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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Pediatr Diabetes. Author manuscript; available in PMC Dec 1, 2012.
Published in final edited form as:
PMCID: PMC3315186
NIHMSID: NIHMS365277

The Environmental Determinants of Diabetes in the Young (TEDDY): Genetic Criteria and International Diabetes Risk Screening of 421,000 infants

Abstract

Aims

The TEDDY study seeks to identify environmental factors influencing the development of type 1 diabetes (T1D) using intensive follow-up of children at elevated genetic risk. The study requires a cost-effective yet accurate screening strategy to identify the high-risk cohort.

Methods

The TEDDY cohort was identified through newborn screening using HLA class II genes based on criteria established with pre-TEDDY data. HLA typing was completed at six international centers using different genotyping methods that can achieve >98% accuracy.

Results

TEDDY developed separate inclusion criteria for the general population (GP) and first degree relatives (FDR) of T1D patients. The FDR eligibility includes nine haplogenotypes (DR3/4, DR4/4, DR4/8, DR3/3, DR4/4b, DR4/1, DR4/13, DR4/9 and DR3/9) for broad HLA diversity, while the GP eligibility includes only the first four haplogenotypes with DRB1*0403 as an exclusion allele. TEDDY has screened 414,714 GP infants, of which 19,906 (4.8%) were eligible, while 1,415 of the 6,333 screened FDR infants (22.2%) were eligible. High resolution confirmation testing of the eligible subjects indicated that the low-cost and low-resolution genotyping techniques employed at the screening centers yielded an accuracy of 99%. There were considerable variations in eligibility rates among the centers for GP (3.5% – 7.4%) and FDR (19% – 32%) subjects. The eligibility rates among US ethnic groups were 0.9%, 1.3%, 5.0% and 6.9% for Asians, Black, Caucasians and Hispanics, respectively.

Conclusions

Different low-cost and low-resolution genotyping methods are useful for the efficient and accurate identification of a high-risk cohort for follow-up based on the TEDDY HLA inclusion criteria (ClinicalTrials.gov NCT00279318).

Keywords: genetic screening, prediction, autoimmunity, type 1 diabetes, population-based

INTRODUCTION

Type 1 diabetes (T1D) results from poorly defined interactions between susceptibility genes and environmental determinants. T1D susceptibility is primarily defined by genetic factors within the HLA complex on chromosome 6. The main disease factors are the HLA-DQ molecule encoded by DQA1 and DQB1 genes and the HLA-DR molecule defined by DRB1 alleles [1]. Additionally, recent genome-wide association studies (GWAS) have identified forty other association intervals that may harbor T1D susceptibility/protection genes [25]. In contrast to the rapid progress in finding T1D genes, identification and confirmation of environmental determinants remain a formidable challenge. The reasons underlying the lack of progress are multi-faceted. First, different categories and large numbers of environmental determinants could contribute to the triggering or protection of T1D. Although many candidates have been suggested by previous studies [6, 7], few have been definitively proven beyond reasonable doubt. Second, exposures may occur any time before the onset of disease, from in utero to disease onset. Third, environmental determinants may differ in different populations, partly depending on the genetic architecture. Fourth, the individual risk of developing T1D in the general population is not very high and quite variable in different populations. Therefore, large study populations with elevated T1D risk must be identified. Although first degree relatives (FDR) of T1D patients certainly have elevated risk, subjects from the general population (GP) must be included as well because 85–90% of diagnosed patients do not have a FDR with the disease.

Identification of environmental determinants requires frequent follow-up studies of large number of subjects from early in life until disease onset for a variety of exposures using both epidemiological and laboratory methodologies. To accomplish such ambitious goals, long term multi-center prospective studies on a cohort at high risk of developing the disease are necessary. The Environmental Determinants of Diabetes in the Young (TEDDY), a NIH-funded prospective observational study, was designed to accomplish this goal. The TEDDY design addresses the main concerns related to the studies of environmental exposures [8]. TEDDY has identified a large cohort of infants that have increased genetic risk for developing islet autoantibodies and T1D by screening several hundred thousand newborns. The high-risk cohort is closely monitored beginning at approximately three months of age for the development of islet autoantibodies and T1D for 15 years, during which environmental exposures are extensively and intensively measured. These exposures include diet, infectious agents, psychosocial stress, and other lifestyle and location-based factors. Exposures are captured via frequent biological samples from the participating children as well as extensive questionnaire-acquired data [8].

For the TEDDY study to be cost-effective, the intention was to apply the long and intense follow-up protocol only to children at elevated risk of T1D. Development of a high-risk study cohort of sufficient size required multiple strategies including an international consortium of large clinical centers, screening of both FDR and GP infants, and study inclusion criteria based upon genetic risk screening applicable in this diverse setting. Despite the available information on multiple T1D susceptibility genes, the only genes useful for screening purpose were, and still are, the HLA class II genes (DRB1, DQA1 and DQB1), which account for some 50% of the total genetic contribution to T1D. Therefore, these genes were chosen for TEDDY screening. Here, we describe the development of the TEDDY HLA strategy, its successful implementation in the screening centers, the overall results as the screening nears completion, and the associated quality control programs and outcomes.

