) in 41 (82%) children. IA-2A affinity was not associated with epitope specificity or HLA class II haplotype. On follow-up, affinity increased or remained high, and IA-2A were commonly against epitopes within the protein tyrosine phosphatase-like IA-2 domain and the homologue protein IA-2β. IA-2A were preceded or accompanied by other islet autoantibodies in 49 (98%) children, of which 34 progressed to diabetes. IA-2A affinity did not stratify diabetes risk. In conclusion, the IA-2A response in children is intense with rapid maturation against immunogenic epitopes and a strong association with diabetes development.
Clin Immunol 2012; 145(3):224-229. [IF 4.046]
2) Genetic association of zinc transporter 8 (ZnT8) autoantibodies in type 1 diabetes cases. Autoantibodies to zinc transporter 8 (ZnT8A) are associated with risk of type 1 diabetes. Outside of the SLC30A8 gene itself, little is known about the genetic basis of ZnT8A positivity. Here, we hypothesised that other loci in addition to SLC30A8 are associated with ZnT8A. ZnT8A was measured in 2,239 British type 1 diabetes cases diagnosed before age 17 years, with a median duration of diabetes of four years. Cases were tested at over 775,000 loci genome wide (including 53 type 1 diabetes associated regions) for association with ZnT8A positivity. ZnT8A was also measured in 855 type 1 diabetes affected family members. Only FCRL3 on chromosome 1q23.1 and the human leukocyte antigen (HLA) class I region were associated with ZnT8A positivity. rs7522061T>C was the most associated SNP in the FCRL3 region (P = 1.13x10-16). The association was confirmed in 855 type 1 diabetes affected family members (P ≤ 9.20x10-4). rs9258750A>G was the most associated variant in the HLA (P = 2.06 x 10-9 and P = 0.0014 in family cases). ZnT8A positivity was not associated with HLA-DRB1, HLA-DQB1, HLA-A, HLA-B or HLA-C (P > 0.05). Unexpectedly, the two loci associated with ZnT8A positivity did not alter risk of type 1 diabetes and, the 53 type 1 diabetes risk loci did not influence positivity to this disease-specific autoantibody. In conclusion, ZnT8A are not primary pathogenic factors in the disease. Nevertheless, ZnT8A testing in combination with
other autoantibodies facilitates disease prediction, despite the biomarker not being under the same genetic control as the disease.
Diabetologia 2012; 55(7):1978-1984. [IF 6.814]
3) Characteristics of rapid versus slow progression to type 1 diabetes in multiple islet autoantibody-positive children. Islet autoantibody-positive children progress to type 1 diabetes at variable rates. In this study, we asked whether characteristic autoantibody and/or gene profiles can be defined for extreme progression phenotypes. Autoantibodies to insulin (IAA), glutamic acid decarboxylase (GADA), insulinoma-associated antigen-2 (IA-2A) and zinc transporter 8 (ZnT8A) were measured in follow-up sera and genotyping for type 1 diabetes susceptibility genes (HLA-DR/DQ, INS VNTR, and single nucleotide polymorphisms at PTPN22, PTPN2, ERBB3, IL2, SH2B3, CTLA4, IFIH1, KIAA0350, CD25, IL18RAP, IL10, COBL) was performed in DNA samples of children born to a parent with type 1 diabetes and prospectively followed from birth for up to 22 years. Of 1650 children followed, 23 developed multiple autoantibodies and progressed to diabetes within 3 years (Rapid Progressors), while 24 children developed multiple autoantibodies and remained non-diabetic for more than 10 years from seroconversion (Slow Progressors). Rapid and Slow Progressors were similar with respect to HLA-DR/DQ genotypes, the development of IAA, GADA, and ZnT8A, and the progression to multiple autoantibodies. In contrast, IA-2A development was considerably delayed in Slow Progressors. Furthermore, both groups were effectively distinguished by the combined presence or absence of type 1 diabetes susceptibility alleles of non-HLA genes, most notably IL2, CD25, IL10, and IFIH1, and discrimination was improved among children carrying high-risk HLA-DR/DQ genotypes. In conclusion, our data suggest that genotypes of non-HLA type 1 diabetes susceptibility genes influence the likelihood or rate of diabetes progression amongst children with multiple islet autoantibodies.
4) Immune Platform at IDF and Central Laboratory for the measurement of islet autoantibodies for the CNDM. We provide an Immune Platform at the IDF for high-end phenotyping of humoral autoimmune responses in T1D. The platform is open for collaborations with investigators at HMGU and from outside.
In 2012, we have provided core measurements of islet autoantibodies for the Network (e.g. DPV study, DiMelli study, TEENDIAB study, CNDM pediatric diabetes biomaterials bank [pedBMB] project). The centralized high-quality measurement of all major T1D-associated autoantibodies (IAA, GADA, IA-2A, ZnT8A) improves diabetes classification and allows determination of autoantibody phenotypes at diabetes onset. Furthermore, it allows direct comparison of autoantibody results between different Network studies and, in case of GADA and IA-2A, large NIH-funded international studies (e.g. TEDDY) because of the use of harmonized autoantibody detection methods. This expert diagnostic service is unique in the field of diabetes in Germany.
It is expected that there will be further collaborations with CNDM studies within the next years.
C. Research Group Gestational diabetes mellitus – Dr. oec. troph. Sandra Hummel
Overview
Gestational diabetes (GDM), defined as glucose intolerance that begins or is first detected during pregnancy, has a prevalence of 2-6% with an increasing trend across most racial/ethnic groups studied and is the most frequent metabolic problem during gestation. GDM is associated with an increase in perinatal morbidity and mortality as well as a greater frequency of long-term complications in the mother and her offspring. It is expected that the increase in the prevalence of GDM heralds a future increase in both atherosclerotic disease and type 2 diabetes (T2D). Women with GDM are a high-risk population for the development of diabetes mellitus. Risk estimates for T2D after GDM range from 17% to 63% within 5 to 16 years after pregnancy, depending upon the ethnic background of the study population and the detection method for GDM and glucose intolerance.
GDM is not only associated with complications for the mothers. Several studies indicated that in utero exposure to GDM is a strong risk factor for overweight and T2D in the offspring during childhood and adolescence. As the prevalence of pregnancies complicated by GDM is increasing, the number of children at high risk for overweight combined with a high risk to develop GDM themselves will also further increase.
The main aims of our research group are to 1.) stratify risk for the development of postpartum diabetes in mothers with GDM, 2.) to investigate mechanisms underlying postpartum development of diabetes in mothers with GDM, 3.) to conduct a prevention trial in women with recent insulin-treated GDM using life style intervention and vildagliptin after pregnancy, and 4) to identify risk factors and mechanisms underlying the increased overweight risk in offspring of mothers with GDM.
To address these aims, more than 800 mothers with GDM together with their offspring are currently followed in two cohort studies: the Prospective German Gestational-diabetes-study and the Postpartum outcomes in women with Gestational Diabetes and their offspring POGO-study. In both studies women with GDM and their offspring are followed for the development of impaired glucose tolerance (IGT) and T2D in the mother postpartum, and insulin resistance and obesity in the offspring. At follow-up, an OGTT is performed and blood samples (Plasma, Serum, DNA, RNA, PBMC) as well as stool samples are collected and stored to enable further genetic,
metabolomic and gut-microbiome studies. Furthermore we are conducting a study aiming to prevent postpartum onset of diabetes in women with GDM. In this placebo-controlled phase II study (PINGUIN) mothers are treated with life style intervention and the DPP4-inhibitor Vildagliptin to prevent postpartum development of T2D.
Main Results
The prospective German GDM study enrolled a total of 302 mothers with GDM and their offspring at birth between 1989 and 1999. Both, mothers and their offspring were followed at 9 months, 2, 5, 8, 11, 14, 17 and 20 years after birth, and blood samples and DNA were collected. In the mothers an OGTT was performed at each visit for the detection of postpartum diabetes. Demographic data that included the age at delivery, the numbers of preceding pregnancies, the duration of gestation, diabetes treatment during pregnancy, and family history of T1D or T2D were obtained shortly after delivery. In offspring, data on height and weight were collected at each follow-up visit. Insulin resistance (HOMA-IR) was determined at age 8 and 11 years. Furthermore, data on maternal BMI during early pregnancy, birth weight, and maternal smoking behavior during pregnancy were collected by questionnaire shortly after birth as well as breastfeeding habits were recorded at age 9 months and 2 years.
1) Predictors of postpartum diabetes in women with GDM. To investigate whether breastfeeding influences short and long term postpartum diabetes outcomes, women (n=304) with GDM participating in the prospective German GDM study were followed from delivery for up to 19 years postpartum for diabetes development. Postpartum diabetes developed in 147 women and was dependant on the treatment received during pregnancy (insulin versus diet), body mass index, and presence/absence of islet autoantibodies (Figure 5). Among islet autoantibody-negative women, breastfeeding was associated with median time to diabetes of 12.3 years as compared to 2.3 years in women who did not breastfeed. The lowest postpartum diabetes risk was observed in women who breastfed for >3 months (Figure 6). Based on these results, we recommend that breastfeeding should be encouraged among these women, as it offers a safe and feasible low-cost intervention to reduce the risk of subsequent diabetes in this high-risk population.