METHODS

Pre-TEDDY data collection

In order to develop a HLA screening strategy for TEDDY, HLA data on the healthy background population, T1D patients and their FDR were assembled from the six TEDDY clinical centers based in Colorado (COL), Washington State (WAS), Georgia/Florida (GEO), Finland (FIN), Germany (GER) and Sweden (SWE). All centers provided historical data on their background populations that were used to develop the TEDDY HLA strategy for GP subjects. Two centers (Germany and Sweden) also provided FDR data. Some of the pre-TEDDY data from this study have been published previously [914].

TEDDY genotyping methods

Samples were in all cases obtained from subjects under informed consent of parents and with IRB/Ethics Board approval. Each center was allowed to develop its own genotyping methods as long as a minimum accuracy of 98% was achieved. Five HLA screening laboratories were chosen for TEDDY screening and they employed four different genotyping strategies. Screening genotyping results were expected to be available by the time the infant was 2 months of age. Low-cost genotyping was achieved by adopting a two-stage screening strategy in four laboratories. In the first stage, approximately 90% of the ineligible subjects are excluded by the presence of specific alleles that can be detected inexpensively. In the second stage, detailed genotyping of DQB1 and DQA1 or DQB1 and DRB1 alleles are determined. For the general population, the DRB1*0403 allele is usually determined by a restriction digest of the exon 2 amplicon. The first-stage strategy used by the Finnish and Swedish screening laboratories was to exclude certain resistant alleles while requiring certain susceptible alleles, and was previously described [15]. The WAS laboratory used a first-stage strategy of exclusion of DQB1*05, DQB1*06, DQB1*0301 and DQA1*02 followed by direct exon 2 sequencing of specific DQB1 and DQA1 alleles in the second stage genotyping. The GEO screening laboratory excluded subjects with DQB1*05, DQB1*06 and DQB1*0301 using allele-specific amplifications in the first stage. The potentially eligible subjects were further genotyped for DQB1 by denaturing gradient gel electrophoresis using a previously published protocol [9] and DRB1 by Luminex beads. Samples from the COL center were genotyped in the laboratory of Dr. Erlich using a reverse line blot SSOP technique with a panel of immobilized probes for DRB1 and DQB1 alleles [11]. The same laboratory also served as the TEDDY HLA Reference Laboratory to carry out confirmatory tests of enrolled subjects from all six clinical centers using separate DRB1, DQA1 and DQB1 reverse line blots, each with a much higher resolution panel of immobilized probes [16].

RESULTS

Development of the TEDDY HLA strategy

To design a study-wide TEDDY HLA strategy, the TEDDY investigators assembled HLA genotyping data from all six TEDDY clinical centers. These data represent the populations near the three US centers and three European centers. All six datasets consisted primarily of Caucasian subjects from the study areas. Odds ratios (OR) for association with T1D were calculated for each haplogenotype in feach population. Genotypes were then ranked by the OR in each of the study populations. Interestingly, the rank order for the top five high risk haplogenotypes were identical for all six data sets. From the combined data set, we were able to identify nine high-risk haplogenotypes which had an estimated relative risk of > 3 in all six data sets (Table 1). Although several other haplogenotypes had increased OR in one or several data sets, their OR were not consistent in all study populations and thus were excluded from further consideration.

Table 1
HLA eligibility for FDR and the general population newborns#

During the TEDDY design stage, consensus favored the adoption of inclusion of specific HLA haplogenotypes eligible for TEDDY follow-up with specific exclusion of dominantly protective alleles. The data in supplemental Table 1 summarizes the cumulative frequencies of the top two, four or nine haplogenotypes in T1D and control populations, the estimated OR and absolute risk (AR), in each of the six clinical centers and the combined data for all populations. As expected, inclusion of the top two haplogenotypes (DR3/4 and DR4/4, denoted A and B, respectively) is a strategy that yields the highest specificity (96.7%) and good AR (5.5%), but only 39.3% of the future T1D cases can be identified by these two haplogenotypes. In contrast, inclusion of nine genotypes (A – I) would increase the average sensitivity to 63% while decreasing the specificity to 90% and the AR to 2.4%. By consensus, the TEDDY adopted the compromise strategy that included four high risk haplogenotypes (A – D) for the GP infants (Table 1). The GP inclusion criteria was expected to yield a sensitivity of 50%, a specificity of 94%, an average OR of 10 and average AR of 3.4%, assuming equal screening numbers of Caucasians in all six clinical centers. Using these inclusion criteria, 5.7% of the GP infants were expected to be eligible for follow-up studies. It should be noted that the pre-TEDDY estimates included all DR4 subtypes in the calculation while haplotypes with a DRB1*0403 subtype are excluded from the actual TEDDY follow-up, which should decrease the observed eligibility rate below the 5.7% estimate.

Because FDR subjects had higher risk compared to GP subjects, it was agreed to expand the inclusion criteria to include all nine haplogenotypes in Table 1 for FDR infants. It should be noted that DRB1*0403 was not used as an exclusion criteria for FDR subjects. An estimated 31% of the FDR population would be eligible for follow-up, and an estimated 69% of future T1D cases from the FDR population would be included in the eligible population with an estimated absolute risk of 13%.

Screening results

From September 2004 to February 2010, TEDDY screened a total of 414,714 GP newborns. Of these newborns, 19,906 were found to be eligible for follow-up, representing 4.8% of the screened GP subjects (Table 2). The overall eligibility rate was lower than the eligibility rate estimated using pre-TEDDY data. More than one third (39.5%) of the eligible GP infants were DR3/4 (haplogenotype A), while each of the other three eligible genotypes accounted for approximately 20% of the entire cohort of eligible infants (Supplemental Figure 1). There was considerable variability in the total eligibility rate as well as the frequencies of the eligible genotypes across the six clinical centers (Table 2). Most notably, the Swedish center had the highest eligibility rate (7.4%) (p < 0.0001) compared to all other centers, which ranged from 3.5% to 5.6%. This was primarily due to the high frequency of the DR3/4 haplogenotype at the Swedish center (p<0.0001 versus the other centers). The overall eligibility rates for the Finnish and Colorado clinical centers (5.6% and 5.5%, respectively) were also higher than the German (4.0%), Washington (4.0%) and Georgia/Florida (3.5%) clinical centers (Table 2).

Table 2
TEDDY HLA screening and eligibility results for GP (top) and FDR (bottom) newborns.

TEDDY also screened 6,333 FDR subjects, of which 1,415 were eligible for the follow-up studies based on the nine eligible haplogenotypes (Table 2). The mean eligibility rate for all six major clinical centers and two small centers was 22.2%. The eligibility rates were quite similar in five of the six large clinical centers (19.1% – 23.2%), while the Finnish center had a higher eligibility rate for FDR (31.2%) compared to the other centers (p<0.0001). As expected, the DR3/4 genotype was the most common haplogenotype in five of the six major clinical centers; however, DR4/1 (haplogenotype F) was the most common eligible haplogenotype in the Finnish center (29.2% of the Finish eligible genotypes). Interestingly, the greater overall eligibility rate for FDR in the Finnish center is primarily due to this greater DR4/1 frequency among eligible Finnish FDR, compared to the other centers (p<0.0001). The DR4/9 (haplogenotype H) is also significantly more common among eligible Finnish FDR versus the other centers (p<0.0001). DR4/1 and DR4/4 (haplogenotype B) are the second and third most common haplogenotypes in the overall study population (20.1% and 15.6%, respectively). DR3/3 (haplogenotype D) and DR4/8 (haplogenotype C) represent 12.7% and 8.5% of the overall FDR eligible population, respectively. The other three genotypes are less common, together representing only 9.2% of the overall eligible population (Table 2 and Figure 1).

Ethnic differences in eligibility rate

While the newborns screened in the three European centers are primarily Caucasians, the screened newborns in the US included all minority populations reflecting the increasingly diverse characteristics of these screening centers. Overall, the screened US cohort includes Asian-Americans (6%), Hispanics (10%), African-Americans (14%), Caucasians (58%), and other ethnic groups (13%). While Caucasians represent 56–60% of the screened cohort in all three US TEDDY centers, each center has a different predominant minority group, Hispanics in the Colorado center (27%), African-Americans in the Georgia/Florida center (26%), and Asian-Americans in the Washington State center (10%). The entire GP cohort screened in the US centers was analyzed for the distribution of eligible genotypes according to race/ethnicity (Table 3). The DR3/3 genotype is the most common (~50%) eligible genotypes in both Asian-American and African-American groups. In contrast, the DR3/4 genotype is common in both Hispanics and Caucasians. Surprisingly, the DR4/4 and DR4/8 genotypes are very common in the Hispanic group and these two genotypes are primarily responsible for the high eligibility rate for the Hispanic group in Colorado. Overall, the eligibility rates are significantly lower for Asian-American (0.9%) and African-American (1.3%) infants than for Hispanic (6.9%) and Caucasian (5.0%) infants (Table 3).

Table 3
Screening results in different ethnic groups in the three US centers.