32 2 1 1 Islet Aab-positive
39 10 2 1 Islet Aab-negative,
insulin-treated, BMI >30 kg/m2
53 15 9 6 4 2 Islet Aab-negative,
insulin-treated, BMI =30 kg/m2
48 21 13 11 7 5 Islet Aab-negative, diet-treated,
BMI >30 kg/m2
132 73 58 41 33 17 Islet Aab-negative, diet-treated,
BMI =30 kg/m2
Follow up af ter delivery (years) C u m u la ti v e p o s tp a rt u m d ia b e te s ( % ) 0 3 6 9 12 15 100 80 60 40 20 0
Figure 5: Combining risk factors for classification of postpartum diabetes risk. Life table analysis of patients with GDM categorized as islet autoantibody-positive; islet autoantibody-negative, insulin- or diet-treated during pregnancy and having a BMI >30 or ≤30 kg/m² in early pregnancy (Ziegler et al., Diabetes 2012).
C u m u la ti v e p o s tp a rt u m d ia b e te s ( % ) 100 80 60 40 20 0 0 3 6 9 12 15
Follow up af ter delivery (years)
No breastf eeding
breastf eeding =3 months
breastf eeding >3 months
54 17 8 7 4 3 No breastf eeding
81 38 26 15 10 5 breastf eeding =3 months
100 49 37 31 26 17 breastf eeding >3 months
Figure 6: Cumulative life-table risk of postpartum diabetes in islet autoantibody negative women with gestational diabetes who breastfed for >3 months compared with those who breastfed for ≤3 months
(p=0.029) or did not breastfeed (p=0.002).
2) Predictors of overweight and insulin resistance during childhood in offspring of mothers with GDM. In a current analysis we were studying the influence of Type 2 Diabetes (T2D) susceptibility alleles at the HHEX-IDE and CDKAL1 loci on weight development in offspring of mothers with GDM by analyzing a total of 549 records on weight, height and BMI were from 185 offspring aged 1 to 17 years participating in the prospective German GDM offspring study.
This analysis revealed that the T2D risk allele at the HHEX-IDE locus is associated with reduced BMI-SDS over the entire observation period (-0.28 SDS per risk allele, p=8.1E-03). After stratification by age tertiles the HHEX-IDE risk allele was strongly associated with reduced BMI-SDS (-0.38 SDS, p=3.6E-03) in the first age tertile (1.0-2.3 years) whereas this association was less pronounced later in childhood (8.1-16.7 years). After stratification by maternal obesity, the HHEX-IDE risk allele was associated with reduced BMI-SDS in offspring of non-obese mothers only (-0.40 SDS, p=0.001). In contrast, the T2D risk allele at the CDKAL1 locus was not associated with childhood growth. Based on these results we conclude that the T2D susceptibility allele at the HHEX-IDE locus is associated with reduced BMI in offspring of mothers with GDM, especially in children up to 2.3 years of age and in offspring of mothers with BMI<30kg/m². The finding that the genetic influences did
not persist during puberty may indicate that the relationship between reduced BMI and the T2D associated allele of the HHEX-IDE gene in early childhood changes towards increased BMI during adulthood, a relationship that appears to be more consistent with the association of the genotype with T2D later in life. However a longer follow-up of this cohort will be required to ascertain this.
Figure 7: Effect of HHEX-IDE risk alleles on BMI-SDS. Effect per allele (95% CI) on BMI-SDS stratified by A) age tertile (n=183 records each, 1. tertile: 1.0-2.3 years, 2. tertile: 2.4-8.1 years, 3. tertile: 8.1-16.7 years) and B) maternal BMI≥30kg/m² in early pregnancy.
3) Postpartum outcomes in women with Gestational Diabetes and their Offspring (POGO). Results from our previous studies have shown that mothers with GDM are at increased risk for T2D postpartum, and that their offspring are at increased risk for childhood obesity and insulin resistance. We found that maternal obesity and insulin treatment increases the risk for postpartum development of T2D in GDM mothers, while breastfeeding seemed to have protective effects; however the mechanisms behind these associations are still unclear.
To identify mechanisms related to the development of T2D postpartum in mothers with GDM and overweight/obesity in offspring of mothers with GDM, we have designed a cohort study in mothers who were referred for screening of GDM to the outpatient clinic of Prof. Dr. Ziegler, PD Dr. Hummel and PD Dr. Füchtenbusch in Munich (Klinikum Schwabing), and their offspring. More than 1800 mothers with and without GDM and their children aged 1 to 11 years will be invited to participate in this follow-up study called Postpartum outcomes in women with Gestational Diabetes and their Offspring (POGO).
At the study visit, demographic, nutritional and anthropometric data are recorded. Additionally, data about physical activity, metabolism and genetic susceptibility are collected using accelerometers, breath gas analyses, 75g oral glucose tolerance