Because the annual incidence of T1D varies greatly among these ethnic groups, it is important to view the HLA eligibility rates in the context of the annual incidences to determine whether the eligibility is proportional to the incidence in each ethnic group. For this purpose, we used the annual incidence of T1D in each of the ethnic groups from the SEARCH study, which includes regions identical or highly similar to each of the three US TEDDY Centers [17]. For comparative purposes, we calculated the relative eligibility rates observed in TEDDY and the relative incidence rates based on published SEARCH data, both normalized relative to Caucasians. Finally, we determined the ratio of these two relative rates, which is denoted as the weighted eligibility rate. As shown in Table 3, relative eligibility based on the TEDDY inclusion criteria differed significantly among ethnic groups, ranging from 18% in Asian Americans to 138% in Hispanics. The relative incidence rates also differed significantly among ethnic groups, being 22% in Asian Americans and in the 50% range for Hispanics and African Americans, relative to Caucasians (Table 3). Importantly, the derived weighted eligibility rates clearly show that: 1) African-Americans are under-represented by the eligibility criteria (47%), 2) Hispanics are over-represented (266%), and 3) Asian-Americans are represented nearly proportionally to their incidence (81%). A similar analysis was not made for the FDR eligibility criteria at this time due to the much smaller number of subjects.

Quality control programs for HLA genotyping

TEDDY developed two quality control programs to ensure the quality and accuracy of the HLA screening data. The first component is an annual HLA proficiency test administered by the Newborn Screening Branch of the National Center for Environmental Health at the Centers for Disease Control (CDC). For each test, a set of 50 coded blood samples (40 designated as GP and 10 as FDR), are genotyped by the participating laboratories using their genotyping methods. The typing results (eligibility status and eligible genotype code) are returned to the CDC within 30 days of receiving the test samples. A minimum accuracy of 98% is deemed acceptable. Failure to meet this requirement calls for an immediate repeat test with a different set of samples. The genotyping laboratory is suspended if it fails both consecutive tests. Four separate tests were carried out in 2004, 2005, 2006 and 2008, respectively. Each laboratory passed all tests with 100% accuracy with the exception that one laboratory scored 98% once, and one laboratory scored 96% once. Based on the established TEDDY quality control procedures, the latter necessitated a repeat test, which was passed with 100% accuracy.

The second quality control program consists of confirmatory repeat genotyping of all eligible subjects by the central HLA reference laboratory. The confirmatory genotyping serves three primary purposes: 1) identify genotyping errors or inaccuracies that occurred in the screening laboratories, 2) identify potential sample mislabeling that occurred anywhere from hospitals to clinical centers to genotyping laboratories, and 3) perform high resolution genotyping for three HLA class II loci, DRB1, DQA1 and DQB1. To achieve these goals, a blood sample is collected at the 9-month or 12-month follow-up visit for each enrolled infant. Genotyping results on the new sample from the HLA reference laboratory are considered the gold standard, and are compared with the initial screening results from the clinical centers. A minimum agreement of 98% must be achieved by each clinical center. This requirement is more stringent than the proficiency test because all errors including genotyping mistakes, sample contaminations, errors in inferred haplotypes and sample labeling mistakes can contribute to the overall discordance rate. Despite the multiple sources of potential errors, the screening laboratories using low cost and low resolution genotyping methods yielded remarkably accurate data, as shown by the 98–100% accuracies in all screening laboratories and the 99% accuracy for the overall cohort (supplemental Table 1). The confirmatory test ensures that genotyping results are 100% correct for all infants who continue enrollment in the long-term follow-up phase of the TEDDY study.

DISCUSSION

HLA class II genes are the most important susceptibility genes for T1D, accounting for approximately 50% of the genetic contribution to the disease. The HLA criteria used to select a high-risk population are not trivial, because of the extremely high degree of polymorphism in these genes, ethnic variability [18,19] and the hierarchical nature of the risks conferred by the large number of distinct haplogenotypes. These selection criteria are also compromises between considerations of sensitivity, specificity and typing costs [11,2025]. Investigators may exclude specific alleles or haplotypes, include specific haplotypes, or use a combination of inclusion and exclusion criteria. For example, the US DAISY study required the susceptibility haplotypes DRB1*03 and/or DRB1*04-DQB1*0302 for inclusion, while DRB1*15/16 was used as an exclusion criterion (25). The Finnish DIPP study used DQB1*0302/X where X was either DQB1*02 or any allele other than DQB1*0602 or DQB1*0301 (13). The TRIGR study of first-degree relatives required susceptibility haplotypes DQB1*0302, DQA1*05-DQB1*02 and/or DQA1*03-DQB1*02, excluded all subjects with DQB1*0602 or DQB1*0301, and conditionally excluded DQB1*0603 or DQA1*0201-DQB1*02 depending on the susceptibility haplotype (26).

Others, like TEDDY, have used strategies with detailed inclusion haplogenotypes. For example, the Belgian Diabetes Registry defined a list of four susceptibility genotypes including DQA1*0301-DQB1*0302/DQA1*0501-DQB1*0201, DQA1*0301-DQB1*0302/DQA1*0301-DQB1*0302, DQA1*0501-DQB1*0201/DQA1*0501-DQB1*0201 and DQA1*0301-DQB1*0302/X where X is any one of eleven generally disease-neutral DQA1-DQB1 haplotypes (27). The TEDDY strategy includes the first three of these susceptibility genotypes, but limits the fourth for general population subjects to X = DQA1*0401-DQB1*0402. The latter choice increased the overall risk level of the TEDDY cohort by limiting the size of the moderate risk portion of the included subjects. For GP screening, TEDDY also excluded DRB1*0403 from eligible DR4 haplotypes since these haplotypes are generally disease-resistant (28). Neither limitation was necessary for FDR due to their higher absolute disease risk.

For TEDDY, the HLA screening strategy had to meet many requirements: 1) identify an eligible cohort with high risk for developing islet autoantibodies and T1D, the primary and secondary endpoints of the TEDDY study, 2) minimize the number of subjects requiring screening to accumulate the cohort, 3) select a relatively genetically homogenous cohort in order to achieve sufficient power to identify environmental determinants, 4) include a sufficiently diverse set of HLA genotypes to determine whether there are different environmental determinants for different HLA genotypes, 5) employ laboratory methods that are both accurate and highly cost-effective, 6) allow efficient risk stratification for both GP and FDR populations, 7) be applicable to an international multi-site study studying multiple ethnic groups, and 8) provide screening results quickly enough to recruit subjects to follow-up by the deadline of 4.5 months of age per the TEDDY protocol [8]. The TEDDY HLA strategy was a successful compromise to fulfill all these requirements under many constraints.

The TEDDY screening laboratories utilized a variety of genotyping strategies to accomplish the goal. These methods were usually simple, low-cost and could efficiently handle tens of thousands of samples a year. These strategies worked exceptionally well as shown by the near perfect score on proficiency tests as well as the 99% confirmation rate for retyping of eligible subjects. This level of performance over 421,000 screened subjects is remarkable given the demand for low genotyping cost, the large numbers of samples to be collected, and the rapid turn-around time required. In fact, the median time to completion of screening typing was 41 days of age, and 95% of infants had complete TEDDY genotyping by 74 days of age.

Of the 414,714 GP newborns screened by TEDDY, 4.8% were genetically eligible for the follow-up study. This observed eligibility rate is significantly less than the 5.7% eligibility rate estimated using the pre-TEDDY data from all six clinical centers. The lesser overall eligibility rate is primarily due to over-estimates in the Colorado center (8.5% estimated versus 5.6% observed) and the Washington center (6.0% estimated versus 4.0% observed). For the four other major TEDDY centers (German, Swedish, Finnish and Georgia/Florida centers) observed rates were similar to estimated rates. The lower observed eligibility rate in Washington was partly explained by the 44% non-Caucasian infants in their screened cohort, which is much greater than that in pre-TEDDY sample due to increasing ethnic diversity in the region. The over-estimated eligibility rate in the Colorado population may be due to a combination of factors such as inclusion of DRB1*0403 subjects in the pre-TEDDY data, and other differences in genotyping methodologies between pre-TEDDY and TEDDY. In fact, for all centers, the eligibility estimates using pre-TEDDY data did not exclude DRB1*0403, which is excluded for the TEDDY GP cohort.

The TEDDY strategy may not appear to be easy to implement for genotyping purpose because it includes very specific haplogenotypes and excludes the protective DRB1*0403 allele for GP infants. However, the TEDDY strategy actually does promote economic and accurate genotyping because the four GP genotypes consist of only three haplotypes: DR4 (DRB1*04-DQA1*0301-DQB1*0302), DR3 (DRB1*03-DQA1*0501-DQB1*0201), and DR8 (DRB1*08-DQA1*0401-DQB1*0402).

The TEDDY data on different ethnic groups in the US provided valuable information for future population screening for T1D. For various reasons discussed earlier, TEDDY elected to adopt a uniform HLA strategy for all ethnic groups. It was not surprising that different ethnic groups had highly different eligibility rates. We indeed observed very low eligibility rates for two populations (0.9% and 1.3% for Asian-American and African-American, respectively) and high eligibility rate for the Hispanic group (6.9%). Since these ethnic groups also have lower T1D incidence, the lower eligibility rates may be appropriate if the eligibility rates are proportional to the annual disease incidence in the corresponding populations. This is indeed true for the Asian-American population. However, African-Americans are under-represented even after correction for the disease incidence (Table 3). As for Africans in general, many African-American T1D patients have a greater diversity of HLA haplotypes. Additional T1D risk haplogenotypes would therefore be required to increase the sensitivity of screening for this group. On the other hand, the Hispanic group is over-represented by the TEDDY inclusion criteria (Table 3). These results suggest that ethnic-specific criteria, while more difficult to implement, should be considered for population-wide screening to maximize sensitivity and specificity. Nevertheless, efficient and accurate TEDDY HLA screening of more than 414,000 infants from multiple international sites, diverse ethnic groups, and different risk strata (FDR versus GP) was successfully completed. This experience supports the notion that population-wide genetic screening for T1D risk may ultimately be a practical goal for public health infrastructures as a part of population-wide T1D prediction and prevention in the future.

Supplementary Material

supplemental material

Acknowledgements

This study was funded by DK 63829, 63861, 63821, 63865, 63863, 63836 and 63790 and Contract No. HHSN267200700014C from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Child Health and Human Development (NICHD), National Institute of Environmental Health Sciences (NIEHS), Juvenile Diabetes Research Foundation (JDRF), and Centers for Disease Control and Prevention (CDC). We wish to thank all TEDDY children and their families for their participation in the study and thank all office staff and laboratory personnel who made significant contribution to the study. Dr. Ingrid Kockum provided unpublished FDR genotype data from the Swedish Childhood Diabetes Registry Study.

Abbreviations

AR
absolute risk
FDR
First Degree Relative
GP
General Population
T1D
Type 1 diabetes mellitus
TEDDY
The Environmental Determinants of Diabetes in the Young
US
United States

Appendix

The Teddy Study Group

Colorado Clinical Center: Marian Rewers, M.D., Ph.D., PI1,4,6,10,11, Katherine Barriga12, Judith Baxter9,12,15, George Eisenbarth, M.D., Ph.D., Nicole Frank2, Patricia Gesualdo2,12,14,15, Michelle Hoffman12,13,14, Lisa Ide, Jill Norris, Ph.D.2,12, Jessie Robinson12, Kathleen Waugh7,12,15. University of Colorado at Denver and Health Sciences Center, Barbara Davis Center for Childhood Diabetes.

Georgia/Florida Clinical Center: Jin-Xiong She, Ph.D.,PI1,3,4,11, Desmond Schatz, M.D.*4,5,7,8, Diane Hopkins12, Leigh Steed6,12,13,14,15, Angela Choate*12, Katherine Silvis2, Meena Shankar*2, Yi-Hua Huang, Ph.D., Ping Yang, Hong-Jie Wang, Jessica Leggett, Kim English, Richard McIndoe, Ph.D., Angela Dequesada*12, Michael Haller, M.D.*14, Stephen W. Anderson, M.D.^ Medical College of Georgia, *University of Florida, ^Pediatric Endocrine Associates, Atlanta.

Germany Clinical Center: Anette G. Ziegler, M.D.,PI1,3,4,11, Heike Boerschmann14, Ezio Bonifacio, Ph.D.*5, Melanie Bunk, Johannes Försch, Lydia Henneberger2,12, Michael Hummel, M.D.13, Sandra Hummel, Ph.D.2, Gesa Joslowski¥2, Mathilde Kersting Ph.D.¥2, Annette Knopff7, Nadja Kocher, Sibylle Koletzko, M.D.¶13, Stephanie Krause, Claudia Lauber, Ulrike Mollenhauer, Claudia Peplow*, Maren Pflüger6, Daniela Pöhlmann, Claudia Ramminger, Sargol Rash-Sur, Roswith Roth, Ph.D.^9, Julia Schenkel, Leonore Thümer, Katja Voit, Christiane Winkler Ph.D.2,12,15, Marina Zwilling, Diabetes Research Institute, *Center for Regenerative Therapies, TU Dresden, ^Institute of Psychology, University of Graz, Austria, Dr. von Hauner Children´s Hospital, Department of Gastroenterology, Ludwig Maximillians University Munich, ¥Research Institute for Child Nutrition, Dortmund.

Finland Clinical Center: Olli G. Simell, M.D., Ph.D.,PI¥^1,4,11,13, Kirsti Nanto-Salonen, M.D., Ph.D.¥ ^12, Jorma Ilonen, M.D., Ph.D.¥ ¶3, Mikael Knip, M.D., Ph.D.*±, Riitta Veijola, M.D., Ph.D. µ¤, Tuula Simell, Ph.D.¥^9,12, Heikki Hyöty, M.D., Ph.D.*±6, Suvi M. Virtanen, M.D., Ph.D.*§2, Carina Kronberg-Kippilä§2, Maija Torma¥^12,14, Barbara Simell¥^12,15, Eeva Ruohonen¥^, Minna Romo¥^, Elina Mantymaki¥^, Heidi Schroderus*±, Mia Nyblom*±, Aino Steniusµ¤. ¥University of Turku, *University of Tampere, µUniversity of Oulu, ^Turku University Hospital, ±Tampere University Hospital, ¤Oulu University Hospital, §National Public Health Institute, Finland, University of Kuopio.

Sweden Clinical Center: Åke Lernmark, Ph.D.,PI1,3,4,8,10,11,15, Daniel Agardh, M.D., Ph.D.13, Peter Almgren, Eva Andersson, Carin Andrén-Aronsson2,13, Maria Ask, Ulla-Marie Karlsson, Corrado Cilio, M.D.5, Ph.D., Jenny Bremer, Emilie Ericson-Hallström, Thomas Gard, Joanna Gerardsson, Ulrika Gustavsson, Gertie Hansson12,14, Monica Hansen, Susanne Hyberg, Rasmus Håkansson, Sten Ivarsson, M.D.,Ph.D.6, , Fredrik Johansen, Helena Larsson M.D., Ph.D.14, Barbro Lernmark, Ph.D.9,12, Maria Markan, Theodosia Massadakis, Jessica Melin, Maria Månsson-Martinez, Anita Nilsson, Emma Nilsson, Kobra Rahmati, Sara Rang, Monica Sedig Järvirova, Sara Sibthorpe, Birgitta Sjöberg, Carina Törn, Ph.D.3,15, Anne Wallin, Åsa Wimar. Lund University.

Washington Clinical Center: William A. Hagopian, M.D., Ph.D., PI1,3,4, 5, 6,7,11,13, 14, Xiang Yan, M.D., Michael Killian6,7,12,13, Claire Cowen Crouch12,14,15, Kristen M. Hay2, Stephen Ayres, Carissa Adams, Brandi Bratrude, Greer Fowler, Czarina Franco, Carla Hammar, Diana Heaney, Patrick Marcus, Arlene Meyer, Denise Mulenga, Elizabeth Scott, Jennifer Skidmore, Erin Small, Joshua Stabbert, Viktoria Stepitova. Pacific Northwest Diabetes Research Institute.

Pennsylvania Satellite Center: Dorothy Becker, M.D., Margaret Franciscus12, MaryEllen Dalmagro-Elias2, Ashi Daftary, M.D. Children’s Hospital of Pittsburgh of UPMC.

Data Coordinating Center: Jeffrey P. Krischer, Ph.D.,PI1,4,5,10,11, Michael Abbondondolo, Lori Ballard3,9,14,15, Rasheedah Brown12,15, David Cuthbertson, Christopher Eberhard, Veena Gowda, Hye-Seung Lee, Ph.D.3,6,13,15, Shu Liu, Kristian Lynch, Ph.D.9, Jamie Malloy, Cristina McCarthy12,15, Wendy McLeod2,5,6,13,15, Laura Smith, Ph.D.9, Stephen Smith, Susan Smith12,15, Ulla Uusitalo, Ph.D.2,15, Kendra Vehik, Ph.D. 4,5,9,14,15, Jimin Yang, Ph.D.2,15. University of South Florida.

Project officer: Beena Akolkar, Ph.D.1,3,4,5,7,10,11, National Institutes of Diabetes and Digestive and Kidney Diseases.

Other contributors: Thomas Briese, Ph.D.6,15, Columbia University, Henry Erlich, Ph.D.3, Children’s Hospital Oakland Research Institute, Suzanne Bennett Johnson, Ph.D.9,12, Florida State University, Steve Oberste, Ph.D.6, Centers for Disease Control and Prevention.

Committees:

1Ancillary Studies, 2Diet, 3Genetics 4Human Subjects/Publicity/Publications, 5Immune Markers, 6Infectious Agents, 7Laboratory Implementation, 8Maternal Studies, 9Psychosocial, 10Quality Assurance, 11Steering, 12Study Coordinators, 13Celiac Disease, 14Clinical Implementation, 15Quality Assurance Subcommittee on Data Quality.

Footnotes

*Members of the TEDDY Study Group are listed in the Appendix (Acknowledgement)

Reference List

1. She JX. Susceptibility to type I diabetes: HLA-DQ and DR revisited. Immunol Today. 1996;17:323–329. [PubMed]
2. Smyth DJ, Cooper JD, Bailey R, et al. A genome-wide association study of nonsynonymous SNPs identifies a type 1 diabetes locus in the interferon-induced helicase (IFIH1) region. Nat Genet. 2006;38:617–619. [PubMed]
3. WTCCC. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447:661–678. [PMC free article] [PubMed]
4. Barrett JC, Clayton DG, Concannon P, et al. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet. 2009;41:703–707. [PMC free article] [PubMed]
5. Hakonarson H, Grant SFA, Bradfield JP, et al. A genome-wide association study identifies KIAA0350 as a type 1 diabetes gene. Nature. 2007;448:591–594. [PubMed]
6. Knip M, Veijola R, Virtanen SM, et al. Environmental triggers and determinants of type 1 diabetes. Diabetes. 2005;54:S125–S136. [PubMed]
7. Peng H, Hagopian W. Environmental factors in the development of Type 1 diabetes. Reviews in Endocrine & Metabolic Disorders. 2006;7:149–162. [PubMed]
8. TEDDY Study Group. The Environmental Determinants of Diabetes in the Young (TEDDY) study: study design. Pediatr Diabetes. 2007;8:286–298. [PubMed]
9. She JX, Bui MM, Tian XH, et al. Additive susceptibility to insulin-dependent diabetes conferred by HLA- DQB1 and insulin genes. Autoimmunity. 1994;18:195–203. [PubMed]
10. Bonifacio E, Hummel M, Walter M, et al. IDDM1 and multiple family history of type 1 diabetes combine to identify neonates at high risk for type 1 diabetes. Diabetes Care. 2005;27:2695–2700. [PubMed]
11. Emery LM, Babu S, Bugawan TL, et al. Newborn HLA-DR,DQ genotype screening: age- and ethnicity-specific type 1 diabetes risk estimates. Pediatr Diabetes. 2005;6:136–144. [PMC free article] [PubMed]
12. Kockum I, Sanjeevi CB, Eastman S, et al. Complex interaction between HLA DR and DQ in conferring risk for childhood type 1 diabetes. Eur J Immunogenet. 1999;26:361–372. [PubMed]
13. Hermann R, Turpeinen H, Laine AP, et al. HLA DR-DQ-encoded genetic determinants of childhood-onset type 1 diabetes in Finland: an analysis of 622 nuclear families. Tissue Antigens. 2003;62:162–169. [PubMed]
14. Wion E, Brantley M, Stevens J, et al. Population-Wide Infant Screening for HLA-Based Type 1 Diabetes Risk Via Dried Bloodspots from the Public Health Infrastructure. Annals NY Acad Sci. 2003;1005:400–404. [PubMed]
15. Kiviniemi M, Hermann R, Nurmi J, et al. A high-throughput population screening system for the estimation of genetic risk for type 1 diabetes: an application for the TEDDY (The Environmental Determinants of Diabetes in the Young) study. Diabetes Technol Ther. 2007;9:460–472. [PubMed]
16. Erlich H, Valdes AM, Noble J, et al. HLA DR-DQ haplotypes and genotypes and type 1 diabetes risk: analysis of the type 1 diabetes genetics consortium families. Diabetes. 2008;57:1084–1092. [PubMed]
17. Liese AD, D'Agostino RB, Jr, Hamman RF, et al. The burden of diabetes mellitus among US youth: prevalence estimates from the SEARCH for Diabetes in Youth Study. Pediatrics. 2006;118:1510–1518. [PubMed]
18. Agrawal S, Khan F, Bharadwaj U. Human genetic variation studies and HLA class II loci. Int J Immunogenet. 2007;34:247–252. [PubMed]
19. Thomson G, Valdes AM, Noble JA, et al. Relative predispositional effects of HLA class II DRB1-DQB1 haplotypes and genotypes on type 1 diabetes: a meta-analysis. Tissue Antigens. 2007;70:110–127. [PubMed]
20. Carmichael SK, Johnson SB, Baughcum A, et al. Prospective assessment in newborns of diabetes autoimmunity (PANDA): maternal understanding of infant diabetes risk. Genet Med. 2003;5:77–83. [PubMed]
21. Nejentsev S, Sjoroos M, Soukka T, et al. Population-based genetic screening for the estimation of Type 1 diabetes mellitus risk in Finland: selective genotyping of markers in the HLA-DQB1, HLA-DQA1 and HLA-DRB1 loci. Diabet Med. 1999;16:985–992. [PubMed]
22. Kupila A, Muona P, Simell T, et al. Feasibility of genetic and immunological prediction of type I diabetes in a population-based birth cohort. Diabetologia. 2001;44:290–297. [PubMed]
23. Ilonen J, Sjoroos M, Knip M, et al. Estimation of genetic risk for type 1 diabetes. Am J Med Genet. 2002;115:30–36. [PubMed]
24. Ilonen J, Reijonen H, Knip M, et al. Population-based genetic screening for IDDM susceptibility as a source of HLA-genotyped control subjects. Diabetologia. 1996;39:123. [PubMed]
25. Rewers M, Bugawan TL, Norris JM, et al. Newborn screening for HLA markers associated with IDDM: diabetes autoimmunity study in the young (DAISY) Diabetologia. 1996;39:807–812. [PubMed]
26. Akerblom HK, Knip M, Becker D, et al. The TRIGR Trial: Tesing the potential link between weaning diet and Type 1 diabetes. Immunology, Endocrine and Metabolic Agents- Medicinal Chemistry. 2007;7:251–263.
27. Van der Auwera BJ, Schuit FC, Weets I, et al. Relative and absolute HLA-DQA1-DQB1 linked risk for developing type I diabetes before 40 years of age in the Belgian population: implications for future prevention studies. Hum Immunol. 2002;63:40–50. [PubMed]
28. Van der Auwera B, Van Waeyenberge C, Schuit F, et al. DRB1*0403 protects against IDDM in Caucasians with the high-risk heterozygous DQA1*0301-DQB1*0302/DQA1*0501-DQB1*0201 genotype. Diabetes. 1995;44:527–530. [PubMed]